Pandas create new column using if else condition

In this quick tutorial, we will learn how to create a new column using if else condition on an existing column in a Pandas dataframe.

To add new column using a condional on existing column we will use Numpy’s where function. So, let us load both numby and Pandas to get started.

import pandas as pd
import numpy as np 

We. will use one of the built-in datasets from Seaborn package. Let us load Seaborn and the health expenditure dataset.

import seaborn as sns
healthexp =  sns.load_dataset("healthexp")

Our health expenditure dataset looks like this.

healthexp.head()

	Year	Country	Spending_USD	Life_Expectancy
0	1970	Germany	252.311	70.6
1	1970	France	192.143	72.2
2	1970	Great Britain	123.993	71.9
3	1970	Japan	150.437	72.0
4	1970	USA	326.961	70.9

We will create a new column using the existing Country column with if condition. Our new column has Yes value if the country value for that row is USA, No if it is not. We use Numpy’s where() function to check the country values and create a new column.

healthexp['Is_USA'] = np.where(healthexp["Country"] == 'USA', 
                               "Yes",
                               "No")
healthexp.head()

Year	Country	Spending_USD	Life_Expectancy	USA	Is_USA
0	1970	Germany	252.311	70.6	False	No
1	1970	France	192.143	72.2	False	No
2	1970	Great Britain	123.993	71.9	False	No
3	1970	Japan	150.437	72.0	False	No
4	1970	USA	326.961	70.9	True	Yes

In the second example, we create a boolean column based on the value of Country column. If the country column value is USA, we have True else False.

healthexp['Is_USA'] = np.where(healthexp["Country"] == 'USA', True, False)
healthexp.head()

Year	Country	Spending_USD	Life_Expectancy	USA	Is_USA
0	1970	Germany	252.311	70.6	False	False
1	1970	France	192.143	72.2	False	False
2	1970	Great Britain	123.993	71.9	False	False
3	1970	Japan	150.437	72.0	False	False
4	1970	USA	326.961	70.9	True	True