Let's finalize this analysis by writing a function to complete all the steps of this recipe at once and add the ability to choose a specific type of crime:

>>> ADJ_2017 = .748

>>> def count_crime(df, offense_cat):
        df = df[df['OFFENSE_CATEGORY_ID'] == offense_cat]
        weekday = df['REPORTED_DATE'].dt.weekday_name
        year = df['REPORTED_DATE'].dt.year

        ct = df.groupby([year, weekday]).size().unstack()
        ct.loc[2017] = ct.loc[2017].div(ADJ_2017).astype('int')

        pop = pd.read_csv('data/denver_pop.csv', index_col='Year')
        pop = pop.squeeze().div(100000)

        ct = ct.div(pop, axis=0).astype('int')
        ct = ct.reindex(columns=days)
        sns.heatmap(ct, cmap='Greys')
        return ct

>>> count_crime(crime, 'auto-theft')