check for any missing dates in the index

assuming data is daily non business dates:

df.index.to_series().diff().dt.days > 1

Example:

As a minimal example, take this:

>>> df
              GWA_BTC   GWA_ETH  GWA_LTC  GWA_XLM  GWA_XRP
Date                                                      
2013-01-19  11,826.36  1,068.45   195.00     0.51     1.82
2013-01-20  13,062.68  1,158.71   207.58     0.52     1.75
2013-01-28  12,326.23  1,108.90   197.36     0.48     1.55
2013-01-29  11,397.52  1,038.21   184.92     0.47     1.43

And we can find the missing dates between 2013-01-19 and 2013-01-29

Method 1:

See @Vaishali's answer

Use .difference to find the difference between your datetime index and the set of all dates within your range:

pd.date_range('2013-01-19', '2013-01-29').difference(df.index)

Which returns:

DatetimeIndex(['2013-01-21', '2013-01-22', '2013-01-23', '2013-01-24',
               '2013-01-25', '2013-01-26', '2013-01-27'],
              dtype='datetime64[ns]', freq=None)

Method 2:

You can re-index your dataframe using all dates within your desired daterange, and find where reindex has inserted NaNs.

And to find missing dates between 2013-01-19 and 2013-01-29:

>>> df.reindex(pd.date_range('2013-01-19', '2013-01-29')).isnull().all(1)

2013-01-19    False
2013-01-20    False
2013-01-21     True
2013-01-22     True
2013-01-23     True
2013-01-24     True
2013-01-25     True
2013-01-26     True
2013-01-27     True
2013-01-28    False
2013-01-29    False
Freq: D, dtype: bool

Those values with True are the missing dates in your original dataframe

You can use DatetimeIndex.difference(other)

pd.date_range(start = '2013-01-19', end = '2018-01-29' ).difference(df.index)

It returns the elements not present in the other