Does weather affect the arrest rate?
Analyzing Police Activity with pandas
Kevin Markham
Founder, Data School
Driver gender and vehicle searches
ri.search_conducted.mean()
0.0382153092354627
ri.groupby('driver_gender').search_conducted.mean()
driver_gender
F 0.019181
M 0.045426
ri.groupby(['violation', 'driver_gender']).search_conducted.mean()
violation driver_gender
Equipment F 0.039984
M 0.071496
Moving violation F 0.039257
M 0.061524
Other F 0.041018
M 0.046191
Registration/plates F 0.054924
M 0.108802
Seat belt F 0.017301
M 0.035119
Speeding F 0.008309
M 0.027885
search_rate = ri.groupby(['violation', 'driver_gender']).search_conducted.mean()search_rate
violation driver_gender
Equipment F 0.039984
M 0.071496
Moving violation F 0.039257
M 0.061524
... ... ...
type(search_rate)type(search_rate.index)
pandas.core.series.Seriespandas.core.indexes.multi.MultiIndex
violation driver_gender
Equipment F 0.039984
M 0.071496
Moving violation F 0.039257
M 0.061524
... ... ...
search_rate.loc['Equipment']
driver_gender
F 0.039984
M 0.071496
search_rate.loc['Equipment', 'M']
0.07149643705463182
Converting a multi-indexed Series to a DataFrame
search_rate.unstack()
driver_gender F M
violation
Equipment 0.039984 0.071496
Moving violation 0.039257 0.061524
Other 0.041018 0.046191
... ... ...
type(search_rate.unstack())
pandas.core.frame.DataFrame
Converting a multi-indexed Series to a DataFrame
ri.pivot_table(index='violation',
columns='driver_gender',
values='search_conducted')
driver_gender F M
violation
Equipment 0.039984 0.071496
Moving violation 0.039257 0.061524
Other 0.041018 0.046191
... ... ...
Let's practice!
Analyzing Police Activity with pandas
