To rename index values of a Pandas DataFrame, you can use the rename() method or the index attribute. In this article, I will explain multiple ways of how to rename a single index value and multiple index values of the pandas DataFrame using functions like DataFrame.rename(), DataFrame.index property with examples. When you update multiple row index values, you would need to create a Dict with key-value pair and use it with rename() function.
Related: pandas Rename Column of DataFrame & pandas rename Index Name
Key Points –
- Pandas offers the
rename()method for altering index values within a DataFrame. - The
rename()function facilitates the renaming process through various parameters likeindex,columns, andlevel. - Renaming index values is pivotal for enhancing data interpretation, ensuring consistency, and aligning with analytical objectives.
- Utilizing
rename()allows for seamless transformation of index labels without altering the underlying data. - Renaming index values aids in improving data clarity, consistency, and facilitates effective data analysis without altering the underlying data structure.
Quick Examples of Rename Index Values
Following are quick examples of renaming index values of DataFrame.
# Quick examples of rename index values
# Assign Indexs to DataFrame.index
df.index = ['index_1', 'index_2', 'index_3', 'index_4']
# Using DataFrame.rename() to rename index values
df2 = df.rename(index={'r3': 'Index_3'})
# Rename multiple index value
df2 = df.rename(index={'r1':'Index_1','r2':'Index_2'})
# Changing the DataFrame inplace
df.rename(index={'r3': 'Index_3','r4': 'Index_4'}, inplace=True)
# Append a value to DataFrame index row Using lambda function
df2 = df.rename(index=lambda Index: Index+'_Index')
To run some examples of renaming index values of pandas DataFrame, let’s create a Pandas DataFrame.
# 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(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
Changing the Row Index Using DataFrame.index() Attribute
In order to rename/change the values for all row index, create a list with new indexes and assign it to DataFrame.index attribute. Note that If your DataFrame is small and for a huge DataFrame this is not an idle solution.
# Change the row indexes
df.index = ['index_1', 'index_2', 'index_3', 'index_4']
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount
index_1 Spark 20000 30days 1000
index_2 PySpark 25000 40days 2300
index_3 Python 22000 35days 1200
index_4 pandas 30000 50days 2000
Rename Specific Index Values
pandas.DataFrame.rename() accepts a dictionary as a parameter for the index values you want to rename. Simply pass a dictionary with key-value pairs where the key is the existing index you wish to rename, and the value is your desired new index value. The below examples changes the index to r3 from Index_3.
# Using DataFrame.rename() to rename index values
df = pd.DataFrame(technologies,index=index_labels)
df2 = df.rename(index={'r3': 'Index_3'})
print(df2)
Yields below output.
# Output:
Courses Fee Duration Discount
r1 Spark 20000 30days 1000
r2 PySpark 25000 40days 2300
Index_3 Python 22000 35days 1200
r4 pandas 30000 50days 2000
Rename Multiple Row Indexes
You can also use the same approach to rename multiple index values or all indexes of Pandas DataFrame.
# Rename multiple index value
df = pd.DataFrame(technologies,index=index_labels)
df2 = df.rename(index={'r1':'Index_1','r2':'Index_2'})
print(df2)
Yields below output.
# Output:
Courses Fee Duration Discount
Index_1 Spark 20000 30days 1000
Index_2 PySpark 25000 40days 2300
r3 Python 22000 35days 1200
r4 pandas 30000 50days 2000
Change the DataFrame Inplace
DataFrame.rename() takes parameter inplace=True to change the DataFrame inplace. In this case, no new DataFrame is returned, and the return value is None.
# Changing the DataFrame inplace
df = pd.DataFrame(technologies,index=index_labels)
df.rename(index={'r3': 'Index_3','r4': 'Index_4'}, inplace=True)
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount
r1 Spark 20000 30days 1000
r2 PySpark 25000 40days 2300
Index_3 Python 22000 35days 1200
Index_4 pandas 30000 50days 2000
Append a Value to DataFrame Index
Another approach would be using the lambda function. The below example add’s ‘_Index’ to all index values. This approach helps you to update/rename all indexes
# Append a value to DataFrame index row Using lambda function
df2 = df.rename(index=lambda Index: Index+'_Index')
print(df2)
Yields below output.
# Output:
Courses Fee Duration Discount
r1_Index Spark 20000 30days 1000
r2_Index PySpark 25000 40days 2300
r3_Index Python 22000 35days 1200
r4_Index pandas 30000 50days 2000
Complete Example for Rename Index Values
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)
# Change the row indexes
df.index = ['index_1', 'index_2', 'index_3', 'index_4']
print(df)
# Using DataFrame.rename() to rename index values
df2 = df.rename(index={'r3': 'Index_3'})
print(df2)
# Rename multiple index value
df2 = df.rename(index={'r1':'Index_1','r2':'Index_2'})
print(df2)
# Changing the DataFrame inplace
df.rename(index={'r3': 'Index_3','r4': 'Index_4'}, inplace=True)
print(df)
# Append a value to DataFrame index row Using lambda function
df2 = df.rename(index=lambda Index: Index+'_Index')
print(df2)
FAQ on Rename Index Values of Pandas DataFrame
Pandas provides the rename() method which can be applied directly to the DataFrame object. You can specify the new index values using a dictionary-like mapping or a function.
Renaming index values using the rename() method in Pandas does not modify the original DataFrame. Instead, it returns a new DataFrame with the updated index values, leaving the original DataFrame unchanged.
You can selectively rename index values within a DataFrame using the rename() method in Pandas. By specifying the indices you want to rename and their corresponding new values, you can effectively rename index values selectively without affecting the rest of the DataFrame’s index.
You can rename index values selectively within a DataFrame in Pandas. By using the rename() method and specifying the indices you want to rename along with their new values, you can effectively rename index values selectively without altering the entire index.
The rename() method allows for renaming both row and column index values simultaneously by specifying the index and columns parameters accordingly.
Conclusion
In this article, you have learned to rename index values of pandas DataFrame using DataFrame.index, DataFrame.rename() and lambda functions with examples. Additionally, you covered how to rename both single and multi-level index values.
Happy Learning !!
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