Reverting from multiindex to single index dataframe in pandas

Question

                       NI
YEAR MONTH datetime        
2000 1     2000-01-01   NaN
           2000-01-02   NaN
           2000-01-03   NaN
           2000-01-04   NaN
           2000-01-05   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?

user308827 · Accepted Answer · 2018-07-10 14:09:43Z

100

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'])

edited Jul 10, 2018 at 14:09

user308827

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answered Oct 4, 2015 at 20:51

EdChum

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4 Comments

Dzmitry Lazerka Over a year ago

What if it's dist_df.columns are MultiIndex, not rows?

EdChum Over a year ago

@DzmitryLazerka sorry I don't understand your comment, if you have a question please post a new question, answering questions in comments is not good form on SO

onlyphantom Over a year ago

@DzmitryLazerka to flatten a multiindex (hierarchical) column / row index, you can use .to_flat_index(). See this answer for code example

meesinlid Over a year ago

Would this change the order of the array?

Shujaath Khan · Accepted Answer · 2020-07-25 09:13:05Z

8

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

answered Jul 25, 2020 at 9:13

Shujaath Khan

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