CODEWITHSUNDEEP
pythonpandaspivot
Marina
Marina

Reputation: 349

Python Pandas: Rename columns after pivoting

I have a pandas dataframe that is pivoted. I do not know how to change column names so that I can continue working with the pivoted dataframe in a natural way. Below is a minimal working example. 

df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
                            'two'],
                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
                    'baz': [1, 2, 3, 4, 5, 6],
                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})

After pivoting with 

df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])

the output is:

      baz       zoo
bar   A  B  C   A  B  C
foo
one   1  2  3   x  y  z
two   4  5  6   q  w  t

What would be the following step to do in order to obtain below output?

   A_baz  B_baz  C_baz   A_zoo  B_zoo  C_zoo

one   1    2       3       x      y      z
two   4    5       6       q      w      t

Thanks a lot!

Upvotes: 5

Views: 14134

Answers (2)

sammywemmy
sammywemmy

Reputation: 28644

One option is with pivot_wider from pyjanitor, using the names_glue parameter to reshape the column names:

# pip install pyjanitor
import pandas as pd
import janitor

df.pivot_wider(
    index = 'foo', 
    names_from = 'bar', 
    values_from = ['baz', 'zoo'], 
    names_glue = "{bar}_{_value}")

   foo A_baz B_baz C_baz A_zoo B_zoo C_zoo
0  one     1     2     3     x     y     z
1  two     4     5     6     q     w     t

in the names_glue string template, _value serves as a placeholder for values from values_from. Anything in the {} brackets should either be from names_from or _value to represent values_from.

Upvotes: 2

jezrael
jezrael

Reputation: 862511

Use f-strings with list comprehension:

#python 3.6+
df.columns = [f'{j}_{i}' for i, j in df.columns]
#lower python versions
#df.columns = ['{}_{}'.format(j, i) for i, j in df.columns]
print (df)
    A_baz B_baz C_baz A_zoo B_zoo C_zoo
foo                                    
one     1     2     3     x     y     z
two     4     5     6     q     w     t

Or DataFrame.swaplevel with map and join:

df = df.pivot(index='foo', columns='bar', values=['baz', 'zoo']).swaplevel(0,1,axis=1)

df.columns = df.columns.map('_'.join)
print (df)
    A_baz B_baz C_baz A_zoo B_zoo C_zoo
foo                                    
one     1     2     3     x     y     z
two     4     5     6     q     w     t

Upvotes: 12

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