3

I have a large pandas dataframe that contains many columns.

I would like to change the order of the columns so that only a subset of them appears first. I dont care about the ordering of the rest (and there are too many variables to list them all)

For instance, if my dataframe is like this

a b c d e f g h i
5 8 7 2 1 4 1 2 3
1 4 2 2 3 4 1 5 3

I would like to specify a subset of the columns

mysubset=['d','f'] and reorder the dataframe such that the order of the columns is now

d,f,a,b,c,e,g,h,i

Is there a way to do that in a panda-esque way?

Derptastic
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ℕʘʘḆḽḘ
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5 Answers5

7

You could use a column mask:

>>> mysubset = ["d","f"]
>>> mask = df.columns.isin(mysubset)
>>> pd.concat([df.loc[:,mask], df.loc[:,~mask]], axis=1)
   d  f  a  b  c  e  g  h  i
0  2  4  5  8  7  1  1  2  3
1  2  4  1  4  2  3  1  5  3

or use sorted:

>>> mysubset = ["d","f"]
>>> df[sorted(df, key=lambda x: x not in mysubset)]
   d  f  a  b  c  e  g  h  i
0  2  4  5  8  7  1  1  2  3
1  2  4  1  4  2  3  1  5  3

which works because x not in mysubset will be False for d and f, and False < True.

DSM
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3

I usually do something like this:

mysubset = ['d', 'f']
othercols = [c for c in df.columns if c not in mysubset]
df = df[mysubset+othercols]
root
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2

use a multi-index to do that :

priority=[ 0 if x in {'d','f'} else 1 for x in df.columns]
newdf=df.T.set_index([priority,df.columns]).sort_index().T

Then you have :

In [3]: newdf
Out[3]: 
   0     1                  
   d  f  a  b  c  e  g  h  i
0  2  4  5  8  7  1  1  2  3
1  2  4  1  4  2  3  1  5  3
B. M.
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2

To move an entire subset of columns, you could do this:

#!/usr/bin/python

import numpy as np
import pandas as pd

dates = pd.date_range('20130101',periods=6)
df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))
print df
cols = df.columns.tolist()
print cols
mysubset = ['B','D']
for idx, item in enumerate(mysubset):
    cols.remove(item)
    cols.insert(idx, item) 
print cols
df = df[cols]
print df

Here I moved B and D first and let the others trailing. Output:

                   A         B         C         D
2013-01-01  0.905122 -0.004839 -0.697663 -1.307550
2013-01-02  0.651998 -1.092546  0.594493  0.341066
2013-01-03  0.355832 -0.840057  0.016989  0.377502
2013-01-04 -0.544407  0.826708 -0.889118  0.871769
2013-01-05  0.190630  0.717418  1.325479 -0.882652
2013-01-06  2.730582  0.195908 -0.657642  1.606263
['A', 'B', 'C', 'D']
['B', 'D', 'A', 'C']
                   B         D         A         C
2013-01-01 -0.004839 -1.307550  0.905122 -0.697663
2013-01-02 -1.092546  0.341066  0.651998  0.594493
2013-01-03 -0.840057  0.377502  0.355832  0.016989
2013-01-04  0.826708  0.871769 -0.544407 -0.889118
2013-01-05  0.717418 -0.882652  0.190630  1.325479
2013-01-06  0.195908  1.606263  2.730582 -0.657642

For more, read this answer.

Community
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gsamaras
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0
a=list('abcdefghi')
b=list('dfabceghi')
ind = pd.Series(range(9),index=b).reindex(a)
df.sort_index(axis=1,inplace=True,key=lambda x:ind)

The benefit of the above approach is inplace=True , and costs lower memory and time when df is a large dataframe. If your dataframe is in common shape:

df.filter(b)

may be more pythonic.

Anthony
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