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

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)

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

assuming data is daily non business dates:

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

I can't post a comment but you can probably traverse each value and add 24 hours to the previous value to see if the date matches?

import pandas as pd

a = [1,2,3,4,5]
b = [1,0.4,0.3,0.5,0.2]

df = pd.DataFrame({'a':a , 'b': b})

for i in range(len(df)):
    prev = df.loc[i,'a']
    if i is 0:
        continue
    else:
         # Add 1 day to the current value and check with prev value

You can use DatetimeIndex.difference and add freq param, so you can check for missing days, hours, minutes, depending on the frequency you are using:

pd.date_range(df.index.min(), df.index.max(), freq="1min").difference(df.index)