diff --git a/Vikas Dongare Assignment2.ipynb b/Vikas Dongare Assignment2.ipynb new file mode 100644 index 0000000..c68d60b --- /dev/null +++ b/Vikas Dongare Assignment2.ipynb @@ -0,0 +1,1219 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "#%matplotlib notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "import the dataset into a dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdEmployeeNameJobTitleBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitsYearNotesAgencyStatus
01NATHANIEL FORDGENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY167411.180.00400184.25NaN567595.43567595.432011NaNSan FranciscoNaN
12GARY JIMENEZCAPTAIN III (POLICE DEPARTMENT)155966.02245131.88137811.38NaN538909.28538909.282011NaNSan FranciscoNaN
23ALBERT PARDINICAPTAIN III (POLICE DEPARTMENT)212739.13106088.1816452.60NaN335279.91335279.912011NaNSan FranciscoNaN
34CHRISTOPHER CHONGWIRE ROPE CABLE MAINTENANCE MECHANIC77916.0056120.71198306.90NaN332343.61332343.612011NaNSan FranciscoNaN
45PATRICK GARDNERDEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)134401.609737.00182234.59NaN326373.19326373.192011NaNSan FranciscoNaN
..........................................
148649148650Roy I TilleryCustodian0.000.000.000.00.000.002014NaNSan FranciscoNaN
148650148651Not providedNot providedNaNNaNNaNNaN0.000.002014NaNSan FranciscoNaN
148651148652Not providedNot providedNaNNaNNaNNaN0.000.002014NaNSan FranciscoNaN
148652148653Not providedNot providedNaNNaNNaNNaN0.000.002014NaNSan FranciscoNaN
148653148654Joe LopezCounselor, Log Cabin Ranch0.000.00-618.130.0-618.13-618.132014NaNSan FranciscoNaN
\n", + "

148654 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " Id EmployeeName \\\n", + "0 1 NATHANIEL FORD \n", + "1 2 GARY JIMENEZ \n", + "2 3 ALBERT PARDINI \n", + "3 4 CHRISTOPHER CHONG \n", + "4 5 PATRICK GARDNER \n", + "... ... ... \n", + "148649 148650 Roy I Tillery \n", + "148650 148651 Not provided \n", + "148651 148652 Not provided \n", + "148652 148653 Not provided \n", + "148653 148654 Joe Lopez \n", + "\n", + " JobTitle BasePay \\\n", + "0 GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY 167411.18 \n", + "1 CAPTAIN III (POLICE DEPARTMENT) 155966.02 \n", + "2 CAPTAIN III (POLICE DEPARTMENT) 212739.13 \n", + "3 WIRE ROPE CABLE MAINTENANCE MECHANIC 77916.00 \n", + "4 DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT) 134401.60 \n", + "... ... ... \n", + "148649 Custodian 0.00 \n", + "148650 Not provided NaN \n", + "148651 Not provided NaN \n", + "148652 Not provided NaN \n", + "148653 Counselor, Log Cabin Ranch 0.00 \n", + "\n", + " OvertimePay OtherPay Benefits TotalPay TotalPayBenefits Year \\\n", + "0 0.00 400184.25 NaN 567595.43 567595.43 2011 \n", + "1 245131.88 137811.38 NaN 538909.28 538909.28 2011 \n", + "2 106088.18 16452.60 NaN 335279.91 335279.91 2011 \n", + "3 56120.71 198306.90 NaN 332343.61 332343.61 2011 \n", + "4 9737.00 182234.59 NaN 326373.19 326373.19 2011 \n", + "... ... ... ... ... ... ... \n", + "148649 0.00 0.00 0.0 0.00 0.00 2014 \n", + "148650 NaN NaN NaN 0.00 0.00 2014 \n", + "148651 NaN NaN NaN 0.00 0.00 2014 \n", + "148652 NaN NaN NaN 0.00 0.00 2014 \n", + "148653 0.00 -618.13 0.0 -618.13 -618.13 2014 \n", + "\n", + " Notes Agency Status \n", + "0 NaN San Francisco NaN \n", + "1 NaN San Francisco NaN \n", + "2 NaN San Francisco NaN \n", + "3 NaN San Francisco NaN \n", + "4 NaN San Francisco NaN \n", + "... ... ... ... \n", + "148649 NaN San Francisco NaN \n", + "148650 NaN San Francisco NaN \n", + "148651 NaN San Francisco NaN \n", + "148652 NaN San Francisco NaN \n", + "148653 NaN San Francisco NaN \n", + "\n", + "[148654 rows x 13 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df=pd.read_csv(\"Salaries.csv\")\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "display the column names" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Id', 'EmployeeName', 'JobTitle', 'BasePay', 'OvertimePay', 'OtherPay',\n", + " 'Benefits', 'TotalPay', 'TotalPayBenefits', 'Year', 'Notes', 'Agency',\n", + " 'Status'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "display the number of rows and cols" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of rows: 148654\n", + "Number of columns: 13\n" + ] + } + ], + "source": [ + "print(\"Number of rows: \", len(df))\n", + "print(\"Number of columns: \", len(df.axes[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "display the dataframe info (types of data in columns and not null values etc.)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 148654 entries, 0 to 148653\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Id 148654 non-null int64 \n", + " 1 EmployeeName 148654 non-null object \n", + " 2 JobTitle 148654 non-null object \n", + " 3 BasePay 148045 non-null float64\n", + " 4 OvertimePay 148650 non-null float64\n", + " 5 OtherPay 148650 non-null float64\n", + " 6 Benefits 112491 non-null float64\n", + " 7 TotalPay 148654 non-null float64\n", + " 8 TotalPayBenefits 148654 non-null float64\n", + " 9 Year 148654 non-null int64 \n", + " 10 Notes 0 non-null float64\n", + " 11 Agency 148654 non-null object \n", + " 12 Status 0 non-null float64\n", + "dtypes: float64(8), int64(2), object(3)\n", + "memory usage: 14.7+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "display stats of the dataframe like count, mean, std, max, 25% etc....." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitsYearNotesStatus
count148654.000000148045.000000148650.000000148650.000000112491.000000148654.000000148654.000000148654.0000000.00.0
mean74327.50000066325.4488415066.0598863648.76729725007.89315174768.32197293692.5548112012.522643NaNNaN
std42912.85779542764.63549511454.3805598056.60186615402.21585850517.00527462793.5334831.117538NaNNaN
min1.000000-166.010000-0.010000-7058.590000-33.890000-618.130000-618.1300002011.000000NaNNaN
25%37164.25000033588.2000000.0000000.00000011535.39500036168.99500044065.6500002012.000000NaNNaN
50%74327.50000065007.4500000.000000811.27000028628.62000071426.61000092404.0900002013.000000NaNNaN
75%111490.75000094691.0500004658.1750004236.06500035566.855000105839.135000132876.4500002014.000000NaNNaN
max148654.000000319275.010000245131.880000400184.25000096570.660000567595.430000567595.4300002014.000000NaNNaN
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" + ], + "text/plain": [ + " Id BasePay OvertimePay OtherPay \\\n", + "count 148654.000000 148045.000000 148650.000000 148650.000000 \n", + "mean 74327.500000 66325.448841 5066.059886 3648.767297 \n", + "std 42912.857795 42764.635495 11454.380559 8056.601866 \n", + "min 1.000000 -166.010000 -0.010000 -7058.590000 \n", + "25% 37164.250000 33588.200000 0.000000 0.000000 \n", + "50% 74327.500000 65007.450000 0.000000 811.270000 \n", + "75% 111490.750000 94691.050000 4658.175000 4236.065000 \n", + "max 148654.000000 319275.010000 245131.880000 400184.250000 \n", + "\n", + " Benefits TotalPay TotalPayBenefits Year Notes \\\n", + "count 112491.000000 148654.000000 148654.000000 148654.000000 0.0 \n", + "mean 25007.893151 74768.321972 93692.554811 2012.522643 NaN \n", + "std 15402.215858 50517.005274 62793.533483 1.117538 NaN \n", + "min -33.890000 -618.130000 -618.130000 2011.000000 NaN \n", + "25% 11535.395000 36168.995000 44065.650000 2012.000000 NaN \n", + "50% 28628.620000 71426.610000 92404.090000 2013.000000 NaN \n", + "75% 35566.855000 105839.135000 132876.450000 2014.000000 NaN \n", + "max 96570.660000 567595.430000 567595.430000 2014.000000 NaN \n", + "\n", + " Status \n", + "count 0.0 \n", + "mean NaN \n", + "std NaN \n", + "min NaN \n", + "25% NaN \n", + "50% NaN \n", + "75% NaN \n", + "max NaN " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "display null values per column" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Id 0\n", + "EmployeeName 0\n", + "JobTitle 0\n", + "BasePay 609\n", + "OvertimePay 4\n", + "OtherPay 4\n", + "Benefits 36163\n", + "TotalPay 0\n", + "TotalPayBenefits 0\n", + "Year 0\n", + "Notes 148654\n", + "Agency 0\n", + "Status 148654\n", + "dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "remove columns will all values as NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdEmployeeNameJobTitleBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitsYearAgency
01NATHANIEL FORDGENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY167411.180.00400184.25NaN567595.43567595.432011San Francisco
12GARY JIMENEZCAPTAIN III (POLICE DEPARTMENT)155966.02245131.88137811.38NaN538909.28538909.282011San Francisco
23ALBERT PARDINICAPTAIN III (POLICE DEPARTMENT)212739.13106088.1816452.60NaN335279.91335279.912011San Francisco
34CHRISTOPHER CHONGWIRE ROPE CABLE MAINTENANCE MECHANIC77916.0056120.71198306.90NaN332343.61332343.612011San Francisco
45PATRICK GARDNERDEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)134401.609737.00182234.59NaN326373.19326373.192011San Francisco
....................................
148649148650Roy I TilleryCustodian0.000.000.000.00.000.002014San Francisco
148650148651Not providedNot providedNaNNaNNaNNaN0.000.002014San Francisco
148651148652Not providedNot providedNaNNaNNaNNaN0.000.002014San Francisco
148652148653Not providedNot providedNaNNaNNaNNaN0.000.002014San Francisco
148653148654Joe LopezCounselor, Log Cabin Ranch0.000.00-618.130.0-618.13-618.132014San Francisco
\n", + "

148654 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Id EmployeeName \\\n", + "0 1 NATHANIEL FORD \n", + "1 2 GARY JIMENEZ \n", + "2 3 ALBERT PARDINI \n", + "3 4 CHRISTOPHER CHONG \n", + "4 5 PATRICK GARDNER \n", + "... ... ... \n", + "148649 148650 Roy I Tillery \n", + "148650 148651 Not provided \n", + "148651 148652 Not provided \n", + "148652 148653 Not provided \n", + "148653 148654 Joe Lopez \n", + "\n", + " JobTitle BasePay \\\n", + "0 GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY 167411.18 \n", + "1 CAPTAIN III (POLICE DEPARTMENT) 155966.02 \n", + "2 CAPTAIN III (POLICE DEPARTMENT) 212739.13 \n", + "3 WIRE ROPE CABLE MAINTENANCE MECHANIC 77916.00 \n", + "4 DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT) 134401.60 \n", + "... ... ... \n", + "148649 Custodian 0.00 \n", + "148650 Not provided NaN \n", + "148651 Not provided NaN \n", + "148652 Not provided NaN \n", + "148653 Counselor, Log Cabin Ranch 0.00 \n", + "\n", + " OvertimePay OtherPay Benefits TotalPay TotalPayBenefits Year \\\n", + "0 0.00 400184.25 NaN 567595.43 567595.43 2011 \n", + "1 245131.88 137811.38 NaN 538909.28 538909.28 2011 \n", + "2 106088.18 16452.60 NaN 335279.91 335279.91 2011 \n", + "3 56120.71 198306.90 NaN 332343.61 332343.61 2011 \n", + "4 9737.00 182234.59 NaN 326373.19 326373.19 2011 \n", + "... ... ... ... ... ... ... \n", + "148649 0.00 0.00 0.0 0.00 0.00 2014 \n", + "148650 NaN NaN NaN 0.00 0.00 2014 \n", + "148651 NaN NaN NaN 0.00 0.00 2014 \n", + "148652 NaN NaN NaN 0.00 0.00 2014 \n", + "148653 0.00 -618.13 0.0 -618.13 -618.13 2014 \n", + "\n", + " Agency \n", + "0 San Francisco \n", + "1 San Francisco \n", + "2 San Francisco \n", + "3 San Francisco \n", + "4 San Francisco \n", + "... ... \n", + "148649 San Francisco \n", + "148650 San Francisco \n", + "148651 San Francisco \n", + "148652 San Francisco \n", + "148653 San Francisco \n", + "\n", + "[148654 rows x 11 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dropna(axis=1, how='all')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "display number of unique values in each column" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Id 148654\n", + "EmployeeName 110811\n", + "JobTitle 2159\n", + "BasePay 109489\n", + "OvertimePay 65998\n", + "OtherPay 83225\n", + "Benefits 98465\n", + "TotalPay 138486\n", + "TotalPayBenefits 142098\n", + "Year 4\n", + "Notes 0\n", + "Agency 1\n", + "Status 0\n", + "dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "mean of total pay of all people based on year" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "2011 71744.103871\n", + "2012 74113.262265\n", + "2013 77611.443142\n", + "2014 75463.918140\n", + "Name: TotalPay, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('Year').mean()['TotalPay']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "how many people have 0 overtime pay" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "77321" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.OvertimePay==0].count()['OvertimePay']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "max, min, mean, median and other stats of TotalPay of people having 0 OvertimePay" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 77321.000000\n", + "mean 60229.348901\n", + "std 49307.912350\n", + "min -618.130000\n", + "25% 13290.450000\n", + "50% 58158.590000\n", + "75% 91115.090000\n", + "max 567595.430000\n", + "Name: TotalPay, dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.OvertimePay==0].describe()['TotalPay']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "find Id of that person with max TotalPay you got in previous question" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "Name: Id, dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.TotalPay==567595.430000].Id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "name of employee with total pay benefits = 87619.78" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12345 REBECCA CHIU\n", + "Name: EmployeeName, dtype: object" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.TotalPay==87619.78].EmployeeName" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "how many people have BasePay > 150000 and OvertimePay > 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "156" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df['BasePay']> 150000) & (df['OvertimePay']> 100000)].size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "which job title generally has highest average TotalPayBenefits" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "110533 Chief Investment Officer\n", + "Name: JobTitle, dtype: object" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#df[df['TotalPayBenefits']== df['TotalPayBenefits'].max()]['JobTitle']\n", + "df[df['TotalPayBenefits']==(df.groupby('JobTitle')['TotalPayBenefits'].mean().max())]['JobTitle']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many employees are POLICE" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2512" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# .str.contains()\n", + "df[df['JobTitle'].str.contains('POLICE')] ['JobTitle'].size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/VikasDongare-Assignment-1.ipynb b/VikasDongare-Assignment-1.ipynb new file mode 100644 index 0000000..6491189 --- /dev/null +++ b/VikasDongare-Assignment-1.ipynb @@ -0,0 +1,358 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make a python list => \\[1,2,3,4,5\\]\n", + "\n", + "Convert it into numpy array and print it" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3 4 5]\n" + ] + } + ], + "source": [ + "list1 = [1,2,3,4,5]\n", + "list1_np = np.array(list1)\n", + "print(list1_np)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make a python matrix (3 x 3) => \\[[1,2,3],[4,5,6],[7,8,9]\\]\n", + "\n", + "Convert it into numpy array and print it" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2 3]\n", + " [4 5 6]\n", + " [7 8 9]]\n" + ] + } + ], + "source": [ + "matrix = [[1,2,3],[4,5,6],[7,8,9]]\n", + "matrix_np = np.array(matrix)\n", + "print(matrix_np)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make a matrix (3 x 3) using built-in methods (like arange(), reshape() etc.):\n", + "\n", + "\\[ [1,3,5],\n", + "\n", + " [7,9,11],\n", + " \n", + " [13,15,17] \\]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1 3 5]\n", + " [ 7 9 11]\n", + " [13 15 17]]\n" + ] + } + ], + "source": [ + "matrix1 = np.arange(1,18,2)\n", + "matrix1 = matrix1.reshape(3,3)\n", + "print(matrix1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a numpy array with 10 random numbers from 0 to 10 (there should be few numbers greater than 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[9 2 1 2 0 5 7 1 9 1]\n" + ] + } + ], + "source": [ + "random_array = np.random.randint(0,10,10)\n", + "print(random_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create numpy array => \\[1,2,3,4,5\\] and convert it to 2D array with 5 rows" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3 4 5]\n", + "(5,)\n" + ] + } + ], + "source": [ + "np_array = np.array([1,2,3,4,5])\n", + "np_array = np_array.reshape(5,)\n", + "print(np_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the shape of the above created array" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5,)\n" + ] + } + ], + "source": [ + "print(np_array.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a numpy array with 10 elements in it. Access and print its 3rd, 4th and 9th element." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3rd element : 10\n", + "4th element : 54\n", + "9th element : 89\n" + ] + } + ], + "source": [ + "npy_array = np.array([0,50,10,54,7,3,87,45,89,34])\n", + "print(\"3rd element : \",npy_array[2])\n", + "print(\"4th element : \",npy_array[3])\n", + "print(\"9th element : \",npy_array[8])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print alternate elements of that array" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "10\n", + "7\n", + "87\n", + "100\n" + ] + } + ], + "source": [ + "i=0\n", + "for i in range(len(npy_array)):\n", + " if i%2 == 0:\n", + " print(npy_array[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Change last 3 elements into 100 using broadcasting and print" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 50 10 54 7 3 87 100 100 100]\n" + ] + } + ], + "source": [ + "npy_array[7:] = 100\n", + "print(npy_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a 5 x 5 matrix (fill it with any element you like), print it.\n", + "\n", + "Then print the middle (3 x 3) matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3 4]\n", + " [ 5 6 7 8 9]\n", + " [10 11 12 13 14]\n", + " [15 16 17 18 19]\n", + " [20 21 22 23 24]]\n" + ] + } + ], + "source": [ + "npy_matrix = np.arange(0,25).reshape(5,5)\n", + "print(npy_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 6 7 8]\n", + " [11 12 13]\n", + " [16 17 18]]\n" + ] + } + ], + "source": [ + "print(npy_matrix[1:4,1:4])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}