Question

Using python code answer the following :

Attrition BusinessT DailyRate Departme DistanceF Education Education Employee Employee Environm Gender HourlyRat Joblnvolv JobLevel JobRole Age JobSatisfa MaritalSta Monthlylr MonthlyR 2 Life Scien Travel Ra 1102 Sales 2 Female 2 Sales Exec 4 Single 41 Yes 1 1 94 5993 19479 1 Travel_Fre Travel Ra 1 Life Scien 2 Research 279 Research 3 Male 2 Married 49 No 1 2 61 5130 24907 1373 Research 2 Other 4 4 Male 1 Laborator 3 Single 37 Yes 2 92 2090 2396 4 Life Scien Travel_Fre 1392 Research 4 Female Research 3 Married 33 No 5 56 2909 23159 Travel_Ra 1 Male 27 No 591 Research 1 Medical 1 Laborator 2 Married 7 40 3468 16632 2 2 Life Scien Travel_Fre 1005 Research 4 Male 32 No 2 79 1 Laborator 4 Single 3068 11864 3 Medical Travel_Ra Travel_Ra 1324 Research 3 Female 81 59 No 2 10 1 Laborator 1 Married 2670 9964 4 1 Life Scien 1358 Research 4 Male 30 No 24 1 67 1 Laborator 3 Divorced 2693 13335 3 Manufact 2 Healthcar 3 Life Scien Travel Fre 216 Research 4 Male 3 Single 38 No 23 1: 44 9526 8787 1299 Research 809 Research 3 Medical Travel_Ra 3 Male 94 36 No 27 13 3 Married 5237 16577 3 Medical 2 Life Scien Travel Ra 16 14 1 Male 84 1 Laborator 2 Married 35 No 4 2426 16479 4 Female Single 29 No Travel Ra 153 Research 2 Laborator 5 1 4C 2 4193 12682

#Q5: What is the Employee ID of highest MonthlyIncome paid employee?

#Q6: What is the average(mean) DailyRate group by Age for all Employees whose age is greater than 58. (hint: use groupby function)

#Q7: How many unique EducationField are there?

Answer 1

#CODE

Question 5

import pandas as pd
import numpy as np
df=pd.read_csv('WA_Fn-UseC_-HR-Employee-Attrition.csv')

df.head()

df.iloc[df['MonthlyIncome'].argmax()]['EmployeeNumber']#argmax() returns the index of maximum element

#Another way to find both gives the result Employee ID:259
df[df['MonthlyIncome']==df['MonthlyIncome'].max()]['EmployeeNumber']

Question 6

ageGreater58=df[df['Age']>58]#Get all rows with age > 58
ageGreater58.groupby('Age')['DailyRate'].mean()#Use groupby and get mean

Question 7

df['EducationField'].unique()

print("The unique education fields: ",len(df['EducationField'].unique()))

jupyter Untitled Last Checkpoint: 03/20/2019 (autosaved) Logout View Help Trusted Python 3 O File a Edit 7 Insert + 3.78 2.49 Cell Run Kernel C Widgets Code + 8 In [1]: import pandas as pd import numpy as np df-pd.read_csv('WA_Fn-UseC_-HR-Employee-Attrition.csv') In [2]: df.head() Out[2]: ent DistanceFromHome Education Education Field EmployeeCount Employee Number ... Relationship Satisfaction Standard Hours StockOptionLevel TotalWorki les 2 Life Sciences h & ient 8 1 Life Sciences h & 2 2 lent Other 4 Life Sciences ient 2 1 Medical In [ ]: In [5]: df.iloc[df [ 'MonthlyIncome'].argmax ['EmployeeNumber']#argmax() returns the index of maximum element C:\ProgramData Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: 'argmax' is deprecated, use 'idxmax' instea d. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values. argmax' to get the position of the maximum now. ****"Entry point for launching an IPython kernel. Out[5]: 259

jupyter Untitled Last Checkpoint. 03/20/2019 (autosaved) Logout File Trusted Python 3 argmax' is deprecated, use 'idxmax' instea Edit View Insert Cell Kernel Widgets Help + 2 B + Run IC Code C:\ProgramData Anaconda3\lib site-packages\ipykernel_launcher.py:1: FutureWarning: d. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values. argmax' to get the position of the maximum now. ** "Entry point for launching an IPython kernel. Out[5]: 259 In [9]: #Another way to find both gives the result Employee ID:259 df[df['MonthlyIncome' ]-=df['MonthlyIncome'].max(['EmployeeNumber'] Out[9]: 190 259 Name: EmployeeNumber, dtype: int64 Question 6 In [14]: ageGreater 58-df[df['Age' ]>58 ]#Get all rows with age > 58 ageGreater58.groupby('Age')['DailyRate'].mean() #Use groupby and get mean Out[14]: Age 59992.3 60 833.2 Name: DailyRate, dtype: float64 Qustion 7 In [15]: df['EducationField'].unique) Out[15]: array(['Life Sciences', 'Other', 'Medical', 'Marketing', Technical Degree', 'Human Resources'l, dtype-object) In [16]: print("The unique education fields: ", len(df['EducationField'].unique()) The unique education fields: 6