A Summary of lecture "Analyzing Police Activity with pandas", via datacamp
# Import the pandas library as pd
import pandas as pd
# Read 'police.csv' into a DataFrame named ri
ri = pd.read_csv('./dataset/police.csv')
# Examine the head of the DataFrame
print(ri.head(5))
# Count the number of missing values in each column
print(ri.isnull().sum())
state stop_date stop_time county_name driver_gender driver_race \
0 RI 2005-01-04 12:55 NaN M White
1 RI 2005-01-23 23:15 NaN M White
2 RI 2005-02-17 04:15 NaN M White
3 RI 2005-02-20 17:15 NaN M White
4 RI 2005-02-24 01:20 NaN F White
violation_raw violation search_conducted search_type \
0 Equipment/Inspection Violation Equipment False NaN
1 Speeding Speeding False NaN
2 Speeding Speeding False NaN
3 Call for Service Other False NaN
4 Speeding Speeding False NaN
stop_outcome is_arrested stop_duration drugs_related_stop district
0 Citation False 0-15 Min False Zone X4
1 Citation False 0-15 Min False Zone K3
2 Citation False 0-15 Min False Zone X4
3 Arrest Driver True 16-30 Min False Zone X1
4 Citation False 0-15 Min False Zone X3
state 0
stop_date 0
stop_time 0
county_name 91741
driver_gender 5205
driver_race 5202
violation_raw 5202
violation 5202
search_conducted 0
search_type 88434
stop_outcome 5202
is_arrested 5202
stop_duration 5202
drugs_related_stop 0
district 0
dtype: int64
# Examine the shape of the DataFrame
print(ri.shape)
# Drop the 'country_name' and 'state' columns
ri.drop(['county_name', 'state'], axis='columns', inplace=True)
# Examine the shape of the DataFrame (again)
print(ri.shape)
(91741, 15) (91741, 13)
# Count the number of missing values in each column
print(ri.isnull().sum())
# Drop all rows that are missing 'driver_gender'
ri.dropna(subset=['driver_gender'], inplace=True)
# Count the number of missing values in each column (again)
print(ri.isnull().sum())
# Examine the shape of the DataFrame
print(ri.shape)
stop_date 0 stop_time 0 driver_gender 5205 driver_race 5202 violation_raw 5202 violation 5202 search_conducted 0 search_type 88434 stop_outcome 5202 is_arrested 5202 stop_duration 5202 drugs_related_stop 0 district 0 dtype: int64 stop_date 0 stop_time 0 driver_gender 0 driver_race 0 violation_raw 0 violation 0 search_conducted 0 search_type 83229 stop_outcome 0 is_arrested 0 stop_duration 0 drugs_related_stop 0 district 0 dtype: int64 (86536, 13)
ri.dtypes
stop_date object stop_time object driver_gender object driver_race object violation_raw object violation object search_conducted bool search_type object stop_outcome object is_arrested object stop_duration object drugs_related_stop bool district object dtype: object
# Examine the head of the 'is_arrested' column
print(ri.is_arrested.head())
# Change the data type of 'is_arrested' to 'bool'
ri['is_arrested'] = ri.is_arrested.astype('bool')
# Check the data type of 'is_arrested'
print(ri['is_arrested'].dtype)
0 False 1 False 2 False 3 True 4 False Name: is_arrested, dtype: object bool
# Concatenate 'stop_date' and 'stop_time' (separated by a space)
combined = ri.stop_date.str.cat(ri.stop_time, sep=' ')
# Convert 'combined' to datetime format
ri['stop_datetime'] = pd.to_datetime(combined)
# Examine the data types of the DataFrame
print(ri.dtypes)
stop_date object stop_time object driver_gender object driver_race object violation_raw object violation object search_conducted bool search_type object stop_outcome object is_arrested bool stop_duration object drugs_related_stop bool district object stop_datetime datetime64[ns] dtype: object
# Set 'stop_datetime' as the index
ri.set_index('stop_datetime', inplace=True)
# Examine the index
print(ri.index)
# Examine the columns
print(ri.columns)
DatetimeIndex(['2005-01-04 12:55:00', '2005-01-23 23:15:00',
'2005-02-17 04:15:00', '2005-02-20 17:15:00',
'2005-02-24 01:20:00', '2005-03-14 10:00:00',
'2005-03-29 21:55:00', '2005-04-04 21:25:00',
'2005-07-14 11:20:00', '2005-07-14 19:55:00',
...
'2015-12-31 13:23:00', '2015-12-31 18:59:00',
'2015-12-31 19:13:00', '2015-12-31 20:20:00',
'2015-12-31 20:50:00', '2015-12-31 21:21:00',
'2015-12-31 21:59:00', '2015-12-31 22:04:00',
'2015-12-31 22:09:00', '2015-12-31 22:47:00'],
dtype='datetime64[ns]', name='stop_datetime', length=86536, freq=None)
Index(['stop_date', 'stop_time', 'driver_gender', 'driver_race',
'violation_raw', 'violation', 'search_conducted', 'search_type',
'stop_outcome', 'is_arrested', 'stop_duration', 'drugs_related_stop',
'district'],
dtype='object')