Create multiindex from existing dataframe

You could simply use groupby in this case, which will create the multi-index automatically when it sums the sales along the requested columns.

df.groupby(['user_id', 'account_num', 'dates']).sales.sum().to_frame()

You should also be able to simply do this:

df.set_index(['user_id', 'account_num', 'dates'])

Although you probably want to avoid any duplicates (e.g. two or more rows with identical user_id, account_num and date values but different sales figures) by summing them, which is why I recommended using groupby.

If you need the multi-index, you can simply access viat new_df.index where new_df is the new dataframe created from either of the two operations above.

And user_id will be level 0 and account_num will be level 1.

For clarification of future users I would like to add the following:

As said by Alexander,

df.set_index(['user_id', 'account_num', 'dates'])

with a possible inplace=True does the job.

The type(df) gives

pandas.core.frame.DataFrame

whereas type(df.index) is indeed the expected

pandas.core.indexes.multi.MultiIndex