Reverting from multiindex to single index dataframe in pandas

 NIYEAR MONTH datetime 2000 1 2000-01-01 NaN2000-01-02 NaN2000-01-03 NaN2000-01-04 NaN2000-01-05 NaN

In the dataframe above, I have a multilevel index consisting of the columns:

names=[u'YEAR', u'MONTH', u'datetime']

How do I revert to a dataframe with 'datetime' as index and 'YEAR' and 'MONTH' as normal columns?

2

Best Answer

pass level=[0,1] to just reset those levels:

dist_df = dist_df.reset_index(level=[0,1])In [28]:df.reset_index(level=[0,1])Out[28]:YEAR MONTH NIdatetime 2000-01-01 2000 1 NaN2000-01-02 2000 1 NaN2000-01-03 2000 1 NaN2000-01-04 2000 1 NaN2000-01-05 2000 1 NaN

you can pass the label names alternatively:

df.reset_index(level=['YEAR','MONTH'])

Another simple way would be to set columns for dataframe

consolidated_data.columns=country_master

ref: https://riptutorial.com/pandas/example/18695/how-to-change-multiindex-columns-to-standard-columns

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