Datetime selection
Back in Chapter 2, Selection and Assignment, we discussed the many robust ways that pandas allows you to select data from a pd.Series or pd.DataFrame by interacting with their associated row pd.Index. If you happen to create a pd.Index using datetime data, it ends up being represented as a special subclass called a pd.DatetimeIndex. This subclass overrides some functionality of the pd.Index.loc method to give you more flexible selection options tailored to temporal data.
How to do it
pd.date_range is a convenient function that helps you quickly generate a pd.DatetimeIndex. One of the ways to use this function is to specify a starting date with the start= parameter, specify a step frequency with the freq= parameter, and specify the desired length of your pd.DatetimeIndex with the periods= argument.
For instance, to generate a pd.DatetimeIndex that starts on December 27, 2023, and provides 5 days in total with 10 days between each record, you would write...
