How to Return Multiple Dataframes Using @ pandas_udf in Pyspark

Learn how to create a function similar to `train_test_split` in Pyspark using `@ pandas_udf`. This article explains how to return multiple dataframes effectively.
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How to Return Multiple Dataframes Using @ pandas_udf in Pyspark

In the world of data science and analytics, efficiently handling data is a key requirement. Often, you might find yourself needing to split a dataset into training and testing segments. If you're using Pyspark and looking for a way to return multiple dataframes from a user-defined function, you're in the right place!

In this guide, we'll discuss how to create a function similar to train_test_split from Scikit-learn using Pyspark’s @ pandas_udf, which allows for effective manipulation of large datasets. We'll cover how to organize your data, perform the split, and return the results in a structured manner.

The Problem

You may have encountered a situation where you need to use the train_test_split function to separate your dataset into different components: typically, features and labels, and further into training and testing sets. The challenge arises when you're using Pyspark with @ pandas_udf, as it only allows returning a single dataframe. The goal is to figure out how to return multiple dataframes (i.e., X_train, X_test, y_train, and y_test) from your UDF.

Understanding the Solution

To tackle this problem, let’s walk through the process step-by-step:

Using @ pandas_udf

We start by leveraging the @ pandas_udf decorator, which allows us to define a function that operates on a pandas DataFrame and returns a pandas DataFrame. For our purpose, we need to ensure that we set the correct schema and the PandasUDFType.

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Data Preparation

Before performing the split, you need to prepare your dataset:

Feature Columns: Identify the columns that will act as features in your model.

Label: Define the target variable you want to predict.

Example Code:

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Splitting the Dataset

Utilize the train_test_split function to divide your dataset effectively. Here, you can set your desired test size. A commonly used ratio is 80% training data and 20% testing data.

Example Code:

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Returning the Dataframes

To handle the situation where you want to return multiple dataframes, you can concatenate X_test and y_test into a single dataframe and return that. This allows you to work within the constraints of @ pandas_udf, which requires a single output, but still satisfies your requirements.

Example Code:

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In this implementation, instead of attempting to return X_train, X_test, y_train, and y_test separately, we return a concatenated dataframe containing the necessary data for further processing.

Conclusion

Implementing user-defined functions in Pyspark can be challenging, especially when handling data splits. By using the @ pandas_udf decorator efficiently, you can create a function that mimics Scikit-learn’s train_test_split. Even though Pyspark restricts outputs to single dataframes, strategic concatenation can help you achieve your objectives.

Next time you need to split your data in Pyspark, remember these steps and adapt your function accordingly. Happy coding!

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