Pandas: combining header rows of a multiIndex DataFrame

Question

Lets say I have a DataFrame as follows:

first   bar       baz       foo
second  one  two  one  two  one  two three
A       1    2    3    4    5    6   7
B       8    9    10   11   12   13  14

and I want to create a new DataFrame like this:

        barone  bartwo  bazone baztwo  fooone footwo foothree
A       1       2       3      4       5      6      7
B       8       9       10     11      12     13     14

What would be the possible code?

Scott Boston · Accepted Answer · 2019-10-18 04:47:51Z

24

1. Update using Python 3.6+ use f-string formatting with list comprehension:

df.columns = [f'{i}{j}' for i, j in df.columns]

2. Use `map` and `join`:

df.columns = df.columns.map(''.join)

3. If your columns has numeric datatypes, use `map` and `format`:

df.columns = df.columns.map('{0[0]}{0[1]}'.format)

Output:

   barone  bartwo  bazone  baztwo  fooone  footwo  foothree
A       1       2       3       4       5       6         7
B       8       9      10      11      12      13        14

edited Oct 18, 2019 at 4:47

answered Dec 4, 2017 at 15:56

Scott Boston

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Pandas: combining header rows of a multiIndex DataFrame

1 Answer 1

1. Update using Python 3.6+ use f-string formatting with list comprehension:

2. Use `map` and `join`:

3. If your columns has numeric datatypes, use `map` and `format`:

Output:

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Collectives™ on Stack Overflow

1 Answer 1

1. Update using Python 3.6+ use f-string formatting with list comprehension:

2. Use map and join:

3. If your columns has numeric datatypes, use map and format:

Output:

Comments

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2. Use `map` and `join`:

3. If your columns has numeric datatypes, use `map` and `format`: