Does time of day affect arrest rate?

Analyzing Police Activity with pandas

Kevin Markham

Founder, Data School

Analyzing datetime data

apple

    price    volume       date_and_time
0  174.35  20567800 2018-01-08 16:00:00
1  174.33  21584000 2018-01-09 16:00:00
2  155.15  54390500 2018-02-08 16:00:00
3  156.41  70672600 2018-02-09 16:00:00
4  176.94  23774100 2018-03-08 16:00:00
5  179.98  32185200 2018-03-09 16:00:00

Accessing datetime attributes (1)

apple.dtypes

price                   float64
volume                    int64
date_and_time    datetime64[ns]

apple.date_and_time.dt.month

Accessing datetime attributes (2)

apple.set_index('date_and_time', inplace=True)

apple.index

DatetimeIndex(['2018-01-08 16:00:00', '2018-01-09 16:00:00',
               '2018-02-08 16:00:00', '2018-02-09 16:00:00',
               '2018-03-08 16:00:00', '2018-03-09 16:00:00'],
              dtype='datetime64[ns]', name='date_and_time', freq=None)

apple.index.month

Int64Index([1, 1, 2, 2, 3, 3], dtype='int64', name='date_and_time')

dt accessor is not used with a DatetimeIndex

Calculating the monthly mean price

apple.price.mean()

169.52666666666667

apple.groupby(apple.index.month).price.mean()

date_and_time
1    174.34
2    155.78
3    178.46
Name: price, dtype: float64

monthly_price = apple.groupby(apple.index.month).price.mean()

Plotting the monthly mean price

import matplotlib.pyplot as plt

monthly_price.plot()

Line plot: Series index on x-axis, Series values on y-axis

plt.xlabel('Month')
plt.ylabel('Price')
plt.title('Monthly mean stock price for Apple')

plt.show()

Line plot of monthly mean stock price for Apple

Let's practice!

Analyzing Police Activity with pandas