How To Insert A New Column Based On Condition In Python?

Recipe Objective- How to insert a new column based on condition in Python?

Adding a new column in Python is an easy task. Have you tried adding a column with values based on some condition? Like a column with values that depends on the values of another column. For a small data set with few numbers of rows, it may be easy to do it manually, but for a large dataset with hundreds of rows, it may be challenging to do it manually.

We can do this hectic manual work with few lines of code. We can create a function that will do it for all the rows.

This recipe shows how to create a function to insert a Pandas new column based on condition.

Python Pandas ‘Add New Column Based On Condition’

You can follow the below steps in Pandas to create new column based on condition.

Step 1 - Import the library

import pandas as pd

import numpy as np

We have imported pandas and numpy. No other library is needed for this function.

Step 2 - Creating a Sample Dataset

We will create a Dataframe with columns 'bond_name' and 'risk_score'. We will use a print statement to view our initial dataset.

raw_data = {'bond_name': ['govt_bond_1', 'govt_bond_2', 'govt_bond_3', 'pvt_bond_1', 'pvt_bond_2', 'pvt_bond_3', 'pvt_bond_4'], 'risk_score': [1.6, 0.9, 2.3, 3.0, 2.7, 1.8, 4.1]}

df = pd.DataFrame(raw_data, columns = ['bond_name', 'risk_score'])

print(df)

Step 3 - Creating a function to assign values in column

First, we will create an empty list named rating, which we will append and assign values as per the condition.

rating = []

We will create a loop that will iterate over all the rows in column 'risk_score' and assign values in the list. We are using the if-else function to make the condition on which we want to assign the values in the column. Here, we want to assign a rating based on risk_score. The condition which we are making is:

• If the value in risk_score is between 0 and 1, it will assign 'AA' in the rating column.

• If the value in risk_score is between 1 and 2, it will assign 'A' in the rating column.

• If the value in risk_score is between 2 and 3, it will assign 'BB' in the rating column.

• If the value in risk_score is between 3 and 4, it will assign 'B' in the rating column.

• If the value in risk_score is between 4 and 5, it will assign 'C' in the rating column.

• If there is no value in risk_score, then it will assign Not_Rated in the rating column.

rating = [] for row in df['risk_score']: if row < 1.0 : rating.append('AA') elif row < 2.0: rating.append('A') elif row < 3.0: rating.append('BB') elif row < 4.0: rating.append('B') elif row < 5.0: rating.append('C') else: rating.append('Not_Rated')

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Step 5 - Converting list into column of dataset and viewing the final dataset

So finally, we are adding that list as a column in the dataset and printing the final dataset to see the changes.

df['rating'] = rating print(df)

We get the following output-

bond_name risk_score

0 govt_bond_1 1.6

1 govt_bond_2 0.9

2 govt_bond_3 2.3

3 pvt_bond_1 3.0

4 pvt_bond_2 2.7

5 pvt_bond_3 1.8

6 pvt_bond_4 4.1

bond_name risk_score rating

0 govt_bond_1 1.6 A

1 govt_bond_2 0.9 AA

2 govt_bond_3 2.3 BB

3 pvt_bond_1 3.0 B

4 pvt_bond_2 2.7 BB

5 pvt_bond_3 1.8 A

6 pvt_bond_4 4.1 C

Here we see that a new column has been added with the values according to the risk_score.

How to Create New Column in Pandas Dataframe Based on Condition?

The apply() method shows you how to create a new column in a Pandas based on condition. The apply() method takes a function as an argument and applies that function to each row in the DataFrame. The function you pass to the apply() method should return a single value. The function should return a Boolean value when creating a new column based on a condition.

The following code shows how to create a new column called Is_Male in a DataFrame called df based on the value of the Name column:

df['Is_Male'] = df['Name'].apply(lambda name: name.split()[-1] == 'M')

The apply() method is applied to the Name column in this code. The function passed to the apply() method checks the last letter of the name. If the last letter is M, then the function returns True. Otherwise, the function returns False.

Python Pandas ‘Create New Column Based On Other Columns’

In Python Pandas, new column based on another column can be created using the where() method. The where() method takes a condition and a value as arguments. If the condition is met, then the value is returned. Otherwise, another value is returned.

Python Pandas ‘Add Column Based on Other Columns’

You can add column based on other columns, i.e., based on the values of two existing columns, using the assign() method. The assign() method takes a dictionary as an argument, where the keys are the names of the new columns, and the values are the expressions used to fill the columns.