How to Preserve a Fresh Copy of a DataFrame Between Pytest Tests

Описание: Learn how to effectively use fixtures in Pytest to maintain a fresh copy of a file without repeatedly reading from disk. Optimize your DataFrame testing process now!
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How to Preserve a Fresh Copy of a DataFrame Between Pytest Tests

When writing tests for your data manipulation functions, it’s often required to use a dataset that remains consistent across tests. However, repeatedly reading a large file from disk can significantly slow things down. If you are using the Pandas library with Pytest and want to leverage a copy of a DataFrame in your tests without incurring the overhead of additional disk reads, you're in the right place!

In this guide, we will uncover the solution to preserving a fresh version of a DataFrame for each test while ensuring that it only reads from the disk once. Let’s get into the details!

Understanding the Problem

Imagine you have a large dataset stored in a CSV file, which you want to load into a Pandas DataFrame. You might wish to send this DataFrame to multiple tests while confirming its properties without loading it repeatedly from the disk. This not only saves time but also makes your tests run more efficiently.

The Solution: Utilizing Pytest Fixtures

The key to solving this issue lies in using fixtures provided by Pytest. Fixtures allow you to define a setup for your tests, ensuring that you can reuse the same setup code in multiple places efficiently. Here’s a breakdown of how you can achieve this:

Step 1: Create a Session-Scoped Fixture for the Main DataFrame

The first step involves creating a fixture that reads the DataFrame once and maintains it for the duration of the testing session. This session-scoped fixture ensures that the DataFrame only loads from disk once.

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scope="session" means this fixture is created once per test session and remains available until the tests complete.

Step 2: Create a Function-Scoped Fixture for Copies

Next, you will create a fixture that uses the main fixture to generate fresh copies of the DataFrame for each test. This means that each test gets a new copy of the data, preserving the original data and allowing for modifications during tests without affecting the others.

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This fixture is scoped to each test function, ensuring that a deep copy of the DataFrame is provided to each test individually.

Step 3: Writing the Tests

Now that you have set up your fixtures, you can easily write tests that utilize the new get_df_copy fixture. Here's how you can check the properties of your DataFrame:

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In these test cases, both tests access their own fresh copy of the DataFrame, ensuring that any changes in one test do not affect the other.

Conclusion

By using session-scoped and function-scoped fixtures effectively, you can significantly optimize your testing procedure with Pytest. Not only does this method save time by preventing redundant disk reads, but it also ensures that your tests remain clean, isolated, and efficient.

Now you are ready to implement a strategy to maintain fresh copies of your DataFrames, allowing for rapid and reliable testing of your data processing functions. Happy testing!