Applying Custom Functions Row-Wise to Create New Columns in Pandas

Introduction

In data analysis, it’s common to derive new columns based on computations or conditions applied to existing ones. When working with tabular data in Python, the Pandas library is a powerful tool that enables such transformations efficiently. This tutorial will guide you through creating a new column by applying custom logic across multiple existing columns row-wise using Pandas.

Prerequisites

Before diving into this tutorial, ensure you have:

• A basic understanding of Python.
• Familiarity with Pandas dataframes and their operations.
• An installed version of Pandas. If not already installed, use pip install pandas to add it to your environment.

Understanding the Problem

Imagine a scenario where you are working with demographic data, and for each individual in your dataset, you need to categorize them based on ethnicity information spread across several columns. The categorization rules might include priority conditions such as if an individual is marked Hispanic, they take precedence over other ethnicities, or if multiple non-Hispanic ethnicities apply, they should be labeled ‘Two or More’.

The challenge lies in applying these rules row by row to a dataframe and creating a new column reflecting the result of this categorization.

Solution Approach

To solve such problems efficiently, we’ll define a custom function encapsulating our logic. This function will take a single row as input and return the category label according to the specified criteria.

Next, we’ll use Pandas’ apply() method, which allows us to apply a function along an axis of the DataFrame (rows or columns). In our case, we’ll use it across rows (axis=1).

Step-by-Step Tutorial

Setting Up the Dataframe

First, let’s create a sample dataframe similar to what might be encountered in real-world data:

import pandas as pd

# Sample data simulating ethnicity information
data = {
    'lname': ['MOST', 'CRUISE', 'DEPP', 'DICAP', 'BRANDO', 'HANKS', 'DENIRO', 'PACINO', 'WILLIAMS', 'EASTWOOD'],
    'fname': ['JEFF', 'TOM', 'JOHNNY', 'LEO', 'MARLON', 'TOM', 'ROBERT', 'AL', 'ROBIN', 'CLINT'],
    'rno_cd': ['E', 'E', '', '', '', '', 'E', 'E', 'E', 'E'],
    'ERI_Hispanic': [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    'ERI_AmerInd_AKNatv': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'ERI_Asian': [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
    'ERI_Black_Afr.Amer': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'ERI_HI_PacIsl': [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
    'ERI_White': [1, 0, 1, 1, 0, 1, 1, 1, 0, 1],
    'rno_defined': ['White', 'White', 'Unknown', 'Unknown', 'White', 'Unknown', 'White', 'White', 'White', 'White']
}

df = pd.DataFrame(data)

Defining the Custom Function

Now we will write a function that follows our criteria for ethnic categorization:

def label_race(row):
    if row['ERI_Hispanic'] == 1:
        return 'Hispanic'
    elif (row['ERI_AmerInd_AKNatv'] + row['ERI_Asian'] +
          row['ERI_Black_Afr.Amer'] + row['ERI_HI_PacIsl'] +
          row['ERI_White']) > 1:
        return 'Two or More'
    elif row['ERI_AmerInd_AKNatv'] == 1:
        return 'A/I AK Native'
    elif row['ERI_Asian'] == 1:
        return 'Asian'
    elif row['ERI_Black_Afr.Amer'] == 1:
        return 'Black/AA'
    elif row['ERI_HI_PacIsl'] == 1:
        return 'Haw/Pac Isl.'
    elif row['ERI_White'] == 1:
        return 'White'
    else:
        return 'Other'

Applying the Function

With our function ready, let’s apply it to each row in the dataframe:

df['ethnic_category'] = df.apply(label_race, axis=1)

Now df contains an additional column named 'ethnic_category', with labels assigned according to the logic we specified.

Verifying Results

To ensure our function worked as expected, let’s look at a sample of our dataframe:

print(df[['lname', 'fname', 'ethnic_category']].head())

The output should reflect the correct ethnic categories for each individual based on our rules.

Best Practices and Tips

• It’s vital to use vectorized operations where possible in Pandas, as they are faster than applying functions row-wise. However, when complex logic is involved that depends on multiple columns, apply() with custom functions can be quite handy.
• Always validate the results after transformations, especially when applying conditional logic across rows or columns.
• If you receive a SettingWithCopyWarning, it means that Pandas has detected an operation that might not be modifying your DataFrame in-place. Use .copy() to avoid this warning if necessary.

Conclusion

In this tutorial, we covered how to create a new column by applying custom logic across multiple existing columns in a Pandas dataframe. By defining and using a function with apply(), we managed to categorize each individual row based on our criteria. This method is very useful for situations that require bespoke calculations or conditions.