Pandas Set Index Name to DataFrame

Use pandas.DataFrame.rename_axis() to set the index name/title, in order to get the index use DataFrame.index.name property and the same could be used to set the index name as well. When you create a DataFrame, by default pandas create an index name as 'Index'. By using the approaches mentioned in this article, you can set the custom name to index.

Alternatively, the index name can be set using df.index.rename(), df.index.set_names().

Key Points –

• The index name provides a label for the index, enhancing clarity and usability in DataFrame operations.
• Use the set_index() method to establish one or more columns as the DataFrame's index while optionally naming it.
• You can directly assign a name to the index using the df.index.name attribute.
• The inplace parameter in set_index() allows modifications to be made directly to the original DataFrame.
• The drop parameter in set_index() determines whether the original column used for the index should be retained in the DataFrame.
• Use the reset_index() method to revert to the default integer index and convert the current index back to a column.

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Quick Examples of Pandas Set Index Name

Following are quick examples of setting the index name to pandas DataFrame.

# Quick examples of pandas set index name

# Example 1: Get name of the index column of DataFrame
index_name=df.index.name

# Example 2: Set index name
df.index.name='Index1'

# Example 3: Set column as index
df = pd.DataFrame(technologies).set_index('Courses')

# Example 4: Rename index
df = df.rename_axis('Courses1')

# Example 5: Get pandas index title/name by index and columns parameter
df = df.rename_axis(index='Courses', columns="Courses1")

# Example 6: Removing index and columns names to set it none
df = df.rename_axis(index=None, columns=None)

# Example 7: Using df.index.rename 
# Get pandas index title/name
df.index.rename('Courses1', inplace=True)

# Example 8: Add multilevel index 
# Using set_index() 
df2 = df.set_index(['Courses', 'Duration'], append=True)

# Example 9: Rename Single index from multi level
df2.index = df2.index.set_names('Courses_Duration', level=2)

# Example 10: Rename all indexes
df2.index=df2.index.rename(['Index','Courses_Name','Courses_Duration'])

To run some examples of set index names to Pandas DataFrame, let’s create Pandas DataFrame using data from a dictionary.

# Create DataFrame
import pandas as pd

technologies = {
    'Courses':["Spark","PySpark","Python","pandas"],
    'Fee' :[20000,25000,22000,30000],
    'Duration':['30days','40days','35days','50days'],
    'Discount':[1000,2300,1200,2000]
              }
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print("Create DataFrame:\n", df)

Yields below output.

By default, the DataFrame assigns an Index name as Index and this is not shown on the DataFrame output.

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Get Index Name From Pandas DataFrame

As I said above, Pandas assign a default name to the Index column, you can get this using DataFrame.index.name

# Get name of the index column of DataFrame.
index_name=df.index.name

Now, let's set a custom Index name to the DataFrame.

# Set/Change the DataFrame index name
df.index.name = 'Index_Name'
print("Set the index name to DataFrame:\n", df)

Yields below output.

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Assign Column as Index using set_index()

Use pandas.DataFrame.set_index() method to set a column as an index. In the below example, I am setting the column Courses as Index. When you do this, the column name is assigned as an index name and it will be removed from columns.

# Set Existing column as Index
df = pd.DataFrame(technologies).set_index('Courses')
print(df)

# Output:
#           Fee Duration  Discount
# Courses                          
# Spark    20000   30days      1000
# PySpark  25000   40days      2300
# Python   22000   35days      1200
# Pandas   30000   50days      2000

# To Get index as columns name.
print (df.index.name)

# Output:
# Courses

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Pandas Set Index Name using rename_axis()

You can also rename the Index name using rename_axis(), just pass the index name you wanted as an argument to this method. This method also takes several arguments like inplace=True, axis=1 and more, refer to pandas documentation for more details.

# Set Index Name using rename_axis() method
df = df.rename_axis('Courses1')
print(df)

# Output:
#            Fee Duration  Discount
# Courses1                          
# Spark     20000   30days      1000
# PySpark   25000   40days      2300
# Python    22000   35days      1200
# Pandas    30000   50days      2000

It sets the name of the index column of the DataFrame to Courses1. Now let's see how to rename the axis of the Index column. For example, set the axis name as Courses_Name.

# Rename index column by rename_axis() method
df = pd.DataFrame(technologies).set_index('Courses').rename_axis('Courses_Name', axis=1)
print(df)

Yields below output.

# Output:
Courses_Name    Fee Duration  Discount
Courses                               
Spark         20000   30days      1000
PySpark       25000   40days      2300
Python        22000   35days      1200
Pandas        30000   50days      2000

You can also use the parameters index and column in order to get pandas index title/name. For example-

# Get pandas index title/name by index and Column parameter
df = pd.DataFrame(technologies,index=index_labels)
df = df.rename_axis(index='RowNumber', columns="Row")
print(df)

# Output: Set Name and Index axis
# Row        Courses    Fee Duration  Discount
# RowNumber                                   
# r1           Spark  20000   30days      1000
# r2         PySpark  25000   40days      2300
# r3          Python  22000   35days      1200
# r4          pandas  30000   50days      2000

# Removing index and columns names.
df = df.rename_axis(index=None, columns=None)
print(df)

# Output: Index and Columns to set none
.
#           Fee Duration  Discount
# Spark    20000   30days      1000
# PySpark  25000   40days      2300
# Python   22000   35days      1200
# pandas   30000   50days      2000

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Using DataFrame.index.rename() get Pandas Index Title/Name

In this section, I will use df.index.rename with inplace=True parameter in order to set the index name on the existing DataFrame.

# Using de.index.rename get pandas index title/name.
df.index.rename('Row', inplace=True)
print(df)

Yields below output.

# Output:
     Courses    Fee Duration  Discount
Row                                   
r1     Spark  20000   30days      1000
r2   PySpark  25000   40days      2300
r3    Python  22000   35days      1200
r4    pandas  30000   50days      2000

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Use set_index() to Add Multiple Index

To add multiple indexes using DataFrame.set_index(), you can pass a list of column names to the keys parameter. Below are examples of append columns Courses and Duration to row Index.

# Add Multilevel index using set_index() 
df2 = df.set_index(['Courses', 'Duration'], append=True)
print(df2)

Yields below output.

# Output:
                          Fee  Discount
Row   Courses Duration                 
r1    Spark   30days    20000      1000
r2    PySpark 40days    25000      2300
r3    Python  35days    22000      1200
r4    pandas  50days    30000      2000

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Rename Multi Level Index Names Using index.set_names()

By using DataFrame.index.set_names() you can change the index of a specific level when you have multiple levels of row indexes. For example-

# Rename Single index from multi Level
df2.index = df2.index.set_names('Courses_Duration', level=2)
print(df2)

Yields below output.

# Output:

                                   Fee  Discount
Row   Courses Courses_Duration                 
r1    Spark      30days          20000      1000
r2    PySpark    40days          25000      2300
r3    Python     35days          22000      1200
r4    pandas     50days          30000      2000

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Rename Multiple of Indexes

When you have multiple rows indices and if you want to rename multiple indices at the same time, use DataFrame.index.rename(). Note that you need to specify all indices as a parameter.

# Rename all indexes
df2.index=df2.index.rename(['Row','Courses_Name','Courses_Duration'])
print(df2)

Yields below output

# Output:
                                       Fee  Discount
Row  Courses_Name Courses_Duration                 
r1    Spark        30days            20000      1000
r2    PySpark      40days            25000      2300
r3    Python       35days            22000      1200
r4    pandas       50days            30000      2000

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Complete Examples of Set Index Name

import pandas as pd
technologies = {
    'Courses':["Spark","PySpark","Python","pandas"],
    'Fee' :[20000,25000,22000,30000],
    'Duration':['30days','40days','35days','50days'],
    'Discount':[1000,2300,1200,2000]
              }
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print(df)

# Get name of the index column of DataFrame
df.index.name
'Index'
df.index.name='Index1'
print(df)

# Get pandas index/name by set_index
df = pd.DataFrame(technologies).set_index('Courses')
print(df)

# To get Index and Column names
print (df.index.name)
print (df.columns.name)

# Rename a column by rename_axis method
df = df.rename_axis('Courses1')
print(df)

# Rename index column by rename_axis() method
df = pd.DataFrame(technologies).set_index('Courses').rename_axis('Courses_Name', axis=1)
print(df)

# Get pandas index title/name by index and Column parameter
df = df.rename_axis(index='RowNumber', columns="Row")
print(df)

# Removing index and columns names to set it none
df = df.rename_axis(index=None, columns=None)
print(df)

# Using de.indx.rename get pandas index title/name
df.index.rename('Row', inplace=True)
print(df)

# Add multilevel index using set_index() 
df2 = df.set_index(['Courses', 'Duration'], append=True)
print(df2)

# Rename Single index from multi level
df2.index = df2.index.set_names('Courses_Duration', level=2)
print(df2)

# Rename all indexes
df2.index=df2.index.rename(['Row','Courses_Name','Courses_Duration'])
print(df2)

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FAQ on Pandas Set Index Name to DataFrame

You can set the index name using the index.name attribute. For example: df.index.name = 'my_index'.

You can specify the index name directly when creating a DataFrame by using the index parameter: df = pd.DataFrame(data, index=index_name).

For a MultiIndex, you can set multiple index names by assigning a list to the names attribute: df.index.names = ['level_1', 'level_2'].

You can set the index name after setting the column as the index using the set_index() method. For example: df.set_index('column_name', inplace=True).

You can reset the index and remove the index name by using reset_index(drop=True) or reset_index() without any arguments to retain the index name as a column.

The index name can be set to any string or variable, such as df.index.name = my_variable.

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Conclusion

In this article, I have explained about getting and setting index column names using set_index(), rename_axis() methods, also renaming the index by rename_axis() with several parameters.

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