Pandas: Selecting columns (with regex) and renaming them (with a list)

Question

I am trying to do 2 things in Python:

Select the names of specific columns using a regex
Rename these selected columns using a list of names (the names are unfortunately stored in their own weird dataframe)

I am new to python and pandas but did a bunch of googling and am getting the TypeError: Index does not support mutable operations error. Here's what I am doing.

import pandas as pd
import numpy as np


df=pd.DataFrame(data=np.array([
    [1, 3, 3, 4, 5,9,5], 
    [1, 2, 4, 4, 5,8,4], 
    [1, 2, 3, 'a', 5,7,3],
    [1, 2, 3, 4, 'e',6,2],
    ['f', 2, 3, 4, 5,6,1]     
    ]),
    columns=[
        'a',
        'car-b', 
        'car-c',
        'car-d',
        'car-e',
        'car-f',
        'car-g'])

#Select the NAMES of the columns that contain 'car' in them as I want to change these column names
names_to_change = df.columns[df.columns.str.contains("car")]
names_to_change 

#Here is the dataset that has the names that I want to use to replace these
#This is just how the names are stored in the workflow
new_names=pd.DataFrame(data=np.array([
    ['new_1','new_3','new_5'],
    ['new_2','new_4','new_6'] 
    ]))
new_names

#My approach is to transform the new names into a list
new_names_list=pd.melt(new_names).iloc[:,1].tolist()
new_names_list

#Now I figure I would use .columns to do the replacement
#But this returnts the mutability error
df.columns[df.columns.str.contains("car")]=new_names_list

#This also returns the same error
df.columns = df.columns[df.columns.str.contains("car")].tolist()+new_names_list
Traceback (most recent call last):

  File "C:\Users\zsg876\AppData\Local\Temp/ipykernel_1340/261138782.py", line 44, in <module>
    df.columns[df.columns.str.contains("car")]=new_names_list

  File "C:\Users\zsg876\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 4585, in __setitem__
    raise TypeError("Index does not support mutable operations")

TypeError: Index does not support mutable operations

I tried a bunch of different methods (this was no help: how to rename columns in pandas using a list) and haven't had much luck. I am coming over from R where renaming columns was a lot simpler -- you'd just pass a vector using names().

I take it the workflow is different here? Appreciate any suggestions!

UPDATE:

This seems to do the trick, but I am not sure why exactly. I figured replacing one list with another of equal length would work, but that does not seem to be the case. Can anyone educate me here?

col_rename_dict=dict(zip(names_to_change,new_names_list))

df.rename(columns=col_rename_dict, inplace=True)

user17242583 · Accepted Answer · 2022-03-25 15:45:30Z

2

You can use df.filter(like='car').columns to get the names of columns containing car, and you can use new_names.to_numpy().T.ravel to efficiently convert the new_names dataframe into an array of the new names. Then, you can use zip and dict to convert the two arrays into a dict where the keys are the old column names and the values are the new column names. Then, simple pass that to df.rename with axis=1:

old_names = df.filter(like='car').columns
new_names = new_names.to_numpy().T.ravel()
df = df.rename(dict(zip(old_names, new_names)), axis=1)

Output:

>>> df
   a new_1 new_2 new_3 new_4 new_5 new_6
0  1     3     3     4     5     9     5
1  1     2     4     4     5     8     4
2  1     2     3     a     5     7     3
3  1     2     3     4     e     6     2
4  f     2     3     4     5     6     1

answered Mar 25, 2022 at 15:45

user17242583

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2 Comments

vashts85 Over a year ago

Thank you! What is the T command doing -- transposing? Figured it might be simpler to ask here than google "T". And, why do we need to use a dict method in pandas.rename?

user17242583 Over a year ago

Yep, T is an alias for transpose(). The dict just tells pandas what to rename, and what to rename to. You can overwrite all the columns if you want with `df = df.set_axis(new_names, axis=1)

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