CODEWITHSUNDEEP
pandas
jtcloud
jtcloud

Reputation: 559

In pandas, how to set_index with using column index instead of referring to column names?

For example:

We have a Pandas dataFrame foo with 2 columns ['A', 'B'].

I want to do function like foo.set_index([0,1]) instead of foo.set_index(['A', 'B'])

Have tried foo.set_index([[0,.1]]) as well but came with this error:

Length mismatch: Expected axis has 9 elements, new values have 2 elements

Upvotes: 11

Views: 23219

Answers (2)

citynorman
citynorman

Reputation: 5292

This worked for me, the other answer didn't.

# single column
df.set_index(df.columns[1])
# multi column
df.set_index(df.columns[[1, 0]].tolist())

Upvotes: 2

unutbu
unutbu

Reputation: 880877

If the column index is unique you could use:

df.set_index(list(df.columns[cols]))

where cols is a list of ordinal indices.

For example,

In [77]: np.random.seed(2016)

In [79]: df = pd.DataFrame(np.random.randint(10, size=(5,4)), columns=list('ABCD'))

In [80]: df
Out[80]: 
   A  B  C  D
0  3  7  2  3
1  8  4  8  7
2  9  2  6  3
3  4  1  9  1
4  2  2  8  9

In [81]: df.set_index(list(df.columns[[0,2]]))
Out[81]: 
     B  D
A C      
3 2  7  3
8 8  4  7
9 6  2  3
4 9  1  1
2 8  2  9

If the DataFrame's column index is not unique, then setting the index by label is impossible and by ordinals more complicated:

import numpy as np
import pandas as pd
np.random.seed(2016)

def set_ordinal_index(df, cols):
    columns, df.columns = df.columns, np.arange(len(df.columns))
    mask = df.columns.isin(cols)
    df = df.set_index(cols)
    df.columns = columns[~mask]
    df.index.names = columns[mask]
    return df

df = pd.DataFrame(np.random.randint(10, size=(5,4)), columns=list('AAAA'))
print(set_ordinal_index(df, [0,2]))

yields

     A  A
A A      
3 2  7  3
8 8  4  7
9 6  2  3
4 9  1  1
2 8  2  9

Upvotes: 16

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