CODEWITHSUNDEEP
pythonpandasdataframeindexingreindex
rhaskett
rhaskett

Reputation: 1962

Reindex DataFrame Columns by Label Series

I have a Series of Labels

pd.Series(['L1', 'L2', 'L3'], ['A', 'B', 'A'])

and a dataframe

pd.DataFrame([[1,2], [3,4]], ['I1', 'I2'], ['A', 'B'])

I'd like to have a dataframe with columns ['L1', 'L2', 'L3'] with the column data from 'A', 'B', 'A' respectively. Like so...

pd.DataFrame([[1,2,1], [3,4,3]], ['I1', 'I2'], ['L1', 'L2', 'L3'])

in a nice pandas way.

Upvotes: 1

Views: 756

Answers (3)

jpp
jpp

Reputation: 164703

You can use loc accessor:

s = pd.Series(['L1', 'L2', 'L3'], ['A', 'B', 'A'])
df = pd.DataFrame([[1,2], [3,4]], ['I1', 'I2'], ['A', 'B'])

res = df.loc[:, s.index]

print(res)

    A  B  A
I1  1  2  1
I2  3  4  3

Or iloc accesor with columns.get_loc:

res = df.iloc[:, s.index.map(df.columns.get_loc)]

Both methods allows accessing duplicate labels / locations, in the same vein as NumPy arrays.

Upvotes: 0

BENY
BENY

Reputation: 323306

Since you mention reindex

#s=pd.Series(['L1', 'L2', 'L3'], ['A', 'B', 'A'])
#df=pd.DataFrame([[1,2], [3,4]], ['I1', 'I2'], ['A', 'B'])
df.reindex(s.index,axis=1).rename(columns=s.to_dict())
Out[598]: 
    L3  L2  L3
I1   1   2   1
I2   3   4   3

Upvotes: 2

Michaelpanicci
Michaelpanicci

Reputation: 206

This will produce the dataframe you described:

import pandas as pd
import numpy as np

data = [['A','B','A','A','B','B'],
        ['B','B','B','A','B','B'],
        ['A','B','A','B','B','B']]

columns = ['L1', 'L2', 'L3', 'L4', 'L5', 'L6']

pd.DataFrame(data, columns = columns)

Upvotes: 1

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