From 630dba83bc18aa0efe6343c4adbe9b1d6fd64b15 Mon Sep 17 00:00:00 2001 From: Pranav25191 <62637230+Pranav25191@users.noreply.github.com> Date: Sun, 6 Sep 2020 14:32:44 +0530 Subject: [PATCH] Panav_Balijapelly_Assignment2_Pandas --- Assignment2 pandas.ipynb | 1107 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 1107 insertions(+) create mode 100644 Assignment2 pandas.ipynb diff --git a/Assignment2 pandas.ipynb b/Assignment2 pandas.ipynb new file mode 100644 index 0000000..12aaceb --- /dev/null +++ b/Assignment2 pandas.ipynb @@ -0,0 +1,1107 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 288, + "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": 289, + "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
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" + ], + "text/plain": [ + " Id EmployeeName JobTitle \\\n", + "0 1 NATHANIEL FORD GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY \n", + "1 2 GARY JIMENEZ CAPTAIN III (POLICE DEPARTMENT) \n", + "2 3 ALBERT PARDINI CAPTAIN III (POLICE DEPARTMENT) \n", + "3 4 CHRISTOPHER CHONG WIRE ROPE CABLE MAINTENANCE MECHANIC \n", + "4 5 PATRICK GARDNER DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT) \n", + "\n", + " BasePay OvertimePay OtherPay Benefits TotalPay TotalPayBenefits \\\n", + "0 167411.18 0.00 400184.25 NaN 567595.43 567595.43 \n", + "1 155966.02 245131.88 137811.38 NaN 538909.28 538909.28 \n", + "2 212739.13 106088.18 16452.60 NaN 335279.91 335279.91 \n", + "3 77916.00 56120.71 198306.90 NaN 332343.61 332343.61 \n", + "4 134401.60 9737.00 182234.59 NaN 326373.19 326373.19 \n", + "\n", + " Year Notes Agency Status \n", + "0 2011 NaN San Francisco NaN \n", + "1 2011 NaN San Francisco NaN \n", + "2 2011 NaN San Francisco NaN \n", + "3 2011 NaN San Francisco NaN \n", + "4 2011 NaN San Francisco NaN " + ] + }, + "execution_count": 289, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df=pd.read_csv(\"Salaries.csv\")\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "display the column names" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "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": 290, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "display the number of rows and cols" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(148654, 13)" + ] + }, + "execution_count": 291, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "display the dataframe info (types of data in columns and not null values etc.)" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "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": 294, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitsYearNotesStatus
count148654.000000148045.000000148650.000000148650.000000112491.000000148654.000000148654.000000148654.0000000.00.0
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max148654.000000319275.010000245131.880000400184.25000096570.660000567595.430000567595.4300002014.000000NaNNaN
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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
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148654 rows × 11 columns

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" + ], + "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": 296, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dropna(how='all',axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "display number of unique values in each column" + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "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": 297, + "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": 305, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "74768.32197169267" + ] + }, + "execution_count": 305, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['TotalPay'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "how many people have 0 overtime pay" + ] + }, + { + "cell_type": "code", + "execution_count": 306, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "77321" + ] + }, + "execution_count": 306, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['OvertimePay'].value_counts()[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "max, min, mean, median and other stats of TotalPay of people having 0 OvertimePay" + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "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": 303, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1=df[(df['OvertimePay']==0)]\n", + "df1[\"TotalPay\"].describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "find Id of that person with max TotalPay you got in previous question" + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 304, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1[(df1['TotalPay']==df1['TotalPay'].max())].iloc[0,0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "name of employee with total pay benefits = 87619.78" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'REBECCA CHIU'" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df['TotalPay']==87619.78)].iloc[0,1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "how many people have BasePay > 150000 and OvertimePay > 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df['BasePay']>150000) & (df[\"OvertimePay\"]>100000)].shape[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "which job title generally has highest average TotalPayBenefits" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Chief Investment Officer'], dtype='object', name='JobTitle')" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3=df.groupby(\"JobTitle\").mean()\n", + "df3[(df3['TotalPayBenefits']==df3['TotalPayBenefits'].max())].index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many employees are POLICE" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of employees are with police 2512\n" + ] + } + ], + "source": [ + "k=df[\"JobTitle\"].value_counts().index\n", + "n=df[\"JobTitle\"].value_counts()\n", + "count=0\n", + "for i in range(len(k)):\n", + " if \"POLICE\" in k[i]:\n", + " count+=n[i]\n", + "print(\"The number of employees are with police\",count)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2512" + ] + }, + "execution_count": 285, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b=df[\"JobTitle\"]\n", + "a=b.str.contains(pat=\"POLICE\")\n", + "\n", + "sum=0\n", + "for i in range(len(b)):\n", + " if a[i]:\n", + " sum=sum+1\n", + " else:\n", + " continue\n", + "sum" + ] + } + ], + "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": "3.8.4rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}