diff --git "a/C\303\263pia_de_Notebooks_NB01_02__Condicionais_hs.ipynb" "b/C\303\263pia_de_Notebooks_NB01_02__Condicionais_hs.ipynb" new file mode 100644 index 000000000..dfefafb07 --- /dev/null +++ "b/C\303\263pia_de_Notebooks_NB01_02__Condicionais_hs.ipynb" @@ -0,0 +1,411 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Cópia de Notebooks/NB01_02__Condicionais.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": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Y-QMrzHhpcu" + }, + "source": [ + "

CONDICIONAIS - IF

\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wYGZ0eGlv--6" + }, + "source": [ + "# **AGENDA**:\n", + "> Veja o **índice** dos itens que serão abordados neste capítulo." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q3FpTG0dh47M" + }, + "source": [ + "___\n", + "# **REFERÊNCIAS**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LWuIj53sVSnA" + }, + "source": [ + "___\n", + "# **CONDICIONAIS**\n", + "> Usado para decidir se uma determinada instrução ou bloco de instruções será executada ou não, isto é, se uma determinada condição for verdadeira, um bloco de instrução será executado." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NyG1l3awJzEq" + }, + "source": [ + "# Não executar o código a seguir:\n", + "if condicao1:\n", + " \n", + "elif condicao2:\n", + " \n", + "elif condicao3:\n", + " \n", + " ...\n", + "elif condicaoN:\n", + " \n", + "else:\n", + " " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FCJBMTh5WX5C" + }, + "source": [ + "## Exemplo 1" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vn5u7CEaWZjH" + }, + "source": [ + "def mensagem(i_idade, i_limite):\n", + " if i_idade > i_limite:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite}'\n", + " print(s_mensagem)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "lW0ME_nVXU4M" + }, + "source": [ + "mensagem(35, 40)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EBBU8Yw2XxUo" + }, + "source": [ + "Nenhuma mensagem? E agora?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xQ23cAjMX1kx", + "outputId": "3612d39b-3f92-40fd-af14-2dfbca6b0697", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "mensagem(45, 40)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "45 é maior que 40\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BeHU0tPuWK4s" + }, + "source": [ + "## Exemplo 2" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gSzCnjS0Fk-d" + }, + "source": [ + "def mensagem2(i_idade, i_limite):\n", + " if i_idade > i_limite:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite}'\n", + " else:\n", + " s_mensagem= f'{i_idade} é menor ou igual a {i_limite}'\n", + " \n", + " print(s_mensagem)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "KxbmxuDwYFX_", + "outputId": "8f1faff1-de34-4967-865f-17453f7992af", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "mensagem2(35, 40)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "35 é menor ou igual a 40\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lToDO6pzWPGL" + }, + "source": [ + "## Exemplo 3" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "a1NlziSbGrIl", + "outputId": "ffed270b-c16f-4d30-cdaf-80ae96898a94", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197 + } + }, + "source": [ + "def mensagem3(i_idade, i_limite1, i_limite2, i_limite3, i_limite4):\n", + " if ((i_idade > i_limite1) and (i_idade < i_limite2)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite1} e menor que {i_limite2}'\n", + " \n", + " elif ((i_idade > i_limite3) and (i_idade < i_limite4)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite3} e menor que {i_limite4}'\n", + " \n", + " else:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite4}'\n", + " \n", + "print(s_mensagem)" + ], + "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[1;32m 9\u001b[0m \u001b[0ms_mensagem\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34mf'{i_idade} é maior que {i_limite4}'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms_mensagem\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 's_mensagem' is not defined" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V8FF3lFLYqui" + }, + "source": [ + "Porque temos um erro nesta função?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "y5F09RKGYyoX" + }, + "source": [ + "**Resposta**: por causa da indentação! A forma correta é:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vR-oFyzAY5UC" + }, + "source": [ + "def mensagem3(i_idade, i_limite1, i_limite2, i_limite3, i_limite4):\n", + " if ((i_idade > i_limite1) and (i_idade < i_limite2)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite1} e menor que {i_limite2}'\n", + " elif ((i_idade > i_limite3) and (i_idade < i_limite4)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite3} e menor que {i_limite4}'\n", + " else:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite4}'\n", + " \n", + " print(s_mensagem)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QgkBOGKdYgGU", + "outputId": "701f4620-817f-41e0-e9d7-f6b06adf6b3d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "mensagem3(35, 10, 20, 30, 40)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "35 é maior que 30 e menor que 40\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LLk7bhjSwZch" + }, + "source": [ + "___\n", + "# **Wrap Up**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lJvjcjm8NQ85" + }, + "source": [ + "___\n", + "# Exercícios\n", + "## **Exercício 1**: \n", + "Escreva uma função em Python que receba um número inteiro i_limite e, na sequência, imprime os números inteiros de 0 a i_limite;\n", + "\n", + "## **outros exercícios**: \n", + "Nos sites abaixo você vai encontrar exercícios de Python:\n", + "### https://pynative.com/python-if-else-and-for-loop-exercise-with-solutions/;\n", + "### https://www.w3resource.com/python-exercises/" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Gi091pZrwbnY" + }, + "source": [ + "# Exercicio 1\n", + "def imprime_inteiros(i_limite):\n", + " for i_inteiro in range(i_limite+1):\n", + " print(i_inteiro, end=' ')\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Vq2andamdPsZ", + "outputId": "7b7f7864-9402-4b13-86eb-c14aa95b55e5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "imprime_inteiros(10)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 1 2 3 4 5 6 7 8 9 10 " + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MG_rtFd0eGgb" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Notebooks/3DP_1_Feature Engineering_Fase1-TESTE.ipynb b/Notebooks/3DP_1_Feature Engineering_Fase1-TESTE.ipynb new file mode 100644 index 000000000..13a846cc6 --- /dev/null +++ b/Notebooks/3DP_1_Feature Engineering_Fase1-TESTE.ipynb @@ -0,0 +1,4856 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + }, + "colab": { + "name": "Copy of 10. Feature Selection Techniques.ipynb", + "provenance": [], + "toc_visible": true, + "include_colab_link": true + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ngLc7b9XiKxN" + }, + "source": [ + "# 3DP_FEATURE ENGINEERING - FASE 1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JNj9RdXbXmWq" + }, + "source": [ + "## Carrega as biblotecas:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "T9JCQatsiKxR" + }, + "source": [ + "from sklearn import feature_selection\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ], + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_ebv3nAzU2ac" + }, + "source": [ + "## Carrega o dataframe\n", + "* A seguir, os principais atributos/features do dataframe:\n", + " * **PassengerID**: ID do passageiro;\n", + " * **Survived**: Indicador, sendo 1= Passageiro sobreviveu e 0= Passageiro morreu;\n", + " * **Pclass**: Classe em que o passageiro viaja (1 classe, 2 classe, 3 classe, etc);\n", + " * **Age**: Idade do Passageiro;\n", + " * **SibSp**: Número de parentes a bordo (esposa, irmãos, pais e etc);\n", + " * **Parch**: Número de pais/crianças a bordo;\n", + " * **Fare**: Valor pago pela viagem;\n", + " * **Cabin**: Cabine do Passageiro;\n", + " * **Embarked**: A porta pelo qual o Passageiro embarcou.\n", + " * **Name**: Nome do Passageiro;\n", + " * **Sex**: Sexo do Passageiro." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8M5uO9r-Vtze" + }, + "source": [ + "url_train= 'https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/Titanic_With_MV.csv'\n", + "url_test= 'https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/Titanic_test.csv'\n", + "\n", + "# Carrega os dataframes de treinamento e teste e define 'PassengerId' como chave\n", + "df_train= pd.read_csv(url_train, index_col='PassengerId')\n", + "df_test= pd.read_csv(url_test, index_col='PassengerId')\n", + "\n", + "# Faz uma cópia dos dados originais da variável resposta 'Survived'\n", + "df_train_Survived = df_train[\"Survived\"].copy()\n", + "\n", + "# merge train and test\n", + "df = df_train.append(df_test, sort= False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QvbVxLs4ZZ0B" + }, + "source": [ + "## Entendendo o dataframe" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yr4kSh-vZcam", + "outputId": "8b2389b7-ad00-4a86-8748-3104a3ec5996", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Número de linhas/instâncias do dataframe\n", + "df.shape" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(1309, 11)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 229 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "icC1tMH1ZhlA", + "outputId": "64a5866c-06ec-4401-bfcc-2d753e070ac5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "source": [ + "# Colunas do dataframe\n", + "df.columns" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n", + " 'Fare', 'Cabin', 'Embarked'],\n", + " dtype='object')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 230 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fsVfiqfjwXcX", + "outputId": "2390399f-b753-448b-eba7-3817c8b8e35d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "source": [ + "# Tratar o nome das colunas usando lower\n", + "df.columns= [cols.lower() for cols in df.columns]\n", + "\n", + "# Verificar se o nome das variáveis estão ok\n", + "df.columns" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['survived', 'pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',\n", + " 'fare', 'cabin', 'embarked'],\n", + " dtype='object')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 231 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "D2opFNkvZ2sf", + "outputId": "e3a5efc3-4e57-4d31-d909-db35cff48129", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 289 + } + }, + "source": [ + "# Informações gerais sobre o dataframe\n", + "df.info()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 1309 entries, 1 to 1309\n", + "Data columns (total 11 columns):\n", + "survived 891 non-null float64\n", + "pclass 1309 non-null int64\n", + "name 1309 non-null object\n", + "sex 1309 non-null object\n", + "age 1046 non-null float64\n", + "sibsp 1309 non-null int64\n", + "parch 1309 non-null int64\n", + "ticket 1309 non-null object\n", + "fare 1308 non-null float64\n", + "cabin 295 non-null object\n", + "embarked 1307 non-null object\n", + "dtypes: float64(3), int64(3), object(5)\n", + "memory usage: 122.7+ KB\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MeHf-pPtaFfM" + }, + "source": [ + "O que você diria do output acima? Que informações você consegue abstrair disso?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GAM06rMgaMnZ", + "outputId": "ae88c0f3-7f46-48e2-fb0a-8494ff4ed1b3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + } + }, + "source": [ + "# Visualizando parte do dataframe\n", + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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survivedpclassnamesexagesibspparchticketfarecabinembarked
PassengerId
10.03Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
21.01Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
31.03Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
50.03Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n", + "
" + ], + "text/plain": [ + " survived pclass ... cabin embarked\n", + "PassengerId ... \n", + "1 0.0 3 ... NaN S\n", + "2 1.0 1 ... C85 C\n", + "3 1.0 3 ... NaN S\n", + "4 1.0 1 ... C123 S\n", + "5 0.0 3 ... NaN S\n", + "\n", + "[5 rows x 11 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 233 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ngoNsaGgaXsJ" + }, + "source": [ + "## Deletar atributos/features que não são de interesse\n", + "* Eu não vejo, a priori, valor na variável 'ticket'. Portanto, vou deletá-la do dataframe." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BIihFnbTaj6K", + "outputId": "c4220be8-0f4c-455c-d087-4404e8360790", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 269 + } + }, + "source": [ + "df= df.drop(['ticket'], axis=1) # axis= 1 indica que se trata de uma operação na coluna do dataframe. Lembre-se: axis= 0 indica operação nas linhas do dataframe.\n", + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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survivedpclassnamesexagesibspparchfarecabinembarked
PassengerId
10.03Braund, Mr. Owen Harrismale22.0107.2500NaNS
21.01Cumings, Mrs. John Bradley (Florence Briggs Th...female38.01071.2833C85C
31.03Heikkinen, Miss. Lainafemale26.0007.9250NaNS
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01053.1000C123S
50.03Allen, Mr. William Henrymale35.0008.0500NaNS
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" + ], + "text/plain": [ + " survived pclass ... cabin embarked\n", + "PassengerId ... \n", + "1 0.0 3 ... NaN S\n", + "2 1.0 1 ... C85 C\n", + "3 1.0 3 ... NaN S\n", + "4 1.0 1 ... C123 S\n", + "5 0.0 3 ... NaN S\n", + "\n", + "[5 rows x 10 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 234 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C8Sb5kOJasHr" + }, + "source": [ + "Observe que a coluna 'ticket' foi de fato deletada do dataframe." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3bbuq8rOawGN" + }, + "source": [ + "A seguir, crio a variável 'survived2' para ajudar no entendimento dos dados:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "AyyfGIGya1bw", + "outputId": "13c90a85-66e7-4b87-9ea6-56dc2f1241ec", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 371 + } + }, + "source": [ + "df['survived2'] = df['survived']\n", + "df['survived2'] = df['survived2'].map({0:'Died',1:'Survived'})\n", + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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survivedpclassnamesexagesibspparchfarecabinembarkedsurvived2
PassengerId
10.03Braund, Mr. Owen Harrismale22.0107.2500NaNSDied
21.01Cumings, Mrs. John Bradley (Florence Briggs Th...female38.01071.2833C85CSurvived
31.03Heikkinen, Miss. Lainafemale26.0007.9250NaNSSurvived
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01053.1000C123SSurvived
50.03Allen, Mr. William Henrymale35.0008.0500NaNSDied
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" + ], + "text/plain": [ + " survived pclass ... embarked survived2\n", + "PassengerId ... \n", + "1 0.0 3 ... S Died\n", + "2 1.0 1 ... C Survived\n", + "3 1.0 3 ... S Survived\n", + "4 1.0 1 ... S Survived\n", + "5 0.0 3 ... S Died\n", + "\n", + "[5 rows x 11 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 235 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Cegg0IcQa6NL" + }, + "source": [ + "## Entendendo as variáveis Originais do Dataframe\n", + "* Vamos verificar como as variáveis estão preenchidas a fim de corrigir possíveis problemas de preenchimento." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_WCbklv0bDlp" + }, + "source": [ + "A função a seguir nos ajudará com o Data Visualization, cruzando a variável-resposta 'Survived' com qualquer outra passada à função:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "epxI-F2UbGGS" + }, + "source": [ + "def Avalia_Taxa_Sobrevivencia(df, column):\n", + " title_xt = pd.crosstab(df[column], df['survived2'])\n", + " print(pd.crosstab(df[column], df['survived2'], margins=True))\n", + " title_xt_pct = title_xt.div(title_xt.sum(1).astype(float), axis=0)\n", + " \n", + " title_xt_pct.plot(kind='bar', stacked=True, title='Taxa de Sobrevivência dos Passageiros', \n", + " color= ['r', 'g'])\n", + " plt.xlabel(column)\n", + " plt.ylabel('Taxa de Sobrevivência')\n", + " plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),shadow=True, ncol=2)\n", + " plt.show()\n", + "\n", + "def Catplot_Graph(x, y, hue= 'survived2', col= None):\n", + " plt.rcdefaults()\n", + " g= sns.catplot(x= x, y= y, hue= hue, palette={'Died':'red','Survived':'blue'}, col= col, data=df, kind= 'bar', height=4, aspect=.7)\n", + " plt.show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a11nwzJKbNE-" + }, + "source": [ + "### Variável 'sex'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j56d6Z6ZbQ2m" + }, + "source": [ + "Vamos avaliar o preenchimento desta variável." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5X-0G4xNbU_b", + "outputId": "4f602695-5eb0-4b4e-a7bf-ad996fdac034", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + } + }, + "source": [ + "df['sex'].value_counts()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "male 833\n", + "female 458\n", + "m 4\n", + "M 3\n", + "f 2\n", + "W 1\n", + "MALE 1\n", + "w 1\n", + "Woman 1\n", + "F 1\n", + "fEMALE 1\n", + "Men 1\n", + "mALE 1\n", + "Female 1\n", + "Name: sex, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 237 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eS2lsALObZwX" + }, + "source": [ + "Qual sua opinião sobre esse preenchimento?\n", + "\n", + "Algum problema?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AYQCZ9HObhXk" + }, + "source": [ + "Aqui temos vários problemas... Olhando para estes resultados, você concorda que 'male', 'm', 'MALE', M', 'mALE' e 'Men' se trata da mesma informação?\n", + "\n", + "Da mesma forma, 'female', 'f', 'F', 'Female', 'fEMALE', 'Woman', 'w' e 'W' também se trata da mesma informação?\n", + "\n", + "Então, vamos fazer o seguinte:\n", + "\n", + "Toda vez que eu encontrar um desses valores: ['m', 'MALE', 'M', 'mALE', 'Men'], vou substituir por 'male'; Toda vez que eu encontrar um desses valores: ['f', 'F', 'Female', 'fEMALE', 'Woman', 'w', 'W'], vou substituit por 'female'. O comando a seguir faz estas substituições:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hQx_tNBQblst" + }, + "source": [ + "Definindo o dicionário para fazermos as substituições dos valores inconsistentes:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LPZzQwRfbnSi", + "outputId": "b8f78b06-59b2-4843-e7cd-9378f86a1df1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + } + }, + "source": [ + "dSex= {}\n", + "dSex.update(dict.fromkeys(['m', 'MALE', 'M', 'mALE', 'Men', 'male'], 'male'))\n", + "dSex.update(dict.fromkeys(['f', 'F', 'Female', 'fEMALE', 'Woman', 'w', 'W', 'female'], 'female'))\n", + "dSex" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'F': 'female',\n", + " 'Female': 'female',\n", + " 'M': 'male',\n", + " 'MALE': 'male',\n", + " 'Men': 'male',\n", + " 'W': 'female',\n", + " 'Woman': 'female',\n", + " 'f': 'female',\n", + " 'fEMALE': 'female',\n", + " 'female': 'female',\n", + " 'm': 'male',\n", + " 'mALE': 'male',\n", + " 'male': 'male',\n", + " 'w': 'female'}" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 238 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YQ3lwKRKbsx0" + }, + "source": [ + "Aplica a transformação:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "idBwRNI7bvCC", + "outputId": "4b83067f-3096-4425-cf46-e7eb365c5e56", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "source": [ + "df['sex2']= df['sex'].map(dSex)\n", + "df['sex2'].value_counts()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "male 843\n", + "female 466\n", + "Name: sex2, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 239 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FzDl78rfb3p5" + }, + "source": [ + "Qual a conclusão? Este preenchimento faz mais sentido que o anterior?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YPOpGyCpb_Yy" + }, + "source": [ + "**Atenção:** Os comandos abaixo são uma alternativa ao map() aplicado anteriormente para corrigir os atributos da variável 'Sex':\n", + "\n", + "```\n", + "df['Sex2'] = df['Sex'].replace(['m', 'MALE', 'M', 'mALE', 'Men'], 'male')\n", + "df['Sex3'] = df['Sex2'].replace(['f', 'F', 'Female', 'fEMALE', 'Woman', 'w', 'W'], 'female') \n", + "df.Sex3.value_counts()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "muDUjFZecMAK" + }, + "source": [ + "Ok, de fato corrigimos os problemas de preenchimento da variável 'sex'. então, vamos renomear nossa variável para o que tínhamos antes:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iWiYCTj8b1Dq", + "outputId": "9f36189e-58a0-4b47-bbb2-a123a8a6ee11", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 371 + } + }, + "source": [ + "# Deleta as variáveis 'sex':\n", + "df= df.drop(columns= ['sex'], axis= 1)\n", + "\n", + "# Renomea a variável auxiliar 'sex2' para 'sex':\n", + "df= df.rename(columns= {'sex2': 'sex'})\n", + "\n", + "# Mostra os dados:\n", + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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survivedpclassnameagesibspparchfarecabinembarkedsurvived2sex
PassengerId
10.03Braund, Mr. Owen Harris22.0107.2500NaNSDiedmale
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31.03Heikkinen, Miss. Laina26.0007.9250NaNSSurvivedfemale
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.1000C123SSurvivedfemale
50.03Allen, Mr. William Henry35.0008.0500NaNSDiedmale
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" + ], + "text/plain": [ + " survived pclass ... survived2 sex\n", + "PassengerId ... \n", + "1 0.0 3 ... Died male\n", + "2 1.0 1 ... Survived female\n", + "3 1.0 3 ... Survived female\n", + "4 1.0 1 ... Survived female\n", + "5 0.0 3 ... Died male\n", + "\n", + "[5 rows x 11 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 240 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XxKmFfe9cSxE", + "outputId": "22eed0ae-4d82-4d36-984d-c34ea9987709", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + } + }, + "source": [ + "sns.catplot(x=\"sex\", kind=\"count\", data=df)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 241 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-tYbYZW1V_eO" + }, + "source": [ + "### Variável 'cabin'\n", + "* No caso da variável 'cabin', vamos construir as variáveis 'deck' e 'seat'" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GCflGdANp4jU", + "outputId": "1bc2cf22-9273-4a5f-d78f-4a4f83b341b2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "set(df['cabin'])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'A10',\n", + " 'A11',\n", + " 'A14',\n", + " 'A16',\n", + " 'A18',\n", + " 'A19',\n", + " 'A20',\n", + " 'A21',\n", + " 'A23',\n", + " 'A24',\n", + " 'A26',\n", + " 'A29',\n", + " 'A31',\n", + " 'A32',\n", + " 'A34',\n", + " 'A36',\n", + " 'A5',\n", + " 'A6',\n", + " 'A7',\n", + " 'A9',\n", + " 'B10',\n", + " 'B101',\n", + " 'B102',\n", + " 'B11',\n", + " 'B18',\n", + " 'B19',\n", + " 'B20',\n", + " 'B22',\n", + " 'B24',\n", + " 'B26',\n", + " 'B28',\n", + " 'B3',\n", + " 'B30',\n", + " 'B35',\n", + " 'B36',\n", + " 'B37',\n", + " 'B38',\n", + " 'B39',\n", + " 'B4',\n", + " 'B41',\n", + " 'B42',\n", + " 'B45',\n", + " 'B49',\n", + " 'B5',\n", + " 'B50',\n", + " 'B51 B53 B55',\n", + " 'B52 B54 B56',\n", + " 'B57 B59 B63 B66',\n", + " 'B58 B60',\n", + " 'B61',\n", + " 'B69',\n", + " 'B71',\n", + " 'B73',\n", + " 'B77',\n", + " 'B78',\n", + " 'B79',\n", + " 'B80',\n", + " 'B82 B84',\n", + " 'B86',\n", + " 'B94',\n", + " 'B96 B98',\n", + " 'C101',\n", + " 'C103',\n", + " 'C104',\n", + " 'C105',\n", + " 'C106',\n", + " 'C110',\n", + " 'C111',\n", + " 'C116',\n", + " 'C118',\n", + " 'C123',\n", + " 'C124',\n", + " 'C125',\n", + " 'C126',\n", + " 'C128',\n", + " 'C130',\n", + " 'C132',\n", + " 'C148',\n", + " 'C2',\n", + " 'C22 C26',\n", + " 'C23 C25 C27',\n", + " 'C28',\n", + " 'C30',\n", + " 'C31',\n", + " 'C32',\n", + " 'C39',\n", + " 'C45',\n", + " 'C46',\n", + " 'C47',\n", + " 'C49',\n", + " 'C50',\n", + " 'C51',\n", + " 'C52',\n", + " 'C53',\n", + " 'C54',\n", + " 'C55 C57',\n", + " 'C6',\n", + " 'C62 C64',\n", + " 'C65',\n", + " 'C68',\n", + " 'C7',\n", + " 'C70',\n", + " 'C78',\n", + " 'C80',\n", + " 'C82',\n", + " 'C83',\n", + " 'C85',\n", + " 'C86',\n", + " 'C87',\n", + " 'C89',\n", + " 'C90',\n", + " 'C91',\n", + " 'C92',\n", + " 'C93',\n", + " 'C95',\n", + " 'C97',\n", + " 'C99',\n", + " 'D',\n", + " 'D10 D12',\n", + " 'D11',\n", + " 'D15',\n", + " 'D17',\n", + " 'D19',\n", + " 'D20',\n", + " 'D21',\n", + " 'D22',\n", + " 'D26',\n", + " 'D28',\n", + " 'D30',\n", + " 'D33',\n", + " 'D34',\n", + " 'D35',\n", + " 'D36',\n", + " 'D37',\n", + " 'D38',\n", + " 'D40',\n", + " 'D43',\n", + " 'D45',\n", + " 'D46',\n", + " 'D47',\n", + " 'D48',\n", + " 'D49',\n", + " 'D50',\n", + " 'D56',\n", + " 'D6',\n", + " 'D7',\n", + " 'D9',\n", + " 'E10',\n", + " 'E101',\n", + " 'E12',\n", + " 'E121',\n", + " 'E17',\n", + " 'E24',\n", + " 'E25',\n", + " 'E31',\n", + " 'E33',\n", + " 'E34',\n", + " 'E36',\n", + " 'E38',\n", + " 'E39 E41',\n", + " 'E40',\n", + " 'E44',\n", + " 'E45',\n", + " 'E46',\n", + " 'E49',\n", + " 'E50',\n", + " 'E52',\n", + " 'E58',\n", + " 'E60',\n", + " 'E63',\n", + " 'E67',\n", + " 'E68',\n", + " 'E77',\n", + " 'E8',\n", + " 'F',\n", + " 'F E46',\n", + " 'F E57',\n", + " 'F E69',\n", + " 'F G63',\n", + " 'F G73',\n", + " 'F2',\n", + " 'F33',\n", + " 'F38',\n", + " 'F4',\n", + " 'G6',\n", + " 'T',\n", + " nan}" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 242 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7E6yje89u7KF" + }, + "source": [ + "Como podemos ver, trata-se de uma variável categórica com vários níveis. Portanto, vamos capturar somente a primeira letra da variável 'cabin'. Para tal, vamos utilizar a função slice().\n", + "\n", + "> slice() - Get substring from a given string using slice object;" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wmZLlSaArR6F" + }, + "source": [ + "A seguir, capturamos a primeira letra da variável 'Cabin':" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hUZTJU0MvVxP", + "outputId": "1b05c1a5-65af-4d90-9a51-696ee929af48", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + } + }, + "source": [ + "# definindo a variável 'deck' que representará a primeira letra da variável 'cabin'\n", + "df[\"deck\"] = df[\"cabin\"].str.slice(0,1) # slice(inicio, tamanho_da_string)\n", + "df['deck'].value_counts()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "C 94\n", + "B 65\n", + "D 46\n", + "E 41\n", + "A 22\n", + "F 21\n", + "G 5\n", + "T 1\n", + "Name: deck, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 243 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6myhrth0rZ6t" + }, + "source": [ + "A seguir, vamos extrair a parte numérica da variável 'cabin' usando Expressões Regulares:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8UXkACPmsfwN" + }, + "source": [ + "# Importar a biblioiteca para Expressões Regulares\n", + "import re" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QKk-fnW4rf4o", + "outputId": "1c9c1b59-19ce-4e10-80ea-61b80a9443fd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + } + }, + "source": [ + "# Primeiramente, usamos a função split() para separar o conteúdo da variável em colunas: \n", + "new = df[\"cabin\"].str.split(\" \", n= 3, expand = True) \n", + "new.head(5)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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0123
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" + ], + "text/plain": [ + " 0 1 2 3\n", + "PassengerId \n", + "1 NaN NaN NaN NaN\n", + "2 C85 None None None\n", + "3 NaN NaN NaN NaN\n", + "4 C123 None None None\n", + "5 NaN NaN NaN NaN" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 245 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dFqoR-Xew9gX" + }, + "source": [ + "Observe acima que o comando gera quantos splits da variável eu quiser. No entanto, por simplicidade, me interessa somente o primeiro split." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_M7vA6WoVG05" + }, + "source": [ + "Agora, vou extrair o número do assento do passageiro usando Expressões Regulares:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rVH5o9KT_IH3", + "outputId": "fbb20adc-123a-4e37-f163-2c980568598f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 136 + } + }, + "source": [ + "# Aqui está o conteúdo de new[0]:\n", + "new[0].head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "PassengerId\n", + "1 NaN\n", + "2 C85\n", + "3 NaN\n", + "4 C123\n", + "5 NaN\n", + "Name: 0, dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 246 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "P7NTcsGOxxSX", + "outputId": "61481b94-bf2c-4259-894b-95e34abc7483", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + } + }, + "source": [ + "new2= new[0].str.extract('(\\d+)')\n", + "new2.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0\n", + "PassengerId \n", + "1 NaN\n", + "2 85\n", + "3 NaN\n", + "4 123\n", + "5 NaN" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 247 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bf8vw2Mc18bQ" + }, + "source": [ + "Por fim, vou carregar esta informação ao dataframe df:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6l6EoRvsxRXn", + "outputId": "760b25ed-ee3a-4a5e-da60-61eaee32c5a8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 527 + } + }, + "source": [ + "df[\"seat\"]= new2\n", + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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survivedpclassnameagesibspparchfarecabinembarkedsurvived2sexdeckseat
PassengerId
10.03Braund, Mr. Owen Harris22.0107.2500NaNSDiedmaleNaNNaN
21.01Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.2833C85CSurvivedfemaleC85
31.03Heikkinen, Miss. Laina26.0007.9250NaNSSurvivedfemaleNaNNaN
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.1000C123SSurvivedfemaleC123
50.03Allen, Mr. William Henry35.0008.0500NaNSDiedmaleNaNNaN
\n", + "
" + ], + "text/plain": [ + " survived pclass ... deck seat\n", + "PassengerId ... \n", + "1 0.0 3 ... NaN NaN\n", + "2 1.0 1 ... C 85\n", + "3 1.0 3 ... NaN NaN\n", + "4 1.0 1 ... C 123\n", + "5 0.0 3 ... NaN NaN\n", + "\n", + "[5 rows x 13 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 248 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LK4V61uy3N9s" + }, + "source": [ + "Por fim, excluir a variável 'cabin':" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4uAr55J43NY7" + }, + "source": [ + "df= df.drop(columns= [\"cabin\"], axis=1, errors=\"ignore\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qZuH7YJXZCgY" + }, + "source": [ + "### Variável 'embarked'" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nTPikhrIZGya", + "outputId": "fd84dd6a-7289-40d1-feab-9e5191b91258", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + } + }, + "source": [ + "df['embarked'].value_counts()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "S 914\n", + "C 270\n", + "Q 123\n", + "Name: embarked, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 250 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ixbZsuqOZsOc", + "outputId": "f7ace000-ed9d-455a-a4ce-a8722ecb9dd3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + } + }, + "source": [ + "sns.catplot(x=\"embarked\", kind=\"count\", data=df)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 251 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VvdU8aAwZNvG" + }, + "source": [ + "Não vejo problemas com esta variável. Vamos em frente..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QBWfecCEF9ie" + }, + "source": [ + "### Variável 'pclass'" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5TajFI92F_UI", + "outputId": "b762b729-d4c2-4e80-9dd2-334f67ea72ec", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + } + }, + "source": [ + "df['pclass'].value_counts()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "3 709\n", + "1 323\n", + "2 277\n", + "Name: pclass, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 252 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "atG9HFsYGNHE" + }, + "source": [ + "Algum problema com esta variável?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hdBdLzIPg3xD", + "outputId": "c6e49200-d76b-4025-9331-4ba999a09b3f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + } + }, + "source": [ + "sns.catplot(x=\"pclass\", kind=\"count\", data=df)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 253 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Qrnc6VUKSTNp" + }, + "source": [ + "### Variável 'parch'" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2i4ed-0zSvJc", + "outputId": "029427d3-2436-4fe8-f957-9b8ec2baf4ec", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + } + }, + "source": [ + "df['parch'].value_counts()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 1002\n", + "1 170\n", + "2 113\n", + "3 8\n", + "5 6\n", + "4 6\n", + "9 2\n", + "6 2\n", + "Name: parch, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 254 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "qd7u__6KZ6DM", + "outputId": "b9638d31-849f-4888-fe06-a39852a1a9ce", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + } + }, + "source": [ + "sns.catplot(x=\"parch\", kind=\"count\", data=df)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 255 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z9vM3vktC7BG" + }, + "source": [ + "#### Exercício:\n", + "* Criar o atributo 'sozinho_parch', onde sozinho= 1 significa que o passageiro viaja sozinho e 0, caso contrário." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Nd4TyOYjs-HW" + }, + "source": [ + "# Função para retornar 0 ou 1 em função dos valores de variavel\n", + "def sozinho(variavel):\n", + " if (variavel == 0):\n", + " return 1\n", + " else:\n", + " return 0" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5oByiBuos_B3", + "outputId": "ca493249-7147-4273-e3ac-cf22ff8ecec7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 527 + } + }, + "source": [ + "df['sozinho_parch'] = df['parch'].map(sozinho)\n", + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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survivedpclassnameagesibspparchfareembarkedsurvived2sexdeckseatsozinho_parch
PassengerId
10.03Braund, Mr. Owen Harris22.0107.2500SDiedmaleNaNNaN1
21.01Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.2833CSurvivedfemaleC851
31.03Heikkinen, Miss. Laina26.0007.9250SSurvivedfemaleNaNNaN1
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.1000SSurvivedfemaleC1231
50.03Allen, Mr. William Henry35.0008.0500SDiedmaleNaNNaN1
\n", + "
" + ], + "text/plain": [ + " survived pclass ... seat sozinho_parch\n", + "PassengerId ... \n", + "1 0.0 3 ... NaN 1\n", + "2 1.0 1 ... 85 1\n", + "3 1.0 3 ... NaN 1\n", + "4 1.0 1 ... 123 1\n", + "5 0.0 3 ... NaN 1\n", + "\n", + "[5 rows x 13 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 257 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C1ICby1oSd41" + }, + "source": [ + "### Variável 'sibsp'" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5n7JNEQqTNjz", + "outputId": "dc13b210-2928-488d-84e9-22a36929848a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 153 + } + }, + "source": [ + "df['sibsp'].value_counts()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 891\n", + "1 319\n", + "2 42\n", + "4 22\n", + "3 20\n", + "8 9\n", + "5 6\n", + "Name: sibsp, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 258 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NLfMhiy0x4u5" + }, + "source": [ + "* Algum problema?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nayYFRK9g8iV", + "outputId": "feb5e2e5-a924-49ee-8f1c-7d3f56745e5f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + } + }, + "source": [ + "sns.catplot(x=\"sibsp\", kind=\"count\", data=df)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 259 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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21.01Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.2833CSurvivedfemaleC8510
31.03Heikkinen, Miss. Laina26.0007.9250SSurvivedfemaleNaNNaN11
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.1000SSurvivedfemaleC12310
50.03Allen, Mr. William Henry35.0008.0500SDiedmaleNaNNaN11
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" + ], + "text/plain": [ + " survived pclass ... sozinho_parch sozinho_sibsp\n", + "PassengerId ... \n", + "1 0.0 3 ... 1 0\n", + "2 1.0 1 ... 1 0\n", + "3 1.0 3 ... 1 1\n", + "4 1.0 1 ... 1 0\n", + "5 0.0 3 ... 1 1\n", + "\n", + "[5 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 260 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0MO9jj2NvGp_" + }, + "source": [ + "### Variável 'fare'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UuWlMV6XvQHs" + }, + "source": [ + "Transformações: arredondar variável Fare." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "boAj64RHvQHu" + }, + "source": [ + "df['fare']= round(df['fare'], 0)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3CIqHUJpvcPa" + }, + "source": [ + "### Variável 'age'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VULFXjvap3qZ" + }, + "source": [ + "Transformações: arredondar variável 'age'." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kpNhCRxcp7h9" + }, + "source": [ + "df['age']= round(df['age'], 0)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B0fZMKKpdHIl" + }, + "source": [ + "## Derivar outros atributos/features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H6n6PzWjoSYf" + }, + "source": [ + "### Variável 'mv_age':\n", + "* Variável (dummy) que assume os valores 1, se o valor de age> 0 e 0, caso contrário." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QluOnZD7kHFW", + "outputId": "26077a35-f1ea-4d12-bf39-3733787d9168", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 527 + } + }, + "source": [ + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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21.01Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.0CSurvivedfemaleC8510
31.03Heikkinen, Miss. Laina26.0008.0SSurvivedfemaleNaNNaN11
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.0SSurvivedfemaleC12310
50.03Allen, Mr. William Henry35.0008.0SDiedmaleNaNNaN11
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" + ], + "text/plain": [ + " survived pclass ... sozinho_parch sozinho_sibsp\n", + "PassengerId ... \n", + "1 0.0 3 ... 1 0\n", + "2 1.0 1 ... 1 0\n", + "3 1.0 3 ... 1 1\n", + "4 1.0 1 ... 1 0\n", + "5 0.0 3 ... 1 1\n", + "\n", + "[5 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 263 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qKMVIXGDsNkh" + }, + "source": [ + "Para construir a variável 'mv_age', vamos utilizar a função pd.isna(). Por exemplo, o comando abaixo verifica se cada linha/observação da variável 'age' é um NaN." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UHzKFytXsNkh", + "outputId": "45bc64e2-5708-493a-9e2e-3f2ac06c37ab", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "df['age'].isna().sum()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "263" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 264 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lW4NYZrjsNkk" + }, + "source": [ + "A seguir, criamos uma variável auxiliar intitulada 'mv_aux', que receberá 'True', caso 'age' seja NaN e 'False', caso contrário.\n", + "\n", + "Veja abaixo:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-bTvVuVpsNkl", + "outputId": "3efb4a54-5d14-40f1-b620-5fb9cdbeff72", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 527 + } + }, + "source": [ + "df['mv_aux']= df['age'].isna()\n", + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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survivedpclassnameagesibspparchfareembarkedsurvived2sexdeckseatsozinho_parchsozinho_sibspmv_aux
PassengerId
10.03Braund, Mr. Owen Harris22.0107.0SDiedmaleNaNNaN10False
21.01Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.0CSurvivedfemaleC8510False
31.03Heikkinen, Miss. Laina26.0008.0SSurvivedfemaleNaNNaN11False
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.0SSurvivedfemaleC12310False
50.03Allen, Mr. William Henry35.0008.0SDiedmaleNaNNaN11False
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" + ], + "text/plain": [ + " survived pclass ... sozinho_sibsp mv_aux\n", + "PassengerId ... \n", + "1 0.0 3 ... 0 False\n", + "2 1.0 1 ... 0 False\n", + "3 1.0 3 ... 1 False\n", + "4 1.0 1 ... 0 False\n", + "5 0.0 3 ... 1 False\n", + "\n", + "[5 rows x 15 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 265 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "R_gk5S4nsNko", + "outputId": "0f69dcb5-75e6-4280-da3d-bab15059d47c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 527 + } + }, + "source": [ + "# Adiciona a nova coluna baseado no dicionario \n", + "df['mv_age'] = df['mv_aux'].map({True: 1, False: 0})\n", + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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survivedpclassnameagesibspparchfareembarkedsurvived2sexdeckseatsozinho_parchsozinho_sibspmv_auxmv_age
PassengerId
10.03Braund, Mr. Owen Harris22.0107.0SDiedmaleNaNNaN10False0
21.01Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.0CSurvivedfemaleC8510False0
31.03Heikkinen, Miss. Laina26.0008.0SSurvivedfemaleNaNNaN11False0
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.0SSurvivedfemaleC12310False0
50.03Allen, Mr. William Henry35.0008.0SDiedmaleNaNNaN11False0
\n", + "
" + ], + "text/plain": [ + " survived pclass ... mv_aux mv_age\n", + "PassengerId ... \n", + "1 0.0 3 ... False 0\n", + "2 1.0 1 ... False 0\n", + "3 1.0 3 ... False 0\n", + "4 1.0 1 ... False 0\n", + "5 0.0 3 ... False 0\n", + "\n", + "[5 rows x 16 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 266 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IHzMcHl8sNkq" + }, + "source": [ + "Deleta a variável auxiliar 'mv_aux':" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DYKh0uMYsNks", + "outputId": "ddcc284c-af6f-4761-8dc4-bc25e4ac94b2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 527 + } + }, + "source": [ + "df= df.drop(columns= ['mv_aux'], axis=1)\n", + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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survivedpclassnameagesibspparchfareembarkedsurvived2sexdeckseatsozinho_parchsozinho_sibspmv_age
PassengerId
10.03Braund, Mr. Owen Harris22.0107.0SDiedmaleNaNNaN100
21.01Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.0CSurvivedfemaleC85100
31.03Heikkinen, Miss. Laina26.0008.0SSurvivedfemaleNaNNaN110
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.0SSurvivedfemaleC123100
50.03Allen, Mr. William Henry35.0008.0SDiedmaleNaNNaN110
\n", + "
" + ], + "text/plain": [ + " survived pclass ... sozinho_sibsp mv_age\n", + "PassengerId ... \n", + "1 0.0 3 ... 0 0\n", + "2 1.0 1 ... 0 0\n", + "3 1.0 3 ... 1 0\n", + "4 1.0 1 ... 0 0\n", + "5 0.0 3 ... 1 0\n", + "\n", + "[5 rows x 15 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 267 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "34-Qbd_QrC8W" + }, + "source": [ + "Qual a relação entre a variável 'mv_age' e a variável-resposta?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bhY8-UjyrC8Z", + "outputId": "f9c29d9b-3ba4-4eb4-ffa6-1f8b6d55a264", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 383 + } + }, + "source": [ + "Avalia_Taxa_Sobrevivencia(df, 'mv_age')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "survived2 Died Survived All\n", + "mv_age \n", + "0 424 290 714\n", + "1 125 52 177\n", + "All 549 342 891\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "umQ3fIKlsNlN" + }, + "source": [ + "### Variável 'age_category'\n", + "* Construir a variável 'age_category' baseado na variável 'age'." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "W66GkyuKkhFe" + }, + "source": [ + "def Age_Category(age):\n", + " if (age <= 1):\n", + " return 1\n", + " elif (age <= 5):\n", + " return 2\n", + " elif(age <= 10):\n", + " return 3\n", + " elif (age <= 15):\n", + " return 4\n", + " elif (age <= 20):\n", + " return 5\n", + " elif (age <= 25):\n", + " return 6\n", + " elif(age < 30):\n", + " return 7\n", + " elif(age < 35):\n", + " return 8\n", + " elif(age < 40):\n", + " return 9\n", + " elif(age < 45):\n", + " return 10\n", + " elif(age < 50):\n", + " return 11\n", + " elif(age < 60):\n", + " return 12\n", + " elif(age < 70):\n", + " return 13\n", + " elif(age < 80):\n", + " return 14\n", + " else:\n", + " return 15" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "TnLzC6hCkuBL" + }, + "source": [ + "df['age_category'] = df['age'].map(Age_Category)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "kG8td6HPsNlP", + "outputId": "a1debdae-ae3e-41e4-fc8f-52696809fc11", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "set(df['age_category']) # Esse comando mostra os NaN's da variável" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 271 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B_3s5cgxfNKQ" + }, + "source": [ + "### Variável 'title'\n", + "\n", + "* Para fins de Data Manipulation, vamos capturar o tratamento dos passageiros contido na variável 'Name'. Ou seja, 'Mr.', 'Mrs.', 'Miss' e etc...\n", + "\n", + "> Fonte: As funções get_title e title_map foram extraídas de https://www.kaggle.com/tjsauer/titanic-survival-python-solution" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gslSjRdDoJFY", + "outputId": "68f27f6d-d52b-48e8-c2e3-ad86acf44792", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 527 + } + }, + "source": [ + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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21.01Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.0CSurvivedfemaleC851009
31.03Heikkinen, Miss. Laina26.0008.0SSurvivedfemaleNaNNaN1107
41.01Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.0SSurvivedfemaleC1231009
50.03Allen, Mr. William Henry35.0008.0SDiedmaleNaNNaN1109
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" + ], + "text/plain": [ + " survived pclass ... mv_age age_category\n", + "PassengerId ... \n", + "1 0.0 3 ... 0 6\n", + "2 1.0 1 ... 0 9\n", + "3 1.0 3 ... 0 7\n", + "4 1.0 1 ... 0 9\n", + "5 0.0 3 ... 0 9\n", + "\n", + "[5 rows x 16 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 272 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nfIG6toGfhd5" + }, + "source": [ + "def get_title(name):\n", + " if '.' in name:\n", + " return name.split(',')[1].split('.')[0].strip()\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "def title_map(title):\n", + " if title in ['Mr', 'Ms']:\n", + " return 1\n", + " elif title in ['Master']:\n", + " return 2\n", + " elif title in ['Ms','Mlle','Miss']:\n", + " return 3\n", + " elif title in [\"Mme\", \"Ms\", \"Mrs\"]:\n", + " return 4\n", + " elif title in [\"Jonkheer\", \"Don\", \"Sir\", \"the Countess\", \"Dona\", \"Lady\"]:\n", + " return 5\n", + " elif title in [\"Capt\", \"Col\", \"Major\", \"Dr\", \"Rev\"]:\n", + " return 6\n", + " else:\n", + " return 7" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7qNUwnCepe_x" + }, + "source": [ + "Captura o tratamento dos passageiros:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "r-Ltf33vgJ6Q", + "outputId": "930abc58-2b12-434e-9e93-c3da788dc000", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "df['title'] = df['name'].apply(get_title).apply(title_map) \n", + "set(df['title']) # Esse comando mostra os NaN's da variável" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{1, 2, 3, 4, 5, 6}" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 274 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D3hY0WVhpRYK" + }, + "source": [ + "Drop a coluna 'Name', pois não vamos mais precisar dela em nossas análises:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Y8i3xKCes5WF" + }, + "source": [ + "df= df.drop(columns= [\"name\"], axis=1)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7Sl1uFdwpW3y" + }, + "source": [ + "Apresenta o conteúdo do dataframe:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2uFnw-pZpan-", + "outputId": "a72224f4-41de-406a-da4c-07be3c824184", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + } + }, + "source": [ + "df.head(10)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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survivedpclassagesibspparchfareembarkedsurvived2sexdeckseatsozinho_parchsozinho_sibspmv_ageage_categorytitle
PassengerId
10.0322.0107.0SDiedmaleNaNNaN10061
21.0138.01071.0CSurvivedfemaleC8510094
31.0326.0008.0SSurvivedfemaleNaNNaN11073
41.0135.01053.0SSurvivedfemaleC12310094
50.0335.0008.0SDiedmaleNaNNaN11091
60.03NaN008.0QDiedmaleNaNNaN111151
70.0154.00052.0SDiedmaleE46110121
80.032.03121.0SDiedmaleNaNNaN00022
91.0327.00211.0SSurvivedfemaleNaNNaN01074
101.0214.01030.0CSurvivedfemaleNaNNaN10044
\n", + "
" + ], + "text/plain": [ + " survived pclass age ... mv_age age_category title\n", + "PassengerId ... \n", + "1 0.0 3 22.0 ... 0 6 1\n", + "2 1.0 1 38.0 ... 0 9 4\n", + "3 1.0 3 26.0 ... 0 7 3\n", + "4 1.0 1 35.0 ... 0 9 4\n", + "5 0.0 3 35.0 ... 0 9 1\n", + "6 0.0 3 NaN ... 1 15 1\n", + "7 0.0 1 54.0 ... 0 12 1\n", + "8 0.0 3 2.0 ... 0 2 2\n", + "9 1.0 3 27.0 ... 0 7 4\n", + "10 1.0 2 14.0 ... 0 4 4\n", + "\n", + "[10 rows x 16 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 276 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ci5FKBazGidH" + }, + "source": [ + "### Variável 'family_size'\n", + "* As variáveis 'sibsp' e 'parch' estão relacionadas ao grupo familiar. Portanto, vamos criar a variável 'family_size', da seguinte forma:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DICRTPxhGvt5" + }, + "source": [ + "df['family_size']= df['sibsp']+df['parch']+1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "6tLLmTrZhDvG", + "outputId": "6d120e96-a9b6-4291-d7ad-f64b3dba1c6a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + } + }, + "source": [ + "sns.catplot(x=\"family_size\", kind=\"count\", data=df)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 278 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ], + "text/plain": [ + " survived pclass age ... age_category title family_size\n", + "PassengerId ... \n", + "1 0.0 3 22.0 ... 6 1 2\n", + "2 1.0 1 38.0 ... 9 4 2\n", + "3 1.0 3 26.0 ... 7 3 1\n", + "4 1.0 1 35.0 ... 9 4 2\n", + "5 0.0 3 35.0 ... 9 1 1\n", + "\n", + "[5 rows x 17 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 282 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "htQ1dODRwfHw", + "outputId": "e7ea64a5-ff9e-4a2e-e4d9-d224192a4a7b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 275 + } + }, + "source": [ + "df.plot.scatter('age','fare', s= 50, c= 'survived')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 291 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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CZV9PksvlpNFoaPPmzZLrKa1kZ2dTx44d7e4/ICCAtFot/fLLLz5vr0uXLqRS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\naNOmDdq0aYP9+/cjNTW1WGVTKBT4/PP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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p6KuSmVpeo_6" + }, + "source": [ + "# Conclusão" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GxVLsfqXesFC" + }, + "source": [ + "df.head(50)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aidvIN0ZyLx2" + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oAIrbEJ5nsiz" + }, + "source": [ + "# Salvar cópia do dataframe" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "f2ktaBhAnxPi" + }, + "source": [ + "df.to_csv(\"df_3DP_FE1.csv\", sep= ',', index = True, header=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GhB-30cIXtJZ" + }, + "source": [ + "# Exercícios\n", + "* Para cada dataframe a seguir, avalie o que necessita ser feito em termos de qualidade de dados." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "caFkC6oCmUKK" + }, + "source": [ + "## Exercício 1 - Predict Breast Cancer" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vhOM-Z9zmf-f" + }, + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.datasets import load_breast_cancer\n", + "\n", + "cancer = load_breast_cancer()\n", + "X= cancer['data']\n", + "y= cancer['target']\n", + "\n", + "df_cancer = pd.DataFrame(np.c_[X, y], columns= np.append(cancer['feature_names'], ['target']))\n", + "df_cancer['target'] = df_cancer['target'].map({0: 'malign', 1: 'benign'})\n", + "df_cancer.head()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1qruqUDqnvMc" + }, + "source": [ + "## Exercício 2 - Predict Boston Housing Price" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "trxK8YXNnsam" + }, + "source": [ + "from sklearn.datasets import load_boston\n", + "\n", + "boston = load_boston()\n", + "X= boston['data']\n", + "y= boston['target']\n", + "\n", + "df_boston = pd.DataFrame(np.c_[X, y], columns= np.append(boston['feature_names'], ['target']))\n", + "df_boston.head()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-CawPH2nb5cl" + }, + "source": [ + "## Exercícios 3 - Diabetes\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_lVjBS7QcZuT" + }, + "source": [ + "from sklearn.datasets import load_diabetes\n", + "\n", + "diabetes = load_diabetes()\n", + "X= diabetes['data']\n", + "y= diabetes['target']\n", + "\n", + "df_diabetes = pd.DataFrame(np.c_[X, y], columns= np.append(diabetes['feature_names'], ['target']))\n", + "df_diabetes.head()" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Notebooks/NB01_02__Condicionais: if, elif, else_hs.ipynb b/Notebooks/NB01_02__Condicionais: if, elif, else_hs.ipynb new file mode 100644 index 000000000..e27c4913c --- /dev/null +++ b/Notebooks/NB01_02__Condicionais: if, elif, else_hs.ipynb @@ -0,0 +1,373 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Notebooks/NB01_02__Condicionais.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": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Y-QMrzHhpcu" + }, + "source": [ + "

CONDICIONAIS - IF

\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wYGZ0eGlv--6" + }, + "source": [ + "# **AGENDA**:\n", + "> Veja o **índice** dos itens que serão abordados neste capítulo." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q3FpTG0dh47M" + }, + "source": [ + "___\n", + "# **REFERÊNCIAS**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LWuIj53sVSnA" + }, + "source": [ + "___\n", + "# **CONDICIONAIS**\n", + "> Usado para decidir se uma determinada instrução ou bloco de instruções será executada ou não, isto é, se uma determinada condição for verdadeira, um bloco de instrução será executado." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NyG1l3awJzEq" + }, + "source": [ + "# Não executar o código a seguir:\n", + "if condicao1:\n", + " \n", + "elif condicao2:\n", + " \n", + "elif condicao3:\n", + " \n", + " ...\n", + "elif condicaoN:\n", + " \n", + "else:\n", + " " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FCJBMTh5WX5C" + }, + "source": [ + "## Exemplo 1" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vn5u7CEaWZjH" + }, + "source": [ + "def mensagem(i_idade, i_limite):\n", + " if i_idade > i_limite:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite}'\n", + " print(s_mensagem)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "lW0ME_nVXU4M" + }, + "source": [ + "mensagem(35, 40)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EBBU8Yw2XxUo" + }, + "source": [ + "Nenhuma mensagem? E agora?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xQ23cAjMX1kx", + "outputId": "3612d39b-3f92-40fd-af14-2dfbca6b0697", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "mensagem(45, 40)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "45 é maior que 40\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BeHU0tPuWK4s" + }, + "source": [ + "## Exemplo 2" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gSzCnjS0Fk-d" + }, + "source": [ + "def mensagem2(i_idade, i_limite):\n", + " if i_idade > i_limite:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite}'\n", + " else:\n", + " s_mensagem= f'{i_idade} é menor ou igual a {i_limite}'\n", + " \n", + " print(s_mensagem)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "KxbmxuDwYFX_", + "outputId": "8f1faff1-de34-4967-865f-17453f7992af", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "mensagem2(35, 40)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "35 é menor ou igual a 40\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lToDO6pzWPGL" + }, + "source": [ + "## Exemplo 3" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "a1NlziSbGrIl", + "outputId": "ffed270b-c16f-4d30-cdaf-80ae96898a94", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197 + } + }, + "source": [ + "def mensagem3(i_idade, i_limite1, i_limite2, i_limite3, i_limite4):\n", + " if ((i_idade > i_limite1) and (i_idade < i_limite2)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite1} e menor que {i_limite2}'\n", + " \n", + " elif ((i_idade > i_limite3) and (i_idade < i_limite4)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite3} e menor que {i_limite4}'\n", + " \n", + " else:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite4}'\n", + " \n", + "print(s_mensagem)" + ], + "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[1;32m 9\u001b[0m \u001b[0ms_mensagem\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34mf'{i_idade} é maior que {i_limite4}'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms_mensagem\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 's_mensagem' is not defined" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V8FF3lFLYqui" + }, + "source": [ + "Porque temos um erro nesta função?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "y5F09RKGYyoX" + }, + "source": [ + "**Resposta**: por causa da indentação! A forma correta é:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vR-oFyzAY5UC" + }, + "source": [ + "def mensagem3(i_idade, i_limite1, i_limite2, i_limite3, i_limite4):\n", + " if ((i_idade > i_limite1) and (i_idade < i_limite2)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite1} e menor que {i_limite2}'\n", + " elif ((i_idade > i_limite3) and (i_idade < i_limite4)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite3} e menor que {i_limite4}'\n", + " else:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite4}'\n", + " \n", + " print(s_mensagem)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QgkBOGKdYgGU", + "outputId": "701f4620-817f-41e0-e9d7-f6b06adf6b3d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "mensagem3(35, 10, 20, 30, 40)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "35 é maior que 30 e menor que 40\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LLk7bhjSwZch" + }, + "source": [ + "___\n", + "# **Wrap Up**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lJvjcjm8NQ85" + }, + "source": [ + "___\n", + "# Exercícios\n", + "## **Exercício 1**: \n", + "Escreva uma função em Python que receba um número inteiro i_limite e, na sequência, imprime os números inteiros de 0 a i_limite;\n", + "\n", + "## **outros exercícios**: \n", + "Nos sites abaixo você vai encontrar exercícios de Python:\n", + "### https://pynative.com/python-if-else-and-for-loop-exercise-with-solutions/;\n", + "### https://www.w3resource.com/python-exercises/" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Gi091pZrwbnY" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Notebooks/NB01_02__Condicionais_hs.ipynb b/Notebooks/NB01_02__Condicionais_hs.ipynb new file mode 100644 index 000000000..bacbbc5eb --- /dev/null +++ b/Notebooks/NB01_02__Condicionais_hs.ipynb @@ -0,0 +1,373 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Notebooks/NB01_02__Condicionais.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": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Y-QMrzHhpcu" + }, + "source": [ + "

CONDICIONAIS - IF

\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wYGZ0eGlv--6" + }, + "source": [ + "# **AGENDA**:\n", + "> Veja o **índice** dos itens que serão abordados neste capítulo." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q3FpTG0dh47M" + }, + "source": [ + "___\n", + "# **REFERÊNCIAS**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LWuIj53sVSnA" + }, + "source": [ + "___\n", + "# **CONDICIONAIS**\n", + "> Usado para decidir se uma determinada instrução ou bloco de instruções será executada ou não, isto é, se uma determinada condição for verdadeira, um bloco de instrução será executado." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NyG1l3awJzEq" + }, + "source": [ + "# Não executar o código a seguir:\n", + "if condicao1:\n", + " \n", + "elif condicao2:\n", + " \n", + "elif condicao3:\n", + " \n", + " ...\n", + "elif condicaoN:\n", + " \n", + "else:\n", + " " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FCJBMTh5WX5C" + }, + "source": [ + "## Exemplo 1" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vn5u7CEaWZjH" + }, + "source": [ + "def mensagem(i_idade, i_limite):\n", + " if i_idade > i_limite:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite}'\n", + " print(s_mensagem)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "lW0ME_nVXU4M" + }, + "source": [ + "mensagem(35, 40)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EBBU8Yw2XxUo" + }, + "source": [ + "Nenhuma mensagem? E agora?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xQ23cAjMX1kx", + "outputId": "3612d39b-3f92-40fd-af14-2dfbca6b0697", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "mensagem(45, 40)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "45 é maior que 40\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BeHU0tPuWK4s" + }, + "source": [ + "## Exemplo 2" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gSzCnjS0Fk-d" + }, + "source": [ + "def mensagem2(i_idade, i_limite):\n", + " if i_idade > i_limite:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite}'\n", + " else:\n", + " s_mensagem= f'{i_idade} é menor ou igual a {i_limite}'\n", + " \n", + " print(s_mensagem)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "KxbmxuDwYFX_", + "outputId": "8f1faff1-de34-4967-865f-17453f7992af", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "mensagem2(35, 40)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "35 é menor ou igual a 40\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lToDO6pzWPGL" + }, + "source": [ + "## Exemplo 3" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "a1NlziSbGrIl", + "outputId": "ffed270b-c16f-4d30-cdaf-80ae96898a94", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197 + } + }, + "source": [ + "def mensagem3(i_idade, i_limite1, i_limite2, i_limite3, i_limite4):\n", + " if ((i_idade > i_limite1) and (i_idade < i_limite2)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite1} e menor que {i_limite2}'\n", + " \n", + " elif ((i_idade > i_limite3) and (i_idade < i_limite4)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite3} e menor que {i_limite4}'\n", + " \n", + " else:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite4}'\n", + " \n", + "print(s_mensagem)" + ], + "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[1;32m 9\u001b[0m \u001b[0ms_mensagem\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34mf'{i_idade} é maior que {i_limite4}'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms_mensagem\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 's_mensagem' is not defined" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V8FF3lFLYqui" + }, + "source": [ + "Porque temos um erro nesta função?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "y5F09RKGYyoX" + }, + "source": [ + "**Resposta**: por causa da indentação! A forma correta é:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vR-oFyzAY5UC" + }, + "source": [ + "def mensagem3(i_idade, i_limite1, i_limite2, i_limite3, i_limite4):\n", + " if ((i_idade > i_limite1) and (i_idade < i_limite2)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite1} e menor que {i_limite2}'\n", + " elif ((i_idade > i_limite3) and (i_idade < i_limite4)):\n", + " s_mensagem= f'{i_idade} é maior que {i_limite3} e menor que {i_limite4}'\n", + " else:\n", + " s_mensagem= f'{i_idade} é maior que {i_limite4}'\n", + " \n", + " print(s_mensagem)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QgkBOGKdYgGU", + "outputId": "701f4620-817f-41e0-e9d7-f6b06adf6b3d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "mensagem3(35, 10, 20, 30, 40)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "35 é maior que 30 e menor que 40\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LLk7bhjSwZch" + }, + "source": [ + "___\n", + "# **Wrap Up**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lJvjcjm8NQ85" + }, + "source": [ + "___\n", + "# Exercícios\n", + "## **Exercício 1**: \n", + "Escreva uma função em Python que receba um número inteiro i_limite e, na sequência, imprime os números inteiros de 0 a i_limite;\n", + "\n", + "## **outros exercícios**: \n", + "Nos sites abaixo você vai encontrar exercícios de Python:\n", + "### https://pynative.com/python-if-else-and-for-loop-exercise-with-solutions/;\n", + "### https://www.w3resource.com/python-exercises/" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Gi091pZrwbnY" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Notebooks/NB02__Numpy_hs.ipynb b/Notebooks/NB02__Numpy_hs.ipynb new file mode 100644 index 000000000..e390345b9 --- /dev/null +++ b/Notebooks/NB02__Numpy_hs.ipynb @@ -0,0 +1,4746 @@ +{ + "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": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6QhLXoatkvKR" + }, + "source": [ + "

NUMPY

\n", + "\n", + "> NumPy é um pacote para computação científica e álgebra linear para Python.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b8EZupp68vW8" + }, + "source": [ + "# **AGENDA**:\n", + "> Neste capítulo, vamos abordar os seguintes assuntos:\n", + "\n", + "* NumPy\n", + "* Criar arrays\n", + "* Criar Arrays Multidimensionais\n", + "* Selecionar itens\n", + "* Aplicar funções como max(), min() e etc\n", + "* Calcular Estatísticas Descritivas: média e variância\n", + "* Reshaping\n", + "* Tansposta de um array\n", + "* Autovalores e Autovetores\n", + "* Wrap Up\n", + "* Exercícios" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cO5t3xCO8kyK" + }, + "source": [ + "___\n", + "# **NOTAS E OBSERVAÇÕES**\n", + "\n", + "* Nosso foco com o NumPy é facilitar o uso do Pandas;" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z2IFUG4GSB0Z" + }, + "source": [ + "___\n", + "# **CHEETSHEET**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jYLeDVH-SNCg" + }, + "source": [ + "![Numpy](https://github.com/MathMachado/Materials/blob/master/numpy_basics-1.png?raw=true)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0mKvExmgUFOk" + }, + "source": [ + "# **ESCALAR, VETORES, MATRIZES E TENSORES**\n", + "\n", + "![Tensor](https://github.com/MathMachado/Materials/blob/master/tensor.png?raw=true)\n", + "\n", + "Source: [PyTorch for Deep Learning: A Quick Guide for Starters](https://towardsdatascience.com/pytorch-for-deep-learning-a-quick-guide-for-starters-5b60d2dbb564)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o00pYRIkXiAU" + }, + "source": [ + "## Import Statement - Primeiros exemplos\n", + "> Como exemplo, considere gerar uma amostra aleatória de tamanho 10 da Distribuição Normal(0, 1):" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l_XuvcUDWNDk" + }, + "source": [ + "## Importar a library NumPy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "am_ZTIGaapCo" + }, + "source": [ + "### **Opção 1**: Importar a biblioteca NumPy COM alias" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "b4irLw6BWVVZ" + }, + "source": [ + "import numpy as np" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "JK54ga7dXnJu" + }, + "source": [ + "# Set up o número de casas decimais para o NumPy:\n", + "np.set_printoptions(precision = 2, suppress = True)\n", + "\n", + "'''\n", + "Define seed por questões de reproducibilidade, ou seja, \n", + "garante que todos vamos gerar os mesmos números aleatórios\n", + "'''\n", + "np.random.seed(seed = 20111974)\n", + "\n", + "# Gera 10 números aleatórios a partir da Distribuição Normal(media, desvio_padrao)\n", + "media = 0\n", + "desvio_padrao = 1\n", + "a_numeros1 = np.random.normal(media, desvio_padrao, size = 10) # Array 1D de size = 10\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3-0934isZUm6" + }, + "source": [ + "**Observação**: Altere o valor de [precision] para 4, 2 e 0 e observe o que acontece." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9ob_8S_bYYa2" + }, + "source": [ + "### **Opção 2**: Importar a biblioteca NumPy SEM alias" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NcGd1ho_XDXU" + }, + "source": [ + "import numpy" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zFYH6J5-Ydjl" + }, + "source": [ + "# Set up o número de casas decimais para o NumPy:\n", + "numpy.set_printoptions(precision = 2, suppress = True)\n", + "\n", + "'''\n", + "Define seed por questões de reproducibilidade, ou seja, \n", + "garante que todos vamos gerar os mesmos números aleatórios\n", + "'''\n", + "numpy.random.seed(seed = 20111974)\n", + "\n", + "# Gera 10 números aleatórios a partir da Distribuição Normal(mu, desvio_padrao)\n", + "media = 0\n", + "desvio_padrao = 1\n", + "numpy.random.normal(size = 10)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AwWSzYrZWfvA" + }, + "source": [ + "### **Opção 3**: Importar funções específicas da biblioteca NumPy" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bfYJzcqRa5eu" + }, + "source": [ + "from numpy import set_printoptions\n", + "from numpy.random import seed, normal" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Xj6fbpvubH_p" + }, + "source": [ + "# Set up o número de casas decimais para o NumPy:\n", + "set_printoptions(precision = 2, suppress = True)\n", + "\n", + "'''\n", + "Define seed por questões de reproducibilidade, ou seja, \n", + "garante que todos vamos gerar os mesmos números aleatórios\n", + "'''\n", + "seed(seed = 20111974)\n", + "\n", + "# Gera 10 números aleatórios a partir da Distribuição Normal(mu, desvio_padrao)\n", + "media = 0\n", + "desvio_padrao = 1 \n", + "np.random.normal(size = 10)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "00RerJPChnuP" + }, + "source": [ + "___\n", + "# **Estatísticas Descriticas com NumPy**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Qa6ro1VJlShd" + }, + "source": [ + "## Exemplo 1\n", + "> Vamos voltar ao mesmo exemplo anterior, mas desta vez, usando a opção 1 (com alias):\n", + "\n", + "* Gerar uma amostra aleatória de tamanho 10 da Distribuiçao Normal(0, 1)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "31dSBU8khvFk" + }, + "source": [ + "# Set up o número de casas decimais para o NumPy:\n", + "np.set_printoptions(precision = 2, suppress = True)\n", + "\n", + "# Define seed\n", + "np.random.seed(seed = 20111974)\n", + "\n", + "# Gera 10 números aleatórios a partir da Distribuição Normal(media, desvio_padrao)\n", + "media = 0\n", + "desvio_padrao = 1\n", + "\n", + "np.random\n", + "a_numeros1 = np.random.normal(media, desvio_padrao, size = 10) # Array 1D de size = 10\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wa2t0P3nevTh" + }, + "source": [ + "Conferindo a média e desvio-padrão do array gerado:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "drUyk3f5ekDq" + }, + "source": [ + "f'Distribuição N({np.mean(a_numeros1)}, {np.std(a_numeros1)})'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XSp7Hd-Gib67" + }, + "source": [ + "Estávamos à espera de media = 0 e sigma = 1. Certo? Porque isso não aconteceu?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HP_8VSgygXOF" + }, + "source": [ + "## **Laboratório 1**\n", + "> Altere os valores de [size] para 100, 1.000, 10.000, 100.000 e 1.000.000 e relate o que acontece com a média e desvio padrão." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4TbmVbdcg6iU" + }, + "source": [ + "## **Minha solução**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-qdiqBVHg-gd" + }, + "source": [ + "# Define a média e o desvio-padrão\n", + "media = 0\n", + "desvio_padrao = 1\n", + "\n", + "# Define seed\n", + "np.random.seed(seed = 20111974)\n", + "l_lista_numeros = [10, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000, 1000000000]\n", + "\n", + "for i_size in l_lista_numeros:\n", + " a_numeros1 = np.random.normal(media, desvio_padrao, size = i_size)\n", + " print(f'Size: {i_size}--> Distribuição: N({np.mean(a_numeros1)}, {np.std(a_numeros1)})')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bp-YuviQwWqE" + }, + "source": [ + "Com relação à Distribuição Normal($\\mu, \\sigma$), temos que:\n", + "\n", + "![NormalDistribution](https://github.com/MathMachado/Materials/blob/master/NormalDistribution.PNG?raw=true)\n", + "\n", + "Fonte: [Normal Distribution](https://towardsdatascience.com/understanding-the-68-95-99-7-rule-for-a-normal-distribution-b7b7cbf760c2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KwHBY3Enk04N" + }, + "source": [ + "## Lei Forte dos Grandes Números - LFGN\n", + "> Por favor, leia o que diz a [Law of large numbers](https://en.wikipedia.org/wiki/Law_of_large_numbers). --> 3 minutos.\n", + "\n", + "* O que você aprendeu com isso?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BhwmSkAjlszT" + }, + "source": [ + "## Exemplo 2\n", + "> Vamos nos aprofundar um pouco mais no que diz a LFGN. Para isso, vamos simular o lançamento de dados. Como sabemos, os dados possuem 6 lados numerados de 1 a 6, com igual probabilidade. Certo?\n", + "\n", + "A LFGN nos diz que à medida que N (o tamanho da amostra ou número de dados) cresce, então a média dos dados converge para o valor esperado. Isso quer dizer que:\n", + "\n", + "$$\\frac{1+2+3+4+5+6}{6}= \\frac{21}{6}= 3,5$$\n", + "\n", + "Ou seja, à medida que N (o tamanho da amostra) cresce, espera-se que a média dos dados se aproxime de 3,5. Ok?\n", + "\n", + "Vamos ver se isso é verdade..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-QcJXf6roj0D" + }, + "source": [ + "Vamos usar o método np.random.randint (= função randint definido na classe np.random), a seguir:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A2u0RzLOrRE2" + }, + "source": [ + "O que significa ou qual é a interpretação do resultado abaixo?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "B3-X_VBerUfa" + }, + "source": [ + "# Define seed\n", + "import numpy as np\n", + "np.random.seed(seed = 20111974)\n", + "\n", + "# Simular 100 lançamentos de um dado:\n", + "a_dados_simulados = np.random.randint(1, 7, size = 100)\n", + "a_dados_simulados" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "m8Of2MMIrbF3" + }, + "source": [ + "# Importar o pandas, pois vamos precisar do método pd.value_counts():\n", + "import pandas as pd\n", + "pd.value_counts(a_dados_simulados)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "54VwED8Br8rx" + }, + "source": [ + "**Interpretação**: Isso quer dizer que fizemos a simulação de lançamento de um dado 100 vezes. Acima, a frequência com que cada lado do dado aparece.\n", + "\n", + "Eu estava à espera de frequência igual para cada um dos lados, isto é, por volta dos 16 ou 17. Ou seja:\n", + "\n", + "$$\\frac{100}{6}= 16,66$$\n", + "\n", + "Mas ok, vamos continuar com nosso experimento..." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HT_Dak-umC6I" + }, + "source": [ + "# Definir a semente\n", + "np.random.seed(20111974)\n", + "\n", + "for i_size in [10, 30, 50, 75, 100, 1000, 10000, 100000, 1000000]:\n", + " a_dados_simulados = np.random.randint(1, 7, size = i_size)\n", + " print(f'Size: {i_size} --> Média: {np.mean(a_dados_simulados)}')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "edWNNOnXtbtd" + }, + "source": [ + "E agora, como você interpreta esses resultados?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eL6gXThkYcSf" + }, + "source": [ + "## Calcular percentis\n", + "> Boxplot" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jlGOQfXfPf0D" + }, + "source": [ + "![BoxPlot](https://github.com/MathMachado/Materials/blob/master/boxplot.png?raw=true)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "grtEXG2BoNRt" + }, + "source": [ + "Considere o array de retornos (simulados) a seguir:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DjPKKq01YjF9" + }, + "source": [ + "import numpy as np\n", + "np.random.seed(20111974)\n", + "\n", + "# Simulando Retornos de ativos financeiros com a distribuição Normal(0, 1):\n", + "a_retornos = np.random.normal(0, 1, 100)\n", + "print(f'Média: {np.mean(a_retornos)}')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ajjlfqgssLVO" + }, + "source": [ + "a_retornos" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XZ3m06gv9lei" + }, + "source": [ + "A seguir, o boxplot do array a_retornos:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QtuwJP449tBQ" + }, + "source": [ + "# Import da biblioteca seaborn: Uma das principais libraries para Data Visualization (outras: matplotlib)\n", + "import seaborn as sns\n", + "\n", + "sns.boxplot(y = a_retornos)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "o9ujdjxNY6qE" + }, + "source": [ + "# Vamos usar o método np.percentile(array, q = [p1, p2, p3, ..., p99])\n", + "percentis = np.percentile(a_retornos, q = [1, 5, 25, 50, 55, 75, 99])\n", + "\n", + "# Primeiro Quartil\n", + "q1 = percentis[2]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c75g2Egco2lc" + }, + "source": [ + "Em qual posição do array a_retornos se encontra Q3?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nZr-A82Zo8Kb" + }, + "source": [ + "q3 = percentis[5]\n", + "\n", + "# ou de trás para a frente do conteúdo da lista:\n", + "q3_2 = percentis[-2]\n", + "print(q3, q3_2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "sWrnESPQT4JM" + }, + "source": [ + "# lim_inferior e lim_superior para detecção de outliers\n", + "lim_inferior = q1 - 1.5 * (q3 - q1)\n", + "lim_superior = q3 + 1.5 * (q3 - q1)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Yb4-ZJlUUYsi" + }, + "source": [ + "f'Limite Inferior: {lim_inferior}; Limite Superior: {lim_superior}'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Jr6oXIHlUxOe" + }, + "source": [ + "np.min(a_retornos)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "UxE47cN0U54X" + }, + "source": [ + "np.max(a_retornos)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OTB9HnIac499" + }, + "source": [ + "___\n", + "# **Ordenar itens de um array**\n", + "> Considere o array a seguir:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Jgj8Yw46dBMx" + }, + "source": [ + "np.random.seed(20111974)\n", + "a_numeros1 = np.random.random(10)\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cC9272GFdRln" + }, + "source": [ + "Ordenando os itens de a_numeros1..." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YUP90nBVdUeF" + }, + "source": [ + "np.sort(a_numeros1)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lG763cDGj-yB" + }, + "source": [ + "___\n", + "# **Obter ajuda**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ehxPlD3EkEYL" + }, + "source": [ + "help(np.random.normal)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1Q_konJVaBsV" + }, + "source": [ + "___\n", + "# **Criar arrays 1D**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DddZT5kadYJ7" + }, + "source": [ + "import numpy as np\n", + "np.set_printoptions(precision = 2, suppress = True)\n", + "np.random.seed(seed = 20111974)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jaqd-VnF3yIt" + }, + "source": [ + "Criar o array 1D a_numeros1, com os seguintes números:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "E3niz_zHaF3e" + }, + "source": [ + "a_numeros1 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DyfXbW_ZKJBS" + }, + "source": [ + "Qual a dimensão de a_numeros1?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gbHlydALKB3R" + }, + "source": [ + "# Dimensão do array\n", + "a_numeros1.ndim" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "am9otElpKNPa" + }, + "source": [ + "Qual o shape (dimensão) do array a_numeros1?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "juJJ74d2wale" + }, + "source": [ + "# Números de itens no array\n", + "a_numeros1.shape" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BHg4Rre3GwPy" + }, + "source": [ + "O array a_numeros1 poderia ter sido criado usando a função np.arange(inicio, fim, step):" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "I3fyusN7G5Zn" + }, + "source": [ + "# Lembre-se que o número 10 é exclusive.\n", + "a_numeros2 = np.arange(start = 0, stop = 10, step = 1)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IHCEpmUxXsaK" + }, + "source": [ + "Outra alternativa seria usar np.linspace(start = 0, stop = 10, num = 9). Acompanhe a seguir:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JB9Y_x3RX1GX" + }, + "source": [ + "# Com np.linspace, o valor 9 é inclusive.\n", + "a_numeros3 = np.linspace(0, 9, 10)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P6MR8MPeYOZm" + }, + "source": [ + "Compare os resultados de a_numeros1, a_numeros2 e a_numeros3 a seguir:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tWEzge6HYSFu" + }, + "source": [ + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "lUNlFVKYYT9f" + }, + "source": [ + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Xo8Lid5fYVPW" + }, + "source": [ + "a_numeros3" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V9aW7C4vHAcF" + }, + "source": [ + "Ou seja, a_numeros1 é igual a a_numeros2 que também é igual a a_numeros3. Ok?\n", + "\n", + "**ATENÇÃO**: Observe que a sintaxe para criar a_numeros3 é ligeiramente diferente da sintaxe usada para criar a_numeros1 e a_numeros2. Abaixo, a sintaxe do comando np.linspace:\n", + "\n", + "![](https://github.com/MathMachado/Materials/blob/master/linspace_sintaxe.PNG?raw=true)\n", + "\n", + "Source: [HOW TO USE THE NUMPY LINSPACE FUNCTION](https://www.sharpsightlabs.com/blog/numpy-linspace/)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KNnwZa3uvYqE" + }, + "source": [ + "Soma 2 à cada item de a_numeros1:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Jt2KVyviw0bp" + }, + "source": [ + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "arROkhWXbdTW" + }, + "source": [ + "a_numeros2 = a_numeros1 + 2\n", + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZJx2vG86vdVi" + }, + "source": [ + "Multiplicar por 10 cada item de a_numeros1:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Vm7abO6Ebkun" + }, + "source": [ + "a_numeros1 = a_numeros1*10\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0Ev1xnBwaYJG" + }, + "source": [ + "___\n", + "# **Criar Arrays Multidimensionais**\n", + "> Ao criarmos, por exemplo, um array 2D, então a chamamos de matriz." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gHaeAug5vjjd" + }, + "source": [ + "Criar o array com 2 linhas e 3 colunas usando números aleatórios:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VDi0vIPSYR4F" + }, + "source": [ + "np.random.seed(20111974)\n", + "a_numeros1 = np.random.randn(2, 3)\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DIdd-nA3tJjV" + }, + "source": [ + "## Dimensão de um array\n", + "> Dimensão é o número de linhas e colunas da matriz." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pKvjjnkrK-v7" + }, + "source": [ + "a_numeros1.shape" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-DHS5jXELCfa" + }, + "source": [ + "a_numeros1 é um array 2D (ou matriz), ou seja, 2 linhas, onde cada linha tem 3 elementos." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HJI6X1wvv4Bg" + }, + "source": [ + "Criar um array com 3 linhas e 3 colunas:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hXPbWh3Tv26T" + }, + "source": [ + "a_numeros2 = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])\n", + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "we6ZJOICc7bQ" + }, + "source": [ + "# Número de linhas e colunas de a_numeros1:\n", + "a_numeros1.shape" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "f0ocwuI1dED6" + }, + "source": [ + "# Número de linhas e colunas de a_numeros2\n", + "a_numeros2.shape" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "CApPtnW0YuRP" + }, + "source": [ + "# Somar 2 à cada elemento de a_numeros2\n", + "a_numeros2 = a_numeros2+2\n", + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "M87aGmxRY3RW" + }, + "source": [ + "# Multiplicar por 10 cada elemento de a_numeros2\n", + "a_numeros2 = a_numeros2*10\n", + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qZt93y1IL_v7" + }, + "source": [ + "___\n", + "# **Copiar arrays**\n", + "> Considere o array abaixo:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sH2FTXj5MRRC" + }, + "source": [ + "np.random.seed(20111974)\n", + "a_numeros1 = np.random.randn(2, 3)\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VtgKeMt6MYrr" + }, + "source": [ + "Fazendo a cópia de a_numeros1..." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "K0hOHR3IMa-o" + }, + "source": [ + "a_numeros1_copia = a_numeros1.copy()\n", + "a_numeros1_copia" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lFpmcR0HkCar" + }, + "source": [ + "___\n", + "# **Operações com arrays**\n", + "> Considere um array com temperaturas em Farenheit dado por:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VnagcUqVkLhW" + }, + "source": [ + "# Define a seed\n", + "np.random.seed(20111974)\n", + "\n", + "a_temperatura_farenheit = np.array(np.random.randint(0, 100, 10))\n", + "a_temperatura_farenheit " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "VrjNKfXxk1yv" + }, + "source": [ + "type(a_temperatura_farenheit)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o1STejhrk0kZ" + }, + "source": [ + "Transformando a temperatura Fahrenheit em Celsius..." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "E_jXflR_lNy3" + }, + "source": [ + "a_temperatura_celsius = 5*a_temperatura_farenheit/9 - 5*32/9\n", + "a_temperatura_celsius" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "U4pCv0pNqPZI" + }, + "source": [ + "# O mesmo resultado, porém, escrito de forma diferente:\n", + "a_temperatura_celsius = (5/9)*a_temperatura_farenheit - (160/9)\n", + "a_temperatura_celsius" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1UT4YD2FawUA" + }, + "source": [ + "___\n", + "# **Selecionar itens**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pqOv8P1za1m8" + }, + "source": [ + "# Selecionar o segundo item de a_numeros1 (lembre-se que no Python arrays começam com indice = 0)\n", + "a_numeros1[1]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TIwVKk6AyRv6" + }, + "source": [ + "Dado a_numeros2 abaixo:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zoDmbXo6bCeu" + }, + "source": [ + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iJXSPp-0yb4w" + }, + "source": [ + "... selecionar o item da linha 2, coluna 3 do array a_numeros2:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sJiVfnlzcjRv" + }, + "source": [ + "a_numeros2[1, 2]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Xl5HwJIMcv2e" + }, + "source": [ + "# Selecionar o último elemento de a_numeros1 --> Lembre-se que a_numeros1 é um array. Desta forma, teremos o último elemento do array!\n", + "a_numeros1[-1]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ezTH0HsyrnAl" + }, + "source": [ + "Veja..." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OBv9EM54rYX3" + }, + "source": [ + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Po3WLFC-rod8" + }, + "source": [ + "a_temperatura_celsius[-1]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4qJJ2HCedW4h" + }, + "source": [ + "___\n", + "# **Aplicar funções como max(), min() e etc**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_meTJdUsda4e" + }, + "source": [ + "f'O máximo de a_numeros1 é: {np.max(a_numeros1)}'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "m-wiBkAidnhN" + }, + "source": [ + "f'O mínimo de a_numeros1 é: {np.min(a_numeros1)}'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "lmupnRHQdtwh" + }, + "source": [ + "f'O máximo de a_numeros2 é: {np.max(a_numeros2)}'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "H2z7oB6Bd786" + }, + "source": [ + "f'O máximo de cada LINHA de a_numeros2 é: {np.max(a_numeros2, axis = 1)}' # Aqui, axis = 1 é que diz ao numpy que estamos interessados nas linhas" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "gj2ZBDsWeMyk" + }, + "source": [ + "f'O máximo de cada COLUNA de a_numeros2 é: {np.max(a_numeros2, axis = 0)}' # axis = 0, diz ao numpy que estamos interessados nas colunas." + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7_tEfm2IecIU" + }, + "source": [ + "___\n", + "# **Calcular Estatísticas Descritivas: média e variância**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lIY5jx3ueh7q" + }, + "source": [ + "f'A média de a_numeros1 é: {np.mean(a_numeros1)}'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "VmqSELRReuAW" + }, + "source": [ + "f'A média de a_numeros2 é: {np.mean(a_numeros2)}'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Gxap-Wg5e2_H" + }, + "source": [ + "f'O Desvio Padrão de a_numeros2 é: {np.std(a_numeros2)}'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R0GcljGtfBvP" + }, + "source": [ + "___\n", + "# **Reshaping**\n", + "> Muito útil em Machine Learning." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vfEmw01j8zux" + }, + "source": [ + "## Exemplo 1\n", + "* O array a_numeros2 tem a seguinte forma:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-Lb3VZCCfK_a" + }, + "source": [ + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "YWN_nN-4fD7u" + }, + "source": [ + "# reshaping para 9 linhas e 1 coluna:\n", + "a_numeros2.reshape(9, 1) # a_numeros2.reshape(9,-1) produz o mesmo resultado." + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "id9ILRRt7SwY" + }, + "source": [ + "## Mais um exemplo de Reshape\n", + "> Dado o array 1D abaixo, reshape para um array 3D com 2 colunas." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9RA9Ht2b7Swd", + "outputId": "eadedfd5-fd6c-49c8-db5c-6f8f30d45f36", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Define seed\n", + "np.random.seed(20111974)\n", + "a_numeros1 = np.array(np.random.randint(1, 10, size = 15))\n", + "a_numeros1" + ], + "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])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8KxR4xZT7cRv" + }, + "source": [ + "### Solução\n", + "> Temos 15 elementos em a_numeros1 para construir (\"reshape\") um array 3D com 2 colunas.\n", + "\n", + "A princípio, a solução seria..." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VMdHl1Il7wLw", + "outputId": "d51c7263-f523-4af8-9606-ee93cab66f1c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 162 + } + }, + "source": [ + "a_numeros1.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": "error", + "ename": "ValueError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\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[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_numeros1 = np.array(np.random.randint(1, 10, size = 16)) # Observe que agora temos 16 elementos\n", + "a_numeros1" + ], + "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": 151 + } + }, + "source": [ + "a_numeros1.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": 151 + } + }, + "source": [ + "# OU --> Neste caso, estamos reshaping o array em 8 linhas e 2 colunas\n", + "a_numeros1.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_numeros1 = np.random.randn(2, 3)\n", + "a_numeros1" + ], + "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_numeros1.reshape(-1, 2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AdwU5ygt9Svq" + }, + "source": [ + "Poderia ser..." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5uBeokKc9Uo-" + }, + "source": [ + "a_numeros1.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_numeros1.reshape(3, 2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "91o5vycQfdKW" + }, + "source": [ + "___\n", + "# **Transposta**\n", + "* O array a_numeros2 tem a seguinte forma:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RsZwyuhoffjb" + }, + "source": [ + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "A3MzTVoGfiyO" + }, + "source": [ + "# Transposta do array a_numeros2 é dado por:\n", + "a_numeros2.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_numeros1 = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])\n", + "a_numeros2 = np.array([[6, 2], [5, 3]])\n", + "a_numeros3 = np.array([[1, 3, 5],[2, 5, 1],[2, 3, 8]])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "7zmHHWWlvaYB" + }, + "source": [ + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "3fHKyhOJvcak" + }, + "source": [ + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vQG7yyfjwLg9" + }, + "source": [ + "a_numeros3" + ], + "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_numeros1)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QSvViNwzwnhI" + }, + "source": [ + "np.linalg.det(a_numeros2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "o8jwsnccw5id" + }, + "source": [ + "np.linalg.det(a_numeros3)" + ], + "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_numeros2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "KsdEt1kIvsM_" + }, + "source": [ + "np.linalg.inv(a_numeros1)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VA_F7_7kccpn" + }, + "source": [ + "Porque não temos a inversa de a_numeros1?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ANPBCnmVwOf4" + }, + "source": [ + "np.linalg.inv(a_numeros3)" + ], + "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", + "![Empilhar1](https://github.com/MathMachado/Materials/blob/master/Empilhar1.PNG?raw=true)\n", + "\n", + "## Exemplo 2\n", + "\n", + "![Empilhar2](https://github.com/MathMachado/Materials/blob/master/Empilhar2.PNG?raw=true)\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_numeros1 = np.random.randn(5, 8)\n", + "\n", + "np.random.seed(19741120)\n", + "a_numeros2 = 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_numeros1" + ], + "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_numeros2" + ], + "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_numeros1, a_numeros2], 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_numeros1, a_numeros2]" + ], + "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_numeros1, a_numeros2])" + ], + "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", + "![Concatenar1](https://github.com/MathMachado/Materials/blob/master/Concatenar1.PNG?raw=true)\n", + "\n", + "# Exemplo 2\n", + "\n", + "![Concatenar2](https://github.com/MathMachado/Materials/blob/master/Concatenar2.PNG?raw=true)" + ] + }, + { + "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_numeros1 = np.random.randint(0, 10, 100).reshape(-1, 10)\n", + "a_numeros2 = 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_numeros1" + ], + "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_numeros2" + ], + "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_numeros2 do lado de a_numeros1.\n", + "np.c_[a_numeros1, a_numeros2]" + ], + "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_numeros1 = np.arange(10, 0, -1)\n", + "a_numeros1" + ], + "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_numeros1" + ], + "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_numeros1 > 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_numeros2 = a_numeros1[l_indices]\n", + "a_numeros2" + ], + "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_numeros1[a_numeros1 > 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_numeros1 > 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_numeros1 > 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_numeros1[a_numeros1 > 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_numeros1" + ], + "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_numeros1 = 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_numeros1[i] = -1*a_numeros1[i]\n", + "\n", + "a_numeros2 = a_numeros1.copy()\n", + "a_numeros2" + ], + "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_numeros2 < 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_numeros2[a_numeros2 < 0] = 0\n", + "a_numeros2" + ], + "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_numeros2 = a_numeros1.copy()\n", + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1bSD9Fs6P5wW" + }, + "source": [ + "a_numeros2 = np.where(a_numeros2 <= 0, 0, 1)\n", + "a_numeros2" + ], + "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", + "![Z_Score](https://github.com/MathMachado/Materials/blob/master/Z_Score.png?raw=true)" + ] + }, + { + "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": [ + "![BoxPlot](https://github.com/MathMachado/Materials/blob/master/boxplot.png?raw=true)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z3VZdU8rICZA" + }, + "source": [ + "## Desafio\n", + "> Substituir os outliers do array por:\n", + "1. Q1-1.5\\*(Q3 - Q1), se ponto < Q1-1.5\\*IQR\n", + "2. Q3+1.5\\*(Q3 - Q1), se ponto > Q3+1.5\\*IQR" + ] + }, + { + "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", + "\n", + "Obs.: Use np.random.seed(20111974)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RL0Zb0fyDory", + "outputId": "1e5a0d7e-833c-4de5-aedc-56a4d836ea73", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 446 + } + }, + "source": [ + "np.random.seed(19741120)\n", + "a_numeros1 = np.array(np.random.normal(100, 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, 10)\n", + "np.sort(l_indices)\n", + "\n", + "a_numeros1_copia = a_numeros1.copy()\n", + "for i in l_indices:\n", + " a_numeros1_copia[i] = 2*a_numeros1_copia[i]\n", + "\n", + "a_numeros1_copia" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 92.26622483, 88.94525348, 202.01256141, 88.54282712,\n", + " 78.47337726, 92.47442751, 78.47005101, 96.69822268,\n", + " 177.90685389, 103.28896747, 100.10101983, 86.67863666,\n", + " 96.60541955, 99.87009928, 100.53428231, 98.13587989,\n", + " 103.94738054, 91.06457686, 94.93326767, 92.53390871,\n", + " 118.35863649, 87.94631286, 112.01848858, 105.1160897 ,\n", + " 94.30477141, 90.66561289, 97.50274717, 219.69742669,\n", + " 111.93333668, 122.99564969, 101.66570222, 107.13574148,\n", + " 95.47489218, 109.21639184, 107.3421263 , 121.78111913,\n", + " 99.43447875, 112.53259996, 96.29607519, 114.38552019,\n", + " 217.12921824, 98.87427612, 192.91994052, 109.41366709,\n", + " 99.13038373, 85.09992988, 200.16973311, 108.67052746,\n", + " 116.34090601, 113.63210628, 99.78246389, 95.46983552,\n", + " 96.28887641, 99.52839308, 77.2662565 , 109.53187379,\n", + " 107.10054804, 92.01167315, 96.83422097, 84.16471762,\n", + " 192.44970327, 97.02396587, 92.65757933, 94.42967769,\n", + " 94.02193121, 96.78511833, 113.48163484, 113.01668438,\n", + " 105.02843445, 107.58835306, 110.94932036, 99.23942748,\n", + " 93.74530061, 98.11132421, 84.62965767, 101.61893629,\n", + " 97.35568508, 94.27312327, 105.55501746, 105.45183177,\n", + " 91.55201318, 207.3344281 , 103.85618579, 98.93911584,\n", + " 119.4216084 , 91.53859352, 79.86676575, 106.45458816,\n", + " 311.17594425, 106.44862258, 113.52153879, 111.68130043,\n", + " 100.4550085 , 93.80661302, 98.87736992, 122.53185044,\n", + " 112.21271855, 101.34656943, 104.99984125, 94.74563688])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 56 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZnmykyahLWX9", + "outputId": "3bbfbafd-8715-4f72-de0e-693f2dcd9887", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "# Algumas estatísticas descritivas:\n", + "f'Média: {np.mean(a_numeros1)}; Mediana: {np.median(a_numeros1)}; STD: {np.std(a_numeros1)}'" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'Média: 100.18633633362035; Mediana: 99.33695311387913; STD: 10.028092450008492'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 55 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ILhNe80xW5C6" + }, + "source": [ + "### Solução do desafio" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "U993i1GJg2hk", + "outputId": "4f8185a8-327a-4502-8998-c81cfd28cc20", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 269 + } + }, + "source": [ + "# Import a biblioteca seaborn:\n", + "import seaborn as sns\n", + "sns.boxplot(y = a_numeros1_copia)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 57 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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pQMZdkgpk3CWpQMZdkgpk3CWpQMZdkgpk3CWpQMZdkgoUmVn3DETEEvB03XNIHVwN/KLuIaQN/GVmjm+0YSDiLg2yiFjIzOm655D64WkZSSqQcZekAhl3aXNH6x5A6pfn3CWpQB65S1KBjLskFci4S1KBjLskFci4S1KB/h9mcr6bwEF81QAAAABJRU5ErkJggg==\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_numeros1_copia[i] < lim_inferior então a_numeros1_copia[i]= Mediana\n", + " * Se a_numeros1_copia[i] > lim_superior então a_numeros1_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" + }, + "source": [ + "# Algumas estatísticas descritivas:\n", + "f'Média: {np.mean(a_numeros1_copia)}; Mediana: {np.median(a_numeros1_copia)}; STD: {np.std(a_numeros1_copia)}'" + ], + "execution_count": null, + "outputs": [] + }, + { + "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_numeros1_copia, q = [25])\n", + "Q3= np.percentile(a_numeros1_copia, q = [75])\n", + "Q2= np.percentile(a_numeros1_copia, q = [50])\n", + "IQR = Q3-Q1\n", + "lim_inferior = Q1-1.5*IQR\n", + "lim_superior = Q3+1.5*IQR" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "VF2NJ3rCeI1_" + }, + "source": [ + "f'Q1: {Q1}; Q3: {Q3}; lim_inferior: {lim_inferior}; lim_superior: {lim_superior}'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JjnwJ7HwMxcl" + }, + "source": [ + "3. Substituir\n", + "* Se a_numeros1[i] < lim_inferior então a_numeros1[i]= Mediana\n", + "* Se a_numeros1[i] > Lia_Sup então a_numeros1[i]= Mediana" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hcAn-IwVfbcI" + }, + "source": [ + "a_numeros2 = a_numeros1_copia.copy()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "J3SSE45oM9oh" + }, + "source": [ + "a_numeros2[a_numeros2 < lim_inferior[0]] = Q2[0]\n", + "a_numeros2[a_numeros2 > lim_superior[0]] = Q2[0]\n", + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VEGFio0Nfj7O" + }, + "source": [ + "4. Estatísticas Descritivas para avaliarmos o impacto:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gX1LZHFqfjFQ" + }, + "source": [ + "# Algumas estatísticas descritivas:\n", + "f'Média: {np.mean(a_numeros2)}; Mediana: {np.median(a_numeros2)}; STD: {np.std(a_numeros2)}'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "-xnguZ7XgyvK" + }, + "source": [ + "# Import a biblioteca seaborn:\n", + "import seaborn as sns\n", + "sns.boxplot(y = a_numeros2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "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_numeros1 = np.random.randint(0, 100, 100)\n", + "a_numeros1" + ], + "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_numeros1)" + ], + "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", + "![Difference](https://github.com/MathMachado/Materials/blob/master/set_Difference.PNG?raw=true)\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_numeros1 = np.array([0, 1, 2, 4, 5, 7, 8, 8]) # array de valores que serão excluidos em a_numeros1. Observe que '3' não pertence a a_numeros1.\n", + "a_numeros2 = np.array([1, 6, 7, 3])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zXJ00pOMorM-" + }, + "source": [ + "np.setdiff1d(a_numeros1, a_numeros2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8GXZNgjfo8lO" + }, + "source": [ + "Observe que o resultado são os elementos de a_numeros1 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_numeros1 = np.arange(10)\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nxDpCMg7T7Rj" + }, + "source": [ + "a_numeros2 = np.array([1, 5, 7])\n", + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "3LU3qYyiUXqm" + }, + "source": [ + "np.setdiff1d(a_numeros1, a_numeros2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mzZEytrRUioU" + }, + "source": [ + "Observe que os elementos de a_numeros2 foram deletados de a_numeros1. 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", + "![DifferenceSymetric](https://github.com/MathMachado/Materials/blob/master/set_DifferenceSymetric.PNG?raw=true)\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_numeros1 = np.array([0, 1, 2, 4, 5, 7, 8]) # Observe que [1, 4, 7] pertencem a a_numeros1, mas 3, não. Portanto:\n", + "a_numeros2 = np.array([1, 4, 7, 3])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Tqd_9XO5p7bo" + }, + "source": [ + "np.setxor1d(a_numeros1, a_numeros2)" + ], + "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", + "![Union](https://github.com/MathMachado/Materials/blob/master/set_Union.PNG?raw=true)\n", + "\n", + "Fonte: [Python Set](https://www.learnbyexample.org/python-set/)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1LZxorw2p2mg" + }, + "source": [ + "a_numeros1 = np.array([0, 1, 2, 4, 5, 7, 8, 8])\n", + "\n", + "# Observe que [1, 4, 7] pertencem a a_numeros1, mas 3, não. Portanto:\n", + "a_numeros2 = np.array([1, 4, 7, 3])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "COsZEmSwuY5L" + }, + "source": [ + "np.union1d(a_numeros1, a_numeros2)" + ], + "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", + "![Intersection](https://github.com/MathMachado/Materials/blob/master/set_Intersection.PNG?raw=true)\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_numeros1 = np.arange(10)\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "pZTHhHxGSRfB" + }, + "source": [ + "a_numeros2 = np.arange(8, 18)\n", + "a_numeros2" + ], + "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_numeros1, a_numeros2)" + ], + "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_numeros2 tem a seguinte forma:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iYlZGKFUfw-R" + }, + "source": [ + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "6EfvIbBNf02Z" + }, + "source": [ + "# Calcula autovalores e autovetores:\n", + "a_Autovalores, a_Autovetores= np.linalg.eig(a_numeros2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v3GtQQvAz9QU" + }, + "source": [ + "Os autovalores do array a_numeros2 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_numeros2 são:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6m4YFAwsf_rA" + }, + "source": [ + "a_Autovetores" + ], + "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_numeros1 = 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_numeros1[i_indices] = np.nan" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "2ZkbMPXMawYh" + }, + "source": [ + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z7Bs75NvbSjx" + }, + "source": [ + "Ok, inserimos aleatoriamente 14 NaN's no array a_numeros1. 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" + }, + "source": [ + "np.isnan(a_numeros1).sum()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y7hh5uowoa3U" + }, + "source": [ + "Ok, temos 14 NaN's em a_numeros1." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iVLQf_bqbyNU" + }, + "source": [ + "Ok, agora eu quero saber os índices desses NaN's." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kJHxjZiwb5HM" + }, + "source": [ + "i_indices= np.where(np.isnan(a_numeros1))\n", + "i_indices" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "W_jHGNImok7L" + }, + "source": [ + "# Checando...\n", + "a_numeros1[2]" + ], + "execution_count": null, + "outputs": [] + }, + { + "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" + }, + "source": [ + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "e497B492fFru" + }, + "source": [ + "a_numeros1[~np.isnan(a_numeros1)]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RpvKfJU_fmA6" + }, + "source": [ + "Observe que os NaN's foram excluidos." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_Dv8MmNYg8zN" + }, + "source": [ + "___\n", + "# **Converter lista em array**\n", + "> Considere a lista a seguir:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "but6T9dVhFYb" + }, + "source": [ + "l_Lista = [np.random.randint(0, 10, 10)]\n", + "l_Lista" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "xytj4Eo4hTh9" + }, + "source": [ + "type(l_Lista)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qrINdcruhWcH" + }, + "source": [ + "Convertendo a minha lista para array:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RoSyaX0OhZSE" + }, + "source": [ + "a_numeros = np.asarray(l_Lista)\n", + "a_numeros" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "dMjTdbBUhlrk" + }, + "source": [ + "type(a_numeros)" + ], + "execution_count": null, + "outputs": [] + }, + { + "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_numeros = np.asarray(t_numeros)\n", + "a_numeros" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "PSgQDmRWh3g5" + }, + "source": [ + "type(a_numeros)" + ], + "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_numeros1 = np.arange(5)\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "d3zrlf_Ci73Z" + }, + "source": [ + "np.random.seed(20111974)\n", + "a_numeros1 = np.append(a_numeros1, [np.random.randint(0, 10, 3), np.random.randint(0, 10, 3), np.random.randint(0, 10, 3)])\n", + "a_numeros1" + ], + "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_numeros1 = np.array(np.random.randint(0, 10, 6))\n", + "\n", + "np.random.seed(19741120)\n", + "a_numeros2 = np.array(np.random.randint(0, 10, 6))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "febs9AUHvs6n" + }, + "source": [ + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "C9OEd-iavvBm" + }, + "source": [ + "a_numeros2" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "KJWjtaWKv0MJ" + }, + "source": [ + "np.column_stack((a_numeros1, a_numeros2)) # Atenção aos parênteses em (a_numeros1, a_numeros2)." + ], + "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_numeros1 = np.array(np.random.randint(0, 10, 6))\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7bOJiKDKxEsC" + }, + "source": [ + "Suponha que eu queira excluir os valores '8' de a_numeros1. Os índices dos valores '8' são: [0, 1, 3]. Portanto, temos:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "SSjueEvjxTJO" + }, + "source": [ + "a_numeros1 = np.delete(a_numeros1, [0, 1, 3])\n", + "a_numeros1" + ], + "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" + }, + "source": [ + "np.random.seed(20111974)\n", + "a_numeros1 = np.array(np.random.randint(0, 10, 100))\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s_tdQBsax4rQ" + }, + "source": [ + "Suponha que eu queira saber quantas vezes o número/elemento '2' aparece em a_numeros1." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6yIlk7pWyAtf" + }, + "source": [ + "l_itens_unicos, i_count = np.unique(a_numeros1, return_counts=True)\n", + "l_itens_unicos" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DyvrIwS9yZIR" + }, + "source": [ + "O que significa o output acima?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uO-MPMhXyV9H" + }, + "source": [ + "i_count" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zwoezXrPyofK" + }, + "source": [ + "Qual a interpretação do output acima?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HgYycSG7yr5e" + }, + "source": [ + "np.asarray((l_itens_unicos, i_count))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SwIZiJAiy06T" + }, + "source": [ + "Qual a interpretação do output acima?" + ] + }, + { + "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_numeros1 = [2, 4, 6]\n", + "a_numeros2 = [0, 8]\n", + "a_numeros4 = [1, 5]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "cEZH6l-Czx7y" + }, + "source": [ + "np.meshgrid(a_numeros1, a_numeros2, a_numeros4)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "btvmDkEcz0tH" + }, + "source": [ + "np.array(np.meshgrid(a_numeros1, a_numeros2, a_numeros4))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z0xhO7rGz059" + }, + "source": [ + "np.array(np.meshgrid(a_numeros1, a_numeros2, a_numeros4)).T" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "eMv4lFnD0Enn" + }, + "source": [ + "# Resultado final\n", + "a_numeros3 = np.array(np.meshgrid(a_numeros1, a_numeros2, a_numeros4)).T.reshape(-1,3)\n", + "a_numeros3" + ], + "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" + }, + "source": [ + "a_numeros1 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kq1zt-uO1HXv" + }, + "source": [ + "### **Minha solução**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YFmK_n2M1Ks9" + }, + "source": [ + "a_numeros1[a_numeros1 % 2 == 0]" + ], + "execution_count": null, + "outputs": [] + }, + { + "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_numeros1." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XLZ-DIWU1WFs" + }, + "source": [ + "a_numeros1 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9c4QWJno1WVB" + }, + "source": [ + "### **Minha solução**\n", + "* Primeiramente, precisamos calcular a mediana.\n", + "* Depois, substituimos os valores pares de a_numeros1 pela mediana encontrada anteriormente. Ok?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rx7NGAO01Wfb" + }, + "source": [ + "a_numeros1[a_numeros1 % 2 == 0] = np.median(a_numeros1)\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2c_AphX82qp8" + }, + "source": [ + "Verificando..." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9kVta0Cr13Z9" + }, + "source": [ + "f'A média de a_numeros1 é: {np.median(a_numeros1)}'" + ], + "execution_count": null, + "outputs": [] + }, + { + "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-" + }, + "source": [ + "# Define seed\n", + "np.random.seed(20111974)\n", + "a_numeros1 = np.array(np.random.randint(1, 10, size = 15))\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dKzEX8TK5b4Z" + }, + "source": [ + "### **Minha solução**\n", + "* O array 1D a_numeros1 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": "I-j5yVD04249" + }, + "source": [ + "a_numeros1.reshape(5, 3) \n", + "# Poderia ser a_numeros1.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_numeros1 = np.array(np.random.randint(1, 10, size = 16))\n", + "a_numeros1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7iICnOyG6fcj" + }, + "source": [ + "### **Minha solução**\n", + "* O array 1D a_numeros1 acima possui 16 elementos. Queremos transformá-lo num array 3D com 2 colunas." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vdq5ybuD6fcn" + }, + "source": [ + "a_numeros1.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