Dates are complicated. Just think of the Y2K bug, the pending Year 2038 problem, and the confusion caused by time zones. It's a mess. We encounter dates naturally when dealing with the time-series data. Pandas can create date ranges, resample time-series data, and perform date arithmetic operations.
Create a range of dates starting from January 1 1900 and lasting 42 days, as follows:
print("Date range", pd.date_range('1/1/1900', periods=42, freq='D'))
January has less than 42 days, so the end date falls in February, as you can check for yourself:
Date range <class 'pandas.tseries.index.DatetimeIndex'> [1900-01-01, ..., 1900-02-11] Length: 42, Freq: D, Timezone: None
The following table from the Pandas official documentation (refer to http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases) describes the frequencies used in Pandas:
|
Short code |
Description |
|---|---|
|
|
Business day frequency |
|
|
Custom business day frequency (experimental... |