rename columns of second dataframe with column names of first dataframe based on a list-CodePudding

I want to rename the name of the columns of df2 by the name of the columns of df1 and print the new df2 dataframe. I also want to drop the columns that are not listed in "df1_cols_to_rename_df2" from the new df2

import pandas as pd
    
    
data1 = {'first_column':  ['1', '2', '2'],
            'second_column': ['1', '2', '2'],
           'third column':['1', '2', '2'],
          'fourth_column':['1', '2', '2'],
           'fifth_column':['1', '2', '2'],
            }
    
data2 = {'1st_column':  ['1', '2', '4'],
            'some_column': ['1', '2', '2'],
            '3rd_column':['1', '2', '2'],
            '4th_column':['7', '2', '2'],
            '5th_column':['1', '4', '2'],
            '2nd_column':['1', '5', '3'],
            }
    
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

df1_cols_to_rename_df2 = {'first_column':['1st_column'], 'second_column':['2nd_column'], 'third column':['3rd_column'],'fourth_column':['4th_column']

so this would be the expected output

CodePudding user response：

d={ df1_cols_to_rename_df2[k][0]: k for k in df1_cols_to_rename_df2.keys()}


df2.loc[:, df2.columns.isin(d.keys())].rename(columns=d )

first_column    third column    fourth_column   second_column
0   1   1   7   1
1   2   2   2   5
2   4   2   2   3

CodePudding user response：

We can use the rename method to change the column names like so :

df2 = df2.rename(columns={'1st_column': 'first_column', 
                          '2nd_column': 'second_column',
                          '3rd_column': 'third_column',
                          '4th_column': 'fourth_column',
                          '5th_column': 'fifth_column'})

Then to only keep the desired columns :

df2 = df2[df1.columns]

Output :

    first_column    second_column   third_column    fourth_column   fifth_column
0   1               1               1               7               1
1   2               5               2               2               4
2   4               3               2               2               2