By using rename_axis()
, Index.rename()
functions you can rename the row index name/label of a pandas DataFrame. Besides these, there are several ways like df.index.names = ['Index']
, rename_axis()
, set_index()
to rename the index. In this article, I will explain multiple ways of how to rename a single index and multiple indexes of the Pandas DataFrame.
Related: Pandas Rename Column and Pandas Rename or Change Row index Values.
Key Points –
- Pandas provides the
rename_axis()
method to rename the index of a DataFrame. - This method allows users to change the name of the index without altering the underlying data.
- Users can specify the new name for the index using the
name
parameter of therename_axis()
method. - Renaming the index can be particularly useful when preparing data for visualization or when exporting data to different formats.
- The
rename_axis()
method in Pandas offers a straightforward way to rename the index of a DataFrame without modifying the data itself. - Renaming the index can improve the interpretability and clarity of DataFrame operations and analyses.
1. Quick Examples of Rename DataFrame Index
If you are in a hurry, below are some of the quick examples of renaming pandas DataFrame index.
# Quick examples of rename dataframe index
# Add/Change name to Index using df.index.name
df.index.names = ['Index']
# Add/Change name to Index using df.index.rename()
df.index = df.index.rename('Index')
df.index.rename('Index', inplace=True)
# Add/Change name to Index using df.rename_axis()
df2 = df.rename_axis('Index')
df.rename_axis('Index', inplace=True)
# DataFrame.rename_axis() to change axis parameter
df2=df2.rename_axis('Attributes', axis='columns')
# Add Multilevel index using set_index()
df2 = df.set_index(['Courses', 'Duration'], append=True)
# Rename Single index from multi Level
df2.index = df2.index.set_names('Courses_Duration', level=2)
# Rename All indexes
df2.index=df2.index.rename(['Index','Courses_Name','Courses_Duration'])
Now, let’s create a DataFrame with a few rows and columns, execute these examples and validate results. Our DataFrame contains column names Courses
, Fee
, Duration
, and Discount
.
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)
Yields below output.
# Output:
Courses Fee Duration Discount
r1 Spark 20000 30days 1000
r2 PySpark 25000 40days 2300
r3 Python 22000 35days 1200
r4 pandas 30000 50days 2000
2. Using rename_axis() to Rename Pandas DataFrame Index
Use DataFrame.rename_axis() to add/rename the column Index. Above DataFrame doesn’t have an Index name and will use this method to add an index label first. Note that this method by default returns a new DataFrame after adding an Index. Use inplace=False
to update the existing DataFrame.
# Rename row with index
df1 = df.rename_axis('Index')
print(df1)
Yield the below output.
# Output:
Courses Fee Duration Discount
Index
r1 Spark 20000 30days 1000
r2 PySpark 25000 40days 2300
r3 Python 22000 35days 1200
r4 pandas 30000 50days 2000
Another simple way to add/rename an Index is using DataFrame.index.rename()
and df.index.names = ['Index']
. These updates the index on the existing DataFrame.
# Using DataFrame.index.rename to change Index
df.index = df.index.rename('Index')
(or)
df.index.names = ['Index']
print(df)
Now, let’s rename the row Index to New_Index
from Index
label.
# Rename Index
df2 = df.rename_axis('New_Index')
Also, add a column name to Index.
df2=df2.rename_axis('Attributes', axis='columns')
print(df2)
Yields below output.
# Output:
Attributes Courses Fee Duration Discount
New_Index
r1 Spark 20000 30days 1000
r2 PySpark 25000 40days 2300
r3 Python 22000 35days 1200
r4 pandas 30000 50days 2000
4. Add Multiple Index using DataFrame.set_index()
Use DataFrame.set_index() to set the one or multiple existing columns as indexes. Below examples appends 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
Index Courses Duration
r1 Spark 30days 20000 1000
r2 PySpark 40days 25000 2300
r3 Python 35days 22000 1200
r4 pandas 50days 30000 2000
5. 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 level of row indexes.
# Rename Single index from multi Level
df2.index = df2.index.set_names('Courses_Duration', level=2)
print(df2)
Yields below output.
# Output:
Fee Discount
Index Courses Courses_Duration
r1 Spark 30days 20000 1000
r2 PySpark 40days 25000 2300
r3 Python 35days 22000 1200
r4 pandas 50days 30000 2000
6. Rename Multiple/All Level of Indexes
When you have multiple rows indices and if you wanted to rename multiple indices at the same time, use DataFrame.index.rename()
. Note that you need to specify all indices as a param.
# Rename All indexes
df2.index=df2.index.rename(['Index','Courses_Name','Courses_Duration'])
print(df2)
Yields below output
# Output:
Fee Discount
Index Courses_Name Courses_Duration
r1 Spark 30days 20000 1000
r2 PySpark 40days 25000 2300
r3 Python 35days 22000 1200
r4 pandas 50days 30000 2000
13. Complete Example For Rename DataFrame Index
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)
# Add/Change name to Index using df.index.name
df.index.names = ['Index']
print(df)
# Add/Change name to Index using df.rename_axis()
df1 = df.rename_axis('Index')
print(df1)
# Add/Change name to Index using df.index.rename()
df.index = df.index.rename('Index')
print(df)
# DataFrame.rename_axis() to change axis parameter
df2 = df.rename_axis('New_Index')
df2=df2.rename_axis('Attributes', axis='columns')
print(df2)
# 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(['Index','Courses_Name','Courses_Duration'])
print(df2)
Frequently Asked Questions on Rename Index of DataFrame
Renaming the index of a DataFrame can enhance readability and improve clarity in data analysis. It allows you to provide more descriptive labels to the index, making it easier to understand the data.
You can rename the index of a DataFrame using the rename_axis()
method in Pandas. This method allows you to specify the new name for the index.
Renaming the index of a DataFrame using the rename_axis()
method does not modify the underlying data in the DataFrame. It only changes the name of the index. The rename_axis()
method operates on the labels of the index, not on the data itself. Therefore, it preserves the integrity and contents of the DataFrame while allowing you to provide a more descriptive name for the index.
You can rename the index to multiple levels, also known as a multi-index or hierarchical index, using the rename_axis()
method. This allows for more complex indexing and slicing operations on the DataFrame.
You can rename the index inplace by setting the inplace
parameter to True
when using the rename_axis()
method. This will modify the DataFrame in place without the need to reassign it to a new variable.
Conclusion
In this article, you have learned how to rename pandas DataFrame index using set_axis()
, set_index()
, index.set_names()
and more methods with examples. You have also learned how to rename single and multilevel Index names.
Happy Learning !!
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