Pandas Dataframe Rename Index

To rename the index of a Pandas DataFrame, rename() method is most easier way to rename specific index values in a pandas dataFrame; allows to selectively change index names without affecting other values.

import pandas as pd

data = {'Name': ['John', 'Alice', 'Bob', 'Eve'],
        'Age': [25, 30, 22, 35], 
        'Gender': ['Male', 'Female', 'Male', 'Female'], 
        'Salary': [50000, 55000, 40000, 70000]}
df = pd.DataFrame(data)

df.rename(index={0: 'Row1', 1: 'Row2'}, inplace=True)
print(df)

       Name  Age  Gender  Salary
Row1   John   25    Male   50000
Row2  Alice   30  Female   55000
2       Bob   22    Male   40000
3       Eve   35  Female   70000

Let’s explore various methods for renaming the index of a Pandas DataFrame, focusing on the rename_axis() method, direct assignment, the set_index()  method, and multi-level indexing. For Other methods implementation we use the same dataset that we have used in above example.

1. Using rename axis

rename_axis() only changes the label of the axis and does not affect the data.

# Rename the index axis to 'Employee ID'
df_renamed_index = df.rename_axis('Employee ID')
print(df_renamed_index)

              Name  Age  Gender  Salary
Employee ID                            
0             John   25    Male   50000
1            Alice   30  Female   55000
2              Bob   22    Male   40000
...

2. Renaming the Index Values Using set index

The set_index() method allows you to set one or more columns as the new index for the DataFrame.Use the set_index() method to set a column (e.g., "Name") as the new index.

# Set 'Name' as the new index
df_with_name_index = df.set_index('Name')
print(df_with_name_index)

       Age  Gender  Salary
Name                      
John    25    Male   50000
Alice   30  Female   55000
Bob     22    Male   40000
Eve     35  Female   70000

3. Renaming the Index In-Place Using index.name

If you want to directly rename the index of a DataFrame in place (without creating a new DataFrame), you can modify the name attribute of the index object.

# Rename the index in place
df.index.name = 'Employee ID'
print(df)

              Name  Age  Gender  Salary
Employee ID                            
0             John   25    Male   50000
1            Alice   30  Female   55000
2              Bob   22    Male   40000
...

4. Resetting the Index Using reset index

If you’ve set a custom index using set_index() and want to revert back to the default integer index, you can use the reset_index() method. This method restores the default integer index and optionally keeps the previous index as a column.

# Set 'Name' as index and then reset it
df_with_name_index = df.set_index('Name')
df_reset_index = df_with_name_index.reset_index()
print(df_reset_index)

    Name  Age  Gender  Salary
0   John   25    Male   50000
1  Alice   30  Female   55000
2    Bob   22    Male   40000
3    Eve   35  Female   70000

5. Setting Multi-level Index

Pandas allows for more advanced indexing, such as multi-level (hierarchical) indexing, where you can use multiple columns as the index. This is particularly useful for handling complex datasets.

# Set 'Gender' and 'Age' as multi-level index
df_multi_index = df.set_index(['Gender', 'Age'])
print(df_multi_index)

             Name  Salary
Gender Age               
Male   25    John   50000
Female 30   Alice   55000
Male   22     Bob   40000
Female 35     Eve   70000

Here are some key takeaways:

• Use rename() to change specific index values and set_index() can set a column as the new index in dataframe.
• reset_index() reverts to the default integer index.
• we use rename_axis() to that renames the axis labels