Rename specific column(s) in pandas

Question

I\'ve got a dataframe called data. How would I rename the only one column header? For example gdp to log(gdp)?

data =
         


        

Answer 1

There are at least five different ways to rename specific columns in pandas, and I have listed them below along with links to the original answers. I also timed these methods and found them to perform about the same (though YMMV depending on your data set and scenario). The test case below is to rename columns A M N Z to A2 M2 N2 Z2 in a dataframe with columns A to Z containing a million rows.

# Import required modules
import numpy as np
import pandas as pd
import timeit

# Create sample data
df = pd.DataFrame(np.random.randint(0,9999,size=(1000000, 26)), columns=list('ABCDEFGHIJKLMNOPQRSTUVWXYZ'))

# Standard way - https://stackoverflow.com/a/19758398/452587
def method_1():
    df_renamed = df.rename(columns={'A': 'A2', 'M': 'M2', 'N': 'N2', 'Z': 'Z2'})

# Lambda function - https://stackoverflow.com/a/16770353/452587
def method_2():
    df_renamed = df.rename(columns=lambda x: x + '2' if x in ['A', 'M', 'N', 'Z'] else x)

# Mapping function - https://stackoverflow.com/a/19758398/452587
def rename_some(x):
    if x=='A' or x=='M' or x=='N' or x=='Z':
        return x + '2'
    return x
def method_3():
    df_renamed = df.rename(columns=rename_some)

# Dictionary comprehension - https://stackoverflow.com/a/58143182/452587
def method_4():
    df_renamed = df.rename(columns={col: col + '2' for col in df.columns[
        np.asarray([i for i, col in enumerate(df.columns) if 'A' in col or 'M' in col or 'N' in col or 'Z' in col])
    ]})

# Dictionary comprehension - https://stackoverflow.com/a/38101084/452587
def method_5():
    df_renamed = df.rename(columns=dict(zip(df[['A', 'M', 'N', 'Z']], ['A2', 'M2', 'N2', 'Z2'])))

print('Method 1:', timeit.timeit(method_1, number=10))
print('Method 2:', timeit.timeit(method_2, number=10))
print('Method 3:', timeit.timeit(method_3, number=10))
print('Method 4:', timeit.timeit(method_4, number=10))
print('Method 5:', timeit.timeit(method_5, number=10))

Output:

Method 1: 3.650640267
Method 2: 3.163998427
Method 3: 2.998530871
Method 4: 2.9918436889999995
Method 5: 3.2436501520000007

Use the method that is most intuitive to you and easiest for you to implement in your application.