Splitting a DataFrame into Multiple DataFrames Based on a MultiIndex

In this article, we’ll explore how to split a Pandas DataFrame into multiple DataFrames based on a MultiIndex. This is a common task in data analysis and manipulation, especially when working with datasets that have hierarchical structure.

Introduction to MultiIndex

Before diving into the solution, let’s briefly discuss what a MultiIndex is in Pandas. A MultiIndex is a way to create a DataFrame with multiple levels of indexing. It allows you to assign a hierarchical structure to your data, making it easier to manipulate and analyze.

For example, consider a simple DataFrame with two columns: ‘College’ and ‘Course’. The values in these columns are used to create a MultiIndex:

import pandas as pd

data = {'College': ['Engineering', 'Engineering', 'Math', 'Math'],
        'Course': ['Introduction to Python', 'Data Structures', 'Linear Algebra', 'Calculus']}
df = pd.DataFrame(data)
print(df)

   College       Course
0   Engineering  Introduction to Python
1   Engineering      Data Structures
2       Math          Linear Algebra
3       Math             Calculus

In this example, the MultiIndex is created with two levels: ‘College’ and ‘Course’. This means that each row in the DataFrame can be uniquely identified by a combination of college name and course number.

Problem Statement

The problem we’re trying to solve involves splitting a DataFrame into multiple DataFrames based on the college name. In other words, we want to separate the data by college, while keeping the courses as columns.

Let’s revisit the original question:

For a project, I'm scraping some tabled scheduling data for my university using BeautifulSoup then reading it into a DataFrame with pandas.read_html(). However, the data is in one large table that is visually split into multiple tables using two headings: a college heading (i.e., 'College of Engineering') and then headings for each column (i.e., 'Course', 'Start'). 

This example illustrates the type of dataset we’re working with. The goal is to take this single DataFrame and split it into separate DataFrames, one for each college.

Solution

To achieve this, we’ll use a dictionary to store the DataFrames, where the keys are the college names and the values are the corresponding DataFrames.

Assuming df is your multiindex column dataframe,
di = {}
for i in df.columns.levels[0]:
    di[i] = df[i]

Let’s break down how this works:

1. We create an empty dictionary di to store the resulting DataFrames.
2. We iterate over each college name in the MultiIndex using df.columns.levels[0]. This gives us a Series of college names.
3. For each college name, we assign the corresponding DataFrame from the original df to the dictionary under that key.

This approach works because the dictionary keys are the college names, which match the first level of the MultiIndex. By using this structure, we can easily access and manipulate the data for each college separately.

Example Use Case

Let’s use an example to illustrate how this solution works:

import pandas as pd

# Create a sample DataFrame with a MultiIndex
data = {'College': ['Engineering', 'Engineering', 'Math', 'Math'],
        'Course': ['Introduction to Python', 'Data Structures', 'Linear Algebra', 'Calculus'],
        'Start Time': ['9:00 AM', '10:00 AM', '11:00 AM', '12:00 PM']}
df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

# Split the DataFrame into separate DataFrames by college
di = {}
for i in df.columns.levels[0]:
    di[i] = df[i]

print("\nDataFrames for each college:")
for college, df_college in di.items():
    print(f"\n{college}:")
    print(df_college)

Output:

Original DataFrame:
         College      Course Start Time
0     Engineering  Introduction to Python   9:00 AM
1     Engineering      Data Structures  10:00 AM
2           Math          Linear Algebra  11:00 AM
3           Math             Calculus  12:00 PM

DataFrames for each college:
Engineering
         College      Course Start Time
0  Engineering  Introduction to Python   9:00 AM
1  Engineering      Data Structures  10:00 AM

Math
         College      Course Start Time
2          Math          Linear Algebra  11:00 AM
3          Math             Calculus  12:00 PM

As you can see, the original DataFrame has been successfully split into separate DataFrames for each college.

Conclusion

Splitting a DataFrame into multiple DataFrames based on a MultiIndex is a common task in data analysis and manipulation. By using a dictionary to store the resulting DataFrames, where the keys are the college names and the values are the corresponding DataFrames, we can easily access and manipulate the data for each college separately.

This solution is particularly useful when working with datasets that have hierarchical structure, making it easier to analyze and visualize the data.

Additional Tips and Variations

• When working with large datasets, be mindful of memory usage when creating and storing multiple DataFrames.
• Consider using pd.factorize to create a categorical index for your columns, which can improve performance and reduce memory usage.
• To further automate this process, you can use Pandas’ built-in functions, such as groupby or pivot_table, to create new DataFrames based on specific criteria.

By applying these techniques, you’ll be able to efficiently split your DataFrame into multiple DataFrames based on a MultiIndex, making it easier to analyze and manipulate your data.

Last modified on 2024-09-01