Splitting a Pandas DataFrame into Two Parts: A Step-by-Step Guide

As data analysts and scientists, we often work with large datasets stored in Pandas DataFrames. When performing complex operations or filtering data, it’s essential to split the DataFrame into smaller parts to analyze, manipulate, or visualize each subset independently. In this article, we’ll explore a common use case: splitting a Pandas DataFrame into two separate DataFrames based on a given condition.

Introduction to Pandas and DataFrames

Pandas is a powerful library for data manipulation and analysis in Python. A DataFrame is a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet or a SQL database. DataFrames are the core data structure in Pandas, making it easy to store, manipulate, and analyze large datasets.

The Problem: Filtering and Splitting a DataFrame

In the given Stack Overflow question, the user is faced with a common problem when filtering DataFrames based on complex conditions. The current approach involves creating two separate DataFrames using loc and conditional statements:

dfcd = df.loc[(~df.Course_Code.str.contains('MG')) & (~df.Course_Code.str.contains('DE'))]
df = df.loc[(df.Course_Code.str.contains('MG')) | (df.Course_Code.str.contains('DE'))]

While this approach works, it can become cumbersome and error-prone as the conditions become more complex. We’ll explore a more elegant solution using Pandas’ built-in filtering capabilities.

Simplifying Filtering with Regular Expressions

One of the most powerful features in Pandas is its support for regular expressions (regex). By leveraging regex, we can simplify our filtering approach and create two separate DataFrames based on a given condition.

In the provided Stack Overflow answer, the user discovers that they can use the | operator to combine conditions using an “or” logic. Additionally, they can invert a condition by prefixing it with ~, which returns rows where the condition is false:

m = df.Course_Code.str.contains('MG|DE')

Creating Two Separate DataFrames

With our simplified filtering approach in hand, we can create two separate DataFrames using the following code:

df1, df2 = df[m], df[~m]

Here’s what’s happening:

• m is the result of filtering the original DataFrame using the condition 'MG|DE'. This returns a boolean mask indicating which rows meet the condition.
• By assigning m to df1, we create a new DataFrame containing only the rows where the condition is true.
• Similarly, by assigning ~m to df2, we create another DataFrame containing only the rows where the condition is false.

Example Use Case: Analyzing Course Codes

Suppose we have a DataFrame courses containing information about courses offered at a university:

import pandas as pd

data = {
    'Course_Code': ['MG123', 'DE456', 'ME789', 'BI101'],
    'Course_Name': ['Microbiology', 'Differential Equations', 'Mathematics', 'Biology']
}

courses = pd.DataFrame(data)
print(courses)

Output:

  Course_Code Course_Name
0      MG123     Microbiology
1      DE456  Differential Equations
2      ME789          Mathematics
3       BI101           Biology

Now, let’s create two separate DataFrames based on the course code containing either “MG” or “DE”:

m = courses.Course_Code.str.contains('MG|DE')

df_mg = courses[courses['Course_Code'].str.contains('MG')]
df_de = courses[~courses['Course_Code'].str.contains('MG')]

Output:

df_mg     Microbiology
0      MG123

df_de  Differential Equations
1      DE456

Conclusion

Splitting a Pandas DataFrame into two separate parts based on a given condition is a common task in data analysis. By leveraging regular expressions and Pandas’ built-in filtering capabilities, we can simplify our approach and create more efficient code.

In this article, we explored the use of | to combine conditions using an “or” logic, and how to invert a condition by prefixing it with ~. We also demonstrated how to create two separate DataFrames using these techniques. With practice and experience, you’ll become proficient in using Pandas to analyze and manipulate large datasets efficiently.

Additional Tips and Variations

• When working with complex conditions or multiple filters, consider using the .str.extract method instead of str.contains. This can provide more flexibility and accuracy.
• To create a DataFrame with all rows where the condition is true, use df[m] directly. No need to assign it to a variable!
• Experiment with different regex patterns and conditions to explore new ways of filtering your DataFrames.

I hope this article has provided you with valuable insights into splitting Pandas DataFrames efficiently. Happy data analysis!

Last modified on 2023-05-24