How to Create a Method Using Dataframe in Python

In Python’s Pandas library, DataFrames are essential for data manipulation and analysis. Creating methods using DataFrames in Python is a powerful way to handle and process data programmatically. It allows you to encapsulate specific tasks and reuse them efficiently.

In this guide, we’ll explore how to create a method using a DataFrame in Python and demonstrate it with a practical example.

Creating a DataFrame Method:

First, let’s create a sample DataFrame to demonstrate the method creation process. We’ll use a DataFrame named office_stuff representing office supplies.

import pandas as pd

# Create the DataFrame
office_stuff = pd.DataFrame({
   'Date': ['01-03-2023', '01-03-2023', '01-03-2023', '01-03-2023', '02-03-2023', '02-03-2023'],
   'Product_Code': ['A-101', 'A-102', 'A-103', 'B-101', 'B-102', 'B-104'],
   'Product_Name': ['Laptop', 'Mobile', 'Printer', 'Keyboard', 'Scanner', 'Mouse'],
   'Price': [4500, 550, 250, 50, 350, 50],
   'Status': [1, 1, 1, 0, 1, 1]
})

Here’s what the DataFrame looks like:

Define the Method:

Now, we’ll define a method named total_price_by_status. This method calculates the total price of products based on their status (1 or 0).

def total_price_by_status(df, status):
    """
    Calculates the total price of products based on the given status.

    Parameters:
    df (pandas.DataFrame): The DataFrame containing product information.
    status (int): The status to filter by (e.g., 1 for available, 0 for unavailable).

    Returns:
    int: The sum of prices for products with the specified status.
    """
    status_df = df[df['Status'] == status]  # Filter DataFrame by status
    return status_df['Price'].sum()         # Calculate and return the sum of prices

Explanation:

• def total_price_by_status(df, status):: Defines the method with parameters df (DataFrame) and status (integer).
• status_df = df[df['Status'] == status]: Filters the DataFrame to include only rows where the ‘Status’ column matches the given status.
• return status_df['Price'].sum(): Calculates and returns the sum of the ‘Price’ column in the filtered DataFrame.

Using the Method:

To use the method, call total_price_by_status with the office_stuff DataFrame and the desired status (e.g., 1).

# Calculate total price for products with status 1
total_price = total_price_by_status(office_stuff, 1)

# Display the result
print(f'Total price of products with status 1: {total_price}')

Output: 👇️

Total price of products with status 1: 5700

Benefits of Using DataFrame Methods:

Creating methods like total_price_by_status provides several benefits:

• Reusability: The method can be used with different DataFrames and statuses without rewriting the logic.
• Readability: Encapsulating the logic in a named method makes the code easier to understand and maintain.
• Efficiency: Avoids code duplication and ensures consistency in calculations.

Conclusion:

Defining methods that operate on DataFrames in Python is a powerful way to organize and reuse code.

By passing DataFrame columns and applying specific operations, you can efficiently perform various data manipulation tasks.

This method of creating functions for DataFrames enhances code clarity and maintainability, making it a valuable skill for data analysis in Python.