Does gender affect whose vehicle is searched?

Analyzing Police Activity with pandas

Kevin Markham

Founder, Data School

Math with Boolean values

ri.isnull().sum()

stop_date             0
stop_time             0
driver_gender         0
driver_race           0
violation_raw         0
...

True = 1, False = 0

import numpy as np

np.mean([0, 1, 0, 0])

0.25

np.mean([False, True, 
         False, False])

0.25

Mean of Boolean Series represents percentage of True values

Taking the mean of a Boolean Series

ri.is_arrested.value_counts(normalize=True)

False    0.964431
True     0.035569

ri.is_arrested.mean()

0.0355690117407784

ri.is_arrested.dtype

dtype('bool')

Comparing groups using groupby (1)

Study the arrest rate by police district

ri.district.unique()

array(['Zone X4', 'Zone K3', 'Zone X1', 'Zone X3',
       'Zone K1', 'Zone K2'], dtype=object)

ri[ri.district == 'Zone K1'].is_arrested.mean()

0.024349083895853423

Comparing groups using groupby (2)

ri[ri.district == 'Zone K2'].is_arrested.mean()

0.030800588834786546

ri.groupby('district').is_arrested.mean()

district
Zone K1    0.024349
Zone K2    0.030801
Zone K3    0.032311
Zone X1    0.023494
Zone X3    0.034871
Zone X4    0.048038

Grouping by multiple categories

ri.groupby(['district', 'driver_gender']).is_arrested.mean()

district  driver_gender
Zone K1   F                0.019169
          M                0.026588
Zone K2   F                0.022196
...       ...                   ...

ri.groupby(['driver_gender', 'district']).is_arrested.mean()

driver_gender  district
F              Zone K1     0.019169
               Zone K2     0.022196
...            ...              ...

Let's practice!

Analyzing Police Activity with pandas