From 30f1a4318dd4c882cf0197ba258b00dff9968e83 Mon Sep 17 00:00:00 2001
From: MariaJacobs70 <72224154+MariaJacobs70@users.noreply.github.com>
Date: Tue, 6 Oct 2020 17:29:22 -0300
Subject: [PATCH 1/9] Criado usando o Colaboratory
---
...247\303\265es maria aula 06-10-2020.ipynb" | 5789 +++++++++++++++++
1 file changed, 5789 insertions(+)
create mode 100644 "Notebooks/NB02__Numpy - altera\303\247\303\265es maria aula 06-10-2020.ipynb"
diff --git "a/Notebooks/NB02__Numpy - altera\303\247\303\265es maria aula 06-10-2020.ipynb" "b/Notebooks/NB02__Numpy - altera\303\247\303\265es maria aula 06-10-2020.ipynb"
new file mode 100644
index 000000000..5fe378ba1
--- /dev/null
+++ "b/Notebooks/NB02__Numpy - altera\303\247\303\265es maria aula 06-10-2020.ipynb"
@@ -0,0 +1,5789 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "NB02__Numpy.ipynb",
+ "provenance": [],
+ "collapsed_sections": [
+ "n8BIbzQbNWUo",
+ "7eS94uQ4NhVR",
+ "SYOgJpGYVLUu",
+ "CaHFxk98W5if",
+ "ReWUyWiHXCnc",
+ "CqszHxaKHr2h",
+ "tXgF1Wl9gHKY",
+ "Fotx7XUquAo8",
+ "36kmLUYDvsUI",
+ "SWO2GdNovxAp",
+ "vpN54l4vxze5",
+ "u4HOf9SNytSq",
+ "6BQ9oZiD9hg5",
+ "tz5-QdrX9vct",
+ "p1muBgMX8NK4",
+ "FxTC2-U88ajk",
+ "z8EYn0pP25Rh"
+ ],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "NUMPY \u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma_numeros1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# O valor \"-1\" na posição das linhas pede ao NumPy para calcular o número de linhas automaticamente.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 15 into shape (2)"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pZS4b4-y708q"
+ },
+ "source": [
+ "Porque temos esse erro?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4disywvR8HeH"
+ },
+ "source": [
+ "E se fizermos..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3oEAAXTp8I7Z",
+ "outputId": "e8c8a90f-c34a-4304-d9b4-fd7f04ce224f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Define seed\n",
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(1, 10, size = 16)) # Observe que agora temos 16 elementos\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([9, 9, 3, 9, 2, 9, 1, 5, 3, 1, 9, 4, 8, 2, 4, 3])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 21
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iUhth0QV8Rpt"
+ },
+ "source": [
+ "Reshapping..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9D1y7uD88Qip",
+ "outputId": "e7d22bcd-c10f-4ea3-e41b-03f6f98a054f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 153
+ }
+ },
+ "source": [
+ "a_conjunto1.reshape(-1, 2) # O valor \"-1\" na posição das linhas pede ao NumPy para calcular o número de linhas automaticamente."
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[9, 9],\n",
+ " [3, 9],\n",
+ " [2, 9],\n",
+ " [1, 5],\n",
+ " [3, 1],\n",
+ " [9, 4],\n",
+ " [8, 2],\n",
+ " [4, 3]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 22
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ALh-sq7DMnN5",
+ "outputId": "db373349-7910-4f1f-93f3-8ac8f67da8b8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 153
+ }
+ },
+ "source": [
+ "# OU --> Neste caso, estamos reshaping o array em 8 linhas e 2 colunas\n",
+ "a_conjunto1.reshape(8, -1)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[9, 9],\n",
+ " [3, 9],\n",
+ " [2, 9],\n",
+ " [1, 5],\n",
+ " [3, 1],\n",
+ " [9, 4],\n",
+ " [8, 2],\n",
+ " [4, 3]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 26
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yvTnrszn8Yk0"
+ },
+ "source": [
+ "Porque agora deu certo?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LeQ9LqIE8baG"
+ },
+ "source": [
+ "## Último exemplo com reshape\n",
+ "> Considere o array a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "OQOC9iiN8hZT"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.random.randn(2, 3)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Cvce8qBl9Cvq"
+ },
+ "source": [
+ "Queremos agora transformá-la num array de 3 linhas e 2 colunas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QDDsYoVt9Klz"
+ },
+ "source": [
+ "a_conjunto1.reshape(-1, 2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AdwU5ygt9Svq"
+ },
+ "source": [
+ "Poderia ser..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5uBeokKc9Uo-"
+ },
+ "source": [
+ "a_conjunto1.reshape(3, -1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OeRBsobc9aKj"
+ },
+ "source": [
+ "E por fim, também poderia ser..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "MDt8UYYH9dBw"
+ },
+ "source": [
+ "a_conjunto1.reshape(3, 2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "91o5vycQfdKW"
+ },
+ "source": [
+ "___\n",
+ "# **Transposta**\n",
+ "* O array a_conjunto2 tem a seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RsZwyuhoffjb"
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "A3MzTVoGfiyO"
+ },
+ "source": [
+ "# Transposta do array a_conjunto2 é dado por:\n",
+ "a_conjunto2.T"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ij-ZW5IyzXIb"
+ },
+ "source": [
+ "Ou seja, linha virou coluna. Ok?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qLy6ajgpt3lU"
+ },
+ "source": [
+ "# **Inversa da matriz quadrada**\n",
+ "> Se uma matriz é não-singular, então sua inversa existe.\n",
+ "\n",
+ "* Se o determinante de uma matriz is not equal to zero, then the matrix isé diferente de 0, então a matriz é não-singular."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-u7jRq34t9_x"
+ },
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "a_conjunto1 = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])\n",
+ "a_conjunto2 = np.array([[6, 2], [5, 3]])\n",
+ "a_conjunto3 = np.array([[1, 3, 5],[2, 5, 1],[2, 3, 8]])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7zmHHWWlvaYB"
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3fHKyhOJvcak"
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vQG7yyfjwLg9"
+ },
+ "source": [
+ "a_conjunto3"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qa2Yre2rwgRk"
+ },
+ "source": [
+ "## Determinantes da matriz quadrada"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "N6jwuC6twkyc"
+ },
+ "source": [
+ "np.linalg.det(a_conjunto1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QSvViNwzwnhI"
+ },
+ "source": [
+ "np.linalg.det(a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "o8jwsnccw5id"
+ },
+ "source": [
+ "np.linalg.det(a_conjunto3)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kkVaTgzgw_XJ"
+ },
+ "source": [
+ "A seguir, calculamos as inversas das matrizes acima definidas..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "b9FgWvTYvpik"
+ },
+ "source": [
+ "np.linalg.inv(a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KsdEt1kIvsM_"
+ },
+ "source": [
+ "np.linalg.inv(a_conjunto1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VA_F7_7kccpn"
+ },
+ "source": [
+ "Porque não temos a inversa de a_conjunto1?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ANPBCnmVwOf4"
+ },
+ "source": [
+ "np.linalg.inv(a_conjunto3)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XAf9k1egxcdF"
+ },
+ "source": [
+ "# **Resolver sistemas de equações lineares**\n",
+ "> Considere o sistema de euqações lineares abaixo:\n",
+ "\n",
+ "\\begin{equation}\n",
+ "x + 3y + 5z = 10\\\\\n",
+ "2x+ 5y + z = 8 \\\\\n",
+ "2x + 3y + 8z= 3\n",
+ "\\end{equation}\n",
+ "\n",
+ "Ou $Ax = b$. A solução deste sistema de equações é dada por $A^{-1}b$."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oNf5nqaLxhBY"
+ },
+ "source": [
+ "Ou seja, basta encontrarmos a inversa de A e multiplicarmos por b."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "omzC5dGA0btc"
+ },
+ "source": [
+ "A= np.array([[1, 3, 5], [2, 5, 1], [2, 3, 8]])\n",
+ "np.linalg.inv(A)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AiXI3oxB05iE"
+ },
+ "source": [
+ "Agora basta multiplicar a matriz inversa $A^{-1}$ acima por b. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XoGebKDa2Fcd"
+ },
+ "source": [
+ "A_Inv = np.linalg.inv(A)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sKaP0a1QZG-P"
+ },
+ "source": [
+ "b= np.array([10, 8, 3]).reshape(3, -1)\n",
+ "b"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3dAVq8dg19VI"
+ },
+ "source": [
+ "A_Inv.dot(b)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zso6hTnB17cm"
+ },
+ "source": [
+ "Uma forma fácil de se fazer isso é utilizar a expressão abaixo:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ptQHIVll1E4P"
+ },
+ "source": [
+ "b= np.array([[10], [8], [3]])\n",
+ "b"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "X4VL8lyY1Xus"
+ },
+ "source": [
+ "np.linalg.solve(A, b)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fJKmwTS59-Bc"
+ },
+ "source": [
+ "# **Empilhar arrays**\n",
+ "\n",
+ "## Exemplo 1\n",
+ "\n",
+ "\n",
+ "\n",
+ "## Exemplo 2\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rhPTt3EwXden"
+ },
+ "source": [
+ "## Gerar os arrays do exemplo1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zEI-yBy3-E46"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.random.randn(5, 8)\n",
+ "\n",
+ "np.random.seed(19741120)\n",
+ "a_conjunto2 = np.random.randn(8, 8)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UYsAqBRp--79"
+ },
+ "source": [
+ "## Método 1 - Concatenate([A, B])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HgO1ujvhObyE",
+ "outputId": "c40e7ed9-255b-4886-dddf-3b17f2b1be2f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 2.5062768 , 1.11440422, 2.05565501, 0.56482376, 0.29897276,\n",
+ " 1.04930857, -0.12607366, 1.06227632],\n",
+ " [ 1.13807032, 1.37966044, -2.05995563, 0.67474814, 0.72722843,\n",
+ " -0.33923852, 0.43613107, 0.59135489],\n",
+ " [-1.29281877, 1.17712036, -0.98644163, -1.79034143, -1.08913605,\n",
+ " -0.90712825, -1.02291108, -1.36445713],\n",
+ " [-0.29429164, 0.06343709, -1.14196185, -0.50706079, -0.83539436,\n",
+ " -1.41492946, -0.2159062 , -1.16519474],\n",
+ " [-0.60767518, -0.61510925, 1.0771542 , 0.5043687 , 0.02674197,\n",
+ " 1.83494644, 0.34728874, -1.14671885]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 33
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2aQY_klZOeg9",
+ "outputId": "14eb3d9c-d0fc-4b6a-fe19-1790695c838f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 289
+ }
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[-0.77337752, -1.10547465, 0.10062807, -1.14571729, -2.15266227,\n",
+ " -0.75255725, -2.1529949 , -0.33017773],\n",
+ " [-1.10465731, 0.32889675, 0.01010198, -1.33213633, -0.33945805,\n",
+ " -0.01299007, 0.05342823, -0.18641201],\n",
+ " [ 0.39473805, -0.89354231, -0.50667323, -0.74660913, 1.83586365,\n",
+ " -1.20536871, 1.20184886, 0.51160897],\n",
+ " [-0.56952286, -0.93343871, -0.24972528, 0.98487133, 1.19333367,\n",
+ " 2.29956497, 0.16657022, 0.71357415],\n",
+ " [-0.45251078, 0.92163918, 0.73421263, 2.17811191, -0.05655212,\n",
+ " 1.25326 , -0.37039248, 1.43855202],\n",
+ " [ 0.85646091, -0.11257239, -0.35400297, 0.94136671, -0.08696163,\n",
+ " -1.49000701, 0.00848666, 0.86705275],\n",
+ " [ 1.6340906 , 1.36321063, -0.02175361, -0.45301645, -0.37111236,\n",
+ " -0.04716069, -2.27337435, 0.95318738],\n",
+ " [ 0.7100548 , -0.79883269, -0.3165779 , -1.58352824, -0.37751484,\n",
+ " -0.29760341, -0.73424207, -0.55703223]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 34
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bK70vaq8_KMH",
+ "outputId": "f6d400cf-4b54-4990-815b-052f5224aadd",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 459
+ }
+ },
+ "source": [
+ "np.concatenate([a_conjunto1, a_conjunto2], axis = 0) # axis= 0 diz ao NumPy para empilhar as linhas"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 2.5062768 , 1.11440422, 2.05565501, 0.56482376, 0.29897276,\n",
+ " 1.04930857, -0.12607366, 1.06227632],\n",
+ " [ 1.13807032, 1.37966044, -2.05995563, 0.67474814, 0.72722843,\n",
+ " -0.33923852, 0.43613107, 0.59135489],\n",
+ " [-1.29281877, 1.17712036, -0.98644163, -1.79034143, -1.08913605,\n",
+ " -0.90712825, -1.02291108, -1.36445713],\n",
+ " [-0.29429164, 0.06343709, -1.14196185, -0.50706079, -0.83539436,\n",
+ " -1.41492946, -0.2159062 , -1.16519474],\n",
+ " [-0.60767518, -0.61510925, 1.0771542 , 0.5043687 , 0.02674197,\n",
+ " 1.83494644, 0.34728874, -1.14671885],\n",
+ " [-0.77337752, -1.10547465, 0.10062807, -1.14571729, -2.15266227,\n",
+ " -0.75255725, -2.1529949 , -0.33017773],\n",
+ " [-1.10465731, 0.32889675, 0.01010198, -1.33213633, -0.33945805,\n",
+ " -0.01299007, 0.05342823, -0.18641201],\n",
+ " [ 0.39473805, -0.89354231, -0.50667323, -0.74660913, 1.83586365,\n",
+ " -1.20536871, 1.20184886, 0.51160897],\n",
+ " [-0.56952286, -0.93343871, -0.24972528, 0.98487133, 1.19333367,\n",
+ " 2.29956497, 0.16657022, 0.71357415],\n",
+ " [-0.45251078, 0.92163918, 0.73421263, 2.17811191, -0.05655212,\n",
+ " 1.25326 , -0.37039248, 1.43855202],\n",
+ " [ 0.85646091, -0.11257239, -0.35400297, 0.94136671, -0.08696163,\n",
+ " -1.49000701, 0.00848666, 0.86705275],\n",
+ " [ 1.6340906 , 1.36321063, -0.02175361, -0.45301645, -0.37111236,\n",
+ " -0.04716069, -2.27337435, 0.95318738],\n",
+ " [ 0.7100548 , -0.79883269, -0.3165779 , -1.58352824, -0.37751484,\n",
+ " -0.29760341, -0.73424207, -0.55703223]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 35
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CpaXBkm8_BF8"
+ },
+ "source": [
+ "## Método 2 - np.r_[A, B]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3QnVUzAY_teZ",
+ "outputId": "e8adfd85-e760-40f5-d9ac-48353d24ccd2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 459
+ }
+ },
+ "source": [
+ "np.r_[a_conjunto1, a_conjunto2]"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 2.5062768 , 1.11440422, 2.05565501, 0.56482376, 0.29897276,\n",
+ " 1.04930857, -0.12607366, 1.06227632],\n",
+ " [ 1.13807032, 1.37966044, -2.05995563, 0.67474814, 0.72722843,\n",
+ " -0.33923852, 0.43613107, 0.59135489],\n",
+ " [-1.29281877, 1.17712036, -0.98644163, -1.79034143, -1.08913605,\n",
+ " -0.90712825, -1.02291108, -1.36445713],\n",
+ " [-0.29429164, 0.06343709, -1.14196185, -0.50706079, -0.83539436,\n",
+ " -1.41492946, -0.2159062 , -1.16519474],\n",
+ " [-0.60767518, -0.61510925, 1.0771542 , 0.5043687 , 0.02674197,\n",
+ " 1.83494644, 0.34728874, -1.14671885],\n",
+ " [-0.77337752, -1.10547465, 0.10062807, -1.14571729, -2.15266227,\n",
+ " -0.75255725, -2.1529949 , -0.33017773],\n",
+ " [-1.10465731, 0.32889675, 0.01010198, -1.33213633, -0.33945805,\n",
+ " -0.01299007, 0.05342823, -0.18641201],\n",
+ " [ 0.39473805, -0.89354231, -0.50667323, -0.74660913, 1.83586365,\n",
+ " -1.20536871, 1.20184886, 0.51160897],\n",
+ " [-0.56952286, -0.93343871, -0.24972528, 0.98487133, 1.19333367,\n",
+ " 2.29956497, 0.16657022, 0.71357415],\n",
+ " [-0.45251078, 0.92163918, 0.73421263, 2.17811191, -0.05655212,\n",
+ " 1.25326 , -0.37039248, 1.43855202],\n",
+ " [ 0.85646091, -0.11257239, -0.35400297, 0.94136671, -0.08696163,\n",
+ " -1.49000701, 0.00848666, 0.86705275],\n",
+ " [ 1.6340906 , 1.36321063, -0.02175361, -0.45301645, -0.37111236,\n",
+ " -0.04716069, -2.27337435, 0.95318738],\n",
+ " [ 0.7100548 , -0.79883269, -0.3165779 , -1.58352824, -0.37751484,\n",
+ " -0.29760341, -0.73424207, -0.55703223]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 36
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XmSPbDP6_20W"
+ },
+ "source": [
+ "**Obs**.: Eu prefiro este método!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dzVKW_wX_Dzw"
+ },
+ "source": [
+ "## Método 3 - np.vstack([A, B]) = np.r_[A, B]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uL7lEN_mABID",
+ "outputId": "d1ea4d86-2cc1-4e2d-af72-b3a292ef15fd",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 459
+ }
+ },
+ "source": [
+ "np.vstack([a_conjunto1, a_conjunto2])"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 2.5062768 , 1.11440422, 2.05565501, 0.56482376, 0.29897276,\n",
+ " 1.04930857, -0.12607366, 1.06227632],\n",
+ " [ 1.13807032, 1.37966044, -2.05995563, 0.67474814, 0.72722843,\n",
+ " -0.33923852, 0.43613107, 0.59135489],\n",
+ " [-1.29281877, 1.17712036, -0.98644163, -1.79034143, -1.08913605,\n",
+ " -0.90712825, -1.02291108, -1.36445713],\n",
+ " [-0.29429164, 0.06343709, -1.14196185, -0.50706079, -0.83539436,\n",
+ " -1.41492946, -0.2159062 , -1.16519474],\n",
+ " [-0.60767518, -0.61510925, 1.0771542 , 0.5043687 , 0.02674197,\n",
+ " 1.83494644, 0.34728874, -1.14671885],\n",
+ " [-0.77337752, -1.10547465, 0.10062807, -1.14571729, -2.15266227,\n",
+ " -0.75255725, -2.1529949 , -0.33017773],\n",
+ " [-1.10465731, 0.32889675, 0.01010198, -1.33213633, -0.33945805,\n",
+ " -0.01299007, 0.05342823, -0.18641201],\n",
+ " [ 0.39473805, -0.89354231, -0.50667323, -0.74660913, 1.83586365,\n",
+ " -1.20536871, 1.20184886, 0.51160897],\n",
+ " [-0.56952286, -0.93343871, -0.24972528, 0.98487133, 1.19333367,\n",
+ " 2.29956497, 0.16657022, 0.71357415],\n",
+ " [-0.45251078, 0.92163918, 0.73421263, 2.17811191, -0.05655212,\n",
+ " 1.25326 , -0.37039248, 1.43855202],\n",
+ " [ 0.85646091, -0.11257239, -0.35400297, 0.94136671, -0.08696163,\n",
+ " -1.49000701, 0.00848666, 0.86705275],\n",
+ " [ 1.6340906 , 1.36321063, -0.02175361, -0.45301645, -0.37111236,\n",
+ " -0.04716069, -2.27337435, 0.95318738],\n",
+ " [ 0.7100548 , -0.79883269, -0.3165779 , -1.58352824, -0.37751484,\n",
+ " -0.29760341, -0.73424207, -0.55703223]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 37
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "68icJ-2ZAdRj"
+ },
+ "source": [
+ "# Concatenar arrays\n",
+ "\n",
+ "## Exemplo 1\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Exemplo 2\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OplgK9YoQi9o"
+ },
+ "source": [
+ "## Concatenar os elementos de dois arrays - np.c_[A, B]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lpdsbTEKQ9EY"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.random.randint(0, 10, 100).reshape(-1, 10)\n",
+ "a_conjunto2 = np.random.randint(0, 2, 10).reshape(-1, 1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JPxhGsaSSMk2",
+ "outputId": "47727fe9-05f1-4ff7-ec0a-04579120cf78",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[8, 8, 2, 8, 9, 1, 8, 0, 4, 2],\n",
+ " [0, 8, 9, 3, 7, 1, 3, 2, 9, 7],\n",
+ " [7, 9, 5, 6, 8, 7, 0, 9, 3, 9],\n",
+ " [3, 1, 8, 6, 3, 5, 4, 1, 2, 9],\n",
+ " [8, 6, 6, 1, 0, 9, 2, 0, 7, 5],\n",
+ " [5, 4, 4, 2, 7, 2, 7, 9, 3, 1],\n",
+ " [5, 0, 1, 2, 3, 8, 7, 5, 4, 0],\n",
+ " [5, 9, 6, 6, 1, 3, 6, 0, 4, 9],\n",
+ " [2, 1, 0, 9, 1, 4, 2, 9, 7, 9],\n",
+ " [5, 3, 7, 6, 3, 9, 8, 4, 3, 0]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 39
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9ZyUPfybTfej",
+ "outputId": "ac27a20e-1622-4cb9-d6f6-74ee467bdb72",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[1],\n",
+ " [0],\n",
+ " [0],\n",
+ " [0],\n",
+ " [0],\n",
+ " [1],\n",
+ " [0],\n",
+ " [0],\n",
+ " [0],\n",
+ " [1]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 40
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nS1cPG3aRug1",
+ "outputId": "c70cf891-ae8f-445d-c271-c6b7f7da1738",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "# colocando o array a_conjunto2 do lado de a_conjunto1.\n",
+ "np.c_[a_conjunto1, a_conjunto2]"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[8, 8, 2, 8, 9, 1, 8, 0, 4, 2, 1],\n",
+ " [0, 8, 9, 3, 7, 1, 3, 2, 9, 7, 0],\n",
+ " [7, 9, 5, 6, 8, 7, 0, 9, 3, 9, 0],\n",
+ " [3, 1, 8, 6, 3, 5, 4, 1, 2, 9, 0],\n",
+ " [8, 6, 6, 1, 0, 9, 2, 0, 7, 5, 0],\n",
+ " [5, 4, 4, 2, 7, 2, 7, 9, 3, 1, 1],\n",
+ " [5, 0, 1, 2, 3, 8, 7, 5, 4, 0, 0],\n",
+ " [5, 9, 6, 6, 1, 3, 6, 0, 4, 9, 0],\n",
+ " [2, 1, 0, 9, 1, 4, 2, 9, 7, 9, 0],\n",
+ " [5, 3, 7, 6, 3, 9, 8, 4, 3, 0, 1]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 41
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kIgU1YBw0OeM"
+ },
+ "source": [
+ "___\n",
+ "# **Selecionar itens que satisfazem condições**\n",
+ "> Considere o array a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "e2pL5anBV0DI",
+ "outputId": "f37cd827-ee00-49ba-994d-77cab3a24421",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1 = np.arange(10, 0, -1)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 42
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "i9HuZZAfV302"
+ },
+ "source": [
+ "Selecionar somente os itens > 7:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZCESvr7iXMkV"
+ },
+ "source": [
+ "## Usando np.where()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BdrAQLHkTS-v",
+ "outputId": "44a6e480-1b6c-4dad-ee29-2fcb4ada5097",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 45
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "O_ZBaWxfWA9o",
+ "outputId": "fae44244-ff29-4b04-cd2d-a4c768487e75",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Índices do array que atendem a condição\n",
+ "l_indices = np.where(a_conjunto1 > 7)\n",
+ "l_indices"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(array([0, 1, 2]),)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 44
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EdWlfPOZWPME"
+ },
+ "source": [
+ "**Atenção**: Capturamos os índices. Para selecionar os itens, basta fazer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tOxs3iYQWWxu",
+ "outputId": "b402fdfd-c6e0-4170-b35c-c7c5cd2ca85e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto2 = a_conjunto1[l_indices]\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 9, 8])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 46
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PGsENqkaXRjh"
+ },
+ "source": [
+ "## Alternativa: Usando []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "YbdRNk1WXTLT",
+ "outputId": "062b157c-00fb-4f8f-d207-a0c8e9871e48",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1[a_conjunto1 > 7]"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 9, 8])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 47
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jijpzFxcSQC8"
+ },
+ "source": [
+ "Acho que vale a pena quebrar esta solução para entendermos melhor como as coisas funcionam:#"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rujhP2LQSWsq"
+ },
+ "source": [
+ " # Primeiro, avalie o resultado de a_conjunto1 > 7:"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "FYZaBsasSb3N",
+ "outputId": "0a190896-249c-4d7c-ea0d-a20a53536446",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ }
+ },
+ "source": [
+ "a_conjunto1 > 7"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([ True, True, True, False, False, False, False, False, False,\n",
+ " False])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 48
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mvEof-UKaaVG"
+ },
+ "source": [
+ "a_conjunto1[a_conjunto1 > 7]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nO4FiBmDUZOT",
+ "outputId": "9f54e601-d95a-444c-bd59-28947e332248",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([-1, -1, -1, 7, 6, 5, 4, 3, 2, 1])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 52
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ci5lT9nmSfsX"
+ },
+ "source": [
+ "Agora, com este resultado, fica fácil entender como o Python seleciona os elementos. Consegue explicar?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1v5Lfin0GGKD"
+ },
+ "source": [
+ "# Substituir itens baseado em condições\n",
+ "> Substituir os valores negativos do array abaixo por 0."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CLY_u0ePWdN7"
+ },
+ "source": [
+ "## Gerar o exemplo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NUANFy-fNXf5"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(0, 10, size = 100))\n",
+ "\n",
+ "# Lista aleatória de índices que vou alterar\n",
+ "np.random.seed(20111974)\n",
+ "l_indices= np.random.randint(0, 99, 9)\n",
+ "\n",
+ "for i in l_indices:\n",
+ " a_conjunto1[i] = -1*a_conjunto1[i]\n",
+ "\n",
+ "a_conjunto2 = a_conjunto1.copy()\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dWVyI40uN2d2"
+ },
+ "source": [
+ "# Indices a serem multiplicados por -1:\n",
+ "l_indices"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3Whuu854OJDZ"
+ },
+ "source": [
+ "## Substituir os valores negativos por 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sr268Rp8b-Se",
+ "outputId": "82514805-b350-45c4-a3fc-7cb24c847b7f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto2 < 0"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([False, False, False])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 50
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "C-eKqPrfOQF6"
+ },
+ "source": [
+ "a_conjunto2[a_conjunto2 < 0] = 0\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eDLM0_JSZlfB"
+ },
+ "source": [
+ "Observe acima que os valores negativos foram substituídos por 0, como queríamos."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AEHJ0rA3dHHU"
+ },
+ "source": [
+ "## Substituir os valores negativos por 0 e os positivos por 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "y32J8SRNZwRF"
+ },
+ "source": [
+ "a_conjunto2 = a_conjunto1.copy()\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1bSD9Fs6P5wW"
+ },
+ "source": [
+ "a_conjunto2 = np.where(a_conjunto2 <= 0, 0, 1)\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "i027scjl0qkm"
+ },
+ "source": [
+ "___\n",
+ "# Outliers\n",
+ "> Qualquer ponto/observação que é incomum quando comparado com todos os outros pontos/observações."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UnDTqRnZHQ3W"
+ },
+ "source": [
+ "## Z-Score\n",
+ "\n",
+ "* Z-Score pode ser utilizado para detectar Outliers.\n",
+ "* É a diferença entre o valor e a média da amostra expressa como o número de desvios-padrão. \n",
+ "* Se o escore z for menor que 2,5 ou maior que 2,5, o valor estará nos 5% do menor ou maior valor (2,5% dos valores em ambas as extremidades da distribuição). No entanto, é pratica comum utilizarmos 3 ao invés dos 2,5.\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "N7gb2zhtd0uM"
+ },
+ "source": [
+ "## IQR Score\n",
+ "\n",
+ "* O Intervalo interquartil (IQR) é uma medida de dispersão estatística, sendo igual à diferença entre os percentis 75 (Q3) e 25 (Q1), ou entre quartis superiores e inferiores, IQR = Q3 - Q1."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lMmWOKNvghI7"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DUw_a-MjWvBc"
+ },
+ "source": [
+ "### Desafio para resolverem\n",
+ "> **Objetivo**: Simular aleatoriamente o salário de 1.000 pessoas com distribuição N(1.045; 100). \n",
+ "* Identificar os _outliers_ da distribuição que acabamos de simular;\n",
+ "* Qual a média da distribuição que simulamos?\n",
+ "* Qual o desvio-padrão;\n",
+ "* Plotar o Boxplot da distribuição dos dados;\n",
+ "* Quantas pessoas > Q3 + 1.5*(Q3-Q1)\n",
+ "* Substituir os outliers do array por:\n",
+ " * Q1-1.5*(Q3 - Q1), se ponto < Q1-1.5*(Q3-Q1)\n",
+ " * Q3+1.5*(Q3 - Q1), se ponto > Q3+1.5*(Q3-Q1)\n",
+ "\n",
+ "Obs.: Use np.random.seed(20111974)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "L9ntAdS_oOAh"
+ },
+ "source": [
+ "### Geração aleatória do array a_salarios com distribuição $N(\\mu, \\sigma)$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RL0Zb0fyDory",
+ "outputId": "2a3d2b33-579c-406d-d662-da4458f164e6",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "import numpy as np\n",
+ "np.random.seed(20111974)\n",
+ "np.set_printoptions(precision = 2, suppress = True)\n",
+ "\n",
+ "media = 1045\n",
+ "desvio_padrao = 100\n",
+ "i_tamanho = 1000\n",
+ "\n",
+ "a_salarios = np.array(np.random.normal(media, desvio_padrao, size = i_tamanho))\n",
+ "a_salarios[:30]"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1295.63, 1156.44, 1250.57, 1101.48, 1074.9 , 1149.93, 1032.39,\n",
+ " 1151.23, 1158.81, 1182.97, 839. , 1112.47, 1117.72, 1011.08,\n",
+ " 1088.61, 1104.14, 915.72, 1162.71, 946.36, 865.97, 936.09,\n",
+ " 954.29, 942.71, 908.55, 1015.57, 1051.34, 930.8 , 994.29,\n",
+ " 961.46, 903.51])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 5
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Fc3a-yhViCTs"
+ },
+ "source": [
+ "### Geração aleatória dos índices que serão (manualmente) alterados"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Iakt6i1cgEcB",
+ "outputId": "9cc09094-5420-4078-a387-e22a58c13f7a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Lista aleatória de índices que vou alterar\n",
+ "np.random.seed(19741120)\n",
+ "l_indices = np.random.randint(0, 999, 10)\n",
+ "\n",
+ "# Estas são as posições que serão alteradas\n",
+ "np.sort(l_indices)"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([ 14, 105, 208, 349, 484, 567, 615, 616, 622, 847])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 6
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oXwME1rciHkw"
+ },
+ "source": [
+ "### Cópia dos salários para compararmos o ANTES e DEPOIS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BEtnua7sgp_y",
+ "outputId": "85b67195-c61a-4f05-ea8f-fc5b05d8973b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "# cópia do array a_salarios\n",
+ "a_salarios_copia = a_salarios.copy()\n",
+ "a_salarios_copia2 = a_salarios.copy()\n",
+ "\n",
+ "a_salarios[:30]"
+ ],
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1295.63, 1156.44, 1250.57, 1101.48, 1074.9 , 1149.93, 1032.39,\n",
+ " 1151.23, 1158.81, 1182.97, 839. , 1112.47, 1117.72, 1011.08,\n",
+ " 1088.61, 1104.14, 915.72, 1162.71, 946.36, 865.97, 936.09,\n",
+ " 954.29, 942.71, 908.55, 1015.57, 1051.34, 930.8 , 994.29,\n",
+ " 961.46, 903.51])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 7
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "So8qj3Yrh-Az"
+ },
+ "source": [
+ "### Alteração (manual dos salários): 2 alternativas\n",
+ "> Vamos medir o tempo para avaliarmos o que é mais rápido. Qual solução é mais rápida?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Z0613on8z5VH"
+ },
+ "source": [
+ "from timeit import default_timer as timer\n",
+ "from datetime import timedelta"
+ ],
+ "execution_count": 8,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NpvvholVxMhs",
+ "outputId": "2dbfff71-3249-4fd8-fd48-c9e1356dde34",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Índices a serem alterados\n",
+ "l_indices"
+ ],
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([567, 14, 616, 484, 208, 105, 349, 615, 622, 847])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 9
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BqXsmMdm1yF-"
+ },
+ "source": [
+ "#### Solução 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "FiiOrlnbgKOD",
+ "outputId": "82a3c137-568d-4776-d952-11d00b1e40e7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Alteração dos salários dos índices propostos\n",
+ "start = timer()\n",
+ "for i_indice in l_indices:\n",
+ " a_salarios_copia[i_indice] = 2*a_salarios[i_indice] # Loop para os índices a serem alterados (manualmente)\n",
+ "\n",
+ "a_salarios_copia[:30]\n",
+ "end = timer()\n",
+ "print(timedelta(seconds=end-start))"
+ ],
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0:00:00.000094\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FgvKC-aFzWpZ"
+ },
+ "source": [
+ "#### Solução 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XWlQC5Jazt26",
+ "outputId": "8640d081-99ae-4235-e2de-620d6152193b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "start = timer()\n",
+ "a_salarios_copia2[l_indices] = 2*a_salarios_copia2[l_indices]\n",
+ "a_salarios_copia2[:30]\n",
+ "end = timer()\n",
+ "print(timedelta(seconds=end-start))"
+ ],
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0:00:00.000090\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "U92w03afhrmC"
+ },
+ "source": [
+ "### Compare"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Ls-jCFCYhtD8",
+ "outputId": "04b7eff2-67d0-4f8f-8812-0b4be9bb7cb7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ }
+ },
+ "source": [
+ "# Antes\n",
+ "a_salarios[l_indices]"
+ ],
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([ 826.43, 1088.61, 1121.95, 833.96, 1165.97, 1081.13, 1078.51,\n",
+ " 1094.67, 904.32, 1128.66])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 12
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nwwU06OahzD2",
+ "outputId": "e18448a4-97f4-452c-da95-3d1db69b1033",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ }
+ },
+ "source": [
+ "# Depois\n",
+ "a_salarios_copia[l_indices]"
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1652.85, 2177.23, 2243.89, 1667.93, 2331.93, 2162.26, 2157.02,\n",
+ " 2189.34, 1808.63, 2257.32])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "qyUUdHmtisJS",
+ "outputId": "779e41e6-cb77-4966-b5d5-fe4d5b4f2ab2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "# 30 primeiras elementos de a_salarios\n",
+ "a_salarios[:30]"
+ ],
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1295.63, 1156.44, 1250.57, 1101.48, 1074.9 , 1149.93, 1032.39,\n",
+ " 1151.23, 1158.81, 1182.97, 839. , 1112.47, 1117.72, 1011.08,\n",
+ " 1088.61, 1104.14, 915.72, 1162.71, 946.36, 865.97, 936.09,\n",
+ " 954.29, 942.71, 908.55, 1015.57, 1051.34, 930.8 , 994.29,\n",
+ " 961.46, 903.51])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "CJ1FEjlCi0-n",
+ "outputId": "5c6c8845-8f83-4047-9f4f-3034d2ec2af7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "# 30 primeiras posições de a_salarios_copia\n",
+ "a_salarios_copia[:30]"
+ ],
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1295.63, 1156.44, 1250.57, 1101.48, 1074.9 , 1149.93, 1032.39,\n",
+ " 1151.23, 1158.81, 1182.97, 839. , 1112.47, 1117.72, 1011.08,\n",
+ " 2177.23, 1104.14, 915.72, 1162.71, 946.36, 865.97, 936.09,\n",
+ " 954.29, 942.71, 908.55, 1015.57, 1051.34, 930.8 , 994.29,\n",
+ " 961.46, 903.51])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wKbSUgxxiOUL"
+ },
+ "source": [
+ "### Algumas Estatísticas descritivas:\n",
+ "#### Antes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZnmykyahLWX9",
+ "outputId": "b2c70db1-2870-48e7-c031-94f122415bc8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "f'Média: {np.mean(a_salarios)}; Mediana: {np.median(a_salarios)}; STD: {np.std(a_salarios)}'"
+ ],
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Média: 1047.150212238584; Mediana: 1047.631166829137; STD: 101.18708333868835'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 16
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "80H92CIjibYJ"
+ },
+ "source": [
+ "#### Depois"
+ ],
+ "execution_count": 17,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5iO-BAikieHJ",
+ "outputId": "ea72b3f5-5682-4971-e1f5-26aec68bc43c",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "f'Média: {np.mean(a_salarios_copia)}; Mediana: {np.median(a_salarios_copia)}; STD: {np.std(a_salarios_copia)}'"
+ ],
+ "execution_count": 18,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Média: 1057.4744151862524; Mediana: 1048.089607774499; STD: 144.64306489539533'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 18
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ILhNe80xW5C6"
+ },
+ "source": [
+ "### Solução do desafio"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "U993i1GJg2hk",
+ "outputId": "bf91af51-aac7-4008-8cc3-342573752205",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 271
+ }
+ },
+ "source": [
+ "# Import a biblioteca seaborn:\n",
+ "import seaborn as sns\n",
+ "sns.boxplot(y = a_salarios_copia)"
+ ],
+ "execution_count": 19,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 19
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAADtCAYAAABTaKWmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWIklEQVR4nO3df4zcdZ3H8eeL3QUBT2m3a8Vtua1OvQv+OCUjkBhyKG3ZEmP9485gLnZQco0CpRgSA9jYAHrx1EhoT0l6oWF7IXCc4tmEdsvW846YXLFbDigtaCdQaNdS1imgpgjd7fv+mE9xWPbHzOx2Zybf1yOZ9Pt9fz8z8/4S9jWffL/fma8iAjMzy4bTGt2AmZnNHoe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5llyJShL2mhpF9I2idpr6Q1Y7bfKCkkzUvrkrReUlHSk5IuqBhbkLQ/PQozvztmZjaZ9irGjAA3RsRjkv4C2C1pICL2SVoILANeqBi/HFicHhcBdwEXSZoLrAPyQKTX2RIRL8/g/piZ2SSmDP2IOAwcTst/kPQ00A3sA+4Avg78rOIpK4DNUf7W105J50g6F7gUGIiIowCSBoBe4L6J3nvevHnR09NTx26ZmWXX7t27fxcRXeNtq2am/yZJPcDHgUclrQCGIuIJSZXDuoGDFeuHUm2i+tj3WAWsAjjvvPMYHByspUUzs8yT9PxE26o+kSvpncBPgBsoH/K5BfjmtLsbIyI2RkQ+IvJdXeN+UJmZWZ2qCn1JHZQD/96IeBD4ALAIeELSAWAB8Jik9wJDwMKKpy9ItYnqZmY2S6q5ekfA3cDTEfEDgIjYExHviYieiOihfKjmgoh4EdgCrExX8VwMvJrOC2wHlkmaI2kO5RPA20/NbpmZ2XiqOab/SeCLwB5Jj6faLRGxdYLxW4ErgCJwDPgSQEQclXQ7sCuNu+3kSV0zM5sd1Vy980tAU4zpqVgO4NoJxm0CNtXWolnzKZVK3Hrrraxbt47Ozs5Gt2NWNX8j16wOfX197Nmzh82bNze6FbOaOPTNalQqlejv7yci6O/vp1QqNbols6o59M1q1NfXx4kTJwAYHR31bN9aikPfrEY7duxgZGQEgJGREQYGBhrckVn1HPpmNVqyZAnt7eVrINrb21m6dGmDOzKrnkPfrEaFQoHTTiv/6bS1tbFy5coGd2RWPYe+WY06Ozvp7e1FEr29vb5k01pKTT+4ZmZlhUKBAwcOeJZvLcczfTOzDHHom9XBX86yVuXQN6tR5Zeztm3b5i9nWUtx6JvVqK+vj+PHjwNw/Phxz/atpTj0zWo0MDBA+XcFISJ4+OGHG9yRWfUc+mY1mj9//qTrZs3MoW9WoyNHjky6btbMHPpmNVq6dCnlG8qBJJYtW9bgjsyqV83tEhdK+oWkfZL2SlqT6t+T9IykJyX9VNI5Fc+5WVJR0q8lXV5R7021oqSbTs0umZ1ahULhLb+94y9oWSupZqY/AtwYEecDFwPXSjofGAA+HBEfBX4D3AyQtl0JfAjoBX4kqU1SG/BDYDlwPvCFNNaspXR2dtLd3Q1Ad3e3f4bBWsqUoR8RhyPisbT8B+BpoDsiHo6IkTRsJ7AgLa8A7o+I1yPiOcr3yr0wPYoR8WxEvAHcn8aatZRSqcTQ0BAAQ0NDvk7fWkpNx/Ql9QAfBx4ds+nLwLa03A0crNh2KNUmqo99j1WSBiUNDg8P19Ke2azo6+t7y+/p+zp9ayVVh76kdwI/AW6IiN9X1L9B+RDQvTPRUERsjIh8ROS7urpm4iXNZpSv07dWVlXoS+qgHPj3RsSDFfWrgM8A/xAn/wpgCFhY8fQFqTZR3ayl+Dp9a2XVXL0j4G7g6Yj4QUW9F/g68NmIOFbxlC3AlZLOkLQIWAz8CtgFLJa0SNLplE/2bpm5XTGbHYcPH5503ayZVfN7+p8EvgjskfR4qt0CrAfOAAbSNcs7I+IrEbFX0gPAPsqHfa6NiFEASdcB24E2YFNE7J3RvTGbBR0dHbz++utvWTdrFVOGfkT8EtA4m7ZO8pxvA98ep751sueZtYI//vGPk66bNTN/I9esRj09PZOumzUzh75ZjdauXTvpulkzc+ib1SiXy705u+/p6SGXyzW2IbMaOPTN6rB27VrOPvtsz/Kt5VRz9Y6ZjZHL5XjooYca3YZZzTzTNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0DczyxCHvplZhlRz56yFkn4haZ+kvZLWpPpcSQOS9qd/56S6JK2XVJT0pKQLKl6rkMbvl1Q4dbtlZmbjqWamPwLcGBHnAxcD10o6H7gJ+HlELAZ+ntYBllO+ReJiYBVwF5Q/JIB1wEXAhcC6kx8UZmY2O6YM/Yg4HBGPpeU/AE8D3cAKoC8N6wM+l5ZXAJujbCdwjqRzgcuBgYg4GhEvAwNA74zujZmZTaqmY/qSeoCPA48C8yPi5B2hXwTmp+Vu4GDF0w6l2kR1MzObJVWHvqR3Aj8BboiI31dui4gAYiYakrRK0qCkweHh4Zl4STMzS6oKfUkdlAP/3oh4MJWPpMM2pH9fSvUhYGHF0xek2kT1t4iIjRGRj4h8V1dXLftiZmZTqObqHQF3A09HxA8qNm0BTl6BUwB+VlFfma7iuRh4NR0G2g4skzQnncBdlmpmZjZLqrlz1ieBLwJ7JD2earcA3wEekHQ18Dzw+bRtK3AFUASOAV8CiIijkm4HdqVxt0XE0RnZCzMzq4rKh+ObUz6fj8HBwUa3YWbWUiTtjoj8eNv8jVwzswxx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTN6lAqlbj++usplUqNbsWsJg59szr09fWxZ88eNm/e3OhWzGpSze0SN0l6SdJTFbWPSdop6fF0E/MLU12S1ksqSnpS0gUVzylI2p8ehfHey6wVlEol+vv7iQj6+/s927eWUs1M/x6gd0ztu8CtEfEx4JtpHWA5sDg9VgF3AUiaC6wDLgIuBNal++SatZy+vj5OnDgBwOjoqGf71lKmDP2IeAQYey/bAN6Vlt8N/DYtrwA2R9lO4BxJ5wKXAwMRcTQiXgYGePsHiVlL2LFjByMjIwCMjIwwMDDQ4I7MqlfvMf0bgO9JOgh8H7g51buBgxXjDqXaRPW3kbQqHTIaHB4errM9s1NnyZIltLe3A9De3s7SpUsb3JFZ9eoN/a8CX4uIhcDXgLtnqqGI2BgR+YjId3V1zdTLms2YQqHAaaeV/3Ta2tpYuXJlgzsyq169oV8AHkzL/0H5OD3AELCwYtyCVJuobtZyOjs76e3tRRK9vb10dnY2uiWzqtUb+r8F/jYtfxrYn5a3ACvTVTwXA69GxGFgO7BM0px0AndZqpm1pEKhwEc+8hHP8q3ltE81QNJ9wKXAPEmHKF+F84/AnZLagT9RvlIHYCtwBVAEjgFfAoiIo5JuB3alcbdFxNiTw2Yto7Ozk/Xr1ze6DbOaKSIa3cOE8vl8DA4ONroNM7OWIml3ROTH2+Zv5JqZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb1YH30TFWpVD36wOvomKtSqHvlmNSqUS27ZtIyLYtm2bZ/vWUhz6ZjXq6+t78/f0jx8/7tm+tRSHvlmNBgYGOPnzJRHBww8/3OCOzKrn0Der0fz58yddN2tmDn2zGh05cmTSdbNm5tA3q9HSpUuRBIAkli1b1uCOzKrn0DerUaFQoKOjA4COjg7fSMVaypShL2mTpJckPTWmvlrSM5L2SvpuRf1mSUVJv5Z0eUW9N9WKkm6a2d0wmz2Vt0tcvny5b5doLWXKO2cB9wD/Arx5XZqkTwErgL+JiNclvSfVzweuBD4EvA/YIemD6Wk/BJYCh4BdkrZExL6Z2hGz2VQoFDhw4IBn+dZypgz9iHhEUs+Y8leB70TE62nMS6m+Arg/1Z+TVOTPN00vRsSzAJLuT2Md+taSfLtEa1X1HtP/IHCJpEcl/Y+kT6R6N3CwYtyhVJuobmZms6iawzsTPW8ucDHwCeABSe+fiYYkrSLdaP28886biZc0M7Ok3pn+IeDBKPsVcAKYBwwBCyvGLUi1iepvExEbIyIfEfmurq462zMzs/HUG/r/CXwKIJ2oPR34HbAFuFLSGZIWAYuBXwG7gMWSFkk6nfLJ3i3Tbd7MzGoz5eEdSfcBlwLzJB0C1gGbgE3pMs43gEKUf4xkr6QHKJ+gHQGujYjR9DrXAduBNmBTROw9BftjZmaT0MkfjmpG+Xw+BgcHG92GmVlLkbQ7IvLjbfM3cs3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0DczyxCHvplZhkwZ+pI2SXop3SVr7LYbJYWkeWldktZLKkp6UtIFFWMLkvanR2Fmd8PMzKpRzUz/HqB3bFHSQmAZ8EJFeTnl++IuBlYBd6WxcynfZvEi4EJgnaQ502ncrJFKpRLXX389pVKp0a2Y1WTK0I+IR4Cj42y6A/g6UHm/xRXA5ijbCZwj6VzgcmAgIo5GxMvAAON8kJi1ir6+Pvbs2cPmzZsb3YpZTeo6pi9pBTAUEU+M2dQNHKxYP5RqE9XHe+1VkgYlDQ4PD9fTntkpVSqV6O/vJyLo7+/3bN9aSs2hL+ks4BbgmzPfDkTExojIR0S+q6vrVLyF2bT09fVx4sQJAEZHRz3bt5ZSz0z/A8Ai4AlJB4AFwGOS3gsMAQsrxi5ItYnqZi1nx44djIyMADAyMsLAwECDOzKrXs2hHxF7IuI9EdETET2UD9VcEBEvAluAlekqnouBVyPiMLAdWCZpTjqBuyzVzFrOJZdcMum6WTOr5pLN+4D/Bf5K0iFJV08yfCvwLFAE/hW4BiAijgK3A7vS47ZUM2s5ETH1ILMmpWb+Hzifz8fg4GCj2zB7iyuuuIJjx469uX7WWWexdevWBnZk9laSdkdEfrxt/kauWY2WLFlCW1sbAG1tbSxdurTBHZlVr73RDVjr2LBhA8VisdFtNNzx48cZHR0F4MSJE+zfv581a9Y0uKvGyuVyrF69utFtWBU80zerUUdHB+3t5fnS3Llz6ejoaHBHZtXzTN+q5pncn11zzTU8//zzbNy4kc7Ozka3Y1Y1z/TN6tDR0UEul3PgW8tx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGVLNnbM2SXpJ0lMVte9JekbSk5J+Kumcim03SypK+rWkyyvqvalWlHTTzO+KmZlNpZqZ/j1A75jaAPDhiPgo8BvgZgBJ5wNXAh9Kz/mRpDZJbcAPgeXA+cAX0lgzM5tFU4Z+RDwCHB1TezgiRtLqTmBBWl4B3B8Rr0fEc5TvlXthehQj4tmIeAO4P401M7NZNBPH9L8MbEvL3cDBim2HUm2i+ttIWiVpUNLg8PDwDLRnZmYnTSv0JX0DGAHunZl2ICI2RkQ+IvJdXV0z9bJmZsY07pwl6SrgM8BlERGpPAQsrBi2INWYpG5mZrOkrpm+pF7g68BnI+JYxaYtwJWSzpC0CFgM/ArYBSyWtEjS6ZRP9m6ZXutmZlarKWf6ku4DLgXmSToErKN8tc4ZwIAkgJ0R8ZWI2CvpAWAf5cM+10bEaHqd64DtQBuwKSL2noL9MTOzSUwZ+hHxhXHKd08y/tvAt8epbwW21tSdmZnNKH8j18wsQxz6ZmYZ4tA3M8uQui/ZzIoNGzZQLBYb3YY1mZP/T6xZs6bBnVizyeVyrF69utFtTMihP4ViscjjTz3N6FlzG92KNZHT3ih/NWX3s0ca3Ik1k7ZjR6ce1GAO/SqMnjWX1/76ika3YWZN7sxnmv8CRR/TNzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDPHVO1MYGhqi7dirLXFW3swaq+1YiaGhkakHNpBn+mZmGeKZ/hS6u7t58fV2X6dvZlM685mtdHfPb3Qbk/JM38wsQ6YMfUmbJL0k6amK2lxJA5L2p3/npLokrZdUlPSkpAsqnlNI4/dLKpya3TEzs8lUM9O/B+gdU7sJ+HlELAZ+ntYBllO+ReJiYBVwF5Q/JCjfcesi4EJg3ckPCjMzmz1Thn5EPAKM/RWhFUBfWu4DPldR3xxlO4FzJJ0LXA4MRMTRiHgZGODtHyRmZnaK1XtMf35EHE7LLwInz1x0Awcrxh1KtYnqbyNplaRBSYPDw8N1tmdmZuOZ9onciAggZqCXk6+3MSLyEZHv6uqaqZc1MzPqv2TziKRzI+JwOnzzUqoPAQsrxi1ItSHg0jH1/67zvWdd27Gj/nKWvcVpf/o9ACfe8a4Gd2LNpPx7+s19yWa9ob8FKADfSf/+rKJ+naT7KZ+0fTV9MGwH/qni5O0y4Ob62549uVyu0S1YEyoW/wBA7v3N/Qdus21+02fGlKEv6T7Ks/R5kg5RvgrnO8ADkq4Gngc+n4ZvBa4AisAx4EsAEXFU0u3ArjTutoho/lvMQFPf9swa5+RtEu+8884Gd2JWmylDPyK+MMGmy8YZG8C1E7zOJmBTTd2ZmdmM8jdyzcwyxKFvZpYhDn0zswxx6JuZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MMceibmWWIQ9/MLEMc+mZmGeLQNzPLEIe+mVmGTCv0JX1N0l5JT0m6T9I7JC2S9KikoqR/l3R6GntGWi+m7T0zsQNmZla9ukNfUjdwPZCPiA8DbcCVwD8Dd0REDngZuDo95Wrg5VS/I40zM7NZNN3DO+3AmZLagbOAw8CngR+n7X3A59LyirRO2n6ZJE3z/c3MrAZ1h35EDAHfB16gHPavAruBVyJiJA07BHSn5W7gYHruSBrfOfZ1Ja2SNChpcHh4uN72zMxsHNM5vDOH8ux9EfA+4Gygd7oNRcTGiMhHRL6rq2u6L2dmZhWmc3hnCfBcRAxHxHHgQeCTwDnpcA/AAmAoLQ8BCwHS9ncDpWm8v5mZ1Wg6of8CcLGks9Kx+cuAfcAvgL9LYwrAz9LylrRO2v5fERHTeH8zM6tR+9RDxhcRj0r6MfAYMAL8H7AReAi4X9K3Uu3u9JS7gX+TVASOUr7Sx1rIhg0bKBaLjW6jKZz877BmzZoGd9Iccrkcq1evbnQbVoW6Qx8gItYB68aUnwUuHGfsn4C/n877mTWLjo4OXnnlFV577TXOPPPMRrdjVrVphb5li2dyf3bVVVfxyiuv8MYbb7Bx48ZGt2NWNf8Mg1mNisUiBw4cAODAgQM+5GUtxaFvVqNvfetbk66bNTOHvlmNTs7yJ1o3a2YOfbMa9fT0TLpu1swc+mY1Wrt27aTrZs3MoW9Wo1wu9+bsvqenh1wu19iGzGrg0Derw9q1azn77LM9y7eW4+v0zeqQy+V46KGHGt2GWc080zczyxCHvplZhjj0zcwyxKFvZpYhauaftJc0DDzf6D7MJjAP+F2jmzAbx19GxLi3Hmzq0DdrZpIGIyLf6D7MauHDO2ZmGeLQNzPLEIe+Wf189xRrOT6mb2aWIZ7pm5lliEPfzCxDHPpmZhni0DczyxCHvplZhvw/5I+5LV0j8I0AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VtenLK1uK1Pi"
+ },
+ "source": [
+ "Consegue identificar os outliers do array?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e3sHuGVGFBdW"
+ },
+ "source": [
+ "## Objetivo\n",
+ "> Substituir os outliers por mediana. \n",
+ "\n",
+ "* Como fazer isso?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RSegPNKCI-dS"
+ },
+ "source": [
+ "### Siga os passos a seguir\n",
+ "1. Calcule estatísticas descritivas antes das transformações par avaliar o impacto;\n",
+ " * Calcule média, mediana e desvio-padrão dos dados originais;\n",
+ "2. Calcule os valores a seguir:\n",
+ " * Q1, Q3\n",
+ " * IQR = Q3-Q1\n",
+ " * lim_inferior = Q1-1.5\\*IQR\n",
+ " * lim_superior = Q3+1.5\\*IQR\n",
+ "3. Proceda à substituição:\n",
+ " * Se a_salarios_copia[i] < lim_inferior então a_salarios_copia[i]= Mediana\n",
+ " * Se a_salarios_copia[i] > lim_superior então a_salarios_copia[i]= Mediana\n",
+ "4. Calcule as estatísticas descritivas após as substituições e compare com os valores antes das transformações."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9DQ7YnWaFn4v"
+ },
+ "source": [
+ "### Minha solução\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RBXJbTeGLC7Q"
+ },
+ "source": [
+ "1. Estatísticas Descritivas antes das transformações:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QueKYn7MLG12",
+ "outputId": "11daf3fe-c4c9-446e-cb46-cf6378c02779",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "# Algumas estatísticas descritivas:\n",
+ "f'Média: {np.mean(a_salarios_copia)}; Mediana: {np.median(a_salarios_copia)}; STD: {np.std(a_salarios_copia)}'"
+ ],
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Média: 1057.4744151862524; Mediana: 1048.089607774499; STD: 144.64306489539533'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 20
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oOBJ8INWL5fo"
+ },
+ "source": [
+ "Observe o quanto nossos dados estão distorcidos dos valores originalmente utilizados."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MX-fJeh2MBTD"
+ },
+ "source": [
+ "2. Calcular Q1, Q3 e IQR"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JlsPiQeGMGeU"
+ },
+ "source": [
+ "Q1 = np.percentile(a_salarios_copia, q = [25])\n",
+ "Q3 = np.percentile(a_salarios_copia, q = [75])\n",
+ "Q2 = np.percentile(a_salarios_copia, q = [50])\n",
+ "IQR = Q3-Q1\n",
+ "lim_inferior = Q1-1.5*IQR\n",
+ "lim_superior = Q3+1.5*IQR"
+ ],
+ "execution_count": 21,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VF2NJ3rCeI1_",
+ "outputId": "e8e38919-ee69-4d21-db00-1abb7bd4fb9b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "f'Q1: {Q1}; Q3: {Q3}; lim_inferior: {lim_inferior}; lim_superior: {lim_superior}'"
+ ],
+ "execution_count": 22,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Q1: [974.41]; Q3: [1119.81]; lim_inferior: [756.33]; lim_superior: [1337.89]'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 22
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JjnwJ7HwMxcl"
+ },
+ "source": [
+ "3. Substituir\n",
+ "* Se a_conjunto1[i] < lim_inferior então a_conjunto1[i] = Mediana\n",
+ "* Se a_conjunto1[i] > Lia_Sup então a_conjunto1[i] = Mediana"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "hcAn-IwVfbcI"
+ },
+ "source": [
+ "a_salarios2 = a_salarios_copia.copy()"
+ ],
+ "execution_count": 23,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "J3SSE45oM9oh",
+ "outputId": "53db1a1d-8483-40f9-8cbc-196b79e449ff",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "a_salarios2[a_salarios2 < lim_inferior[0]] = Q2[0]\n",
+ "#para todos que atendam essa condição ele vai receber o valor da mediana\n",
+ "a_salarios2[a_salarios2 > lim_superior[0]] = Q2[0]\n",
+ "a_salarios2[:30]"
+ ],
+ "execution_count": 24,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1295.63, 1156.44, 1250.57, 1101.48, 1074.9 , 1149.93, 1032.39,\n",
+ " 1151.23, 1158.81, 1182.97, 839. , 1112.47, 1117.72, 1011.08,\n",
+ " 1048.09, 1104.14, 915.72, 1162.71, 946.36, 865.97, 936.09,\n",
+ " 954.29, 942.71, 908.55, 1015.57, 1051.34, 930.8 , 994.29,\n",
+ " 961.46, 903.51])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 24
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VEGFio0Nfj7O"
+ },
+ "source": [
+ "4. Estatísticas Descritivas para avaliarmos o impacto:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gX1LZHFqfjFQ",
+ "outputId": "31a986a8-79bd-4c04-daf2-3fa1e8ca9f44",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "# Algumas estatísticas descritivas:\n",
+ "f'Média: {np.mean(a_salarios2)}; Mediana: {np.median(a_salarios2)}; STD: {np.std(a_salarios2)}'"
+ ],
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Média: 1047.3019702056902; Mediana: 1048.089607774499; STD: 98.3265929249586'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 25
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-xnguZ7XgyvK",
+ "outputId": "98be8554-e55a-4d5b-e418-4712377c627e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 269
+ }
+ },
+ "source": [
+ "# Import a biblioteca seaborn:\n",
+ "import seaborn as sns\n",
+ "sns.boxplot(y = a_salarios2)"
+ ],
+ "execution_count": 26,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 26
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uEPFcBjFhETQ"
+ },
+ "source": [
+ "Como podem ver, os outliers desapareceram, como queríamos."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tHfzjW_ymKuR"
+ },
+ "source": [
+ "___\n",
+ "# **Valores únicos**\n",
+ "> Considere o array a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HzmQgWZVmUUD"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.random.randint(0, 100, 100)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Dm9ky1F1mrNA"
+ },
+ "source": [
+ "Quem são os valores únicos do array?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "G-LPRqc-mS5j"
+ },
+ "source": [
+ "np.unique(a_conjunto1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uXZZoTd6nMuq"
+ },
+ "source": [
+ "___\n",
+ "# **Diferença entre dois arrays**\n",
+ "> O resultado é um array com os **valores únicos de A que não estão em B**. Na teoria de conjuntos escrevemos $A - B = A - A \\cap B$.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Fonte: [Python Set](https://www.learnbyexample.org/python-set/)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uW6i3m9q1ZNs"
+ },
+ "source": [
+ "\n",
+ "* Vamos ver como isso funciona na prática:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vw05sfe22mfk"
+ },
+ "source": [
+ "## Exemplo 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Qqw2do90nQ7k"
+ },
+ "source": [
+ "a_conjunto1 = np.array([0, 1, 2, 4, 5, 7, 8, 8]) # array de valores que serão excluidos em a_conjunto1. Observe que '3' não pertence a a_conjunto1.\n",
+ "a_conjunto2 = np.array([1, 6, 7, 3])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zXJ00pOMorM-"
+ },
+ "source": [
+ "np.setdiff1d(a_conjunto1, a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8GXZNgjfo8lO"
+ },
+ "source": [
+ "Observe que o resultado são os elementos de a_conjunto1 que não pertencem a x_Y. Mas como fica o '3' nesta história?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aJSu6VKb2oc_"
+ },
+ "source": [
+ "## Exemplo 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "N1wahElXTqoB"
+ },
+ "source": [
+ "a_conjunto1 = np.arange(10)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nxDpCMg7T7Rj"
+ },
+ "source": [
+ "a_conjunto2 = np.array([1, 5, 7])\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3LU3qYyiUXqm"
+ },
+ "source": [
+ "np.setdiff1d(a_conjunto1, a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mzZEytrRUioU"
+ },
+ "source": [
+ "Observe que os elementos de a_conjunto2 foram deletados de a_conjunto1. Ok?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gJRcoVRUnaY9"
+ },
+ "source": [
+ "___\n",
+ "# Diferença Simétrica\n",
+ "* Em teoria de conjuntos, chamamos de Diferença Simétrica e escrevemos $(A \\cup B)- (A \\cap B)$.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Fonte: [Python Set](https://www.learnbyexample.org/python-set/)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2Uzzm85Kup3H"
+ },
+ "source": [
+ "* Vamos ver como isso funciona na prática:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1z5wZ8VwpsWN"
+ },
+ "source": [
+ "import numpy as np\n",
+ "a_conjunto1 = np.array([0, 1, 2, 4, 5, 7, 8]) # Observe que [1, 4, 7] pertencem a a_conjunto1, mas 3, não. Portanto:\n",
+ "a_conjunto2 = np.array([1, 4, 7, 3])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Tqd_9XO5p7bo"
+ },
+ "source": [
+ "np.setxor1d(a_conjunto1, a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_meurG3mqS5Y"
+ },
+ "source": [
+ "Como explicamos ou interpretamos este resultado?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Kc8JoKe2nj2n"
+ },
+ "source": [
+ "___\n",
+ "# **União de dois arrays**\n",
+ "> Retorna os valores **únicos** dos dois arrays. Na teoria dos conjuntos, escrevemos:\n",
+ "\n",
+ "$$A \\cup B$$\n",
+ "\n",
+ "\n",
+ "\n",
+ "Fonte: [Python Set](https://www.learnbyexample.org/python-set/)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1LZxorw2p2mg"
+ },
+ "source": [
+ "a_conjunto1 = np.array([0, 1, 2, 4, 5, 7, 8, 8])\n",
+ "\n",
+ "# Observe que [1, 4, 7] pertencem a a_conjunto1, mas 3, não. Portanto:\n",
+ "a_conjunto2 = np.array([1, 4, 7, 3])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "COsZEmSwuY5L"
+ },
+ "source": [
+ "np.union1d(a_conjunto1, a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b53bR-GYRu_3"
+ },
+ "source": [
+ "___\n",
+ "# **Selecionar itens comuns dos arrays X e Y**\n",
+ "* Na teoria de conjuntos, chamamos de intersecção e escrevemos $X \\cap Y$.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Fonte: [Python Set](https://www.learnbyexample.org/python-set/)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "n2ec2tqqR1Gw"
+ },
+ "source": [
+ "* Considere os arrays a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rXVQQvBqR4J-"
+ },
+ "source": [
+ "a_conjunto1 = np.arange(10)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "pZTHhHxGSRfB"
+ },
+ "source": [
+ "a_conjunto2 = np.arange(8, 18)\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MxB2_qHpScMB"
+ },
+ "source": [
+ "Quais são os elementos comuns à X e Y?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "e-rncJHtSfw0"
+ },
+ "source": [
+ "np.intersect1d(a_conjunto1, a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3Bb39sWdfqaF"
+ },
+ "source": [
+ "___\n",
+ "# **Autovalores e Autovetores**\n",
+ "> Autovetor e Autovalor são um dos tópicos mais importantes em Machine Learning.\n",
+ "\n",
+ "Por definição, o escalar $\\lambda$ e o vetor $v$ são autovalor e autovetor da matriz $A$ se\n",
+ "\n",
+ "$$Av = \\lambda v$$\n",
+ "\n",
+ "## Leitura Adicional:\n",
+ "\n",
+ "* [Machine Learning & Linear Algebra — Eigenvalue and eigenvector](https://medium.com/@jonathan_hui/machine-learning-linear-algebra-eigenvalue-and-eigenvector-f8d0493564c9)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XZBKq8nGCUbL"
+ },
+ "source": [
+ "* O array a_conjunto2 tem a seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iYlZGKFUfw-R"
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6EfvIbBNf02Z"
+ },
+ "source": [
+ "# Calcula autovalores e autovetores:\n",
+ "a_autovalores, a_autovalores= np.linalg.eig(a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "v3GtQQvAz9QU"
+ },
+ "source": [
+ "Os autovalores do array a_conjunto2 são:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WvZGyBR1f9vP"
+ },
+ "source": [
+ "a_autovalores"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AuuDRJVh0FC8"
+ },
+ "source": [
+ "Os autovetores do array a_conjunto2 são:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6m4YFAwsf_rA"
+ },
+ "source": [
+ "a_autovalores"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DASn2Un9ZNV-"
+ },
+ "source": [
+ "___\n",
+ "# **Encontrar Missing Values (NaN)**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TKilWBsSXtR4"
+ },
+ "source": [
+ "## Gerar o exemplo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lqLI2ER_ZUMY"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.random.random(100)\n",
+ "\n",
+ "# Inserindo 15 NaN's no array:\n",
+ "np.random.seed(20111974)\n",
+ "l_indices_aleatorios= np.random.randint(0, 100, size = 15)\n",
+ "\n",
+ "for i_indices in l_indices_aleatorios:\n",
+ " #print(i_indices)\n",
+ " a_conjunto1[i_indices] = np.nan"
+ ],
+ "execution_count": 27,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2ZkbMPXMawYh",
+ "outputId": "af5865c5-95fe-4df1-8712-b77543e860c5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": 28,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.53, 0.57, nan, 0.65, 0.86, 0.6 , 0.87, 0.46, nan, 0.64, 0.55,\n",
+ " 0.35, 0.32, nan, 0.85, 0.76, 0.66, 0.33, 0.35, 0.42, 0.31, 0.27,\n",
+ " 0.31, 0.36, 0.6 , 0.02, 0.36, nan, 0.28, 0.37, nan, 0.44, 0.2 ,\n",
+ " 0.21, 0.65, 0.82, 0.72, 0.5 , 0.17, 0.6 , nan, 0.14, nan, 0.71,\n",
+ " 0.07, 0.56, nan, 0.84, 0.21, 0.85, 0.63, 0.38, 0.91, 0.34, 0.07,\n",
+ " 0.1 , 0.85, 0.12, 0.94, 0.16, nan, 0.91, 0.59, 0.37, 0.72, 0.07,\n",
+ " 0.48, 0.78, 0.97, 0.72, 0.29, 0.33, 0.95, 0.24, 0.98, 0.85, 0.63,\n",
+ " 0.57, 0.67, 0.88, nan, nan, nan, 0.68, 0.29, 0.33, 0.98, 0.17,\n",
+ " nan, 0.92, 0.98, 0.76, 0.31, 0.97, 0.08, 0.56, nan, 0.49, 0.07,\n",
+ " 0.11])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 28
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z7Bs75NvbSjx"
+ },
+ "source": [
+ "Ok, inserimos aleatoriamente 14 NaN's no array a_conjunto1. Agora, vamos contar quantos NaN's (já sabemos a resposta!)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hL1Wn0vdX8ur"
+ },
+ "source": [
+ "## Identificar os NaN's"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5R-n3H0xbd6d",
+ "outputId": "9dc32bc4-bb41-4c02-bc64-c98461f3cefd",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "np.isnan(a_conjunto1).sum()\n",
+ "## isnan retorna um valor boleano se é nulo ou não"
+ ],
+ "execution_count": 29,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "14"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 29
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "81IoQ-EVbI5X",
+ "outputId": "fcddc325-8c6c-4545-cf1c-349af38ca954",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "np.isnan\n",
+ "## é uma função"
+ ],
+ "execution_count": 31,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 31
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PB-GAJ71bc7i",
+ "outputId": "0a9a431e-986d-40da-c841-544d25727b38",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 221
+ }
+ },
+ "source": [
+ "array_nulos = np.isnan(a_conjunto1)\n",
+ "array_nulos"
+ ],
+ "execution_count": 32,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([False, False, True, False, False, False, False, False, True,\n",
+ " False, False, False, False, True, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False, False,\n",
+ " True, False, False, True, False, False, False, False, False,\n",
+ " False, False, False, False, True, False, True, False, False,\n",
+ " False, True, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, True, False, False,\n",
+ " False, False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False, True,\n",
+ " True, True, False, False, False, False, False, True, False,\n",
+ " False, False, False, False, False, False, True, False, False,\n",
+ " False])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 32
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Y7hh5uowoa3U"
+ },
+ "source": [
+ "Ok, temos 14 NaN's em a_conjunto1."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iVLQf_bqbyNU"
+ },
+ "source": [
+ "Ok, agora eu quero saber os índices desses NaN's."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kJHxjZiwb5HM",
+ "outputId": "57d4bf56-64bd-4969-9281-d311ac926119",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "i_indices= np.where(np.isnan(a_conjunto1))\n",
+ "## o where retorna a posiçao do array que é true\n",
+ "i_indices"
+ ],
+ "execution_count": 30,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(array([ 2, 8, 13, 27, 30, 40, 42, 46, 60, 80, 81, 82, 88, 96]),)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 30
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "W_jHGNImok7L",
+ "outputId": "cbdcf2d2-3edf-4b8a-9a42-29aa20cbcd94",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Checando... que a posição 2 é nan\n",
+ "a_conjunto1[2]"
+ ],
+ "execution_count": 33,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "nan"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 33
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iPhHAhDYcMWO"
+ },
+ "source": [
+ "Vamos conferir se está correto? Para isso, basta comparar com l_indices_aleatorios:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gxQYslRCe11G"
+ },
+ "source": [
+ "___\n",
+ "# **Deletar NaN's de um array**\n",
+ "> Considere o mesmo array que acabamos de trabalhar. Agora eu quero excluir os NaN's identificados."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AeBARFqNfNnN",
+ "outputId": "eb361064-326a-451c-c4fe-0d14b861fbe8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": 34,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.53, 0.57, nan, 0.65, 0.86, 0.6 , 0.87, 0.46, nan, 0.64, 0.55,\n",
+ " 0.35, 0.32, nan, 0.85, 0.76, 0.66, 0.33, 0.35, 0.42, 0.31, 0.27,\n",
+ " 0.31, 0.36, 0.6 , 0.02, 0.36, nan, 0.28, 0.37, nan, 0.44, 0.2 ,\n",
+ " 0.21, 0.65, 0.82, 0.72, 0.5 , 0.17, 0.6 , nan, 0.14, nan, 0.71,\n",
+ " 0.07, 0.56, nan, 0.84, 0.21, 0.85, 0.63, 0.38, 0.91, 0.34, 0.07,\n",
+ " 0.1 , 0.85, 0.12, 0.94, 0.16, nan, 0.91, 0.59, 0.37, 0.72, 0.07,\n",
+ " 0.48, 0.78, 0.97, 0.72, 0.29, 0.33, 0.95, 0.24, 0.98, 0.85, 0.63,\n",
+ " 0.57, 0.67, 0.88, nan, nan, nan, 0.68, 0.29, 0.33, 0.98, 0.17,\n",
+ " nan, 0.92, 0.98, 0.76, 0.31, 0.97, 0.08, 0.56, nan, 0.49, 0.07,\n",
+ " 0.11])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 34
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "e497B492fFru",
+ "outputId": "68f697f4-4778-43e9-9b4f-87d930eccc46",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 153
+ }
+ },
+ "source": [
+ "a_conjunto1[~np.isnan(a_conjunto1)]\n",
+ "## o til nega a condição e retorna tudo que é falso\n",
+ "## colo o np.isnan gera o arrau com true e false, se eu falo que quero a negação e peço as posições com o falso\n",
+ "## eu estou retirando os nan"
+ ],
+ "execution_count": 36,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.53, 0.57, 0.65, 0.86, 0.6 , 0.87, 0.46, 0.64, 0.55, 0.35, 0.32,\n",
+ " 0.85, 0.76, 0.66, 0.33, 0.35, 0.42, 0.31, 0.27, 0.31, 0.36, 0.6 ,\n",
+ " 0.02, 0.36, 0.28, 0.37, 0.44, 0.2 , 0.21, 0.65, 0.82, 0.72, 0.5 ,\n",
+ " 0.17, 0.6 , 0.14, 0.71, 0.07, 0.56, 0.84, 0.21, 0.85, 0.63, 0.38,\n",
+ " 0.91, 0.34, 0.07, 0.1 , 0.85, 0.12, 0.94, 0.16, 0.91, 0.59, 0.37,\n",
+ " 0.72, 0.07, 0.48, 0.78, 0.97, 0.72, 0.29, 0.33, 0.95, 0.24, 0.98,\n",
+ " 0.85, 0.63, 0.57, 0.67, 0.88, 0.68, 0.29, 0.33, 0.98, 0.17, 0.92,\n",
+ " 0.98, 0.76, 0.31, 0.97, 0.08, 0.56, 0.49, 0.07, 0.11])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 36
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RpvKfJU_fmA6"
+ },
+ "source": [
+ "Observe que os NaN's foram excluidos."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7ereghZPcdh4"
+ },
+ "source": [
+ "EXERCÍCIO - ATRIBUIR A MEDIANA AOS VALORES DA AMOSTRA\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_Dv8MmNYg8zN"
+ },
+ "source": [
+ "___\n",
+ "# **Converter lista em array**\n",
+ "> Considere a lista a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "but6T9dVhFYb",
+ "outputId": "001bc55c-fe58-40ab-ff3c-3ea90d4e71d7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "l_Lista = [np.random.randint(0, 10, 10)]\n",
+ "l_Lista"
+ ],
+ "execution_count": 37,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[array([8, 9, 3, 7, 1, 3, 2, 9, 7, 7])]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 37
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xytj4Eo4hTh9",
+ "outputId": "4c2d1778-13ec-4717-a3a8-d538cfcf896a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "type(l_Lista)"
+ ],
+ "execution_count": 38,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "list"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 38
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qrINdcruhWcH"
+ },
+ "source": [
+ "Convertendo a minha lista para array:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RoSyaX0OhZSE",
+ "outputId": "8b194262-97d5-45ee-9336-3a5c0a5ad802",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto = np.asarray(l_Lista)\n",
+ "a_conjunto"
+ ],
+ "execution_count": 39,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[8, 9, 3, 7, 1, 3, 2, 9, 7, 7]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 39
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dMjTdbBUhlrk",
+ "outputId": "4d140e4f-2e4c-4aa7-8e99-481cb4d1cdc3",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "type(a_conjunto)"
+ ],
+ "execution_count": 40,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "numpy.ndarray"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 40
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Mbm3ZP9DhxDI"
+ },
+ "source": [
+ "___\n",
+ "# Converter tupla em array\n",
+ "> Considere a tupla a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cZxEFYLAh3S_"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "t_numeros = ([np.random.randint(0, 10, 3)], [np.random.randint(0, 10, 3)], [np.random.randint(0, 10, 3)])\n",
+ "t_numeros"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vlTXUJviiAml"
+ },
+ "source": [
+ "type(t_numeros)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "yEaOlq8oh3oh"
+ },
+ "source": [
+ "a_conjunto = np.asarray(t_numeros)\n",
+ "a_conjunto"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PSgQDmRWh3g5"
+ },
+ "source": [
+ "type(a_conjunto)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pH-Ht6yMiqJN"
+ },
+ "source": [
+ "___\n",
+ "# Acrescentar elementos à um array\n",
+ "> Considere o array a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dFaDZInZiwoo"
+ },
+ "source": [
+ "a_conjunto1 = np.arange(5)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "d3zrlf_Ci73Z"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.append(a_conjunto1, [np.random.randint(0, 10, 3), np.random.randint(0, 10, 3), np.random.randint(0, 10, 3)])\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eFRhtk13ojqA"
+ },
+ "source": [
+ "___\n",
+ "# **Converter array 1D num array 2D**\n",
+ "> Considere os arrays a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wYhBgW9Zu6ZP"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(0, 10, 6))\n",
+ "\n",
+ "np.random.seed(19741120)\n",
+ "a_conjunto2 = np.array(np.random.randint(0, 10, 6))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "febs9AUHvs6n"
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "C9OEd-iavvBm"
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KJWjtaWKv0MJ"
+ },
+ "source": [
+ "np.column_stack((a_conjunto1, a_conjunto2)) # Atenção aos parênteses em (a_conjunto1, a_conjunto2)."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xr_WZXJ7pi2D"
+ },
+ "source": [
+ "___\n",
+ "# **Excluir um elemento específico do array usando indices**\n",
+ "> Considere os arrays a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tS0ZzOs8w0dw"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(0, 10, 6))\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7bOJiKDKxEsC"
+ },
+ "source": [
+ "Suponha que eu queira excluir os valores '8' de a_conjunto1. Os índices dos valores '8' são: [0, 1, 3]. Portanto, temos:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SSjueEvjxTJO"
+ },
+ "source": [
+ "a_conjunto1 = np.delete(a_conjunto1, [0, 1, 3])\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mZkGZ2Rgp--5"
+ },
+ "source": [
+ "___\n",
+ "# **Frequência dos valores únicos de um array**\n",
+ "> Considere o array a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Z2BWKfH0xvQ8",
+ "outputId": "0405171f-5590-434a-87be-39d44e18ce17",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(0, 10, 100))\n",
+ "a_conjunto1"
+ ],
+ "execution_count": 41,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([8, 8, 2, 8, 9, 1, 8, 0, 4, 2, 0, 8, 9, 3, 7, 1, 3, 2, 9, 7, 7, 9,\n",
+ " 5, 6, 8, 7, 0, 9, 3, 9, 3, 1, 8, 6, 3, 5, 4, 1, 2, 9, 8, 6, 6, 1,\n",
+ " 0, 9, 2, 0, 7, 5, 5, 4, 4, 2, 7, 2, 7, 9, 3, 1, 5, 0, 1, 2, 3, 8,\n",
+ " 7, 5, 4, 0, 5, 9, 6, 6, 1, 3, 6, 0, 4, 9, 2, 1, 0, 9, 1, 4, 2, 9,\n",
+ " 7, 9, 5, 3, 7, 6, 3, 9, 8, 4, 3, 0])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 41
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s_tdQBsax4rQ"
+ },
+ "source": [
+ "Suponha que eu queira saber quantas vezes o número/elemento '2' aparece em a_conjunto1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6yIlk7pWyAtf",
+ "outputId": "01f739af-34ea-448c-992f-ee587482c359",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "l_itens_unicos, i_count = np.unique(a_conjunto1, return_counts=True)\n",
+ "## é uma função que retorna os itens unicos e a quantidade que aparecem\n",
+ "l_itens_unicos"
+ ],
+ "execution_count": 42,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 42
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DyvrIwS9yZIR"
+ },
+ "source": [
+ "O que significa o output acima?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uO-MPMhXyV9H",
+ "outputId": "4f477738-6362-4177-a6ec-dd559dc9dc71",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "i_count"
+ ],
+ "execution_count": 43,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 10, 10, 11, 8, 8, 8, 10, 10, 15])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 43
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zwoezXrPyofK"
+ },
+ "source": [
+ "Qual a interpretação do output acima?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HgYycSG7yr5e",
+ "outputId": "02fe1140-2976-4715-e211-20d27baf3c87",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ }
+ },
+ "source": [
+ "np.asarray((l_itens_unicos, i_count))"
+ ],
+ "execution_count": 44,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
+ " [10, 10, 10, 11, 8, 8, 8, 10, 10, 15]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 44
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SwIZiJAiy06T"
+ },
+ "source": [
+ "Qual a interpretação do output acima?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Wy_tqAPgdchD"
+ },
+ "source": [
+ "é a frequencia com que cada um aparece"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JpNRpN2Dql3N"
+ },
+ "source": [
+ "___\n",
+ "# **Combinações possíveis de outros arrays**\n",
+ "> Considere o exemplo a seguir:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BUr89dH4zLXD"
+ },
+ "source": [
+ "a_conjunto1 = [2, 4, 6]\n",
+ "a_conjunto2 = [0, 8]\n",
+ "a_conjunto4 = [1, 5]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cEZH6l-Czx7y"
+ },
+ "source": [
+ "np.meshgrid(a_conjunto1, a_conjunto2, a_conjunto4)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "btvmDkEcz0tH"
+ },
+ "source": [
+ "np.array(np.meshgrid(a_conjunto1, a_conjunto2, a_conjunto4))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Z0xhO7rGz059"
+ },
+ "source": [
+ "np.array(np.meshgrid(a_conjunto1, a_conjunto2, a_conjunto4)).T"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "eMv4lFnD0Enn"
+ },
+ "source": [
+ "# Resultado final\n",
+ "a_conjunto3 = np.array(np.meshgrid(a_conjunto1, a_conjunto2, a_conjunto4)).T.reshape(-1,3)\n",
+ "a_conjunto3"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Rz80YANfAh2k"
+ },
+ "source": [
+ "___\n",
+ "# **Wrap Up**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_cyhMsAVXxGC"
+ },
+ "source": [
+ "___\n",
+ "# **Exercícios**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kNjovMw3uJ3R"
+ },
+ "source": [
+ "## Exercício 1 - Selecionar os números pares\n",
+ "> Dado o 1D array abaixo, selecionar somente os números pares."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "isDzQjwjBX3V",
+ "outputId": "ad54cd80-fa6e-4772-a869-1b7f98b09725",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
+ "a_conjunto1"
+ ],
+ "execution_count": 45,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 45
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Kq1zt-uO1HXv"
+ },
+ "source": [
+ "### **Minha solução**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "YFmK_n2M1Ks9",
+ "outputId": "496556f7-9ff2-40f7-8cbf-2a1588793e40",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1[a_conjunto1 % 2 == 0]"
+ ],
+ "execution_count": 46,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0, 2, 4, 6, 8])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 46
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sScYG0hp05vb"
+ },
+ "source": [
+ "___\n",
+ "## Exercício 2 - Substituir pela mediana\n",
+ "> Dado o array 1D abaixo, substituir os números pares pela mediana de a_conjunto1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XLZ-DIWU1WFs",
+ "outputId": "aebcdfd3-b244-4cb2-f33b-a8083d241d17",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
+ "a_conjunto1"
+ ],
+ "execution_count": 47,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 47
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9c4QWJno1WVB"
+ },
+ "source": [
+ "### **Minha solução**\n",
+ "* Primeiramente, precisamos calcular a mediana.\n",
+ "* Depois, substituimos os valores pares de a_conjunto1 pela mediana encontrada anteriormente. Ok?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rx7NGAO01Wfb",
+ "outputId": "575c869f-1a28-49db-9516-47c84cecacc2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1[a_conjunto1 % 2 == 0] = np.median(a_conjunto1)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": 48,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([4, 1, 4, 3, 4, 5, 4, 7, 4, 9])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 48
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2c_AphX82qp8"
+ },
+ "source": [
+ "Verificando..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9kVta0Cr13Z9",
+ "outputId": "cb8af387-8353-49f1-cf9e-37ee7c77b607",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "f'A média de a_conjunto1 é: {np.median(a_conjunto1)}'"
+ ],
+ "execution_count": 49,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'A média de a_conjunto1 é: 4.0'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 49
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "L9O-Hf5x26TY"
+ },
+ "source": [
+ "___\n",
+ "## Exercício 3 - Reshape\n",
+ "> Dado o array 1D abaixo, reshape para um array 2D com 3 colunas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0_laUvtB4Wl-",
+ "outputId": "34954fdf-6e28-477c-ac93-03b510461a21",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Define seed\n",
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(1, 10, size = 15))\n",
+ "a_conjunto1"
+ ],
+ "execution_count": 50,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([9, 9, 3, 9, 2, 9, 1, 5, 3, 1, 9, 4, 8, 2, 4])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 50
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dKzEX8TK5b4Z"
+ },
+ "source": [
+ "### **Minha solução**\n",
+ "* O array 1D a_conjunto1 acima possui 15 elementos. Como queremos transformá-lo num array 2D com 3 colunas, então cada coluna terá 5 elementos."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "r5hJ-wMwjXPR",
+ "outputId": "2eadc741-755c-46d2-bd8c-ad0d3cc8ad8e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "a_conjunto1.reshape(-1,3)"
+ ],
+ "execution_count": 51,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[9, 9, 3],\n",
+ " [9, 2, 9],\n",
+ " [1, 5, 3],\n",
+ " [1, 9, 4],\n",
+ " [8, 2, 4]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 51
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "I-j5yVD04249"
+ },
+ "source": [
+ "a_conjunto1.reshape(5, 3) \n",
+ "# Poderia ser a_conjunto1.reshape(-1, 3), onde \"-1\" pede para o NumPy calcular o número de linhas. "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F1vfS8jE6L0_"
+ },
+ "source": [
+ "___\n",
+ "## Exercício 4 - Reshape\n",
+ "> Dado o array 1D abaixo, reshape para um array 3D com 2 colunas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xcN-bez56L1D"
+ },
+ "source": [
+ "# Define seed\n",
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(1, 10, size = 16))\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7iICnOyG6fcj"
+ },
+ "source": [
+ "### **Minha solução**\n",
+ "* O array 1D a_conjunto1 acima possui 16 elementos. Queremos transformá-lo num array 3D com 2 colunas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vdq5ybuD6fcn"
+ },
+ "source": [
+ "a_conjunto1.reshape(-1, 2) # O valor \"-1\" na posição das linhas pede ao NumPy para calcular o número de linhas automaticamente."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "haQfWPcCs_H0"
+ },
+ "source": [
+ "## Exercício 5\n",
+ "Para mais exercícios envolvendo arrays, visite a página [Python: Array Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/array/)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LQQL0JS2tnc0"
+ },
+ "source": [
+ "## Exercício 6\n",
+ "Para mais exercícios envolvendo matemática, viste a página [Python Math: - Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/math/index.php)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qNskKFy9t4D5"
+ },
+ "source": [
+ "## Exercício 7\n",
+ "Para mais exercícios envolvendo NumPy em geral, visite a página [NumPy Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/numpy/index.php)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qqc1AiHXuKZ5"
+ },
+ "source": [
+ "## Exercício 8\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jYrgc3KvtmLy"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
From b7eb0e404bb8b4b6ed419eb273f7e9410c25dc13 Mon Sep 17 00:00:00 2001
From: MariaJacobs70 <72224154+MariaJacobs70@users.noreply.github.com>
Date: Wed, 7 Oct 2020 14:42:36 -0300
Subject: [PATCH 2/9] Criado usando o Colaboratory
---
Notebooks/NB02__Numpy_alterado.ipynb | 5789 ++++++++++++++++++++++++++
1 file changed, 5789 insertions(+)
create mode 100644 Notebooks/NB02__Numpy_alterado.ipynb
diff --git a/Notebooks/NB02__Numpy_alterado.ipynb b/Notebooks/NB02__Numpy_alterado.ipynb
new file mode 100644
index 000000000..4ea92acae
--- /dev/null
+++ b/Notebooks/NB02__Numpy_alterado.ipynb
@@ -0,0 +1,5789 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "NB02__Numpy.ipynb",
+ "provenance": [],
+ "collapsed_sections": [
+ "n8BIbzQbNWUo",
+ "7eS94uQ4NhVR",
+ "SYOgJpGYVLUu",
+ "CaHFxk98W5if",
+ "ReWUyWiHXCnc",
+ "CqszHxaKHr2h",
+ "tXgF1Wl9gHKY",
+ "Fotx7XUquAo8",
+ "36kmLUYDvsUI",
+ "SWO2GdNovxAp",
+ "vpN54l4vxze5",
+ "u4HOf9SNytSq",
+ "6BQ9oZiD9hg5",
+ "tz5-QdrX9vct",
+ "p1muBgMX8NK4",
+ "FxTC2-U88ajk",
+ "z8EYn0pP25Rh"
+ ],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "NUMPY \u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma_numeros1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# O valor \"-1\" na posição das linhas pede ao NumPy para calcular o número de linhas automaticamente.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 15 into shape (2)"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pZS4b4-y708q"
+ },
+ "source": [
+ "Porque temos esse erro?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4disywvR8HeH"
+ },
+ "source": [
+ "E se fizermos..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3oEAAXTp8I7Z",
+ "outputId": "e8c8a90f-c34a-4304-d9b4-fd7f04ce224f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Define seed\n",
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(1, 10, size = 16)) # Observe que agora temos 16 elementos\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([9, 9, 3, 9, 2, 9, 1, 5, 3, 1, 9, 4, 8, 2, 4, 3])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 21
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iUhth0QV8Rpt"
+ },
+ "source": [
+ "Reshapping..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9D1y7uD88Qip",
+ "outputId": "e7d22bcd-c10f-4ea3-e41b-03f6f98a054f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 153
+ }
+ },
+ "source": [
+ "a_conjunto1.reshape(-1, 2) # O valor \"-1\" na posição das linhas pede ao NumPy para calcular o número de linhas automaticamente."
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[9, 9],\n",
+ " [3, 9],\n",
+ " [2, 9],\n",
+ " [1, 5],\n",
+ " [3, 1],\n",
+ " [9, 4],\n",
+ " [8, 2],\n",
+ " [4, 3]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 22
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ALh-sq7DMnN5",
+ "outputId": "db373349-7910-4f1f-93f3-8ac8f67da8b8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 153
+ }
+ },
+ "source": [
+ "# OU --> Neste caso, estamos reshaping o array em 8 linhas e 2 colunas\n",
+ "a_conjunto1.reshape(8, -1)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[9, 9],\n",
+ " [3, 9],\n",
+ " [2, 9],\n",
+ " [1, 5],\n",
+ " [3, 1],\n",
+ " [9, 4],\n",
+ " [8, 2],\n",
+ " [4, 3]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 26
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yvTnrszn8Yk0"
+ },
+ "source": [
+ "Porque agora deu certo?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LeQ9LqIE8baG"
+ },
+ "source": [
+ "## Último exemplo com reshape\n",
+ "> Considere o array a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "OQOC9iiN8hZT"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.random.randn(2, 3)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Cvce8qBl9Cvq"
+ },
+ "source": [
+ "Queremos agora transformá-la num array de 3 linhas e 2 colunas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QDDsYoVt9Klz"
+ },
+ "source": [
+ "a_conjunto1.reshape(-1, 2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AdwU5ygt9Svq"
+ },
+ "source": [
+ "Poderia ser..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5uBeokKc9Uo-"
+ },
+ "source": [
+ "a_conjunto1.reshape(3, -1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OeRBsobc9aKj"
+ },
+ "source": [
+ "E por fim, também poderia ser..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "MDt8UYYH9dBw"
+ },
+ "source": [
+ "a_conjunto1.reshape(3, 2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "91o5vycQfdKW"
+ },
+ "source": [
+ "___\n",
+ "# **Transposta**\n",
+ "* O array a_conjunto2 tem a seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RsZwyuhoffjb"
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "A3MzTVoGfiyO"
+ },
+ "source": [
+ "# Transposta do array a_conjunto2 é dado por:\n",
+ "a_conjunto2.T"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ij-ZW5IyzXIb"
+ },
+ "source": [
+ "Ou seja, linha virou coluna. Ok?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qLy6ajgpt3lU"
+ },
+ "source": [
+ "# **Inversa da matriz quadrada**\n",
+ "> Se uma matriz é não-singular, então sua inversa existe.\n",
+ "\n",
+ "* Se o determinante de uma matriz is not equal to zero, then the matrix isé diferente de 0, então a matriz é não-singular."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-u7jRq34t9_x"
+ },
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "a_conjunto1 = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])\n",
+ "a_conjunto2 = np.array([[6, 2], [5, 3]])\n",
+ "a_conjunto3 = np.array([[1, 3, 5],[2, 5, 1],[2, 3, 8]])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7zmHHWWlvaYB"
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3fHKyhOJvcak"
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vQG7yyfjwLg9"
+ },
+ "source": [
+ "a_conjunto3"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qa2Yre2rwgRk"
+ },
+ "source": [
+ "## Determinantes da matriz quadrada"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "N6jwuC6twkyc"
+ },
+ "source": [
+ "np.linalg.det(a_conjunto1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QSvViNwzwnhI"
+ },
+ "source": [
+ "np.linalg.det(a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "o8jwsnccw5id"
+ },
+ "source": [
+ "np.linalg.det(a_conjunto3)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kkVaTgzgw_XJ"
+ },
+ "source": [
+ "A seguir, calculamos as inversas das matrizes acima definidas..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "b9FgWvTYvpik"
+ },
+ "source": [
+ "np.linalg.inv(a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KsdEt1kIvsM_"
+ },
+ "source": [
+ "np.linalg.inv(a_conjunto1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VA_F7_7kccpn"
+ },
+ "source": [
+ "Porque não temos a inversa de a_conjunto1?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ANPBCnmVwOf4"
+ },
+ "source": [
+ "np.linalg.inv(a_conjunto3)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XAf9k1egxcdF"
+ },
+ "source": [
+ "# **Resolver sistemas de equações lineares**\n",
+ "> Considere o sistema de euqações lineares abaixo:\n",
+ "\n",
+ "\\begin{equation}\n",
+ "x + 3y + 5z = 10\\\\\n",
+ "2x+ 5y + z = 8 \\\\\n",
+ "2x + 3y + 8z= 3\n",
+ "\\end{equation}\n",
+ "\n",
+ "Ou $Ax = b$. A solução deste sistema de equações é dada por $A^{-1}b$."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oNf5nqaLxhBY"
+ },
+ "source": [
+ "Ou seja, basta encontrarmos a inversa de A e multiplicarmos por b."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "omzC5dGA0btc"
+ },
+ "source": [
+ "A= np.array([[1, 3, 5], [2, 5, 1], [2, 3, 8]])\n",
+ "np.linalg.inv(A)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AiXI3oxB05iE"
+ },
+ "source": [
+ "Agora basta multiplicar a matriz inversa $A^{-1}$ acima por b. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XoGebKDa2Fcd"
+ },
+ "source": [
+ "A_Inv = np.linalg.inv(A)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sKaP0a1QZG-P"
+ },
+ "source": [
+ "b= np.array([10, 8, 3]).reshape(3, -1)\n",
+ "b"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3dAVq8dg19VI"
+ },
+ "source": [
+ "A_Inv.dot(b)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zso6hTnB17cm"
+ },
+ "source": [
+ "Uma forma fácil de se fazer isso é utilizar a expressão abaixo:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ptQHIVll1E4P"
+ },
+ "source": [
+ "b= np.array([[10], [8], [3]])\n",
+ "b"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "X4VL8lyY1Xus"
+ },
+ "source": [
+ "np.linalg.solve(A, b)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fJKmwTS59-Bc"
+ },
+ "source": [
+ "# **Empilhar arrays**\n",
+ "\n",
+ "## Exemplo 1\n",
+ "\n",
+ "\n",
+ "\n",
+ "## Exemplo 2\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rhPTt3EwXden"
+ },
+ "source": [
+ "## Gerar os arrays do exemplo1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zEI-yBy3-E46"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.random.randn(5, 8)\n",
+ "\n",
+ "np.random.seed(19741120)\n",
+ "a_conjunto2 = np.random.randn(8, 8)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UYsAqBRp--79"
+ },
+ "source": [
+ "## Método 1 - Concatenate([A, B])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HgO1ujvhObyE",
+ "outputId": "c40e7ed9-255b-4886-dddf-3b17f2b1be2f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 2.5062768 , 1.11440422, 2.05565501, 0.56482376, 0.29897276,\n",
+ " 1.04930857, -0.12607366, 1.06227632],\n",
+ " [ 1.13807032, 1.37966044, -2.05995563, 0.67474814, 0.72722843,\n",
+ " -0.33923852, 0.43613107, 0.59135489],\n",
+ " [-1.29281877, 1.17712036, -0.98644163, -1.79034143, -1.08913605,\n",
+ " -0.90712825, -1.02291108, -1.36445713],\n",
+ " [-0.29429164, 0.06343709, -1.14196185, -0.50706079, -0.83539436,\n",
+ " -1.41492946, -0.2159062 , -1.16519474],\n",
+ " [-0.60767518, -0.61510925, 1.0771542 , 0.5043687 , 0.02674197,\n",
+ " 1.83494644, 0.34728874, -1.14671885]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 33
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2aQY_klZOeg9",
+ "outputId": "14eb3d9c-d0fc-4b6a-fe19-1790695c838f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 289
+ }
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[-0.77337752, -1.10547465, 0.10062807, -1.14571729, -2.15266227,\n",
+ " -0.75255725, -2.1529949 , -0.33017773],\n",
+ " [-1.10465731, 0.32889675, 0.01010198, -1.33213633, -0.33945805,\n",
+ " -0.01299007, 0.05342823, -0.18641201],\n",
+ " [ 0.39473805, -0.89354231, -0.50667323, -0.74660913, 1.83586365,\n",
+ " -1.20536871, 1.20184886, 0.51160897],\n",
+ " [-0.56952286, -0.93343871, -0.24972528, 0.98487133, 1.19333367,\n",
+ " 2.29956497, 0.16657022, 0.71357415],\n",
+ " [-0.45251078, 0.92163918, 0.73421263, 2.17811191, -0.05655212,\n",
+ " 1.25326 , -0.37039248, 1.43855202],\n",
+ " [ 0.85646091, -0.11257239, -0.35400297, 0.94136671, -0.08696163,\n",
+ " -1.49000701, 0.00848666, 0.86705275],\n",
+ " [ 1.6340906 , 1.36321063, -0.02175361, -0.45301645, -0.37111236,\n",
+ " -0.04716069, -2.27337435, 0.95318738],\n",
+ " [ 0.7100548 , -0.79883269, -0.3165779 , -1.58352824, -0.37751484,\n",
+ " -0.29760341, -0.73424207, -0.55703223]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 34
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bK70vaq8_KMH",
+ "outputId": "f6d400cf-4b54-4990-815b-052f5224aadd",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 459
+ }
+ },
+ "source": [
+ "np.concatenate([a_conjunto1, a_conjunto2], axis = 0) # axis= 0 diz ao NumPy para empilhar as linhas"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 2.5062768 , 1.11440422, 2.05565501, 0.56482376, 0.29897276,\n",
+ " 1.04930857, -0.12607366, 1.06227632],\n",
+ " [ 1.13807032, 1.37966044, -2.05995563, 0.67474814, 0.72722843,\n",
+ " -0.33923852, 0.43613107, 0.59135489],\n",
+ " [-1.29281877, 1.17712036, -0.98644163, -1.79034143, -1.08913605,\n",
+ " -0.90712825, -1.02291108, -1.36445713],\n",
+ " [-0.29429164, 0.06343709, -1.14196185, -0.50706079, -0.83539436,\n",
+ " -1.41492946, -0.2159062 , -1.16519474],\n",
+ " [-0.60767518, -0.61510925, 1.0771542 , 0.5043687 , 0.02674197,\n",
+ " 1.83494644, 0.34728874, -1.14671885],\n",
+ " [-0.77337752, -1.10547465, 0.10062807, -1.14571729, -2.15266227,\n",
+ " -0.75255725, -2.1529949 , -0.33017773],\n",
+ " [-1.10465731, 0.32889675, 0.01010198, -1.33213633, -0.33945805,\n",
+ " -0.01299007, 0.05342823, -0.18641201],\n",
+ " [ 0.39473805, -0.89354231, -0.50667323, -0.74660913, 1.83586365,\n",
+ " -1.20536871, 1.20184886, 0.51160897],\n",
+ " [-0.56952286, -0.93343871, -0.24972528, 0.98487133, 1.19333367,\n",
+ " 2.29956497, 0.16657022, 0.71357415],\n",
+ " [-0.45251078, 0.92163918, 0.73421263, 2.17811191, -0.05655212,\n",
+ " 1.25326 , -0.37039248, 1.43855202],\n",
+ " [ 0.85646091, -0.11257239, -0.35400297, 0.94136671, -0.08696163,\n",
+ " -1.49000701, 0.00848666, 0.86705275],\n",
+ " [ 1.6340906 , 1.36321063, -0.02175361, -0.45301645, -0.37111236,\n",
+ " -0.04716069, -2.27337435, 0.95318738],\n",
+ " [ 0.7100548 , -0.79883269, -0.3165779 , -1.58352824, -0.37751484,\n",
+ " -0.29760341, -0.73424207, -0.55703223]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 35
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CpaXBkm8_BF8"
+ },
+ "source": [
+ "## Método 2 - np.r_[A, B]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3QnVUzAY_teZ",
+ "outputId": "e8adfd85-e760-40f5-d9ac-48353d24ccd2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 459
+ }
+ },
+ "source": [
+ "np.r_[a_conjunto1, a_conjunto2]"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 2.5062768 , 1.11440422, 2.05565501, 0.56482376, 0.29897276,\n",
+ " 1.04930857, -0.12607366, 1.06227632],\n",
+ " [ 1.13807032, 1.37966044, -2.05995563, 0.67474814, 0.72722843,\n",
+ " -0.33923852, 0.43613107, 0.59135489],\n",
+ " [-1.29281877, 1.17712036, -0.98644163, -1.79034143, -1.08913605,\n",
+ " -0.90712825, -1.02291108, -1.36445713],\n",
+ " [-0.29429164, 0.06343709, -1.14196185, -0.50706079, -0.83539436,\n",
+ " -1.41492946, -0.2159062 , -1.16519474],\n",
+ " [-0.60767518, -0.61510925, 1.0771542 , 0.5043687 , 0.02674197,\n",
+ " 1.83494644, 0.34728874, -1.14671885],\n",
+ " [-0.77337752, -1.10547465, 0.10062807, -1.14571729, -2.15266227,\n",
+ " -0.75255725, -2.1529949 , -0.33017773],\n",
+ " [-1.10465731, 0.32889675, 0.01010198, -1.33213633, -0.33945805,\n",
+ " -0.01299007, 0.05342823, -0.18641201],\n",
+ " [ 0.39473805, -0.89354231, -0.50667323, -0.74660913, 1.83586365,\n",
+ " -1.20536871, 1.20184886, 0.51160897],\n",
+ " [-0.56952286, -0.93343871, -0.24972528, 0.98487133, 1.19333367,\n",
+ " 2.29956497, 0.16657022, 0.71357415],\n",
+ " [-0.45251078, 0.92163918, 0.73421263, 2.17811191, -0.05655212,\n",
+ " 1.25326 , -0.37039248, 1.43855202],\n",
+ " [ 0.85646091, -0.11257239, -0.35400297, 0.94136671, -0.08696163,\n",
+ " -1.49000701, 0.00848666, 0.86705275],\n",
+ " [ 1.6340906 , 1.36321063, -0.02175361, -0.45301645, -0.37111236,\n",
+ " -0.04716069, -2.27337435, 0.95318738],\n",
+ " [ 0.7100548 , -0.79883269, -0.3165779 , -1.58352824, -0.37751484,\n",
+ " -0.29760341, -0.73424207, -0.55703223]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 36
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XmSPbDP6_20W"
+ },
+ "source": [
+ "**Obs**.: Eu prefiro este método!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dzVKW_wX_Dzw"
+ },
+ "source": [
+ "## Método 3 - np.vstack([A, B]) = np.r_[A, B]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uL7lEN_mABID",
+ "outputId": "d1ea4d86-2cc1-4e2d-af72-b3a292ef15fd",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 459
+ }
+ },
+ "source": [
+ "np.vstack([a_conjunto1, a_conjunto2])"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 2.5062768 , 1.11440422, 2.05565501, 0.56482376, 0.29897276,\n",
+ " 1.04930857, -0.12607366, 1.06227632],\n",
+ " [ 1.13807032, 1.37966044, -2.05995563, 0.67474814, 0.72722843,\n",
+ " -0.33923852, 0.43613107, 0.59135489],\n",
+ " [-1.29281877, 1.17712036, -0.98644163, -1.79034143, -1.08913605,\n",
+ " -0.90712825, -1.02291108, -1.36445713],\n",
+ " [-0.29429164, 0.06343709, -1.14196185, -0.50706079, -0.83539436,\n",
+ " -1.41492946, -0.2159062 , -1.16519474],\n",
+ " [-0.60767518, -0.61510925, 1.0771542 , 0.5043687 , 0.02674197,\n",
+ " 1.83494644, 0.34728874, -1.14671885],\n",
+ " [-0.77337752, -1.10547465, 0.10062807, -1.14571729, -2.15266227,\n",
+ " -0.75255725, -2.1529949 , -0.33017773],\n",
+ " [-1.10465731, 0.32889675, 0.01010198, -1.33213633, -0.33945805,\n",
+ " -0.01299007, 0.05342823, -0.18641201],\n",
+ " [ 0.39473805, -0.89354231, -0.50667323, -0.74660913, 1.83586365,\n",
+ " -1.20536871, 1.20184886, 0.51160897],\n",
+ " [-0.56952286, -0.93343871, -0.24972528, 0.98487133, 1.19333367,\n",
+ " 2.29956497, 0.16657022, 0.71357415],\n",
+ " [-0.45251078, 0.92163918, 0.73421263, 2.17811191, -0.05655212,\n",
+ " 1.25326 , -0.37039248, 1.43855202],\n",
+ " [ 0.85646091, -0.11257239, -0.35400297, 0.94136671, -0.08696163,\n",
+ " -1.49000701, 0.00848666, 0.86705275],\n",
+ " [ 1.6340906 , 1.36321063, -0.02175361, -0.45301645, -0.37111236,\n",
+ " -0.04716069, -2.27337435, 0.95318738],\n",
+ " [ 0.7100548 , -0.79883269, -0.3165779 , -1.58352824, -0.37751484,\n",
+ " -0.29760341, -0.73424207, -0.55703223]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 37
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "68icJ-2ZAdRj"
+ },
+ "source": [
+ "# Concatenar arrays\n",
+ "\n",
+ "## Exemplo 1\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Exemplo 2\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OplgK9YoQi9o"
+ },
+ "source": [
+ "## Concatenar os elementos de dois arrays - np.c_[A, B]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lpdsbTEKQ9EY"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.random.randint(0, 10, 100).reshape(-1, 10)\n",
+ "a_conjunto2 = np.random.randint(0, 2, 10).reshape(-1, 1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JPxhGsaSSMk2",
+ "outputId": "47727fe9-05f1-4ff7-ec0a-04579120cf78",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[8, 8, 2, 8, 9, 1, 8, 0, 4, 2],\n",
+ " [0, 8, 9, 3, 7, 1, 3, 2, 9, 7],\n",
+ " [7, 9, 5, 6, 8, 7, 0, 9, 3, 9],\n",
+ " [3, 1, 8, 6, 3, 5, 4, 1, 2, 9],\n",
+ " [8, 6, 6, 1, 0, 9, 2, 0, 7, 5],\n",
+ " [5, 4, 4, 2, 7, 2, 7, 9, 3, 1],\n",
+ " [5, 0, 1, 2, 3, 8, 7, 5, 4, 0],\n",
+ " [5, 9, 6, 6, 1, 3, 6, 0, 4, 9],\n",
+ " [2, 1, 0, 9, 1, 4, 2, 9, 7, 9],\n",
+ " [5, 3, 7, 6, 3, 9, 8, 4, 3, 0]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 39
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9ZyUPfybTfej",
+ "outputId": "ac27a20e-1622-4cb9-d6f6-74ee467bdb72",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[1],\n",
+ " [0],\n",
+ " [0],\n",
+ " [0],\n",
+ " [0],\n",
+ " [1],\n",
+ " [0],\n",
+ " [0],\n",
+ " [0],\n",
+ " [1]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 40
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nS1cPG3aRug1",
+ "outputId": "c70cf891-ae8f-445d-c271-c6b7f7da1738",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "# colocando o array a_conjunto2 do lado de a_conjunto1.\n",
+ "np.c_[a_conjunto1, a_conjunto2]"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[8, 8, 2, 8, 9, 1, 8, 0, 4, 2, 1],\n",
+ " [0, 8, 9, 3, 7, 1, 3, 2, 9, 7, 0],\n",
+ " [7, 9, 5, 6, 8, 7, 0, 9, 3, 9, 0],\n",
+ " [3, 1, 8, 6, 3, 5, 4, 1, 2, 9, 0],\n",
+ " [8, 6, 6, 1, 0, 9, 2, 0, 7, 5, 0],\n",
+ " [5, 4, 4, 2, 7, 2, 7, 9, 3, 1, 1],\n",
+ " [5, 0, 1, 2, 3, 8, 7, 5, 4, 0, 0],\n",
+ " [5, 9, 6, 6, 1, 3, 6, 0, 4, 9, 0],\n",
+ " [2, 1, 0, 9, 1, 4, 2, 9, 7, 9, 0],\n",
+ " [5, 3, 7, 6, 3, 9, 8, 4, 3, 0, 1]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 41
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kIgU1YBw0OeM"
+ },
+ "source": [
+ "___\n",
+ "# **Selecionar itens que satisfazem condições**\n",
+ "> Considere o array a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "e2pL5anBV0DI",
+ "outputId": "f37cd827-ee00-49ba-994d-77cab3a24421",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1 = np.arange(10, 0, -1)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 42
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "i9HuZZAfV302"
+ },
+ "source": [
+ "Selecionar somente os itens > 7:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZCESvr7iXMkV"
+ },
+ "source": [
+ "## Usando np.where()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BdrAQLHkTS-v",
+ "outputId": "44a6e480-1b6c-4dad-ee29-2fcb4ada5097",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 45
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "O_ZBaWxfWA9o",
+ "outputId": "fae44244-ff29-4b04-cd2d-a4c768487e75",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Índices do array que atendem a condição\n",
+ "l_indices = np.where(a_conjunto1 > 7)\n",
+ "l_indices"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(array([0, 1, 2]),)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 44
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EdWlfPOZWPME"
+ },
+ "source": [
+ "**Atenção**: Capturamos os índices. Para selecionar os itens, basta fazer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tOxs3iYQWWxu",
+ "outputId": "b402fdfd-c6e0-4170-b35c-c7c5cd2ca85e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto2 = a_conjunto1[l_indices]\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 9, 8])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 46
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PGsENqkaXRjh"
+ },
+ "source": [
+ "## Alternativa: Usando []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "YbdRNk1WXTLT",
+ "outputId": "062b157c-00fb-4f8f-d207-a0c8e9871e48",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1[a_conjunto1 > 7]"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 9, 8])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 47
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jijpzFxcSQC8"
+ },
+ "source": [
+ "Acho que vale a pena quebrar esta solução para entendermos melhor como as coisas funcionam:#"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rujhP2LQSWsq"
+ },
+ "source": [
+ " # Primeiro, avalie o resultado de a_conjunto1 > 7:"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "FYZaBsasSb3N",
+ "outputId": "0a190896-249c-4d7c-ea0d-a20a53536446",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ }
+ },
+ "source": [
+ "a_conjunto1 > 7"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([ True, True, True, False, False, False, False, False, False,\n",
+ " False])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 48
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mvEof-UKaaVG"
+ },
+ "source": [
+ "a_conjunto1[a_conjunto1 > 7]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nO4FiBmDUZOT",
+ "outputId": "9f54e601-d95a-444c-bd59-28947e332248",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([-1, -1, -1, 7, 6, 5, 4, 3, 2, 1])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 52
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ci5lT9nmSfsX"
+ },
+ "source": [
+ "Agora, com este resultado, fica fácil entender como o Python seleciona os elementos. Consegue explicar?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1v5Lfin0GGKD"
+ },
+ "source": [
+ "# Substituir itens baseado em condições\n",
+ "> Substituir os valores negativos do array abaixo por 0."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CLY_u0ePWdN7"
+ },
+ "source": [
+ "## Gerar o exemplo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NUANFy-fNXf5"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(0, 10, size = 100))\n",
+ "\n",
+ "# Lista aleatória de índices que vou alterar\n",
+ "np.random.seed(20111974)\n",
+ "l_indices= np.random.randint(0, 99, 9)\n",
+ "\n",
+ "for i in l_indices:\n",
+ " a_conjunto1[i] = -1*a_conjunto1[i]\n",
+ "\n",
+ "a_conjunto2 = a_conjunto1.copy()\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dWVyI40uN2d2"
+ },
+ "source": [
+ "# Indices a serem multiplicados por -1:\n",
+ "l_indices"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3Whuu854OJDZ"
+ },
+ "source": [
+ "## Substituir os valores negativos por 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sr268Rp8b-Se",
+ "outputId": "82514805-b350-45c4-a3fc-7cb24c847b7f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto2 < 0"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([False, False, False])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 50
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "C-eKqPrfOQF6"
+ },
+ "source": [
+ "a_conjunto2[a_conjunto2 < 0] = 0\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eDLM0_JSZlfB"
+ },
+ "source": [
+ "Observe acima que os valores negativos foram substituídos por 0, como queríamos."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AEHJ0rA3dHHU"
+ },
+ "source": [
+ "## Substituir os valores negativos por 0 e os positivos por 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "y32J8SRNZwRF"
+ },
+ "source": [
+ "a_conjunto2 = a_conjunto1.copy()\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1bSD9Fs6P5wW"
+ },
+ "source": [
+ "a_conjunto2 = np.where(a_conjunto2 <= 0, 0, 1)\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "i027scjl0qkm"
+ },
+ "source": [
+ "___\n",
+ "# Outliers\n",
+ "> Qualquer ponto/observação que é incomum quando comparado com todos os outros pontos/observações."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UnDTqRnZHQ3W"
+ },
+ "source": [
+ "## Z-Score\n",
+ "\n",
+ "* Z-Score pode ser utilizado para detectar Outliers.\n",
+ "* É a diferença entre o valor e a média da amostra expressa como o número de desvios-padrão. \n",
+ "* Se o escore z for menor que 2,5 ou maior que 2,5, o valor estará nos 5% do menor ou maior valor (2,5% dos valores em ambas as extremidades da distribuição). No entanto, é pratica comum utilizarmos 3 ao invés dos 2,5.\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "N7gb2zhtd0uM"
+ },
+ "source": [
+ "## IQR Score\n",
+ "\n",
+ "* O Intervalo interquartil (IQR) é uma medida de dispersão estatística, sendo igual à diferença entre os percentis 75 (Q3) e 25 (Q1), ou entre quartis superiores e inferiores, IQR = Q3 - Q1."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lMmWOKNvghI7"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DUw_a-MjWvBc"
+ },
+ "source": [
+ "### Desafio para resolverem\n",
+ "> **Objetivo**: Simular aleatoriamente o salário de 1.000 pessoas com distribuição N(1.045; 100). \n",
+ "* Identificar os _outliers_ da distribuição que acabamos de simular;\n",
+ "* Qual a média da distribuição que simulamos?\n",
+ "* Qual o desvio-padrão;\n",
+ "* Plotar o Boxplot da distribuição dos dados;\n",
+ "* Quantas pessoas > Q3 + 1.5*(Q3-Q1)\n",
+ "* Substituir os outliers do array por:\n",
+ " * Q1-1.5*(Q3 - Q1), se ponto < Q1-1.5*(Q3-Q1)\n",
+ " * Q3+1.5*(Q3 - Q1), se ponto > Q3+1.5*(Q3-Q1)\n",
+ "\n",
+ "Obs.: Use np.random.seed(20111974)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "L9ntAdS_oOAh"
+ },
+ "source": [
+ "### Geração aleatória do array a_salarios com distribuição $N(\\mu, \\sigma)$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RL0Zb0fyDory",
+ "outputId": "2a3d2b33-579c-406d-d662-da4458f164e6",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "import numpy as np\n",
+ "np.random.seed(20111974)\n",
+ "np.set_printoptions(precision = 2, suppress = True)\n",
+ "\n",
+ "media = 1045\n",
+ "desvio_padrao = 100\n",
+ "i_tamanho = 1000\n",
+ "\n",
+ "a_salarios = np.array(np.random.normal(media, desvio_padrao, size = i_tamanho))\n",
+ "a_salarios[:30]"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1295.63, 1156.44, 1250.57, 1101.48, 1074.9 , 1149.93, 1032.39,\n",
+ " 1151.23, 1158.81, 1182.97, 839. , 1112.47, 1117.72, 1011.08,\n",
+ " 1088.61, 1104.14, 915.72, 1162.71, 946.36, 865.97, 936.09,\n",
+ " 954.29, 942.71, 908.55, 1015.57, 1051.34, 930.8 , 994.29,\n",
+ " 961.46, 903.51])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 5
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Fc3a-yhViCTs"
+ },
+ "source": [
+ "### Geração aleatória dos índices que serão (manualmente) alterados"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Iakt6i1cgEcB",
+ "outputId": "9cc09094-5420-4078-a387-e22a58c13f7a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Lista aleatória de índices que vou alterar\n",
+ "np.random.seed(19741120)\n",
+ "l_indices = np.random.randint(0, 999, 10)\n",
+ "\n",
+ "# Estas são as posições que serão alteradas\n",
+ "np.sort(l_indices)"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([ 14, 105, 208, 349, 484, 567, 615, 616, 622, 847])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 6
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oXwME1rciHkw"
+ },
+ "source": [
+ "### Cópia dos salários para compararmos o ANTES e DEPOIS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BEtnua7sgp_y",
+ "outputId": "85b67195-c61a-4f05-ea8f-fc5b05d8973b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "# cópia do array a_salarios\n",
+ "a_salarios_copia = a_salarios.copy()\n",
+ "a_salarios_copia2 = a_salarios.copy()\n",
+ "\n",
+ "a_salarios[:30]"
+ ],
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1295.63, 1156.44, 1250.57, 1101.48, 1074.9 , 1149.93, 1032.39,\n",
+ " 1151.23, 1158.81, 1182.97, 839. , 1112.47, 1117.72, 1011.08,\n",
+ " 1088.61, 1104.14, 915.72, 1162.71, 946.36, 865.97, 936.09,\n",
+ " 954.29, 942.71, 908.55, 1015.57, 1051.34, 930.8 , 994.29,\n",
+ " 961.46, 903.51])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 7
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "So8qj3Yrh-Az"
+ },
+ "source": [
+ "### Alteração (manual dos salários): 2 alternativas\n",
+ "> Vamos medir o tempo para avaliarmos o que é mais rápido. Qual solução é mais rápida?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Z0613on8z5VH"
+ },
+ "source": [
+ "from timeit import default_timer as timer\n",
+ "from datetime import timedelta"
+ ],
+ "execution_count": 8,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NpvvholVxMhs",
+ "outputId": "2dbfff71-3249-4fd8-fd48-c9e1356dde34",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Índices a serem alterados\n",
+ "l_indices"
+ ],
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([567, 14, 616, 484, 208, 105, 349, 615, 622, 847])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 9
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BqXsmMdm1yF-"
+ },
+ "source": [
+ "#### Solução 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "FiiOrlnbgKOD",
+ "outputId": "82a3c137-568d-4776-d952-11d00b1e40e7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Alteração dos salários dos índices propostos\n",
+ "start = timer()\n",
+ "for i_indice in l_indices:\n",
+ " a_salarios_copia[i_indice] = 2*a_salarios[i_indice] # Loop para os índices a serem alterados (manualmente)\n",
+ "\n",
+ "a_salarios_copia[:30]\n",
+ "end = timer()\n",
+ "print(timedelta(seconds=end-start))"
+ ],
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0:00:00.000094\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FgvKC-aFzWpZ"
+ },
+ "source": [
+ "#### Solução 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XWlQC5Jazt26",
+ "outputId": "8640d081-99ae-4235-e2de-620d6152193b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "start = timer()\n",
+ "a_salarios_copia2[l_indices] = 2*a_salarios_copia2[l_indices]\n",
+ "a_salarios_copia2[:30]\n",
+ "end = timer()\n",
+ "print(timedelta(seconds=end-start))"
+ ],
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0:00:00.000090\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "U92w03afhrmC"
+ },
+ "source": [
+ "### Compare"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Ls-jCFCYhtD8",
+ "outputId": "04b7eff2-67d0-4f8f-8812-0b4be9bb7cb7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ }
+ },
+ "source": [
+ "# Antes\n",
+ "a_salarios[l_indices]"
+ ],
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([ 826.43, 1088.61, 1121.95, 833.96, 1165.97, 1081.13, 1078.51,\n",
+ " 1094.67, 904.32, 1128.66])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 12
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nwwU06OahzD2",
+ "outputId": "e18448a4-97f4-452c-da95-3d1db69b1033",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ }
+ },
+ "source": [
+ "# Depois\n",
+ "a_salarios_copia[l_indices]"
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1652.85, 2177.23, 2243.89, 1667.93, 2331.93, 2162.26, 2157.02,\n",
+ " 2189.34, 1808.63, 2257.32])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "qyUUdHmtisJS",
+ "outputId": "779e41e6-cb77-4966-b5d5-fe4d5b4f2ab2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "# 30 primeiras elementos de a_salarios\n",
+ "a_salarios[:30]"
+ ],
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1295.63, 1156.44, 1250.57, 1101.48, 1074.9 , 1149.93, 1032.39,\n",
+ " 1151.23, 1158.81, 1182.97, 839. , 1112.47, 1117.72, 1011.08,\n",
+ " 1088.61, 1104.14, 915.72, 1162.71, 946.36, 865.97, 936.09,\n",
+ " 954.29, 942.71, 908.55, 1015.57, 1051.34, 930.8 , 994.29,\n",
+ " 961.46, 903.51])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "CJ1FEjlCi0-n",
+ "outputId": "5c6c8845-8f83-4047-9f4f-3034d2ec2af7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "# 30 primeiras posições de a_salarios_copia\n",
+ "a_salarios_copia[:30]"
+ ],
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1295.63, 1156.44, 1250.57, 1101.48, 1074.9 , 1149.93, 1032.39,\n",
+ " 1151.23, 1158.81, 1182.97, 839. , 1112.47, 1117.72, 1011.08,\n",
+ " 2177.23, 1104.14, 915.72, 1162.71, 946.36, 865.97, 936.09,\n",
+ " 954.29, 942.71, 908.55, 1015.57, 1051.34, 930.8 , 994.29,\n",
+ " 961.46, 903.51])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wKbSUgxxiOUL"
+ },
+ "source": [
+ "### Algumas Estatísticas descritivas:\n",
+ "#### Antes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZnmykyahLWX9",
+ "outputId": "b2c70db1-2870-48e7-c031-94f122415bc8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "f'Média: {np.mean(a_salarios)}; Mediana: {np.median(a_salarios)}; STD: {np.std(a_salarios)}'"
+ ],
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Média: 1047.150212238584; Mediana: 1047.631166829137; STD: 101.18708333868835'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 16
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "80H92CIjibYJ"
+ },
+ "source": [
+ "#### Depois"
+ ],
+ "execution_count": 17,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5iO-BAikieHJ",
+ "outputId": "ea72b3f5-5682-4971-e1f5-26aec68bc43c",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "f'Média: {np.mean(a_salarios_copia)}; Mediana: {np.median(a_salarios_copia)}; STD: {np.std(a_salarios_copia)}'"
+ ],
+ "execution_count": 18,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Média: 1057.4744151862524; Mediana: 1048.089607774499; STD: 144.64306489539533'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 18
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ILhNe80xW5C6"
+ },
+ "source": [
+ "### Solução do desafio"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "U993i1GJg2hk",
+ "outputId": "bf91af51-aac7-4008-8cc3-342573752205",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 271
+ }
+ },
+ "source": [
+ "# Import a biblioteca seaborn:\n",
+ "import seaborn as sns\n",
+ "sns.boxplot(y = a_salarios_copia)"
+ ],
+ "execution_count": 19,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 19
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VtenLK1uK1Pi"
+ },
+ "source": [
+ "Consegue identificar os outliers do array?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e3sHuGVGFBdW"
+ },
+ "source": [
+ "## Objetivo\n",
+ "> Substituir os outliers por mediana. \n",
+ "\n",
+ "* Como fazer isso?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RSegPNKCI-dS"
+ },
+ "source": [
+ "### Siga os passos a seguir\n",
+ "1. Calcule estatísticas descritivas antes das transformações par avaliar o impacto;\n",
+ " * Calcule média, mediana e desvio-padrão dos dados originais;\n",
+ "2. Calcule os valores a seguir:\n",
+ " * Q1, Q3\n",
+ " * IQR = Q3-Q1\n",
+ " * lim_inferior = Q1-1.5\\*IQR\n",
+ " * lim_superior = Q3+1.5\\*IQR\n",
+ "3. Proceda à substituição:\n",
+ " * Se a_salarios_copia[i] < lim_inferior então a_salarios_copia[i]= Mediana\n",
+ " * Se a_salarios_copia[i] > lim_superior então a_salarios_copia[i]= Mediana\n",
+ "4. Calcule as estatísticas descritivas após as substituições e compare com os valores antes das transformações."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9DQ7YnWaFn4v"
+ },
+ "source": [
+ "### Minha solução\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RBXJbTeGLC7Q"
+ },
+ "source": [
+ "1. Estatísticas Descritivas antes das transformações:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QueKYn7MLG12",
+ "outputId": "11daf3fe-c4c9-446e-cb46-cf6378c02779",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "# Algumas estatísticas descritivas:\n",
+ "f'Média: {np.mean(a_salarios_copia)}; Mediana: {np.median(a_salarios_copia)}; STD: {np.std(a_salarios_copia)}'"
+ ],
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Média: 1057.4744151862524; Mediana: 1048.089607774499; STD: 144.64306489539533'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 20
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oOBJ8INWL5fo"
+ },
+ "source": [
+ "Observe o quanto nossos dados estão distorcidos dos valores originalmente utilizados."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MX-fJeh2MBTD"
+ },
+ "source": [
+ "2. Calcular Q1, Q3 e IQR"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JlsPiQeGMGeU"
+ },
+ "source": [
+ "Q1 = np.percentile(a_salarios_copia, q = [25])\n",
+ "Q3 = np.percentile(a_salarios_copia, q = [75])\n",
+ "Q2 = np.percentile(a_salarios_copia, q = [50])\n",
+ "IQR = Q3-Q1\n",
+ "lim_inferior = Q1-1.5*IQR\n",
+ "lim_superior = Q3+1.5*IQR"
+ ],
+ "execution_count": 21,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VF2NJ3rCeI1_",
+ "outputId": "e8e38919-ee69-4d21-db00-1abb7bd4fb9b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "f'Q1: {Q1}; Q3: {Q3}; lim_inferior: {lim_inferior}; lim_superior: {lim_superior}'"
+ ],
+ "execution_count": 22,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Q1: [974.41]; Q3: [1119.81]; lim_inferior: [756.33]; lim_superior: [1337.89]'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 22
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JjnwJ7HwMxcl"
+ },
+ "source": [
+ "3. Substituir\n",
+ "* Se a_conjunto1[i] < lim_inferior então a_conjunto1[i] = Mediana\n",
+ "* Se a_conjunto1[i] > Lia_Sup então a_conjunto1[i] = Mediana"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "hcAn-IwVfbcI"
+ },
+ "source": [
+ "a_salarios2 = a_salarios_copia.copy()"
+ ],
+ "execution_count": 23,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "J3SSE45oM9oh",
+ "outputId": "53db1a1d-8483-40f9-8cbc-196b79e449ff",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "a_salarios2[a_salarios2 < lim_inferior[0]] = Q2[0]\n",
+ "#para todos que atendam essa condição ele vai receber o valor da mediana\n",
+ "a_salarios2[a_salarios2 > lim_superior[0]] = Q2[0]\n",
+ "a_salarios2[:30]"
+ ],
+ "execution_count": 24,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1295.63, 1156.44, 1250.57, 1101.48, 1074.9 , 1149.93, 1032.39,\n",
+ " 1151.23, 1158.81, 1182.97, 839. , 1112.47, 1117.72, 1011.08,\n",
+ " 1048.09, 1104.14, 915.72, 1162.71, 946.36, 865.97, 936.09,\n",
+ " 954.29, 942.71, 908.55, 1015.57, 1051.34, 930.8 , 994.29,\n",
+ " 961.46, 903.51])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 24
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VEGFio0Nfj7O"
+ },
+ "source": [
+ "4. Estatísticas Descritivas para avaliarmos o impacto:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gX1LZHFqfjFQ",
+ "outputId": "31a986a8-79bd-4c04-daf2-3fa1e8ca9f44",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "# Algumas estatísticas descritivas:\n",
+ "f'Média: {np.mean(a_salarios2)}; Mediana: {np.median(a_salarios2)}; STD: {np.std(a_salarios2)}'"
+ ],
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Média: 1047.3019702056902; Mediana: 1048.089607774499; STD: 98.3265929249586'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 25
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-xnguZ7XgyvK",
+ "outputId": "98be8554-e55a-4d5b-e418-4712377c627e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 269
+ }
+ },
+ "source": [
+ "# Import a biblioteca seaborn:\n",
+ "import seaborn as sns\n",
+ "sns.boxplot(y = a_salarios2)"
+ ],
+ "execution_count": 26,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 26
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uEPFcBjFhETQ"
+ },
+ "source": [
+ "Como podem ver, os outliers desapareceram, como queríamos."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tHfzjW_ymKuR"
+ },
+ "source": [
+ "___\n",
+ "# **Valores únicos**\n",
+ "> Considere o array a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HzmQgWZVmUUD"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.random.randint(0, 100, 100)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Dm9ky1F1mrNA"
+ },
+ "source": [
+ "Quem são os valores únicos do array?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "G-LPRqc-mS5j"
+ },
+ "source": [
+ "np.unique(a_conjunto1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uXZZoTd6nMuq"
+ },
+ "source": [
+ "___\n",
+ "# **Diferença entre dois arrays**\n",
+ "> O resultado é um array com os **valores únicos de A que não estão em B**. Na teoria de conjuntos escrevemos $A - B = A - A \\cap B$.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Fonte: [Python Set](https://www.learnbyexample.org/python-set/)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uW6i3m9q1ZNs"
+ },
+ "source": [
+ "\n",
+ "* Vamos ver como isso funciona na prática:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vw05sfe22mfk"
+ },
+ "source": [
+ "## Exemplo 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Qqw2do90nQ7k"
+ },
+ "source": [
+ "a_conjunto1 = np.array([0, 1, 2, 4, 5, 7, 8, 8]) # array de valores que serão excluidos em a_conjunto1. Observe que '3' não pertence a a_conjunto1.\n",
+ "a_conjunto2 = np.array([1, 6, 7, 3])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zXJ00pOMorM-"
+ },
+ "source": [
+ "np.setdiff1d(a_conjunto1, a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8GXZNgjfo8lO"
+ },
+ "source": [
+ "Observe que o resultado são os elementos de a_conjunto1 que não pertencem a x_Y. Mas como fica o '3' nesta história?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aJSu6VKb2oc_"
+ },
+ "source": [
+ "## Exemplo 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "N1wahElXTqoB"
+ },
+ "source": [
+ "a_conjunto1 = np.arange(10)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nxDpCMg7T7Rj"
+ },
+ "source": [
+ "a_conjunto2 = np.array([1, 5, 7])\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3LU3qYyiUXqm"
+ },
+ "source": [
+ "np.setdiff1d(a_conjunto1, a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mzZEytrRUioU"
+ },
+ "source": [
+ "Observe que os elementos de a_conjunto2 foram deletados de a_conjunto1. Ok?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gJRcoVRUnaY9"
+ },
+ "source": [
+ "___\n",
+ "# Diferença Simétrica\n",
+ "* Em teoria de conjuntos, chamamos de Diferença Simétrica e escrevemos $(A \\cup B)- (A \\cap B)$.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Fonte: [Python Set](https://www.learnbyexample.org/python-set/)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2Uzzm85Kup3H"
+ },
+ "source": [
+ "* Vamos ver como isso funciona na prática:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1z5wZ8VwpsWN"
+ },
+ "source": [
+ "import numpy as np\n",
+ "a_conjunto1 = np.array([0, 1, 2, 4, 5, 7, 8]) # Observe que [1, 4, 7] pertencem a a_conjunto1, mas 3, não. Portanto:\n",
+ "a_conjunto2 = np.array([1, 4, 7, 3])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Tqd_9XO5p7bo"
+ },
+ "source": [
+ "np.setxor1d(a_conjunto1, a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_meurG3mqS5Y"
+ },
+ "source": [
+ "Como explicamos ou interpretamos este resultado?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Kc8JoKe2nj2n"
+ },
+ "source": [
+ "___\n",
+ "# **União de dois arrays**\n",
+ "> Retorna os valores **únicos** dos dois arrays. Na teoria dos conjuntos, escrevemos:\n",
+ "\n",
+ "$$A \\cup B$$\n",
+ "\n",
+ "\n",
+ "\n",
+ "Fonte: [Python Set](https://www.learnbyexample.org/python-set/)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1LZxorw2p2mg"
+ },
+ "source": [
+ "a_conjunto1 = np.array([0, 1, 2, 4, 5, 7, 8, 8])\n",
+ "\n",
+ "# Observe que [1, 4, 7] pertencem a a_conjunto1, mas 3, não. Portanto:\n",
+ "a_conjunto2 = np.array([1, 4, 7, 3])"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "COsZEmSwuY5L"
+ },
+ "source": [
+ "np.union1d(a_conjunto1, a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b53bR-GYRu_3"
+ },
+ "source": [
+ "___\n",
+ "# **Selecionar itens comuns dos arrays X e Y**\n",
+ "* Na teoria de conjuntos, chamamos de intersecção e escrevemos $X \\cap Y$.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Fonte: [Python Set](https://www.learnbyexample.org/python-set/)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "n2ec2tqqR1Gw"
+ },
+ "source": [
+ "* Considere os arrays a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rXVQQvBqR4J-"
+ },
+ "source": [
+ "a_conjunto1 = np.arange(10)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "pZTHhHxGSRfB"
+ },
+ "source": [
+ "a_conjunto2 = np.arange(8, 18)\n",
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MxB2_qHpScMB"
+ },
+ "source": [
+ "Quais são os elementos comuns à X e Y?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "e-rncJHtSfw0"
+ },
+ "source": [
+ "np.intersect1d(a_conjunto1, a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3Bb39sWdfqaF"
+ },
+ "source": [
+ "___\n",
+ "# **Autovalores e Autovetores**\n",
+ "> Autovetor e Autovalor são um dos tópicos mais importantes em Machine Learning.\n",
+ "\n",
+ "Por definição, o escalar $\\lambda$ e o vetor $v$ são autovalor e autovetor da matriz $A$ se\n",
+ "\n",
+ "$$Av = \\lambda v$$\n",
+ "\n",
+ "## Leitura Adicional:\n",
+ "\n",
+ "* [Machine Learning & Linear Algebra — Eigenvalue and eigenvector](https://medium.com/@jonathan_hui/machine-learning-linear-algebra-eigenvalue-and-eigenvector-f8d0493564c9)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XZBKq8nGCUbL"
+ },
+ "source": [
+ "* O array a_conjunto2 tem a seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iYlZGKFUfw-R"
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6EfvIbBNf02Z"
+ },
+ "source": [
+ "# Calcula autovalores e autovetores:\n",
+ "a_autovalores, a_autovalores= np.linalg.eig(a_conjunto2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "v3GtQQvAz9QU"
+ },
+ "source": [
+ "Os autovalores do array a_conjunto2 são:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WvZGyBR1f9vP"
+ },
+ "source": [
+ "a_autovalores"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AuuDRJVh0FC8"
+ },
+ "source": [
+ "Os autovetores do array a_conjunto2 são:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6m4YFAwsf_rA"
+ },
+ "source": [
+ "a_autovalores"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DASn2Un9ZNV-"
+ },
+ "source": [
+ "___\n",
+ "# **Encontrar Missing Values (NaN)**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TKilWBsSXtR4"
+ },
+ "source": [
+ "## Gerar o exemplo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lqLI2ER_ZUMY"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.random.random(100)\n",
+ "\n",
+ "# Inserindo 15 NaN's no array:\n",
+ "np.random.seed(20111974)\n",
+ "l_indices_aleatorios= np.random.randint(0, 100, size = 15)\n",
+ "\n",
+ "for i_indices in l_indices_aleatorios:\n",
+ " #print(i_indices)\n",
+ " a_conjunto1[i_indices] = np.nan"
+ ],
+ "execution_count": 27,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2ZkbMPXMawYh",
+ "outputId": "af5865c5-95fe-4df1-8712-b77543e860c5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": 28,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.53, 0.57, nan, 0.65, 0.86, 0.6 , 0.87, 0.46, nan, 0.64, 0.55,\n",
+ " 0.35, 0.32, nan, 0.85, 0.76, 0.66, 0.33, 0.35, 0.42, 0.31, 0.27,\n",
+ " 0.31, 0.36, 0.6 , 0.02, 0.36, nan, 0.28, 0.37, nan, 0.44, 0.2 ,\n",
+ " 0.21, 0.65, 0.82, 0.72, 0.5 , 0.17, 0.6 , nan, 0.14, nan, 0.71,\n",
+ " 0.07, 0.56, nan, 0.84, 0.21, 0.85, 0.63, 0.38, 0.91, 0.34, 0.07,\n",
+ " 0.1 , 0.85, 0.12, 0.94, 0.16, nan, 0.91, 0.59, 0.37, 0.72, 0.07,\n",
+ " 0.48, 0.78, 0.97, 0.72, 0.29, 0.33, 0.95, 0.24, 0.98, 0.85, 0.63,\n",
+ " 0.57, 0.67, 0.88, nan, nan, nan, 0.68, 0.29, 0.33, 0.98, 0.17,\n",
+ " nan, 0.92, 0.98, 0.76, 0.31, 0.97, 0.08, 0.56, nan, 0.49, 0.07,\n",
+ " 0.11])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 28
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z7Bs75NvbSjx"
+ },
+ "source": [
+ "Ok, inserimos aleatoriamente 14 NaN's no array a_conjunto1. Agora, vamos contar quantos NaN's (já sabemos a resposta!)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hL1Wn0vdX8ur"
+ },
+ "source": [
+ "## Identificar os NaN's"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5R-n3H0xbd6d",
+ "outputId": "9dc32bc4-bb41-4c02-bc64-c98461f3cefd",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "np.isnan(a_conjunto1).sum()\n",
+ "## isnan retorna um valor boleano se é nulo ou não"
+ ],
+ "execution_count": 29,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "14"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 29
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "81IoQ-EVbI5X",
+ "outputId": "fcddc325-8c6c-4545-cf1c-349af38ca954",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "np.isnan\n",
+ "## é uma função"
+ ],
+ "execution_count": 31,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 31
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PB-GAJ71bc7i",
+ "outputId": "0a9a431e-986d-40da-c841-544d25727b38",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 221
+ }
+ },
+ "source": [
+ "array_nulos = np.isnan(a_conjunto1)\n",
+ "array_nulos"
+ ],
+ "execution_count": 32,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([False, False, True, False, False, False, False, False, True,\n",
+ " False, False, False, False, True, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False, False,\n",
+ " True, False, False, True, False, False, False, False, False,\n",
+ " False, False, False, False, True, False, True, False, False,\n",
+ " False, True, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, True, False, False,\n",
+ " False, False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False, True,\n",
+ " True, True, False, False, False, False, False, True, False,\n",
+ " False, False, False, False, False, False, True, False, False,\n",
+ " False])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 32
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Y7hh5uowoa3U"
+ },
+ "source": [
+ "Ok, temos 14 NaN's em a_conjunto1."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iVLQf_bqbyNU"
+ },
+ "source": [
+ "Ok, agora eu quero saber os índices desses NaN's."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kJHxjZiwb5HM",
+ "outputId": "57d4bf56-64bd-4969-9281-d311ac926119",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "i_indices= np.where(np.isnan(a_conjunto1))\n",
+ "## o where retorna a posiçao do array que é true\n",
+ "i_indices"
+ ],
+ "execution_count": 30,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(array([ 2, 8, 13, 27, 30, 40, 42, 46, 60, 80, 81, 82, 88, 96]),)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 30
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "W_jHGNImok7L",
+ "outputId": "cbdcf2d2-3edf-4b8a-9a42-29aa20cbcd94",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Checando... que a posição 2 é nan\n",
+ "a_conjunto1[2]"
+ ],
+ "execution_count": 33,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "nan"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 33
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iPhHAhDYcMWO"
+ },
+ "source": [
+ "Vamos conferir se está correto? Para isso, basta comparar com l_indices_aleatorios:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gxQYslRCe11G"
+ },
+ "source": [
+ "___\n",
+ "# **Deletar NaN's de um array**\n",
+ "> Considere o mesmo array que acabamos de trabalhar. Agora eu quero excluir os NaN's identificados."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AeBARFqNfNnN",
+ "outputId": "eb361064-326a-451c-c4fe-0d14b861fbe8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": 34,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.53, 0.57, nan, 0.65, 0.86, 0.6 , 0.87, 0.46, nan, 0.64, 0.55,\n",
+ " 0.35, 0.32, nan, 0.85, 0.76, 0.66, 0.33, 0.35, 0.42, 0.31, 0.27,\n",
+ " 0.31, 0.36, 0.6 , 0.02, 0.36, nan, 0.28, 0.37, nan, 0.44, 0.2 ,\n",
+ " 0.21, 0.65, 0.82, 0.72, 0.5 , 0.17, 0.6 , nan, 0.14, nan, 0.71,\n",
+ " 0.07, 0.56, nan, 0.84, 0.21, 0.85, 0.63, 0.38, 0.91, 0.34, 0.07,\n",
+ " 0.1 , 0.85, 0.12, 0.94, 0.16, nan, 0.91, 0.59, 0.37, 0.72, 0.07,\n",
+ " 0.48, 0.78, 0.97, 0.72, 0.29, 0.33, 0.95, 0.24, 0.98, 0.85, 0.63,\n",
+ " 0.57, 0.67, 0.88, nan, nan, nan, 0.68, 0.29, 0.33, 0.98, 0.17,\n",
+ " nan, 0.92, 0.98, 0.76, 0.31, 0.97, 0.08, 0.56, nan, 0.49, 0.07,\n",
+ " 0.11])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 34
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "e497B492fFru",
+ "outputId": "68f697f4-4778-43e9-9b4f-87d930eccc46",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 153
+ }
+ },
+ "source": [
+ "a_conjunto1[~np.isnan(a_conjunto1)]\n",
+ "## o til nega a condição e retorna tudo que é falso\n",
+ "## colo o np.isnan gera o arrau com true e false, se eu falo que quero a negação e peço as posições com o falso\n",
+ "## eu estou retirando os nan"
+ ],
+ "execution_count": 36,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.53, 0.57, 0.65, 0.86, 0.6 , 0.87, 0.46, 0.64, 0.55, 0.35, 0.32,\n",
+ " 0.85, 0.76, 0.66, 0.33, 0.35, 0.42, 0.31, 0.27, 0.31, 0.36, 0.6 ,\n",
+ " 0.02, 0.36, 0.28, 0.37, 0.44, 0.2 , 0.21, 0.65, 0.82, 0.72, 0.5 ,\n",
+ " 0.17, 0.6 , 0.14, 0.71, 0.07, 0.56, 0.84, 0.21, 0.85, 0.63, 0.38,\n",
+ " 0.91, 0.34, 0.07, 0.1 , 0.85, 0.12, 0.94, 0.16, 0.91, 0.59, 0.37,\n",
+ " 0.72, 0.07, 0.48, 0.78, 0.97, 0.72, 0.29, 0.33, 0.95, 0.24, 0.98,\n",
+ " 0.85, 0.63, 0.57, 0.67, 0.88, 0.68, 0.29, 0.33, 0.98, 0.17, 0.92,\n",
+ " 0.98, 0.76, 0.31, 0.97, 0.08, 0.56, 0.49, 0.07, 0.11])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 36
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RpvKfJU_fmA6"
+ },
+ "source": [
+ "Observe que os NaN's foram excluidos."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7ereghZPcdh4"
+ },
+ "source": [
+ "EXERCÍCIO - ATRIBUIR A MEDIANA AOS VALORES DA AMOSTRA\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_Dv8MmNYg8zN"
+ },
+ "source": [
+ "___\n",
+ "# **Converter lista em array**\n",
+ "> Considere a lista a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "but6T9dVhFYb",
+ "outputId": "001bc55c-fe58-40ab-ff3c-3ea90d4e71d7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "l_Lista = [np.random.randint(0, 10, 10)]\n",
+ "l_Lista"
+ ],
+ "execution_count": 37,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[array([8, 9, 3, 7, 1, 3, 2, 9, 7, 7])]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 37
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xytj4Eo4hTh9",
+ "outputId": "4c2d1778-13ec-4717-a3a8-d538cfcf896a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "type(l_Lista)"
+ ],
+ "execution_count": 38,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "list"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 38
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qrINdcruhWcH"
+ },
+ "source": [
+ "Convertendo a minha lista para array:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RoSyaX0OhZSE",
+ "outputId": "8b194262-97d5-45ee-9336-3a5c0a5ad802",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto = np.asarray(l_Lista)\n",
+ "a_conjunto"
+ ],
+ "execution_count": 39,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[8, 9, 3, 7, 1, 3, 2, 9, 7, 7]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 39
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dMjTdbBUhlrk",
+ "outputId": "4d140e4f-2e4c-4aa7-8e99-481cb4d1cdc3",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "type(a_conjunto)"
+ ],
+ "execution_count": 40,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "numpy.ndarray"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 40
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Mbm3ZP9DhxDI"
+ },
+ "source": [
+ "___\n",
+ "# Converter tupla em array\n",
+ "> Considere a tupla a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cZxEFYLAh3S_"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "t_numeros = ([np.random.randint(0, 10, 3)], [np.random.randint(0, 10, 3)], [np.random.randint(0, 10, 3)])\n",
+ "t_numeros"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vlTXUJviiAml"
+ },
+ "source": [
+ "type(t_numeros)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "yEaOlq8oh3oh"
+ },
+ "source": [
+ "a_conjunto = np.asarray(t_numeros)\n",
+ "a_conjunto"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PSgQDmRWh3g5"
+ },
+ "source": [
+ "type(a_conjunto)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pH-Ht6yMiqJN"
+ },
+ "source": [
+ "___\n",
+ "# Acrescentar elementos à um array\n",
+ "> Considere o array a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dFaDZInZiwoo"
+ },
+ "source": [
+ "a_conjunto1 = np.arange(5)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "d3zrlf_Ci73Z"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.append(a_conjunto1, [np.random.randint(0, 10, 3), np.random.randint(0, 10, 3), np.random.randint(0, 10, 3)])\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eFRhtk13ojqA"
+ },
+ "source": [
+ "___\n",
+ "# **Converter array 1D num array 2D**\n",
+ "> Considere os arrays a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wYhBgW9Zu6ZP"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(0, 10, 6))\n",
+ "\n",
+ "np.random.seed(19741120)\n",
+ "a_conjunto2 = np.array(np.random.randint(0, 10, 6))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "febs9AUHvs6n"
+ },
+ "source": [
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "C9OEd-iavvBm"
+ },
+ "source": [
+ "a_conjunto2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KJWjtaWKv0MJ"
+ },
+ "source": [
+ "np.column_stack((a_conjunto1, a_conjunto2)) # Atenção aos parênteses em (a_conjunto1, a_conjunto2)."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xr_WZXJ7pi2D"
+ },
+ "source": [
+ "___\n",
+ "# **Excluir um elemento específico do array usando indices**\n",
+ "> Considere os arrays a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tS0ZzOs8w0dw"
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(0, 10, 6))\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7bOJiKDKxEsC"
+ },
+ "source": [
+ "Suponha que eu queira excluir os valores '8' de a_conjunto1. Os índices dos valores '8' são: [0, 1, 3]. Portanto, temos:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SSjueEvjxTJO"
+ },
+ "source": [
+ "a_conjunto1 = np.delete(a_conjunto1, [0, 1, 3])\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mZkGZ2Rgp--5"
+ },
+ "source": [
+ "___\n",
+ "# **Frequência dos valores únicos de um array**\n",
+ "> Considere o array a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Z2BWKfH0xvQ8",
+ "outputId": "0405171f-5590-434a-87be-39d44e18ce17",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(0, 10, 100))\n",
+ "a_conjunto1"
+ ],
+ "execution_count": 41,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([8, 8, 2, 8, 9, 1, 8, 0, 4, 2, 0, 8, 9, 3, 7, 1, 3, 2, 9, 7, 7, 9,\n",
+ " 5, 6, 8, 7, 0, 9, 3, 9, 3, 1, 8, 6, 3, 5, 4, 1, 2, 9, 8, 6, 6, 1,\n",
+ " 0, 9, 2, 0, 7, 5, 5, 4, 4, 2, 7, 2, 7, 9, 3, 1, 5, 0, 1, 2, 3, 8,\n",
+ " 7, 5, 4, 0, 5, 9, 6, 6, 1, 3, 6, 0, 4, 9, 2, 1, 0, 9, 1, 4, 2, 9,\n",
+ " 7, 9, 5, 3, 7, 6, 3, 9, 8, 4, 3, 0])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 41
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s_tdQBsax4rQ"
+ },
+ "source": [
+ "Suponha que eu queira saber quantas vezes o número/elemento '2' aparece em a_conjunto1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6yIlk7pWyAtf",
+ "outputId": "01f739af-34ea-448c-992f-ee587482c359",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "l_itens_unicos, i_count = np.unique(a_conjunto1, return_counts=True)\n",
+ "## é uma função que retorna os itens unicos e a quantidade que aparecem\n",
+ "l_itens_unicos"
+ ],
+ "execution_count": 42,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 42
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DyvrIwS9yZIR"
+ },
+ "source": [
+ "O que significa o output acima?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uO-MPMhXyV9H",
+ "outputId": "4f477738-6362-4177-a6ec-dd559dc9dc71",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "i_count"
+ ],
+ "execution_count": 43,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([10, 10, 10, 11, 8, 8, 8, 10, 10, 15])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 43
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zwoezXrPyofK"
+ },
+ "source": [
+ "Qual a interpretação do output acima?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HgYycSG7yr5e",
+ "outputId": "02fe1140-2976-4715-e211-20d27baf3c87",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ }
+ },
+ "source": [
+ "np.asarray((l_itens_unicos, i_count))"
+ ],
+ "execution_count": 44,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
+ " [10, 10, 10, 11, 8, 8, 8, 10, 10, 15]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 44
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SwIZiJAiy06T"
+ },
+ "source": [
+ "Qual a interpretação do output acima?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Wy_tqAPgdchD"
+ },
+ "source": [
+ "é a frequencia com que cada um aparece"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JpNRpN2Dql3N"
+ },
+ "source": [
+ "___\n",
+ "# **Combinações possíveis de outros arrays**\n",
+ "> Considere o exemplo a seguir:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BUr89dH4zLXD"
+ },
+ "source": [
+ "a_conjunto1 = [2, 4, 6]\n",
+ "a_conjunto2 = [0, 8]\n",
+ "a_conjunto4 = [1, 5]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cEZH6l-Czx7y"
+ },
+ "source": [
+ "np.meshgrid(a_conjunto1, a_conjunto2, a_conjunto4)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "btvmDkEcz0tH"
+ },
+ "source": [
+ "np.array(np.meshgrid(a_conjunto1, a_conjunto2, a_conjunto4))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Z0xhO7rGz059"
+ },
+ "source": [
+ "np.array(np.meshgrid(a_conjunto1, a_conjunto2, a_conjunto4)).T"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "eMv4lFnD0Enn"
+ },
+ "source": [
+ "# Resultado final\n",
+ "a_conjunto3 = np.array(np.meshgrid(a_conjunto1, a_conjunto2, a_conjunto4)).T.reshape(-1,3)\n",
+ "a_conjunto3"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Rz80YANfAh2k"
+ },
+ "source": [
+ "___\n",
+ "# **Wrap Up**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_cyhMsAVXxGC"
+ },
+ "source": [
+ "___\n",
+ "# **Exercícios**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kNjovMw3uJ3R"
+ },
+ "source": [
+ "## Exercício 1 - Selecionar os números pares\n",
+ "> Dado o 1D array abaixo, selecionar somente os números pares."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "isDzQjwjBX3V",
+ "outputId": "ad54cd80-fa6e-4772-a869-1b7f98b09725",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
+ "a_conjunto1"
+ ],
+ "execution_count": 45,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 45
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Kq1zt-uO1HXv"
+ },
+ "source": [
+ "### **Minha solução**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "YFmK_n2M1Ks9",
+ "outputId": "496556f7-9ff2-40f7-8cbf-2a1588793e40",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1[a_conjunto1 % 2 == 0]"
+ ],
+ "execution_count": 46,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0, 2, 4, 6, 8])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 46
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sScYG0hp05vb"
+ },
+ "source": [
+ "___\n",
+ "## Exercício 2 - Substituir pela mediana\n",
+ "> Dado o array 1D abaixo, substituir os números pares pela mediana de a_conjunto1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XLZ-DIWU1WFs",
+ "outputId": "aebcdfd3-b244-4cb2-f33b-a8083d241d17",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
+ "a_conjunto1"
+ ],
+ "execution_count": 47,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 47
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9c4QWJno1WVB"
+ },
+ "source": [
+ "### **Minha solução**\n",
+ "* Primeiramente, precisamos calcular a mediana.\n",
+ "* Depois, substituimos os valores pares de a_conjunto1 pela mediana encontrada anteriormente. Ok?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rx7NGAO01Wfb",
+ "outputId": "575c869f-1a28-49db-9516-47c84cecacc2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "a_conjunto1[a_conjunto1 % 2 == 0] = np.median(a_conjunto1)\n",
+ "a_conjunto1"
+ ],
+ "execution_count": 48,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([4, 1, 4, 3, 4, 5, 4, 7, 4, 9])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 48
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2c_AphX82qp8"
+ },
+ "source": [
+ "Verificando..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9kVta0Cr13Z9",
+ "outputId": "cb8af387-8353-49f1-cf9e-37ee7c77b607",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "f'A média de a_conjunto1 é: {np.median(a_conjunto1)}'"
+ ],
+ "execution_count": 49,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'A média de a_conjunto1 é: 4.0'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 49
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "L9O-Hf5x26TY"
+ },
+ "source": [
+ "___\n",
+ "## Exercício 3 - Reshape\n",
+ "> Dado o array 1D abaixo, reshape para um array 2D com 3 colunas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0_laUvtB4Wl-",
+ "outputId": "34954fdf-6e28-477c-ac93-03b510461a21",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "# Define seed\n",
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(1, 10, size = 15))\n",
+ "a_conjunto1"
+ ],
+ "execution_count": 50,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([9, 9, 3, 9, 2, 9, 1, 5, 3, 1, 9, 4, 8, 2, 4])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 50
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dKzEX8TK5b4Z"
+ },
+ "source": [
+ "### **Minha solução**\n",
+ "* O array 1D a_conjunto1 acima possui 15 elementos. Como queremos transformá-lo num array 2D com 3 colunas, então cada coluna terá 5 elementos."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "r5hJ-wMwjXPR",
+ "outputId": "2eadc741-755c-46d2-bd8c-ad0d3cc8ad8e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102
+ }
+ },
+ "source": [
+ "a_conjunto1.reshape(-1,3)"
+ ],
+ "execution_count": 51,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[9, 9, 3],\n",
+ " [9, 2, 9],\n",
+ " [1, 5, 3],\n",
+ " [1, 9, 4],\n",
+ " [8, 2, 4]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 51
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "I-j5yVD04249"
+ },
+ "source": [
+ "a_conjunto1.reshape(5, 3) \n",
+ "# Poderia ser a_conjunto1.reshape(-1, 3), onde \"-1\" pede para o NumPy calcular o número de linhas. "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F1vfS8jE6L0_"
+ },
+ "source": [
+ "___\n",
+ "## Exercício 4 - Reshape\n",
+ "> Dado o array 1D abaixo, reshape para um array 3D com 2 colunas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xcN-bez56L1D"
+ },
+ "source": [
+ "# Define seed\n",
+ "np.random.seed(20111974)\n",
+ "a_conjunto1 = np.array(np.random.randint(1, 10, size = 16))\n",
+ "a_conjunto1"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7iICnOyG6fcj"
+ },
+ "source": [
+ "### **Minha solução**\n",
+ "* O array 1D a_conjunto1 acima possui 16 elementos. Queremos transformá-lo num array 3D com 2 colunas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vdq5ybuD6fcn"
+ },
+ "source": [
+ "a_conjunto1.reshape(-1, 2) # O valor \"-1\" na posição das linhas pede ao NumPy para calcular o número de linhas automaticamente."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "haQfWPcCs_H0"
+ },
+ "source": [
+ "## Exercício 5\n",
+ "Para mais exercícios envolvendo arrays, visite a página [Python: Array Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/array/)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LQQL0JS2tnc0"
+ },
+ "source": [
+ "## Exercício 6\n",
+ "Para mais exercícios envolvendo matemática, viste a página [Python Math: - Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/math/index.php)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qNskKFy9t4D5"
+ },
+ "source": [
+ "## Exercício 7\n",
+ "Para mais exercícios envolvendo NumPy em geral, visite a página [NumPy Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/numpy/index.php)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qqc1AiHXuKZ5"
+ },
+ "source": [
+ "## Exercício 8\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jYrgc3KvtmLy"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
From a568ce97c04618fae60156c588d5f3bea7a057a1 Mon Sep 17 00:00:00 2001
From: MariaJacobs70 <72224154+MariaJacobs70@users.noreply.github.com>
Date: Wed, 7 Oct 2020 15:45:59 -0300
Subject: [PATCH 3/9] Criado usando o Colaboratory
---
Notebooks/NB07__Dictionaries_alterado.ipynb | 2270 +++++++++++++++++++
1 file changed, 2270 insertions(+)
create mode 100644 Notebooks/NB07__Dictionaries_alterado.ipynb
diff --git a/Notebooks/NB07__Dictionaries_alterado.ipynb b/Notebooks/NB07__Dictionaries_alterado.ipynb
new file mode 100644
index 000000000..aeea3db03
--- /dev/null
+++ b/Notebooks/NB07__Dictionaries_alterado.ipynb
@@ -0,0 +1,2270 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "NB07__Dictionaries.ipynb",
+ "provenance": [],
+ "collapsed_sections": [
+ "n8BIbzQbNWUo",
+ "7eS94uQ4NhVR",
+ "SYOgJpGYVLUu",
+ "CaHFxk98W5if",
+ "ReWUyWiHXCnc",
+ "CqszHxaKHr2h",
+ "tXgF1Wl9gHKY",
+ "Fotx7XUquAo8",
+ "36kmLUYDvsUI",
+ "SWO2GdNovxAp",
+ "vpN54l4vxze5",
+ "u4HOf9SNytSq",
+ "6BQ9oZiD9hg5",
+ "tz5-QdrX9vct",
+ "p1muBgMX8NK4",
+ "FxTC2-U88ajk",
+ "z8EYn0pP25Rh"
+ ],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "DICIONÁRIOS \u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md_Paises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'd_Paises' is not defined"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IJEMg2LKHyGa"
+ },
+ "source": [
+ "___\n",
+ "# **OBTER VALOR PARA UMA KEY ESPECÍFICA**\n",
+ "* d_dicionario.get(key)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dzgBhsphjSQm"
+ },
+ "source": [
+ "Qual o valor para key= 1?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "FUfTjqktjW60",
+ "outputId": "678ab629-6cff-4fe1-e03f-d90709a98f26",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "d_paises.get(1)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'Italy'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 11
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tyJ0KsloIBoD"
+ },
+ "source": [
+ "___\n",
+ "# **COPIAR DICIONÁRIO**\n",
+ "* d_dicionario.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XL17EmvMkkky",
+ "outputId": "65846bc2-87a2-42cf-eb17-e3fccb00c9a4",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "d_paises2 = d_paises.copy()\n",
+ "d_paises2"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{1: 'Italy', 2: 'Denmark', 3: 'France'}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 28
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8V25l2ZoIG4B"
+ },
+ "source": [
+ "___\n",
+ "# **REMOVER TODOS OS ITENS DO DICIONÁRIO**\n",
+ "* d_dicionario.clear()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "r-8Gs1gYjqLN"
+ },
+ "source": [
+ "d_paises.clear()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ro_42gzDjsdV",
+ "outputId": "a2c2a25b-40ef-4842-f2f7-3ac85404d195",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "d_paises"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pCzKkKoujv7G"
+ },
+ "source": [
+ "Como esperado, removemos todos os itens do dicionário d_paises. Entretanto, o dicionário d_paises continua a existir!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MKtPwGVsIaLQ"
+ },
+ "source": [
+ "___\n",
+ "# **DELETAR O DICIONÁRIO**\n",
+ "* del d_dicionario"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8wvM-o7Lj7A0"
+ },
+ "source": [
+ "del d_paises"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wK83ZURYkD_T",
+ "outputId": "03254461-9939-4ef9-de30-c4b59c920674",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 166
+ }
+ },
+ "source": [
+ "d_paises"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "NameError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdCountries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'dCountries' is not defined"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aSe3veUB1lo_"
+ },
+ "source": [
+ "Como esperado, pois agora o dicionário já não existe mais. Ok?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "STtkGUvEg7d1"
+ },
+ "source": [
+ "___\n",
+ "# **ITERAR PELO DICIONÁRIO**\n",
+ "* Considere o dicionário d_frutas a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "IG8hKSvcfalZ"
+ },
+ "source": [
+ "# Definindo os valores iniciais do dicionário d_frutas:\n",
+ "d_frutas = {'Avocado': 0.35, \n",
+ " 'Apple': 0.40, \n",
+ " 'Apricot': 0.25, \n",
+ " 'Banana': 0.30, \n",
+ " 'Blackcurrant': 0.70, \n",
+ " 'Blackberry': 0.55, \n",
+ " 'Blueberry': 0.45, \n",
+ " 'Cherry': 0.50, \n",
+ " 'Coconut': 0.75, \n",
+ " 'Fig': 0.60, \n",
+ " 'Grape': 0.65, \n",
+ " 'Kiwi': 0.20, \n",
+ " 'Lemon': 0.15, \n",
+ " 'Mango': 0.80, \n",
+ " 'Nectarine': 0.75, \n",
+ " 'Orange': 0.25, \n",
+ " 'Papaya': 0.30,\n",
+ " 'Passion Fruit': 0.45,\n",
+ " 'Peach': 0.55,\n",
+ " 'Pineapple': 0.55,\n",
+ " 'Plum': 0.60,\n",
+ " 'Raspberry': 0.40,\n",
+ " 'Strawberry': 0.50,\n",
+ " 'Watermelon': 0.45}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ppRkK_jJJG6W"
+ },
+ "source": [
+ "Mostrando os itens do dicionário d_frutas:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bI7Ctf0ohyz8",
+ "outputId": "5af38cf4-aaf8-4efa-f682-502bfb5022a6",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 425
+ }
+ },
+ "source": [
+ "d_frutas"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Apple': 0.4,\n",
+ " 'Apricot': 0.25,\n",
+ " 'Avocado': 0.35,\n",
+ " 'Banana': 0.3,\n",
+ " 'Blackberry': 0.55,\n",
+ " 'Blackcurrant': 0.7,\n",
+ " 'Blueberry': 0.45,\n",
+ " 'Cherry': 0.5,\n",
+ " 'Coconut': 0.75,\n",
+ " 'Fig': 0.6,\n",
+ " 'Grape': 0.65,\n",
+ " 'Kiwi': 0.2,\n",
+ " 'Lemon': 0.15,\n",
+ " 'Mango': 0.8,\n",
+ " 'Nectarine': 0.75,\n",
+ " 'Orange': 0.25,\n",
+ " 'Papaya': 0.3,\n",
+ " 'Passion Fruit': 0.45,\n",
+ " 'Peach': 0.55,\n",
+ " 'Pineapple': 0.55,\n",
+ " 'Plum': 0.6,\n",
+ " 'Raspberry': 0.4,\n",
+ " 'Strawberry': 0.5,\n",
+ " 'Watermelon': 0.45}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wXFfyiyPtD35"
+ },
+ "source": [
+ "Qual o valor para a fruta 'Apple'? Para responder à esta pergunta, basta lembrar que 'Apple' é uma key do dicionário d_frutas. Certo?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JpreyE_LtCcU",
+ "outputId": "cee4be2d-7980-4a3d-85fb-17561d1bb1ff",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "d_frutas['Apple']"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "0.4"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 21
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JBMf8SbAJmiq"
+ },
+ "source": [
+ "## Iterar pelas keys do dicionário:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rMro_tY8kepo",
+ "outputId": "4488c243-6792-4efa-b271-e546270b129d",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 425
+ }
+ },
+ "source": [
+ "for key in d_frutas.keys():\n",
+ " print(key)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Avocado\n",
+ "Apple\n",
+ "Apricot\n",
+ "Banana\n",
+ "Blackcurrant\n",
+ "Blackberry\n",
+ "Blueberry\n",
+ "Cherry\n",
+ "Coconut\n",
+ "Fig\n",
+ "Grape\n",
+ "Kiwi\n",
+ "Lemon\n",
+ "Mango\n",
+ "Nectarine\n",
+ "Orange\n",
+ "Papaya\n",
+ "Passion Fruit\n",
+ "Peach\n",
+ "Pineapple\n",
+ "Plum\n",
+ "Raspberry\n",
+ "Strawberry\n",
+ "Watermelon\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yDkOLvRFJxco"
+ },
+ "source": [
+ "## Iterar pelos itens (key, value) do dicionário"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DpFB1g-3kDSt",
+ "outputId": "7ac51581-edfa-418d-a1e0-d297ebdffca7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 425
+ }
+ },
+ "source": [
+ "for item in d_frutas.items():\n",
+ " print(item) "
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "('Avocado', 0.35)\n",
+ "('Apple', 0.4)\n",
+ "('Apricot', 0.25)\n",
+ "('Banana', 0.3)\n",
+ "('Blackcurrant', 0.7)\n",
+ "('Blackberry', 0.55)\n",
+ "('Blueberry', 0.45)\n",
+ "('Cherry', 0.5)\n",
+ "('Coconut', 0.75)\n",
+ "('Fig', 0.6)\n",
+ "('Grape', 0.65)\n",
+ "('Kiwi', 0.2)\n",
+ "('Lemon', 0.15)\n",
+ "('Mango', 0.8)\n",
+ "('Nectarine', 0.75)\n",
+ "('Orange', 0.25)\n",
+ "('Papaya', 0.3)\n",
+ "('Passion Fruit', 0.45)\n",
+ "('Peach', 0.55)\n",
+ "('Pineapple', 0.55)\n",
+ "('Plum', 0.6)\n",
+ "('Raspberry', 0.4)\n",
+ "('Strawberry', 0.5)\n",
+ "('Watermelon', 0.45)\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8z6qO74fJ6Q1"
+ },
+ "source": [
+ "## Iterar pelos valores do dicionário"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tjJ6qRF8nr4v",
+ "outputId": "3e75843b-2d45-4b4c-a3f2-24ffc0e60a7a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 425
+ }
+ },
+ "source": [
+ "for value in d_frutas.values():\n",
+ " print(value)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0.35\n",
+ "0.4\n",
+ "0.25\n",
+ "0.3\n",
+ "0.7\n",
+ "0.55\n",
+ "0.45\n",
+ "0.5\n",
+ "0.75\n",
+ "0.6\n",
+ "0.65\n",
+ "0.2\n",
+ "0.15\n",
+ "0.8\n",
+ "0.75\n",
+ "0.25\n",
+ "0.3\n",
+ "0.45\n",
+ "0.55\n",
+ "0.55\n",
+ "0.6\n",
+ "0.4\n",
+ "0.5\n",
+ "0.45\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-LmEUroVKDUA"
+ },
+ "source": [
+ "## Iterar pela key e valor do dicionário"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oRhZ_Zq9oQIg",
+ "outputId": "be168183-30b4-4f96-ae2c-3f313acbc558",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 425
+ }
+ },
+ "source": [
+ "for key, value in d_frutas.items():\n",
+ " print(\"%s --> %s\" %(key, value))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Avocado --> 0.35\n",
+ "Apple --> 0.4\n",
+ "Apricot --> 0.25\n",
+ "Banana --> 0.3\n",
+ "Blackcurrant --> 0.7\n",
+ "Blackberry --> 0.55\n",
+ "Blueberry --> 0.45\n",
+ "Cherry --> 0.5\n",
+ "Coconut --> 0.75\n",
+ "Fig --> 0.6\n",
+ "Grape --> 0.65\n",
+ "Kiwi --> 0.2\n",
+ "Lemon --> 0.15\n",
+ "Mango --> 0.8\n",
+ "Nectarine --> 0.75\n",
+ "Orange --> 0.25\n",
+ "Papaya --> 0.3\n",
+ "Passion Fruit --> 0.45\n",
+ "Peach --> 0.55\n",
+ "Pineapple --> 0.55\n",
+ "Plum --> 0.6\n",
+ "Raspberry --> 0.4\n",
+ "Strawberry --> 0.5\n",
+ "Watermelon --> 0.45\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Fotx7XUquAo8"
+ },
+ "source": [
+ "___\n",
+ "# **VERIFICAR SE UMA KEY ESPECÍFICA PERTENCE AO DICIONÁRIO**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ju__WsSoKXtk"
+ },
+ "source": [
+ "A fruta 'Apple' (que em nosso caso, é uma key) existe no dicionário?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-gkEKNZPTeMp",
+ "outputId": "3540aadd-996a-4abd-cfcb-c22e49b75aaa",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "'Apple' in d_frutas.keys()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 75
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fMzBeFMIusv7"
+ },
+ "source": [
+ "A fruta 'Coconut' pertence ao dicionário d_frutas?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SKtEwmBCuxyi",
+ "outputId": "1df7263c-a64f-4eaf-8d4d-a55cac03d2bc",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "'Coconut' in fruits.keys()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 77
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rrH8ArqsK6Bd"
+ },
+ "source": [
+ "___\n",
+ "# **VERIFICAR SE VALOR PERTENCE AO DICIONÁRIO**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DbWpbuLTK9sn",
+ "outputId": "e9fafa6d-284e-4862-8f25-9419ff702dec",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "0.4 in d_frutas.values()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "36kmLUYDvsUI"
+ },
+ "source": [
+ "## Adicionar novos itens ao dicionário\n",
+ "* Considere o dicionário d_frutas2 abaixo:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5Rwq4-UG4--u"
+ },
+ "source": [
+ "d_frutas2 = {'Grapefruit': 1.0 }"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vljceM6_5H9o"
+ },
+ "source": [
+ "O comando abaixo adiciona o dicionário d_frutas2 ao dicionário d_frutas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7BD_mYMM5O5o",
+ "outputId": "2b185546-255e-4ad0-e8c9-10564fcbe2b0",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 442
+ }
+ },
+ "source": [
+ "d_frutas.update(d_frutas2)\n",
+ "d_frutas"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Apple': 0.4,\n",
+ " 'Apricot': 0.25,\n",
+ " 'Avocado': 0.35,\n",
+ " 'Banana': 0.3,\n",
+ " 'Blackberry': 0.55,\n",
+ " 'Blackcurrant': 0.7,\n",
+ " 'Blueberry': 0.45,\n",
+ " 'Cherry': 0.5,\n",
+ " 'Coconut': 0.75,\n",
+ " 'Fig': 0.6,\n",
+ " 'Grape': 0.65,\n",
+ " 'Grapefruit': 1.0,\n",
+ " 'Kiwi': 0.2,\n",
+ " 'Lemon': 0.15,\n",
+ " 'Mango': 0.8,\n",
+ " 'Nectarine': 0.75,\n",
+ " 'Orange': 0.25,\n",
+ " 'Papaya': 0.3,\n",
+ " 'Passion Fruit': 0.45,\n",
+ " 'Peach': 0.55,\n",
+ " 'Pineapple': 0.55,\n",
+ " 'Plum': 0.6,\n",
+ " 'Raspberry': 0.4,\n",
+ " 'Strawberry': 0.5,\n",
+ " 'Watermelon': 0.45}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 79
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ffh-94lo55n4"
+ },
+ "source": [
+ "Agora, considere o dicionário d_frutas3 abaixo:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JMAq_jbP5---"
+ },
+ "source": [
+ "d_frutas3 = {'Apple': 0.70}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Jd6B2cy-6KmY"
+ },
+ "source": [
+ "Qual o resultado do comando abaixo?\n",
+ "\n",
+ "* Atenção: A fruta 'Apple' (é uma key do dicionário d_frutas) tem valor 0.40. E no dicionário d_frutas3 a fruta 'Apple' tem valor 0.70."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "E4GKdTw76PXI"
+ },
+ "source": [
+ "d_frutas.update(d_frutas3)\n",
+ "d_frutas"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HMmDfrln6o0c"
+ },
+ "source": [
+ "Como esperado, como key= 'Apple' existe no dicionário d_frutas, então o Python atualizou o valor de key= 'Apple' para 0.70."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SWO2GdNovxAp"
+ },
+ "source": [
+ "## Modificar keys e valores"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DX9UTy4TwlAw"
+ },
+ "source": [
+ "Suponha que queremos aplicar um desconto de 10% para cada fruta do nosso dicionário.\n",
+ "\n",
+ "* Como fazemos isso?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZziGmKGmwqwn"
+ },
+ "source": [
+ "for key, value in d_frutas.items():\n",
+ " d_frutas[key] = round(value * 0.9, 2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s1B-yN8lM-C1"
+ },
+ "source": [
+ "Mostra d_frutas com os valores atualizados:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zZLa85knxBtY",
+ "outputId": "2c7c12f8-8885-4f34-a0d1-1323e98a9437",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 442
+ }
+ },
+ "source": [
+ "d_frutas"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Apple': 0.63,\n",
+ " 'Apricot': 0.23,\n",
+ " 'Avocado': 0.32,\n",
+ " 'Banana': 0.27,\n",
+ " 'Blackberry': 0.5,\n",
+ " 'Blackcurrant': 0.63,\n",
+ " 'Blueberry': 0.41,\n",
+ " 'Cherry': 0.45,\n",
+ " 'Coconut': 0.68,\n",
+ " 'Fig': 0.54,\n",
+ " 'Grape': 0.59,\n",
+ " 'Grapefruit': 0.9,\n",
+ " 'Kiwi': 0.18,\n",
+ " 'Lemon': 0.14,\n",
+ " 'Mango': 0.72,\n",
+ " 'Nectarine': 0.68,\n",
+ " 'Orange': 0.23,\n",
+ " 'Papaya': 0.27,\n",
+ " 'Passion Fruit': 0.41,\n",
+ " 'Peach': 0.5,\n",
+ " 'Pineapple': 0.5,\n",
+ " 'Plum': 0.54,\n",
+ " 'Raspberry': 0.36,\n",
+ " 'Strawberry': 0.45,\n",
+ " 'Watermelon': 0.41}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 84
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vpN54l4vxze5"
+ },
+ "source": [
+ "## Deletar keys do dicionário\n",
+ "* Deletar uma key significa deletar todo o item {key: value}, ok?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eDlthLStNIwR"
+ },
+ "source": [
+ "Suponha que queremos deletar a fruta 'Avocado' do dicionário d_frutas.\n",
+ "\n",
+ "* Como fazer isso?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fnpzHZU_x5Y1"
+ },
+ "source": [
+ "for key in list(d_frutas.keys()): # Dica: use a função list para melhorar a performance computacional\n",
+ " if key == 'Avocado':\n",
+ " del d_frutas[key] # Deleta key = 'Avocado'"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VyPUrobONqvI"
+ },
+ "source": [
+ "Mostra o dicionário d_frutas atualizado:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "IwnsHejhyT4l",
+ "outputId": "b910699c-9729-4a27-bd78-3a283c82ac39",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 425
+ }
+ },
+ "source": [
+ "d_frutas"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Apple': 0.63,\n",
+ " 'Apricot': 0.23,\n",
+ " 'Banana': 0.27,\n",
+ " 'Blackberry': 0.5,\n",
+ " 'Blackcurrant': 0.63,\n",
+ " 'Blueberry': 0.41,\n",
+ " 'Cherry': 0.45,\n",
+ " 'Coconut': 0.68,\n",
+ " 'Fig': 0.54,\n",
+ " 'Grape': 0.59,\n",
+ " 'Grapefruit': 0.9,\n",
+ " 'Kiwi': 0.18,\n",
+ " 'Lemon': 0.14,\n",
+ " 'Mango': 0.72,\n",
+ " 'Nectarine': 0.68,\n",
+ " 'Orange': 0.23,\n",
+ " 'Papaya': 0.27,\n",
+ " 'Passion Fruit': 0.41,\n",
+ " 'Peach': 0.5,\n",
+ " 'Pineapple': 0.5,\n",
+ " 'Plum': 0.54,\n",
+ " 'Raspberry': 0.36,\n",
+ " 'Strawberry': 0.45,\n",
+ " 'Watermelon': 0.41}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 86
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "u4HOf9SNytSq"
+ },
+ "source": [
+ "## Filtrar/Selecionar itens baseado em condições\n",
+ "Em algumas situações você vai querer filtrar os itens do dicionário que satisfaçam alguma(s) condições.\n",
+ "\n",
+ "* Considere o exemplo a seguir: queremos selecionar/filtrar somente as frutas com preços maiores que 0.4."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EwqxWiVlyvgH"
+ },
+ "source": [
+ "d_frutas_filtro = {}\n",
+ "for key, value in d_frutas.items():\n",
+ " if value > 0.5:\n",
+ " d_frutas_filtro.update({key: value})"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eb0jmAKWOtYt"
+ },
+ "source": [
+ "Mostra o resultado do dicionário d_frutas_Selected:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SsStWM5k1s-Q",
+ "outputId": "f6af5b61-2333-41c7-a28a-0f6a67b0a949",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 170
+ }
+ },
+ "source": [
+ "d_frutas_filtro"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Apple': 0.63,\n",
+ " 'Blackcurrant': 0.63,\n",
+ " 'Coconut': 0.68,\n",
+ " 'Fig': 0.54,\n",
+ " 'Grape': 0.59,\n",
+ " 'Grapefruit': 0.9,\n",
+ " 'Mango': 0.72,\n",
+ " 'Nectarine': 0.68,\n",
+ " 'Plum': 0.54}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 89
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "u1ve6xIGOjrE"
+ },
+ "source": [
+ " Como se pode ver, somente a fruta 'Blackberry' satifaz esta condição."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KJqpPrfkCk9L"
+ },
+ "source": [
+ "## Cálculos com os itens do dicionário"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "exD8HXodCqg6"
+ },
+ "source": [
+ "from collections import Counter"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "llCLTysdCuwB"
+ },
+ "source": [
+ "Somando os valores de todas as frutas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uG0VP1MNCroX",
+ "outputId": "8221b07b-610d-4a7c-cb14-86d6f63e5be3",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "sum(d_frutas.values())"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "11.450000000000001"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 22
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "a5MBNCF-C5-4"
+ },
+ "source": [
+ "Quantos itens existem no dicionário:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AkvygR0PC9bT",
+ "outputId": "254eff41-8336-4fe6-d6ad-4d52544d74a9",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "len(list(d_frutas))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "24"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 25
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xBNFaklq8OC9"
+ },
+ "source": [
+ "## Sortear itens do dicionário - sorted(d_dicionario.items(), reverse= True/False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WULJMjHA-mal"
+ },
+ "source": [
+ "Ordem alfabética (por key):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SH0WIKZ8-Ylr",
+ "outputId": "b9cea719-637e-40a5-9e79-eb67aeb47887",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 425
+ }
+ },
+ "source": [
+ "d_frutas_ordenadas = sorted(d_frutas.items(), reverse = False)\n",
+ "d_frutas_ordenadas"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[('Apple', 0.4),\n",
+ " ('Apricot', 0.25),\n",
+ " ('Avocado', 0.35),\n",
+ " ('Banana', 0.3),\n",
+ " ('Blackberry', 0.55),\n",
+ " ('Blackcurrant', 0.7),\n",
+ " ('Blueberry', 0.45),\n",
+ " ('Cherry', 0.5),\n",
+ " ('Coconut', 0.75),\n",
+ " ('Fig', 0.6),\n",
+ " ('Grape', 0.65),\n",
+ " ('Kiwi', 0.2),\n",
+ " ('Lemon', 0.15),\n",
+ " ('Mango', 0.8),\n",
+ " ('Nectarine', 0.75),\n",
+ " ('Orange', 0.25),\n",
+ " ('Papaya', 0.3),\n",
+ " ('Passion Fruit', 0.45),\n",
+ " ('Peach', 0.55),\n",
+ " ('Pineapple', 0.55),\n",
+ " ('Plum', 0.6),\n",
+ " ('Raspberry', 0.4),\n",
+ " ('Strawberry', 0.5),\n",
+ " ('Watermelon', 0.45)]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 12
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "T4Li1Q2d-pnZ"
+ },
+ "source": [
+ "Ordem reversa (por key):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PoBOmfpM_A_a",
+ "outputId": "4cd9a21c-a2ad-462c-acb0-26ba7a0a4e5d",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 425
+ }
+ },
+ "source": [
+ "d_frutas_ordenadas_reverse = sorted(d_frutas.items(), reverse = True)\n",
+ "d_frutas_ordenadas_reverse"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[('Watermelon', 0.45),\n",
+ " ('Strawberry', 0.5),\n",
+ " ('Raspberry', 0.4),\n",
+ " ('Plum', 0.6),\n",
+ " ('Pineapple', 0.55),\n",
+ " ('Peach', 0.55),\n",
+ " ('Passion Fruit', 0.45),\n",
+ " ('Papaya', 0.3),\n",
+ " ('Orange', 0.25),\n",
+ " ('Nectarine', 0.75),\n",
+ " ('Mango', 0.8),\n",
+ " ('Lemon', 0.15),\n",
+ " ('Kiwi', 0.2),\n",
+ " ('Grape', 0.65),\n",
+ " ('Fig', 0.6),\n",
+ " ('Coconut', 0.75),\n",
+ " ('Cherry', 0.5),\n",
+ " ('Blueberry', 0.45),\n",
+ " ('Blackcurrant', 0.7),\n",
+ " ('Blackberry', 0.55),\n",
+ " ('Banana', 0.3),\n",
+ " ('Avocado', 0.35),\n",
+ " ('Apricot', 0.25),\n",
+ " ('Apple', 0.4)]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 11
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FxTC2-U88ajk"
+ },
+ "source": [
+ "## Função filter()\n",
+ "* A função filter() aplica um filtro no dicionário, retornando apenas os itens que satisfaz as condições do filtro."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iJq1clvOHVG2",
+ "outputId": "16a779ef-48c9-497c-8c7c-a1612aa9aa03",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 425
+ }
+ },
+ "source": [
+ "d_frutas"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Apple': 0.4,\n",
+ " 'Apricot': 0.25,\n",
+ " 'Avocado': 0.35,\n",
+ " 'Banana': 0.3,\n",
+ " 'Blackberry': 0.55,\n",
+ " 'Blackcurrant': 0.7,\n",
+ " 'Blueberry': 0.45,\n",
+ " 'Cherry': 0.5,\n",
+ " 'Coconut': 0.75,\n",
+ " 'Fig': 0.6,\n",
+ " 'Grape': 0.65,\n",
+ " 'Kiwi': 0.2,\n",
+ " 'Lemon': 0.15,\n",
+ " 'Mango': 0.8,\n",
+ " 'Nectarine': 0.75,\n",
+ " 'Orange': 0.25,\n",
+ " 'Papaya': 0.3,\n",
+ " 'Passion Fruit': 0.45,\n",
+ " 'Peach': 0.55,\n",
+ " 'Pineapple': 0.55,\n",
+ " 'Plum': 0.6,\n",
+ " 'Raspberry': 0.4,\n",
+ " 'Strawberry': 0.5,\n",
+ " 'Watermelon': 0.45}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 2
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qtTKvNeJNycl"
+ },
+ "source": [
+ "### Filtrando por key:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uIDW5FhwAiSs",
+ "outputId": "52599d3f-ff13-4894-f697-ce7290bff9d5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "source": [
+ "d_frutas2 = {k: v for k, v in filter(lambda t: t[0] == 'Apple', d_frutas.items())}\n",
+ "d_frutas2"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Apple': 0.4}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 6
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nUMGIzxeNt_U"
+ },
+ "source": [
+ "### Filtrando por valor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tvHcQatANltL",
+ "outputId": "8feaf5b1-1db8-4391-8950-248ba8ab46c5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 187
+ }
+ },
+ "source": [
+ "d_frutas3 = {k: v for k, v in filter(lambda t: t[1] > 0.5, d_frutas.items())}\n",
+ "d_frutas3"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Blackberry': 0.55,\n",
+ " 'Blackcurrant': 0.7,\n",
+ " 'Coconut': 0.75,\n",
+ " 'Fig': 0.6,\n",
+ " 'Grape': 0.65,\n",
+ " 'Mango': 0.8,\n",
+ " 'Nectarine': 0.75,\n",
+ " 'Peach': 0.55,\n",
+ " 'Pineapple': 0.55,\n",
+ " 'Plum': 0.6}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 7
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qA_XhCdmA6Gn"
+ },
+ "source": [
+ "___\n",
+ "# **EXERCÍCIOS**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RSpyl_URgNyE"
+ },
+ "source": [
+ "## Exercício 1\n",
+ "* É possível sortear os itens de um dicionário? Explique sua resposta."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CXqc9kHch6Mm"
+ },
+ "source": [
+ "## Exercício 2\n",
+ "* É possível termos um dicionário do tipo abaixo?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0BBWO9Zth_mc"
+ },
+ "source": [
+ "d_colaboradores= {'Gerentes': ['A', 'B', 'C'], 'Programadores': ['B', 'D', 'E', 'F', 'G'], 'Gerentes_Projeto': ['A', 'E']}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TNiJSG_uiePb"
+ },
+ "source": [
+ "Como acessar o Gerente 'A'?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ntVcr_3XwaQ-"
+ },
+ "source": [
+ "## Exercício 3\n",
+ "Consulte a página [Python Data Types: Dictionary - Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/dictionary/) para mais exercícios relacionados à dicionários."
+ ]
+ }
+ ]
+}
\ No newline at end of file
From 27328b97f577baf578c03c9f20f6c635ab609551 Mon Sep 17 00:00:00 2001
From: MariaJacobs70 <72224154+MariaJacobs70@users.noreply.github.com>
Date: Wed, 7 Oct 2020 16:52:23 -0300
Subject: [PATCH 4/9] Criado usando o Colaboratory
---
Notebooks/NB07__Dictionaries_alterado.ipynb | 692 ++++++++++++++------
1 file changed, 494 insertions(+), 198 deletions(-)
diff --git a/Notebooks/NB07__Dictionaries_alterado.ipynb b/Notebooks/NB07__Dictionaries_alterado.ipynb
index aeea3db03..96c177534 100644
--- a/Notebooks/NB07__Dictionaries_alterado.ipynb
+++ b/Notebooks/NB07__Dictionaries_alterado.ipynb
@@ -109,15 +109,28 @@
{
"cell_type": "code",
"metadata": {
- "id": "FxuJ7Awd8f5a"
+ "id": "FxuJ7Awd8f5a",
+ "outputId": "2b09e013-655c-492e-8902-c0706b3f7b4d",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
},
"source": [
"# Definição da lista l_frutas:\n",
"l_frutas = ['Avocado', 'Apple', 'Apricot', 'Banana', 'Blackcurrant', 'Blackberry', 'Blueberry', 'Cherry', 'Coconut', 'Fig', 'Grape', 'Kiwi', 'Lemon', 'Mango', 'Nectarine', \n",
- " 'Orange', 'Papaya','Passion Fruit','Peach','Pineapple','Plum','Raspberry','Strawberry','Watermelon']"
+ " 'Orange', 'Papaya','Passion Fruit','Peach','Pineapple','Plum','Raspberry','Strawberry','Watermelon']\n",
+ "print(l_frutas)"
],
- "execution_count": null,
- "outputs": []
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "['Avocado', 'Apple', 'Apricot', 'Banana', 'Blackcurrant', 'Blackberry', 'Blueberry', 'Cherry', 'Coconut', 'Fig', 'Grape', 'Kiwi', 'Lemon', 'Mango', 'Nectarine', 'Orange', 'Papaya', 'Passion Fruit', 'Peach', 'Pineapple', 'Plum', 'Raspberry', 'Strawberry', 'Watermelon']\n"
+ ],
+ "name": "stdout"
+ }
+ ]
},
{
"cell_type": "code",
@@ -128,7 +141,7 @@
"# Definição da lista l_precos_frutas:\n",
"l_precos_frutas = [0.35, 0.40, 0.25, 0.30, 0.70, 0.55, 0.45, 0.50, 0.75, 0.60, 0.65, 0.20, 0.15, 0.80, 0.75, 0.25, 0.30,0.45,0.55,0.55,0.60,0.40,0.50,0.45]"
],
- "execution_count": null,
+ "execution_count": 3,
"outputs": []
},
{
@@ -144,17 +157,16 @@
"cell_type": "code",
"metadata": {
"id": "qT_4sYxA9dyn",
- "outputId": "a8badcb1-7f11-4b2e-8629-48a36cc95f9d",
+ "outputId": "e8276718-3d1c-4dc2-c4ee-0ac975b7ff18",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 425
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_frutas = dict(zip(l_frutas, l_precos_frutas))\n",
"d_frutas"
],
- "execution_count": null,
+ "execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
@@ -189,7 +201,7 @@
"metadata": {
"tags": []
},
- "execution_count": 36
+ "execution_count": 4
}
]
},
@@ -251,17 +263,16 @@
"cell_type": "code",
"metadata": {
"id": "N_2J839X4lps",
- "outputId": "54ca32b8-7dda-4907-8aa5-b327444bd458",
+ "outputId": "56ab2a1f-4c41-4335-9697-38bce1eb51f6",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 134
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_dia_semana = {'Seg': 'Segunda', 'Ter': 'Terça', 'Qua': 'Quarta', 'Qui': 'Quinta', 'Sex': 'Sexta', 'Sab': 'Sabado', 'Dom': 'Domingo'}\n",
"d_dia_semana"
],
- "execution_count": null,
+ "execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
@@ -279,7 +290,7 @@
"metadata": {
"tags": []
},
- "execution_count": 1
+ "execution_count": 5
}
]
},
@@ -297,20 +308,23 @@
"cell_type": "code",
"metadata": {
"id": "eHuvY7BWQKhQ",
- "outputId": "87fabef2-0891-4994-a4ce-cdd1e23218b1",
+ "outputId": "649df9a0-587f-4e6c-a19a-4654e919bd93",
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 34
+ "height": 35
}
},
"source": [
"d_dia_semana['Seg']"
],
- "execution_count": null,
+ "execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
"text/plain": [
"'Segunda'"
]
@@ -318,7 +332,7 @@
"metadata": {
"tags": []
},
- "execution_count": 2
+ "execution_count": 6
}
]
},
@@ -345,17 +359,16 @@
"cell_type": "code",
"metadata": {
"id": "2iPWXPBLfOlr",
- "outputId": "7925813c-77e2-4651-bdb2-f0e1144aecdb",
+ "outputId": "550e6ef2-9a6a-41b1-8b36-95c3e2e5efbe",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_paises = {} # Também podemos usar a função dict() para criar o dicionário vazio da seguinte forma: d_paises= dict()\n",
"d_paises"
],
- "execution_count": null,
+ "execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
@@ -367,7 +380,7 @@
"metadata": {
"tags": []
},
- "execution_count": 4
+ "execution_count": 7
}
]
},
@@ -386,16 +399,15 @@
"cell_type": "code",
"metadata": {
"id": "voPYpGIGff3o",
- "outputId": "7fab37f5-8ed1-46d8-b47b-62a100ee2196",
+ "outputId": "d0748385-60a7-4b87-b7e2-794fbb8e9abe",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"type(d_paises)"
],
- "execution_count": null,
+ "execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
@@ -407,7 +419,7 @@
"metadata": {
"tags": []
},
- "execution_count": 5
+ "execution_count": 8
}
]
},
@@ -434,29 +446,28 @@
"cell_type": "code",
"metadata": {
"id": "EXZ7eEZofnza",
- "outputId": "bacf6377-b5cc-4f29-f6c0-347516550f37",
+ "outputId": "fcbde537-da11-4573-ed38-bd4d48a96f60",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_paises[1] = 'Italy'\n",
"d_paises"
],
- "execution_count": null,
+ "execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "{1: 'Italy'}"
+ "{1: 'Italy', 2: 'Denmark', 3: 'Brazil'}"
]
},
"metadata": {
"tags": []
},
- "execution_count": 6
+ "execution_count": 12
}
]
},
@@ -473,29 +484,28 @@
"cell_type": "code",
"metadata": {
"id": "GAXSzSiufv1u",
- "outputId": "94f7e900-0452-4908-c0fe-2783a735bd01",
+ "outputId": "ba60506d-647b-4d40-b316-dc2c693c6f8c",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_paises[2] = 'Denmark'\n",
"d_paises"
],
- "execution_count": null,
+ "execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "{1: 'Italy', 2: 'Denmark'}"
+ "{1: 'Italy', 2: 'Denmark', 3: 'Brazil'}"
]
},
"metadata": {
"tags": []
},
- "execution_count": 7
+ "execution_count": 13
}
]
},
@@ -512,17 +522,16 @@
"cell_type": "code",
"metadata": {
"id": "FN7km8C9gAjM",
- "outputId": "7863cccb-b0aa-47b2-901b-ba72a5574d4f",
+ "outputId": "9927805c-54bf-4a7b-8e1e-134353f3ff40",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_paises[3]= 'Brazil'\n",
"d_paises"
],
- "execution_count": null,
+ "execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
@@ -534,7 +543,7 @@
"metadata": {
"tags": []
},
- "execution_count": 8
+ "execution_count": 14
}
]
},
@@ -561,10 +570,9 @@
"cell_type": "code",
"metadata": {
"id": "Rr6DtJnDgU5I",
- "outputId": "f02c2c47-e5aa-43cd-886b-345c52ed31bd",
+ "outputId": "763dc0a4-de82-428c-d9d8-940412330956",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
@@ -572,7 +580,7 @@
"d_paises[3]= 'France'\n",
"d_paises"
],
- "execution_count": null,
+ "execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
@@ -584,7 +592,7 @@
"metadata": {
"tags": []
},
- "execution_count": 9
+ "execution_count": 15
}
]
},
@@ -613,16 +621,16 @@
"cell_type": "code",
"metadata": {
"id": "ALwbHwi4iwky",
- "outputId": "bb0d57fb-2742-4eb1-9d82-9309142d21f5",
+ "outputId": "8aa9b44e-313b-42a6-e885-523185c0f4c5",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
- "d_paises.keys()"
+ "d_paises.keys()\n",
+ "d_paises."
],
- "execution_count": null,
+ "execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
@@ -634,7 +642,7 @@
"metadata": {
"tags": []
},
- "execution_count": 10
+ "execution_count": 16
}
]
},
@@ -691,27 +699,27 @@
"cell_type": "code",
"metadata": {
"id": "LraTwXjdjG3m",
- "outputId": "b3d6d55e-20ad-4f88-a783-9ba1c4fd8654",
+ "outputId": "f7e69608-c6bf-45c2-a08a-f7e76e1c4b2f",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 162
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_paises.items()"
],
- "execution_count": null,
+ "execution_count": 17,
"outputs": [
{
- "output_type": "error",
- "ename": "NameError",
- "evalue": "ignored",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md_Paises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m: name 'd_Paises' is not defined"
- ]
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "dict_items([(1, 'Italy'), (2, 'Denmark'), (3, 'France')])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 17
}
]
},
@@ -739,20 +747,23 @@
"cell_type": "code",
"metadata": {
"id": "FUfTjqktjW60",
- "outputId": "678ab629-6cff-4fe1-e03f-d90709a98f26",
+ "outputId": "acf5b8d0-7b30-4f3d-87ef-a1c9d259652b",
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 34
+ "height": 35
}
},
"source": [
"d_paises.get(1)"
],
- "execution_count": null,
+ "execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
"text/plain": [
"'Italy'"
]
@@ -760,7 +771,7 @@
"metadata": {
"tags": []
},
- "execution_count": 11
+ "execution_count": 18
}
]
},
@@ -779,17 +790,16 @@
"cell_type": "code",
"metadata": {
"id": "XL17EmvMkkky",
- "outputId": "65846bc2-87a2-42cf-eb17-e3fccb00c9a4",
+ "outputId": "9aea7f6c-bc39-4d39-bbf7-78c2ff4d7c8c",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_paises2 = d_paises.copy()\n",
"d_paises2"
],
- "execution_count": null,
+ "execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
@@ -801,7 +811,7 @@
"metadata": {
"tags": []
},
- "execution_count": 28
+ "execution_count": 19
}
]
},
@@ -824,23 +834,22 @@
"source": [
"d_paises.clear()"
],
- "execution_count": null,
+ "execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ro_42gzDjsdV",
- "outputId": "a2c2a25b-40ef-4842-f2f7-3ac85404d195",
+ "outputId": "355082b3-19d2-4d8b-8347-c2a6b08d6487",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_paises"
],
- "execution_count": null,
+ "execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
@@ -852,7 +861,7 @@
"metadata": {
"tags": []
},
- "execution_count": 13
+ "execution_count": 21
}
]
},
@@ -967,7 +976,7 @@
" 'Strawberry': 0.50,\n",
" 'Watermelon': 0.45}"
],
- "execution_count": null,
+ "execution_count": 22,
"outputs": []
},
{
@@ -983,16 +992,15 @@
"cell_type": "code",
"metadata": {
"id": "bI7Ctf0ohyz8",
- "outputId": "5af38cf4-aaf8-4efa-f682-502bfb5022a6",
+ "outputId": "f9aa2e05-8a41-45c0-a32e-22b1b8976c69",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 425
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_frutas"
],
- "execution_count": null,
+ "execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
@@ -1027,7 +1035,7 @@
"metadata": {
"tags": []
},
- "execution_count": 8
+ "execution_count": 23
}
]
},
@@ -1044,16 +1052,15 @@
"cell_type": "code",
"metadata": {
"id": "JpreyE_LtCcU",
- "outputId": "cee4be2d-7980-4a3d-85fb-17561d1bb1ff",
+ "outputId": "f2f7d1e4-51ff-40ff-af58-5577a34dd137",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_frutas['Apple']"
],
- "execution_count": null,
+ "execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
@@ -1065,7 +1072,7 @@
"metadata": {
"tags": []
},
- "execution_count": 21
+ "execution_count": 24
}
]
},
@@ -1082,17 +1089,16 @@
"cell_type": "code",
"metadata": {
"id": "rMro_tY8kepo",
- "outputId": "4488c243-6792-4efa-b271-e546270b129d",
+ "outputId": "dec61759-d1b5-4379-8846-cce26973209d",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 425
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"for key in d_frutas.keys():\n",
" print(key)"
],
- "execution_count": null,
+ "execution_count": 25,
"outputs": [
{
"output_type": "stream",
@@ -1139,17 +1145,16 @@
"cell_type": "code",
"metadata": {
"id": "DpFB1g-3kDSt",
- "outputId": "7ac51581-edfa-418d-a1e0-d297ebdffca7",
+ "outputId": "a0b41867-9ca7-414d-a451-34aa0f6e4232",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 425
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"for item in d_frutas.items():\n",
" print(item) "
],
- "execution_count": null,
+ "execution_count": 26,
"outputs": [
{
"output_type": "stream",
@@ -1183,6 +1188,17 @@
}
]
},
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KxGaOrYSdLxp"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -1196,17 +1212,16 @@
"cell_type": "code",
"metadata": {
"id": "tjJ6qRF8nr4v",
- "outputId": "3e75843b-2d45-4b4c-a3f2-24ffc0e60a7a",
+ "outputId": "6798f545-3f24-40b9-83d9-8c4a5e073389",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 425
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"for value in d_frutas.values():\n",
" print(value)"
],
- "execution_count": null,
+ "execution_count": 27,
"outputs": [
{
"output_type": "stream",
@@ -1297,6 +1312,34 @@
}
]
},
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bDb1IHuddfwH",
+ "outputId": "b66c5df5-6028-4cad-e480-60c92cdaa9f8",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "d_frutas.items()"
+ ],
+ "execution_count": 28,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "dict_items([('Avocado', 0.35), ('Apple', 0.4), ('Apricot', 0.25), ('Banana', 0.3), ('Blackcurrant', 0.7), ('Blackberry', 0.55), ('Blueberry', 0.45), ('Cherry', 0.5), ('Coconut', 0.75), ('Fig', 0.6), ('Grape', 0.65), ('Kiwi', 0.2), ('Lemon', 0.15), ('Mango', 0.8), ('Nectarine', 0.75), ('Orange', 0.25), ('Papaya', 0.3), ('Passion Fruit', 0.45), ('Peach', 0.55), ('Pineapple', 0.55), ('Plum', 0.6), ('Raspberry', 0.4), ('Strawberry', 0.5), ('Watermelon', 0.45)])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 28
+ }
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -1397,16 +1440,15 @@
"cell_type": "code",
"metadata": {
"id": "DbWpbuLTK9sn",
- "outputId": "e9fafa6d-284e-4862-8f25-9419ff702dec",
+ "outputId": "f10aeaa8-4cd7-41a9-b9f9-7e7bbe6a7fd5",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"0.4 in d_frutas.values()"
],
- "execution_count": null,
+ "execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
@@ -1418,7 +1460,7 @@
"metadata": {
"tags": []
},
- "execution_count": 14
+ "execution_count": 29
}
]
},
@@ -1440,7 +1482,7 @@
"source": [
"d_frutas2 = {'Grapefruit': 1.0 }"
],
- "execution_count": null,
+ "execution_count": 30,
"outputs": []
},
{
@@ -1456,17 +1498,16 @@
"cell_type": "code",
"metadata": {
"id": "7BD_mYMM5O5o",
- "outputId": "2b185546-255e-4ad0-e8c9-10564fcbe2b0",
+ "outputId": "39cdea31-d4ff-4064-e59f-adf178840a82",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 442
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_frutas.update(d_frutas2)\n",
"d_frutas"
],
- "execution_count": null,
+ "execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
@@ -1502,7 +1543,7 @@
"metadata": {
"tags": []
},
- "execution_count": 79
+ "execution_count": 31
}
]
},
@@ -1523,7 +1564,7 @@
"source": [
"d_frutas3 = {'Apple': 0.70}"
],
- "execution_count": null,
+ "execution_count": 32,
"outputs": []
},
{
@@ -1540,14 +1581,55 @@
{
"cell_type": "code",
"metadata": {
- "id": "E4GKdTw76PXI"
+ "id": "E4GKdTw76PXI",
+ "outputId": "6a14210d-d1e4-4dce-9465-72af35a60671",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
},
"source": [
"d_frutas.update(d_frutas3)\n",
"d_frutas"
],
- "execution_count": null,
- "outputs": []
+ "execution_count": 33,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Apple': 0.7,\n",
+ " 'Apricot': 0.25,\n",
+ " 'Avocado': 0.35,\n",
+ " 'Banana': 0.3,\n",
+ " 'Blackberry': 0.55,\n",
+ " 'Blackcurrant': 0.7,\n",
+ " 'Blueberry': 0.45,\n",
+ " 'Cherry': 0.5,\n",
+ " 'Coconut': 0.75,\n",
+ " 'Fig': 0.6,\n",
+ " 'Grape': 0.65,\n",
+ " 'Grapefruit': 1.0,\n",
+ " 'Kiwi': 0.2,\n",
+ " 'Lemon': 0.15,\n",
+ " 'Mango': 0.8,\n",
+ " 'Nectarine': 0.75,\n",
+ " 'Orange': 0.25,\n",
+ " 'Papaya': 0.3,\n",
+ " 'Passion Fruit': 0.45,\n",
+ " 'Peach': 0.55,\n",
+ " 'Pineapple': 0.55,\n",
+ " 'Plum': 0.6,\n",
+ " 'Raspberry': 0.4,\n",
+ " 'Strawberry': 0.5,\n",
+ " 'Watermelon': 0.45}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 33
+ }
+ ]
},
{
"cell_type": "markdown",
@@ -1587,7 +1669,7 @@
"for key, value in d_frutas.items():\n",
" d_frutas[key] = round(value * 0.9, 2)"
],
- "execution_count": null,
+ "execution_count": 34,
"outputs": []
},
{
@@ -1603,16 +1685,15 @@
"cell_type": "code",
"metadata": {
"id": "zZLa85knxBtY",
- "outputId": "2c7c12f8-8885-4f34-a0d1-1323e98a9437",
+ "outputId": "ee34b820-6961-4d88-d0e2-628efd922e70",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 442
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_frutas"
],
- "execution_count": null,
+ "execution_count": 35,
"outputs": [
{
"output_type": "execute_result",
@@ -1648,7 +1729,7 @@
"metadata": {
"tags": []
},
- "execution_count": 84
+ "execution_count": 35
}
]
},
@@ -1683,7 +1764,7 @@
" if key == 'Avocado':\n",
" del d_frutas[key] # Deleta key = 'Avocado'"
],
- "execution_count": null,
+ "execution_count": 36,
"outputs": []
},
{
@@ -1699,16 +1780,15 @@
"cell_type": "code",
"metadata": {
"id": "IwnsHejhyT4l",
- "outputId": "b910699c-9729-4a27-bd78-3a283c82ac39",
+ "outputId": "e5029d7e-9c8d-4e37-940e-a375ba57c78d",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 425
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_frutas"
],
- "execution_count": null,
+ "execution_count": 37,
"outputs": [
{
"output_type": "execute_result",
@@ -1743,7 +1823,7 @@
"metadata": {
"tags": []
},
- "execution_count": 86
+ "execution_count": 37
}
]
},
@@ -1770,7 +1850,21 @@
" if value > 0.5:\n",
" d_frutas_filtro.update({key: value})"
],
- "execution_count": null,
+ "execution_count": 38,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fu1jgbfUhPRD"
+ },
+ "source": [
+ "d_frutas_filtro = {}\n",
+ "for key, value in d_frutas.items():\n",
+ " if value == 0.5:\n",
+ " d_frutas_filtro.update({key: value})"
+ ],
+ "execution_count": 40,
"outputs": []
},
{
@@ -1786,36 +1880,27 @@
"cell_type": "code",
"metadata": {
"id": "SsStWM5k1s-Q",
- "outputId": "f6af5b61-2333-41c7-a28a-0f6a67b0a949",
+ "outputId": "3fc3a8ef-4627-40d7-e24e-f2bee8e22e66",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 170
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_frutas_filtro"
],
- "execution_count": null,
+ "execution_count": 41,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "{'Apple': 0.63,\n",
- " 'Blackcurrant': 0.63,\n",
- " 'Coconut': 0.68,\n",
- " 'Fig': 0.54,\n",
- " 'Grape': 0.59,\n",
- " 'Grapefruit': 0.9,\n",
- " 'Mango': 0.72,\n",
- " 'Nectarine': 0.68,\n",
- " 'Plum': 0.54}"
+ "{'Blackberry': 0.5, 'Peach': 0.5, 'Pineapple': 0.5}"
]
},
"metadata": {
"tags": []
},
- "execution_count": 89
+ "execution_count": 41
}
]
},
@@ -2071,51 +2156,50 @@
"cell_type": "code",
"metadata": {
"id": "iJq1clvOHVG2",
- "outputId": "16a779ef-48c9-497c-8c7c-a1612aa9aa03",
+ "outputId": "2c89ba89-3377-4337-87f1-050360788da9",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 425
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_frutas"
],
- "execution_count": null,
+ "execution_count": 42,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "{'Apple': 0.4,\n",
- " 'Apricot': 0.25,\n",
- " 'Avocado': 0.35,\n",
- " 'Banana': 0.3,\n",
- " 'Blackberry': 0.55,\n",
- " 'Blackcurrant': 0.7,\n",
- " 'Blueberry': 0.45,\n",
- " 'Cherry': 0.5,\n",
- " 'Coconut': 0.75,\n",
- " 'Fig': 0.6,\n",
- " 'Grape': 0.65,\n",
- " 'Kiwi': 0.2,\n",
- " 'Lemon': 0.15,\n",
- " 'Mango': 0.8,\n",
- " 'Nectarine': 0.75,\n",
- " 'Orange': 0.25,\n",
- " 'Papaya': 0.3,\n",
- " 'Passion Fruit': 0.45,\n",
- " 'Peach': 0.55,\n",
- " 'Pineapple': 0.55,\n",
- " 'Plum': 0.6,\n",
- " 'Raspberry': 0.4,\n",
- " 'Strawberry': 0.5,\n",
- " 'Watermelon': 0.45}"
+ "{'Apple': 0.63,\n",
+ " 'Apricot': 0.23,\n",
+ " 'Banana': 0.27,\n",
+ " 'Blackberry': 0.5,\n",
+ " 'Blackcurrant': 0.63,\n",
+ " 'Blueberry': 0.41,\n",
+ " 'Cherry': 0.45,\n",
+ " 'Coconut': 0.68,\n",
+ " 'Fig': 0.54,\n",
+ " 'Grape': 0.59,\n",
+ " 'Grapefruit': 0.9,\n",
+ " 'Kiwi': 0.18,\n",
+ " 'Lemon': 0.14,\n",
+ " 'Mango': 0.72,\n",
+ " 'Nectarine': 0.68,\n",
+ " 'Orange': 0.23,\n",
+ " 'Papaya': 0.27,\n",
+ " 'Passion Fruit': 0.41,\n",
+ " 'Peach': 0.5,\n",
+ " 'Pineapple': 0.5,\n",
+ " 'Plum': 0.54,\n",
+ " 'Raspberry': 0.36,\n",
+ " 'Strawberry': 0.45,\n",
+ " 'Watermelon': 0.41}"
]
},
"metadata": {
"tags": []
},
- "execution_count": 2
+ "execution_count": 42
}
]
},
@@ -2171,38 +2255,36 @@
"cell_type": "code",
"metadata": {
"id": "tvHcQatANltL",
- "outputId": "8feaf5b1-1db8-4391-8950-248ba8ab46c5",
+ "outputId": "6ca05107-f13c-4175-9d60-41a7615fd233",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 187
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
"d_frutas3 = {k: v for k, v in filter(lambda t: t[1] > 0.5, d_frutas.items())}\n",
"d_frutas3"
],
- "execution_count": null,
+ "execution_count": 43,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "{'Blackberry': 0.55,\n",
- " 'Blackcurrant': 0.7,\n",
- " 'Coconut': 0.75,\n",
- " 'Fig': 0.6,\n",
- " 'Grape': 0.65,\n",
- " 'Mango': 0.8,\n",
- " 'Nectarine': 0.75,\n",
- " 'Peach': 0.55,\n",
- " 'Pineapple': 0.55,\n",
- " 'Plum': 0.6}"
+ "{'Apple': 0.63,\n",
+ " 'Blackcurrant': 0.63,\n",
+ " 'Coconut': 0.68,\n",
+ " 'Fig': 0.54,\n",
+ " 'Grape': 0.59,\n",
+ " 'Grapefruit': 0.9,\n",
+ " 'Mango': 0.72,\n",
+ " 'Nectarine': 0.68,\n",
+ " 'Plum': 0.54}"
]
},
"metadata": {
"tags": []
},
- "execution_count": 7
+ "execution_count": 43
}
]
},
@@ -2239,13 +2321,33 @@
{
"cell_type": "code",
"metadata": {
- "id": "0BBWO9Zth_mc"
+ "id": "0BBWO9Zth_mc",
+ "outputId": "6f948288-be8e-4541-94b9-b067c17f13e4",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
},
"source": [
- "d_colaboradores= {'Gerentes': ['A', 'B', 'C'], 'Programadores': ['B', 'D', 'E', 'F', 'G'], 'Gerentes_Projeto': ['A', 'E']}"
+ "d_colaboradores= {'Gerentes': ['A', 'B', 'C'], 'Programadores': ['B', 'D', 'E', 'F', 'G'], 'Gerentes_Projeto': ['A', 'E']}\n",
+ "d_colaboradores"
],
- "execution_count": null,
- "outputs": []
+ "execution_count": 45,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Gerentes': ['A', 'B', 'C'],\n",
+ " 'Gerentes_Projeto': ['A', 'E'],\n",
+ " 'Programadores': ['B', 'D', 'E', 'F', 'G']}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 45
+ }
+ ]
},
{
"cell_type": "markdown",
@@ -2256,6 +2358,182 @@
"Como acessar o Gerente 'A'?"
]
},
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XH3BgvYfi01G",
+ "outputId": "d2fe69fa-5c0b-47b9-9413-e9c2019c19d1",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 129
+ }
+ },
+ "source": [
+ "d_colaboradores{'Gerentes'[0]}"
+ ],
+ "execution_count": 46,
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "SyntaxError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m d_colaboradores{'Gerentes'[0]}\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kCILrtFPjBTe",
+ "outputId": "4d8c8aca-341d-4528-8ba2-53a898683c49",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "d_colaboradores.get('Gerentes')\n"
+ ],
+ "execution_count": 50,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "['A', 'B', 'C']"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 50
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "emYGFLuJk65D",
+ "outputId": "8fe63e1f-8421-4d10-916e-58520b8d71fe",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "list_gerentes=d_colaboradores.get('Gerentes')\n",
+ "list_gerentes[0]"
+ ],
+ "execution_count": 53,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'A'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 53
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "B8ZbnSYNlZgt",
+ "outputId": "06d1ea72-8a6d-49fd-9d1d-83cf8a080b24",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "source": [
+ "d_colaboradores.get('Gerentes')[0]\n",
+ "\n"
+ ],
+ "execution_count": 57,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'A'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 57
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4JnYRazemjq5"
+ },
+ "source": [
+ "retornar os cargos que o funcionario !A! ocupa\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SX2h28bXj33b",
+ "outputId": "8be812f8-d86a-4a47-d439-55c229c216c9",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "d_colaboradores['Gerentes']"
+ ],
+ "execution_count": 51,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "['A', 'B', 'C']"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 51
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XCkq6tN9mqgn"
+ },
+ "source": [
+ "retornar do dicinario d_colaboradores somente o progarmador cujo nome seja 'E'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BSXBKuQvm0y0"
+ },
+ "source": [
+ "quais são os colaboradores que \n",
+ "a)são ao mesmo tempo gerente e gerente de projeto\n",
+ "b) gerentes de projetos e programadores"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -2265,6 +2543,24 @@
"## Exercício 3\n",
"Consulte a página [Python Data Types: Dictionary - Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/dictionary/) para mais exercícios relacionados à dicionários."
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZWRUI3Q0m0GQ"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eKwYMbxymyai"
+ },
+ "source": [
+ ""
+ ]
}
]
}
\ No newline at end of file
From 40a63768549d78be4d8544162889e06e0beae38c Mon Sep 17 00:00:00 2001
From: MariaJacobs70 <72224154+MariaJacobs70@users.noreply.github.com>
Date: Wed, 7 Oct 2020 21:59:25 -0300
Subject: [PATCH 5/9] Criado usando o Colaboratory
---
Notebooks/NB09_01__Functions_alterado.ipynb | 1549 +++++++++++++++++++
1 file changed, 1549 insertions(+)
create mode 100644 Notebooks/NB09_01__Functions_alterado.ipynb
diff --git a/Notebooks/NB09_01__Functions_alterado.ipynb b/Notebooks/NB09_01__Functions_alterado.ipynb
new file mode 100644
index 000000000..5859a10e7
--- /dev/null
+++ b/Notebooks/NB09_01__Functions_alterado.ipynb
@@ -0,0 +1,1549 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "NB09_01__Functions.ipynb",
+ "provenance": [],
+ "private_outputs": true,
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "FUNÇÕES "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "etxNlyRYo39A"
+ },
+ "source": [
+ "def show_hello_world():\n",
+ " print('Hello World!')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "G6I9PFvZpBgR"
+ },
+ "source": [
+ "type(show_hello_world)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_meNdNygpIbv"
+ },
+ "source": [
+ "show_hello_world()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6zfLd8HwpPpg"
+ },
+ "source": [
+ "___\n",
+ "# **DOCUMENTAR FUNÇÕES COM COMMENTS/DOCSTRING**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3yzgBxtNpRi_"
+ },
+ "source": [
+ "def show_hello_world():\n",
+ " '''\n",
+ " Esta função faz um cumprimento: 'Hello World!'\n",
+ " Inputs: \n",
+ " param1: djdjdjdjdj\n",
+ " param2: fjrjirjjirjir\n",
+ " '''\n",
+ " print('Hello World!')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0rBaxjpmpbm1"
+ },
+ "source": [
+ "show_hello_world()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6ThOwDQp4TfR"
+ },
+ "source": [
+ "# Se quisermos ver a documentação da função, basta invocar o statement __doc__ da seguinte forma:\n",
+ "show_hello_world.__doc__"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9YZ2afpNA4st"
+ },
+ "source": [
+ "OU..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uSnwA4BVA5_t"
+ },
+ "source": [
+ "help(show_hello_world)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "whbnnMA5p1Jw"
+ },
+ "source": [
+ "___\n",
+ "# **FUNÇÕES COM ARGUMENTOS**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "O3bSjLA_qTTc"
+ },
+ "source": [
+ "Definir a função mostra_nome com dois argumentos: s_primeiro_nome e s_ultimo_nome:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9jWyCCPPp4yS"
+ },
+ "source": [
+ "def mostra_nome(s_primeiro_nome, s_ultimo_nome):\n",
+ " print(f'Olá, meu nome é {s_primeiro_nome} {s_ultimo_nome}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VOB3Ip63qIzr"
+ },
+ "source": [
+ "mostra_nome('Nelio', 'Machado')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Oi0c_GuesfcL"
+ },
+ "source": [
+ "Neste caso, o primeiro argumento da função (s_primeiro_nome) vai receber o valor 'Nelio' e o segundo argumento da função (s_ultimo_nome) vai receber 'Machado'."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qkMblpnLsITO"
+ },
+ "source": [
+ "No entanto, também podemos invocar a função da seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "TTli7e6xsMCo"
+ },
+ "source": [
+ "mostra_nome(s_ultimo_nome = 'Machado', s_primeiro_nome = 'Nelio')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rmatMmhTsaVc"
+ },
+ "source": [
+ "Observe que o resultado é o mesmo. No entanto, desta forma, estamos dizendo o valor específico que cada parâmetro irá receber."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PnNYrgJ6VQo9"
+ },
+ "source": [
+ "## PEP8 + Annotations = Códigos mais fáceis de entender e atualizar\n",
+ "\n",
+ "> Observe abaixo quando combinamos PEP8 + Annotations para tornar o código Python ainda mais detalhado. O objetivo de _Annotations_ é deixar o código mais claro, sem mudar o comportamento da função. No exemplo abaixo, os argumentos da função s_primeiro_nome e s_ultimo_nome são argumentos do tipo _str_ e a função retorna um _output_ do tipo _str_."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "aU2Sob37VVmi"
+ },
+ "source": [
+ "def mostra_nome2(s_primeiro_nome: str, s_ultimo_nome: str) -> str:\n",
+ " print(f'Olá, meu nome é {s_primeiro_nome} {s_ultimo_nome}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iIvqS73mXNam"
+ },
+ "source": [
+ "mostra_nome2(s_ultimo_nome = 'Machado', s_primeiro_nome = 'Nelio')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rSnrtFNtXrbN"
+ },
+ "source": [
+ "# **\\*args**\n",
+ "> \\*args permite que você passe mais argumentos do que o número de argumentos formais que você definiu anteriormente."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aT0_PeuEvXiP"
+ },
+ "source": [
+ "## Exemplo 1\n",
+ "> Considere a função (simples) para imprimir o nome completo de um cliente."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Npbi_Hy0bUec"
+ },
+ "source": [
+ "# definimos a função mostra_nome3 da seguinte forma:\n",
+ "def mostra_nome3(*args):\n",
+ " nome = ' '.join(args)\n",
+ " \n",
+ " print(f'Olá, meu nome é {nome}.')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dFzM0gA3_9za"
+ },
+ "source": [
+ "mostra_nome3('Nelio', 'Machado')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "370bpgaSvDbJ"
+ },
+ "source": [
+ "E agora, a função recebe qualquer quantidade de parâmetros."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4kYcu6PEX-Nz"
+ },
+ "source": [
+ "mostra_nome3('Pedro', 'de', 'Alcantara', 'Francisco', 'Antonio', 'Joao', 'Carlos', 'Xavier', 'de', 'Paula', 'Miguel', 'Rafael', 'Joaquim', 'Jose', 'Gonzaga', 'Pascoal', 'Cipriano', 'Serafim')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KMgngPmFimxb"
+ },
+ "source": [
+ "Observe que desta forma pouco importa a quantidade de parâmetros que passamos á função."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Y9pDa6ZRjo0U"
+ },
+ "source": [
+ "## Exemplo 2\n",
+ "* Suponha que estamos insteressados em desenvolver uma função que multiplica dois números (passados como parâmetros)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1A-vhsHxv1YE"
+ },
+ "source": [
+ "Antes de vermos a solução usando \\*args, vamos ver como seria nossa função se \\*args não existisse."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cCDwruF8j5i5"
+ },
+ "source": [
+ "### Forma \"Normal\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_R03BiwLjtwB"
+ },
+ "source": [
+ "# Definição da função\n",
+ "def multiplicar_numeros(x1, x2):\n",
+ " '''\n",
+ " Objetivo: Esta função multiplica DOIS números passados como argumentos.\n",
+ " Autor: Nelio Machado\n",
+ " Data: 04/10/2020\n",
+ " '''\n",
+ " return x1 * x2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0eVm1Qj9kDtd"
+ },
+ "source": [
+ "print(multiplicar_numeros(3, 4))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4h9Nhkickf_8"
+ },
+ "source": [
+ "### Usando \\*args"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9Kf89meJkjw8"
+ },
+ "source": [
+ "def multiplicar_numeros2(*args):\n",
+ " '''\n",
+ " Objetivo: Esta função multiplica vários números passados como argumentos.\n",
+ " Autor: Nelio Machado\n",
+ " Data: 04/10/2020\n",
+ " '''\n",
+ " print(args)\n",
+ " print(type(args))\n",
+ " x = 1\n",
+ " for N in args:\n",
+ " x *= N\n",
+ " \n",
+ " return x"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZuIzwitWk7by"
+ },
+ "source": [
+ "print(multiplicar_numeros2(1, 2, 3, 4, 5))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "U5kyPu792gMN"
+ },
+ "source": [
+ "Eu também posso fazer da seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oc2NJmJf2s7X"
+ },
+ "source": [
+ "args= (1, 2, 3, 4, 5)\n",
+ "print(multiplicar_numeros2(*args))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "38jVie_IjMXI"
+ },
+ "source": [
+ "# \\**kwargs\n",
+ "\n",
+ "* \\**kwargs é usado para passar um dicionário de comprimento variável para uma função.\n",
+ "* Argumento do tipo {chave: valor};\n",
+ "\n",
+ "* Para exemplificar o uso de \\**kwargs, vou usar parte do dicionário dFruits que definimos na sessão [Dictionaries](Dictionaries.ipynb). Qualquer dúvida, volte áquele capítulo para relembrar os principais conceitos."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "yAntQ724nMbv"
+ },
+ "source": [
+ "# Definindo a função para receber parâmetros em forma de dicionário:\n",
+ "def imprime_frutas(**kwargs):\n",
+ " '''\n",
+ " Objetivo: Esta função imprime as frutas contidas em kwargs.\n",
+ " Autor: Nelio Machado\n",
+ " Data: 04/10/2020\n",
+ " '''\n",
+ " for key, value in kwargs.items():\n",
+ " print(f'O valor de {key} é {value}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jpmSk9mfxww3"
+ },
+ "source": [
+ "Atenção à forma como os itens são passados à função!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "88-1lStInaVs"
+ },
+ "source": [
+ "imprime_frutas(Avocado = 0.35, Apple = 0.4, Apricot = 0.25, Banana = 0.30)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-jb_kkLiyQt8"
+ },
+ "source": [
+ "No entanto, posso passar um dicionário na forma como estamos acostumados, da seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JZJNiLz7wgCy"
+ },
+ "source": [
+ "d_frutas = {'Apple': 0.4, 'Avocado': 0.3, 'Orange': 0.5, 'Lemon': 0.25}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "eUCum4JPEcxD"
+ },
+ "source": [
+ "imprime_frutas(**d_frutas)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iK8-e7a1sXmn"
+ },
+ "source": [
+ "___\n",
+ "# **Python return**\n",
+ "> Uma função Python pode ou não retornar um valor."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HS0dGA55siWw"
+ },
+ "source": [
+ "def par_ou_impar(i_numero1, i_numero2):\n",
+ " '''\n",
+ " Esta função somente avalia se a soma de dois números é par ou impar. \n",
+ " A função retorna odd ou even.\n",
+ " '''\n",
+ " i_soma = i_numero1+i_numero2\n",
+ " i_modulo = i_soma % 2\n",
+ " print(f'A soma é {i_soma}')\n",
+ " if i_modulo > 0:\n",
+ " return 'Odd'\n",
+ " else:\n",
+ " return 'Even' "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mZTG2tDJuIZQ"
+ },
+ "source": [
+ "i_numero1 = int(input('Por favor, informe o primeiro número: '))\n",
+ "i_numero2 = int(input('Por favor, informe o segundo número.: '))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7p_9pq3Du18a"
+ },
+ "source": [
+ "type(i_numero1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4oO7aAjcvCAe"
+ },
+ "source": [
+ "type(i_numero2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Br7yT8UHuKYY"
+ },
+ "source": [
+ "s_resultado = par_ou_impar(i_numero1, i_numero2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "601QnggJuhf-"
+ },
+ "source": [
+ "print(f'O resultado é {s_resultado}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "t6HNf9j9yKcT"
+ },
+ "source": [
+ "Mostra o valor de i_modulo ou i_soma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Yu8RsyDAyXne"
+ },
+ "source": [
+ "i_modulo"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nx3twrLRyaeJ"
+ },
+ "source": [
+ "Python reporta que i_modulo não existe.\n",
+ "Está correta esta informação?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "imkyRO4kyvgV"
+ },
+ "source": [
+ "Considere o exemplo a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kwRiXDA5y19h"
+ },
+ "source": [
+ "i_modulo = 0\n",
+ "\n",
+ "def par_ou_impar_v2(i_numero1, i_numero2):\n",
+ " '''\n",
+ " Esta função somente avalia se a soma de dois números é par ou impar. \n",
+ " A função retorna odd ou even.\n",
+ " '''\n",
+ " i_soma = i_numero1+i_numero2\n",
+ " i_modulo = i_soma % 2\n",
+ " print(f'A soma é {i_soma}')\n",
+ " if i_modulo > 0:\n",
+ " return 'Odd'\n",
+ " else:\n",
+ " return 'Even' "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GYxLSGQLy_Ai"
+ },
+ "source": [
+ "i_numero1 = int(input('Por favor, informe o primeiro número: '))\n",
+ "i_numero2 = int(input('Por favor, informe o segundo número.: '))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NMtv99fjzHGs"
+ },
+ "source": [
+ "s_resultado = par_ou_impar_v2(i_numero1, i_numero2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "qjOHnYDVzNGK"
+ },
+ "source": [
+ "print(f'O resultado é {s_resultado}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pPTecxRfzQUc"
+ },
+ "source": [
+ "Agora, vamos checar o valor de i_modulo..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jkQb2mQzzTEo"
+ },
+ "source": [
+ "i_modulo"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oOlyGxBAzjE3"
+ },
+ "source": [
+ "Porque agora o Python reconhece a variável i_modulo?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dceSkt9Z0BZh"
+ },
+ "source": [
+ "___\n",
+ "# **ESCOPO DE VARIÁVEIS: LOCAL & GLOBAL**\n",
+ "* **Local** - Variável declarada dentro da função. Em outras palavras, é uma variável local/uso da função.\n",
+ "\n",
+ "* **Global** - Variável declarada fora da função. Neste caso, a variável é visível à todo o programa. Entretanto, não se pode alterar o valor da variável dentro da função. Caso queira alterar o valor da variável dentro da função, então é necesário declarar a variável usando a palavra reservada 'global’."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0tIjI9GScPxu"
+ },
+ "source": [
+ "## Exemplo 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QRojHHJ20iTY"
+ },
+ "source": [
+ "def exemplo1():\n",
+ " i_valor = 20\n",
+ " i_valor += 1\n",
+ " print(i_valor)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RdhElmTs0y1c"
+ },
+ "source": [
+ "exemplo1()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Tytq7PnH08pz"
+ },
+ "source": [
+ "O escopo da variável 'i_valor' é local, ou seja, de uso/restrito à função. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "299AK0PA1lIg"
+ },
+ "source": [
+ "i_valor"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gGP4cx17y8EZ"
+ },
+ "source": [
+ "Portanto, o erro acima faz sentido, pois a variável i_valor é restrito á função. Ou seja, fora da função o Python não conhece este valor."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KTV_6Gzxfvpc"
+ },
+ "source": [
+ "## Exemplo 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zyi9AyJwfxTm"
+ },
+ "source": [
+ "i_valor= 100\n",
+ "\n",
+ "def exemplo2():\n",
+ " i_valor = 20\n",
+ " i_valor += 1\n",
+ " print(i_valor)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iEWrboG6gBSs"
+ },
+ "source": [
+ "exemplo2()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JPvT0BHG-vxE"
+ },
+ "source": [
+ "Isso é um tanto estranho! Definimos, fora da função, i_valor= 100 e, dentro da função, redefinimos i_valor= 20. Entretanto, como vimos, exemplo2() retorna 21 como resultado."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "N_t8tIDC-149"
+ },
+ "source": [
+ "Agora, a seguir, fora da função, pedimos para ver o valor de i_valor e temos, como resposta, o valor 100."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "I46Bn4FlgJLu"
+ },
+ "source": [
+ "i_valor"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IQlP5nbngL6E"
+ },
+ "source": [
+ "Saberia nos explicar o que está acontecendo?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "h8PHd6rLgtwK"
+ },
+ "source": [
+ "## Exemplo 3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "qB7_zPQVgvVT"
+ },
+ "source": [
+ "i_valor = 100\n",
+ "\n",
+ "def exemplo3():\n",
+ " global i_valor\n",
+ " i_valor = 20\n",
+ " i_valor += 1\n",
+ " print(i_valor)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2KgQSbYCg8Eq"
+ },
+ "source": [
+ "exemplo3()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Y7yWoojrg_9Z"
+ },
+ "source": [
+ "i_valor"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cGlmbIJGzWG6"
+ },
+ "source": [
+ "Saberia explicar o que acontece neste exemplo?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "X8qFfIoxhFOp"
+ },
+ "source": [
+ "## Exemplo 4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZM-yTLuO1bFh"
+ },
+ "source": [
+ "i_valor = 20\n",
+ "\n",
+ "def exemplo4():\n",
+ " i_valor += 1\n",
+ " print(i_valor)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oLvfPO8w1zwL"
+ },
+ "source": [
+ "exemplo4()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2V7QzpZp2QcM"
+ },
+ "source": [
+ "Qual a razão deste erro?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "w9qI8kln1_C7"
+ },
+ "source": [
+ "i_valor"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AQFFGqLI1FWn"
+ },
+ "source": [
+ "___\n",
+ "# **ARGUMENTOS DEFAULT**\n",
+ "> Considere o exemplo a seguir: toda vez que vai ao supermercado compra 1 pack de leite (contendo 4 garrafas) e 1 garrafão de água de 5L. Portanto, de forma simples, podemos definir nossa função da seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HbcSTiBI4nOj"
+ },
+ "source": [
+ "# Define a função para receber os parâmetros arroz, feijao, leite e água.\n",
+ "def lista_de_compras(arroz, feijao, leite= 1, agua= 1):\n",
+ " '''\n",
+ " Documentação da função: objetivos, autor e data.\n",
+ " '''\n",
+ " print('Lista de Compras:')\n",
+ " print(f'Quantidade de arroz.: {arroz} kilos.') \n",
+ " print(f'Quantidade de feijão: {feijao} kilos.') \n",
+ " print(f'Quantidade de leite.: {leite} pack com 4.') \n",
+ " print(f'Quantidade de água..: {agua} garrafa de 5 litros.') "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vwZnDgoq5pgB"
+ },
+ "source": [
+ "lista_de_compras(5, 3)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "l7bY5BSO7eJF"
+ },
+ "source": [
+ "Como leite= 1 e agua= 1 são valores default's, não precisamos passar esses parâmetros, desde que informamos ao Python o valor default. No entanto, se numa determinada semana precisarmos de 2 pack's de leite, ao invés de 1, devemos informar ao Python o novo valor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "YY4OrFuH7yXi"
+ },
+ "source": [
+ "lista_de_compras(5, 3, 2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-nfrZAvN73YT"
+ },
+ "source": [
+ "Da mesma forma, se numa outra semana precisarmos de 2 garrafões de água ao invés de 1, informamos ao Python da seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Vpoh6TdM7_xb"
+ },
+ "source": [
+ "lista_de_compras(5, 3, 2, 2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "q3qZn9FuVQly"
+ },
+ "source": [
+ "___\n",
+ "# **map()**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Dav8k0JYWi4B"
+ },
+ "source": [
+ "## Exemplo 1\n",
+ "> Suponha que queremos o quadrado de cada número passado à uma função."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "R6NC0i2OVktM"
+ },
+ "source": [
+ "l_numeros= [0, 1, 2, 3, 4, 5]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AVjYlN44Vw2k"
+ },
+ "source": [
+ "def quadrado_do_numero(i_numero):\n",
+ " return i_numero**2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "i_4CHiehV7lD"
+ },
+ "source": [
+ "list(map(quadrado_do_numero, l_numeros))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5tq8QDSPWNf6"
+ },
+ "source": [
+ "OU..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZAfkybybWOcG"
+ },
+ "source": [
+ "for i in map(quadrado_do_numero, l_numeros):\n",
+ " print(i)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "c01V5CEzWlGF"
+ },
+ "source": [
+ "## Exemplo 2\n",
+ "> substituir_truer todos os valores True da lista abaixo por 1 e False por 0."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "qH1ackDZWvKp"
+ },
+ "source": [
+ "import random\n",
+ "\n",
+ "l_dados = []\n",
+ "for i in range(50):\n",
+ " random.seed(i)\n",
+ " l_dados.append(random.choice([True, False]))\n",
+ " \n",
+ "l_dados"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Dt2UKC-WXsxr"
+ },
+ "source": [
+ "def substituir_true(s_String):\n",
+ " if s_String == True:\n",
+ " return 1\n",
+ " else:\n",
+ " return 0"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BIIkPuDEXaM0"
+ },
+ "source": [
+ "list(map(substituir_true, l_dados))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TzkLIH1gYpFQ"
+ },
+ "source": [
+ "___\n",
+ "# **Filter()**\n",
+ "* Filtra elementos baseado em condições."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cjU8YznfZai1"
+ },
+ "source": [
+ "Suponha que agora eu quero filtrar os itens True da lista l_dados."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "a3SeaKJgZlAZ"
+ },
+ "source": [
+ "def filtrar_true(item):\n",
+ " if item == True:\n",
+ " return True\n",
+ " else:\n",
+ " return False"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1Z1APDQtZyXs"
+ },
+ "source": [
+ "list(filter(filtrar_true, l_dados))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xPpFqVUnKEH7"
+ },
+ "source": [
+ "___\n",
+ "# **EXERCÍCIOS**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RDgCRPRs0W6C"
+ },
+ "source": [
+ "## Exercício 1\n",
+ "Construa uma função para retornar o dia da semana a partir de um número, sendo:\n",
+ "\n",
+ "* 1 - Dom\n",
+ "* 2 - Seg\n",
+ "* 3 - Ter\n",
+ "* 4 - Qua\n",
+ "* 5 - Qui\n",
+ "* 6 - Sex\n",
+ "* 7 - Sab"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "H17JO6sLOrG7"
+ },
+ "source": [
+ "### Minha solução"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wX_7XDyB0XSy"
+ },
+ "source": [
+ "def dia_da_semana(dia):\n",
+ " d_palavra= {1: 'Segunda',\n",
+ " 2: 'Terça',\n",
+ " 3: 'Quarta',\n",
+ " 4: 'Quinta',\n",
+ " 5: 'Sexta',\n",
+ " 6: 'Sabado',\n",
+ " 7: 'Domingo' }\n",
+ " return d_palavra.get(dia,\"Dia da semana inválido. Informe um número de 1 a 7\")"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "39toyCRU1Q5T"
+ },
+ "source": [
+ "dia_da_semana(1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wt5hQq__1UEd"
+ },
+ "source": [
+ "dia_da_semana(0)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "N53NOsZjOv9m"
+ },
+ "source": [
+ "## Exercício 2\n",
+ "* Desenvolver uma função que retorna True se s_palavra pertence à uma string e False caso contrário."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7j-HHsxFrX5t"
+ },
+ "source": [
+ "def palavra_está_string (s_palavra, s_string):\n",
+ " if s_palavra in s_string:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EOTAE-oMrYJW"
+ },
+ "source": [
+ "s_string = 'O amor é o fogo que arde sem se ver. É ferida que dói e não se sente. É um contentamento descontente. É dor que desatina sem doer'\n",
+ "s_string"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tfzOT6w0thZg"
+ },
+ "source": [
+ "s_palavra = 'fogo'\n",
+ "palavra_está_string (s_palavra, s_string)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xxunBr3ttnji"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vrBZ_68-PBWl"
+ },
+ "source": [
+ "### Minha solução:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "m4Pi4S8hPC_u"
+ },
+ "source": [
+ "def check_palavra(s_frase, s_palavra):\n",
+ " if s_palavra in s_frase:\n",
+ " return True\n",
+ " else:\n",
+ " return False"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NJeqwxDjPxub"
+ },
+ "source": [
+ "A frase abaixo foi extraída de [+ Bíblia + Camões + Legião Urbana - (Guerra) = Monte Castelo](http://compondoletras.blogspot.com/2013/11/biblia-camoes-legiao-urbana-guerra.html)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Dj_n_beIPRBN"
+ },
+ "source": [
+ "s_frase = 'O amor é o fogo que arde sem se ver. É ferida que dói e não se sente. É um contentamento descontente. É dor que desatina sem doer'\n",
+ "s_frase"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "s40FJ9iCPPY0"
+ },
+ "source": [
+ "s_palavra = 'fogo'"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tzc2eaM7QUFE"
+ },
+ "source": [
+ "A palavra s_palavra está em s_frase?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2tlravrMQXn2"
+ },
+ "source": [
+ "check_palavra(s_frase, s_palavra)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XFBVXsW_rVG2"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pMx9E0xMu1lc"
+ },
+ "source": [
+ "## Exercício 3\n",
+ "Para mais exercícios envolvendo funções, consulte [Python functions - Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/python-functions-exercises.php)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Mw6Wg5hFvFMR"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
From 3c6e45809d6d65537cd280ff84c1557dc497c7bd Mon Sep 17 00:00:00 2001
From: MariaJacobs70 <72224154+MariaJacobs70@users.noreply.github.com>
Date: Wed, 7 Oct 2020 22:56:24 -0300
Subject: [PATCH 6/9] Criado usando o Colaboratory
---
Notebooks/NB07__Dictionaries_alterado.ipynb | 1266 +++++++++++++------
1 file changed, 874 insertions(+), 392 deletions(-)
diff --git a/Notebooks/NB07__Dictionaries_alterado.ipynb b/Notebooks/NB07__Dictionaries_alterado.ipynb
index 96c177534..49d128332 100644
--- a/Notebooks/NB07__Dictionaries_alterado.ipynb
+++ b/Notebooks/NB07__Dictionaries_alterado.ipynb
@@ -110,7 +110,7 @@
"cell_type": "code",
"metadata": {
"id": "FxuJ7Awd8f5a",
- "outputId": "2b09e013-655c-492e-8902-c0706b3f7b4d",
+ "outputId": "aa77d55b-989e-4472-c3cb-6865e05fb0f3",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -118,10 +118,11 @@
"source": [
"# Definição da lista l_frutas:\n",
"l_frutas = ['Avocado', 'Apple', 'Apricot', 'Banana', 'Blackcurrant', 'Blackberry', 'Blueberry', 'Cherry', 'Coconut', 'Fig', 'Grape', 'Kiwi', 'Lemon', 'Mango', 'Nectarine', \n",
- " 'Orange', 'Papaya','Passion Fruit','Peach','Pineapple','Plum','Raspberry','Strawberry','Watermelon']\n",
+ " 'Orange', 'Papaya', 'Passion Fruit', 'Peach', 'Pineapple', 'Plum', 'Raspberry', 'Strawberry', 'Watermelon']\n",
+ "\n",
"print(l_frutas)"
],
- "execution_count": 2,
+ "execution_count": null,
"outputs": [
{
"output_type": "stream",
@@ -135,14 +136,55 @@
{
"cell_type": "code",
"metadata": {
- "id": "jJyxuMQc9Ewy"
+ "id": "jJyxuMQc9Ewy",
+ "outputId": "fc4b18d6-b85b-4ad8-e41d-c53f6314a31d",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
},
"source": [
"# Definição da lista l_precos_frutas:\n",
- "l_precos_frutas = [0.35, 0.40, 0.25, 0.30, 0.70, 0.55, 0.45, 0.50, 0.75, 0.60, 0.65, 0.20, 0.15, 0.80, 0.75, 0.25, 0.30,0.45,0.55,0.55,0.60,0.40,0.50,0.45]"
+ "l_precos_frutas = [0.35, 0.40, 0.25, 0.30, 0.70, 0.55, 0.45, 0.50, 0.75, 0.60, 0.65, 0.20, 0.15, 0.80, 0.75, 0.25, 0.30,0.45,0.55,0.55,0.60,0.40,0.50,0.45]\n",
+ "l_precos_frutas"
],
- "execution_count": 3,
- "outputs": []
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[0.35,\n",
+ " 0.4,\n",
+ " 0.25,\n",
+ " 0.3,\n",
+ " 0.7,\n",
+ " 0.55,\n",
+ " 0.45,\n",
+ " 0.5,\n",
+ " 0.75,\n",
+ " 0.6,\n",
+ " 0.65,\n",
+ " 0.2,\n",
+ " 0.15,\n",
+ " 0.8,\n",
+ " 0.75,\n",
+ " 0.25,\n",
+ " 0.3,\n",
+ " 0.45,\n",
+ " 0.55,\n",
+ " 0.55,\n",
+ " 0.6,\n",
+ " 0.4,\n",
+ " 0.5,\n",
+ " 0.45]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 4
+ }
+ ]
},
{
"cell_type": "markdown",
@@ -157,16 +199,17 @@
"cell_type": "code",
"metadata": {
"id": "qT_4sYxA9dyn",
- "outputId": "e8276718-3d1c-4dc2-c4ee-0ac975b7ff18",
+ "outputId": "1952df07-193f-4827-dc41-5f9068b96436",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
+ "# Definir o dicionário d_frutas: estrutura do tipo {chave1: valor1, chave2: valor2, ..., chaveN: valorN} --> JSON\n",
"d_frutas = dict(zip(l_frutas, l_precos_frutas))\n",
"d_frutas"
],
- "execution_count": 4,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -201,7 +244,7 @@
"metadata": {
"tags": []
},
- "execution_count": 4
+ "execution_count": 5
}
]
},
@@ -263,7 +306,7 @@
"cell_type": "code",
"metadata": {
"id": "N_2J839X4lps",
- "outputId": "56ab2a1f-4c41-4335-9697-38bce1eb51f6",
+ "outputId": "ddfb58dd-dcd8-4a57-b886-6eed048a6b2f",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -272,7 +315,7 @@
"d_dia_semana = {'Seg': 'Segunda', 'Ter': 'Terça', 'Qua': 'Quarta', 'Qui': 'Quinta', 'Sex': 'Sexta', 'Sab': 'Sabado', 'Dom': 'Domingo'}\n",
"d_dia_semana"
],
- "execution_count": 5,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -290,7 +333,7 @@
"metadata": {
"tags": []
},
- "execution_count": 5
+ "execution_count": 6
}
]
},
@@ -308,16 +351,16 @@
"cell_type": "code",
"metadata": {
"id": "eHuvY7BWQKhQ",
- "outputId": "649df9a0-587f-4e6c-a19a-4654e919bd93",
+ "outputId": "aabce6c1-1914-435e-b21b-4f845f82d53f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
- "d_dia_semana['Seg']"
+ "d_dia_semana['Seg'] # A chave aqui é 'Seg'"
],
- "execution_count": 6,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -332,7 +375,7 @@
"metadata": {
"tags": []
},
- "execution_count": 6
+ "execution_count": 7
}
]
},
@@ -359,7 +402,7 @@
"cell_type": "code",
"metadata": {
"id": "2iPWXPBLfOlr",
- "outputId": "550e6ef2-9a6a-41b1-8b36-95c3e2e5efbe",
+ "outputId": "dfe22769-059e-4e95-8dad-85c1654530de",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -368,7 +411,7 @@
"d_paises = {} # Também podemos usar a função dict() para criar o dicionário vazio da seguinte forma: d_paises= dict()\n",
"d_paises"
],
- "execution_count": 7,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -380,7 +423,7 @@
"metadata": {
"tags": []
},
- "execution_count": 7
+ "execution_count": 8
}
]
},
@@ -399,7 +442,7 @@
"cell_type": "code",
"metadata": {
"id": "voPYpGIGff3o",
- "outputId": "d0748385-60a7-4b87-b7e2-794fbb8e9abe",
+ "outputId": "d28c315e-b0a3-46e8-e7e8-4392f5525e94",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -407,7 +450,7 @@
"source": [
"type(d_paises)"
],
- "execution_count": 8,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -419,7 +462,7 @@
"metadata": {
"tags": []
},
- "execution_count": 8
+ "execution_count": 9
}
]
},
@@ -446,7 +489,7 @@
"cell_type": "code",
"metadata": {
"id": "EXZ7eEZofnza",
- "outputId": "fcbde537-da11-4573-ed38-bd4d48a96f60",
+ "outputId": "1716826d-e92b-4d21-a56e-1fa6b1df6663",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -455,19 +498,19 @@
"d_paises[1] = 'Italy'\n",
"d_paises"
],
- "execution_count": 12,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "{1: 'Italy', 2: 'Denmark', 3: 'Brazil'}"
+ "{1: 'Italy'}"
]
},
"metadata": {
"tags": []
},
- "execution_count": 12
+ "execution_count": 10
}
]
},
@@ -484,7 +527,7 @@
"cell_type": "code",
"metadata": {
"id": "GAXSzSiufv1u",
- "outputId": "ba60506d-647b-4d40-b316-dc2c693c6f8c",
+ "outputId": "0a3ab152-c82b-41d4-e6b8-ec4adf30493f",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -493,19 +536,19 @@
"d_paises[2] = 'Denmark'\n",
"d_paises"
],
- "execution_count": 13,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "{1: 'Italy', 2: 'Denmark', 3: 'Brazil'}"
+ "{1: 'Italy', 2: 'Denmark'}"
]
},
"metadata": {
"tags": []
},
- "execution_count": 13
+ "execution_count": 11
}
]
},
@@ -522,7 +565,7 @@
"cell_type": "code",
"metadata": {
"id": "FN7km8C9gAjM",
- "outputId": "9927805c-54bf-4a7b-8e1e-134353f3ff40",
+ "outputId": "fabb845b-cdff-423c-ef51-2d1f6cd2ee30",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -531,7 +574,7 @@
"d_paises[3]= 'Brazil'\n",
"d_paises"
],
- "execution_count": 14,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -543,7 +586,7 @@
"metadata": {
"tags": []
},
- "execution_count": 14
+ "execution_count": 12
}
]
},
@@ -570,7 +613,7 @@
"cell_type": "code",
"metadata": {
"id": "Rr6DtJnDgU5I",
- "outputId": "763dc0a4-de82-428c-d9d8-940412330956",
+ "outputId": "9d79ee40-e0a6-4f47-dfc0-fe0010102b73",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -580,7 +623,7 @@
"d_paises[3]= 'France'\n",
"d_paises"
],
- "execution_count": 15,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -592,7 +635,7 @@
"metadata": {
"tags": []
},
- "execution_count": 15
+ "execution_count": 13
}
]
},
@@ -617,20 +660,47 @@
"# **OBTER KEYS DO DICIONÁRIO**"
]
},
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "FQtAHjJdb0xK",
+ "outputId": "db5582e2-d3cf-47b6-fa62-c503bca4c05b",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "d_paises"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{1: 'Italy', 2: 'Denmark', 3: 'France'}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 14
+ }
+ ]
+ },
{
"cell_type": "code",
"metadata": {
"id": "ALwbHwi4iwky",
- "outputId": "8aa9b44e-313b-42a6-e885-523185c0f4c5",
+ "outputId": "ddc7b4c8-5c59-44a3-8ae7-fdc21524b043",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
- "d_paises.keys()\n",
- "d_paises."
+ "d_paises.keys()"
],
- "execution_count": 16,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -642,7 +712,7 @@
"metadata": {
"tags": []
},
- "execution_count": 16
+ "execution_count": 15
}
]
},
@@ -660,14 +730,13 @@
"cell_type": "code",
"metadata": {
"id": "cp0PPtl3jEKo",
- "outputId": "c7b8739a-caa9-4e58-e6d3-0f86ccd2d950",
+ "outputId": "68a71557-c44a-4a0f-89c6-ffd9db6421b3",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
- "d_paises.values()"
+ "d_paises.items()"
],
"execution_count": null,
"outputs": [
@@ -675,13 +744,13 @@
"output_type": "execute_result",
"data": {
"text/plain": [
- "dict_values(['Italy', 'Denmark', 'France'])"
+ "dict_items([(1, 'Italy'), (2, 'Denmark'), (3, 'France')])"
]
},
"metadata": {
"tags": []
},
- "execution_count": 11
+ "execution_count": 16
}
]
},
@@ -699,27 +768,27 @@
"cell_type": "code",
"metadata": {
"id": "LraTwXjdjG3m",
- "outputId": "f7e69608-c6bf-45c2-a08a-f7e76e1c4b2f",
+ "outputId": "b3d6d55e-20ad-4f88-a783-9ba1c4fd8654",
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 162
}
},
"source": [
"d_paises.items()"
],
- "execution_count": 17,
+ "execution_count": null,
"outputs": [
{
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "dict_items([(1, 'Italy'), (2, 'Denmark'), (3, 'France')])"
- ]
- },
- "metadata": {
- "tags": []
- },
- "execution_count": 17
+ "output_type": "error",
+ "ename": "NameError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md_Paises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'd_Paises' is not defined"
+ ]
}
]
},
@@ -747,23 +816,20 @@
"cell_type": "code",
"metadata": {
"id": "FUfTjqktjW60",
- "outputId": "acf5b8d0-7b30-4f3d-87ef-a1c9d259652b",
+ "outputId": "678ab629-6cff-4fe1-e03f-d90709a98f26",
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 35
+ "height": 34
}
},
"source": [
"d_paises.get(1)"
],
- "execution_count": 18,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
"text/plain": [
"'Italy'"
]
@@ -771,7 +837,7 @@
"metadata": {
"tags": []
},
- "execution_count": 18
+ "execution_count": 11
}
]
},
@@ -790,16 +856,17 @@
"cell_type": "code",
"metadata": {
"id": "XL17EmvMkkky",
- "outputId": "9aea7f6c-bc39-4d39-bbf7-78c2ff4d7c8c",
+ "outputId": "65846bc2-87a2-42cf-eb17-e3fccb00c9a4",
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 35
}
},
"source": [
"d_paises2 = d_paises.copy()\n",
"d_paises2"
],
- "execution_count": 19,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -811,7 +878,7 @@
"metadata": {
"tags": []
},
- "execution_count": 19
+ "execution_count": 28
}
]
},
@@ -834,22 +901,23 @@
"source": [
"d_paises.clear()"
],
- "execution_count": 20,
+ "execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ro_42gzDjsdV",
- "outputId": "355082b3-19d2-4d8b-8347-c2a6b08d6487",
+ "outputId": "a2c2a25b-40ef-4842-f2f7-3ac85404d195",
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 35
}
},
"source": [
"d_paises"
],
- "execution_count": 21,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -861,7 +929,7 @@
"metadata": {
"tags": []
},
- "execution_count": 21
+ "execution_count": 13
}
]
},
@@ -976,7 +1044,7 @@
" 'Strawberry': 0.50,\n",
" 'Watermelon': 0.45}"
],
- "execution_count": 22,
+ "execution_count": null,
"outputs": []
},
{
@@ -992,7 +1060,7 @@
"cell_type": "code",
"metadata": {
"id": "bI7Ctf0ohyz8",
- "outputId": "f9aa2e05-8a41-45c0-a32e-22b1b8976c69",
+ "outputId": "909b3b73-00da-487e-b981-a62073c78fda",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -1000,7 +1068,7 @@
"source": [
"d_frutas"
],
- "execution_count": 23,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -1035,7 +1103,7 @@
"metadata": {
"tags": []
},
- "execution_count": 23
+ "execution_count": 18
}
]
},
@@ -1052,7 +1120,7 @@
"cell_type": "code",
"metadata": {
"id": "JpreyE_LtCcU",
- "outputId": "f2f7d1e4-51ff-40ff-af58-5577a34dd137",
+ "outputId": "8b2e9f8f-66f9-4fa1-eae9-95b8e6df1abb",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -1060,7 +1128,7 @@
"source": [
"d_frutas['Apple']"
],
- "execution_count": 24,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -1072,7 +1140,35 @@
"metadata": {
"tags": []
},
- "execution_count": 24
+ "execution_count": 19
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RMWau2TOclHr",
+ "outputId": "741f0735-17a1-4f4f-ec4c-4df8bc82c987",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 166
+ }
+ },
+ "source": [
+ "d_frutas['blablabla'] # Isso significa que 'blablabla' não faz parte do dicionário!"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "KeyError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md_frutas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'blablabla'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# Isso significa que 'blablabla' não faz parte do dicionário!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m: 'blablabla'"
+ ]
}
]
},
@@ -1088,17 +1184,17 @@
{
"cell_type": "code",
"metadata": {
- "id": "rMro_tY8kepo",
- "outputId": "dec61759-d1b5-4379-8846-cce26973209d",
+ "id": "i-6-pNQCcyXY",
+ "outputId": "a8408cf3-3e7f-4cb5-d19a-2e8e243dba6b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
- "for key in d_frutas.keys():\n",
- " print(key)"
+ "for chave in d_frutas.keys():\n",
+ " print(chave)"
],
- "execution_count": 25,
+ "execution_count": null,
"outputs": [
{
"output_type": "stream",
@@ -1132,29 +1228,114 @@
}
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9u4xJ0FfdCxm"
+ },
+ "source": [
+ "## Iterar pelos valores do dicionário:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vrFPwQPDdFP3",
+ "outputId": "cb739da2-4407-47fe-8495-9c86247bffad",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "for i_valor in d_frutas.values():\n",
+ " print(i_valor)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0.35\n",
+ "0.4\n",
+ "0.25\n",
+ "0.3\n",
+ "0.7\n",
+ "0.55\n",
+ "0.45\n",
+ "0.5\n",
+ "0.75\n",
+ "0.6\n",
+ "0.65\n",
+ "0.2\n",
+ "0.15\n",
+ "0.8\n",
+ "0.75\n",
+ "0.25\n",
+ "0.3\n",
+ "0.45\n",
+ "0.55\n",
+ "0.55\n",
+ "0.6\n",
+ "0.4\n",
+ "0.5\n",
+ "0.45\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
"id": "yDkOLvRFJxco"
},
"source": [
- "## Iterar pelos itens (key, value) do dicionário"
+ "## Iterar pelos itens (chave, valor) do dicionário"
]
},
{
"cell_type": "code",
"metadata": {
- "id": "DpFB1g-3kDSt",
- "outputId": "a0b41867-9ca7-414d-a451-34aa0f6e4232",
+ "id": "H8BCC6qodU6o",
+ "outputId": "9744058d-976e-4309-d878-679af58013aa",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
+ "source": [
+ "d_frutas.items()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "dict_items([('Avocado', 0.35), ('Apple', 0.4), ('Apricot', 0.25), ('Banana', 0.3), ('Blackcurrant', 0.7), ('Blackberry', 0.55), ('Blueberry', 0.45), ('Cherry', 0.5), ('Coconut', 0.75), ('Fig', 0.6), ('Grape', 0.65), ('Kiwi', 0.2), ('Lemon', 0.15), ('Mango', 0.8), ('Nectarine', 0.75), ('Orange', 0.25), ('Papaya', 0.3), ('Passion Fruit', 0.45), ('Peach', 0.55), ('Pineapple', 0.55), ('Plum', 0.6), ('Raspberry', 0.4), ('Strawberry', 0.5), ('Watermelon', 0.45)])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 24
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DpFB1g-3kDSt",
+ "outputId": "7ac51581-edfa-418d-a1e0-d297ebdffca7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 425
+ }
+ },
"source": [
"for item in d_frutas.items():\n",
" print(item) "
],
- "execution_count": 26,
+ "execution_count": null,
"outputs": [
{
"output_type": "stream",
@@ -1188,17 +1369,6 @@
}
]
},
- {
- "cell_type": "code",
- "metadata": {
- "id": "KxGaOrYSdLxp"
- },
- "source": [
- ""
- ],
- "execution_count": null,
- "outputs": []
- },
{
"cell_type": "markdown",
"metadata": {
@@ -1212,16 +1382,17 @@
"cell_type": "code",
"metadata": {
"id": "tjJ6qRF8nr4v",
- "outputId": "6798f545-3f24-40b9-83d9-8c4a5e073389",
+ "outputId": "3e75843b-2d45-4b4c-a3f2-24ffc0e60a7a",
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 425
}
},
"source": [
"for value in d_frutas.values():\n",
" print(value)"
],
- "execution_count": 27,
+ "execution_count": null,
"outputs": [
{
"output_type": "stream",
@@ -1313,35 +1484,7 @@
]
},
{
- "cell_type": "code",
- "metadata": {
- "id": "bDb1IHuddfwH",
- "outputId": "b66c5df5-6028-4cad-e480-60c92cdaa9f8",
- "colab": {
- "base_uri": "https://localhost:8080/"
- }
- },
- "source": [
- "d_frutas.items()"
- ],
- "execution_count": 28,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "dict_items([('Avocado', 0.35), ('Apple', 0.4), ('Apricot', 0.25), ('Banana', 0.3), ('Blackcurrant', 0.7), ('Blackberry', 0.55), ('Blueberry', 0.45), ('Cherry', 0.5), ('Coconut', 0.75), ('Fig', 0.6), ('Grape', 0.65), ('Kiwi', 0.2), ('Lemon', 0.15), ('Mango', 0.8), ('Nectarine', 0.75), ('Orange', 0.25), ('Papaya', 0.3), ('Passion Fruit', 0.45), ('Peach', 0.55), ('Pineapple', 0.55), ('Plum', 0.6), ('Raspberry', 0.4), ('Strawberry', 0.5), ('Watermelon', 0.45)])"
- ]
- },
- "metadata": {
- "tags": []
- },
- "execution_count": 28
- }
- ]
- },
- {
- "cell_type": "markdown",
+ "cell_type": "markdown",
"metadata": {
"id": "Fotx7XUquAo8"
},
@@ -1363,10 +1506,9 @@
"cell_type": "code",
"metadata": {
"id": "-gkEKNZPTeMp",
- "outputId": "3540aadd-996a-4abd-cfcb-c22e49b75aaa",
+ "outputId": "6c248a84-4912-446e-a197-7dc8ea670b85",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
@@ -1384,7 +1526,7 @@
"metadata": {
"tags": []
},
- "execution_count": 75
+ "execution_count": 25
}
]
},
@@ -1401,14 +1543,13 @@
"cell_type": "code",
"metadata": {
"id": "SKtEwmBCuxyi",
- "outputId": "1df7263c-a64f-4eaf-8d4d-a55cac03d2bc",
+ "outputId": "cf52b903-90e0-432f-8b7e-bc878d5c5f5f",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
- "'Coconut' in fruits.keys()"
+ "'Coconut' in d_frutas.keys()"
],
"execution_count": null,
"outputs": [
@@ -1422,7 +1563,7 @@
"metadata": {
"tags": []
},
- "execution_count": 77
+ "execution_count": 29
}
]
},
@@ -1440,7 +1581,7 @@
"cell_type": "code",
"metadata": {
"id": "DbWpbuLTK9sn",
- "outputId": "f10aeaa8-4cd7-41a9-b9f9-7e7bbe6a7fd5",
+ "outputId": "97999d69-9bce-463c-f980-e5a447ccf7a4",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -1448,7 +1589,7 @@
"source": [
"0.4 in d_frutas.values()"
],
- "execution_count": 29,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -1460,7 +1601,7 @@
"metadata": {
"tags": []
},
- "execution_count": 29
+ "execution_count": 30
}
]
},
@@ -1477,13 +1618,31 @@
{
"cell_type": "code",
"metadata": {
- "id": "5Rwq4-UG4--u"
+ "id": "5Rwq4-UG4--u",
+ "outputId": "4b157602-fd78-41a9-ae5b-de42c189d805",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
},
"source": [
- "d_frutas2 = {'Grapefruit': 1.0 }"
+ "d_frutas2 = {'Grapefruit': 1.0}\n",
+ "d_frutas2"
],
- "execution_count": 30,
- "outputs": []
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Grapefruit': 1.0}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 32
+ }
+ ]
},
{
"cell_type": "markdown",
@@ -1498,7 +1657,7 @@
"cell_type": "code",
"metadata": {
"id": "7BD_mYMM5O5o",
- "outputId": "39cdea31-d4ff-4064-e59f-adf178840a82",
+ "outputId": "ea96653d-04e7-4809-a24f-ff97ecdf4a81",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -1507,7 +1666,7 @@
"d_frutas.update(d_frutas2)\n",
"d_frutas"
],
- "execution_count": 31,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -1543,7 +1702,7 @@
"metadata": {
"tags": []
},
- "execution_count": 31
+ "execution_count": 33
}
]
},
@@ -1564,7 +1723,7 @@
"source": [
"d_frutas3 = {'Apple': 0.70}"
],
- "execution_count": 32,
+ "execution_count": null,
"outputs": []
},
{
@@ -1581,55 +1740,14 @@
{
"cell_type": "code",
"metadata": {
- "id": "E4GKdTw76PXI",
- "outputId": "6a14210d-d1e4-4dce-9465-72af35a60671",
- "colab": {
- "base_uri": "https://localhost:8080/"
- }
+ "id": "E4GKdTw76PXI"
},
"source": [
"d_frutas.update(d_frutas3)\n",
"d_frutas"
],
- "execution_count": 33,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "{'Apple': 0.7,\n",
- " 'Apricot': 0.25,\n",
- " 'Avocado': 0.35,\n",
- " 'Banana': 0.3,\n",
- " 'Blackberry': 0.55,\n",
- " 'Blackcurrant': 0.7,\n",
- " 'Blueberry': 0.45,\n",
- " 'Cherry': 0.5,\n",
- " 'Coconut': 0.75,\n",
- " 'Fig': 0.6,\n",
- " 'Grape': 0.65,\n",
- " 'Grapefruit': 1.0,\n",
- " 'Kiwi': 0.2,\n",
- " 'Lemon': 0.15,\n",
- " 'Mango': 0.8,\n",
- " 'Nectarine': 0.75,\n",
- " 'Orange': 0.25,\n",
- " 'Papaya': 0.3,\n",
- " 'Passion Fruit': 0.45,\n",
- " 'Peach': 0.55,\n",
- " 'Pineapple': 0.55,\n",
- " 'Plum': 0.6,\n",
- " 'Raspberry': 0.4,\n",
- " 'Strawberry': 0.5,\n",
- " 'Watermelon': 0.45}"
- ]
- },
- "metadata": {
- "tags": []
- },
- "execution_count": 33
- }
- ]
+ "execution_count": null,
+ "outputs": []
},
{
"cell_type": "markdown",
@@ -1663,14 +1781,113 @@
{
"cell_type": "code",
"metadata": {
- "id": "ZziGmKGmwqwn"
+ "id": "RV-YOkrffa3h",
+ "outputId": "6a7a1330-7ecd-4924-bf73-f3e0bc16baae",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "d_frutas.keys()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "dict_keys(['Avocado', 'Apple', 'Apricot', 'Banana', 'Blackcurrant', 'Blackberry', 'Blueberry', 'Cherry', 'Coconut', 'Fig', 'Grape', 'Kiwi', 'Lemon', 'Mango', 'Nectarine', 'Orange', 'Papaya', 'Passion Fruit', 'Peach', 'Pineapple', 'Plum', 'Raspberry', 'Strawberry', 'Watermelon', 'Grapefruit'])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 34
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tV8k5w2Bf1Oq",
+ "outputId": "4ee0496f-ab86-4c6d-fc9f-b04863017bb0",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "d_frutas.items()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "dict_items([('Avocado', 0.35), ('Apple', 0.4), ('Apricot', 0.25), ('Banana', 0.3), ('Blackcurrant', 0.7), ('Blackberry', 0.55), ('Blueberry', 0.45), ('Cherry', 0.5), ('Coconut', 0.75), ('Fig', 0.6), ('Grape', 0.65), ('Kiwi', 0.2), ('Lemon', 0.15), ('Mango', 0.8), ('Nectarine', 0.75), ('Orange', 0.25), ('Papaya', 0.3), ('Passion Fruit', 0.45), ('Peach', 0.55), ('Pineapple', 0.55), ('Plum', 0.6), ('Raspberry', 0.4), ('Strawberry', 0.5), ('Watermelon', 0.45), ('Grapefruit', 1.0)])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 37
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZziGmKGmwqwn",
+ "outputId": "30ba046d-0815-4df5-d36b-1459fae1bb9a",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
},
"source": [
"for key, value in d_frutas.items():\n",
- " d_frutas[key] = round(value * 0.9, 2)"
+ " d_frutas[key] = round((value * 0.9), 2) # Isso representa um desconto de 10% no valor das frutas\n",
+ "\n",
+ "d_frutas"
],
- "execution_count": 34,
- "outputs": []
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Apple': 0.32,\n",
+ " 'Apricot': 0.21,\n",
+ " 'Avocado': 0.29,\n",
+ " 'Banana': 0.24,\n",
+ " 'Blackberry': 0.45,\n",
+ " 'Blackcurrant': 0.57,\n",
+ " 'Blueberry': 0.37,\n",
+ " 'Cherry': 0.41,\n",
+ " 'Coconut': 0.61,\n",
+ " 'Fig': 0.49,\n",
+ " 'Grape': 0.53,\n",
+ " 'Grapefruit': 0.81,\n",
+ " 'Kiwi': 0.16,\n",
+ " 'Lemon': 0.13,\n",
+ " 'Mango': 0.65,\n",
+ " 'Nectarine': 0.61,\n",
+ " 'Orange': 0.21,\n",
+ " 'Papaya': 0.24,\n",
+ " 'Passion Fruit': 0.37,\n",
+ " 'Peach': 0.45,\n",
+ " 'Pineapple': 0.45,\n",
+ " 'Plum': 0.49,\n",
+ " 'Raspberry': 0.32,\n",
+ " 'Strawberry': 0.41,\n",
+ " 'Watermelon': 0.37}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 39
+ }
+ ]
},
{
"cell_type": "markdown",
@@ -1685,15 +1902,16 @@
"cell_type": "code",
"metadata": {
"id": "zZLa85knxBtY",
- "outputId": "ee34b820-6961-4d88-d0e2-628efd922e70",
+ "outputId": "2c7c12f8-8885-4f34-a0d1-1323e98a9437",
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 442
}
},
"source": [
"d_frutas"
],
- "execution_count": 35,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -1729,7 +1947,7 @@
"metadata": {
"tags": []
},
- "execution_count": 35
+ "execution_count": 84
}
]
},
@@ -1764,7 +1982,7 @@
" if key == 'Avocado':\n",
" del d_frutas[key] # Deleta key = 'Avocado'"
],
- "execution_count": 36,
+ "execution_count": null,
"outputs": []
},
{
@@ -1780,7 +1998,7 @@
"cell_type": "code",
"metadata": {
"id": "IwnsHejhyT4l",
- "outputId": "e5029d7e-9c8d-4e37-940e-a375ba57c78d",
+ "outputId": "f0f819ca-117e-4a52-8458-196476c52aad",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -1788,42 +2006,42 @@
"source": [
"d_frutas"
],
- "execution_count": 37,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "{'Apple': 0.63,\n",
- " 'Apricot': 0.23,\n",
- " 'Banana': 0.27,\n",
- " 'Blackberry': 0.5,\n",
- " 'Blackcurrant': 0.63,\n",
- " 'Blueberry': 0.41,\n",
- " 'Cherry': 0.45,\n",
- " 'Coconut': 0.68,\n",
- " 'Fig': 0.54,\n",
- " 'Grape': 0.59,\n",
- " 'Grapefruit': 0.9,\n",
- " 'Kiwi': 0.18,\n",
- " 'Lemon': 0.14,\n",
- " 'Mango': 0.72,\n",
- " 'Nectarine': 0.68,\n",
- " 'Orange': 0.23,\n",
- " 'Papaya': 0.27,\n",
- " 'Passion Fruit': 0.41,\n",
- " 'Peach': 0.5,\n",
- " 'Pineapple': 0.5,\n",
- " 'Plum': 0.54,\n",
- " 'Raspberry': 0.36,\n",
- " 'Strawberry': 0.45,\n",
- " 'Watermelon': 0.41}"
+ "{'Apple': 0.32,\n",
+ " 'Apricot': 0.21,\n",
+ " 'Banana': 0.24,\n",
+ " 'Blackberry': 0.45,\n",
+ " 'Blackcurrant': 0.57,\n",
+ " 'Blueberry': 0.37,\n",
+ " 'Cherry': 0.41,\n",
+ " 'Coconut': 0.61,\n",
+ " 'Fig': 0.49,\n",
+ " 'Grape': 0.53,\n",
+ " 'Grapefruit': 0.81,\n",
+ " 'Kiwi': 0.16,\n",
+ " 'Lemon': 0.13,\n",
+ " 'Mango': 0.65,\n",
+ " 'Nectarine': 0.61,\n",
+ " 'Orange': 0.21,\n",
+ " 'Papaya': 0.24,\n",
+ " 'Passion Fruit': 0.37,\n",
+ " 'Peach': 0.45,\n",
+ " 'Pineapple': 0.45,\n",
+ " 'Plum': 0.49,\n",
+ " 'Raspberry': 0.32,\n",
+ " 'Strawberry': 0.41,\n",
+ " 'Watermelon': 0.37}"
]
},
"metadata": {
"tags": []
},
- "execution_count": 37
+ "execution_count": 41
}
]
},
@@ -1846,25 +2064,12 @@
},
"source": [
"d_frutas_filtro = {}\n",
+ "\n",
"for key, value in d_frutas.items():\n",
" if value > 0.5:\n",
" d_frutas_filtro.update({key: value})"
],
- "execution_count": 38,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "fu1jgbfUhPRD"
- },
- "source": [
- "d_frutas_filtro = {}\n",
- "for key, value in d_frutas.items():\n",
- " if value == 0.5:\n",
- " d_frutas_filtro.update({key: value})"
- ],
- "execution_count": 40,
+ "execution_count": null,
"outputs": []
},
{
@@ -1880,7 +2085,7 @@
"cell_type": "code",
"metadata": {
"id": "SsStWM5k1s-Q",
- "outputId": "3fc3a8ef-4627-40d7-e24e-f2bee8e22e66",
+ "outputId": "a473e0a5-1ece-47ff-b8ae-2a460d0b3dc2",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -1888,19 +2093,28 @@
"source": [
"d_frutas_filtro"
],
- "execution_count": 41,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "{'Blackberry': 0.5, 'Peach': 0.5, 'Pineapple': 0.5}"
+ "{'Blackberry': 0.55,\n",
+ " 'Blackcurrant': 0.7,\n",
+ " 'Coconut': 0.75,\n",
+ " 'Fig': 0.6,\n",
+ " 'Grape': 0.65,\n",
+ " 'Mango': 0.8,\n",
+ " 'Nectarine': 0.75,\n",
+ " 'Peach': 0.55,\n",
+ " 'Pineapple': 0.55,\n",
+ " 'Plum': 0.6}"
]
},
"metadata": {
"tags": []
},
- "execution_count": 41
+ "execution_count": 3
}
]
},
@@ -2031,10 +2245,9 @@
"cell_type": "code",
"metadata": {
"id": "SH0WIKZ8-Ylr",
- "outputId": "b9cea719-637e-40a5-9e79-eb67aeb47887",
+ "outputId": "0352afbe-7189-4b41-9428-7980a76c8442",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 425
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
@@ -2047,36 +2260,36 @@
"output_type": "execute_result",
"data": {
"text/plain": [
- "[('Apple', 0.4),\n",
- " ('Apricot', 0.25),\n",
- " ('Avocado', 0.35),\n",
- " ('Banana', 0.3),\n",
- " ('Blackberry', 0.55),\n",
- " ('Blackcurrant', 0.7),\n",
- " ('Blueberry', 0.45),\n",
- " ('Cherry', 0.5),\n",
- " ('Coconut', 0.75),\n",
- " ('Fig', 0.6),\n",
- " ('Grape', 0.65),\n",
- " ('Kiwi', 0.2),\n",
- " ('Lemon', 0.15),\n",
- " ('Mango', 0.8),\n",
- " ('Nectarine', 0.75),\n",
- " ('Orange', 0.25),\n",
- " ('Papaya', 0.3),\n",
- " ('Passion Fruit', 0.45),\n",
- " ('Peach', 0.55),\n",
- " ('Pineapple', 0.55),\n",
- " ('Plum', 0.6),\n",
- " ('Raspberry', 0.4),\n",
- " ('Strawberry', 0.5),\n",
- " ('Watermelon', 0.45)]"
+ "[('Apple', 0.32),\n",
+ " ('Apricot', 0.21),\n",
+ " ('Banana', 0.24),\n",
+ " ('Blackberry', 0.45),\n",
+ " ('Blackcurrant', 0.57),\n",
+ " ('Blueberry', 0.37),\n",
+ " ('Cherry', 0.41),\n",
+ " ('Coconut', 0.61),\n",
+ " ('Fig', 0.49),\n",
+ " ('Grape', 0.53),\n",
+ " ('Grapefruit', 0.81),\n",
+ " ('Kiwi', 0.16),\n",
+ " ('Lemon', 0.13),\n",
+ " ('Mango', 0.65),\n",
+ " ('Nectarine', 0.61),\n",
+ " ('Orange', 0.21),\n",
+ " ('Papaya', 0.24),\n",
+ " ('Passion Fruit', 0.37),\n",
+ " ('Peach', 0.45),\n",
+ " ('Pineapple', 0.45),\n",
+ " ('Plum', 0.49),\n",
+ " ('Raspberry', 0.32),\n",
+ " ('Strawberry', 0.41),\n",
+ " ('Watermelon', 0.37)]"
]
},
"metadata": {
"tags": []
},
- "execution_count": 12
+ "execution_count": 46
}
]
},
@@ -2156,7 +2369,7 @@
"cell_type": "code",
"metadata": {
"id": "iJq1clvOHVG2",
- "outputId": "2c89ba89-3377-4337-87f1-050360788da9",
+ "outputId": "66747a5b-c319-4b99-eda0-afcb187ea867",
"colab": {
"base_uri": "https://localhost:8080/"
}
@@ -2164,42 +2377,42 @@
"source": [
"d_frutas"
],
- "execution_count": 42,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "{'Apple': 0.63,\n",
- " 'Apricot': 0.23,\n",
- " 'Banana': 0.27,\n",
- " 'Blackberry': 0.5,\n",
- " 'Blackcurrant': 0.63,\n",
- " 'Blueberry': 0.41,\n",
- " 'Cherry': 0.45,\n",
- " 'Coconut': 0.68,\n",
- " 'Fig': 0.54,\n",
- " 'Grape': 0.59,\n",
- " 'Grapefruit': 0.9,\n",
- " 'Kiwi': 0.18,\n",
- " 'Lemon': 0.14,\n",
- " 'Mango': 0.72,\n",
- " 'Nectarine': 0.68,\n",
- " 'Orange': 0.23,\n",
- " 'Papaya': 0.27,\n",
- " 'Passion Fruit': 0.41,\n",
- " 'Peach': 0.5,\n",
- " 'Pineapple': 0.5,\n",
- " 'Plum': 0.54,\n",
- " 'Raspberry': 0.36,\n",
- " 'Strawberry': 0.45,\n",
- " 'Watermelon': 0.41}"
+ "{'Apple': 0.32,\n",
+ " 'Apricot': 0.21,\n",
+ " 'Banana': 0.24,\n",
+ " 'Blackberry': 0.45,\n",
+ " 'Blackcurrant': 0.57,\n",
+ " 'Blueberry': 0.37,\n",
+ " 'Cherry': 0.41,\n",
+ " 'Coconut': 0.61,\n",
+ " 'Fig': 0.49,\n",
+ " 'Grape': 0.53,\n",
+ " 'Grapefruit': 0.81,\n",
+ " 'Kiwi': 0.16,\n",
+ " 'Lemon': 0.13,\n",
+ " 'Mango': 0.65,\n",
+ " 'Nectarine': 0.61,\n",
+ " 'Orange': 0.21,\n",
+ " 'Papaya': 0.24,\n",
+ " 'Passion Fruit': 0.37,\n",
+ " 'Peach': 0.45,\n",
+ " 'Pineapple': 0.45,\n",
+ " 'Plum': 0.49,\n",
+ " 'Raspberry': 0.32,\n",
+ " 'Strawberry': 0.41,\n",
+ " 'Watermelon': 0.37}"
]
},
"metadata": {
"tags": []
},
- "execution_count": 42
+ "execution_count": 47
}
]
},
@@ -2216,14 +2429,13 @@
"cell_type": "code",
"metadata": {
"id": "uIDW5FhwAiSs",
- "outputId": "52599d3f-ff13-4894-f697-ce7290bff9d5",
+ "outputId": "b266365b-417a-4f9f-9fda-c033446472e8",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
- "d_frutas2 = {k: v for k, v in filter(lambda t: t[0] == 'Apple', d_frutas.items())}\n",
+ "d_frutas2 = {chave: valor for chave, valor in filter(lambda t: t[0] == 'Apple', d_frutas.items())}\n",
"d_frutas2"
],
"execution_count": null,
@@ -2238,7 +2450,50 @@
"metadata": {
"tags": []
},
- "execution_count": 6
+ "execution_count": 4
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3XmPlpNqBVMl"
+ },
+ "source": [
+ "### A expressão acima é equivalente à expressão abaixo:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_5j19I7tiHgp",
+ "outputId": "87e3bd82-8ec6-4f59-c8e2-74aaa80858d3",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "d_filtro = {}\n",
+ "\n",
+ "for chave, valor in d_frutas.items():\n",
+ " if chave == 'Apple':\n",
+ " d_filtro.update({chave: valor})\n",
+ "\n",
+ "d_filtro"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'Apple': 0.4}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 8
}
]
},
@@ -2248,43 +2503,55 @@
"id": "nUMGIzxeNt_U"
},
"source": [
- "### Filtrando por valor:"
+ "### Filtrando por valor:\n",
+ "\n",
+ "Equivalente a:\n",
+ "\n",
+ "```\n",
+ "d_frutas3 = {}\n",
+ "\n",
+ "for key, value in d_frutas.items():\n",
+ " if value > 0.5:\n",
+ " d_frutas3.update({key: value})\n",
+ "```"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tvHcQatANltL",
- "outputId": "6ca05107-f13c-4175-9d60-41a7615fd233",
+ "outputId": "8feaf5b1-1db8-4391-8950-248ba8ab46c5",
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 187
}
},
"source": [
"d_frutas3 = {k: v for k, v in filter(lambda t: t[1] > 0.5, d_frutas.items())}\n",
"d_frutas3"
],
- "execution_count": 43,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "{'Apple': 0.63,\n",
- " 'Blackcurrant': 0.63,\n",
- " 'Coconut': 0.68,\n",
- " 'Fig': 0.54,\n",
- " 'Grape': 0.59,\n",
- " 'Grapefruit': 0.9,\n",
- " 'Mango': 0.72,\n",
- " 'Nectarine': 0.68,\n",
- " 'Plum': 0.54}"
+ "{'Blackberry': 0.55,\n",
+ " 'Blackcurrant': 0.7,\n",
+ " 'Coconut': 0.75,\n",
+ " 'Fig': 0.6,\n",
+ " 'Grape': 0.65,\n",
+ " 'Mango': 0.8,\n",
+ " 'Nectarine': 0.75,\n",
+ " 'Peach': 0.55,\n",
+ " 'Pineapple': 0.55,\n",
+ " 'Plum': 0.6}"
]
},
"metadata": {
"tags": []
},
- "execution_count": 43
+ "execution_count": 7
}
]
},
@@ -2322,16 +2589,16 @@
"cell_type": "code",
"metadata": {
"id": "0BBWO9Zth_mc",
- "outputId": "6f948288-be8e-4541-94b9-b067c17f13e4",
+ "outputId": "18783570-d8b5-4fa1-9f19-747b0db288da",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
- "d_colaboradores= {'Gerentes': ['A', 'B', 'C'], 'Programadores': ['B', 'D', 'E', 'F', 'G'], 'Gerentes_Projeto': ['A', 'E']}\n",
+ "d_colaboradores = {'Gerentes': ['A', 'B', 'C'], 'Programadores': ['B', 'D', 'E', 'F', 'G'], 'Gerentes_Projeto': ['A', 'E']}\n",
"d_colaboradores"
],
- "execution_count": 45,
+ "execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
@@ -2345,7 +2612,7 @@
"metadata": {
"tags": []
},
- "execution_count": 45
+ "execution_count": 2
}
]
},
@@ -2361,71 +2628,74 @@
{
"cell_type": "code",
"metadata": {
- "id": "XH3BgvYfi01G",
- "outputId": "d2fe69fa-5c0b-47b9-9413-e9c2019c19d1",
+ "id": "rGvVgyz7jxwn",
+ "outputId": "c4e02509-6910-46c5-d906-b7d6f542dfb3",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 129
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
- "d_colaboradores{'Gerentes'[0]}"
+ "d_colaboradores['Gerentes']"
],
- "execution_count": 46,
+ "execution_count": null,
"outputs": [
{
- "output_type": "error",
- "ename": "SyntaxError",
- "evalue": "ignored",
- "traceback": [
- "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m d_colaboradores{'Gerentes'[0]}\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
- ]
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "['A', 'B', 'C']"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 50
}
]
},
{
"cell_type": "code",
"metadata": {
- "id": "kCILrtFPjBTe",
- "outputId": "4d8c8aca-341d-4528-8ba2-53a898683c49",
+ "id": "c-VwXvdij3QQ",
+ "outputId": "f4344858-8ebf-4e0c-b336-e7a6ed4a43a2",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
- "d_colaboradores.get('Gerentes')\n"
+ "d_colaboradores['Programadores']"
],
- "execution_count": 50,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "['A', 'B', 'C']"
+ "['B', 'D', 'E', 'F', 'G']"
]
},
"metadata": {
"tags": []
},
- "execution_count": 50
+ "execution_count": 51
}
]
},
{
"cell_type": "code",
"metadata": {
- "id": "emYGFLuJk65D",
- "outputId": "8fe63e1f-8421-4d10-916e-58520b8d71fe",
+ "id": "WV0WaGB4kCiP",
+ "outputId": "171e4ea0-c66f-49c2-f4ea-deb44b315d43",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
- "list_gerentes=d_colaboradores.get('Gerentes')\n",
- "list_gerentes[0]"
+ "s_gerentes = d_colaboradores['Gerentes']\n",
+ "s_gerentes[0]"
],
- "execution_count": 53,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@@ -2440,127 +2710,339 @@
"metadata": {
"tags": []
},
- "execution_count": 53
+ "execution_count": 62
}
]
},
{
"cell_type": "code",
"metadata": {
- "id": "B8ZbnSYNlZgt",
- "outputId": "06d1ea72-8a6d-49fd-9d1d-83cf8a080b24",
+ "id": "yRrG7wUgkf6K",
+ "outputId": "122c0ff9-47af-4a50-874e-42779aa3c068",
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
+ "base_uri": "https://localhost:8080/"
}
},
"source": [
- "d_colaboradores.get('Gerentes')[0]\n",
- "\n"
+ "s_gerente_A = d_colaboradores.values()\n",
+ "s_gerente_A"
],
- "execution_count": 57,
+ "execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
"text/plain": [
- "'A'"
+ "dict_values([['A', 'B', 'C'], ['B', 'D', 'E', 'F', 'G'], ['A', 'E']])"
]
},
"metadata": {
"tags": []
},
- "execution_count": 57
+ "execution_count": 55
}
]
},
{
"cell_type": "markdown",
"metadata": {
- "id": "4JnYRazemjq5"
+ "id": "ntVcr_3XwaQ-"
+ },
+ "source": [
+ "## Exercício 3\n",
+ "Consulte a página [Python Data Types: Dictionary - Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/dictionary/) para mais exercícios relacionados à dicionários."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7u5-o8dzlryA"
},
"source": [
- "retornar os cargos que o funcionario !A! ocupa\n"
+ "## **Exercício 4**\n",
+ "\n",
+ "Retornar do dicionário d_colaboradores somente o Programador cujo nome seja 'E'. "
]
},
{
"cell_type": "code",
"metadata": {
- "id": "SX2h28bXj33b",
- "outputId": "8be812f8-d86a-4a47-d439-55c229c216c9",
+ "id": "W97EV00T1ejw",
+ "outputId": "f043e621-a387-4db4-bd19-a1410af98f46",
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 35
}
},
"source": [
- "d_colaboradores['Gerentes']"
+ "chave='Gerentes'\n",
+ "d_colaboradores['Gerentes'][0]\n",
+ "d_colaboradores[chave][0]"
],
- "execution_count": 51,
+ "execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
"text/plain": [
- "['A', 'B', 'C']"
+ "'A'"
]
},
"metadata": {
"tags": []
},
- "execution_count": 51
+ "execution_count": 24
}
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
"metadata": {
- "id": "XCkq6tN9mqgn"
+ "id": "1CGVsatkzuu4",
+ "outputId": "d6beb963-ec37-4f40-944b-4b9a5bf264b0",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 129
+ }
},
"source": [
- "retornar do dicinario d_colaboradores somente o progarmador cujo nome seja 'E'"
+ "for key in d_colaboradores.keys():\n",
+ " i_posição=0\n",
+ " for i_indice in d_colaboradores[key][i_indice]:\n",
+ " if 'E' in d_colaboradores.get(key):\n",
+ " valor = d_colaboradores.get(key)\n",
+ " for i_posição < valor:\n",
+ " d_colaboradores.get(key)[i_posição]\n",
+ " print(d_colaboradores.get(key)[i_posição])\n",
+ " valor = d_colaboradores.get(key)\n",
+ " tamanho=len(valor)\n",
+ " print(tamanho)\n",
+ " print(valor)\n",
+ " valor[valor == 'E']\n",
+ " print(valor)\n",
+ " print(key)\n",
+ " print(i_indice)\n",
+ " #print(d_colaboradores[key][i_indice])\n",
+ " print(d_colaboradores.values())"
+ ],
+ "execution_count": 42,
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "SyntaxError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m6\u001b[0m\n\u001b[0;31m for i_posição < valor:\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ ]
+ }
]
},
{
"cell_type": "markdown",
"metadata": {
- "id": "BSXBKuQvm0y0"
+ "id": "zfnP-CArmPb4"
},
"source": [
- "quais são os colaboradores que \n",
- "a)são ao mesmo tempo gerente e gerente de projeto\n",
- "b) gerentes de projetos e programadores"
+ "## **Exercício 5**\n",
+ "\n",
+ "Retornar qual é o cargo do funcionário (todas as pessoas da organização) que se chama 'A'."
]
},
{
"cell_type": "markdown",
"metadata": {
- "id": "ntVcr_3XwaQ-"
+ "id": "qc6xDbMGvwX9"
},
"source": [
- "## Exercício 3\n",
- "Consulte a página [Python Data Types: Dictionary - Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/dictionary/) para mais exercícios relacionados à dicionários."
+ "'A' in "
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
"metadata": {
- "id": "ZWRUI3Q0m0GQ"
+ "id": "zIUF3u6Bv7r5",
+ "outputId": "8d5657c7-71d9-452e-ea1e-1e6a24f212a9",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
},
"source": [
- ""
+ "for key in d_colaboradores.keys():\n",
+ " if 'A' in d_colaboradores[key]:\n",
+ " print(key)\n"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Gerentes\n",
+ "Gerentes_Projeto\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "O0QqPmYdxDHY",
+ "outputId": "bb30b080-9356-4238-d33c-9a8222963096",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "cargos = []\n",
+ "for key in d_colaboradores.keys():\n",
+ " if 'A' in d_colaboradores[key]:\n",
+ " cargos.append(key)\n",
+ "print(cargos)"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "['Gerentes', 'Gerentes_Projeto']\n"
+ ],
+ "name": "stdout"
+ }
]
},
{
"cell_type": "markdown",
"metadata": {
- "id": "eKwYMbxymyai"
+ "id": "VzjLVeFvmnjk"
+ },
+ "source": [
+ "## **Exercício 6**\n",
+ "\n",
+ "* Quais são os colabores que são ao mesmo tempo:\n",
+ " * Gerente de Projeto e Gerente (funcional)?\n",
+ " * Gerentes de Projeto e Programadores?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wnCi3kWcl8Sb",
+ "outputId": "4a9f4bdd-6e59-494f-c4bb-e093c409f842",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ " import numpy as np\n",
+ " gerente_e_gerenteproj=[]\n",
+ " gerente_e_gerenteproj=np.intersect1d(d_colaboradores['Gerentes'],d_colaboradores['Gerentes_Projeto'])\n",
+ " print(gerente_e_gerenteproj, 'são gerentes e gerentes de projeto')"
+ ],
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "['A'] são gerentes e gerentes de projeto\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "hKS4X7c2zfX9",
+ "outputId": "ecf5c9cd-2c86-4cb9-9060-b517c37ddd7f",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "print(np.intersect1d(d_colaboradores['Gerentes'],d_colaboradores['Gerentes_Projeto']), 'são gerentes e gerentes de projeto')"
+ ],
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "['A'] são gerentes e gerentes de projeto\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "k1N4T6fVyMCR",
+ "outputId": "f11a1554-ef09-49f7-a0ef-0618e1112dbe",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "gerente_e_programador=np.intersect1d(d_colaboradores['Gerentes'],d_colaboradores['Programadores'])\n",
+ "print(gerente_e_programador, 'são gerentes e programadores')"
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "['B'] são gerentes e programadores\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PmJHD32dzeV2"
},
"source": [
""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GT82fPS0zGJ0",
+ "outputId": "69184b7c-6027-4bd0-ee64-5e7962608c14",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "print(np.intersect1d(d_colaboradores['Gerentes'],d_colaboradores['Programadores']), 'são gerentes e programadores')"
+ ],
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "['B'] são gerentes e programadores\n"
+ ],
+ "name": "stdout"
+ }
]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "smjv-gaKzaUl"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
}
]
}
\ No newline at end of file
From 497bf8229fec9c3732e3a4ddfc5a13e951216a0f Mon Sep 17 00:00:00 2001
From: MariaJacobs70 <72224154+MariaJacobs70@users.noreply.github.com>
Date: Thu, 8 Oct 2020 15:39:56 -0300
Subject: [PATCH 7/9] Criado usando o Colaboratory
---
Notebooks/NB09_01__Functions_alterado2.ipynb | 1662 ++++++++++++++++++
1 file changed, 1662 insertions(+)
create mode 100644 Notebooks/NB09_01__Functions_alterado2.ipynb
diff --git a/Notebooks/NB09_01__Functions_alterado2.ipynb b/Notebooks/NB09_01__Functions_alterado2.ipynb
new file mode 100644
index 000000000..092a9649b
--- /dev/null
+++ b/Notebooks/NB09_01__Functions_alterado2.ipynb
@@ -0,0 +1,1662 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "NB09_01__Functions.ipynb",
+ "provenance": [],
+ "private_outputs": true,
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "FUNÇÕES "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "etxNlyRYo39A"
+ },
+ "source": [
+ "def show_hello_world():\n",
+ " print('Hello World!')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "G6I9PFvZpBgR"
+ },
+ "source": [
+ "type(show_hello_world)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_meNdNygpIbv"
+ },
+ "source": [
+ "show_hello_world()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6zfLd8HwpPpg"
+ },
+ "source": [
+ "___\n",
+ "# **DOCUMENTAR FUNÇÕES COM COMMENTS/DOCSTRING**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3yzgBxtNpRi_"
+ },
+ "source": [
+ "def show_hello_world():\n",
+ " '''\n",
+ " Esta função faz um cumprimento: 'Hello World!'\n",
+ " Inputs: \n",
+ " param1: djdjdjdjdj\n",
+ " param2: fjrjirjjirjir\n",
+ " '''\n",
+ " print('Hello World!')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0rBaxjpmpbm1"
+ },
+ "source": [
+ "show_hello_world()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6ThOwDQp4TfR"
+ },
+ "source": [
+ "# Se quisermos ver a documentação da função, basta invocar o statement __doc__ da seguinte forma:\n",
+ "show_hello_world.__doc__"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9YZ2afpNA4st"
+ },
+ "source": [
+ "OU..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uSnwA4BVA5_t"
+ },
+ "source": [
+ "help(show_hello_world)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "whbnnMA5p1Jw"
+ },
+ "source": [
+ "___\n",
+ "# **FUNÇÕES COM ARGUMENTOS**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "O3bSjLA_qTTc"
+ },
+ "source": [
+ "Definir a função mostra_nome com dois argumentos: s_primeiro_nome e s_ultimo_nome:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9jWyCCPPp4yS"
+ },
+ "source": [
+ "def mostra_nome(s_primeiro_nome, s_ultimo_nome):\n",
+ " print(f'Olá, meu nome é {s_primeiro_nome} {s_ultimo_nome}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VOB3Ip63qIzr"
+ },
+ "source": [
+ "mostra_nome('Nelio', 'Machado')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Oi0c_GuesfcL"
+ },
+ "source": [
+ "Neste caso, o primeiro argumento da função (s_primeiro_nome) vai receber o valor 'Nelio' e o segundo argumento da função (s_ultimo_nome) vai receber 'Machado'."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qkMblpnLsITO"
+ },
+ "source": [
+ "No entanto, também podemos invocar a função da seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "TTli7e6xsMCo"
+ },
+ "source": [
+ "mostra_nome(s_ultimo_nome = 'Machado', s_primeiro_nome = 'Nelio')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rmatMmhTsaVc"
+ },
+ "source": [
+ "Observe que o resultado é o mesmo. No entanto, desta forma, estamos dizendo o valor específico que cada parâmetro irá receber."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PnNYrgJ6VQo9"
+ },
+ "source": [
+ "## PEP8 + Annotations = Códigos mais fáceis de entender e atualizar\n",
+ "\n",
+ "> Observe abaixo quando combinamos PEP8 + Annotations para tornar o código Python ainda mais detalhado. O objetivo de _Annotations_ é deixar o código mais claro, sem mudar o comportamento da função. No exemplo abaixo, os argumentos da função s_primeiro_nome e s_ultimo_nome são argumentos do tipo _str_ e a função retorna um _output_ do tipo _str_."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "aU2Sob37VVmi"
+ },
+ "source": [
+ "def mostra_nome2(s_primeiro_nome: str, s_ultimo_nome: str) -> str:\n",
+ " print(f'Olá, meu nome é {s_primeiro_nome} {s_ultimo_nome}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iIvqS73mXNam"
+ },
+ "source": [
+ "mostra_nome2(s_ultimo_nome = 'Machado', s_primeiro_nome = 'Nelio')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rSnrtFNtXrbN"
+ },
+ "source": [
+ "# **\\*args**\n",
+ "> \\*args permite que você passe mais argumentos do que o número de argumentos formais que você definiu anteriormente."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aT0_PeuEvXiP"
+ },
+ "source": [
+ "## Exemplo 1\n",
+ "> Considere a função (simples) para imprimir o nome completo de um cliente."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Npbi_Hy0bUec"
+ },
+ "source": [
+ "# definimos a função mostra_nome3 da seguinte forma:\n",
+ "def mostra_nome3(*args):\n",
+ " nome = ' '.join(args)\n",
+ " \n",
+ " print(f'Olá, meu nome é {nome}.')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dFzM0gA3_9za"
+ },
+ "source": [
+ "mostra_nome3('Nelio', 'Machado')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "370bpgaSvDbJ"
+ },
+ "source": [
+ "E agora, a função recebe qualquer quantidade de parâmetros."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4kYcu6PEX-Nz"
+ },
+ "source": [
+ "mostra_nome3('Pedro', 'de', 'Alcantara', 'Francisco', 'Antonio', 'Joao', 'Carlos', 'Xavier', 'de', 'Paula', 'Miguel', 'Rafael', 'Joaquim', 'Jose', 'Gonzaga', 'Pascoal', 'Cipriano', 'Serafim')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KMgngPmFimxb"
+ },
+ "source": [
+ "Observe que desta forma pouco importa a quantidade de parâmetros que passamos á função."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Y9pDa6ZRjo0U"
+ },
+ "source": [
+ "## Exemplo 2\n",
+ "* Suponha que estamos insteressados em desenvolver uma função que multiplica dois números (passados como parâmetros)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1A-vhsHxv1YE"
+ },
+ "source": [
+ "Antes de vermos a solução usando \\*args, vamos ver como seria nossa função se \\*args não existisse."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cCDwruF8j5i5"
+ },
+ "source": [
+ "### Forma \"Normal\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_R03BiwLjtwB"
+ },
+ "source": [
+ "# Definição da função\n",
+ "def multiplicar_numeros(x1, x2):\n",
+ " '''\n",
+ " Objetivo: Esta função multiplica DOIS números passados como argumentos.\n",
+ " Autor: Nelio Machado\n",
+ " Data: 04/10/2020\n",
+ " '''\n",
+ " return x1 * x2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0eVm1Qj9kDtd"
+ },
+ "source": [
+ "print(multiplicar_numeros(3, 4))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4h9Nhkickf_8"
+ },
+ "source": [
+ "### Usando \\*args"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9Kf89meJkjw8"
+ },
+ "source": [
+ "def multiplicar_numeros2(*args):\n",
+ " '''\n",
+ " Objetivo: Esta função multiplica vários números passados como argumentos.\n",
+ " Autor: Nelio Machado\n",
+ " Data: 04/10/2020\n",
+ " '''\n",
+ " print(args)\n",
+ " print(type(args))\n",
+ " x = 1\n",
+ " for N in args:\n",
+ " x *= N\n",
+ " \n",
+ " return x"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZuIzwitWk7by"
+ },
+ "source": [
+ "print(multiplicar_numeros2(1, 2, 3, 4, 5))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "U5kyPu792gMN"
+ },
+ "source": [
+ "Eu também posso fazer da seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oc2NJmJf2s7X"
+ },
+ "source": [
+ "args= (1, 2, 3, 4, 5)\n",
+ "print(multiplicar_numeros2(*args))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "38jVie_IjMXI"
+ },
+ "source": [
+ "# \\**kwargs\n",
+ "\n",
+ "* \\**kwargs é usado para passar um dicionário de comprimento variável para uma função.\n",
+ "* Argumento do tipo {chave: valor};\n",
+ "\n",
+ "* Para exemplificar o uso de \\**kwargs, vou usar parte do dicionário dFruits que definimos na sessão [Dictionaries](Dictionaries.ipynb). Qualquer dúvida, volte áquele capítulo para relembrar os principais conceitos."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "yAntQ724nMbv"
+ },
+ "source": [
+ "# Definindo a função para receber parâmetros em forma de dicionário:\n",
+ "def imprime_frutas(**kwargs):\n",
+ " '''\n",
+ " Objetivo: Esta função imprime as frutas contidas em kwargs.\n",
+ " Autor: Nelio Machado\n",
+ " Data: 04/10/2020\n",
+ " '''\n",
+ " for key, value in kwargs.items():#o .itens()devolve dois valores chave e valor\n",
+ " print(f'O valor de {key} é {value}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_FaKEXk_d9jQ"
+ },
+ "source": [
+ "[15:25] Vinicius Fabri Brenck\n",
+ "Isso aqui tb funciona normalmente\n",
+ "\n",
+ "[15:25] Vinicius Fabri Brenck\n",
+ "def imprimir_normal(dicionario): for key, value in dicionario.items(): print('O valor de {} é {}'.format(key, value))imprimir_normal(d_frutas)\n",
+ "\n",
+ "[15:25] Vinicius Fabri Brenck\n",
+ "O resultado é exatamente o mesmo\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sdGIG-c-cuOW"
+ },
+ "source": [
+ "##\n",
+ "##o próprio construtor de dicionário pode ser chamado assim.\n",
+ "\n",
+ "##dict(a=1, b=2) >>> {'a':1, 'b':2}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "eYI0dxKyeBZJ"
+ },
+ "source": [
+ "##[15:25] Vinicius Fabri Brenck\n",
+ "##Isso aqui tb funciona normalmente\n",
+ "\n",
+ "##[15:25] Vinicius Fabri Brenck\n",
+ "def imprimir_normal(dicionario): for key, value in dicionario.items(): print('O valor de {} é {}'.format(key, value))imprimir_normal(d_frutas)\n",
+ "\n",
+ "##[15:25] Vinicius Fabri Brenck\n",
+ "##O resultado é exatamente o mesmo\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jFrnCnJWdJjw"
+ },
+ "source": [
+ "d_frutas['blblba'] = 0.1\n",
+ "d_frutas"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jpmSk9mfxww3"
+ },
+ "source": [
+ "Atenção à forma como os itens são passados à função!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "88-1lStInaVs"
+ },
+ "source": [
+ "imprime_frutas(Avocado = 0.35, Apple = 0.4, Apricot = 0.25, Banana = 0.30)## essa coisa esquisita é uma forma de passar o dicionario"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-jb_kkLiyQt8"
+ },
+ "source": [
+ "No entanto, posso passar um dicionário na forma como estamos acostumados, da seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JZJNiLz7wgCy"
+ },
+ "source": [
+ "d_frutas = {'Apple': 0.4, 'Avocado': 0.3, 'Orange': 0.5, 'Lemon': 0.25}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "eUCum4JPEcxD"
+ },
+ "source": [
+ "imprime_frutas(**d_frutas) ## a sintaxe é ** nome do dicionário"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3VuoDBftdnfi"
+ },
+ "source": [
+ "Cristiano:\n",
+ "eu fazia a seguinte analogia:\n",
+ "\n",
+ "*iterable explode um iterável em argumentos posicionias.\n",
+ "\n",
+ "**mapper explode um mapaeamento (dicionário) em argumentos nomeados."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iK8-e7a1sXmn"
+ },
+ "source": [
+ "___\n",
+ "# **Python return**\n",
+ "> Uma função Python pode ou não retornar um valor."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HS0dGA55siWw"
+ },
+ "source": [
+ "def par_ou_impar(i_numero1, i_numero2):\n",
+ " '''\n",
+ " Esta função somente avalia se a soma de dois números é par ou impar. \n",
+ " A função retorna odd ou even.\n",
+ " '''\n",
+ " i_soma = i_numero1+i_numero2\n",
+ " i_modulo = i_soma % 2\n",
+ " print(f'A soma é {i_soma}')\n",
+ " if i_modulo > 0:\n",
+ " return 'Odd'\n",
+ " else:\n",
+ " return 'Even' "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mZTG2tDJuIZQ"
+ },
+ "source": [
+ "i_numero1 = int(input('Por favor, informe o primeiro número: '))\n",
+ "i_numero2 = int(input('Por favor, informe o segundo número.: '))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7p_9pq3Du18a"
+ },
+ "source": [
+ "type(i_numero1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4oO7aAjcvCAe"
+ },
+ "source": [
+ "type(i_numero2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Br7yT8UHuKYY"
+ },
+ "source": [
+ "s_resultado = par_ou_impar(i_numero1, i_numero2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "601QnggJuhf-"
+ },
+ "source": [
+ "print(f'O resultado é {s_resultado}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "t6HNf9j9yKcT"
+ },
+ "source": [
+ "Mostra o valor de i_modulo ou i_soma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Yu8RsyDAyXne"
+ },
+ "source": [
+ "i_modulo"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nx3twrLRyaeJ"
+ },
+ "source": [
+ "Python reporta que i_modulo não existe.\n",
+ "Está correta esta informação?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "imkyRO4kyvgV"
+ },
+ "source": [
+ "Considere o exemplo a seguir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kwRiXDA5y19h"
+ },
+ "source": [
+ "i_modulo = 0\n",
+ "\n",
+ "def par_ou_impar_v2(i_numero1, i_numero2):\n",
+ " '''\n",
+ " Esta função somente avalia se a soma de dois números é par ou impar. \n",
+ " A função retorna odd ou even.\n",
+ " '''\n",
+ " i_soma = i_numero1+i_numero2\n",
+ " i_modulo = i_soma % 2\n",
+ " print(f'A soma é {i_soma}')\n",
+ " if i_modulo > 0:\n",
+ " return 'Odd'\n",
+ " else:\n",
+ " return 'Even' "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GYxLSGQLy_Ai"
+ },
+ "source": [
+ "i_numero1 = int(input('Por favor, informe o primeiro número: '))\n",
+ "i_numero2 = int(input('Por favor, informe o segundo número.: '))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NMtv99fjzHGs"
+ },
+ "source": [
+ "s_resultado = par_ou_impar_v2(i_numero1, i_numero2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "qjOHnYDVzNGK"
+ },
+ "source": [
+ "print(f'O resultado é {s_resultado}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pPTecxRfzQUc"
+ },
+ "source": [
+ "Agora, vamos checar o valor de i_modulo..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jkQb2mQzzTEo"
+ },
+ "source": [
+ "i_modulo"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oOlyGxBAzjE3"
+ },
+ "source": [
+ "Porque agora o Python reconhece a variável i_modulo?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dceSkt9Z0BZh"
+ },
+ "source": [
+ "___\n",
+ "# **ESCOPO DE VARIÁVEIS: LOCAL & GLOBAL**\n",
+ "* **Local** - Variável declarada dentro da função. Em outras palavras, é uma variável local/uso da função.\n",
+ "\n",
+ "* **Global** - Variável declarada fora da função. Neste caso, a variável é visível à todo o programa. Entretanto, não se pode alterar o valor da variável dentro da função. Caso queira alterar o valor da variável dentro da função, então é necesário declarar a variável usando a palavra reservada 'global’."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0tIjI9GScPxu"
+ },
+ "source": [
+ "## Exemplo 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QRojHHJ20iTY"
+ },
+ "source": [
+ "def exemplo1():\n",
+ " i_valor = 20\n",
+ " i_valor += 1\n",
+ " print(i_valor)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RdhElmTs0y1c"
+ },
+ "source": [
+ "exemplo1()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Tytq7PnH08pz"
+ },
+ "source": [
+ "O escopo da variável 'i_valor' é local, ou seja, de uso/restrito à função. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "299AK0PA1lIg"
+ },
+ "source": [
+ "i_valor"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gGP4cx17y8EZ"
+ },
+ "source": [
+ "Portanto, o erro acima faz sentido, pois a variável i_valor é restrito á função. Ou seja, fora da função o Python não conhece este valor."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KTV_6Gzxfvpc"
+ },
+ "source": [
+ "## Exemplo 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zyi9AyJwfxTm"
+ },
+ "source": [
+ "i_valor= 100\n",
+ "\n",
+ "def exemplo2():\n",
+ " i_valor = 20\n",
+ " i_valor += 1\n",
+ " print(i_valor)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iEWrboG6gBSs"
+ },
+ "source": [
+ "exemplo2()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JPvT0BHG-vxE"
+ },
+ "source": [
+ "Isso é um tanto estranho! Definimos, fora da função, i_valor= 100 e, dentro da função, redefinimos i_valor= 20. Entretanto, como vimos, exemplo2() retorna 21 como resultado."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "N_t8tIDC-149"
+ },
+ "source": [
+ "Agora, a seguir, fora da função, pedimos para ver o valor de i_valor e temos, como resposta, o valor 100."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "I46Bn4FlgJLu"
+ },
+ "source": [
+ "i_valor"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IQlP5nbngL6E"
+ },
+ "source": [
+ "Saberia nos explicar o que está acontecendo?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "h8PHd6rLgtwK"
+ },
+ "source": [
+ "## Exemplo 3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "qB7_zPQVgvVT"
+ },
+ "source": [
+ "i_valor = 100\n",
+ "\n",
+ "def exemplo3():\n",
+ " global i_valor\n",
+ " i_valor = 20\n",
+ " i_valor += 1\n",
+ " print(i_valor)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2KgQSbYCg8Eq"
+ },
+ "source": [
+ "exemplo3()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Y7yWoojrg_9Z"
+ },
+ "source": [
+ "i_valor"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cGlmbIJGzWG6"
+ },
+ "source": [
+ "Saberia explicar o que acontece neste exemplo?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "X8qFfIoxhFOp"
+ },
+ "source": [
+ "## Exemplo 4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZM-yTLuO1bFh"
+ },
+ "source": [
+ "i_valor = 20\n",
+ "\n",
+ "def exemplo4(): ## dentro de exemplo4 eu não defini a variavel i_valor, para dar certo tenho que colocar o global i_valor\n",
+ " i_valor += 1\n",
+ " print(i_valor)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oLvfPO8w1zwL"
+ },
+ "source": [
+ "exemplo4()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WvA2CjOGfPr9"
+ },
+ "source": [
+ "i_valor = 20\n",
+ "\n",
+ "def exemplo4(): ## dentro de exemplo4 eu não defini a variavel i_valor, para dar certo tenho que colocar o global i_valor\n",
+ " glocal i_valor += 1\n",
+ " print(i_valor)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Qyl5gKHEfTZN"
+ },
+ "source": [
+ "exemplo4()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2V7QzpZp2QcM"
+ },
+ "source": [
+ "Qual a razão deste erro?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "w9qI8kln1_C7"
+ },
+ "source": [
+ "i_valor"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AQFFGqLI1FWn"
+ },
+ "source": [
+ "___\n",
+ "# **ARGUMENTOS DEFAULT**\n",
+ "> Considere o exemplo a seguir: toda vez que vai ao supermercado compra 1 pack de leite (contendo 4 garrafas) e 1 garrafão de água de 5L. Portanto, de forma simples, podemos definir nossa função da seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HbcSTiBI4nOj"
+ },
+ "source": [
+ "# Define a função para receber os parâmetros arroz, feijao, leite e água.\n",
+ "def lista_de_compras(arroz, feijao, leite= 1, agua= 1):\n",
+ " '''\n",
+ " Documentação da função: objetivos, autor e data.\n",
+ " '''\n",
+ " print('Lista de Compras:')\n",
+ " print(f'Quantidade de arroz.: {arroz} kilos.') \n",
+ " print(f'Quantidade de feijão: {feijao} kilos.') \n",
+ " print(f'Quantidade de leite.: {leite} pack com 4.') \n",
+ " print(f'Quantidade de água..: {agua} garrafa de 5 litros.') "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vwZnDgoq5pgB"
+ },
+ "source": [
+ "lista_de_compras(5, 3)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "l7bY5BSO7eJF"
+ },
+ "source": [
+ "Como leite= 1 e agua= 1 são valores default's, não precisamos passar esses parâmetros, desde que informamos ao Python o valor default. No entanto, se numa determinada semana precisarmos de 2 pack's de leite, ao invés de 1, devemos informar ao Python o novo valor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "YY4OrFuH7yXi"
+ },
+ "source": [
+ "lista_de_compras(5, 3, 2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-nfrZAvN73YT"
+ },
+ "source": [
+ "Da mesma forma, se numa outra semana precisarmos de 2 garrafões de água ao invés de 1, informamos ao Python da seguinte forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Vpoh6TdM7_xb"
+ },
+ "source": [
+ "lista_de_compras(5, 3, 2, 2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "q3qZn9FuVQly"
+ },
+ "source": [
+ "___\n",
+ "# **map()**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Dav8k0JYWi4B"
+ },
+ "source": [
+ "## Exemplo 1\n",
+ "> Suponha que queremos o quadrado de cada número passado à uma função."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "R6NC0i2OVktM"
+ },
+ "source": [
+ "l_numeros= [0, 1, 2, 3, 4, 5]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AVjYlN44Vw2k"
+ },
+ "source": [
+ "def quadrado_do_numero(i_numero):\n",
+ " return i_numero**2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "i_4CHiehV7lD"
+ },
+ "source": [
+ "list(map(quadrado_do_numero, l_numeros)) ### o map precisa de dois parametro :a função é o parametro e um iterable (tupla,array)\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ckT2g4CcgNKV"
+ },
+ "source": [
+ "map(quadrado_do_numero, l_numeros) ## se eu não der o list ele deixa o resultado na memoria que é um iteravel"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5tq8QDSPWNf6"
+ },
+ "source": [
+ "OU..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZAfkybybWOcG"
+ },
+ "source": [
+ "for i in map(quadrado_do_numero, l_numeros):\n",
+ " print(i)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "c01V5CEzWlGF"
+ },
+ "source": [
+ "## Exemplo 2\n",
+ "> substituir_truer todos os valores True da lista abaixo por 1 e False por 0."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "qH1ackDZWvKp"
+ },
+ "source": [
+ "import random\n",
+ "\n",
+ "l_dados = []\n",
+ "for i in range(50):\n",
+ " random.seed(i)\n",
+ " l_dados.append(random.choice([True, False]))\n",
+ " \n",
+ "l_dados"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Dt2UKC-WXsxr"
+ },
+ "source": [
+ "def substituir_true(s_String):\n",
+ " if s_String == True:\n",
+ " return 1\n",
+ " else:\n",
+ " return 0"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BIIkPuDEXaM0"
+ },
+ "source": [
+ "list(map(substituir_true, l_dados))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TzkLIH1gYpFQ"
+ },
+ "source": [
+ "___\n",
+ "# **Filter()**\n",
+ "* Filtra elementos baseado em condições."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cjU8YznfZai1"
+ },
+ "source": [
+ "Suponha que agora eu quero filtrar os itens True da lista l_dados."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "a3SeaKJgZlAZ"
+ },
+ "source": [
+ "def filtrar_true(item):\n",
+ " if item == True:\n",
+ " return True\n",
+ " else:\n",
+ " return False"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1Z1APDQtZyXs"
+ },
+ "source": [
+ "list(filter(filtrar_true, l_dados))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xPpFqVUnKEH7"
+ },
+ "source": [
+ "___\n",
+ "# **EXERCÍCIOS**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RDgCRPRs0W6C"
+ },
+ "source": [
+ "## Exercício 1\n",
+ "Construa uma função para retornar o dia da semana a partir de um número, sendo:\n",
+ "\n",
+ "* 1 - Dom\n",
+ "* 2 - Seg\n",
+ "* 3 - Ter\n",
+ "* 4 - Qua\n",
+ "* 5 - Qui\n",
+ "* 6 - Sex\n",
+ "* 7 - Sab"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "H17JO6sLOrG7"
+ },
+ "source": [
+ "### Minha solução"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wX_7XDyB0XSy"
+ },
+ "source": [
+ "def dia_da_semana(dia):\n",
+ " d_palavra= {1: 'Segunda',\n",
+ " 2: 'Terça',\n",
+ " 3: 'Quarta',\n",
+ " 4: 'Quinta',\n",
+ " 5: 'Sexta',\n",
+ " 6: 'Sabado',\n",
+ " 7: 'Domingo' }\n",
+ " return d_palavra.get(dia,\"Dia da semana inválido. Informe um número de 1 a 7\")"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "39toyCRU1Q5T"
+ },
+ "source": [
+ "dia_da_semana(1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wt5hQq__1UEd"
+ },
+ "source": [
+ "dia_da_semana(0)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "N53NOsZjOv9m"
+ },
+ "source": [
+ "## Exercício 2\n",
+ "* Desenvolver uma função que retorna True se s_palavra pertence à uma string e False caso contrário."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7j-HHsxFrX5t"
+ },
+ "source": [
+ "def palavra_está_string (s_palavra, s_string):\n",
+ " if s_palavra in s_string:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EOTAE-oMrYJW"
+ },
+ "source": [
+ "s_string = 'O amor é o fogo que arde sem se ver. É ferida que dói e não se sente. É um contentamento descontente. É dor que desatina sem doer'\n",
+ "s_string"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tfzOT6w0thZg"
+ },
+ "source": [
+ "s_palavra = 'fogo'\n",
+ "palavra_está_string (s_palavra, s_string)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xxunBr3ttnji"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vrBZ_68-PBWl"
+ },
+ "source": [
+ "### Minha solução:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "m4Pi4S8hPC_u"
+ },
+ "source": [
+ "def check_palavra(s_frase, s_palavra):\n",
+ " if s_palavra in s_frase:\n",
+ " return True\n",
+ " else:\n",
+ " return False"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NJeqwxDjPxub"
+ },
+ "source": [
+ "A frase abaixo foi extraída de [+ Bíblia + Camões + Legião Urbana - (Guerra) = Monte Castelo](http://compondoletras.blogspot.com/2013/11/biblia-camoes-legiao-urbana-guerra.html)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Dj_n_beIPRBN"
+ },
+ "source": [
+ "s_frase = 'O amor é o fogo que arde sem se ver. É ferida que dói e não se sente. É um contentamento descontente. É dor que desatina sem doer'\n",
+ "s_frase"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "s40FJ9iCPPY0"
+ },
+ "source": [
+ "s_palavra = 'fogo'"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tzc2eaM7QUFE"
+ },
+ "source": [
+ "A palavra s_palavra está em s_frase?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2tlravrMQXn2"
+ },
+ "source": [
+ "check_palavra(s_frase, s_palavra)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XFBVXsW_rVG2"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pMx9E0xMu1lc"
+ },
+ "source": [
+ "## Exercício 3\n",
+ "Para mais exercícios envolvendo funções, consulte [Python functions - Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/python-functions-exercises.php)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Mw6Wg5hFvFMR"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
From 5c20b86b727ad16f8b875d54bc8b407001a65315 Mon Sep 17 00:00:00 2001
From: MariaJacobs70 <72224154+MariaJacobs70@users.noreply.github.com>
Date: Wed, 21 Oct 2020 07:34:55 -0300
Subject: [PATCH 8/9] Criado usando o Colaboratory
---
...0_04__3DP_4_Anomaly_Detection mexido.ipynb | 4445 +++++++++++++++++
1 file changed, 4445 insertions(+)
create mode 100644 Notebooks/NB10_04__3DP_4_Anomaly_Detection mexido.ipynb
diff --git a/Notebooks/NB10_04__3DP_4_Anomaly_Detection mexido.ipynb b/Notebooks/NB10_04__3DP_4_Anomaly_Detection mexido.ipynb
new file mode 100644
index 000000000..43cdff8b9
--- /dev/null
+++ b/Notebooks/NB10_04__3DP_4_Anomaly_Detection mexido.ipynb
@@ -0,0 +1,4445 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "NB10_04__3DP_4_Anomaly_Detection.ipynb",
+ "provenance": [],
+ "collapsed_sections": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "3DP_4 - ANOMALY/OUTLIER DETECTION \n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " survived \n",
+ " pclass \n",
+ " sex \n",
+ " age \n",
+ " sibsp \n",
+ " parch \n",
+ " fare \n",
+ " embarked \n",
+ " class \n",
+ " who \n",
+ " adult_male \n",
+ " deck \n",
+ " embark_town \n",
+ " alive \n",
+ " alone \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " female \n",
+ " 38.0 \n",
+ " 1 \n",
+ " 0 \n",
+ " 71.2833 \n",
+ " C \n",
+ " First \n",
+ " woman \n",
+ " False \n",
+ " C \n",
+ " Cherbourg \n",
+ " yes \n",
+ " False \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 1 \n",
+ " 1 \n",
+ " female \n",
+ " 35.0 \n",
+ " 1 \n",
+ " 0 \n",
+ " 53.1000 \n",
+ " S \n",
+ " First \n",
+ " woman \n",
+ " False \n",
+ " C \n",
+ " Southampton \n",
+ " yes \n",
+ " False \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 0 \n",
+ " 1 \n",
+ " male \n",
+ " 54.0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 51.8625 \n",
+ " S \n",
+ " First \n",
+ " man \n",
+ " True \n",
+ " E \n",
+ " Southampton \n",
+ " no \n",
+ " True \n",
+ " \n",
+ " \n",
+ " 10 \n",
+ " 1 \n",
+ " 3 \n",
+ " female \n",
+ " 4.0 \n",
+ " 1 \n",
+ " 1 \n",
+ " 16.7000 \n",
+ " S \n",
+ " Third \n",
+ " child \n",
+ " False \n",
+ " G \n",
+ " Southampton \n",
+ " yes \n",
+ " False \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " 1 \n",
+ " 1 \n",
+ " female \n",
+ " 58.0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 26.5500 \n",
+ " S \n",
+ " First \n",
+ " woman \n",
+ " False \n",
+ " C \n",
+ " Southampton \n",
+ " yes \n",
+ " True \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8FIo9tD1FQ0u",
+ "outputId": "b8761d55-6258-405e-934f-97d477538e9b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 426
+ }
+ },
+ "source": [
+ "boxplot_sobreviveu(df_titanic, 'age')"
+ ],
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fCqj102y9Kfo",
+ "outputId": "e3580cba-ce0b-4a2b-cdaf-e3421d9ef741",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 170
+ }
+ },
+ "source": [
+ "# Descrever o dataframe, variável 'fare'\n",
+ "df_titanic_ss['fare'].describe()"
+ ],
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "count 1.820000e+02\n",
+ "mean 2.537653e-16\n",
+ "std 1.002759e+00\n",
+ "min -1.034601e+00\n",
+ "25% -6.452479e-01\n",
+ "50% -2.873576e-01\n",
+ "75% 1.452571e-01\n",
+ "max 5.681797e+00\n",
+ "Name: fare, dtype: float64"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 11
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SMcvIb1K_69n",
+ "outputId": "975fc065-ffdd-4e67-f3c0-103d02276998",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 472
+ }
+ },
+ "source": [
+ "plt.scatter(range(df_titanic_ss.shape[0]), np.sort(df_titanic_ss['fare'].values)) ## está mexendo com o dataframe normalizado\n",
+ "plt.xlabel('index')\n",
+ "plt.ylabel('Fares')\n",
+ "plt.title(\"Distribuição da variável Fare\")\n",
+ "\n",
+ "sns.despine()"
+ ],
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NCcXnHHYIlM4",
+ "outputId": "c49ba369-ac5a-47b2-85a0-b25cffdfe4fe",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 284
+ }
+ },
+ "source": [
+ "df_titanic_ss.describe()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " survived \n",
+ " pclass \n",
+ " age \n",
+ " sibsp \n",
+ " parch \n",
+ " fare \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 182.000000 \n",
+ " 182.000000 \n",
+ " 1.820000e+02 \n",
+ " 182.000000 \n",
+ " 182.000000 \n",
+ " 1.820000e+02 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 0.675824 \n",
+ " 1.192308 \n",
+ " 1.464030e-17 \n",
+ " 0.467033 \n",
+ " 0.478022 \n",
+ " 2.537653e-16 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 0.469357 \n",
+ " 0.516411 \n",
+ " 1.002759e+00 \n",
+ " 0.645007 \n",
+ " 0.755869 \n",
+ " 1.002759e+00 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 0.000000 \n",
+ " 1.000000 \n",
+ " -2.220506e+00 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " -1.034601e+00 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 0.000000 \n",
+ " 1.000000 \n",
+ " -7.437173e-01 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " -6.452479e-01 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 2.411064e-02 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " -2.873576e-01 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 7.759421e-01 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.452571e-01 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 1.000000 \n",
+ " 3.000000 \n",
+ " 2.839480e+00 \n",
+ " 3.000000 \n",
+ " 4.000000 \n",
+ " 5.681797e+00 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Qa28Hc3ZC6FV"
+ },
+ "source": [
+ "___\n",
+ "## Kurtosis\n",
+ "> Kurtosis é uma medida estatística que define com que intensidade as caudas de uma distribuição diferem das caudas de uma distribuição Normal. Em outras palavras, a curtose identifica se as caudas de uma determinada distribuição contêm valores extremos.\n",
+ ">> A Kurtosis de uma distribuição Normal padrão é igual a 3. Portanto, se Kurtosis-3 > 0, então isso é o que chamamos de excesso de Kurtosis.\n",
+ ">>> **Alta Kurtosis é um indicador de que os dados possuem caudas pesadas ou outliers**.\n",
+ "\n",
+ "* **Dica muito importante**: Normalize os dados antes!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ynyNHZqmD-tb"
+ },
+ "source": [
+ "___\n",
+ "## Skewness\n",
+ "> É o grau de distorção da distribuição, ou seja, mede a falta de simetria na distribuição de dados, diferenciando valores extremos em uma cauda versus na outra. Uma distribuição simétrica terá uma assimetria de 0.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Source: [Skew and Kurtosis: 2 Important Statistics terms you need to know in Data Science](https://codeburst.io/2-important-statistics-terms-you-need-to-know-in-data-science-skewness-and-kurtosis-388fef94eeaa)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Uoo3xVhBFixi"
+ },
+ "source": [
+ "### Interpretando a Skewness (Rule of Thumb)\n",
+ "* Se -0.5 < Skewness < 0.5: Dados razoavelmente simétricos;\n",
+ "* Se -1 < Skewness < -0.5: Dados moderadamente negativa;\n",
+ "* Se 0.5 < Skewness < 1: Dados moderadamente positiva;\n",
+ "* Se Skewness < -1: Dados altamente negativa;\n",
+ "* Se Skewness > 1: Dados altamente positiva.\n",
+ "\n",
+ "> **Dica**: Normalize os dados antes!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oHg3nyjUTiRu",
+ "outputId": "eaacbc04-e67b-4aa6-fec1-5d9cc97fa449",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 50
+ }
+ },
+ "source": [
+ "# Cálculo das medidas de Skewness e Kurtosis para 'fare'\n",
+ "print(f\"Skewness: {df_titanic_ss['fare'].skew()}\")\n",
+ "print(f\"Kurtosis: {df_titanic_ss['fare'].kurt()}\")"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Skewness: 2.7073683146429004\n",
+ "Kurtosis: 10.690697893681472\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "V2jCZLGVH3Qu"
+ },
+ "source": [
+ "Olhando para as medidas de Skewness e Kurtosis logo acima, qual a conclusão?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0nnFS8vi_rOe",
+ "outputId": "dcd9091c-3c1d-4388-caaf-efb28f8eba55",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 170
+ }
+ },
+ "source": [
+ "# Distribuição da variável 'age'\n",
+ "df_titanic_ss['age'].describe()"
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "count 1.820000e+02\n",
+ "mean 1.464030e-17\n",
+ "std 1.002759e+00\n",
+ "min -2.220506e+00\n",
+ "25% -7.437173e-01\n",
+ "50% 2.411064e-02\n",
+ "75% 7.759421e-01\n",
+ "max 2.839480e+00\n",
+ "Name: age, dtype: float64"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "h9ZmvO1b_4sF",
+ "outputId": "a5ca3bfd-2d10-4f88-88c0-01d58d543c16",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 472
+ }
+ },
+ "source": [
+ "plt.scatter(range(df_titanic_ss.shape[0]), np.sort(df_titanic_ss['age'].values))\n",
+ "plt.xlabel('index')\n",
+ "plt.ylabel('age')\n",
+ "plt.title(\"Distribuição da variável age\")\n",
+ "sns.despine()"
+ ],
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GIAYrDJyCT6r",
+ "outputId": "7355ec62-b4d1-4586-d92c-3f7f2b16f222",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 472
+ }
+ },
+ "source": [
+ "sns.distplot(df_titanic_ss['age'])\n",
+ "plt.title(\"Distribuição da variável age\")\n",
+ "sns.despine()"
+ ],
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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3d7PXev755/Hll1/innvuwTPPPAMvLy+sW7cO6enp+Pbbb83/c29NbGwsZs+ejX/961+oqKjAqFGjkJSU1OroQHvz3MjMmTPx/vvvY+nSpRg4cKC5L+jaa2zZsgXTp0/H5MmTzcvfo6OjUV1d3e7rXMvd3R1jxozBW2+9haamJgQFBWH79u0tCksTBwcH3H///fjqq69QU1ODt99+u8U5H3zwAcaMGYOBAwdi/vz5iIyMREFBAZKTk5GTk4MTJ050KGN739u//OUv2LhxI+688048/fTT5qXgoaGhKC0tNY92ubu7Y9WqVXjkkUcwZMgQzJo1C76+vsjKysLWrVsxevRo/POf/+xQRiIAXApOJIq/LQU3fanVajEgIEC88847xXfffbfZcmuT65eCJyUliffdd5/Yo0cPUa1Wiz169BBnz54tXrx4sdnzvv/+ezE6OlpUqVTNlgKPHz9e7N+/f6v52loK/uWXX4qLFy8W/fz8RCcnJ3Hy5Mktlm2Loii+8847YlBQkKjRaMTRo0eLhw8fbtdScFEUxdOnT4vTp08X3d3dRQBinz59xBdffNH8eFlZmZiYmCj6+PiIrq6uYkJCgnj+/PlWl6CnpqaKM2bMED08PERHR0dx+PDh4k8//dTqz3y9uro68ZlnnhG9vb1FFxcX8d577xWzs7NbLAXvSJ62GAwGMSQkRAQgvvLKK60+/tprr4lhYWGiRqMRBw8eLP7000/i3LlzxbCwMPN5pvf0rbfeavEarb3fWVlZ4rRp00StVit6eHiIs2bNEvPz81v8jCY7d+4UAYiCIIjZ2dmt/iypqaninDlzxICAANHBwUEMCgoSp0yZIm7evNl8TnuXgnfkvT127Jg4duxYUaPRiMHBweLy5cvF9957TwQg5ufnNzt39+7dYkJCgqjVakVHR0cxKipKnDdvnnj48OEb5iFqiyCK140xEhG1IT4+Hn/5y1/My5OJOuLZZ5/Fhx9+iOrqaos0sxO1hT03RNRu9957b7MtKIjaUldX1+z7kpISbNiwAWPGjGFhQ12OPTdEdFNffvklampq8M0335jvu0J0IyNHjsSECRPQr18/FBQU4NNPP0VlZSVefPFFqaORHWBxQ0Q3debMGbz99tsIDAzEm2++KXUcsgGTJk3C5s2b8dFHH0EQBAwZMgSffvopxo0bJ3U0sgPsuSEiIiJZYc8NERERyQqLGyIiIpIVuytuRFFEZWVli7tsEhERkTzYXXFTVVUFrVbb7Jb1REREJB92V9wQERGRvLG4ISIiIllhcUNERESywuKGiIiIZIXFDREREckKixsiIiKSFRY3REREJCssboiIiEhWWNwQERGRrLC4ISIiIllhcUNERESywuKGiIiIZIXFDREREckKixsiIiKSFRY3REREJCssboiIiEhWWNwQERGRrKikDkBE8vPFwSzJrv3wiFDJrk1E1oEjN0RERCQrLG6IiIhIVljcEBERkaywuCEiIiJZYXFDREREssLihoiIiGSFxQ0RERHJCosbIiIikhUWN0RERCQrLG6IiIhIVljcEBERkaywuCEiIiJZYXFDREREssLihoiIiGSFxQ0RERHJCosbIiIikhUWN0RERCQrLG6IiIhIVljcEBERkaywuCEiIiJZYXFDREREssLihoiIiGSFxQ0RERHJilUUNx988AHCw8Ph6OiIESNGICUlpV3P++qrryAIAqZNm9bFCYmIiMhWSF7cbNq0CQsXLsTSpUtx9OhRDBo0CAkJCSgsLLzh8zIyMrBo0SKMHTu2m5ISERGRLZC8uFmxYgXmz5+PxMREREdHY/Xq1XB2dsaaNWvafI5er8fvfvc7vPzyy4iMjOzGtERERGTtJC1uGhsbceTIEcTHx5uPKRQKxMfHIzk5uc3nLVu2DH5+fnj00Udveo2GhgZUVlY2+yIiIiL5krS4KS4uhl6vh7+/f7Pj/v7+yM/Pb/U5+/btw6effoqPP/64XddYvnw5tFqt+SskJOSWcxMREZH1knxaqiOqqqrwyCOP4OOPP4aPj0+7nrN48WJUVFSYv7Kzs7s4JREREUlJJeXFfXx8oFQqUVBQ0Ox4QUEBAgICWpyfmpqKjIwM3HvvveZjBoMBAKBSqXDhwgVERUU1e45Go4FGo+mC9ERERGSNJB25UavVGDp0KJKSkszHDAYDkpKSMHLkyBbn9+3bF6dOncLx48fNX1OnTsXtt9+O48ePc8qJiIiIpB25AYCFCxdi7ty5iIuLw/Dhw7Fy5UrU1NQgMTERADBnzhwEBQVh+fLlcHR0xIABA5o938PDAwBaHCciIiL7JHlxM3PmTBQVFWHJkiXIz89HbGwstm3bZm4yzsrKgkJhU61BREREJCFBFEVR6hDdqbKyElqtFhUVFXB3d5c6DpEsfXEwS7JrPzwiVLJrE5F14JAIERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGsqKQOQETyIYoiDmeWYevJPBRUNqC4ugEGUYSLRgVPZzX6BbqhX6A7nNX86CGirsNPGCK6ZTq9AV+lZOGj/6Yhu7SuxeOV9U24UlGPs1cqoRByMSLCG/H9/OGkVkqQlojkjsUNEd2SXy8W4eUfzyCtqAYA4KJWok+AGyJ8XOHjqoZKoUBNYxNyympxJq8SVyrqkZxWghM55ZgS0wOxIR4S/wREJDcsboioU+p1erz+83l89r8MAICXixrPxvfCg0ND8O9juS3O7+3vhjv6+uNyYTV+OpmHwqoGfH04GwWV9bgz2h8KQejmn4CI5IrFDRF1WE5ZLR5bdxjn86sAAPNGhWPhXb3h7uhw0+f29HPF03f0wi/nCvDrxSL8erEIJTWNmBkXAqWCBQ4R3ToWN0TUISdzyvHousMoqmqAj6sabz04CLf38evQaygVAhL6B8DPTYMtx3JxOrcCDgoBDwwN5ggOEd0yFjdE1G57Lxbh8Q1HUKfTo2+AG9YmDkOg1qnTrzc41BNOaiU2HsjEsexyOKuVmDQwEAILHCK6BbzPDRG1y+4LhXhs/WHU6fQY19sX3/yfkbdU2Jj0DXDHA0OCAQD7U0twKKPsll+TiOwbixsiuqnd5wvx+PojaGwyIKG/Pz6ZEwe3dvTXtNfgUE8kRPsDAH46mYe88pbLyYmI2ovFDRHdUEp6Kf7PxiNo1BswaWAA/vnwEKhVlv/oGNvbF30D3NBkEPFlShbqdXqLX4OI7AOLGyJq09m8Sjy67hAamgyY2NcP784aDAdl13xsKAQBM4YGw8PJASU1jfjxRF6XXIeI5I/FDRG1Kq+8DvPWpqCqvgnDwj3xwe+GdFlhY+KsVmHmsBAIAI5ll+PC1aXmREQdweKGiFqoaWjCo+sOo7CqAb39XfHJ3GFwdOierRLCvF0wKsobAPDd8Vw0cHqKiDqIxQ0RNaM3iPjjV8dw7kolfFzVWDNvGLROlmsebo87owPg5aJGRZ0O287kd+u1icj2sbghombeTbqEX84VQqNS4OM5cQj2dO72DGqVAtMHBwEwNjRz9RQRdQSLGyIy23OhEO/vugQAeP2BgRgc6ilZlihfV8QEayEC+PFkHkRRlCwLEdkWFjdEBMC4X9Szm45DFIHf3xaK6YODpY6Eu/sHwEEpILOkFidzK6SOQ0Q2gsUNEaGhSY8nPz+K8lodBgVr8eKUaKkjAQA8nNUY19sXALDtdD4amwwSJyIiW8Dihojw95/O4mROBTycHfDB74ZAo+qelVHtMa6XLzycHFBRp0NyarHUcYjIBrC4IbJz3x3LxcYDWRAEYOXMWEkaiG/EQanAnVe3Zvj1UhFqG5skTkRE1o7FDZEdyy6txd++Ow0AePqOXpjQx0/iRK0bFOKBAHdH1OsM+PVCkdRxiMjKsbghslMGg4hF35xAdUMT4sI88ceJvaSO1CaFICChv3H0JjmtBOW1jRInIiJrppI6AJEcfXEwS9LrPzwi9KbnrNmfjoPppXBWK/HOQ4OgVAjdkKzzevu7IdzbBRklNdhzoQjTrt4Hh4joehy5IbJDFwuq8Ob2CwCAv02ORpi3i8SJbk4QBHPvzZHMMpRx9IaI2sDihsjONDYZ8KdNx9HYZMDtfXwxe3iI1JHaLcLHBZG+LtCLIntviKhNLG6I7Mz7uy7hTF4lPJwd8MYDMRAE656Out7Evhy9IaIbY3FDZEdO5pTjg92XAQCvThsIP3dHiRN1HEdviOhmWNwQ2YkmvQHPf3sKBhG4d1APTI4JlDpSp3H0hohuhMUNkZ1Ysz8dZ68Yp6OW3msd2yt01rWjN3s4ekNE12FxQ2QHsktr8Y+dxt2+X7inH3xcNRInunW/jd6UcvSGiJphcUNkB17+8QzqdHrcFumFB+Ok3+3bEiJ8XBDl6wKDCOy5UCh1HCKyIixuiGRu9/lC/HKuECqFgFemDbC51VE3wt4bImoNixsiGWto0uPlH88AABJHh6Onn5vEiSwr/GrvjUEEV04RkRmLGyIZ+3RfOjJKauHrpsEzVrx31K24dvSGe04REcDihki2iqoa8MEu4z1tFt/TF26ODhIn6hoRPi6I9Lm6cuoiR2+IiMUNkWy9m3QRNY16xARrMS1W3ptM3tHPDwBwJKMMeeV1EqchIqmxuCGSocuF1fgyJRsA8MKkflBY+Y7ftyrSxxURV0dvVu1JlToOEUmMxQ2RDL3+83noDSLi+/njtkhvqeN0i4l9jaM3mw5l40oFR2+I7BmLGyKZySqpwS/nCqBUCHj+nr5Sx+k2kb6uCPd2QaPegNUcvSGyayxuiGRm57kCAMCMIcHo6ecqcZruNfFq782XKdnIr6iXOA0RSUUldQAispy0omqkFtVAKQgI9XbGFwezpI7UrSJ9XDAs3BOHMsqw+tdUvDS1v9SRiEgCHLkhkglRFM2jNnHhnvB0VkucqPsJgoBn43sDAL5IyUJBJUdviOwRixsimUgtqkFmSS1UCgET+vhJHUcyo6K8ERfmicYmA1b/yt4bInvE4oZIJnZf3TxyWIQXtE7yvGFfewiCgD/GG+/G/MXBLBRy9IbI7rC4IZKBrJIapBcbe23G9vSROo7kxvT0wZBQDzQ0GbD61zSp4xBRN2NxQyQDpm0HYkM94GGHvTbXEwQBf7rT2Huz8UAmcnnXYiK7wuKGyMblV9TjfH4VBADje/lKHcdqjOnpg5GR3mjUG7By50Wp4xBRN2JxQ2Tj/nvJOGrTP0gLHzeNxGmshyAI+MvdfQAA3x7NwaWCKokTEVF3YXFDZMMq63U4mVMBABjXi7021xsc6omE/v4wiMBb2y9IHYeIugmLGyIbdjCtBHpRRJiXM4I9naWOY5X+nNAHCgHYcbYAB9NKpI5DRN2AxQ2RjdLpDTiYXgoAGM0VUm3q6eeGmcNCAQCvbD0Hg0GUOBERdTUWN0Q26nh2OWob9fBwdkC/QHep41i1hXf2hqtGhVO5Ffj3sVyp4xBRF2NxQ2SDRFHE/1KLAQCjIr2hVAgSJ7Juvm4a/N/bewIA3tx+HrWNTRInIqKuxOKGyAZlltSioLIBDkoBQ8O8pI5jExJHhyPEywkFlQ14f9dlqeMQURdicUNkg1IyjL02g4I94KRWSpzGNjg6KLFkinGX8I/3puFyIZeGE8kVixsiG1PT0IRTucbl3yMivCVOY1vujPZHfD8/NBlEvPjdGYgim4uJ5IjFDZGNOZpVBr1BRJCHE4I8naSOY3OW3tsfGpUCyWkl+O44m4uJ5MgqipsPPvgA4eHhcHR0xIgRI5CSktLmuVu2bEFcXBw8PDzg4uKC2NhYbNiwoRvTEknHIIpIubr8e0QEe206I8TLGc9MNO4a/vKPZ1FU1SBxIiKyNMmLm02bNmHhwoVYunQpjh49ikGDBiEhIQGFhYWtnu/l5YW//vWvSE5OxsmTJ5GYmIjExERs3769m5MTdb+M4hqU1DRCo1IgJthD6jg2a8G4SEQHuqO8Vocl35+WOg4RWZjkxc2KFSswf/58JCYmIjo6GqtXr4azszPWrFnT6vkTJkzA9OnT0a9fP0RFReGPf1D3ilYAACAASURBVPwjYmJisG/fvm5OTtT9jmSWAQBigj2gVkn+62uzHJQKvPVgDFQKAT+fzsfWk1ekjkREFiTpp2NjYyOOHDmC+Ph48zGFQoH4+HgkJyff9PmiKCIpKQkXLlzAuHHjWj2noaEBlZWVzb6IbFGDTo/TecZG4qFhnhKnsX39e2jx5IQoAMBfvzuF/Ip6iRMRkaVIWtwUFxdDr9fD39+/2XF/f3/k5+e3+byKigq4urpCrVZj8uTJeP/993HnnXe2eu7y5cuh1WrNXyEhIRb9GYi6y6ncCuj0InxcNQhhI7FFPHVHLwwIMk5PLfz6OLdmIJIJmxzXdnNzw/Hjx3Ho0CG8+uqrWLhwIfbs2dPquYsXL0ZFRYX5Kzs7u3vDElnI0SzjlNTQUA8IAu9IbAlqlQLvzhoMJwcl/pdago/+myZ1JCKyAJWUF/fx8YFSqURBQUGz4wUFBQgICGjzeQqFAj17Gm+lHhsbi3PnzmH58uWYMGFCi3M1Gg00Go1FcxN1t5LqBmSU1EIAEBvKKSlLivJ1xUtTo/Hct6fw9vYLiAvzRFw4V6IR2TJJR27UajWGDh2KpKQk8zGDwYCkpCSMHDmy3a9jMBjQ0MDlnCRfx7LLAQA9/VyhdXKQOI38PBQXgnsH9UCTQcSTnx9FYRX7b4hsmeTTUgsXLsTHH3+MdevW4dy5c3jiiSdQU1ODxMREAMCcOXOwePFi8/nLly/Hzp07kZaWhnPnzuGdd97Bhg0b8Pvf/16qH4GoS4miiBNXi5vYEC7/7gqCIOD1+weil58rCqsa8NQXx6DTG6SORUSdJOm0FADMnDkTRUVFWLJkCfLz8xEbG4tt27aZm4yzsrKgUPxWg9XU1ODJJ59ETk4OnJyc0LdvX2zcuBEzZ86U6kcg6lJ55fUoqWmESiEgOtBd6jiy5aJRYdXvh2LaB/uRkl6KJd+fxmvTB7K/icgGCaKdba5SWVkJrVaLiooKuLvzfxTUNb44mGWx1/rPqSvYd7kYA4O0mD081GKvK1cPj7i192jn2QIs2HAYogg8d3dfPHF1uTgR2Q7Jp6WIqG0GUcTJHOOU1KBgrcRp7MOd0f5YMiUaAPDGtvP46WSexImIqKNY3BBZsYziGlTWN8HRQYHe/m5Sx7EbiaMjMG9UOABg4dcncDijVNpARNQhLG6IrNiJHOMdifv30EKl5K9rd3pxSjTi+/mjscmA+esPI6O4RupIRNRO/LQkslJ6g4izV7dbiAnilFR3UyoEvDc7FjHBWpTV6jBvbQp3ECeyESxuiKxURkkNahr1cHJQItLXVeo4dslZrcInc+MQ7OmEjJJazFubgsp6ndSxiOgmWNwQWakzV0dtogPdoVRwObJU/NwcseHREfBxVeNMXiUWrD+Mep1e6lhEdAMsboiskEEUcSbPuIP9gCDeskBqET4u+CxxOFw1KhxIK8UfvzqGJt7kj8hqsbghskLZpbWourpKKopTUlZhQJAWH80ZCrVSge1nCvC3707Dzm4TRmQzWNwQWaHTucYpqb4B7lwlZUVGRfngvdmxUAjAV4ey8faOC1JHIqJW8FOTyMqI105J9eAqKWtz94BAvDp9IADgg92p+HRfusSJiOh6LG6IrMyVinqU1+ngoBTQy59TUtZo9vBQ/DmhDwDg7z+dxXfHciVORETXYnFDZGXOXTGO2vTyc4MDp6Ss1pMTovCH0REAgD9vPoGDaSUSJyIiE35yElmZc/nG4qZfILdbsGaCIOBvk/th0sAA6PQiFmw4gtSiaqljERFY3BBZlfLaRuSV10MA0CeAS8CtnUIhYMVDsYgN8UBFnQ6PfnYIFbW8yR+R1FjcEFmR8/lVAIBQL2e4alQSp6H2cHRQ4pO5cQjyMN7F+I+bjkFv4BJxIimxuCGyIqZ+m36BHLWxJT6uGnz4yFA4Oiiw50IR3uEScSJJsbghshL1Oj3Siow7T7O4sT0DgrR444EYAMC/9qRi59kCiRMR2S8WN0RW4nJhNfSiCG8XNXzdNFLHoU64LzbIvIJq0TcnkFteJ3EiIvvE4obISlwqNPbb9A7gKilb9vw9fRETrEVFnQ7PfHkMOu5BRdTtWNwQWQFRFHGxwLiMuLcfixtbplYp8M/ZQ+CmUeFIZhn+ueuy1JGI7A6LGyIrUFjVgIo6HVQKAZG+LlLHoVsU6u2M1+43btHwz92XcTKnXOJERPaFxQ2RFbhUYJySivBx4V2JZeLeQT0wJSYQeoOIhV+fQL1OL3UkIrvBT1EiK3Cx8OqUlD+npOTk7/cNgK+bBpcLq/GPnReljkNkN1jcEEmsscmA9GLjEnBulCkvni5qvH51euqTfek4k1chcSIi+8Dihkhi6cXV0BtEeDg7wNeVS8DlZmI/f0y+Oj31wpZTvHsxUTdgcUMksWtXSQmCIHEa6gpLp0TDTaPCiZwKbDyQKXUcItljcUMksYtXm4l7c0pKtvzcHfGXe/oCAN7afgFFVQ0SJyKSNxY3RBIqqW5ASU0jFAIQ6cviRs5+NzwUMcFaVDc0ce8poi7WqeImLS3N0jmI7JJplVSYtwscHZQSp6GupFAIWHpvNABg0+FsnM5lczFRV+lUcdOzZ0/cfvvt2LhxI+rr6y2dichuXDJPSXEJuD0YGuaF+2J7QBSBl388A1FkczFRV+hUcXP06FHExMRg4cKFCAgIwOOPP46UlBRLZyOStSa9AalFpvvbcErKXjx3d184OihwKKMM28/kSx2HSJY6VdzExsbi3XffRV5eHtasWYMrV65gzJgxGDBgAFasWIGioiJL5ySSnYySWuj0Itw0KgS4O0odh7pJDw8nLBgbCQB4c/sFNHFjTSKLu6WGYpVKhfvvvx/ffPMN3njjDVy+fBmLFi1CSEgI5syZgytXrlgqJ5HsmKakevlzCbi9mT8uEl4uaqQV1WDzkRyp4xDJjupWnnz48GGsWbMGX331FVxcXLBo0SI8+uijyMnJwcsvv4z77ruP01VEbbh0tZmYdyW2rC8OZkkdoV1GRnpj66kreO0/51CvM0Ctsszi1YdHhFrkdYhsWaeKmxUrVmDt2rW4cOECJk2ahPXr12PSpElQKIy/nBEREfjss88QHh5uyaxEslHd0IT8SmMzfhSXgNulERFe2J9ajPJaHQ6klWBcb1+pIxHJRqf+qbBq1So8/PDDyMzMxHfffYcpU6aYCxsTPz8/fPrppxYJSSQ3pr2kAtwd4aq5pQFUslEqpQLxff0BAHsvFaGhibuGE1lKpz5Vd+7cidDQ0BYFjSiKyM7ORmhoKNRqNebOnWuRkERyY1olFenrInESktKgEA/svlCIkppGHEwr5egNkYV0auQmKioKxcXFLY6XlpYiIiLilkMRyV3a1eKGU1L2TakQcHtfPwAcvSGypE4VN23deKq6uhqOjlzSSnQjFXU6FFc3QgAQ7s2RG3s3KNgD3i5q1DbqcSCtVOo4RLLQoWmphQsXAgAEQcCSJUvg7Oxsfkyv1+PgwYOIjY21bEIimTGN2gR5OsFJzS0X7J1p9GbzkRzsu1yMUVHecFBy2z+iW9Gh4ubYsWMAjCM3p06dglqtNj+mVqsxaNAgLFq0yLIJiWQmlVNSdJ1BwR745WwByut0OJpVhhER3lJHIrJpHSpudu/eDQBITEzEu+++C3d39y4JRSRXoigitci4UorNxGSiVAgY3dMHW09dwb5LxRgW7gUFb+xI1GmdGvtcu3YtCxuiTiitaURFnQ5KQUCYF4sb+k1cuCecHJQoqWnE2bxKqeMQ2bR2j9zcf//9+Oyzz+Du7o7777//hudu2bLlloMRyZFp1CbEy9lid6QledColLgt0gu7LxRh76Ui9O/hzm05iDqp3cWNVqs1/6JptdouC0QkZ7/123DUhloaGeWD/14qRk5ZHTJKahHhw78nRJ3R7uJm7dq1rf43EbWPKIrmlVKRbCamVrhqVBgS5omU9FLsvVjE4oaokzo1Ll5XV4fa2lrz95mZmVi5ciV27NhhsWBEclNQ1YCaRj0clAJCvJykjkNWamxPHwgALhRUmfcfI6KO6VRxc99992H9+vUAgPLycgwfPhzvvPMO7rvvPqxatcqiAYnkwjRqE+7tApWC/TbUOm9XDfr3MC7Y2HepSOI0RLapU5+wR48exdixYwEAmzdvRkBAADIzM7F+/Xq89957Fg1IJBephby/DbWPaY+p49nlqKjTSZyGyPZ0qripra2Fm5sbAGDHjh24//77oVAocNtttyEzM9OiAYnkQG8QkVbM+9tQ+wR7OiPCxwUGETiQViJ1HCKb06nipmfPnvjuu++QnZ2N7du346677gIAFBYW8v43RK24UlGHhiYDHB0U6OHBfhu6uVFRxrsUH8oohU5vkDgNkW3pVHGzZMkSLFq0COHh4RgxYgRGjhwJwDiKM3jwYIsGJJKD9KujNuHeLrzzLLVL3wB3aJ0cUNuox6mcCqnjENmUThU3M2bMQFZWFg4fPoxt27aZj0+cOBH/+Mc/LBaOSC5MxQ2X9lJ7KRUCbovwAgAkp5VAFEWJExHZjg7tLXWtgIAABAQENDs2fPjwWw5EJDcGUURGCYsb6ri4cC8knS9EbnkdssvqEOrlLHUkIpvQqeKmpqYGr7/+OpKSklBYWAiDofl8cFpamkXCEclBQWU96nUGqFUKBGrZb0Pt56JRISbYA0ezypCcWoxQr1CpIxHZhE4VN4899hh+/fVXPPLIIwgMDOT+J0Q3YJqSCvNyhlLB3xXqmJFR3jiaVYbTuZWYNFAHN0cHqSMRWb1OFTc///wztm7ditGjR1s6D5HsZLDfhm5BkIcTQr2ckVVai5SMUkzs6y91JCKr16mGYk9PT3h5eVk6C5HsiKKI9BLjViXh3ixuqHNGRhqXhaekl6LJwGXhRDfTqeLm73//O5YsWdJsfykiaqm4uhE1DU1QKQQEe7Lfhjqnf5A73DQqVNU34UxepdRxiKxep6al3nnnHaSmpsLf3x/h4eFwcGg+B3z06FGLhCOydaZ+mxAvZ6iU3E+KOkelUGBYhBd2nS/EgdQSDAr2kDoSkVXrVHEzbdo0S+cgkiUuASdLGR7hhT0XCpFZWouCynr4uztKHYnIanWquFm6dKmlcxDJjiiKze5MTHQr3B0d0DfAHWevVCIlvRT3DuohdSQiq9XpcfLy8nJ88sknWLx4MUpLSwEYp6Nyc3MtFo7IlpXX6lBRp4NCAG++RhYx/Oodi49ll6GxiY3FRG3p1MjNyZMnER8fD61Wi4yMDMyfPx9eXl7YsmULsrKysH79ekvnJLI5plGbYE9nqFXst6Fb19PPFZ7ODiir1eFUbgWGhnlKHYnIKnXqE3fhwoWYN28eLl26BEfH3+Z9J02ahL1791osHJEtSy/hlBRZlkIQMCzcOHqTkl4icRoi69Wp4ubQoUN4/PHHWxwPCgpCfn7+LYcikoPfbt7HKSmynKFhnlAIQHZZHa5U1Ekdh8gqdaq40Wg0qKxsea+FixcvwtfX95ZDEdm6yjodSmoaIQAI48gNWZCbowOiA90BGG/qR0Qtdaq4mTp1KpYtWwadTgcAEAQBWVlZeO655/DAAw9YNCCRLTJNSQV6OMLRQSlxGpKb4RHGOxYfzy5nYzFRKzpV3Lzzzjuorq6Gr68v6urqMH78ePTs2RNubm549dVXLZ2RyOaYp6Q4akNdINLXBV4uajQ0GXAyp1zqOERWp1OrpbRaLXbu3In9+/fjxIkTqK6uxpAhQxAfH2/pfEQ2yXx/G968j7qAQhAwPNwL287kIyWjFHHh3OuP6FodHrkxGAxYs2YNpkyZgscffxyrVq3Cvn37kJeXB1EUOxXigw8+QHh4OBwdHTFixAikpKS0ee7HH3+MsWPHwtPTE56enoiPj7/h+UTdrbSmEYVVDQC4Uoq6zpAwTygFATlldcgrZ2Mx0bU6VNyIooipU6fiscceQ25uLgYOHIj+/fsjMzMT8+bNw/Tp0zscYNOmTVi4cCGWLl2Ko0ePYtCgQUhISEBhYWGr5+/ZswezZ8/G7t27kZycjJCQENx11128eSBZDVOTp5+bBi6aTg2OEt2Uq0aF6B7GxuJDGWwsJrpWh4qbzz77DHv37kVSUhKOHTuGL7/8El999RVOnDiBX375Bbt27erwDfxWrFiB+fPnIzExEdHR0Vi9ejWcnZ2xZs2aVs///PPP8eSTTyI2NhZ9+/bFJ598AoPBgKSkpA5dl6irmIob7idFXc10z5sTOeXQ6dlYTGTSoeLmyy+/xAsvvIDbb7+9xWN33HEHnn/+eXz++eftfr3GxkYcOXKkWa+OQqFAfHw8kpOT2/UatbW10Ol08PJqfc65oaEBlZWVzb6IulJKhvHmauy3oa4W6esCT2cH1OsMOJ1bIXUcIqvRoeLm5MmTuPvuu9t8/J577sGJEyfa/XrFxcXQ6/Xw9/dvdtzf37/dNwN87rnn0KNHjzabmZcvXw6tVmv+CgkJaXc+oo6qrNfhbJ6xgOZKKepqCkHAkKtbMBzOLJM4DZH16FBxU1pa2qIQuZa/vz/KyrrvF+z111/HV199hX//+9/NtoG41uLFi1FRUWH+ys7O7rZ8ZH+OZJTBIALeLmq4OzlIHYfswNBQTwgwrtArqW6QOg6RVehQt6Ner4dK1fZTlEolmpqa2v16Pj4+UCqVKCgoaHa8oKAAAQEBN3zu22+/jddffx2//PILYmJi2jxPo9FAo9G0OxPRrTh4td+GU1LUXTyc1ejl74qLBdU4wtEbIgAdLG5EUcS8efPaLBYaGjr2rwa1Wo2hQ4ciKSkJ06ZNAwBzc/BTTz3V5vPefPNNvPrqq9i+fTvi4uI6dE2irmTazJBTUtSdhoZ5GYubrDI06Q1QKbkLPdm3DhU3c+fOvek5c+bM6VCAhQsXYu7cuYiLi8Pw4cOxcuVK1NTUIDEx0fx6QUFBWL58OQDgjTfewJIlS/DFF18gPDzc3Jvj6uoKV1fXDl2byJJqG5twMsfY1MmRG+pO/QLd4KxWoqq+Cb9eLMLEfm23DxDZgw4VN2vXrrV4gJkzZ6KoqAhLlixBfn4+YmNjsW3bNnNvT1ZWFhSK3/4VsmrVKjQ2NmLGjBnNXmfp0qV46aWXLJ6PqL2OZZWjySCih9YRns7st6Huo1IoMDjEA/tTS7DpUDaLG7J7gtjZ2wrbqMrKSmi1WlRUVMDd3V3qOCQjK3ZexHtJlzAttod5Y0Oi7lJQWY93ky5BpRCQvHgifN3Ya0j2ixOzRBZi6rdhYUNS8Hd3RIinE5oMIrYczZE6DpGkWNwQWUBDkx7Hsoy7Mw+P4CaGJA3TBpqbDmV3eq8/IjlgcUNkASdzKtDQZICPqxpRvmwmJmnEBGnhrFYirbiGN/Uju8bihsgCTPtJDY/wgiAIEqche6VxUGLywEAAxtEbInvF4obIAkw37xsezikpktbMYcYtZraevIKqep3EaYikweKG6BY16Q04kmEauWEzMUlraJgnIn1dUKfT46eTV6SOQyQJFjdEt+hMXiVqGvVwd1Shb4Cb1HHIzgmCgFlXR2++4tQU2SkWN0S36KB5CbgXFAr225D07h8SDJVCwInsclzIr5I6DlG3Y3FDdIsOphmnpEZwSoqshI+rBhP7+QFgYzHZJxY3RLdAbxDNK6VGRLKZmKyHqbH438dy0NCklzgNUfdicUN0C85dqURVQxPcNCpEB3I7D7Ie43r5wt9dg7JaHXaeLZA6DlG3YnFDdAsOpBn7beLCPaFS8teJrIdKqcCDQ42jN5yaInvDT2OiW3DQPCXFfhuyPg/FGYubfZeLkVNWK3Eaou7D4oaokwzX9ttwPymyQqHezhgZ6Q1RBDYf4WaaZD9Y3BB10vn8KlTU6eCiVmJAkFbqOEStMjUWf3M4BwYDN9Mk+8DihqiTTPe3GRruBQf225CVuntAANwcVcgtr8P+1GKp4xB1C34iE3WSqZmYU1JkzRwdlJgWGwSAjcVkP1jcEHXCtf02t7GZmKycaWpqx5kClNU0SpyGqOuxuCHqhEuF1Sir1cHJQYmYYPbbkHUbEKRFdKA7GvUGfHc8V+o4RF2OxQ1RJ5j7bcI82W9DNsE0erPpUDZEkY3FJG/8VCbqBPbbkK2ZFhsEtUqB8/lVOJVbIXUcoi7F4oaog0Txmn6bKPbbkG3QOjvg7v4BANhYTPLH4oaog1KLqlFc3QiNSsF+G7IppqmpH47noa6Rm2mSfLG4Ieqg5DTjqM2QUE9oVEqJ0xC138hIb4R4OaGqoQk/n74idRyiLsPihqiDDl7tt+EScLI1CoXAzTTJLrC4IeoAURRxIM20WSabicn2zBgaDEEwbvqaUVwjdRyiLsHihqgDLhZUo7i6AY4OCgwO9ZA6DlGH9fBwwrhevgCArw9z9IbkicUNUQfsu2zcm2d4hDf7bchmmRqLNx/JQZPeIHEaIstjcUPUAfuvFjdjerLfhmxXfD9/eLmoUVjVgF8vFkkdh8jiWNwQtZNObzDfvG90Tx+J0xB1nlqlwPTB3EyT5IvFDVE7Hc8uR22jHl4uavQLcJc6DtEtMU1N7TpfiKKqBonTEFkWixuidtp3yTglNSrKGwqFIHEaolvT298NsSEeaDKI2HI0R+o4RBbF4oaonf6XaixuOCVFcmHeTPMwN9MkeWFxQ9QO1Q1NOJZVDgAYw+KGZGJKTCCcHJRIK6rBkcwyqeMQWQyLG6J2SEkvQZNBRKiXM0K8nKWOQ2QRbo4OmBwTCICNxSQvLG6I2mHfJa6SInkyTU1tPXUF1Q1NEqchsgwWN0Tt8Nv9bVjckLzEhXki0tcFtY16/HQiT+o4RBbB4oboJgqr6nGhoAqCAIyM4s37SF4EQcBDcb81FhPJAYsbopv432XjlFT/Hu7wclFLnIbI8u4fEgSlQsCxrHJcKqiSOg7RLWNxQ3QTpv2k2G9DcuXn5ojb+/gBYGMxyYNK6gBE1kwURfbbkE354mBWp54XqHU0Pj8lC6HezlApOvdv34dHhHbqeUSWxJEbohtIK67BlYp6qFUKDAv3kjoOUZfp7e8GN40KtY16nL/CqSmybSxuiG7AtOXC0FBPODooJU5D1HWUCgGDQz0BAIczSyVOQ3RrWNwQ3cDuC4UAgPF9fCVOQtT14sKMxc2lgmpU1OkkTkPUeSxuiNpQr9MjOdW4UsrUbEkkZz5uGoR7O0MEuB0D2TQWN0RtSE4rQUOTAT20jujt7yp1HKJuERdm7C07klkKAzfTJBvF4oaoDXvOG6ekJvT1gyAIEqch6h4DgrTQqBQoq9UhvbhG6jhEncLihqgVoihi94UiAJySIvuiVikQE+wBADicwcZisk0sbohakV5cg6zSWqiVCozilgtkZ0yNxWfyKlHbyM00yfawuCFqhWnUZkSkF1w0vNcl2ZdgTycEuDuiySDiWFa51HGIOozFDVErdp0vAACM780l4GR/BEHA8AhjY3FKeilENhaTjWFxQ3SdilodDqQZew3ujPaXOA2RNGJDPOCgFFBU3YCMklqp4xB1CIsbouvsvlAIvUFEH383hHm7SB2HSBKODkoMutpYfIiNxWRjWNwQXWfH2XwAHLUhMk1Nnc6tQG0DG4vJdrC4IbpGvU6PPVebie/qz+KG7FuQhxN6aI2NxUezeMdish0sboiukZxagtpGPQLcHTEwSCt1HCJJCYKAYabG4owyNhaTzWBxQ3QN05RUfDTvSkwEALHBHlCrFCiubuAdi8lmsLghukpvELHzrHHLhbuiAyROQ2QdNNc0FqewsZhsBIsboqsOppeguLoBWicH3BbJuxITmZgai8/kVqKajcVkA1jcEF3108krAICE/v5Qq/irQWQS5OGEIA8n6EURx9hYTDaAn+BEAJr0Bmw7bey3mRLTQ+I0RNZneDjvWEy2g8UNEYD/pZagtKYRXi5qbpRJ1IqYEC00KgVKahqRxsZisnIsbogAbL06JXX3gAColPy1ILqeRqXEoJCrjcXpbCwm68ZPcbJ7jU0GbDtjmpIKlDgNkfUyTU2dzWNjMVk3Fjdk9369WISKOh183TQYEcEpKaK29PBwQrCnsbH4SCYbi8l6sbghu/fN4WwAwLTYHlAqeOM+ohv5rbG4BAY2FpOVYnFDdq2kugG7zhtv3DdjaIjEaYisX0ywBxwdFCir1eFiQZXUcYhapZI6AMnTFwezJL3+wyNC23Xed8fz0GQQEROsRZ8Aty5ORWT71CoF4sK8sO9yMQ6klaBvgLvUkYha4MgN2bXNR3IAAA8ODZY4CZHtGBHhBQHAxYJqlFQ3SB2HqAUWN2S3TudW4NyVSqiVCtw7iDfuI2ovb1cNevsbRzoPpJVInIaoJcmLmw8++ADh4eFwdHTEiBEjkJKS0ua5Z86cwQMPPIDw8HAIgoCVK1d2Y1KSmy9TjFNnd/b3h4ezWuI0RLbFtP/akawyNDYZJE5D1Jykxc2mTZuwcOFCLF26FEePHsWgQYOQkJCAwsLCVs+vra1FZGQkXn/9dQQEcNdm6ryKOh22HM0FAPx+RJjEaYhsTy9/V3i5qFGvM+BEdrnUcYiakbS4WbFiBebPn4/ExERER0dj9erVcHZ2xpo1a1o9f9iwYXjrrbcwa9YsaDSabk5LcvLN4WzU6fTo4++G2yK9pI5DZHMUgmAevUlOK+F+U2RVJCtuGhsbceTIEcTHx/8WRqFAfHw8kpOTLXadhoYGVFZWNvsi+2YwiNhwIBMAMHeUcYqTiDpuaKgnHJQC8ivrkVFSK3UcIjPJipvi4mLo9Xr4+/s3O+7v74/8/HyLXWf58uXQarXmr5AQ3svE3u25WIjMklq4O6owbTAbiYk6y0mtRGyIJwA2FpN1kbyhuKstXrwYFRUV5q/s7GypI5HEPt2XDgCYOSwEzmre6onoVpimdc/kVaCyTidxGiIjyYobHx8fKJVKFBQUX7X3IwAAHsZJREFUNDteUFBg0WZhjUYDd3f3Zl9kv45klmH/5RKoFALmjgqXOg6RzQvUOiHc2xkGETiQztEbsg6SFTdqtRpDhw5FUlKS+ZjBYEBSUhJGjhwpVSySufd3XQIAPDAkGMGezhKnIZKH0T19AAAH00pR16iXOA2RxNNSCxcuxMcff4x169bh3LlzeOKJJ1BTU4PExEQAwJw5c7B48WLz+Y2NjTh+/DiOHz+OxsZG5Obm4vjx47h8+bJUPwLZkBPZ5dhzoQhKhYAnb4+SOg6RbPQLdIeXixp1Oj2+PZojdRwiafeWmjlzJoqKirBkyRLk5+cjNjYW27ZtMzcZZ2VlQaH4rf7Ky8vD4MGDzd+//fbbePvttzF+/Hjs2bOnu+OTjXl/l7EIvi+2B8K8XSROQyQfCkHAqChv/HTyCtbsS8fDw0OhUHAVIklH8m7Kp556Ck899VSrj11fsISHh/NeCtQpB9NK8Mu5AigE4P/e3lPqOESyMzTUE7+cK0BacQ12XyjExH7+N38SUReR/WopIr1BxLKfzgIAZg0PRZSvq8SJiORH46DEsHDjyinTikQiqbC4IdnbfCQbZ/Iq4eaowv+7s7fUcYhka2SkN5QKAf9LLcGZvAqp45AdY3FDslZZr8Nb2y8AAP44sRe8XbltB1FX8XBWY/LAQAAcvSFpsbghWXv5h7Morm5EpI8L5owMlzoOkew9NjYCAPDjiTwUVNZLnIbsFYsbkq1tp/Px7dEcKATgzRkxUKv4152oq8UEe2B4uBd0ehHrkzOkjkN2ip/2JEtFVQ144d+nAACPj49CXDh3/ibqLo9eHb3ZkJyJ6oYmidOQPWJxQ7LTpDfgiY1HUFrTiH6B7vhTPJuIibpTfD9/RPq6oLK+CZ8fyJQ6DtkhFjckK6IoYsuxXBzOLIObowrvz47ldBRRN1MqBDwx3ngX8I//m456HbdkoO7FT32SDVEUsfNcAY5nl0OpELDqd0PR089N6lhEdmna4CAEeTihuLoB3xzOljoO2RkWNyQLoijiP6euYM+FIgDA3+8bgDG9fCRORWS/HJQKPD4+EgCw+tc06PQGiRORPWFxQzZPpzfg26M52J9aAgCYPDAQD48IlTgVET0UFwIfVw1yy+vw/9u78/CoyoPv49+ZSSb7DtkgC2HHsEPYXEAQVKDFt4UWW6RocSlaaa5apHVpn+oLVqz4IFYUi61CUYvAq09lKbKIIkvCDgkQlhBgQkJCloFkyMx5/8BGeUARzMxJJr/PdXGJZ85kfreQzM9z7rnv5TtOmh1HmhGVG2nSHJU1vLLuELmFZ7EAP+zVmkHtdMVGpDEIDrTVr3vzyrpDuD3aG1B8Q+VGmqSaC25W7nXwytpDFFfWEh4UwM8GptMrLcbsaCLyFT/tn0ZUSCCHS5ys3OswO440Eyo30qRU19axLv80L6zKZ/2BEuo8Bh0Swvnl0Pa0T9DkYZHG5j//4wEwd+0hDENXb8T7AswOIHI1511u8hyV7DtVSZ6jqv7SdovwIG6/IZHOSRFYLBaTU4rI1/nZwHRe/+Qwe09Wsi6/hCGd4s2OJH5O5UYapXKnq77QHCl18tVb9a1jQhiQEUe31tHYrCo1Io1dTJidCf3TmLfhMLNW5XNLh5ZY9b0rXqRyI42CxzA4XnaOPEcVeY5KiitrL3k8PiKILsmR3JAcRavoEJNSisj1euCWtizcXMjek5V8tMfByG5JZkcSP6ZyI6Y6XVlDTmE5O46fparmyz1oLEBqXChdkiLpnBRJi/Ag80KKyHcWG2Zn8k0ZvPjvA7ywOp8RNyQQYNO0T/EOlRvxOcMwOFLqZMPBEg4UV9cfDw600iEhgs6JkbRPCCfUrr+eIv7kvpva8LdNRzlc4uT93BOM65tidiTxU3r3EJ9yVNTwr92nOFRysdRYgE5JkfROjaZDYgQBVv2fnIi/Cg8K4BeD2/LM/+xn9r8P8P2eyQQF2MyOJX5I5UZ8wlXnYeU+B58XnMHg4sZ6fdJiuLFdC+J0y0mk2fhp/zTmf3KEkxU1LNpcyKRBbcyOJH5I5Ua8rrDsHO9tO84ZpwuAzORIbs9MIjbM7rXXXLS50GtfW0SuX3CgjV8Obc9vl+7m5Y8PMa5PCmFBeiuShqV7AOI1hmHw+eEzvLahgDNOF5HBFxfzurtfmleLjYg0bmP7tCY9LpQzThcLPj1idhzxQyo34hV1Hg9Lt5/g/+08iceAzFZRPDq0Ax20irBIsxdos/Kr2zoAMG/9YUqra6/yDJFro3IjDe6cq463Pz/GtmPlWIDbb0hkfN8UQuyaOCgiF43ulkxmq0iqaut4cfUBs+OIn1G5kQZVce4CE97YwoHiagJtFiYMSOPmDi21PYKIXMJqtfDkyC4A/GNLIXmOSpMTiT9RuZEGU1VzgXsWbCHnWDnBgVbuHdSGTomRZscSkUaqX0Ycd2Qm4jHgmQ/3a1NNaTAqN9IgzrnquPfNrew8fpaY0EAm35RBWlyY2bFEpJGbfkdn7DYrGw+VsmpfsdlxxE+o3Mh35qrz8MBbOWw9Wk5kcABv3dePpCjt/yQiV5caF8rkmy+udfNfH+zjvMttciLxByo38p0YhsHj7+/ik4OlhNpt/O3eLDJbRZkdS0SakClD2tEqOoQTZ8/z8tqDZscRP6ByI9/Jn1cf4P3cE9isFub+pBc9U2PMjiQiTUyoPYCnRl+cXPzahsMUlFRf5Rki30zlRq7b0u1FzPn4EAD/965MhnSMNzmRiDRVw7skMKRjSy64Daa/vxuPR5OL5fqp3Mh12V5YzrQluwH4xeC2/KhvqsmJRKQps1gs/Nf3Mwm129hypIx/bNUWKnL9VG7kmhVX1vDAWzm46jwM65zAr4d3NDuSiPiBlNjQ+p8nM/+Vh6OixuRE0lSp3Mg1ueD28PCiXE5X1dIhIZzZP+6B1aoF+kSkYUwcmE7P1Giqauv47dLdWvtGrovKjVyT5z7KY+vRciKCApg3oQ/h2s1XRBqQzWrhTz/ohj3Aysd5p1m89bjZkaQJUrmRb+1fu08xf+PFHXxnjetOmxZapE9EGl77hAge++L21B8/3MexM06TE0lTo3Ij30pBSTW/+ecuAB64OYMRNySanEhE/Nl9N7ahX5tYzrnc/OqdHdS5PWZHkiZE5Uau6pyrjofezqG6to6sNrE8NkITiEXEu6xWCy+M605EUAC5hWf5s3YOl2ugCRPyjQzD4HdL93CguJqWEUG8fHdPAmzqxCJyZYs2N+xHuEd1T+YfWwp5ZV0B51xuOiREfO25d/fTkhRykd6l5Bv9Y8txlm6/uALxy+N7Eh8RbHYkEWlGuraKol+bWADe3XacivMXTE4kTYHKjXytPScq+P0HewF4bERH+mXEmZxIRJqjO7smkRQVzDmXm4Wbj3FB82/kKlRu5Ioqay7wi4W5XyzUF8/9N2WYHUlEmqlAm5Wf9EsjJNBGUfl5lm0/ofVv5Bup3MhlDMPgsfd2Ulh2jlbRIcwa210L9YmIqWLD7IzPSsVqge3Hz7LxUKnZkaQRU7mRy/z106Os3FuM3WbllZ/0IjrUbnYkERHaxYdzR2YSAB/tcbCr6KzJiaSxUrmRS+QcK2fGv/YD8MSoznRPiTY5kYjIlwa2jWPAF/P/3ssp4kipFviTy6ncSL0yp4uHF+VS5zEY2S2JCf3TzI4kInIJi8XCyG5JdEmKxO0x+Pumo5woP292LGlkVG4EAI/HIPvdHZyqqCGjRRjP/aAbFovm2YhI42O1WBjXJ4X0uFBq6zz89dMjOCq1g7h8SeVGAHh57SHW5ZcQFGBl7k96aUNMEWnU7AFW7hmQTuuYEM5fcPPGxiPsP1VpdixpJFRuhBV7HPVLm/9xTCadkyJNTiQicnXBgTYmDWxDclQwzto6fvza5+w4rknGonLT7OU5Ksl+dwcAPxuYzrg+KSYnEhH59kLsNu67MYPU2FAqzl/gJ69/zicHS8yOJSZTuWnGypwufv63bZxzuRnULo4nRnY2O5KIyDULsduYNCidQe3icLrcTFqwlcVbGnaPK2laVG6aKVedh4fezqGo/DxpcaHMvbuXNsQUkSYrKMDGX3/Wl7t6tqLOY/D4+7t55sN91GmrhmZJ72bNkGEY/OGDvWw+UkZ4UADz7+mjhfpEpMkLCrDx53Hd+eXQ9gDM33iEu+dv5nSVPknV3KjcNEPzNhxm4eZCLBZ46cc9aJ8QYXYkEZEGYbFYyL6tA6/+9OKnPrccKePOlz5hzf5is6OJD6ncNDNLcoqY+VEeAL+7szNDOyeYnEhEpOHdnpnE8ocH0SkxgtJqF/f9bRvT399NZc0Fs6OJD6jcNCNr9hczbckuAO6/OYOfa6dvEfFjbVuGs2zKIH5+YxsA/rGlkNv+vJ4Ve05pV3E/p3LTTKw/UMJDb1/cWmFMj2Qev72T2ZFERLwuONDGE6O6sGhyP9LjQimurOXBt3OZ8MYW8hxa9M9fqdw0A58eKuX+v2/D5fZw+w2JPD+2O1artlYQkeZjYNsWrJh6Mw8PaYfdZmXjoVLufOkTst/ZwcHiKrPjSQNTufFzq/Y6mPTmVmrrPAzrHM9/j+9JoD7yLSLNUHCgjV+P6Mi/s2/hzq6JeAx4f/sJbntxAw+8tY1dRVrd2F9YjGZ247GyspKoqCgqKiqIjPTvbQaW5BTxmyW7cHsMhndJYM7dPQkKsPnktRdt1gJaIuJbd/dLvabzdxWd5ZW1BazY66g/NqhdHOOzUrmtS4LPfl5Kw1O58UMej8HsNQf57zUHAfg/vVrxpx908+kifSo3IuJr11pu/uNgcRV/WVfA8p0ncXsuviVGhwYypkcrxvVJoUuyf75X+DOVGz/jrK3jN0t28T+7TgHwwM0ZTLu9k8/n2KjciIivXW+5+Y/jZed4Z+tx/plThKPyy4X/2seHM/yGBIZ3SaRrqyjNWWwCVG78SJ6jkikLcykocRJos/DsmK6M62vORpgqNyLia9+13PyH22PwycES3ttWxKp9Di64v3ybTIwMZmjneG5q34IBGS2ICg1skNeUhqVy4wfcHoO/fXaU51bkUVvnISEyiDnje5HVJta0TCo3IuJrDVVuvqri/AXW5Z9m1d5i1uWfxuly1z9msUDXVlEMbNuCfhmx9EqNISpEZacxULlp4g4WV/H4+7vJOVYOwC0dWvLncd2JCw8yNZfKjYj4mwtuD4dLqskvrqagpJqSqtrLzumQEE7vtBh6p8XSOy2G9LhQLBbdxvI1lZsmqszp4sXVB1i0pRC3xyA8KIDpd3ZifN/URnE/WOVGRPxdxfkLHC65WHSOnTnHGafrsnPiwux0ax1F19bRdGsVRbfWUcRHBpuQtnlRuWliSqtrmf/JEd7adLT+8uhtXRL4w/duIDk6xNxwX6FyIyLNzfAbEsg9Vk5OYTk5R8vZdaICV53nsvMSIoPo2ir6YulpFUXX1lG0MPlqu79RuWkCDMNgx/GzLNxcyAc7T1L7xTdLl6RInhjVmYFtW5ic8HIqNyLS3PzvOT+1dW72naxk94kKdhVVsLuogoOnq/Bc4V03PiKIzkmRdEqKoHPixX+2bRmuRVevU4DZAeTKDMMgz1HFij0OPth1ksMlzvrHuqdE88iQdgztHK97uSIijVRQgI2eqTH0TI2pP3bOVce+k5UXy86JCnYVneVwqZPTVbWcriph/YGS+nMDbRbaxUfQOTGCTkkRtIsPJ6NFOK1jQny6bllT1CjKzdy5c3n++edxOBx0796dOXPmkJWV9bXnv/feezz55JMcPXqU9u3b89xzz3HnnXf6MHHDuzhRzUluYTlbj5ax8WApp78yWS0owMrIbkn8tH8aPVOiVWpERJqgUHsAfdJj6ZP+5adZq2vryHdUkeeoJO9UFftPVZLnqKK6to79pyrZf6oStn/5NQJtFlJjQ8loGU5GizDatAijdUwoSdHBJEeFEGLXysqml5t33nmH7OxsXn31Vfr168fs2bMZMWIE+fn5xMfHX3b+Z599xvjx45kxYwajRo1i0aJFjBkzhtzcXDIzM00Ywbd3zlWHo6KG4spaiitrOHbmHAdOV3GwuIojpc5L1lIACAm0MbBtHKO6JzGscwIRwfqIoYiIvwkPCvjiE1ZfXuExDIOi8vP1RSfPUcnhEidHSp3U1nkoKHFS8JUr+l8VG2YnOTqYpKgQkqKCiQ2zExdmJzYsiJiwQOLCgogNsxMdGui3t71Mn3PTr18/+vbty8svvwyAx+MhJSWFRx55hMcff/yy83/0ox/hdDr58MMP64/179+fHj168Oqrr1719bw15+bYGSdvbTpGVU0d1bV1VNXWUV1zgeraOqpr6qj84vg3CbPb6No6ij5psfTLiKVveizBgU2zgWvOjYg0N95YZ+d/83gMTlXWcLikmiOlTg6XODl6xsnJs+c5UX7+knV4vg17gJXwoADCgmyE2QO++H0AwYFW7AE27DYr9gDLF/+8+Cvwi98HWq1YrRZsFrBaLVgtFmxWCzaLhZYRQQzpdPkFCl8x9cqNy+UiJyeH6dOn1x+zWq0MGzaMTZs2XfE5mzZtIjs7+5JjI0aMYNmyZVc8v7a2ltraL2/vVFRUABdLTkM65ijntTV7r3peiN1KQkQw8RHBJEYF0y4+jHbxEbRtGUZydMglt5tc5524zjdoTJ8556wyO4KIiE819PvK14mwQveEILonBAFf3t4yDIPKmjocFec5VVGDo+I8xZW1lJ+7QPm5WsqdFyg/56Lc6aKipg7DgJpaqHFCaQNn7N46it7J/Rv4q14UERFx1akZppab0tJS3G43CQkJlxxPSEggLy/vis9xOBxXPN/hcFzx/BkzZvCHP/zhsuMpKeZsSwBwwLRXFhERb5lsdoBG5DgQ9WvvfO1vc+fF9Dk33jZ9+vRLrvR4PB7KysqIi4trtJNyKysrSUlJ4fjx403m4+oNobmOGzR2jb15jb25jhs09oYYe0RExFXPMbXctGjRApvNRnFx8SXHi4uLSUxMvOJzEhMTr+n8oKAggoIuXRwpOjr6O6T2ncjIyGb3lx+a77hBY9fYm5fmOm7Q2L09dlOnSdvtdnr37s2aNWvqj3k8HtasWcOAAQOu+JwBAwZccj7A6tWrv/Z8ERERaV5Mvy2VnZ3NxIkT6dOnD1lZWcyePRun08mkSZMAuOeee2jVqhUzZswA4NFHH+WWW27hhRdeYOTIkSxevJht27bx2muvmTkMERERaSRsv//9739vZoDMzEyio6N59tlnmTVrFgALFy6kY8eOALz00ksEBAQwZswY4OJE4M6dO/P8888zc+ZMiouLeeONNxg0aJBpY/AGm83G4MGDCQgwvX/6VHMdN2jsGnvzGntzHTdo7L4Yu+nr3IiIiIg0JP9cmlBERESaLZUbERER8SsqNyIiIuJXVG5ERETEr6jcNHLf+973SE1NJTg4mKSkJCZMmMDJkyfNjuV1R48e5b777qNNmzaEhITQtm1bnn76aVwul9nRvO7ZZ59l4MCBhIaGNpkFJ6/X3LlzSU9PJzg4mH79+rFlyxazI/nEhg0bGD16NMnJyVgslq/dG8/fzJgxg759+xIREUF8fDxjxowhPz/f7Fg+8Ze//IVu3brVL2A3YMAAPvroI7Nj+dzMmTOxWCxMnTrVq6+jctPIDRkyhHfffZf8/HyWLFlCQUEBP/zhD82O5XV5eXl4PB7mzZvH3r17efHFF3n11Vf57W9/a3Y0r3O5XIwdO5aHHnrI7Che9c4775Cdnc3TTz9Nbm4u3bt3Z8SIEZw+fdrsaF7ndDrp3r07c+fONTuKT61fv54pU6bw+eefs3r1ai5cuMDw4cNxOp1mR/O61q1bM3PmTHJycti2bRu33nor3//+99m79+obLvuLrVu3Mm/ePLp16+b9FzOkSVm+fLlhsVgMl8tldhSf+9Of/mS0adPG7Bg+s2DBAiMqKsrsGF6TlZVlTJkypf7f3W63kZycbMyYMcPEVL4HGEuXLjU7hilOnz5tAMb69evNjmKKmJgYY/78+WbH8Imqqiqjffv2xurVq41bbrnFePTRR736erpy04SUlZWxcOFCBg4cSGBgoNlxfK6iooLY2FizY0gDcLlc5OTkMGzYsPpjVquVYcOGsWnTJhOTiS9VVFQANLvva7fbzeLFi3E6nc1m66ApU6YwcuTIS77nvUnlpgmYNm0aYWFhxMXFUVhYyPLly82O5HOHDh1izpw5PPDAA2ZHkQZQWlqK2+0mISHhkuMJCQk4HA6TUokveTwepk6dyqBBg8jMzDQ7jk/s3r2b8PBwgoKCePDBB1m6dCldunQxO5bXLV68mNzc3PptlHxB5cYEjz/+OBaL5Rt/5eXl1Z//2GOPsX37dlatWoXNZuOee+7BaKILS1/r2AFOnDjB7bffztixY5k8ebJJyb+b6xm3iD+bMmUKe/bsYfHixWZH8ZmOHTuyY8cONm/ezEMPPcTEiRPZt2+f2bG86vjx4zz66KMsXLiQ4OBgn72utl8wQUlJCWfOnPnGczIyMrDb7ZcdLyoqIiUlhc8++6xJXs681rGfPHmSwYMH079/f958802s1qbZx6/nz/zNN99k6tSpnD171tvxfM7lchEaGso///nP+n3jACZOnMjZs2eb1dVJi8XC0qVLL/nv4O8efvhhli9fzoYNG2jTpo3ZcUwzbNgw2rZty7x588yO4jXLli3jrrvuwmaz1R9zu91YLBasViu1tbWXPNZQmt+uXY1Ay5Ytadmy5XU91+PxAFBbW9uQkXzmWsZ+4sQJhgwZQu/evVmwYEGTLTbw3f7M/ZHdbqd3796sWbOm/k3d4/GwZs0aHn74YZPTibcYhsEjjzzC0qVLWbduXbMuNnDx73xT/Vn+bQ0dOpTdu3dfcmzSpEl06tSJadOmeaXYgMpNo7Z582a2bt3KjTfeSExMDAUFBTz55JO0bdu2SV61uRYnTpxg8ODBpKWlMWvWLEpKSuofS0xMNDGZ9xUWFlJWVkZhYSFut5sdO3YA0K5dO8LDw01O13Cys7OZOHEiffr0ISsri9mzZ+N0Opk0aZLZ0byuurqaQ4cO1f/7kSNH2LFjB7GxsaSmppqYzLumTJnCokWLWL58OREREfXzq6KioggJCTE5nXdNnz6dO+64g9TUVKqqqli0aBHr1q1j5cqVZkfzqoiIiMvmVP1nDqlX51p59bNY8p3s2rXLGDJkiBEbG2sEBQUZ6enpxoMPPmgUFRWZHc3rFixYYABX/OXvJk6ceMVxr1271uxoDW7OnDlGamqqYbfbjaysLOPzzz83O5JPrF279op/xhMnTjQ7mld93ff0ggULzI7mdffee6+RlpZm2O12o2XLlsbQoUONVatWmR3LFL74KLjm3IiIiIhfabqTGERERESuQOVGRERE/IrKjYiIiPgVlRsRERHxKyo3IiIi4ldUbkRERMSvqNyIiIiIX1G5EREREb+iciMiIiJ+ReVGRERE/IrKjYiIiPgVlRsRafRWrFjBjTfeSHR0NHFxcYwaNYqCgoL6xz/77DN69OhBcHAwffr0YdmyZVgslvod1QH27NnDHXfcQXh4OAkJCUyYMIHS0lIzhiMiXqZyIyKNntPpJDs7m23btrFmzRqsVit33XUXHo+HyspKRo8eTdeuXcnNzeWPf/wj06ZNu+T5Z8+e5dZbb6Vnz55s27aNFStWUFxczLhx40wakYh4k3YFF5Emp7S0lJYtW7J79242btzIE088QVFREcHBwQDMnz+fyZMns337dnr06MEzzzzDJ598wsqVK+u/RlFRESkpKeTn59OhQwezhiIiXqArNyLS6B08eJDx48eTkZFBZGQk6enpABQWFpKfn0+3bt3qiw1AVlbWJc/fuXMna9euJTw8vP5Xp06dAC65vSUi/iHA7AAiIlczevRo0tLSeP3110lOTsbj8ZCZmYnL5fpWz6+urmb06NE899xzlz2WlJTU0HFFxGQqNyLSqJ05c4b8/Hxef/11brrpJgA2btxY/3jHjh15++23qa2tJSgoCICtW7de8jV69erFkiVLSE9PJyBAP/ZE/J1uS4lIoxYTE0NcXByvvfYahw4d4uOPPyY7O7v+8bvvvhuPx8P999/P/v37WblyJbNmzQLAYrEAMGXKFMrKyhg/fjxbt26loKCAlStXMmnSJNxutynjEhHvUbkRkUbNarWyePFicnJyyMzM5Fe/+hXPP/98/eORkZF88MEH7Nixgx49evC73/2Op556CqB+Hk5ycjKffvopbreb4cOH07VrV6ZOnUp0dDRWq34MivgbfVpKRPzOwoULmTRpEhUVFYSEhJgdR0R8TDefRaTJ+/vf/05GRgatWrVi586dTJs2jXHjxqnYiDRTKjci0uQ5HA6eeuopHA4HSUlJjB07lmeffdbsWCJiEt2WEhEREb+imXQiIiLiV1RuRERExK+o3IiIiIhfUbkRERERv6JyIyIiIn5F5UZERET8isqNiIiI+BWVGxEREfEr/x/hnf3iy4mnnwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "USy48-H2UXqB",
+ "outputId": "3abad211-8b21-4a78-ea46-0e3587866c92",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ }
+ },
+ "source": [
+ "# Cálculo das medidas de Skewness e Kurtosis para 'age'\n",
+ "print(f\"Skewness: {df_titanic_ss['age'].skew()}\")\n",
+ "print(f\"Kurtosis: {df_titanic_ss['age'].kurt()}\")"
+ ],
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Skewness: 0.01841894050949496\n",
+ "Kurtosis: -0.2309427735598728\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ENQaVw2lItVL"
+ },
+ "source": [
+ "Olhando para as medidas de Skewness e Kurtosis logo acima, qual a conclusão?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Nt0PQIjW-wXd"
+ },
+ "source": [
+ "___\n",
+ "## **Isolation Forest Region**\n",
+ "* Source: [Outlier Detection with Isolation Forest](https://towardsdatascience.com/outlier-detection-with-isolation-forest-3d190448d45e)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tM6Xht76KmUN"
+ },
+ "source": [
+ "### Anomaly Detection para 'fare'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uFuAUh5S778M",
+ "outputId": "7ea4f291-c983-4736-b0c7-2b18a65c17e8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 388
+ }
+ },
+ "source": [
+ "isolation_forest = IsolationForest(n_estimators = 100)\n",
+ "isolation_forest.fit(df_titanic['fare'].values.reshape(-1, 1))\n",
+ "xx = np.linspace(df_titanic['fare'].min(), df_titanic['fare'].max(), len(df_titanic)).reshape(-1, 1)\n",
+ "anomaly_score = isolation_forest.decision_function(xx)\n",
+ "outlier = isolation_forest.predict(xx)\n",
+ "plt.figure(figsize = (10, 4))\n",
+ "plt.plot(xx, anomaly_score, label = 'anomaly score')\n",
+ "plt.fill_between(xx.T[0], np.min(anomaly_score), np.max(anomaly_score), where = outlier == -1, color = 'r', alpha = .4, label = 'outlier region')\n",
+ "plt.legend()\n",
+ "plt.ylabel('anomaly score')\n",
+ "plt.xlabel('fare')\n",
+ "plt.show();"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "FkhRwo1cgYtK",
+ "outputId": "771f93e4-9d22-41cf-d6f0-6ee611326a47",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 195
+ }
+ },
+ "source": [
+ "# Vamos avaliar os dados do dataframe para fare > 200, por exemplo\n",
+ "df_titanic.loc[df_titanic['fare'] > 200].head()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
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+ " 299 \n",
+ " 1 \n",
+ " 1 \n",
+ " female \n",
+ " 50.0 \n",
+ " 0 \n",
+ " 1 \n",
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+ " woman \n",
+ " False \n",
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+ " yes \n",
+ " False \n",
+ " \n",
+ " \n",
+ " 311 \n",
+ " 1 \n",
+ " 1 \n",
+ " female \n",
+ " 18.0 \n",
+ " 2 \n",
+ " 2 \n",
+ " 262.3750 \n",
+ " C \n",
+ " First \n",
+ " woman \n",
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+ " B \n",
+ " Cherbourg \n",
+ " yes \n",
+ " False \n",
+ " \n",
+ " \n",
+ "
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+ "
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+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GivF2cSFS208"
+ },
+ "source": [
+ "Observe no gráfico acima que há duas regiões em que os dados têm baixa probabilidade de aparecer: uma no lado esquerdo da distribuição, outra no lado direito da distribuição."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XtizVySOlPUT",
+ "outputId": "0b387228-ac36-4617-9e06-a231e7b0fbdf",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 195
+ }
+ },
+ "source": [
+ "# Avaliando os dados da cauda esquerda\n",
+ "df_titanic.loc[df_titanic['age'] < 15].head()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
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+ " \n",
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+ " parch \n",
+ " fare \n",
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+ " class \n",
+ " who \n",
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+ "
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+ "\n",
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+ " 80.0 \n",
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+ " 30.0000 \n",
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+ " man \n",
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+ " \n",
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+ " male \n",
+ " 70.0 \n",
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+ " 71.0000 \n",
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+ " First \n",
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+ " \n",
+ " \n",
+ "
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+ "
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+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AChZpGY4Ghc9",
+ "outputId": "eed40cf6-a0e9-438c-e674-4bac462b872f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 195
+ }
+ },
+ "source": [
+ "cols = ['fare', 'age']\n",
+ "df_titanic_ss[cols].head()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
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+ "metadata": {
+ "tags": []
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+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "O7NmDgjRm5EE",
+ "outputId": "0d0379c1-b9ce-4aed-bf59-feb9e9d99091",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 106
+ }
+ },
+ "source": [
+ "# Zoom em alguns outliers...\n",
+ "df1.loc[df1['outlier'] == 1].head()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
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+ " mean \n",
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+ " \n",
+ " sex \n",
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+ " \n",
+ " \n",
+ " female \n",
+ " 33.0 \n",
+ " 89.0 \n",
+ " \n",
+ " \n",
+ " male \n",
+ " 38.0 \n",
+ " 69.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gHRoON0BnLVb",
+ "outputId": "73ec2b80-9edf-445b-8276-baa505178432",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 106
+ }
+ },
+ "source": [
+ "# Zoom em alguns outliers...\n",
+ "df1.loc[df1['outlier'] == 1].head()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
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+ " 69.0 \n",
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+ ]
+ },
+ "metadata": {
+ "tags": []
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+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HLVraGcCnNTA",
+ "outputId": "957c19be-cec9-4d4a-9902-0f49f806198f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 106
+ }
+ },
+ "source": [
+ "# Zoom em alguns outliers...\n",
+ "df1.loc[df1['outlier'] == 1].head()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
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+ "metadata": {
+ "id": "6B-L7MwXg25Z",
+ "outputId": "0f8d806d-9e19-4133-c038-01d96c26b168",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 195
+ }
+ },
+ "source": [
+ "df1.head()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
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+ " 7.07 \n",
+ " 0.0 \n",
+ " 0.469 \n",
+ " 7.185 \n",
+ " 61.1 \n",
+ " 4.9671 \n",
+ " 2.0 \n",
+ " 242.0 \n",
+ " 17.8 \n",
+ " 392.83 \n",
+ " 4.03 \n",
+ " 34.7 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 0.03237 \n",
+ " 0.0 \n",
+ " 2.18 \n",
+ " 0.0 \n",
+ " 0.458 \n",
+ " 6.998 \n",
+ " 45.8 \n",
+ " 6.0622 \n",
+ " 3.0 \n",
+ " 222.0 \n",
+ " 18.7 \n",
+ " 394.63 \n",
+ " 2.94 \n",
+ " 33.4 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 0.06905 \n",
+ " 0.0 \n",
+ " 2.18 \n",
+ " 0.0 \n",
+ " 0.458 \n",
+ " 7.147 \n",
+ " 54.2 \n",
+ " 6.0622 \n",
+ " 3.0 \n",
+ " 222.0 \n",
+ " 18.7 \n",
+ " 396.90 \n",
+ " 5.33 \n",
+ " 36.2 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " sepal length (cm) \n",
+ " sepal width (cm) \n",
+ " petal length (cm) \n",
+ " petal width (cm) \n",
+ " target \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 5.1 \n",
+ " 3.5 \n",
+ " 1.4 \n",
+ " 0.2 \n",
+ " setosa \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 4.9 \n",
+ " 3.0 \n",
+ " 1.4 \n",
+ " 0.2 \n",
+ " setosa \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 4.7 \n",
+ " 3.2 \n",
+ " 1.3 \n",
+ " 0.2 \n",
+ " setosa \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 4.6 \n",
+ " 3.1 \n",
+ " 1.5 \n",
+ " 0.2 \n",
+ " setosa \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 5.0 \n",
+ " 3.6 \n",
+ " 1.4 \n",
+ " 0.2 \n",
+ " setosa \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " age \n",
+ " sex \n",
+ " bmi \n",
+ " bp \n",
+ " s1 \n",
+ " s2 \n",
+ " s3 \n",
+ " s4 \n",
+ " s5 \n",
+ " s6 \n",
+ " target \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0.038076 \n",
+ " 0.050680 \n",
+ " 0.061696 \n",
+ " 0.021872 \n",
+ " -0.044223 \n",
+ " -0.034821 \n",
+ " -0.043401 \n",
+ " -0.002592 \n",
+ " 0.019908 \n",
+ " -0.017646 \n",
+ " 151.0 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " -0.001882 \n",
+ " -0.044642 \n",
+ " -0.051474 \n",
+ " -0.026328 \n",
+ " -0.008449 \n",
+ " -0.019163 \n",
+ " 0.074412 \n",
+ " -0.039493 \n",
+ " -0.068330 \n",
+ " -0.092204 \n",
+ " 75.0 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 0.085299 \n",
+ " 0.050680 \n",
+ " 0.044451 \n",
+ " -0.005671 \n",
+ " -0.045599 \n",
+ " -0.034194 \n",
+ " -0.032356 \n",
+ " -0.002592 \n",
+ " 0.002864 \n",
+ " -0.025930 \n",
+ " 141.0 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " -0.089063 \n",
+ " -0.044642 \n",
+ " -0.011595 \n",
+ " -0.036656 \n",
+ " 0.012191 \n",
+ " 0.024991 \n",
+ " -0.036038 \n",
+ " 0.034309 \n",
+ " 0.022692 \n",
+ " -0.009362 \n",
+ " 206.0 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 0.005383 \n",
+ " -0.044642 \n",
+ " -0.036385 \n",
+ " 0.021872 \n",
+ " 0.003935 \n",
+ " 0.015596 \n",
+ " 0.008142 \n",
+ " -0.002592 \n",
+ " -0.031991 \n",
+ " -0.046641 \n",
+ " 135.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
MACHINE LEARNING WITH PYTHON \n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " v1 \n",
+ " v2 \n",
+ " v3 \n",
+ " v4 \n",
+ " v5 \n",
+ " v6 \n",
+ " v7 \n",
+ " v8 \n",
+ " v9 \n",
+ " v10 \n",
+ " v11 \n",
+ " v12 \n",
+ " v13 \n",
+ " v14 \n",
+ " v15 \n",
+ " v16 \n",
+ " v17 \n",
+ " v18 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0.068441 \n",
+ " 4.211842 \n",
+ " -2.558302 \n",
+ " 3.665482 \n",
+ " -3.835158 \n",
+ " 3.499851 \n",
+ " 2.490856 \n",
+ " 3.665482 \n",
+ " 0.245117 \n",
+ " 0.867172 \n",
+ " 2.865546 \n",
+ " 0.493956 \n",
+ " -5.148596 \n",
+ " 2.865546 \n",
+ " 3.499851 \n",
+ " -0.630619 \n",
+ " -0.978320 \n",
+ " -0.888270 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " -4.824021 \n",
+ " 0.179509 \n",
+ " -2.984473 \n",
+ " 1.033618 \n",
+ " -3.893426 \n",
+ " 3.428734 \n",
+ " -3.334605 \n",
+ " 1.033618 \n",
+ " -0.882780 \n",
+ " -0.753281 \n",
+ " 1.441522 \n",
+ " -1.395514 \n",
+ " -4.002880 \n",
+ " 1.441522 \n",
+ " 3.428734 \n",
+ " 0.339920 \n",
+ " 1.891538 \n",
+ " -6.109676 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 1.389530 \n",
+ " -0.226476 \n",
+ " 1.877400 \n",
+ " 2.713426 \n",
+ " 4.630257 \n",
+ " 0.516455 \n",
+ " -3.743027 \n",
+ " 2.713426 \n",
+ " 1.284039 \n",
+ " 2.030797 \n",
+ " -1.095536 \n",
+ " 1.560159 \n",
+ " -1.014211 \n",
+ " -1.095536 \n",
+ " 0.516455 \n",
+ " -1.477845 \n",
+ " 0.960526 \n",
+ " 2.060204 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 1.145809 \n",
+ " 2.255946 \n",
+ " 0.207364 \n",
+ " 4.665817 \n",
+ " 2.294678 \n",
+ " 6.501306 \n",
+ " 0.964770 \n",
+ " 4.665817 \n",
+ " 0.119410 \n",
+ " 3.196354 \n",
+ " 1.894787 \n",
+ " 3.519138 \n",
+ " -4.757807 \n",
+ " 1.894787 \n",
+ " 6.501306 \n",
+ " -3.789029 \n",
+ " 0.579491 \n",
+ " 1.397106 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " -0.936646 \n",
+ " 3.697163 \n",
+ " -3.363617 \n",
+ " 3.805126 \n",
+ " -1.754430 \n",
+ " 4.954346 \n",
+ " 0.406605 \n",
+ " 3.805126 \n",
+ " -0.824738 \n",
+ " 1.382591 \n",
+ " 1.665704 \n",
+ " -0.649758 \n",
+ " -3.513036 \n",
+ " 1.665704 \n",
+ " 4.954346 \n",
+ " 0.257052 \n",
+ " 0.904244 \n",
+ " -3.071354 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " v1 \n",
+ " v2 \n",
+ " v3 \n",
+ " v4 \n",
+ " v5 \n",
+ " v6 \n",
+ " v7 \n",
+ " v8 \n",
+ " v9 \n",
+ " v10 \n",
+ " v11 \n",
+ " v12 \n",
+ " v13 \n",
+ " v14 \n",
+ " v15 \n",
+ " v16 \n",
+ " v17 \n",
+ " v18 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " 1000.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " -0.085159 \n",
+ " 1.034227 \n",
+ " 0.657408 \n",
+ " 1.405317 \n",
+ " 0.687279 \n",
+ " 1.131560 \n",
+ " 0.108053 \n",
+ " 1.405317 \n",
+ " 1.007023 \n",
+ " 1.048801 \n",
+ " 0.079248 \n",
+ " 0.001650 \n",
+ " -0.365438 \n",
+ " 0.079248 \n",
+ " 1.131560 \n",
+ " -0.027751 \n",
+ " 0.984606 \n",
+ " 0.633624 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 2.002247 \n",
+ " 1.631507 \n",
+ " 3.608772 \n",
+ " 2.256857 \n",
+ " 4.019598 \n",
+ " 4.481832 \n",
+ " 1.981307 \n",
+ " 2.256857 \n",
+ " 1.863288 \n",
+ " 1.643900 \n",
+ " 1.949273 \n",
+ " 1.932641 \n",
+ " 4.160668 \n",
+ " 1.949273 \n",
+ " 4.481832 \n",
+ " 2.065455 \n",
+ " 1.850593 \n",
+ " 3.552991 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " -6.944169 \n",
+ " -4.620754 \n",
+ " -16.300139 \n",
+ " -6.235192 \n",
+ " -12.454256 \n",
+ " -14.305401 \n",
+ " -6.152747 \n",
+ " -6.235192 \n",
+ " -5.484992 \n",
+ " -3.293216 \n",
+ " -7.135349 \n",
+ " -5.705500 \n",
+ " -9.120941 \n",
+ " -7.135349 \n",
+ " -14.305401 \n",
+ " -6.009023 \n",
+ " -5.035184 \n",
+ " -11.439074 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " -1.305566 \n",
+ " -0.089052 \n",
+ " -1.623657 \n",
+ " -0.152888 \n",
+ " -1.854645 \n",
+ " -1.684751 \n",
+ " -1.216983 \n",
+ " -0.152888 \n",
+ " -0.240908 \n",
+ " -0.012710 \n",
+ " -1.209675 \n",
+ " -1.292162 \n",
+ " -3.555363 \n",
+ " -1.209675 \n",
+ " -1.684751 \n",
+ " -1.436673 \n",
+ " -0.261610 \n",
+ " -1.691346 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 0.052523 \n",
+ " 0.994150 \n",
+ " 0.573849 \n",
+ " 1.449931 \n",
+ " 0.812364 \n",
+ " 1.281504 \n",
+ " 0.167091 \n",
+ " 1.449931 \n",
+ " 1.066125 \n",
+ " 1.012899 \n",
+ " 0.180344 \n",
+ " 0.035237 \n",
+ " -0.966638 \n",
+ " 0.180344 \n",
+ " 1.281504 \n",
+ " -0.000190 \n",
+ " 0.975793 \n",
+ " 0.844784 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 1.383853 \n",
+ " 2.071995 \n",
+ " 3.038586 \n",
+ " 2.887141 \n",
+ " 3.413952 \n",
+ " 4.008103 \n",
+ " 1.438719 \n",
+ " 2.887141 \n",
+ " 2.288188 \n",
+ " 2.187202 \n",
+ " 1.439199 \n",
+ " 1.315342 \n",
+ " 2.745806 \n",
+ " 1.439199 \n",
+ " 4.008103 \n",
+ " 1.365369 \n",
+ " 2.256504 \n",
+ " 3.109330 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 4.997172 \n",
+ " 7.354860 \n",
+ " 11.720165 \n",
+ " 8.494566 \n",
+ " 12.844418 \n",
+ " 15.999803 \n",
+ " 6.293550 \n",
+ " 8.494566 \n",
+ " 8.146559 \n",
+ " 6.523180 \n",
+ " 6.252448 \n",
+ " 5.538216 \n",
+ " 11.259350 \n",
+ " 6.252448 \n",
+ " 15.999803 \n",
+ " 6.531561 \n",
+ " 7.646802 \n",
+ " 12.090528 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "p8D975NqsGtj"
+ },
+ "source": [
+ "## Parameter tunning\n",
+ "### Referência\n",
+ "* [Hyperparameter Tuning the Random Forest in Python](https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74)\n",
+ "* [Decision Tree Adventures 2 — Explanation of Decision Tree Classifier Parameters](https://medium.com/datadriveninvestor/decision-tree-adventures-2-explanation-of-decision-tree-classifier-parameters-84776f39a28) - Explica didaticamente e step by step como fazer parameter tunning."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Bfdq5zEhlVsk"
+ },
+ "source": [
+ "# Dicionário de parâmetros para o parameter tunning. Ao todo serão ajustados 2X13X5X5X7= 4.550 modelos. Contando com 10 folds no Cross-Validation, então são 45.500 modelos.\n",
+ "d_parametros_DT= {\"criterion\": [\"gini\", \"entropy\"]} #, \"min_samples_split\": [2, 5, 10, 30, 50, 70, 90, 120, 150, 180, 210, 240, 270, 350, 400], \"max_depth\": [None, 2, 5, 9, 15], \"min_samples_leaf\": [20, 40, 60, 80, 100], \"max_leaf_nodes\": [None, 2, 3, 4, 5, 10, 15]}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "H8gNSs0G0A-L"
+ },
+ "source": [
+ "```\n",
+ "grid_search = GridSearchCV(ml_DT, param_grid= d_parametros_DT, cv = i_CV, n_jobs= -1)\n",
+ "start = time()\n",
+ "grid_search.fit(X_train, y_train)\n",
+ "tempo_elapsed= time()-start\n",
+ "print(f\"\\nGridSearchCV levou {tempo_elapsed:.2f} segundos para estimar {len(grid_search.cv_results_)} modelos candidatos\")\n",
+ "\n",
+ "GridSearchCV levou 1999.12 segundos para estimar 23 modelos candidatos\n",
+ "```\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ap3WMXqDthu9"
+ },
+ "source": [
+ "# Definindo a função para o GridSearchCV\n",
+ "def GridSearchOptimizer(modelo, ml_Opt, d_Parametros, X_train, y_train, X_test, y_test, cv = i_CV):\n",
+ " ml_GridSearchCV = GridSearchCV(modelo, d_Parametros, cv = i_CV, n_jobs= -1, verbose= 10, scoring= 'accuracy')\n",
+ " start = time()\n",
+ " ml_GridSearchCV.fit(X_train, y_train)\n",
+ " tempo_elapsed= time()-start\n",
+ " #print(f\"\\nGridSearchCV levou {tempo_elapsed:.2f} segundos.\")\n",
+ "\n",
+ " # Parâmetros que otimizam a classificação:\n",
+ " print(f'\\nParametros otimizados: {ml_GridSearchCV.best_params_}')\n",
+ " \n",
+ " if ml_Opt == 'ml_DT2':\n",
+ " print(f'\\nDecisionTreeClassifier *********************************************************************************************************')\n",
+ " ml_Opt = DecisionTreeClassifier(criterion= ml_GridSearchCV.best_params_['criterion'], \n",
+ " max_depth= ml_GridSearchCV.best_params_['max_depth'],\n",
+ " max_leaf_nodes= ml_GridSearchCV.best_params_['max_leaf_nodes'],\n",
+ " min_samples_split= ml_GridSearchCV.best_params_['min_samples_leaf'],\n",
+ " min_samples_leaf= ml_GridSearchCV.best_params_['min_samples_split'], \n",
+ " random_state= i_Seed)\n",
+ " \n",
+ " elif ml_Opt == 'ml_RF2':\n",
+ " print(f'\\nRandomForestClassifier *********************************************************************************************************')\n",
+ " ml_Opt = RandomForestClassifier(bootstrap= ml_GridSearchCV.best_params_['bootstrap'], \n",
+ " max_depth= ml_GridSearchCV.best_params_['max_depth'],\n",
+ " max_features= ml_GridSearchCV.best_params_['max_features'],\n",
+ " min_samples_leaf= ml_GridSearchCV.best_params_['min_samples_leaf'],\n",
+ " min_samples_split= ml_GridSearchCV.best_params_['min_samples_split'],\n",
+ " n_estimators= ml_GridSearchCV.best_params_['n_estimators'],\n",
+ " random_state= i_Seed)\n",
+ " \n",
+ " elif ml_Opt == 'ml_AB2':\n",
+ " print(f'\\nAdaBoostClassifier *********************************************************************************************************')\n",
+ " ml_Opt = AdaBoostClassifier(algorithm='SAMME.R', \n",
+ " base_estimator=RandomForestClassifier(bootstrap = False, \n",
+ " max_depth = 10, \n",
+ " max_features = 'auto', \n",
+ " min_samples_leaf = 1, \n",
+ " min_samples_split = 2, \n",
+ " n_estimators = 400), \n",
+ " learning_rate = ml_GridSearchCV.best_params_['learning_rate'], \n",
+ " n_estimators = ml_GridSearchCV.best_params_['n_estimators'], \n",
+ " random_state = i_Seed)\n",
+ " \n",
+ " elif ml_Opt == 'ml_GB2':\n",
+ " print(f'\\nGradientBoostingClassifier *********************************************************************************************************')\n",
+ " ml_Opt = GradientBoostingClassifier(learning_rate = ml_GridSearchCV.best_params_['learning_rate'], \n",
+ " n_estimators = ml_GridSearchCV.best_params_['n_estimators'], \n",
+ " max_depth = ml_GridSearchCV.best_params_['max_depth'], \n",
+ " min_samples_split = ml_GridSearchCV.best_params_['min_samples_split'], \n",
+ " min_samples_leaf = ml_GridSearchCV.best_params_['min_samples_leaf'], \n",
+ " max_features = ml_GridSearchCV.best_params_['max_features'])\n",
+ " \n",
+ " elif ml_Opt == 'ml_XGB2':\n",
+ " print(f'\\nXGBoostingClassifier *********************************************************************************************************')\n",
+ " ml_Opt = XGBoostingClassifier(learning_rate= ml_GridSearchCV.best_params_['learning_rate'], \n",
+ " max_depth= ml_GridSearchCV.best_params_['max_depth'], \n",
+ " colsample_bytree= ml_GridSearchCV.best_params_['colsample_bytree'], \n",
+ " subsample= ml_GridSearchCV.best_params_['subsample'], \n",
+ " gamma= ml_GridSearchCV.best_params_['gamma'], \n",
+ " min_child_weight= ml_GridSearchCV.best_params_['min_child_weight'])\n",
+ " \n",
+ " # Treina novamente usando os parametros otimizados...\n",
+ " ml_Opt.fit(X_train, y_train)\n",
+ "\n",
+ " # Cross-Validation com 10 folds\n",
+ " print(f'\\n********* CROSS-VALIDATION ***********')\n",
+ " a_scores_CV = cross_val_score(ml_Opt, X_train, y_train, cv = i_CV)\n",
+ " print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_scores_CV.mean(),4)}')\n",
+ " print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_scores_CV.std(),4)}')\n",
+ "\n",
+ " # Faz predições com os parametros otimizados...\n",
+ " y_pred = ml_Opt.predict(X_test)\n",
+ " \n",
+ " # Importância das COLUNAS\n",
+ " print(f'\\n********* IMPORTÂNCIA DAS COLUNAS ***********')\n",
+ " df_importancia_variaveis = pd.DataFrame(zip(l_colunas, ml_Opt.feature_importances_), columns= ['coluna', 'importancia'])\n",
+ " df_importancia_variaveis = df_importancia_variaveis.sort_values(by= ['importancia'], ascending=False)\n",
+ " print(df_importancia_variaveis)\n",
+ "\n",
+ " # Matriz de Confusão\n",
+ " print(f'\\n********* CONFUSION MATRIX - PARAMETER TUNNING ***********')\n",
+ " cf_matrix = confusion_matrix(y_test, y_pred)\n",
+ " cf_labels = ['True_Negative', 'False_Positive', 'False_Negative', 'True_Positive']\n",
+ " cf_categories = ['Zero', 'One']\n",
+ " mostra_confusion_matrix(cf_matrix, group_names = cf_labels, categories = cf_categories)\n",
+ "\n",
+ " return ml_Opt, ml_GridSearchCV.best_params_"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "44-BRnNjBT25",
+ "outputId": "da9fa734-cd1d-4731-d6c6-2ff2cbc1d379",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 520
+ }
+ },
+ "source": [
+ "# Invoca a função\n",
+ "ml_DT2, best_params = GridSearchOptimizer(ml_DT, 'ml_DT2', d_parametros_DT, X_train, y_train, X_test, y_test, cv = i_CV)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "stream",
+ "text": [
+ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
+ "[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 1.0s\n",
+ "[Parallel(n_jobs=-1)]: Done 4 tasks | elapsed: 1.1s\n",
+ "[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 1.2s\n",
+ "[Parallel(n_jobs=-1)]: Batch computation too fast (0.1813s.) Setting batch_size=2.\n",
+ "[Parallel(n_jobs=-1)]: Done 14 tasks | elapsed: 1.2s\n"
+ ],
+ "name": "stderr"
+ },
+ {
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Parametros otimizados: {'criterion': 'entropy'}\n",
+ "\n",
+ "DecisionTreeClassifier *********************************************************************************************************\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "stream",
+ "text": [
+ "[Parallel(n_jobs=-1)]: Done 20 out of 20 | elapsed: 1.3s remaining: 0.0s\n",
+ "[Parallel(n_jobs=-1)]: Done 20 out of 20 | elapsed: 1.3s finished\n"
+ ],
+ "name": "stderr"
+ },
+ {
+ "output_type": "error",
+ "ename": "KeyError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Invoca a função\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mml_DT2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbest_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchOptimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mml_DT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ml_DT2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md_parametros_DT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi_CV\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mGridSearchOptimizer\u001b[0;34m(modelo, ml_Opt, d_Parametros, X_train, y_train, X_test, y_test, cv)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'\\nDecisionTreeClassifier *********************************************************************************************************'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m ml_Opt = DecisionTreeClassifier(criterion= ml_GridSearchCV.best_params_['criterion'], \n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mml_GridSearchCV\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'max_depth'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0mmax_leaf_nodes\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mml_GridSearchCV\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'max_leaf_nodes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mmin_samples_split\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mml_GridSearchCV\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'min_samples_leaf'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'max_depth'"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gmCkjGjPJMLr"
+ },
+ "source": [
+ "### Visualizar o resultado"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cIc3ZgaISEd0"
+ },
+ "source": [
+ "from sklearn.tree import export_graphviz\n",
+ "from sklearn.externals.six import StringIO \n",
+ "from IPython.display import Image \n",
+ "import pydotplus\n",
+ "\n",
+ "dot_data = StringIO()\n",
+ "export_graphviz(ml_DT2, out_file = dot_data, filled = True, rounded = True, special_characters = True, feature_names = l_colunas, class_names = ['0','1'])\n",
+ "\n",
+ "graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) \n",
+ "graph.write_png('DecisionTree.png')\n",
+ "Image(graph.create_png())"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e1R2GBkbnV37"
+ },
+ "source": [
+ "## Selecionar as COLUNAS importantes/relevantes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vv7GKBvs6Ybf"
+ },
+ "source": [
+ "# Função desenvolvida para Selecionar COLUNAS relevantes\n",
+ "from sklearn.feature_selection import SelectFromModel\n",
+ "\n",
+ "def seleciona_colunas_relevantes(modelo, X_train, X_test, threshold = 0.05):\n",
+ " # Cria um seletor para selecionar as COLUNAS com importância > threshold\n",
+ " sfm = SelectFromModel(modelo, threshold)\n",
+ " \n",
+ " # Treina o seletor\n",
+ " sfm.fit(X_train, y_train)\n",
+ "\n",
+ " # Mostra o indice das COLUNAS mais importantes\n",
+ " print(f'\\n********** COLUNAS Relevantes ******')\n",
+ " print(sfm.get_support(indices=True))\n",
+ "\n",
+ " # Seleciona somente as COLUNAS relevantes\n",
+ " X_train_I = sfm.transform(X_train)\n",
+ " X_test_I = sfm.transform(X_test)\n",
+ " return X_train_I, X_test_I "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ukMLoEr7nbUf"
+ },
+ "source": [
+ "X_train_DT, X_test_DT = seleciona_colunas_relevantes(ml_DT2, X_train, X_test)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8JjePRQAoqkk"
+ },
+ "source": [
+ "## Treina o classificador com as COLUNAS relevantes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Gt3aCPpfKRxm"
+ },
+ "source": [
+ "best_params"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zq6uCVtzovMt"
+ },
+ "source": [
+ "# Treina usando as COLUNAS relevantes...\n",
+ "ml_DT2.fit(X_train_DT, y_train)\n",
+ "\n",
+ "# Cross-Validation com 10 folds\n",
+ "a_scores_CV = cross_val_score(ml_DT2, X_train_DT, y_train, cv = i_CV)\n",
+ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_scores_CV.mean(),4)}')\n",
+ "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_scores_CV.std(),4)}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Tc7esxqtq-Og"
+ },
+ "source": [
+ "****************************************************************"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "znWy3LE1q-Z3"
+ },
+ "source": [
+ "ml_DT3, best_params2 = GridSearchOptimizer(ml_DT2, 'ml_DT2', d_parametros_DT, X_train_DT, y_train, X_test_DT, y_test, cv = i_CV)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6IhCC6pfq-jL"
+ },
+ "source": [
+ "best_params"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "qw6Dk3kesT0q"
+ },
+ "source": [
+ "best_params2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SbS4ZKN8s-ee"
+ },
+ "source": [
+ "# Cross-Validation com 10 folds\n",
+ "a_scores_CV = cross_val_score(ml_DT3, X_train_DT, y_train, cv = i_CV)\n",
+ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_scores_CV.mean(),4)}')\n",
+ "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_scores_CV.std(),4)}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_at3XP1Bq-qb"
+ },
+ "source": [
+ "***************************************************************"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MZ1-vGRcxJoN"
+ },
+ "source": [
+ "## Valida o modelo usando o dataframe X_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ig9GiUAEw9jr"
+ },
+ "source": [
+ "y_pred_DT = ml_DT2.predict(X_test_DT)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7UZz4UzHDqae"
+ },
+ "source": [
+ "# Calcula acurácia\n",
+ "accuracy_score(y_test, y_pred_DT)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "K3EUMAxxKBur"
+ },
+ "source": [
+ "___\n",
+ "# **RANDOM FOREST**\n",
+ "* Decision Trees possuem estrutura em forma de árvores.\n",
+ "* Random Forest pode ser utilizado tanto para classificação (RandomForestClassifier)quanto para Regressão (RandomForestRegressor).\n",
+ "\n",
+ "* **Vantagens**:\n",
+ " * Não requer tanto data preprocessing;\n",
+ " * Lida bem com COLUNAS categóricas e numéricas;\n",
+ " * É um Boosting Ensemble Method (pois constrói muitas árvores). Estes modelos aprendem com os próprios erros e ajustam as árvores de modo a fazer melhores classificações;\n",
+ " * Mais robusta que uma simples Decision Tree. **Porque?**\n",
+ " * Controla automaticamente overfitting (**porque?**) e frequentemente produz modelos muito robustos e de alta-performance.\n",
+ " * Pode ser utilizado como Feature Selection, pois gera a matriz de importância dos atributos (importance sample). A soma das importâncias soma 100;\n",
+ " * Assim como as Decision Trees, esses modelos capturam facilmente padrões não-lineares presentes nos dados;\n",
+ " * Não requer os dados sejam normalizados;\n",
+ " * Lida bem com Missing Values;\n",
+ " * Não requer suposições (assumptions) sobre a distribuição dos dados por causa da natureza não-paramétrica do algoritmo\n",
+ "\n",
+ "* **Desvantagens**\n",
+ " * **Recomenda-se balancear o dataframe previamente para se evitar esse problema**.\n",
+ "\n",
+ "* **Principais parâmetros**\n",
+ "\n",
+ "## **Referências**:\n",
+ "* [Running Random Forests? Inspect the feature importances with this code](https://towardsdatascience.com/running-random-forests-inspect-the-feature-importances-with-this-code-2b00dd72b92e)\n",
+ "* [Feature importances with forests of trees](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html)\n",
+ "* [Understanding Random Forests Classifiers in Python](https://www.datacamp.com/community/tutorials/random-forests-classifier-python)\n",
+ "* [Understanding Random Forest](https://towardsdatascience.com/understanding-random-forest-58381e0602d2)\n",
+ "* [An Implementation and Explanation of the Random Forest in Python](https://towardsdatascience.com/an-implementation-and-explanation-of-the-random-forest-in-python-77bf308a9b76)\n",
+ "* [Random Forest Simple Explanation](https://medium.com/@williamkoehrsen/random-forest-simple-explanation-377895a60d2d)\n",
+ "* [Random Forest Explained](https://www.youtube.com/watch?v=eM4uJ6XGnSM)\n",
+ "* [Hyperparameter Tuning the Random Forest in Python](https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74) - Explica os principais parâmetros do Random Forest."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cnfDw_GEKBuu"
+ },
+ "source": [
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "\n",
+ "# Instancia...\n",
+ "ml_RF= RandomForestClassifier(n_estimators=100, min_samples_split= 2, max_features=\"auto\", random_state= i_Seed)\n",
+ "\n",
+ "# Treina...\n",
+ "ml_RF.fit(X_train, y_train)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lYa9oaZW__o6",
+ "outputId": "ba94936d-6a1d-49d4-9bb0-23cb56f7fa49",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 214
+ }
+ },
+ "source": [
+ "# Cross-Validation com 10 folds\n",
+ "a_scores_CV = cross_val_score(ml_RF, X_train, y_train, cv = i_CV)\n",
+ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_scores_CV.mean(),4)}')\n",
+ "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_scores_CV.std(),4)}')"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "NameError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Cross-Validation com 10 folds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma_scores_CV\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mml_RF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi_CV\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Média das Acurácias calculadas pelo CV....: {100*round(a_scores_CV.mean(),4)}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'std médio das Acurácias calculadas pelo CV: {100*round(a_scores_CV.std(),4)}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'cross_val_score' is not defined"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AouWUu8vANdb"
+ },
+ "source": [
+ "**Interpretação**: Nosso classificador (RandomForestClassifier) tem uma acurácia média de 96,44% (base de treinamento). Além disso, o std é da ordem de 2,77%, ou seja, pequena. Vamos tentar melhorar a acurácia do classificador usando parameter tunning (GridSearchCV)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vbducxlgAa85",
+ "outputId": "a916adc2-7038-4d7b-8ac5-96f41f971340",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 163
+ }
+ },
+ "source": [
+ "print(f'Acurácias: {a_scores_CV}')"
+ ],
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "NameError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Acurácias: {a_scores_CV}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'a_scores_CV' is not defined"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_lxx-LUw_5sd",
+ "outputId": "c7cccd43-813b-4765-f396-1a70540b3f05",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 180
+ }
+ },
+ "source": [
+ "# Faz predições...\n",
+ "y_pred = ml_RF.predict(X_test)"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "NameError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Faz predições...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mml_RF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'ml_RF' is not defined"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "pQIRO_LpGAkw"
+ },
+ "source": [
+ "# Confusion Matrix\n",
+ "cf_matrix = confusion_matrix(y_test, y_pred)\n",
+ "cf_labels = ['True_Negative','False_Positive','False_Negative','True_Positive']\n",
+ "cf_categories = ['Zero', 'One']\n",
+ "mostra_confusion_matrix(cf_matrix, group_names= cf_labels, categories= cf_categories)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yKLHZ5_C6FJ8"
+ },
+ "source": [
+ "## Parameter tunning\n",
+ "### Referência\n",
+ "* [Hyperparameter Tuning the Random Forest in Python](https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74)\n",
+ "* [Decision Tree Adventures 2 — Explanation of Decision Tree Classifier Parameters](https://medium.com/datadriveninvestor/decision-tree-adventures-2-explanation-of-decision-tree-classifier-parameters-84776f39a28) - Explica didaticamente e step by step como fazer parameter tunning.\n",
+ "* [Optimizing Hyperparameters in Random Forest Classification](https://towardsdatascience.com/optimizing-hyperparameters-in-random-forest-classification-ec7741f9d3f6) - Outro approach para entender parameter tunning. Recomendo fortemente a leitura! "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XOa9naju6FKA"
+ },
+ "source": [
+ "# Dicionário de parâmetros para o parameter tunning.\n",
+ "d_parametros_RF= {'bootstrap': [True, False]} #,\n",
+ "# 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None],\n",
+ "# 'max_features': ['auto', 'sqrt'],\n",
+ "# 'min_samples_leaf': [1, 2, 4],\n",
+ "# 'min_samples_split': [2, 5, 10],\n",
+ "# 'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6__f2jZaTQat"
+ },
+ "source": [
+ "# Invoca a função\n",
+ "ml_RF2, best_params = GridSearchOptimizer(ml_RF, 'ml_RF2', d_parametros_RF, X_train, y_train, X_test, y_test, cv = i_CV)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "crfn-n--KG4n"
+ },
+ "source": [
+ "### Resultado da execução do Random Forest\n",
+ "\n",
+ "```\n",
+ "[Parallel(n_jobs=-1)]: Done 7920 out of 7920 | elapsed: 194.0min finished\n",
+ "best_params= {'bootstrap': False, 'max_depth': 10, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SGTOe5PaRw59"
+ },
+ "source": [
+ "# Como o procedimento acima levou 194 minutos para executar, então vou estimar ml_RF2 abaixo usando os parâmetros acima estimados\n",
+ "best_params= {'bootstrap': False, 'max_depth': 10, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
+ "\n",
+ "ml_RF2= RandomForestClassifier(bootstrap= best_params['bootstrap'], \n",
+ " max_depth= best_params['max_depth'], \n",
+ " max_features= best_params['max_features'], \n",
+ " min_samples_leaf= best_params['min_samples_leaf'], \n",
+ " min_samples_split= best_params['min_samples_split'], \n",
+ " n_estimators= best_params['n_estimators'], \n",
+ " random_state= i_Seed)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HMJcAdLlTQa0"
+ },
+ "source": [
+ "## Visualizar o resultado\n",
+ "> Implementar a visualização do RandomForest."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WWNiy7Z0TQa3"
+ },
+ "source": [
+ "## Selecionar as COLUNAS importantes/relevantes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kOi11YOKTQa4"
+ },
+ "source": [
+ "X_train_RF, X_test_RF = seleciona_colunas_relevantes(ml_RF2, X_train, X_test)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Zn_O7c_DTQbE"
+ },
+ "source": [
+ "## Treina o classificador com as COLUNAS relevantes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "UwEOwzSGTQbF"
+ },
+ "source": [
+ "best_params"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Rr8qDrgvTQbL"
+ },
+ "source": [
+ "# Treina com as COLUNAS relevantes...\n",
+ "ml_RF2.fit(X_train_RF, y_train)\n",
+ "\n",
+ "# Cross-Validation com 10 folds\n",
+ "a_scores_CV = cross_val_score(ml_RF2, X_train_RF, y_train, cv = i_CV)\n",
+ "print(f'Acurácia Media: {100*a_scores_CV.mean():.2f}')\n",
+ "print(f'std médio.....: {100*a_scores_CV.std():.2f}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-mYfQLlsTQbQ"
+ },
+ "source": [
+ "## Valida o modelo usando o dataframe X_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sSD5o1JQTQbR"
+ },
+ "source": [
+ "y_pred_RF = ml_RF2.predict(X_test_RF)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wywF6LymDzKr"
+ },
+ "source": [
+ "# Calcula acurácia\n",
+ "accuracy_score(y_test, y_pred_RF)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hJJsL0IJb6iO"
+ },
+ "source": [
+ "## Estudo do comportamento dos parametros do algoritmo\n",
+ "> Consulte [Optimizing Hyperparameters in Random Forest Classification](https://towardsdatascience.com/optimizing-hyperparameters-in-random-forest-classification-ec7741f9d3f6) para mais detalhes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "navUWMwHi44D"
+ },
+ "source": [
+ "param_range = np.arange(1, 250, 2)\n",
+ "\n",
+ "# Calculate accuracy on training and test set using range of parameter values\n",
+ "train_a_scores_CV, test_a_scores_CV = validation_curve(RandomForestClassifier(), \n",
+ " X_train, \n",
+ " y_train, \n",
+ " param_name=\"n_estimators\", \n",
+ " param_range = param_range, \n",
+ " cv = i_CV, \n",
+ " scoring = \"accuracy\", \n",
+ " n_jobs = -1)\n",
+ "\n",
+ "\n",
+ "# Calculate mean and standard deviation for training set a_scores_CV\n",
+ "train_mean = np.mean(train_a_scores_CV, axis = 1)\n",
+ "train_std = np.std(train_a_scores_CV, axis = 1)\n",
+ "\n",
+ "# Calculate mean and standard deviation for test set a_scores_CV\n",
+ "test_mean = np.mean(test_a_scores_CV, axis = 1)\n",
+ "test_std = np.std(test_a_scores_CV, axis = 1)\n",
+ "\n",
+ "# Plot mean accuracy a_scores_CV for training and test sets\n",
+ "plt.plot(param_range, train_mean, label = \"Training score\", color = \"black\")\n",
+ "plt.plot(param_range, test_mean, label = \"Cross-validation score\", color = \"dimgrey\")\n",
+ "\n",
+ "# Plot accurancy bands for training and test sets\n",
+ "plt.fill_between(param_range, train_mean - train_std, train_mean + train_std, color = \"gray\")\n",
+ "plt.fill_between(param_range, test_mean - test_std, test_mean + test_std, color = \"gainsboro\")\n",
+ "\n",
+ "# Create plot\n",
+ "plt.title(\"Validation Curve With Random Forest\")\n",
+ "plt.xlabel(\"Number Of Trees\")\n",
+ "plt.ylabel(\"Accuracy Score\")\n",
+ "plt.tight_layout()\n",
+ "plt.legend(loc = \"best\")\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rv7TIM9kjsud"
+ },
+ "source": [
+ "param_range = np.arange(1, 250, 2)\n",
+ "\n",
+ "# Calculate accuracy on training and test set using range of parameter values\n",
+ "train_a_scores_CV, test_a_scores_CV = validation_curve(RandomForestClassifier(), \n",
+ " X_train, \n",
+ " y_train, \n",
+ " param_name = \"max_depth\", \n",
+ " param_range = param_range, \n",
+ " cv = i_CV, \n",
+ " scoring = \"accuracy\", \n",
+ " n_jobs = -1)\n",
+ "\n",
+ "# Calculate mean and standard deviation for training set a_scores_CV\n",
+ "train_mean = np.mean(train_a_scores_CV, axis = 1)\n",
+ "train_std = np.std(train_a_scores_CV, axis = 1)\n",
+ "\n",
+ "# Calculate mean and standard deviation for test set a_scores_CV\n",
+ "test_mean = np.mean(test_a_scores_CV, axis = 1)\n",
+ "test_std = np.std(test_a_scores_CV, axis = 1)\n",
+ "\n",
+ "# Plot mean accuracy a_scores_CV for training and test sets\n",
+ "plt.plot(param_range, train_mean, label=\"Training score\", color=\"black\")\n",
+ "plt.plot(param_range, test_mean, label=\"Cross-validation score\", color=\"dimgrey\")\n",
+ "\n",
+ "# Plot accurancy bands for training and test sets\n",
+ "plt.fill_between(param_range, train_mean - train_std, train_mean + train_std, color=\"gray\")\n",
+ "plt.fill_between(param_range, test_mean - test_std, test_mean + test_std, color=\"gainsboro\")\n",
+ "\n",
+ "# Create plot\n",
+ "plt.title(\"Validation Curve With Random Forest\")\n",
+ "plt.xlabel(\"Number Of Trees\")\n",
+ "plt.ylabel(\"Accuracy Score\")\n",
+ "plt.tight_layout()\n",
+ "plt.legend(loc=\"best\")\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lm_fPGYwkJYc"
+ },
+ "source": [
+ "param_range = np.arange(1, 250, 2)\n",
+ "\n",
+ "# Calculate accuracy on training and test set using range of parameter values\n",
+ "train_a_scores_CV, test_a_scores_CV = validation_curve(RandomForestClassifier(), \n",
+ " X_train, \n",
+ " y_train, \n",
+ " param_name='min_samples_leaf', \n",
+ " param_range=param_range,\n",
+ " cv = i_CV, \n",
+ " scoring=\"accuracy\", \n",
+ " n_jobs=-1)\n",
+ "\n",
+ "\n",
+ "# Calculate mean and standard deviation for training set a_scores_CV\n",
+ "train_mean = np.mean(train_a_scores_CV, axis = 1)\n",
+ "train_std = np.std(train_a_scores_CV, axis = 1)\n",
+ "\n",
+ "# Calculate mean and standard deviation for test set a_scores_CV\n",
+ "test_mean = np.mean(test_a_scores_CV, axis = 1)\n",
+ "test_std = np.std(test_a_scores_CV, axis = 1)\n",
+ "\n",
+ "# Plot mean accuracy a_scores_CV for training and test sets\n",
+ "plt.plot(param_range, train_mean, label=\"Training score\", color=\"black\")\n",
+ "plt.plot(param_range, test_mean, label=\"Cross-validation score\", color=\"dimgrey\")\n",
+ "\n",
+ "# Plot accurancy bands for training and test sets\n",
+ "plt.fill_between(param_range, train_mean - train_std, train_mean + train_std, color=\"gray\")\n",
+ "plt.fill_between(param_range, test_mean - test_std, test_mean + test_std, color=\"gainsboro\")\n",
+ "\n",
+ "# Create plot\n",
+ "plt.title(\"Validation Curve With Random Forest\")\n",
+ "plt.xlabel(\"Number Of Trees\")\n",
+ "plt.ylabel(\"Accuracy Score\")\n",
+ "plt.tight_layout()\n",
+ "plt.legend(loc=\"best\")\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "CAqdiSaVlAB8"
+ },
+ "source": [
+ "param_range = np.arange(0.05, 1, 0.05)\n",
+ "\n",
+ "# Calculate accuracy on training and test set using range of parameter values\n",
+ "train_a_scores_CV, test_a_scores_CV = validation_curve(RandomForestClassifier(), \n",
+ " X_train, \n",
+ " y_train, \n",
+ " param_name='min_samples_split', \n",
+ " param_range=param_range,\n",
+ " cv = i_CV, \n",
+ " scoring=\"accuracy\", \n",
+ " n_jobs=-1)\n",
+ "\n",
+ "\n",
+ "# Calculate mean and standard deviation for training set a_scores_CV\n",
+ "train_mean = np.mean(train_a_scores_CV, axis = 1)\n",
+ "train_std = np.std(train_a_scores_CV, axis = 1)\n",
+ "\n",
+ "# Calculate mean and standard deviation for test set a_scores_CV\n",
+ "test_mean = np.mean(test_a_scores_CV, axis = 1)\n",
+ "test_std = np.std(test_a_scores_CV, axis = 1)\n",
+ "\n",
+ "# Plot mean accuracy a_scores_CV for training and test sets\n",
+ "plt.plot(param_range, train_mean, label=\"Training score\", color=\"black\")\n",
+ "plt.plot(param_range, test_mean, label=\"Cross-validation score\", color=\"dimgrey\")\n",
+ "\n",
+ "# Plot accurancy bands for training and test sets\n",
+ "plt.fill_between(param_range, train_mean - train_std, train_mean + train_std, color=\"gray\")\n",
+ "plt.fill_between(param_range, test_mean - test_std, test_mean + test_std, color=\"gainsboro\")\n",
+ "\n",
+ "# Create plot\n",
+ "plt.title(\"Validation Curve With Random Forest\")\n",
+ "plt.xlabel(\"Number Of Trees\")\n",
+ "plt.ylabel(\"Accuracy Score\")\n",
+ "plt.tight_layout()\n",
+ "plt.legend(loc=\"best\")\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cX_gfsbQSdNd"
+ },
+ "source": [
+ "___\n",
+ "# **BOOSTING MODELS**\n",
+ "* São algoritmos muito utilizados nas competições do Kaggle;\n",
+ "* São algoritmos utilizados para melhorar a performance dos algoritmos de Machine Learning;\n",
+ "* Modelos:\n",
+ " - [X] AdaBoost\n",
+ " - [X] XGBoost\n",
+ " - [X] LightGBM\n",
+ " - [X] GradientBoosting\n",
+ " - [X] CatBoost\n",
+ "\n",
+ "## Bagging vs Boosting vc Stacking\n",
+ "### **Bagging**\n",
+ "* Objetivo é reduzir a variância;\n",
+ "\n",
+ "#### Como funciona\n",
+ "* Seleciona várias amostras **COM REPOSIÇÃO** do dataframe de treinamento. Cada amostra é usada para treinar um modelo usando Decision Trees. Como resultado, temos um ensemble de muitas e diferentes modelos (Decision Trees). A média de desses muitos e diferentes modelos (Decision Trees) são usados para produzir o resultado final;\n",
+ "* O resultado final é mais robusto do que usarmos uma simples Decision Tree.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Souce: [Boosting and Bagging: How To Develop A Robust Machine Learning Algorithm](https://hackernoon.com/how-to-develop-a-robust-algorithm-c38e08f32201).\n",
+ "\n",
+ "#### Steps\n",
+ "* Suponha um dataframe X_train (dataframe de treinamento) contendo N observações (instâncias, pontos, linhas) e M COLUNAS (features, atributos).\n",
+ " 1. Bagging seleciona aleatoriamente uma amostra **COM REPOSIÇÃO** de X_train;\n",
+ " 2. Bagging seleciona aleatoriamente M2 (M2 < M) COLUNAS do dataframe extraído do passo (1);\n",
+ " 3. Constroi uma Decision Tree com as M2 COLUNAS do passo (2) e o dataframe obtido no passo (1) e as COLUNAS são avaliadas pela sua habilidade de classificar as observações;\n",
+ " 4. Os passos (1)--> (2)-- (3) são repetidos K vezes (ou seja, K Decision Trees), de forma que as COLUNAS são ranqueadas pelo seu poder preditivo e o resultado final (acurácia, por exemplo) é obtido pela agregação das predições dos K Decision Trees.\n",
+ "\n",
+ "#### Vantagens\n",
+ "* Reduz overfitting;\n",
+ "* Lida bem com dataframes com muitas COLUNAS (high dimensionality);\n",
+ "* Lida automaticamente com Missing Values;\n",
+ "\n",
+ "#### Desvantagem\n",
+ "* A predição final é baseada na média das K Decision Trees, o que pode comprometer a acurácia final.\n",
+ "\n",
+ "___ \n",
+ "### **Boosting**\n",
+ "* Objetivo é melhorar acurácia;\n",
+ "\n",
+ "#### Como funciona\n",
+ "* Os classificadores são usados sequencialmente, de forma que o classificador no passo N aprende com os erros do classificador do passo N-1. Ou seja, o objetivo é melhorar a precisão/acurácia à cada passo aprendendo com o passado.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Source: [Ensemble methods: bagging, boosting and stacking](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205), Joseph Rocca\n",
+ ".\n",
+ "\n",
+ "#### Steps\n",
+ "* Suponha um dataframe X_train (dataframe de treinamento) contendo N observações (instâncias, pontos, linhas) e M COLUNAS (features, atributos).\n",
+ " 1. Boosting seleciona aleatoriamente uma amostra D1 SEM reposição de X_train;\n",
+ " 2. Boosting treina o classificador C1;\n",
+ " 3. Boosting seleciona aleatoriamente a SEGUNDA amostra D2 SEM reposição de X_train e acrescenta à D2 50% das observações que foram classificadas incorretamente para treinar o classificador C2;\n",
+ " 4. Boosting encontra em X_train a amostra D3 que os classificadores C1 e C2 discordam em classificar e treina C3;\n",
+ " 5. Combina (voto) as predições de C1, C2 e C3 para produzir o resultado final.\n",
+ "\n",
+ "#### Vantagens\n",
+ "* Lida bem com dataframes com muitas COLUNAS (high dimensionality);\n",
+ "* Lida automaticamente com Missing Values;\n",
+ "\n",
+ "#### Desvantagem\n",
+ "* Propenso a overfitting. Recomenda-se tratar outliers previamente.\n",
+ "* Requer ajuste cuidadoso dos hyperparameters;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9fgUrkmPk4dr"
+ },
+ "source": [
+ "___\n",
+ "# STACKING\n",
+ "\n",
+ "\n",
+ "\n",
+ "Kd a referência desta figura???"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "B0jxx3ETpOdm"
+ },
+ "source": [
+ "___\n",
+ "# **BOOTSTRAPPING METHODS**\n",
+ "> Antes de falarmos de Boosting ou Bagging, precisamos entender primeiro o que é Bootstrap, pois ambos (Boosting e Bagging) são baseados em Bootstrap.\n",
+ "\n",
+ "* Em Estatística (e em Machine Learning), Bootstrap se refere à extrair amostras aleatórias COM reposição da população X."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SyqazmUuifkE"
+ },
+ "source": [
+ "___\n",
+ "# **ADABOOST(Adaptive Boosting)**\n",
+ "* Quando nada funciona, AdaBoost funciona!\n",
+ "* Foi um dos primeiros algoritmos de Boosting (1995);\n",
+ "* AdaBoost pode ser utilizado tanto para classificação (AdaBoostClassifier) quanto para Regressão (AdaBoostRegressor);\n",
+ "* AdaBoost usam algoritmos DecisionTree como base_estimator;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RU-vzkXqrFVw"
+ },
+ "source": [
+ "## Referências\n",
+ "* [AdaBoost Classifier Example In Python](https://towardsdatascience.com/machine-learning-part-17-boosting-algorithms-adaboost-in-python-d00faac6c464) - Didático e explica exatamente como o AdaBoost funciona.\n",
+ "* [Adaboost for Dummies: Breaking Down the Math (and its Equations) into Simple Terms](https://towardsdatascience.com/adaboost-for-dummies-breaking-down-the-math-and-its-equations-into-simple-terms-87f439757dcf) - Para quem quer entender a matemática por trás do algoritmo.\n",
+ "* [Gradient Boosting and XGBoost](https://medium.com/hackernoon/gradient-boosting-and-xgboost-90862daa6c77)\n",
+ "* [Understanding AdaBoost](https://towardsdatascience.com/understanding-adaboost-2f94f22d5bfe), Akash Desarda.\n",
+ "* [AdaBoost Classifier Example In Python](https://towardsdatascience.com/machine-learning-part-17-boosting-algorithms-adaboost-in-python-d00faac6c464)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6EMrjQDZIMl_"
+ },
+ "source": [
+ "## O que é AdaBoost (Adaptive Boosting)?\n",
+ "* é um dos classificadores do tipo ensemble (combina vários classificadores para aumentar a precisão).\n",
+ "* AdaBoost é um classificador iterativo e forte que combina (ensemble) vários classificadores fracos para melhorar a precisão.\n",
+ "* Qualquer algoritmo de aprendizado de máquina pode ser usado como um classificador de base (parâmetro base_estimator);\n",
+ "\n",
+ "## Parâmetros mais importantes do AdaBoost:\n",
+ "* base_estimator - É um classificador usado para treinar o modelo. Como default, AdaBoost usa o DecisionTreeClassifier. Como dito anteriormente, pode-se utilizar diferentes algoritmos para esse fim.\n",
+ "* n_estimators - Número de base_estimator para treinar iterativamente.\n",
+ "* learning_rate - Controla a contribuição do base_estimator na solução/combinação final;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TzLtHzWNJBix"
+ },
+ "source": [
+ "## Usando diferentes algoritmos para base_estimator\n",
+ "> Como dito anteriormente, pode-se utilizar vários tipos de base_estimator em AdaBoost. Por exemplo, se quisermos usar SVM (Support Vector Machines), devemos proceder da seguinte forma:\n",
+ "\n",
+ "\n",
+ "```\n",
+ "# Importar a biblioteca base_estimator\n",
+ "from sklearn.svm import SVC\n",
+ "\n",
+ "# Treina o classificador (algoritmo)\n",
+ "ml_SVC= SVC(probability=True, kernel='linear')\n",
+ "\n",
+ "# Constroi o modelo AdaBoost\n",
+ "ml_AB = AdaBoostClassifier(n_estimators= 50, base_estimator=ml_SVC, learning_rate=1)\n",
+ "```\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hrj4a4s6hMMB"
+ },
+ "source": [
+ "## Vantagens\n",
+ "* AdaBoost é fácil de implementar;\n",
+ "* AdaBoost corrige os erros do base_estimator iterativamente e melhora a acurácia;\n",
+ "* Faz o Feature Selection automaticamente (**Porque**?);\n",
+ "* Pode-se usar muitos algoritos como base_estimator ;\n",
+ "* Como é um método ensemble, então o modelo final é pouco propenso à overfitting.\n",
+ "\n",
+ "## Desvantagens\n",
+ "* AdaBoost é sensível a ruídos nos dados;\n",
+ "* Altamente impactado por outliers (contribui para overfitting), pois o algoritmo tenta se ajustr a cada ponto da mehor forma possível;\n",
+ "* AdaBoost é mais lento que XGBoost;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bgJmu7YLiyv7"
+ },
+ "source": [
+ "No exemplo a seguir, vou usar RandomForestClassifier com os parâmetros otimizados, ou seja:\n",
+ "\n",
+ "```\n",
+ "best_params= {'bootstrap': False, 'max_depth': 10, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
+ "```\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5VCRNyZT3qvc"
+ },
+ "source": [
+ "best_params= {'bootstrap': False, 'max_depth': 10, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1gIboJdriq61"
+ },
+ "source": [
+ "from sklearn.ensemble import AdaBoostClassifier\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "\n",
+ "# Instancia RandomForestClassifier - Parâmetros otimizados!\n",
+ "ml_RF2= RandomForestClassifier(bootstrap= best_params['bootstrap'], \n",
+ " max_depth= best_params['max_depth'], \n",
+ " max_features= best_params['max_features'], \n",
+ " min_samples_leaf= best_params['min_samples_leaf'], \n",
+ " min_samples_split= best_params['min_samples_split'], \n",
+ " n_estimators= best_params['n_estimators'], \n",
+ " random_state= i_Seed)\n",
+ "# Instancia AdaBoostClassifier\n",
+ "ml_AB= AdaBoostClassifier(n_estimators=100, base_estimator= ml_RF2, random_state= i_Seed)\n",
+ "\n",
+ "# Treina...\n",
+ "ml_AB.fit(X_train, y_train)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "A4Cs81OLD40y"
+ },
+ "source": [
+ "# Cross-Validation com 10 folds\n",
+ "a_scores_CV = cross_val_score(ml_AB, X_train, y_train, cv = i_CV)\n",
+ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_scores_CV.mean(),4)}')\n",
+ "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_scores_CV.std(),4)}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F7Ce5L38ECoC"
+ },
+ "source": [
+ "**Interpretação**: Nosso classificador (AdaBoostClassifier) tem uma acurácia média de 96,72% (base de treinamento). Além disso, o std é da ordem de 2,54%, ou seja, pequena. Vamos tentar melhorar a acurácia do classificador usando parameter tunning (GridSearchCV)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "t5GfnBwEifkO"
+ },
+ "source": [
+ "print(f'Acurácias: {a_scores_CV}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Q9rSpuXyEPA5"
+ },
+ "source": [
+ "# Faz predições com os parametros otimizados...\n",
+ "y_pred = ml_AB.predict(X_test)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2F9k-_eXGDLa"
+ },
+ "source": [
+ "# Confusion Matrix\n",
+ "cf_matrix = confusion_matrix(y_test, y_pred)\n",
+ "cf_labels = ['True_Negative','False_Positive','False_Negative','True_Positive']\n",
+ "cf_categories = ['Zero', 'One']\n",
+ "mostra_confusion_matrix(cf_matrix, group_names= cf_labels, categories= cf_categories)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XweWTjQ9EXLw"
+ },
+ "source": [
+ "## Parameter tunning"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fcrKzse9EbL_"
+ },
+ "source": [
+ "# Dicionário de parâmetros para o parameter tunning.\n",
+ "d_parametros_AB = {'n_estimators':[50, 100, 200], 'learning_rate':[.001, 0.01, 0.05, 0.1, 0.3,1]}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Susc3I7mFDQX"
+ },
+ "source": [
+ "# Invoca a função\n",
+ "ml_AB2, best_params= GridSearchOptimizer(ml_AB, 'ml_AB2', d_parametros_AB, X_train, y_train, X_test, y_test, cv = i_CV)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "w4JjWsusjNS8"
+ },
+ "source": [
+ "___\n",
+ "# **GRADIENT BOOSTING**\n",
+ "* Gradient boosting pode ser usado para resolver problemas de classificação (GradientBoostingClassifier) e Regressão (GradientBoostingRegressor);\n",
+ "* Gradient boosting são um refinamento do AdaBoost (lembra que AdaBoost foi um dos primeiros métodos de Boosting - criado em 1995). O que Gradient Boosting faz adicionalmente ao AdaBoost é minimizar a loss (função perda), ie, minimizar a diferença entre os valores observados de y e os valores preditos.\n",
+ "* Usa Gradient Descent para encontrar as deficiências nas previsões do passo anterior. Gradient Descent é um algoritmo popular e poderoso e usado em Redes Neurais;\n",
+ "* O objetivo do Gradient Boosting é minimizar 'loss function'. Portanto, Gradient Boosting depende da \"loss function\".\n",
+ "* Gradient boosting usam algoritmos DecisionTree como base_estimator;\n",
+ "\n",
+ "## Vantagens\n",
+ "* Não há necessidade de pre-processing;\n",
+ "* Trabalha normalmente com COLUNAS numéricas ou categóricas;\n",
+ "* Trata automaticamente os Missing Values. Ou seja, não é necessário aplicar métodos de Missing Value Imputation;\n",
+ "\n",
+ "## Desvantagens\n",
+ "* Como Gradient Boosting tenta continuamente minimizar os erros à cada iteração, isso pode enfatizar os outliers e causar overfitting. Portanto, deve-se:\n",
+ " * Tratar os outliers previamente OU\n",
+ " * Usar Cross-Validation para neutralizar os efeitos dos outliers (**Eu prefiro este método, pois toma menos tempo**);\n",
+ "* Computacionalmene caro. Geralmente são necessários muitas árvores (> 1000) para se obter bons resultados;\n",
+ "* Devido à flexibilidade (muitos parâmetros para ajustar), então é necessário usar GridSearchCV para encontrar a combinação ótima dos hyperparameters;\n",
+ "\n",
+ "## Referências\n",
+ "* [Gradient Boosting Decision Tree Algorithm Explained](https://towardsdatascience.com/machine-learning-part-18-boosting-algorithms-gradient-boosting-in-python-ef5ae6965be4) - Didático e detalhista.\n",
+ "* [Predicting Wine Quality with Gradient Boosting Machines](https://towardsdatascience.com/predicting-wine-quality-with-gradient-boosting-machines-a-gmb-tutorial-d950b1542065)\n",
+ "* [Parameter Tuning in Gradient Boosting (GBM) with Python](https://www.datacareer.de/blog/parameter-tuning-in-gradient-boosting-gbm/)\n",
+ "* [Tune Learning Rate for Gradient Boosting with XGBoost in Python](https://machinelearningmastery.com/tune-learning-rate-for-gradient-boosting-with-xgboost-in-python/)\n",
+ "* [In Depth: Parameter tuning for Gradient Boosting](https://medium.com/all-things-ai/in-depth-parameter-tuning-for-gradient-boosting-3363992e9bae) - Muito bom\n",
+ "* [Complete Machine Learning Guide to Parameter Tuning in Gradient Boosting (GBM) in Python](https://www.analyticsvidhya.com/blog/2016/02/complete-guide-parameter-tuning-gradient-boosting-gbm-python/)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Q4bUCZs2jNTA"
+ },
+ "source": [
+ "from sklearn.ensemble import GradientBoostingClassifier\n",
+ "\n",
+ "# Instancia...\n",
+ "ml_GB=GradientBoostingClassifier(n_estimators=100, min_samples_split= 2)\n",
+ "\n",
+ "# Treina...\n",
+ "ml_GB.fit(X_train, y_train)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-dr6dyjdXwvd"
+ },
+ "source": [
+ "# Cross-Validation com 10 folds\n",
+ "a_scores_CV = cross_val_score(ml_GB, X_train, y_train, cv = i_CV)\n",
+ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_scores_CV.mean(),4)}')\n",
+ "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_scores_CV.std(),4)}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VlC3y3M5YaGG"
+ },
+ "source": [
+ "print(f'Acurácias: {a_scores_CV}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vnLvQ0ZDYNjB"
+ },
+ "source": [
+ "**Interpretação**: Nosso classificador (GradientBoostingClassifier) tem uma acurácia média de 96,86% (base de treinamento). Além disso, o std é da ordem de 2,52%, ou seja, pequena. Vamos tentar melhorar a acurácia do classificador usando parameter tunning (GridSearchCV)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "D2n1RKZuXq3D"
+ },
+ "source": [
+ "# Faz precições...\n",
+ "y_pred = ml_GB.predict(X_test)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8r6JCzQRGFa0"
+ },
+ "source": [
+ "# Confusion Matrix\n",
+ "cf_matrix = confusion_matrix(y_test, y_pred)\n",
+ "cf_labels = ['True_Negative','False_Positive','False_Negative','True_Positive']\n",
+ "cf_categories = ['Zero', 'One']\n",
+ "mostra_confusion_matrix(cf_matrix, group_names = cf_labels, categories = cf_categories)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KFv-Q2AD5uCk"
+ },
+ "source": [
+ "## Parameter tunning\n",
+ "> Consulte [Complete Machine Learning Guide to Parameter Tuning in Gradient Boosting (GBM) in Python](https://www.analyticsvidhya.com/blog/2016/02/complete-guide-parameter-tuning-gradient-boosting-gbm-python/) para detalhes sobre os parâmetros, significado e etc."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wgU040AcjNTF"
+ },
+ "source": [
+ "# Dicionário de parâmetros para o parameter tunning.\n",
+ "d_parametros_GB= {'learning_rate': [1, 0.5, 0.25, 0.1, 0.05, 0.01]} #,\n",
+ "# 'n_estimators': [1, 2, 4, 8, 16, 32, 64, 100, 200],\n",
+ "# 'max_depth': [5, 10, 15, 20, 25, 30],\n",
+ "# 'min_samples_split': [0.1, 0.3, 0.5, 0.7, 0.9],\n",
+ "# 'min_samples_leaf': [0.1, 0.2, 0.3, 0.4, 0.5],\n",
+ "# 'max_features': list(range(1, X_train.shape[1]))}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "v5KLFlpTjNTH"
+ },
+ "source": [
+ "# Invoca a função\n",
+ "ml_GB2, best_params= GridSearchOptimizer(ml_GB, 'ml_GB2', d_parametros_GB, X_train, y_train, X_test, y_test, cv = i_CV)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YQ6ERz3fi9i2"
+ },
+ "source": [
+ "### Resultado da execução do Gradient Boosting"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RSa7uKw13mKG"
+ },
+ "source": [
+ "```\n",
+ "[Parallel(n_jobs=-1)]: Done 275400 out of 275400 | elapsed: 93.7min finished\n",
+ "\n",
+ "Parametros otimizados: {'learning_rate': 1, 'max_depth': 30, 'max_features': 11, 'min_samples_leaf': 0.1, 'min_samples_split': 0.1, 'n_estimators': 100}\n",
+ "```\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wiJpA2PyjDjR"
+ },
+ "source": [
+ "# Como o procedimento acima levou 93 minutos para executar, então vou estimar ml_GB2 abaixo usando os parâmetros acima estimados\n",
+ "best_params= {'learning_rate': 1, 'max_depth': 30, 'max_features': 11, 'min_samples_leaf': 0.1, 'min_samples_split': 0.1, 'n_estimators': 100}\n",
+ "\n",
+ "#ml_GB2= GradientBoostingClassifier(learning_rate= best_params['learning_rate'], \n",
+ "# max_depth= best_params['max_depth'],\n",
+ "# max_features= best_params['max_features'],\n",
+ "# min_samples_leaf= best_params['min_samples_leaf'],\n",
+ "# min_samples_split= best_params['min_samples_split'],\n",
+ "# n_estimators= best_params['n_estimators'],\n",
+ "# random_state= i_Seed)\n",
+ "\n",
+ "ml_GB2= GradientBoostingClassifier(learning_rate= best_params['learning_rate'], \n",
+ " max_depth= best_params['max_depth'],\n",
+ " min_samples_leaf= best_params['min_samples_leaf'],\n",
+ " min_samples_split= best_params['min_samples_split'],\n",
+ " n_estimators= best_params['n_estimators'],\n",
+ " random_state= i_Seed)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mb14gJ7-jbVM"
+ },
+ "source": [
+ "## Selecionar as COLUNAS importantes/relevantes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "TAqGZIFYm2sU"
+ },
+ "source": [
+ "X_train_GB, X_test_GB = seleciona_colunas_relevantes(ml_GB2, X_train, X_test)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6yiu6dahnBvC"
+ },
+ "source": [
+ "## Treina o classificador com as COLUNAS relevantes "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "APrtWN18nc4t"
+ },
+ "source": [
+ "best_params"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VS0mLdOmnXAY"
+ },
+ "source": [
+ "# Treina com as COLUNAS relevantes\n",
+ "ml_GB2.fit(X_train_GB, y_train)\n",
+ "\n",
+ "# Cross-Validation com 10 folds\n",
+ "a_scores_CV = cross_val_score(ml_GB2, X_train_GB, y_train, cv = i_CV)\n",
+ "print(f'Acurácia Media: {100*a_scores_CV.mean():.2f}')\n",
+ "print(f'std médio.....: {100*a_scores_CV.std():.2f}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vmc9PP_Rn1TN"
+ },
+ "source": [
+ "## Valida o modelo usando o dataframe X_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "e3mnIALvnzP2"
+ },
+ "source": [
+ "y_pred_GB = ml_GB2.predict(X_test_GB)\n",
+ "\n",
+ "# Calcula acurácia\n",
+ "accuracy_score(y_test, y_pred_GB)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kwP9Z2GnkV7r"
+ },
+ "source": [
+ "___\n",
+ "# **XGBOOST (eXtreme Gradient Boosting)**\n",
+ "* XGBoost é uma melhoria de Gradient Boosting. As melhorias são em velocidade e performace, além de corrigir as ineficiências do GradientBoosting.\n",
+ "* Algoritmo preferido pelos Kaggle Grandmasters;\n",
+ "* Paralelizável;\n",
+ "* Estado-da-arte em termos de Machine Learning;\n",
+ "\n",
+ "## Parâmetros relevantes e seus valores iniciais\n",
+ "Consulte [Complete Guide to Parameter Tuning in XGBoost with codes in Python](https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/) para detalhes completos sobre os parâmetros, significado e etc.\n",
+ "\n",
+ "* n_estimators = 100 (100 caso o dataframe for grande. Se o dataframe for médio/pequeno, então 1000) - É o número de árvores desejamos construir;\n",
+ "* max_depth= 3 - Determina quão profundo cada árvore pode crescer durante qualquer round de treinamento. Valores típicos no intervalo [3, 10];\n",
+ "* learning rate= 0.01 - Usado para evitar overfitting, intervalo: [0, 1];\n",
+ "* alpha (somente para problemas de Regressão) - L1 regularization nos pesos. Valores altos resulta em mais regularization;\n",
+ "* lambda (somente para problemas de Regressão) - L2 regularization nos pesos.\n",
+ "* colsample_bytree: 1 - porcentagem de COLUNAS usados por cada árvore. Alto valor pode causar overfitting;\n",
+ "* subsample: 0.8 - porcentagem de amostras usadas por árvore. Um valor baixo pode levar a overfitting;\n",
+ "* gamma: 1 - Controla se um determinado nó será dividido com base na redução esperada na perda após a divisão. Um valor mais alto leva a menos divisões.\n",
+ "* objective: Define a \"loss function\". As opções são:\n",
+ " * reg:linear - Para resolver problemas de regressão;\n",
+ " * reg:logistic - Para resolver problemas de classificação;\n",
+ " * binary:logistic - Para resolver problemas de classificação com cálculo de probabilidades;\n",
+ "\n",
+ "# Referências\n",
+ "* [How exactly XGBoost Works?](https://medium.com/@pushkarmandot/how-exactly-xgboost-works-a320d9b8aeef)\n",
+ "* [Fine-tuning XGBoost in Python like a boss](https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e)\n",
+ "* [Gentle Introduction of XGBoost Library](https://medium.com/@imoisharma/gentle-introduction-of-xgboost-library-2b1ac2669680)\n",
+ "* [A Beginner’s guide to XGBoost](https://towardsdatascience.com/a-beginners-guide-to-xgboost-87f5d4c30ed7)\n",
+ "* [Exploring XGBoost](https://towardsdatascience.com/exploring-xgboost-4baf9ace0cf6)\n",
+ "* [Feature Importance and Feature Selection With XGBoost in Python](https://machinelearningmastery.com/feature-importance-and-feature-selection-with-xgboost-in-python/)\n",
+ "* [Ensemble Learning case study: Running XGBoost on Google Colab free GPU](https://towardsdatascience.com/running-xgboost-on-google-colab-free-gpu-a-case-study-841c90fef101) - Recomendo\n",
+ "* [Predicting movie revenue with AdaBoost, XGBoost and LightGBM](https://towardsdatascience.com/predicting-movie-revenue-with-adaboost-xgboost-and-lightgbm-262eadee6daa)\n",
+ "* [Tuning XGBoost Hyperparameters with Scikit Optimize](https://towardsdatascience.com/how-to-improve-the-performance-of-xgboost-models-1af3995df8ad)\n",
+ "* [An Example of Hyperparameter Optimization on XGBoost, LightGBM and CatBoost using Hyperopt](https://towardsdatascience.com/an-example-of-hyperparameter-optimization-on-xgboost-lightgbm-and-catboost-using-hyperopt-12bc41a271e) - Interessante\n",
+ "* [XGBOOST vs LightGBM: Which algorithm wins the race !!!](https://towardsdatascience.com/lightgbm-vs-xgboost-which-algorithm-win-the-race-1ff7dd4917d) - LightGBM tem se mostrado interessante.\n",
+ "* [From Zero to Hero in XGBoost Tuning](https://towardsdatascience.com/from-zero-to-hero-in-xgboost-tuning-e48b59bfaf58) - Gostei\n",
+ "* [Build XGBoost / LightGBM models on large datasets — what are the possible solutions?](https://towardsdatascience.com/build-xgboost-lightgbm-models-on-large-datasets-what-are-the-possible-solutions-bf882da2c27d)\n",
+ "* [Selecting Optimal Parameters for XGBoost Model Training](https://towardsdatascience.com/selecting-optimal-parameters-for-xgboost-model-training-c7cd9ed5e45e) - Muito bom!\n",
+ "* [CatBoost vs. Light GBM vs. XGBoost](https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iMM_R4_ukV7x"
+ },
+ "source": [
+ "from xgboost import XGBClassifier\n",
+ "import xgboost as xgb\n",
+ "\n",
+ "# Instancia...\n",
+ "ml_XGB= XGBClassifier(silent=False, \n",
+ " scale_pos_weight=1,\n",
+ " learning_rate=0.01, \n",
+ " colsample_bytree = 1,\n",
+ " subsample = 0.8,\n",
+ " objective='binary:logistic', \n",
+ " n_estimators=1000, \n",
+ " reg_alpha = 0.3,\n",
+ " max_depth= 3, \n",
+ " gamma=1, \n",
+ " max_delta_step=5)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "E4wQMlDEFINR"
+ },
+ "source": [
+ "# Treina...\n",
+ "ml_XGB.fit(X_train, y_train)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zAhsTtwGqMkG"
+ },
+ "source": [
+ "# Cross-Validation com 10 folds\n",
+ "a_scores_CV = cross_val_score(ml_XGB, X_train, y_train, cv = i_CV)\n",
+ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_scores_CV.mean(),4)}')\n",
+ "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_scores_CV.std(),4)}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JNyKX6PkrXOk"
+ },
+ "source": [
+ "**Interpretação**: Nosso classificador (XGBClassifier) tem uma acurácia média de 96,72% (base de treinamento). Além disso, o std é da ordem de 2,02%, ou seja, pequena. Vamos tentar melhorar a acurácia do classificador usando parameter tunning (GridSearchCV)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_h0QYv3FkV73"
+ },
+ "source": [
+ "print(f'Acurácias: {a_scores_CV}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AKhhAZLjkV76"
+ },
+ "source": [
+ "# Faz predições...\n",
+ "y_pred = ml_XGB.predict(X_test)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Ir2Kd1PqGHgz"
+ },
+ "source": [
+ "# Confusion Matrix\n",
+ "cf_matrix = confusion_matrix(y_test, y_pred)\n",
+ "cf_labels = ['True_Negative','False_Positive','False_Negative','True_Positive']\n",
+ "cf_categories = ['Zero', 'One']\n",
+ "mostra_confusion_matrix(cf_matrix, group_names= cf_labels, categories= cf_categories)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jEC7gW4qYpWw"
+ },
+ "source": [
+ "## Parameter tunning\n",
+ "### Leitura Adicional:\n",
+ "* [Fine-tuning XGBoost in Python like a boss](https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e)\n",
+ "* [Complete Guide to Parameter Tuning in XGBoost with codes in Python](https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/)\n",
+ "\n",
+ "> Olhando para os resultados acima, qual o melhor modelo?\n",
+ "\n",
+ "XGBoost? Supondo que sim, agora vamos fazer o fine-tuning dos parâmetros do modelo."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "n3MsUONPwIV9"
+ },
+ "source": [
+ "# Dicionário de parâmetros para XGBoost:\n",
+ "d_parametros_XGB = {'min_child_weight': [i for i in np.arange(1, 13)]} #,\n",
+ "# 'gamma': [i for i in np.arange(0, 5, 0.5)],\n",
+ "# 'subsample': [0.6, 0.8, 1.0],\n",
+ "# 'colsample_bytree': [0.6, 0.8, 1.0],\n",
+ "# 'max_depth': [3, 4, 5, 7, 9],\n",
+ "# 'learning_rate': [i for i in np.arange(0.01, 1, 0.1)]}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "CX27FCKmwSni"
+ },
+ "source": [
+ "# Invoca a função\n",
+ "ml_XGB, best_params= GridSearchOptimizer(ml_XGB, 'ml_XGB2', d_parametros_XGB, X_train, y_train, X_test, y_test, cv = i_CV)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9b7uCuF74Hjv"
+ },
+ "source": [
+ "### Resultado da execução do XGBoostClassifier\n",
+ "\n",
+ "```\n",
+ "[Parallel(n_jobs=-1)]: Done 108000 out of 108000 | elapsed: 372.0min finished\n",
+ "\n",
+ "Parametros otimizados: {'colsample_bytree': 0.8, 'gamma': 0.5, 'learning_rate': 0.51, 'max_depth': 5, 'min_child_weight': 1, 'subsample': 0.6}\n",
+ "```\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "n7E0oyxEtbGi"
+ },
+ "source": [
+ "# Como o procedimento acima levou 372 minutos para executar, então vou estimar ml_XGB2 abaixo usando os parâmetros acima estimados\n",
+ "best_params= {'colsample_bytree': 0.8, 'gamma': 0.5, 'learning_rate': 0.51, 'max_depth': 5, 'min_child_weight': 1, 'subsample': 0.6}\n",
+ "\n",
+ "ml_XGB2= XGBClassifier(min_child_weight= best_params['min_child_weight'], \n",
+ " gamma= best_params['gamma'], \n",
+ " subsample= best_params['subsample'], \n",
+ " colsample_bytree= best_params['colsample_bytree'], \n",
+ " max_depth= best_params['max_depth'], \n",
+ " learning_rate= best_params['learning_rate'], \n",
+ " random_state= i_Seed)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CuqyLHTU5Z-j"
+ },
+ "source": [
+ "## Selecionar as COLUNAS importantes/relevantes\n",
+ "* [The Multiple faces of ‘Feature importance’ in XGBoost](https://towardsdatascience.com/be-careful-when-interpreting-your-features-importance-in-xgboost-6e16132588e7)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QPG3JZIpRZ-T"
+ },
+ "source": [
+ "# plot feature importance\n",
+ "from xgboost import plot_importance\n",
+ "\n",
+ "xgb.plot_importance(ml_XGB2, color = 'red')\n",
+ "plt.title('importance', fontsize = 20)\n",
+ "plt.yticks(fontsize = 10)\n",
+ "plt.ylabel('features', fontsize = 20)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EmpRC2lHW-KP"
+ },
+ "source": [
+ "ml_XGB2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4f9MIEBiyq-5"
+ },
+ "source": [
+ "X_train_XGB, X_test_XGB= seleciona_colunas_relevantes(ml_XGB2, X_train, X_test)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F6EayWaY5nMm"
+ },
+ "source": [
+ "## Treina o classificador com as COLUNAS relevantes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Huy18gKI5qad"
+ },
+ "source": [
+ "best_params"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "E3-PaTdc5vZk"
+ },
+ "source": [
+ "# Treina com as COLUNAS relevantes...\n",
+ "ml_XGB2.fit(X_train_XGB, y_train)\n",
+ "\n",
+ "# Cross-Validation com 10 folds\n",
+ "a_scores_CV = cross_val_score(ml_XGB2, X_train_XGB, y_train, cv = i_CV)\n",
+ "print(f'Acurácia Media: {100*a_scores_CV.mean():.2f}')\n",
+ "print(f'std médio.....: {100*a_scores_CV.std():.2f}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tBdYikDU6NhD"
+ },
+ "source": [
+ "## Valida o modelo usando o dataframe X_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GcvY-VdL6VIZ"
+ },
+ "source": [
+ "y_pred_XGB = ml_XGB2.predict(X_test_XGB)\n",
+ "\n",
+ "# Calcula acurácia\n",
+ "accuracy_score(y_test, y_pred_XGB)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8oLtdH-vTSbC"
+ },
+ "source": [
+ "xgb.to_graphviz(ml_XGB2)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "czXQG3MCHfHM"
+ },
+ "source": [
+ "# KNN - KNEIGHBORSCLASSIFIER"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "llTTXNeyHiwx"
+ },
+ "source": [
+ "# BAGGINGCLASSIFIER"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Fbkekd4QHoZO"
+ },
+ "source": [
+ "# EXTRATREESCLASSIFIER"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "widavwR4HzwE"
+ },
+ "source": [
+ "# SVM\n",
+ "https://data-flair.training/blogs/svm-support-vector-machine-tutorial/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "id_Ubulns6We"
+ },
+ "source": [
+ "# NAIVE BAYES"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3e0m7lEnYOV9"
+ },
+ "source": [
+ "# **IMPORTANCIA DAS COLUNAS**\n",
+ "Source: [Plotting Feature Importances](https://www.kaggle.com/grfiv4/plotting-feature-importances)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fjco0HnNYr-N"
+ },
+ "source": [
+ "def mostra_feature_importances(clf, X_train, y_train=None, \n",
+ " top_n=10, figsize=(8,8), print_table=False, title=\"Feature Importances\"):\n",
+ " '''\n",
+ " plot feature importances of a tree-based sklearn estimator\n",
+ " \n",
+ " Note: X_train and y_train are pandas DataFrames\n",
+ " \n",
+ " Note: Scikit-plot is a lovely package but I sometimes have issues\n",
+ " 1. flexibility/extendibility\n",
+ " 2. complicated models/datasets\n",
+ " But for many situations Scikit-plot is the way to go\n",
+ " see https://scikit-plot.readthedocs.io/en/latest/Quickstart.html\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " clf (sklearn estimator) if not fitted, this routine will fit it\n",
+ " \n",
+ " X_train (pandas DataFrame)\n",
+ " \n",
+ " y_train (pandas DataFrame) optional\n",
+ " required only if clf has not already been fitted \n",
+ " \n",
+ " top_n (int) Plot the top_n most-important features\n",
+ " Default: 10\n",
+ " \n",
+ " figsize ((int,int)) The physical size of the plot\n",
+ " Default: (8,8)\n",
+ " \n",
+ " print_table (boolean) If True, print out the table of feature importances\n",
+ " Default: False\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " the pandas dataframe with the features and their importance\n",
+ " \n",
+ " Author\n",
+ " ------\n",
+ " George Fisher\n",
+ " '''\n",
+ " \n",
+ " __name__ = \"mostra_feature_importances\"\n",
+ " \n",
+ " import pandas as pd\n",
+ " import numpy as np\n",
+ " import matplotlib.pyplot as plt\n",
+ " \n",
+ " from xgboost.core import XGBoostError\n",
+ " from lightgbm.sklearn import LightGBMError\n",
+ " \n",
+ " try: \n",
+ " if not hasattr(clf, 'feature_importances_'):\n",
+ " clf.fit(X_train.values, y_train.values.ravel())\n",
+ "\n",
+ " if not hasattr(clf, 'feature_importances_'):\n",
+ " raise AttributeError(\"{} does not have feature_importances_ attribute\".\n",
+ " format(clf.__class__.__name__))\n",
+ " \n",
+ " except (XGBoostError, LightGBMError, ValueError):\n",
+ " clf.fit(X_train.values, y_train.values.ravel())\n",
+ " \n",
+ " feat_imp = pd.DataFrame({'importance':clf.feature_importances_}) \n",
+ " feat_imp['feature'] = X_train.columns\n",
+ " feat_imp.sort_values(by ='importance', ascending = False, inplace = True)\n",
+ " feat_imp = feat_imp.iloc[:top_n]\n",
+ " \n",
+ " feat_imp.sort_values(by='importance', inplace = True)\n",
+ " feat_imp = feat_imp.set_index('feature', drop = True)\n",
+ " feat_imp.plot.barh(title=title, figsize=figsize)\n",
+ " plt.xlabel('Feature Importance Score')\n",
+ " plt.show()\n",
+ " \n",
+ " if print_table:\n",
+ " from IPython.display import display\n",
+ " print(\"Top {} features in descending order of importance\".format(top_n))\n",
+ " display(feat_imp.sort_values(by = 'importance', ascending = False))\n",
+ " \n",
+ " return feat_imp"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ycu_EIGlYUYn"
+ },
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "from xgboost import XGBClassifier\n",
+ "from sklearn.ensemble import ExtraTreesClassifier\n",
+ "from sklearn.tree import ExtraTreeClassifier\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.ensemble import GradientBoostingClassifier\n",
+ "from sklearn.ensemble import BaggingClassifier\n",
+ "from sklearn.ensemble import AdaBoostClassifier\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from lightgbm import LGBMClassifier\n",
+ "\n",
+ "clfs = [XGBClassifier(), LGBMClassifier(), \n",
+ " ExtraTreesClassifier(), ExtraTreeClassifier(),\n",
+ " BaggingClassifier(), DecisionTreeClassifier(),\n",
+ " GradientBoostingClassifier(), LogisticRegression(),\n",
+ " AdaBoostClassifier(), RandomForestClassifier()]\n",
+ "\n",
+ "for clf in clfs:\n",
+ " try:\n",
+ " _ = mostra_feature_importances(clf, X_train, y_train, top_n=X_train.shape[1], title=clf.__class__.__name__)\n",
+ " except AttributeError as e:\n",
+ " print(e)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EwWkjfC8KEZH"
+ },
+ "source": [
+ "# ENSEMBLE METHODS\n",
+ "https://towardsdatascience.com/using-bagging-and-boosting-to-improve-classification-tree-accuracy-6d3bb6c95e5b\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3Uf1RML7xETY"
+ },
+ "source": [
+ "# WOE e IV\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TBNRfYZCyhMP"
+ },
+ "source": [
+ "## Construção do exemplo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gIIroyyP4ZRZ"
+ },
+ "source": [
+ "df_y.head()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PzQQdrkf1ohX"
+ },
+ "source": [
+ "from random import choices\n",
+ "\n",
+ "df_X2= df_X.copy()\n",
+ "df_X2['tipo']= choices(['A', 'B', 'C', 'D'], k= 1000)\n",
+ "df_X2['idade']= np.random.randint(10, 15, size= 1000)\n",
+ "df_X2['target']= df_y['target']\n",
+ "df_X2.head()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "v-OpwIpx4hXJ"
+ },
+ "source": [
+ "df_X2['target'].value_counts()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "yZfqSvbKzeJ3"
+ },
+ "source": [
+ "def Constroi_Buckets(df, i, k= 10):\n",
+ " coluna= 'v'+ str(i)\n",
+ " df[coluna+'_Bucket']= pd.cut(df[coluna], bins= k, labels= np.arange(1, k+1))\n",
+ " df= df.drop(columns= [coluna], axis= 1)\n",
+ " return df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "V6Nrpsx60HD3"
+ },
+ "source": [
+ "for i in np.arange(1,19):\n",
+ " df_X2= Constroi_Buckets(df_X2, i)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "J2Fbh41-03OB"
+ },
+ "source": [
+ "df_X2.head()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "O9r5BeWVxIr3"
+ },
+ "source": [
+ "# Função para calcular WOE e IV\n",
+ "def calculate_woe_iv(dataset, feature, target):\n",
+ "\n",
+ " def codethem(IV):\n",
+ " if IV < 0.02: return 'Useless'\n",
+ " elif IV >= 0.02 and IV < 0.1: return 'Weak'\n",
+ " elif IV >= 0.1 and IV < 0.3: return 'Medium'\n",
+ " elif IV >= 0.3 and IV < 0.5: return 'Strong'\n",
+ " elif IV >= 0.5: return 'Suspicious'\n",
+ " else: return 'None'\n",
+ "\n",
+ " lst = []\n",
+ " for i in range(dataset[feature].nunique()):\n",
+ " val = list(dataset[feature].unique())[i]\n",
+ " lst.append({\n",
+ " 'Value': val,\n",
+ " 'All': dataset[dataset[feature] == val].count()[feature],\n",
+ " 'Good': dataset[(dataset[feature] == val) & (dataset[target] == 0)].count()[feature],\n",
+ " 'Bad': dataset[(dataset[feature] == val) & (dataset[target] == 1)].count()[feature]\n",
+ " })\n",
+ " \n",
+ " dset = pd.DataFrame(lst)\n",
+ " dset['Distr_Good'] = dset['Good']/dset['Good'].sum()\n",
+ " dset['Distr_Bad'] = dset['Bad']/dset['Bad'].sum()\n",
+ " dset['Mean']= dset['All']/dset['All'].sum()\n",
+ " dset['WoE'] = np.log(dset['Distr_Good']/dset['Distr_Bad'])\n",
+ " dset = dset.replace({'WoE': {np.inf: 0, -np.inf: 0}})\n",
+ " dset['IV'] = (dset['Distr_Good'] - dset['Distr_Bad']) * dset['WoE']\n",
+ " #dset= dset.drop(columns= ['Distr_Good', 'Distr_Bad'], axis= 1)\n",
+ "\n",
+ " dset['Predictive_Power']= dset['IV'].map(codethem)\n",
+ " iv = dset['IV'].sum() \n",
+ " dset = dset.sort_values(by='IV') \n",
+ " return dset, iv"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Y8WGjWH63nx_"
+ },
+ "source": [
+ "df_Lab = df_X2.copy()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-N6xr1MgxTiz"
+ },
+ "source": [
+ "def calcula_Predictive_Power(df_Lab, coluna):\n",
+ " print('WoE and IV for column: {}'.format(coluna))\n",
+ " df, iv = calculate_woe_iv(df_Lab, coluna, 'target')\n",
+ " print(df)\n",
+ " print('IV score: {:.2f}'.format(iv))\n",
+ " print('\\n')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ayqN_7WnxVq9"
+ },
+ "source": [
+ "for i in np.arange(1,19):\n",
+ " coluna= 'v'+str(i)+'_Bucket'\n",
+ " calcula_Predictive_Power(df_Lab, coluna)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qtoJVI4Pyx3I"
+ },
+ "source": [
+ "# **IMBALANCED SAMPLE**\n",
+ "> Alguns objetivos como detectar fraude em transações bancárias ou detecção de intrusão em network tem em comum o fato que a classe de interesse (o que queremos detectar), geralmente é um evento raro\n",
+ "\n",
+ "## Exemplo: Detectar fraude\n",
+ "A proporção de fraudes diante de NÃO-FRAUDES são mais ou menos 1%/99%. Neste caso, ao desenvovermos um modelo para detectar fraudes e o modelo classificar todas as instâncias como NÃO-FRAUDE, então o modelo terá uma acurácia de 99%. No entanto, este modelo não nos ajudará em nada.\n",
+ "\n",
+ "## Necessidade de se usar outras métricas \n",
+ "> Recomenda-se utilizar outras métricas (na verdade, é boa prática usar mais de 1 métrica para medir a performance dos modelos) como, por exemplo, F1-Score, Precision/Specificity, Recall/Sensitivity e AUROC.\n",
+ "\n",
+ "## Como lidar com a amostra desbalanceada?\n",
+ "* Under-sampling\n",
+ "> Seleciona aleatoriamente a classe MAJORITÁRIA (em nosso exemplo, NÃO-FRAUDE) até o número de instâncias da classe MINORITÁRIA (FRAUDE);\n",
+ "\n",
+ "* Over-sampling\n",
+ "> Resample aleatoriamente a classe MINORITÁRIA (em nosso exemplo, FRAUDE) até o número de instâncias da classe MAJORITÁRIA (NÃO-FRAUDE), ou uma proporção da classe MAJORITÁRIA. Veja a bibliotea SMOTE (Synthetic Minority Over-Sampling Techniques);\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2o45zx8zw-aB"
+ },
+ "source": [
+ "## EFEITOS DA AMOSTRA DESBALANCEADA"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cCVTPCB-Xkbd"
+ },
+ "source": [
+ "# TPOT\n",
+ "https://towardsdatascience.com/tpot-automated-machine-learning-in-python-4c063b3e5de9"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2ulXii6JXpWd"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_TWUq-z4X4yZ"
+ },
+ "source": [
+ "___\n",
+ "# FEATURETOOLS\n",
+ "https://medium.com/@rrfd/simple-automatic-feature-engineering-using-featuretools-in-python-for-classification-b1308040e183\n",
+ "\n",
+ "https://www.analyticsvidhya.com/blog/2018/08/guide-automated-feature-engineering-featuretools-python/\n",
+ "\n",
+ "https://mlwhiz.com/blog/2019/05/19/feature_extraction/\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "aZUNOgmSgAmq"
+ },
+ "source": [
+ "!pip install featuretools"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_sxdONzsh9rb"
+ },
+ "source": [
+ "df_X.head()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "p5_ynGo1dBJJ"
+ },
+ "source": [
+ "df_X.shape"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "TqJRJXUhiDqf"
+ },
+ "source": [
+ "from random import choices\n",
+ "\n",
+ "df_X2= df_X.copy()\n",
+ "df_X2['tipo'] = choices(['A', 'B', 'C', 'D'], k = 1000)\n",
+ "df_X2['idade'] = np.random.randint(10, 15, size = 1000)\n",
+ "df_X2['id'] = range(0,1000)\n",
+ "df_X2.head()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nR56bGGngk-W"
+ },
+ "source": [
+ "# Automated feature engineering\n",
+ "import featuretools as ft\n",
+ "import featuretools.variable_types as vtypes\n",
+ "\n",
+ "es= ft.EntitySet(id = 'simulacao')\n",
+ "\n",
+ "# adding a dataframe \n",
+ "es.entity_from_dataframe(entity_id = 'df_X2', dataframe = df_X2, index = 'id')\n",
+ "es"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "IOJ4Tr5Ogk6M"
+ },
+ "source": [
+ "es['df_X2'].variables"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1uXPqHDZgkys"
+ },
+ "source": [
+ "variable_types = {'idade': vtypes.Categorical}\n",
+ " \n",
+ "es.entity_from_dataframe(entity_id = 'df_X2', dataframe = df_X2, index = 'id', variable_types= variable_types)\n",
+ "\n",
+ "es = es.normalize_entity(base_entity_id='df_X2', new_entity_id= 'tipo', index='id')\n",
+ "es = es.normalize_entity(base_entity_id='df_X2', new_entity_id= 'idade', index='id')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dnbYTBqugkvm"
+ },
+ "source": [
+ "es"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "I2v_jetdgkr7"
+ },
+ "source": [
+ "feature_matrix, feature_names = ft.dfs(entityset=es, target_entity = 'df_X2', max_depth = 3, verbose = 3, n_jobs= 1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zZiRBvHXgkoJ"
+ },
+ "source": [
+ "feature_matrix.head()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aWiahwKe2d6U"
+ },
+ "source": [
+ "# **EXERCÍCIOS**\n",
+ "> Encontre algoritmos adequados para ser aplicados aos seguintes problemas:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XbSLkbDB2mzK"
+ },
+ "source": [
+ "## Exercício 1 - Credit Card Fraud Detection\n",
+ "Source: [Credit Card Fraud Detection](https://www.kaggle.com/mlg-ulb/creditcardfraud)\n",
+ "\n",
+ "### Leitura suporte\n",
+ "* [Detecting Credit Card Fraud Using Machine Learning](https://towardsdatascience.com/detecting-credit-card-fraud-using-machine-learning-a3d83423d3b8)\n",
+ "* [Credit Card Fraud Detection](https://towardsdatascience.com/credit-card-fraud-detection-a1c7e1b75f59)\n",
+ "\n",
+ "### Dataframe\n",
+ "* [Creditcard.csv](https://raw.githubusercontent.com/MathMachado/DataFrames/master/creditcard.csv)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oYgK6JXd3MgA"
+ },
+ "source": [
+ "## Exercício 2 - Predicting species on IRIS dataset\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "si0rsJvu3O6O"
+ },
+ "source": [
+ "from sklearn import datasets\n",
+ "import xgboost as xgb\n",
+ "\n",
+ "iris = datasets.load_iris()\n",
+ "X_iris = iris.data\n",
+ "y_iris = iris.target"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zom8t4yWC_UC"
+ },
+ "source": [
+ "## Exercício 3 - Predict Wine Quality\n",
+ "> Estimar a qualidade dos vinhos, numa scala de 0–100. A seguir, a qualidade em função da escala:\n",
+ "\n",
+ "* 95–100 Classic: a great wine\n",
+ "* 90–94 Outstanding: a wine of superior character and style\n",
+ "* 85–89 Very good: a wine with special qualities\n",
+ "* 80–84 Good: a solid, well-made wine\n",
+ "* 75–79 Mediocre: a drinkable wine that may have minor flaws\n",
+ "* 50–74 Not recommended\n",
+ "\n",
+ "Source: [Wine Reviews](https://www.kaggle.com/zynicide/wine-reviews)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "klL2Q9Ria96n"
+ },
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from sklearn import datasets\n",
+ "\n",
+ "Wine = datasets.load_wine()\n",
+ "X_vinho = Wine.data\n",
+ "y_vinho = Wine.target"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lhVhSWBgGijq"
+ },
+ "source": [
+ "## Exercício 4 - Predict Parkinson\n",
+ "Source: https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SVCxHqv0VBJn"
+ },
+ "source": [
+ "## Exercício 5 - Predict survivors from Titanic tragedy\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "CwvB8us4eKNi"
+ },
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "\n",
+ "df_titanic = sns.load_dataset('titanic')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZJrT9YIXVdtx"
+ },
+ "source": [
+ "## Exercício 6 - Predict Loan\n",
+ "> Os dados devem ser obtidos diretamente da fonte: [Loan Default Prediction - Imperial College London](https://www.kaggle.com/c/loan-default-prediction/data)\n",
+ "\n",
+ "* [Bank Loan Default Prediction](https://medium.com/@wutianhao910/bank-loan-default-prediction-94d4902db740)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R8-GVu7ZWeA8"
+ },
+ "source": [
+ "## Exercício 7 - Predict the sales of a store.\n",
+ "* [Predicting expected sales for Bigmart’s stores](https://medium.com/diogo-menezes-borges/project-1-bigmart-sale-prediction-fdc04f07dc1e)\n",
+ "* Dataframes\n",
+ " * [Treinamento](https://raw.githubusercontent.com/MathMachado/DataFrames/master/Big_Mart_Sales_III_train.txt)\n",
+ " * [Validação](https://raw.githubusercontent.com/MathMachado/DataFrames/master/Big_Mart_Sales_III_test.txt)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fv9w86j4Wnwj"
+ },
+ "source": [
+ "## Exercício 8 - [The Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)\n",
+ "> Predict the median value of owner occupied homes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5HYRt8-ug1BT"
+ },
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from sklearn import datasets\n",
+ "\n",
+ "Boston = datasets.load_boston()\n",
+ "X_boston = Boston.data\n",
+ "y_boston = Boston.target"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1UDIaqmtXQ0T"
+ },
+ "source": [
+ "## Exercício 9 - Predict the height or weight of a person.\n",
+ "\n",
+ "http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_Dinov_020108_HeightsWeights"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-7R146nIXmMT"
+ },
+ "source": [
+ "## Exercício 10 - Black Friday Sales Prediction - Predict purchase amount.\n",
+ "\n",
+ "This dataset comprises of sales transactions captured at a retail store. It’s a classic dataset to explore and expand your feature engineering skills and day to day understanding from multiple shopping experiences. This is a regression problem. The dataset has 550,069 rows and 12 columns.\n",
+ "\n",
+ "https://github.com/MathMachado/DataFrames/blob/master/blackfriday.zip\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mQ8FPbuLZlIh"
+ },
+ "source": [
+ "## Exercício 11 - Predict the income class of US population.\n",
+ "\n",
+ "http://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Af4NRrchgPlM"
+ },
+ "source": [
+ "## Exercício 12 - Predicting Cancer\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "c4LOlgZW3P40"
+ },
+ "source": [
+ "from sklearn import datasets\n",
+ "cancer = datasets.load_breast_cancer()\n",
+ "X_cancer = cancer.data\n",
+ "y_cancer = cancer.target"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "74PmpT8Ix0tD"
+ },
+ "source": [
+ "## Exercício 13\n",
+ "Source: [Complete Machine Learning Guide to Parameter Tuning in Gradient Boosting (GBM) in Python](https://www.analyticsvidhya.com/blog/2016/02/complete-guide-parameter-tuning-gradient-boosting-gbm-python/).\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WY8GZMixZ9W9"
+ },
+ "source": [
+ "## Exercício 14 - Predict Diabetes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "y92t6tbOge0S"
+ },
+ "source": [
+ "from sklearn import datasets\n",
+ "Diabetes= datasets.load_diabetes()\n",
+ "\n",
+ "X_diabetes = Diabetes.data\n",
+ "y_diabetes = Diabetes.target"
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}