JIYIK CN >

Python Pandas DataFrame.assign() function assigns new columns to DataFrame.

pandas.DataFrame.assign()grammar

DataFrame.assign(**kwargs)

parameter

Return Value

It returns DataFramea object with the new columns assigned along with the existing columns.

Example code: DataFrame.assign()Method to assign a column

import pandas as pd

df = pd.DataFrame({'Cost Price': 
                   [100, 200], 
                   'Selling Price':
                   [200, 400]})

new_df=df.assign(Profit=df["Selling Price"]-
                        df["Cost Price"])
print(new_df)

The caller DataFrameis

   Cost Price  Selling Price
0         100            200
1         200            400

Output:

   Cost Price  Selling Price  Profit
0         100            200     100
1         200            400     200

ProfitIt assigns a new column to DataFrame, corresponding to the difference between the Selling Priceand columns.Cost Price

We can also assign new columns lambdato by using the function on the callable object .df

import pandas as pd

df = pd.DataFrame({'Cost_Price': 
                   [100, 200], 
                   'Selling_Price': 
                   [200, 400]})

new_df=df.assign(Profit=lambda x: 
                 x.Selling_Price-
                 x.Cost_Price)

print(new_df)

The object of the call DataFrameis

   Cost Price  Selling Price
0         100            200
1         200            400

Output:

   Cost_Price  Selling_Price  Profit
0         100            200     100
1         200            400     200

Sample Code: DataFrame.assign()Method to assign multiple columns

import pandas as pd

df = pd.DataFrame({'Cost_Price': 
                   [100, 200], 
                   'Selling_Price': 
                   [200, 400]})

new_df=df.assign(Cost_Price_Euro =  
                 df['Cost_Price']*1.11,  
                  Selling_Price_Euro = 
                 df['Selling_Price']*1.11)

print(new_df)

The caller DataFrameis

   Cost Price  Selling Price
0         100            200
1         200            400

Output:

   Cost_Price  Selling_Price  Cost_Price_Euro  Selling_Price_Euro
0         100            200            111.0               222.0
1         200            400            222.0               444.0

It assigns two new columns Cost_Price_Euroand to , which are taken from the existing and , respectively .Selling_Price_EurodfCost_PriceSelling_Price

We can also use lambdathe function to assign multiple columns dfto the calling object.

import pandas as pd

df = pd.DataFrame({'Cost_Price': 
                   [100, 200], 
                   'Selling_Price': 
                   [200, 400]})

new_df=df.assign(Cost_Price_Euro = 
                 lambda x: x.Cost_Price*1.11,  
                 Selling_Price_Euro =
                 lambda x: x.Selling_Price*1.11)

print(new_df)

The object of the call DataFrameis,

   Cost Price  Selling Price
0         100            200
1         200            400

Output:

   Cost_Price  Selling_Price  Cost_Price_Euro  Selling_Price_Euro
0         100            200            111.0               222.0
1         200            400            222.0               444.0