Matching Values from One Column to Second Column with Multiple Values

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In this article, we’ll delve into the world of data manipulation and explore how to match values from one column to second column with multiple values. We’ll take a closer look at the problem presented in the Stack Overflow post, analyze the existing code, and provide a more efficient solution using pandas.

Problem Statement

The original code aims to count the number of people working in each department based on the input data. The first column contains names, while the second column shows various departments they are associated with (e.g., Genteic|Biology|Chemical Engineering). However, the provided Python script has several issues, including:

• Ignoring the fact that the input file is a CSV file and using a simple string comparison to process it.
• Not utilizing libraries designed for parsing CSV files.
• Hardcoding department names in the counters dictionary.

Proposed Solution

To address these concerns, we’ll employ a more robust approach using pandas, a popular library for data manipulation in Python. We’ll create a function that takes the input CSV file and returns a dictionary with the count of people working in each department.

Step 1: Import Necessary Libraries

First, let’s import the required libraries:

import pandas as pd
from collections import defaultdict

Step 2: Load the Input CSV File

We’ll load the input CSV file using pd.read_csv():

def load_data(file_path):
    try:
        data = pd.read_csv(file_path)
        return data
    except FileNotFoundError:
        print("File not found. Please check the file path.")
        return None
    except pd.errors.EmptyDataError:
        print("No data in file. Please check the file contents.")
        return None

Step 3: Extract Department Names

Next, we’ll extract department names from the second column using str.split():

def extract_departments(data):
    departments = set()
    for row in data.iterrows():
        professions = row[1][1].split("|")
        departments.update(professions)
    return list(departments)

Step 4: Count People Working in Each Department

Now, we’ll create a dictionary to store the count of people working in each department:

def count_department_employees(data):
    department_employees = defaultdict(int)
    for index, row in data.iterrows():
        name = row[0]
        professions = row[1].split("|")
        for profession in professions:
            department_employees[profession] += 1
    return dict(department_employees)

Step 5: Print the Department Counts

Finally, we’ll print the count of people working in each department:

def main():
    file_path = "input.csv"
    data = load_data(file_path)
    if data is not None:
        departments = extract_departments(data)
        department_employees = count_department_employees(data)
        for department, count in department_employees.items():
            print(f"There are {count} teachers working in {department}")

if __name__ == "__main__":
    main()

Example Usage

To use this code, simply replace the file_path variable with your input CSV file path. The program will load the data, extract department names, count people working in each department, and print the results.

Benefits of the Proposed Solution

The proposed solution offers several benefits over the original code:

• Efficient parsing: We utilize pandas to efficiently parse the CSV file.
• Robust error handling: The load_data() function handles file not found and empty data errors, ensuring a more robust program.
• Improved code organization: Each step is separated into a distinct function, making it easier to understand and maintain.

Conclusion

In this article, we’ve explored how to match values from one column to second column with multiple values using pandas. We’ve presented a proposed solution that addresses the issues in the original code, providing a more efficient and robust approach to data manipulation.

Last modified on 2025-04-02