Calling a Python Function from Another File Using DataFrame

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What will you learn?

In this tutorial, you will learn how to call a function defined in one Python script from another script. Specifically, you will understand the process of passing a DataFrame as an argument when calling the function.

Introduction to the Problem and Solution

Working on complex projects often involves functions spread across multiple files for better organization and reusability. When functions need to be called from one file to another, it’s essential to know how to handle this in Python.

To tackle this issue, we can create a separate script containing the desired function and then import it into the main script where it is needed. We’ll explore this process using DataFrames as arguments when calling the function.

Code

# Importing necessary libraries
import pandas as pd

# Defining the function in 'helper_functions.py'
def process_data(df):
    # Perform data processing operations on the DataFrame df 
    processed_data = df.apply(lambda x: x**2)
    return processed_data

# Calling the 'process_data' function from 'main_script.py'
from helper_functions import process_data

# Creating a sample DataFrame
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)

# Calling the function with df as an argument
processed_df = process_data(df)

print(processed_df)

# Copyright PHD

(_Credit: PythonHelpDesk.com_)

Explanation

  • The process_data function defined in helper_functions.py offers reusable code.
  • Importing process_data into main_script.py makes these operations available within that file.
  • Passing a DataFrame (df) as an argument facilitates easy data manipulation across different scripts.
    How do I ensure that both files are in the same directory?

    Ensure both your main script (main_script.py) and helper functions script (helper_functions.py) are saved in the same directory.

    Can I pass multiple DataFrames or other types of objects as arguments?

    Yes, you can pass multiple DataFrames or any other required objects by including them within parentheses separated by commas when calling your function.

    Do I always need to specify column names while passing DataFrames between files?

    No, unless explicitly required; passing the entire DataFrame is generally sufficient without specifying column names.

    Is there any limit on DataFrame size that can be passed between scripts?

    There’s no strict limit on dataframe size for passing between scripts; consider memory constraints based on system resources.

    What if my helper functions script contains multiple functions? How do I import only one specific function?

    You can selectively import specific functions from a module using syntax like from module_name import specific_function.

    Can I modify my original DataFrame within my called function without affecting its state outside of it?

    Modifying a DataFrame inside a called function does not affect its state outside unless changes are returned and assigned back accordingly.

    Conclusion

    By mastering the concept of calling Python functions from separate files using DataFrames, developers gain enhanced modularity and code reuse capabilities. This understanding promotes cleaner code organization, facilitating project management with improved maintainability and scalability over time.

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