Pandas DataFrame pivot() Method
In this tutorial, we will discuss and learn the Python pandas DataFrame.pivot()
method which helps us to transform or reshape the DataFrame. When this method applied to the DataFrame, it returns the DataFrame which is reshaped and organized by the given index and column values.
The below is the syntax of the DataFrame.pivot()
method.
Syntax
DataFrame.pivot(index=None, columns=None, values=None)
Parameters
index: It represents the str or object or a list of str, which is optional. It indicates the column to use to make new frame’s index. If this parameter is None, it uses the existing index.
columns: It represents the str or object or a list of str. It indicates the column to use to make new frame columns.
values: It represents the str, object, or a list of the previous, optional. It indicates the column or columns to use for populating new frame values. If this parameter is not specified, all remaining columns will be used and the result will have hierarchically indexed columns.
Example 1: Reshape the DataFrame in Pandas
Let's apply the DataFrame.pivot()
method and reshape it by specifying the index and column names. Here, in this example, we change the DataFrame's row axis to 'crop' and column axis to 'state' by passing in the index and column parameter in the DataFrame.pivot()
method.
See the below example. The DataFrame.pivot()
method returns the DataFrame having crops as rows and multiple columns for temperature and humidity.
#importing pandas as pd
import pandas as pd
#creating the DataFrame
df=pd.DataFrame({'crop':['Rice','Wheat','Rice','Wheat','Rice','Wheat'],'state':['karnataka','karnataka','Tamilnadu','Tamilnadu','Kerala','Kerala'],'Tempreture':[29,29,31,31,25,25],'Humidity':[50,50,62,62,45,45]})
df = df.pivot(index='crop',columns='state')
print(df)
![](data:image/png;base64,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)
Example 2: Reshape the DataFrame using the DataFrame.pivot()
Method
In the previous example, we have multiple columns and if we do not want multiple columns, we can specify the column name which we want in the DataFrame.pivot()
method by passing the values
parameter. See the below example.
The DataFrame.pivot()
method returns the DataFrame which consists of only the Temperature column.
#importing pandas as pd
import pandas as pd
#creating the DataFrame
df=pd.DataFrame({'crop':['Rice','Wheat','Rice','Wheat','Rice','Wheat'],'state':['karnataka','karnataka','Tamilnadu','Tamilnadu','Kerala','Kerala'],'Tempreture':[29,29,31,31,25,25],'Humidity':[50,50,62,62,45,45]})
df = df.pivot(index='crop',columns='state',values='Tempreture')
print(df)
![](data:image/png;base64,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)
Example: The DataFrame.pivot()
method raises ValueError
The DataFrame.pivot()
method raises ValueError
while reshaping the DataFrame if there is any duplicates in the DataFrame. See the below example.
#importing pandas as pd
import pandas as pd
#creating the DataFrame
df=pd.DataFrame({'crop':['Rice','Rice','Wheat','Wheat','Rice','Wheat'],'state':['karnataka','karnataka','Tamilnadu','Tamilnadu','Kerala','Kerala'],'Tempreture':[29,29,31,31,25,25],'Humidity':[50,50,62,62,45,45]})
df = df.pivot(index='crop',columns='state',values='Tempreture')
print(df)
![](data:image/png;base64,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)
ValueError: Index contains duplicate entries, cannot reshape
Conclusion
In this tutorial, we learned the Python pandas DataFrame.pivot()
method. We learned syntax, parameters, and solved examples by applying this method on the DataFrame and understood the method.