Understanding the Problem and Background

The problem presented involves two pandas DataFrames, df1 and df2, each with their own set of columns. The goal is to create a mapping between the columns of both DataFrames where there are matching values. This can be achieved by finding the intersection of sets containing the unique values from each column in both DataFrames.

Setting Up the Environment

To tackle this problem, we’ll need to have pandas installed in our Python environment. If you don’t have it installed, you can do so using pip:

pip install pandas

Next, let’s import the necessary libraries and set up a sample DataFrame for testing:

import pandas as pd

# Creating sample DataFrames
df1 = pd.DataFrame({
    'SAP_Name': ['Avi', 'Rison', 'Slesh', 'San', 'Sud'],
    'SAP_Class': ['5', '6', '7', '8', '7'],
})

df2 = pd.DataFrame({
    'Name_Fi': ['Avi', 'Rison', 'Slesh'],
    'Class': ['fgh', 'Rij', 'jkh']
})

print("DataFrame 1:")
print(df1)
print("\nDataFrame 2:")
print(df2)

Understanding the Problem Statement

The original problem statement uses a dictionary comprehension to create two dictionaries, dfs1 and set2, where each key is a column name from one of the DataFrames, and its value is a set containing the unique values in that column. It then iterates over these sets using another dictionary comprehension to find matching columns between the two DataFrames.

Breaking Down the Problem

The problem can be broken down into several steps:

1. Cleaning and Preparing Data: Ensure both DataFrames are clean and in a suitable format for analysis.
2. Identifying Matching Columns: Find columns in df1 that have matching values with any column in df2.
3. Constructing the Mapping Dictionary: Create a dictionary where each key is a unique column from df2, and its value is a list of corresponding column names from df1.

Solution Overview

To solve this problem, we’ll use a combination of pandas’ built-in functions for data manipulation and Python’s set operations.

The proposed solution involves the following steps:

• Convert both DataFrames into dictionaries where each key is a column name, and its value is a set containing unique values in that column.
• Use a dictionary comprehension to create another dictionary where each key is a unique column from df2, and its value is a list of corresponding columns from df1 that have matching values with the key’s column.

Solution Implementation

Below, you can see how these steps are implemented:

# Import necessary libraries
from collections import defaultdict

# Create sample DataFrames
df1 = pd.DataFrame({
    'SAP_Name': ['Avi', 'Rison', 'Slesh', 'San', 'Sud'],
    'SAP_Class': ['5', '6', '7', '8', '7'],
})

df2 = pd.DataFrame({
    'Name_Fi': ['Avi', 'Rison', 'Slesh'],
    'Class': ['fgh', 'Rij', 'jkh']
})

# Define function to find matching columns
def find_matching_columns(df1, df2):
    # Convert DataFrames into dictionaries where each key is a column name,
    # and its value is a set containing unique values in that column.
    dfs1 = {col1: set(df1[col1].drop_duplicates()) for col1 in df1.columns}
    sets2 = {col2: set(df2[col2]) for col2 in df2.columns}

    # Use dictionary comprehension to create another dictionary where each key
    # is a unique column from `df2`, and its value is a list of corresponding
    # columns from `df1` that have matching values with the key's column.
    d = defaultdict(list)
    for col2, v2 in sets2.items():
        for col1, v1 in dfs1.items():
            cond = v2.intersection(v1)
            if cond:
                d[col2].append(col1)

    return dict(d)

# Call function with sample DataFrames
matching_columns = find_matching_columns(df1, df2)

print("Matching Columns:")
for key, value in matching_columns.items():
    print(f"{key}: {value}")

Conclusion and Further Improvements

The proposed solution successfully prints the desired mapping of columns between df1 and df2, where there are matching values. However, there’s room for improvement:

• Data Cleaning: Ensure both DataFrames are clean and free from duplicates to avoid incorrect mappings.
• Error Handling: Implement error handling to handle cases where no matches exist or where the data is malformed.

Summary

In this article, we explored how to print all occurrences of mapped data from two pandas DataFrames using set operations. We used a combination of pandas’ built-in functions for data manipulation and Python’s set operations to create a dictionary mapping columns between the two DataFrames based on matching values.

Last modified on 2023-05-10