How to rename multiple column headers in a Pandas DataFrame?

Here we are going to rename multiple column headers using rename() method. The rename method is used to rename a single column as well as rename multiple columns at a time. And pass columns that contain the new values and inplace = true as an argument. We pass inplace = true because we just modify the working data frame if we pass inplace = false then it returns a new data frame.

Way 1: Using rename() method

1. Import pandas.
2. Create a data frame with multiple columns.
3. Create a dictionary and set key = old name, value= new name of columns header.
4. Assign the dictionary in columns.
5. Call the rename method and pass columns that contain dictionary and inplace=true as an argument.

Example:

Python

# import pandas
import pandas as pd

# create data frame
df = pd.DataFrame({'First Name': ["Mukul", "Rohan", "Mayank",
                                  "Vedansh", "Krishna"],

                   'Location': ["Saharanpur", "Rampur", "Agra",
                                "Saharanpur", "Noida"],

                   'Pay': [56000.0, 55000.0, 61000.0, 45000.0, 62000.0]})

# print original data frame
display(df)

# create a dictionary
# key = old name
# value = new name
dict = {'First Name': 'Name',
        'Location': 'City',
        'Pay': 'Salary'}

# call rename () method
df.rename(columns=dict,
          inplace=True)

# print Data frame after rename columns
display(df)

Output:

Example 2: 

In this example, we will rename the multiple times using the same approach.

Python3

# import pandas
import pandas as pd

# create data frame
df = pd.DataFrame({'First Name': ["Mukul", "Rohan", "Mayank",
                                  "Vedansh", "Krishna"],

                   'Location': ["Saharanpur", "Rampur",
                                "Agra", "Saharanpur", "Noida"],

                   'Pay': [56000.0, 55000.0, 61000.0, 45000.0, 62000.0]})

print("Original DataFrame")

# print original data frame
display(df)

# create a dictionary
# key = old name
# value = new name
dict = {'First Name': 'Name',
        'Location': 'City',
        'Pay': 'Salary'}

print("\nAfter rename")
# call rename () method
df.rename(columns=dict,
          inplace=True)

# print Data frame after rename columns
display(df)

# create a dictionary
# key = old name
# value = new name
dict = {'Name': 'Full Name',
        'City': 'Address',
        'Salary': 'Amount'}

# call rename () method
df.rename(columns=dict,
          inplace=True)

display(df)

Output:

Way 2: Using index

One must follow the below steps as listed in sequential order prior to landing upon implementation part as follows:  

1. Import pandas.
2. Create a data frame with multiple columns.
3. The .column and .values property will return the array of columns.
4. Change the values in the array of columns using slicing.
5. Print the dataframe.

Example

Python3

# import pandas
import pandas as pd

# create data frame
df = pd.DataFrame({'First Name': ["Mukul", "Rohan", "Mayank",
                                  "Vedansh", "Krishna"],

                   'Location': ["Saharanpur", "Rampur", "Agra",
                                "Saharanpur", "Noida"],

                   'Pay': [56000.0, 55000.0, 61000.0, 45000.0, 62000.0]})

# print original data frame
display(df)

# renaming the column by index
df.columns.values[0:2] =["name", "address" ]

# print Data frame after rename columns
display(df)

Output: