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{
"cells": [
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"file_path='https://raw.githubusercontent.com/aaubs/ds-master/main/apps/M1-attrition-streamlit/HR-Employee-Attrition-synth.csv'\n",
"\n",
"data=pd.read_csv(file_path)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 2000 entries, 0 to 1999\n",
"Data columns (total 36 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Unnamed: 0 2000 non-null int64 \n",
" 1 Age 2000 non-null int64 \n",
" 2 Attrition 2000 non-null object\n",
" 3 BusinessTravel 2000 non-null object\n",
" 4 DailyRate 2000 non-null int64 \n",
" 5 Department 2000 non-null object\n",
" 6 DistanceFromHome 2000 non-null int64 \n",
" 7 Education 2000 non-null int64 \n",
" 8 EducationField 2000 non-null object\n",
" 9 EmployeeCount 2000 non-null int64 \n",
" 10 EmployeeNumber 2000 non-null int64 \n",
" 11 EnvironmentSatisfaction 2000 non-null int64 \n",
" 12 Gender 2000 non-null object\n",
" 13 HourlyRate 2000 non-null int64 \n",
" 14 JobInvolvement 2000 non-null int64 \n",
" 15 JobLevel 2000 non-null int64 \n",
" 16 JobRole 2000 non-null object\n",
" 17 JobSatisfaction 2000 non-null int64 \n",
" 18 MaritalStatus 2000 non-null object\n",
" 19 MonthlyIncome 2000 non-null int64 \n",
" 20 MonthlyRate 2000 non-null int64 \n",
" 21 NumCompaniesWorked 2000 non-null int64 \n",
" 22 Over18 2000 non-null object\n",
" 23 OverTime 2000 non-null object\n",
" 24 PercentSalaryHike 2000 non-null int64 \n",
" 25 PerformanceRating 2000 non-null int64 \n",
" 26 RelationshipSatisfaction 2000 non-null int64 \n",
" 27 StandardHours 2000 non-null int64 \n",
" 28 StockOptionLevel 2000 non-null int64 \n",
" 29 TotalWorkingYears 2000 non-null int64 \n",
" 30 TrainingTimesLastYear 2000 non-null int64 \n",
" 31 WorkLifeBalance 2000 non-null int64 \n",
" 32 YearsAtCompany 2000 non-null int64 \n",
" 33 YearsInCurrentRole 2000 non-null int64 \n",
" 34 YearsSinceLastPromotion 2000 non-null int64 \n",
" 35 YearsWithCurrManager 2000 non-null int64 \n",
"dtypes: int64(27), object(9)\n",
"memory usage: 562.6+ KB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Attrition\n",
"No 8033.23208\n",
"Yes 8676.02349\n",
"Name: MonthlyIncome, dtype: float64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('Attrition')['MonthlyIncome'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Attrition Department \n",
"No Human Resources 8212.311828\n",
" Research & Development 8018.842391\n",
" Sales 8028.274311\n",
"Yes Human Resources 7557.600000\n",
" Research & Development 8908.348315\n",
" Sales 8401.754545\n",
"Name: MonthlyIncome, dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby(['Attrition','Department'])['MonthlyIncome'].mean()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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