import pandas as pd

# Read 'police.csv' into a DataFrame named ri


## Does time of day affect arrest rate?

### Calculating the hourly arrest rate

When a police officer stops a driver, a small percentage of those stops ends in an arrest. This is known as the arrest rate. In this exercise, you'll find out whether the arrest rate varies by time of day.

First, you'll calculate the arrest rate across all stops in the ri DataFrame. Then, you'll calculate the hourly arrest rate by using the hour attribute of the index. The hour ranges from 0 to 23, in which:

• 0 = midnight
• 12 = noon
• 23 = 11 PM

### Preprocess

combined = ri.stop_date.str.cat(ri.stop_time, sep=' ')

ri['stop_datetime'] = pd.to_datetime(combined)
ri['is_arrested'] = ri['is_arrested'].astype(bool)

ri.set_index('stop_datetime', inplace=True)

print(ri.is_arrested.mean())

# Calculate the hourly arrest rate
print(ri.groupby(ri.index.hour).is_arrested.mean())

# Save the hourly arrest rate
hourly_arrest_rate = ri.groupby(ri.index.hour).is_arrested.mean()

0.09025408486936048
stop_datetime
0     0.121206
1     0.144250
2     0.144120
3     0.148370
4     0.179310
5     0.178899
6     0.043614
7     0.053497
8     0.073591
9     0.070199
10    0.069306
11    0.075217
12    0.087040
13    0.078964
14    0.080171
15    0.080526
16    0.089505
17    0.107914
18    0.089883
19    0.078508
20    0.091482
21    0.153265
22    0.110715
23    0.108225
Name: is_arrested, dtype: float64


### Plotting the hourly arrest rate

In this exercise, you'll create a line plot from the hourly_arrest_rate object. A line plot is appropriate in this case because you're showing how a quantity changes over time.

This plot should help you to spot some trends that may not have been obvious when examining the raw numbers!

import matplotlib.pyplot as plt

# Create a line plot of 'hourly_arrest_rate'
hourly_arrest_rate.plot()

# Add the xlabel, ylabel, and title
plt.xlabel('Hour')
plt.ylabel('Arrest Rate')
plt.title('Arrest Rate by Time of Day')

Text(0.5, 1.0, 'Arrest Rate by Time of Day')

In a small portion of traffic stops, drugs are found in the vehicle during a search. In this exercise, you'll assess whether these drug-related stops are becoming more common over time.

The Boolean column drugs_related_stop indicates whether drugs were found during a given stop. You'll calculate the annual drug rate by resampling this column, and then you'll use a line plot to visualize how the rate has changed over time.

print(ri.drugs_related_stop.resample('A').mean())

# Save the annual rate of drug-related stops
annual_drug_rate = ri.drugs_related_stop.resample('A').mean()

# Create a line plot of 'annual_drug_rate'
annual_drug_rate.plot()

stop_datetime
2005-12-31    0.006390
2006-12-31    0.006913
2007-12-31    0.007520
2008-12-31    0.006998
2009-12-31    0.009079
2010-12-31    0.009407
2011-12-31    0.009035
2012-12-31    0.009388
2013-12-31    0.012283
2014-12-31    0.013280
2015-12-31    0.011787
Freq: A-DEC, Name: drugs_related_stop, dtype: float64

<matplotlib.axes._subplots.AxesSubplot at 0x1afaf8989c8>

### Comparing drug and search rates

As you saw in the last exercise, the rate of drug-related stops increased significantly between 2005 and 2015. You might hypothesize that the rate of vehicle searches was also increasing, which would have led to an increase in drug-related stops even if more drivers were not carrying drugs.

You can test this hypothesis by calculating the annual search rate, and then plotting it against the annual drug rate. If the hypothesis is true, then you'll see both rates increasing over time.

annual_search_rate = ri.search_conducted.resample('A').mean()

# Concatenate 'annual_drug_rate' and 'annual_search_rate'
annual = pd.concat([annual_drug_rate, annual_search_rate], axis='columns')

# Create subplots from 'annual'
annual.plot(subplots=True)

array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001AFAF939288>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000001AFAF965988>],
dtype=object)

## What violations are caught in each district?

### Tallying violations by district

The state of Rhode Island is broken into six police districts, also known as zones. How do the zones compare in terms of what violations are caught by police?

In this exercise, you'll create a frequency table to determine how many violations of each type took place in each of the six zones. Then, you'll filter the table to focus on the "K" zones, which you'll examine further in the next exercise.

print(pd.crosstab(ri.district, ri.violation))

# Save the frequency table as 'all_zones'
all_zones = pd.crosstab(ri.district, ri.violation)

# Select rows 'Zone K1' through 'Zone K3'
print(all_zones.loc['Zone K1':'Zone K3'])

# Save the smaller table as 'k_zones'
k_zones = all_zones.loc['Zone K1':'Zone K3']

violation  Equipment  Moving violation  Other  Registration/plates  Seat belt  \
district
Zone K1          673              1254    290                  120          0
Zone K2         2061              2962    942                  768        481
Zone K3         2302              2898    706                  695        638
Zone X1          296               671    143                   38         74
Zone X3         2049              3086    769                  671        820
Zone X4         3541              5353   1560                 1411        843

violation  Speeding
district
Zone K1        5960
Zone K2       10448
Zone K3       12323
Zone X1        1119
Zone X3        8779
Zone X4        9795
violation  Equipment  Moving violation  Other  Registration/plates  Seat belt  \
district
Zone K1          673              1254    290                  120          0
Zone K2         2061              2962    942                  768        481
Zone K3         2302              2898    706                  695        638

violation  Speeding
district
Zone K1        5960
Zone K2       10448
Zone K3       12323


### Plotting violations by district

Now that you've created a frequency table focused on the "K" zones, you'll visualize the data to help you compare what violations are being caught in each zone.

First you'll create a bar plot, which is an appropriate plot type since you're comparing categorical data. Then you'll create a stacked bar plot in order to get a slightly different look at the data. Which plot do you find to be more insightful?

k_zones.plot(kind='bar')
plt.savefig('../images/k-zones-plot.png')

k_zones.plot(kind='bar', stacked=True)

<matplotlib.axes._subplots.AxesSubplot at 0x1afafa5bbc8>

## How long might you be stopped for a violation?

### Converting stop durations to numbers

In the traffic stops dataset, the stop_duration column tells you approximately how long the driver was detained by the officer. Unfortunately, the durations are stored as strings, such as '0-15 Min'. How can you make this data easier to analyze?

In this exercise, you'll convert the stop durations to integers. Because the precise durations are not available, you'll have to estimate the numbers using reasonable values:

• Convert '0-15 Min' to 8
• Convert '16-30 Min' to 23
• Convert '30+ Min' to 45
print(ri.stop_duration.unique())

# Create a dictionary that maps strings to integers
mapping = {'0-15 Min': 8, '16-30 Min': 23, '30+ Min': 45}

# Convert the 'stop_duration' strings to intergers using the 'mapping'
ri['stop_minutes'] = ri.stop_duration.map(mapping)

# Print the unique values in 'stop_minutes'
print(ri.stop_minutes.unique())

['0-15 Min' '16-30 Min' nan '30+ Min']
[ 8. 23. nan 45.]


### Plotting stop length

If you were stopped for a particular violation, how long might you expect to be detained?

In this exercise, you'll visualize the average length of time drivers are stopped for each type of violation. Rather than using the violation column in this exercise, you'll use violation_raw since it contains more detailed descriptions of the violations.

print(ri.groupby('violation_raw').stop_minutes.mean())

# Save the resulting Series as 'stop_length'
stop_length = ri.groupby('violation_raw').stop_minutes.mean()

# Sort 'stop_length' by its values and create a horizontal bar plot
stop_length.sort_values().plot(kind='barh')

violation_raw
APB                                 17.967033
Call for Service                    22.140805
Equipment/Inspection Violation      11.445340
Motorist Assist/Courtesy            17.741463
Other Traffic Violation             13.844490
Registration Violation              13.736970
Seatbelt Violation                   9.662815
Special Detail/Directed Patrol      15.123632
Speeding                            10.581509
Suspicious Person                   14.910714
Violation of City/Town Ordinance    13.254144
Warrant                             24.055556
Name: stop_minutes, dtype: float64

<matplotlib.axes._subplots.AxesSubplot at 0x7f5bdd8fc390>