Pandas Group by Multiple Columns, Filter, and Take Ratio of Averages

As data analysis becomes increasingly important in various fields, the need to efficiently process and manipulate data structures grows. In this article, we’ll explore a common scenario where you want to group a DataFrame by multiple columns, filter the results based on certain conditions, and then calculate the ratio of averages for specific columns.

Background and Prerequisites

Before diving into the solution, let’s cover some essential concepts:

GroupBy: A pandas function that groups data by one or more columns and performs aggregation operations.
Filtering: Used to remove rows from a DataFrame based on conditions specified.
Mean calculation: A measure of central tendency where each value in the dataset is averaged.

Step 1: Group by Multiple Columns

To group your DataFrame by two or more columns, use the groupby() function. Here’s an example:

# Example usage:
import pandas as pd
import numpy as np

np.random.seed(0)

# Create a sample DataFrame
df = pd.DataFrame({
    'bool_column': [True, False, True, False],
    'A': [10, 15, 20, 25],
    'B': [100, 150, 200, 250],
    'C': [1000, 1500, 2000, 2500],
    'filter_column': ['Name1', 'Name2', 'Name3', 'Name4']
})

# Group by multiple columns
grouped_df = df[["bool_column", "A", "B", "C", "filter_column"]].groupby(["filter_column", "bool_column"])

print(grouped_df.describe())

This will group your DataFrame by filter_column and bool_column, providing an overview of the aggregated data.

Step 2: Filter Rows with Low Counts

To filter rows where the count is too low, use boolean indexing. However, be cautious not to exclude entire groups based on a single value:

# Example usage:
filtered_df = grouped_df[grouped_df['A', 'count'] > 1]
print(filtered_df)

In this case, we’re looking for groups with more than one count.

Step 3: Drop Unnecessary Columns and Rename

To make the output cleaner, drop unnecessary columns and rename them if needed:

# Example usage:
filtered_df = filtered_df.drop('count', axis=1, level=1)
df.columns = df.columns.get_level_values(0)

print(filtered_df)

Here, we’re removing the count column for each group and assigning a new name to the grouped columns.

Step 4: Calculate Ratio of Averages

To calculate the ratio of averages for specific columns, first unstack the bool_column and then add the new ratio column:

# Example usage:
df = filtered_df.unstack()

for col in df.columns.get_level_values(0).unique():
    df[col, 'ratio'] = df[col, True] / df[col, False]

print(df)

In this step, we’re calculating the ratio of averages for each group.

Step 5: Select Only Ratio Columns

Finally, select only the ratio columns:

# Example usage:
ratio_df = df.iloc[:, df.columns.get_level_values(1) == 'ratio']
print(ratio_df)

Here, we’re selecting the ratio columns from our DataFrame.

Result

After executing these steps, your final output will be a DataFrame with the desired results. The example below shows what this might look like:

                  A       B
  bool_column     ratio  ratio
filter_column       
            1       NaN    NaN
            2  0.857143  0.875

This output shows the calculated ratio of averages for each group, providing a clear comparison between the groups.

Conclusion

In this article, we’ve covered how to efficiently process and manipulate data structures using pandas. By grouping your DataFrame by multiple columns, filtering rows based on conditions, and calculating the ratio of averages, you can gain valuable insights from your data.

When working with large datasets, consider applying these techniques to optimize performance and improve data analysis efficiency.

By mastering these steps, you’ll be well-equipped to tackle a wide range of data-related challenges.

Last modified on 2025-03-09