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To reset the index in pandas, you simply need to chain the function . reset_index() with the dataframe object. On applying the . reset_index() function, the index gets shifted to the dataframe as a separate column.
pandas MultiIndex to ColumnsUse pandas DataFrame. reset_index() function to convert/transfer MultiIndex (multi-level index) indexes to columns. The default setting for the parameter is drop=False which will keep the index values as columns and set the new index to DataFrame starting from zero.
A multi-index dataframe has multi-level, or hierarchical indexing. We can easily convert the multi-level index into the column by the reset_index() method. DataFrame. reset_index() is used to reset the index to default and make the index a column of the dataframe.
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 NI
datetime
2000-01-01 2000 1 NaN
2000-01-02 2000 1 NaN
2000-01-03 2000 1 NaN
2000-01-04 2000 1 NaN
2000-01-05 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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