Repeating columns in DataFrame

Question

What is the right way of repeating columns in DataFrame?

I'm working on df:

  England    Germany    US
0 -3.3199    -3.31      496.68
1 1004.0     4.01       4.01
2 4.9794     4.97       1504.97
3 3.1766     2003.17    3.17

And I'd like to obtain:

  England  England   Germany  Germany   US        US    
0 -3.3199  -3.3199   -3.31    -3.31     496.68    496.68    
1 1004.0   1004.0    4.01     4.01      4.01      4.01 
2 4.9794   4.9794    4.97     4.97      1504.97   1504.97
3 3.1766   3.1766    2003.17  2003.17   3.17      3.17

I tough of getting headers from the original DataFrame and double them:

headers_double = [x for x in headers for i in range(2)]

Subsequently I tried to create df with new headers:

df.columns = [x for x in headers_double]

Unfortunately, my way of thinking was wrong. Any suggestions how to solve this problem?

Oren · Accepted Answer · 2020-11-20 08:59:54Z

5

I just came up with another solution that I want to share. Maybe it will be useful for somebody else.

print(df[np.repeat(df.columns.values,2)])

edited Nov 20, 2020 at 8:59

Oren

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answered Aug 16, 2016 at 2:22

Monica

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1 Comment

sudarsan vs Over a year ago

I tried this but column index remains the same as the original column , am I missing a trick?

Alicia Garcia-Raboso · Accepted Answer · 2016-08-15 21:46:00Z

3

If you only have a few columns and you can name them manually, just select columns from your dataframe duplicating those names.

import io
import pandas as pd

data = io.StringIO('''\
  England    Germany    US
0 -3.3199    -3.31      496.68
1 1004.0     4.01       4.01
2 4.9794     4.97       1504.97
3 3.1766     2003.17    3.17
''')
df = pd.read_csv(data, delim_whitespace=True)

print(df[['England', 'England', 'Germany', 'Germany', 'US', 'US']])

Output:

     England    England  Germany  Germany       US       US
0    -3.3199    -3.3199    -3.31    -3.31   496.68   496.68
1  1004.0000  1004.0000     4.01     4.01     4.01     4.01
2     4.9794     4.9794     4.97     4.97  1504.97  1504.97
3     3.1766     3.1766  2003.17  2003.17     3.17     3.17

If you want to do this more generally, you can get your column names, duplicate them and then select columns. The following results in the same output as above:

print(df[[col for col in df.columns for i in range(2)]])

edited Aug 15, 2016 at 21:46

answered Aug 15, 2016 at 21:30

Alicia Garcia-Raboso

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3 Comments

Monica Over a year ago

I simplified the problem. I have to many columns to do it by hand.

Alicia Garcia-Raboso Over a year ago

Check my latest edit, which addresses duplicating all columns programatically instead of manually.

Monica Over a year ago

Thank you so much, dude!

Reza Reiazi · Accepted Answer · 2021-08-27 16:00:50Z

0

You can use this to replicate all columns or replace ':' with a selected range of columns:

df[df.columns[:].append(df.columns)]

answered Aug 27, 2021 at 16:00

Reza Reiazi

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