Pivot Pandas DataFrame into hierarchical columns/change column hierarchy

I figured it out by myself:

df = df.swaplevel(0,1,axis = 1).sort(axis = 1)

will do

What you need is reorder_levels and then sort the columns, like this:

import pandas as pd

df = pd.read_clipboard()

df
Out[8]:
dim1    Value_V Value_y instance
0   A_1 50.000000   0   instance200
1   A_2 6500.000000 1   instance200
2   A_3 50.000000   0   instance200
3   A_4 4305.922313 1   instance200
In [9]:

df.pivot('instance', 'dim1').reorder_levels([1, 0], axis=1).sort(axis=1)
Out[9]:
dim1        A_1             A_2             A_3             A_4
            Value_V Value_y Value_V Value_y Value_V Value_y Value_V Value_y
instance                                
instance200 50      0       6500    1       50      0       4305.922313 1

I have been battling this problem for a long time. My job requires me to handle large pivot_tables, where there are several dozen indexes and a bit more values. The last, most convenient solution in terms of versatility is this:

def pivot_fix(df):
    df = (df.reset_index().T.reset_index(level=0).T.reset_index(drop=True).
          reset_index(drop=True).reset_index(drop=True).T.reset_index().T)
    df.iloc[0, :df.iloc[0, :].isna().sum()] = df.iloc[1, :df.iloc[0, :].isna().sum()]
    df.columns = df.iloc[0]
    df.drop(df.index[0:2], inplace=True)
    return(df)

using it like that: df = (df.pivot_table(index=['location_id', 'place_name', 'address'], columns='day', values='sum')

I'd like to add that the prior answers are outdated.

df = df.swaplevel(0, 1, axis=1) # Swaps level as desired
df.columns = df.columns.sortlevel(0)[0] # Orders level 0, slices to the cols

You can find more documentation on this method here: https://pandas.pydata.org/docs/reference/api/pandas.MultiIndex.sortlevel.html. It allows a user to directly sort individual levels of a pandas.MultiIndex.