63 csv file read home work 04

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In [6]:

import pandas as pd
sal = pd.read_csv('Salaries.csv')

What was the average (mean) BasePay of all employees per year? (2011-2014) ?

sal.groupby('Year').mean()['BasePay']¶

sal['BasePay']

In [12]:

sal.groupby('Year').mean()['BasePay']

Out[12]:

Year
2011    63595.956517
2012    65436.406857
2013    69630.030216
2014    66564.421924
Name: BasePay, dtype: float64

How many unique job titles are there?

sal['JobTitle'].nunique()¶

explain using unique what len

In [16]:

len(sal['JobTitle'].unique())

Out[16]:

2159

In [17]:

sal['JobTitle'].nunique()

Out[17]:

2159

How many unique job titles are there?

sal['JobTitle'].nunique()¶

What are the top 5 most common jobs?

sal['JobTitle'].value_counts().head(5)¶

In [19]:

sal[ sal['year']== 2013][JobTitle'].value_counts().head(5)

Out[19]:

Transit Operator                7036
Special Nurse                   4389
Registered Nurse                3736
Public Svc Aide-Public Works    2518
Police Officer 3                2421
Name: JobTitle, dtype: int64

How many Job Titles were represented by only one person in 2013? (e.g. Job Titles with only one occurence in 2013?)

##### sum(sal[sal['Year']==2013]['JobTitle'].value_counts() == 1)¶

In [22]:

sum(sal[sal['Year']==2013]['JobTitle'].value_counts()==1)

Out[22]:

202

How many people have the word Chief in their job title? (This is pretty tricky)

In [26]:

def chie(title):
    if 'chief' in title.lower():
        return True
    else:
        return False
sum(sal['JobTitle'].apply(lambda x: chie(x)))

Out[26]:

627

Is there a correlation between length of the Job Title string and Salary?

sal['title_len'] = sal['JobTitle'].apply(len)¶

sal[['title_len','TotalPayBenefits']].corr() # No correlation.¶

In [27]:

Out[27]:

	Id	EmployeeName	JobTitle	BasePay	OvertimePay	OtherPay	Benefits	TotalPay	TotalPayBenefits	Year	Notes	Agency	Status
0	1	NATHANIEL FORD	GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY	167411.18	0.00	400184.25	NaN	567595.43	567595.43	2011	NaN	San Francisco	NaN
1	2	GARY JIMENEZ	CAPTAIN III (POLICE DEPARTMENT)	155966.02	245131.88	137811.38	NaN	538909.28	538909.28	2011	NaN	San Francisco	NaN

In [28]:

def chie(title):
    if 'chief' in title.lower():
        return True
    else:
        return False
def ram(x):
    if 'chief' in x.lower():
        return True
    else:
        return False

In [33]:

ram("kumar ChIef ramesh")

Out[33]:

True

In [35]:

sum(sal['JobTitle'].apply(lambda x: ram(x)))

Out[35]:

627

In [37]:

sal['title_len'] = sal['JobTitle'].apply(len)

In [76]:

#sal[["TotalPayBenefits",'title_len']]
sal[['title_len','TotalPayBenefits']].corr() # No correlation.

Out[76]:

	title_len	TotalPayBenefits
title_len	1.000000	-0.036878
TotalPayBenefits	-0.036878	1.000000