import pandas as pd
import matplotlib.pyplot as plt

Exploring the weather dataset

Plotting the temperature

In this exercise, you'll examine the temperature columns from the weather dataset to assess whether the data seems trustworthy. First you'll print the summary statistics, and then you'll visualize the data using a box plot.

When deciding whether the values seem reasonable, keep in mind that the temperature is measured in degrees Fahrenheit, not Celsius!

weather = pd.read_csv('./dataset/weather.csv')

# Describe the temperature columns
print(weather[['TMIN', 'TAVG', 'TMAX']].describe())

# Create a box plot of the temperature columns
weather[['TMIN', 'TAVG', 'TMAX']].plot(kind='box')
plt.savefig('../images/tmin-boxplot.png')
              TMIN         TAVG         TMAX
count  4017.000000  1217.000000  4017.000000
mean     43.484441    52.493016    61.268608
std      17.020298    17.830714    18.199517
min      -5.000000     6.000000    15.000000
25%      30.000000    39.000000    47.000000
50%      44.000000    54.000000    62.000000
75%      58.000000    68.000000    77.000000
max      77.000000    86.000000   102.000000

Plotting the temperature difference

In this exercise, you'll continue to assess whether the dataset seems trustworthy by plotting the difference between the maximum and minimum temperatures.

What do you notice about the resulting histogram? Does it match your expectations, or do you see anything unusual?

weather['TDIFF'] = weather.TMAX - weather.TMIN

# Describe the 'TDIFF' column
print(weather.TDIFF.describe())

# Create a histogram with 20 bins to visualize 'TDIFF'
weather.TDIFF.plot(kind='hist', bins=20)
count    4017.000000
mean       17.784167
std         6.350720
min         2.000000
25%        14.000000
50%        18.000000
75%        22.000000
max        43.000000
Name: TDIFF, dtype: float64
<matplotlib.axes._subplots.AxesSubplot at 0x7f454980add0>

Categorizing the weather

Counting bad weather conditions

The weather DataFrame contains 20 columns that start with 'WT', each of which represents a bad weather condition. For example:

  • WT05 indicates "Hail"
  • WT11 indicates "High or damaging winds"
  • WT17 indicates "Freezing rain"

For every row in the dataset, each WT column contains either a 1 (meaning the condition was present that day) or NaN (meaning the condition was not present).

In this exercise, you'll quantify "how bad" the weather was each day by counting the number of 1 values in each row.

WT = weather.loc[:, 'WT01':'WT22']

# Calculate the sum of each row in 'WT'
weather['bad_conditions'] = WT.sum(axis='columns')

# Replace missing values in 'bad_conditions' with 0
weather['bad_conditions'] = weather.bad_conditions.fillna(0).astype('int')

# Create a histogram to visualist 'bad_conditions'
weather['bad_conditions'].plot(kind='hist')
<matplotlib.axes._subplots.AxesSubplot at 0x7f45455a6f50>

Rating the weather conditions

In the previous exercise, you counted the number of bad weather conditions each day. In this exercise, you'll use the counts to create a rating system for the weather.

The counts range from 0 to 9, and should be converted to ratings as follows:

  • Convert 0 to 'good'
  • Convert 1 through 4 to 'bad'
  • Convert 5 through 9 to 'worse'
print(weather.bad_conditions.value_counts().sort_index())

# Create a dictionary that maps integers to strings
mapping = {0:'good', 1:'bad', 2:'bad', 3:'bad', 4:'bad', 5:'worse', 
           6:'worse', 7:'worse', 8:'worse', 9:'worse'}

# Convert the 'bad_conditions' integers to strings using the 'mapping'
weather['rating'] = weather.bad_conditions.map(mapping)

# Count the unique values in 'rating'
print(weather.rating.value_counts())
0    1749
1     613
2     367
3     380
4     476
5     282
6     101
7      41
8       4
9       4
Name: bad_conditions, dtype: int64
bad      1836
good     1749
worse     432
Name: rating, dtype: int64

Changing the data type to category

Since the rating column only has a few possible values, you'll change its data type to category in order to store the data more efficiently. You'll also specify a logical order for the categories, which will be useful for future exercises.

NOTE : in pandas 1.0.3, ordered categories must be defined with pd.api.types.CategoricalDtype

cats = pd.api.types.CategoricalDtype(categories=['good', 'bad', 'worse'], ordered=True)

# Change the data type of 'rating' to category
weather['rating'] = weather.rating.astype(cats)

# Examine the head of 'rating'
print(weather.rating.head())
0    bad
1    bad
2    bad
3    bad
4    bad
Name: rating, dtype: category
Categories (3, object): [good < bad < worse]

Merging datasets

Preparing the DataFrames

In this exercise, you'll prepare the traffic stop and weather rating DataFrames so that they're ready to be merged:

  1. With the ri DataFrame, you'll move the stop_datetime index to a column since the index will be lost during the merge.
  2. With the weather DataFrame, you'll select the DATE and rating columns and put them in a new DataFrame.
ri = pd.read_csv('./dataset/police.csv')

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.reset_index(inplace=True)

# Examine the head of 'ri'
print(ri.head())

# Create a DataFrame from the 'DATE' and 'rating' columns
weather_rating = weather[['DATE', 'rating']]

# Examine the head of 'weather_rating'
print(weather_rating.head())
   index state   stop_date stop_time  county_name driver_gender driver_race  \
0      0    RI  2005-01-04     12:55          NaN             M       White   
1      1    RI  2005-01-23     23:15          NaN             M       White   
2      2    RI  2005-02-17     04:15          NaN             M       White   
3      3    RI  2005-02-20     17:15          NaN             M       White   
4      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   

        stop_datetime  
0 2005-01-04 12:55:00  
1 2005-01-23 23:15:00  
2 2005-02-17 04:15:00  
3 2005-02-20 17:15:00  
4 2005-02-24 01:20:00  
         DATE rating
0  2005-01-01    bad
1  2005-01-02    bad
2  2005-01-03    bad
3  2005-01-04    bad
4  2005-01-05    bad

Merging the DataFrames

In this exercise, you'll merge the ri and weather_rating DataFrames into a new DataFrame, ri_weather.

The DataFrames will be joined using the stop_date column from ri and the DATE column from weather_rating. Thankfully the date formatting matches exactly, which is not always the case!

Once the merge is complete, you'll set stop_datetime as the index, which is the column you saved in the previous exercise.

print(ri.shape)

# Merge 'ri' and 'weather_rating' using left join
ri_weather = pd.merge(left=ri, right=weather_rating, 
                      left_on='stop_date', right_on='DATE', how='left')

# Examine the shape of 'ri_weather'
print(ri_weather.shape)

# Set 'stop_datetime' as the index of 'ri_weather'
ri_weather.set_index('stop_datetime', inplace=True)
(91741, 17)
(91741, 19)

Does weather affect the arrest rate?

Comparing arrest rates by weather rating

Do police officers arrest drivers more often when the weather is bad? Find out below!

  • First, you'll calculate the overall arrest rate.
  • Then, you'll calculate the arrest rate for each of the weather ratings you previously assigned.
  • Finally, you'll add violation type as a second factor in the analysis, to see if that accounts for any differences in the arrest rate.

Since you previously defined a logical order for the weather categories, good < bad < worse, they will be sorted that way in the results.

print(ri_weather.is_arrested.mean())

print(ri_weather.groupby('rating').is_arrested.mean())

# Calculate the arrest rate for each 'violation' and 'rating'
print(ri_weather.groupby(['violation', 'rating']).is_arrested.mean())
0.09025408486936048
rating
good     0.086842
bad      0.090479
worse    0.106527
Name: is_arrested, dtype: float64
violation            rating
Equipment            good      0.058995
                     bad       0.066311
                     worse     0.097357
Moving violation     good      0.056227
                     bad       0.058050
                     worse     0.065860
Other                good      0.076923
                     bad       0.087443
                     worse     0.062893
Registration/plates  good      0.081574
                     bad       0.098160
                     worse     0.115625
Seat belt            good      0.028587
                     bad       0.022493
                     worse     0.000000
Speeding             good      0.013404
                     bad       0.013314
                     worse     0.016886
Name: is_arrested, dtype: float64

Selecting from a multi-indexed Series

The output of a single .groupby() operation on multiple columns is a Series with a MultiIndex. Working with this type of object is similar to working with a DataFrame:

  • The outer index level is like the DataFrame rows.
  • The inner index level is like the DataFrame columns.

In this exercise, you'll practice accessing data from a multi-indexed Series using the .loc[] accessor.

arrest_rate = ri_weather.groupby(['violation', 'rating']).is_arrested.mean()

# Print the 'arrest_rate' Series
print(arrest_rate)

# Print the arrest rate for moving violations in bad weather
print(arrest_rate.loc['Moving violation', 'bad'])

# Print the arrest rates for speeding violations in all three weather condtions
print(arrest_rate.loc['Speeding'])
violation            rating
Equipment            good      0.058995
                     bad       0.066311
                     worse     0.097357
Moving violation     good      0.056227
                     bad       0.058050
                     worse     0.065860
Other                good      0.076923
                     bad       0.087443
                     worse     0.062893
Registration/plates  good      0.081574
                     bad       0.098160
                     worse     0.115625
Seat belt            good      0.028587
                     bad       0.022493
                     worse     0.000000
Speeding             good      0.013404
                     bad       0.013314
                     worse     0.016886
Name: is_arrested, dtype: float64
0.05804964058049641
rating
good     0.013404
bad      0.013314
worse    0.016886
Name: is_arrested, dtype: float64

Reshaping the arrest rate data

In this exercise, you'll start by reshaping the arrest_rate Series into a DataFrame. This is a useful step when working with any multi-indexed Series, since it enables you to access the full range of DataFrame methods.

Then, you'll create the exact same DataFrame using a pivot table. This is a great example of how pandas often gives you more than one way to reach the same result!

print(arrest_rate.unstack())

# Create the same DataFrame using a pivot table
print(ri_weather.pivot_table(index='violation', columns='rating', values='is_arrested'))
rating                   good       bad     worse
violation                                        
Equipment            0.058995  0.066311  0.097357
Moving violation     0.056227  0.058050  0.065860
Other                0.076923  0.087443  0.062893
Registration/plates  0.081574  0.098160  0.115625
Seat belt            0.028587  0.022493  0.000000
Speeding             0.013404  0.013314  0.016886
rating                   good       bad     worse
violation                                        
Equipment            0.058995  0.066311  0.097357
Moving violation     0.056227  0.058050  0.065860
Other                0.076923  0.087443  0.062893
Registration/plates  0.081574  0.098160  0.115625
Seat belt            0.028587  0.022493  0.000000
Speeding             0.013404  0.013314  0.016886