Randomly Splitting a Grouped DataFrame in Python

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In this article, we’ll explore how to randomly split a grouped DataFrame in Python. We’ll start with an overview of the problem and then dive into the solution.

Problem Overview

Suppose you have a DataFrame containing player information, including player IDs, years played, and overall scores. You want to split your data into training and testing sets, ensuring that the two sets don’t share any player IDs.

For example:

We’ll show you how to randomly shuffle the rows of this DataFrame, grouped by player ID, and then split it into two subsets with a specified ratio (e.g., 80% for training and 20% for testing).

Solution

To solve this problem, we can follow these steps:

1. Shuffle the rows: Use the sample function to randomly shuffle the rows of the DataFrame.
2. Group by player ID: Use the groupby function to group the shuffled data by player ID.
3. Sample the groups: Use the sample function again to sample a subset of rows from each group, with a specified ratio (e.g., 0.8 for training).
4. Split the data: Split the shuffled data into two subsets: one for training and one for testing.

Here’s the Python code that implements these steps:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    "player_id": [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6],
    "year": [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],
    "overall": [20, 16, 7, 3, 8, 80, 20, 12, 9, 3, 2, 1]
})

# Shuffle the rows
shuffled_df = df.sample(frac=1).reset_index(drop=True)

# Group by player ID and sample a subset of rows
test_ids = shuffled_df["player_id"].drop_duplicates().sample(frac=0.2).values
train_data = shuffled_df[~shuffled_df["player_id"].isin(test_ids)]

# Split the data into training and testing sets
test_data = shuffled_df[shuffled_df["player_id"].isin(test_ids)]

Explanation

Here’s a step-by-step breakdown of what’s happening in the code:

1. We create a sample DataFrame using pd.DataFrame.
2. We shuffle the rows of the DataFrame using sample(frac=1). This randomly rearranges the rows, ensuring that the original order is lost.
3. We drop duplicates from the player IDs and sample 20% of these unique values to create the test set. The remaining players will be in the training set.
4. We split the shuffled data into two subsets: train_data (training set) and test_data (testing set).

Example Use Cases

Here are some example use cases for this technique:

• Sports Analytics: Suppose you want to predict a player’s overall score based on their past performance. You can use this method to split your data into training and testing sets, ensuring that the two sets don’t share any player IDs.
• Customer Segmentation: If you’re building a customer segmentation model, you can use this technique to randomly shuffle your data and then split it into training and testing sets.

Advice

When working with large datasets, it’s essential to ensure that your random shuffling and sampling processes are properly implemented. Here are some tips:

• Use a secure random number generator: Make sure to use a cryptographically secure random number generator (RNG) when generating test IDs.
• Avoid over-sampling or under-sampling: Be cautious not to over-sample or under-sample your data, as this can lead to biased models.

By following these steps and tips, you’ll be able to randomly split your grouped DataFrame in Python and create balanced training and testing sets for your machine learning model.

Last modified on 2024-05-14