Update dataframe header with values from another dataframe

Question

I'm working with census data (using the Census package ). When I select variables with the census API, they pass through in their raw format (e.g. B01001_007) and I'd like to replace the column name with the label (e.g. male 18 to 19 years).

I know this can be done through df.columns = ['male 18 to 19 years', 'male 20 years', 'male 21 years'] but this is tedious.

Is there a way to do some type of mapping that will auto-query into the header in my df below?

Sample data:

import pandas as pd
from pandas import DataFrame

variables_table = pd.DataFrame({'variable': ['B01001_007E','B01001_008E','B01001_009E'],
                    'label': ['male 18 to 19 years','male 20 years','male 21 years']
                    })

variables_table

label                   variable
male 18 to 19 years     B01001_007E
male 20 years           B01001_008E
male 21 years           B01001_009E

Unclean output:

df = pd.DataFrame({'B01001_007E': ['100','200','300'],
                                'B01001_008E': ['300','200','100'],
                                'B01001_009E': ['500','100','200']})
df
B01001_007E     B01001_008E     B01001_009E
100             300             500
200             200             100
300             100             200

Answer 1

df.rename(columns=variables_table.set_index('variable')['label'])
Out: 
  male 18 to 19 years male 20 years male 21 years
0                 100           300           500
1                 200           200           100
2                 300           100           200

Note that variables_table.set_index('variable')['label'] is a Series whose index is 'variable`. It will do the mapping on that index.

This is not an inplace operation. If you want to change the actual dataframe, assign it back to df: df = df.rename(columns=variables_table.set_index('variable')['label']) or use the inplace parameter: df.rename(columns=variables_table.set_index('variable')['label'], inplace=True)