[英]Reverting from multiindex to single index dataframe in pandas
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:在上面的数据框中,我有一个由列组成的多级索引:
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?如何恢复到以“datetime”为索引、以“YEAR”和“MONTH”为普通列的数据框?
pass level=[0,1]
to just reset those levels:通过
level=[0,1]
来重置这些级别:
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:您也可以传递标签名称:
df.reset_index(level=['YEAR','MONTH'])
Another simple way would be to set columns for dataframe另一种简单的方法是为数据框设置列
consolidated_data.columns=country_master
ref: https://riptutorial.com/pandas/example/18695/how-to-change-multiindex-columns-to-standard-columns参考: https : //riptutorial.com/pandas/example/18695/how-to-change-multiindex-columns-to-standard-columns
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