Are drug-related stops on the rise?

Analyzing Police Activity with pandas

Kevin Markham

Founder, Data School

Resampling the price

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

date_and_time
1    174.34
2    155.78
3    178.46

apple.price.resample('M').mean()

date_and_time
2018-01-31    174.34
2018-02-28    155.78
2018-03-31    178.46

Resampling the volume

apple

date_and_time         price    volume          
2018-01-08 16:00:00  174.35  20567800
2018-01-09 16:00:00  174.33  21584000
2018-02-08 16:00:00  155.15  54390500
...                     ...       ...

apple.volume.resample('M').mean()

date_and_time
2018-01-31    21075900
2018-02-28    62531550
2018-03-31    27979650
Freq: M, Name: volume, Length: 3, dtype: float64

Concatenating price and volume

monthly_price = apple.price.resample('M').mean()
monthly_volume = apple.volume.resample('M').mean()

pd.concat([monthly_price, monthly_volume], axis='columns')

date_and_time   price    volume
2018-01-31     174.34  21075900
2018-02-28     155.78  62531550
2018-03-31     178.46  27979650

monthly = pd.concat([monthly_price, monthly_volume],
                     axis='columns')

Plotting price and volume (1)

monthly.plot()
plt.show()

Single line plot of monthly mean price and volume for Apple stock

Plotting price and volume (2)

monthly.plot(subplots=True)
plt.show()

Separate line plots of monthly mean price and volume for Apple stock

Let's practice!

Analyzing Police Activity with pandas