How to Rename the Index of a Pandas DataFrame: Complete Guide with Source Code

You can rename the index of a pandas DataFrame using DataFrame.rename() to map existing labels to new values, DataFrame.rename_axis() to change the index name metadata, DataFrame.set_index() to replace the index with column data, or DataFrame.reset_index() to move the index back into a column.

Working with the pandas-dev/pandas repository, you have several built-in methods to modify the row identifiers of a DataFrame. Whether you need to update specific index labels, change the axis name, or completely rebuild the index structure, the implementation in pandas/core/frame.py provides flexible, non-destructive operations that return new objects by default.

Rename Specific Index Labels with DataFrame.rename()

The DataFrame.rename() method allows you to change individual index values by supplying a mapper—either a dictionary, a function, or a Series. According to the source code in pandas/core/frame.py, this method forwards to DataFrame._rename, which internally calls Index.rename for the row axis.

The method signature in pandas/core/frame.py (around line 6124) is:

def rename(self, mapper=None, *, index=None, columns=None,
           axis=None, copy=True, inplace=False, level=None, 
           errors='ignore'):
    ...

Using a Dictionary Mapping

Pass a dictionary to the index parameter to map specific old labels to new ones:

import pandas as pd

df = pd.DataFrame(
    {"city": ["Paris", "Berlin", "Tokyo"],
     "population": [2.2, 3.6, 13.9]},
    index=["FR", "DE", "JP"]
)

# Rename specific index labels

df_renamed = df.rename(index={"FR": "France", "DE": "Germany"})
print(df_renamed)

Using a Callable Function

You can also pass a function that transforms each label:

# Prepend text to each index label

df_func = df.rename(index=lambda x: f"Country-{x}")
print(df_func)

Change the Index Name with DataFrame.rename_axis()

While rename() modifies the labels themselves, DataFrame.rename_axis() changes the name of the index axis (the .name attribute). This is useful for setting meaningful metadata like "date" or "country_code" without altering the actual row identifiers.

The implementation in pandas/core/frame.py (around line 6165) shows:

def rename_axis(self, mapper=None, *, index=None, columns=None,
                axis=None, copy=True, inplace=False):
    ...

Under the hood, this method invokes Index.rename_axis on the underlying index object located in pandas/core/indexes/base.py.

Setting the Axis Name

# Change the index name metadata

df_named = df.rename_axis("country_code")
print(df_named.index.name)  # Output: "country_code"

Replace the Entire Index with DataFrame.set_index()

To completely replace the current index with new values—such as converting a column into the index or applying a new Index object—use DataFrame.set_index(). This is the appropriate method when you need to rebuild the index structure rather than rename existing labels.

# Replace index with a new Index object

df_new_index = df.set_index(pd.Index(["FRN", "DEN", "JPN"], name="iso"))
print(df_new_index)

Move Index to Column with DataFrame.reset_index()

When you need to undo a previous index assignment or move the current index back into the DataFrame as a regular column, use DataFrame.reset_index(). This effectively reverses set_index() operations.

# Move index back to a column

df_reset = df_new_index.reset_index()
print(df_reset)

# Or drop the index entirely

df_dropped = df_new_index.reset_index(drop=True)

In-Place vs. Copy Operations

All four methods are non-destructive by default, returning new DataFrame objects. To modify the existing object without assignment, set inplace=True:

df.rename(index={"FR": "France"}, inplace=True)

Alternatively, assign the result back to your variable. The copy parameter controls whether underlying data is copied; when copy=True (default), modifications to the new DataFrame do not affect the original.

Summary

• DataFrame.rename(): Changes individual index labels via mapping or functions; forwards to Index.rename in pandas/core/indexes/base.py.
• DataFrame.rename_axis(): Modifies the index name metadata (.name attribute), not the labels themselves.
• DataFrame.set_index(): Replaces the entire index with column data or a new Index object.
• DataFrame.reset_index(): Moves the current index back into the DataFrame as a column or drops it.
• All methods support inplace=True for mutation and are implemented in pandas/core/frame.py.

Frequently Asked Questions

What is the difference between rename and rename_axis in pandas?

DataFrame.rename() changes the actual values of the index labels (the row identifiers), while DataFrame.rename_axis() only changes the name attribute of the index axis (the metadata label for the index itself). Use rename() when you want "FR" to become "France", and use rename_axis() when you want the index to have the name "country_code".

How do I rename a MultiIndex in pandas?

Use DataFrame.rename() with the level parameter to target specific levels of a MultiIndex. For example, df.rename(index={"old": "new"}, level=0) renames labels only in the first level of the index hierarchy. You can also use rename_axis() to change the names of specific levels by passing the mapper and axis parameters.

Can I rename index labels in place without creating a copy?

Yes. Pass inplace=True to DataFrame.rename(), DataFrame.rename_axis(), DataFrame.set_index(), or DataFrame.reset_index(). This modifies the existing DataFrame object rather than returning a new one. Note that the default behavior (inplace=False, copy=True) creates a copy to preserve the original data.

How do I rename the index after using set_index()?

After using set_index(), you can rename the new index labels using df.rename(index={...}) or change the index name using df.rename_axis("new_name"). If you want to replace the index again with different data, simply call set_index() again with the new column or Index object.

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