Dealing with dates

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
B	Business day frequency
C	Custom business day frequency (experimental...