Pandas rename()

import pandas as pd

# create a DataFrame
data = {'Old_Column1': [1, 2, 3]}
df = pd.DataFrame(data)

# rename 'Old_Column1' to 'New_Column1' 
result = df.rename(columns={'Old_Column1': 'New_Column1'})

print(result)

'''
Output

   New_Column1
0  1
1  2
2  3

'''

rename() Syntax

The syntax of the rename() method in Pandas is:

df.rename(columns=None, index=None, inplace=False)

rename() Arguments

The rename() method takes following arguments:

• columns (optional) - a dictionary that specifies the new names for columns
• index (optional) - a dictionary that specifies the new names for index labels
• inplace (optional) - if True, modifies the original DataFrame in place; if False, returns a new DataFrame.

rename() Return Value

The rename() method returns a new DataFrame with the renamed columns and/or index labels.

Example 1: Rename Columns Using a Dictionary

import pandas as pd

data = {'Age': [25, 30, 35], 
       'Income': [50000, 60000, 75000]}
df = pd.DataFrame(data)

# rename columns 'Age' to 'Customer Age' and 'Income' to 'Annual Income'
df.rename(columns={'Age': 'Customer Age', 'Income': 'Annual Income'}, inplace=True)

print(df)

Output

Customer Age  Annual Income
0        25            50000
1       30            60000
2       35            75000

In the above example, we have used the rename() method to rename the columns in the df DataFrame.

The columns parameter is set to a dictionary where the keys are the current column names Age and Income and the values are the new column names Customer Age and Annual Income.

The inplace=True argument modifies the DataFrame in place, so the original DataFrame df is updated with the new column names.

Example 2: Rename Index Labels Using a Dictionary

import pandas as pd

# create a sample DataFrame
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)

# rename index labels using the dictionary
df.rename(index={0: 'Row1', 1: 'Row2', 2: 'Row3'}, inplace=True)

# display the DataFrame with renamed index labels
print(df)

Output

      A  B
Row1  1  4
Row2  2  5
Row3  3  6

In this example, we have used rename() on the df DataFrame to rename its index labels.

We have provided a dictionary to the index parameter, where the keys represent the current index labels 0, 1, 2, and the values represent the new index labels Row1, Row2, Row3.

Example 3: Rename Columns Using a Function

import pandas as pd

data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)

# define a function to rename columns
def column_rename_function(column_name):
    # add a prefix 'new_' to the column name
    return 'new_' + column_name

# rename columns using the function
df.rename(columns=column_rename_function, inplace=True)


print(df)

Output

         new_A   new_B
0           1      4
1           2      5
2           3      6

Here, we defined the function named column_rename_function() that adds a prefix "new_" to the column names.

Then, we pass this function to the rename() method's columns parameter, and when we set inplace=True, it modifies the df DataFrame to have columns with the new names.

The new DataFrame has new column names new_A and new_B.