Adding a new column in Pandas dataframe based on existing columns

Pandas dataframe allows adding new columns to it based on the values of the existing columns.

Example Scenario

Suppose, a dataset contains Football match results of some matches like below:

Here, GF stands for Goals For and GA stands for Goals Against (i.e., number of goals conceded by a team). Now we want to add a new column called Result to this dataframe that will contain the result of the match.

The condition to generate the Result column is:

• If GF > GA, Result should contain W
• If GF < GA, Result should contain L
• Otherwise Result should contain D.

Solution using Dataframe apply method

We can use pandas.DataFrame.apply method to add the new Result column.

import pandas as pd


def get_result(row):
    if row['GF'] == row['GA']:
        return 'D'
    elif row['GF'] > row['GA']:
        return 'W'
    return 'L'


data = pd.read_csv("scores.csv")
data['Result'] = data.apply(lambda row: get_result(row), axis=1)
print(data)

Output:

scores.csv:

Date,Venue,Opponent,GF,GA
08-27-2017,H,Arsenal FC,4,0
09-16-2017,H,Burnley FC,1,1

Explanation

We check each row of the existing dataframe. We assign a value to the newly created Result column based on the condition given in the get_result method. axis=1 in the apply method tells of applying the logic in all rows of the dataframe.

Reference

• Pandas official documentation on pandas.DataFrame.apply