Reindex sublevel of pandas dataframe multiindex

问题

I have a time series dataframe and I would like to reindex it by Trials and Measurements.

Simplified, I have this:

                value
Trial         
    1     0        13
          1         3
          2         4
    2     3       NaN
          4        12
    3     5        34   

Which I want to turn into this:

                  value
Trial    
    1      0        13
           1         3
           2         4
    2      0       NaN
           1        12
    3      0        34

How can I best do this?

回答1:

Just yesterday, the illustrious Andy Hayden added this feature to version 0.13 of pandas, which will be released any day now. See here for usage example he added to the docs.

If you are comfortable installing the development version of pandas from source, you can use it now.

df['Measurements'] = df.reset_index().groupby('Trial').cumcount()

The following code is equivalent, if less pithy, and will work on any recent version of pandas.

grouped = df.reset_index().groupby('Trial')
df['Measurements'] = grouped.apply(lambda x: Series(np.arange(len(x)), x.index))

Finally, df.set_index(['Trial', 'Measurements'], inplace=True) to get your desired result.

来源：https://stackoverflow.com/questions/20104874/reindex-sublevel-of-pandas-dataframe-multiindex