Python Binary Column if GetDate is between dates

Question

I am trying to create a binary column in python based if a given data in the GameDate column is between a set of dates.

Here is what I tried:

df5['pre_season'] = df5.gameDate.apply(lambda x: 1 if pd.to_datetime('2021-02-28') <= x <= pd.to_datetime('2021-03-30'):
                                       elif pd.to_datetime('2017-02-22') <= x <= pd.to_datetime('2017-04-01'):
                                       elif pd.to_datetime('2018-02-21') <= x <= pd.to_datetime('2018-03-27'):
                                       elif pd.to_datetime('2019-02-21') <= x <= pd.to_datetime('2019-03-26'):
                                       elif pd.to_datetime('2020-02-21') <= x <= pd.to_datetime('2020-07-22'):
                                       else 0).astype(int)

Through this I was hoping to create a binary column that call pre_season that would have a 1 or 0 when the GameDate falls in these ranges.

Answer 1

You can either use pandas.Series.dt.to_pydatetime with a listcomp :

dates = pd.date_range(start='01/01/2017', end='01/01/2022')

df5 = pd.DataFrame({'gameDate': dates}).sample(10, ignore_index=True)

date_ranges = list(map(pd.to_datetime, [('2021-02-28', '2021-03-30'),
                                        ('2017-02-22', '2017-04-01'),
                                        ('2018-02-21', '2018-03-27'),
                                        ('2019-02-21', '2019-03-26'),
                                        ('2020-02-21', '2020-07-22')]))

df5['pre_season'] = [1 if any([(start < x.to_pydatetime() < end)
                              for start, end in date_ranges]) else 0
                     for x in df5['gameDate']]

Or use numpy.where with pandas.Series.isin :

df5['pre_season'] = np.where(df5['gameDate'].isin([x for start, end in date_ranges
                                                   for x in pd.date_range(start, end)]), 1, 0)
#array([1, 0, 0, 1, 0, 0, 0, 0, 0, 0])

Output:

    gameDate  pre_season
0 2020-04-20           1
1 2021-12-28           0
2 2020-01-04           0
3 2018-03-07           1
4 2018-05-22           0
5 2017-01-10           0
6 2019-10-20           0
7 2018-10-27           0
8 2017-11-27           0
9 2017-05-24           0