Resampling pandas Dataframe keeping other columns
To add missing indices, use:

full_idx = pd.date_range(start='&lt;start_date&gt;', end='&lt;end_date&gt;', freq='H')

df.reindex(full_idx)


That will put NaN's in SITE_NB and VALUE columns.

If each dataframe has only one value of SITE_NB, you can use:

df['SITE_NB'].fillna(df['SITE_NB'].unique()[0], inplace=True)


which replaces all NaN's with the first non-null values in the column.



If you actually have one dataframe with multiple SITE_NB values, could you please show what that looks like? Are the time indices overlapping in some cases or not?

Also, this answer might work for you: https://stackoverflow.com/questions/32275540/pandas-reindex-dates-in-groupby