Comparing Values Between Categorical Columns in Pandas Datasets

In this article, we will explore a common problem when comparing values between categorical columns in pandas datasets. Specifically, we will discuss how to create a new column that reflects the result of these comparisons. We’ll delve into the world of pandas data manipulation and function application to achieve this.

Introduction

The question provided in the Stack Overflow post revolves around comparing values from two different categorical columns: ‘A’ from the first dataset (df1) and ‘C’ from the second dataset (df2). The comparison is done with a twist: if the value from ‘A’ is present in ‘C’, we need to compare it against another column ‘D’. If ‘B’ matches the corresponding value in ‘D’, we should return “NA” (which stands for Not Available), otherwise, we’ll say “T Not Found.” The main challenge here is handling the cases where either ‘A’ is not present in ‘C’ or when there’s no match between ‘B’ and ‘D.’

Setting Up the Dataset

To illustrate this concept, let’s first create a sample dataset using pandas. We have two datasets: df1 with columns 'A' and 'B,' and df2 with columns 'C' and `‘D.``

import pandas as pd

# Creating df1
data1 = {'A': [1, 2, 3],
         'B': ['p', 's', 'r']}
df1 = pd.DataFrame(data1)

# Creating df2
data2 = {'C': ['3', '2', '5'],
         'D': ['p', 'r', 'q']}
df2 = pd.DataFrame(data2)

Comparing Values Between Categorical Columns

To compare the values from df1['A'] and `df2[‘C’], we can use pandas’ boolean indexing functionality. However, this approach will not directly help us achieve the desired outcome of returning “V Not Found” if ‘A’ is not present in ‘C.’ We need to explore alternative methods that involve checking for membership within a specific set or applying custom comparison functions.

Using Custom Comparison Function

One way to tackle this problem is by implementing a custom function that takes into account both the existence of df1['A'] within df2['C'] and its corresponding match against df2['D'].

Here’s an example code snippet that encapsulates this logic:

def compare_columns(row):
    # Check if row['A'] is present in df2['C']
    if row['A'] not in df2['C'].astype(int).values:
        return 'V Not Found'
    
    # If the value exists, check for a match against df2['D']
    idx = df2[df2['C'].astype(int) == row['A']].index[0]
    if row['B'] == df2.loc[idx, 'D']:
        return 'NA'
    else:
        return 'T Not Found'

df1['E'] = df1.apply(compare_columns, axis=1)

However, this implementation has a few issues. For instance, using row['A'] not in df2['C'].astype(int).values assumes that the values are integers and does not account for cases where they might be strings or other data types.

Improved Custom Comparison Function

To address these concerns, we can modify the comparison function to use more robust membership checks. Instead of relying solely on type conversions, we’ll utilize pandas’ isin() method along with conditional statements to accommodate different data types.

Here’s a revised version of the comparison function:

def compare_columns(row):
    # Check if row['A'] is present in df2['C']
    if row['A'].astype(str) not in df2['C'].values:
        return 'V Not Found'
    
    # If the value exists, check for a match against df2['D']
    idx = df2[df2['C'] == row['A']].index[0]
    if row['B'] == df2.loc[idx, 'D']:
        return 'NA'
    else:
        return 'T Not Found'

df1['E'] = df1.apply(compare_columns, axis=1)

Handling Non-Existent Values

In the previous examples, we relied on the existence of df1['A'] within df2['C'] to determine the outcome. However, if ‘A’ is not present in ‘C,’ our function returns “V Not Found.” This behavior might be desirable or undesirable depending on your specific use case.

To better accommodate this variation, you can modify the comparison function as follows:

def compare_columns(row):
    # Check for a match against df2['D'] regardless of whether row['A'] exists in df2['C']
    idx = df2[df2['C'].astype(int) == row['A']].index[0] if row['A'].astype(str) in df2['C'].values else None
    
    if idx is not None:
        # If a match exists, check for alignment with 'B'
        if row['B'] == df2.loc[idx, 'D']:
            return 'NA'
        else:
            return 'T Not Found'
    
    # Handle the case when 'A' does not exist in 'C'
    return 'V Not Found'

df1['E'] = df1.apply(compare_columns, axis=1)

Applying Custom Comparison Function Using Lambda Expression

In addition to using a regular function, you can also define an anonymous lambda expression that serves the same purpose. Here’s how:

compare_columns_lambda = lambda row: 'V Not Found' if row['A'].astype(str) not in df2['C'].values else ('NA' if row['B'] == df2[df2['C'].astype(int) == row['A']].loc[0, 'D'] else 'T Not Found')

df1['E'] = df1.apply(compare_columns_lambda, axis=1)

Conclusion

In this article, we’ve discussed how to compare values from two categorical columns in pandas datasets while returning a custom result based on the presence and alignment of these values. We explored several approaches, including defining a custom comparison function and utilizing lambda expressions, to handle cases where ‘A’ is not present in ‘C’ or does not match with ‘D.’ By applying these techniques, you can manipulate your data more effectively and make informed decisions within your projects.

Note: The revised comparison functions discussed in the article aim to address potential issues with the original code snippets. However, the implementation might still be optimized further depending on specific requirements and dataset characteristics.

Last modified on 2023-10-09