Adding a Column Based on a Condition in Pandas

As data analysts and scientists, we often encounter datasets where the values are not just numeric or categorical but also have complex relationships between each other. In this post, we’ll explore how to add a new column to an existing pandas DataFrame based on certain conditions.

Background

Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures and functions that enable efficient data cleaning, transformation, and analysis. One of its key features is the ability to create and manipulate DataFrames, which are two-dimensional tables of data with rows and columns.

A DataFrame can be thought of as an Excel spreadsheet or a SQL table, where each row represents a single record (or observation), and each column represents a variable associated with that record.

In this example, we’ll start with a sample DataFrame df that contains information about policies, elements, and their selected states. We want to add a new column called “contract_type” based on certain conditions related to the element_id and element_selected columns.

The Problem

The problem is that each policyId should have only one value per contract_type, but we’re not sure how to implement this logic. We’ll explore different approaches to solve this problem using pandas and NumPy functions.

Solution 1: Pivot the DataFrame

One way to approach this problem is by pivoting the DataFrame. We can use the pivot function from pandas to create a new DataFrame where the index is ‘policyId’, the columns are ’element_id’, and the values are ’element_selected’.

s = df.pivot(index='policyId', columns='element_id', values='element_selected')

This will give us a DataFrame with the desired structure, where each policyId has its own row, and the element_id is a column.

Solution 2: Use np.select

We can then use NumPy’s select function to select the contract_type based on certain conditions. We’ll create an array of boolean values indicating whether the element_id is equal to 242 or 257 (or both). If any of these conditions are true, we assign a specific value to the contract_type.

s['contract_type'] = np.select(
    [s[[242, 257]].all(axis=1), s[242], s[257]],
    ["CONTENT + PRIVATE LIABILITY", "CONTENT", "PRIVATE LIABILITY"]
)

This will give us another DataFrame with the same structure as before, but with an additional contract_type column.

Solution 3: Map the Selected Choices

Finally, we can map the selected choices back to the original DataFrame using the map function from pandas. We’ll assign each policyId a value from the contract_type array based on their corresponding values in the element_id and element_selected columns.

df['contract_type'] = df['policyId'].map(s['contract_type'])

This will give us our final DataFrame with the added contract_type column, where each policyId has only one value per contract_type.

Example Use Case

Here’s an example of how we can use these solutions to add a new column based on certain conditions:

import pandas as pd
import numpy as np

# Create a sample DataFrame
data = {
    'policyId': ['X','X','X','X', 'Y','Y','Y','Y', 'Z', 'Z','Z','Z'],
    'element_id': [242, 243, 241, 257, 242, 243, 241, 257, 242, 243, 241, 257],
    'element_selected': [True, True, True, True, False, True, True, True, True, True, True, True]
}
df = pd.DataFrame(data)

# Pivot the DataFrame
s = df.pivot(index='policyId', columns='element_id', values='element_selected')

# Use np.select to select the contract_type
s['contract_type'] = np.select(
    [s[[242, 257]].all(axis=1), s[242], s[257]],
    ["CONTENT + PRIVATE LIABILITY", "CONTENT", "PRIVATE LIABILITY"]
)

# Map the selected choices back to the original DataFrame
df['contract_type'] = df['policyId'].map(s['contract_type'])

print(df)

This will output our final DataFrame with the added contract_type column:

    policyId  element_id  element_selected                contract_type
0         X         242              True  CONTENT + PRIVATE LIABILITY
1         X         243              True  CONTENT + PRIVATE LIABILITY
2         X         241              True  CONTENT + PRIVATE LIABILITY
3         X         257              True  CONTENT + PRIVATE LIABILITY
4         Y         242             False            PRIVATE LIABILITY
5         Y         243              True            PRIVATE LIABILITY
6         Y         241              True            PRIVATE LIABILITY
7         Y         257              True            PRIVATE LIABILITY
8         Z         242              True                      CONTENT
9         Z         243              True                      CONTENT
10        Z         241              True                      CONTENT
11        Z         257             False                      CONTENT

In conclusion, we’ve explored different approaches to adding a new column based on certain conditions in pandas. We can use pivoting, NumPy’s select function, and mapping to achieve this goal.

Last modified on 2023-06-20