How to Rename Columns in Pandas DataFrame
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Rename Columns in Pandas
DataFrameUsing theDataFrame.columnsMethod -
Rename Columns in Pandas
DataFrameUsingDataFrame.rename()Method -
Rename Columns in Pandas
DataFrameUsingDataFrame.set_axis()Method
Often we need to rename a column in Pandas when performing data analysis. This article will introduce different methods to rename Pandas column names in Pandas DataFrame.
Rename Columns in Pandas DataFrame Using the DataFrame.columns Method
This method is pretty straightforward and lets you rename columns directly. We can assign a list of new column names using DataFrame.columns attribute as follows:
import pandas as pd
example_df = pd.DataFrame(
[["John", 20, 45], ["Peter", 21, 62], ["Scot", 25, 68]],
index=[0, 1, 2],
columns=["Name", "Age", "Marks"],
)
print "\nOriginal DataFrame"
print (pd.DataFrame(example_df))
example_df.columns = ["Name", "Age", "Roll_no"]
print "\nModified DataFrame"
print (pd.DataFrame(example_df))
Output:
Original DataFrame
Name Age Marks
0 John 20 45
1 Peter 21 62
2 Scot 25 68
Modified DataFrame
Name Age Roll_no
0 John 20 45
1 Peter 21 62
2 Scot 25 68
One drawback of this approach is that you have to list the entire column even if only one of the columns needs to be renamed. Specifying the entire column list becomes impractical when you have a large number of columns.
Rename Columns in Pandas DataFrame Using DataFrame.rename() Method
The alternative approach to the previous method is using DataFrame.rename() method. This method is quite handy when we need not rename all columns.
We will need to specify the old column name as key and new names as values.
import pandas as pd
example_df = pd.DataFrame(
[["John", 20, 45, 78], ["Peter", 21, 62, 68], ["Scot", 25, 68, 95]],
index=[0, 1, 2],
columns=["Name", "Age", "Marks", "Roll_no"],
)
print "\nOriginal DataFrame"
print (pd.DataFrame(example_df))
example_df.rename(columns={"Marks": "Roll_no", "Roll_no": "Marks"}, inplace=True)
print "\nModified DataFrame"
print (pd.DataFrame(example_df))
Output:
Original DataFrame
Name Age Marks Roll_no
0 John 20 45 78
1 Peter 21 62 68
2 Scot 25 68 95
Modified DataFrame
Name Age Roll_no Marks
0 John 20 45 78
1 Peter 21 62 68
2 Scot 25 68 95
The most significant advantage of this method is that you can specify as many columns as you want. It is quite useful when you need to rename specific columns and unlike previous methods no need to list the entire column list for the DataFrame.
You might notice that in the DataFrame.rename() function call, we have specified the inplace parameter as True. inplace parameter is by default False and determines whether to return a new pandas DataFrame or not.
Specifying it as True means function call doesn’t return a new pandas DataFrame but changes the existing DataFrame in place.
Rename Columns in Pandas DataFrame Using DataFrame.set_axis() Method
Another convenient method to rename columns of pandas DataFrame. We have to specify the entire column list while using this method.
import pandas as pd
example_df = pd.DataFrame(
[["John", 20, 45, 78], ["Peter", 21, 62, 68], ["Scot", 25, 68, 95]],
index=[0, 1, 2],
columns=["Name", "Age", "Marks", "Roll_no"],
)
print "\nOriginal DataFrame"
print (pd.DataFrame(example_df))
example_df.set_axis(["Name", "Age", "Roll_no", "Marks"], axis="columns", inplace=True)
print "\nModified DataFrame"
print (pd.DataFrame(example_df))
Output:
Original DataFrame
Name Age Marks Roll_no
0 John 20 45 78
1 Peter 21 62 68
2 Scot 25 68 95
Modified DataFrame
Name Age Roll_no Marks
0 John 20 45 78
1 Peter 21 62 68
2 Scot 25 68 95