Pandas Parquet Feature: Fixing Version Compatibility Issues

Aug 15, 2025 by Mei Lin 60 views

Pandas `[parquet]` Feature Availability: A Version Compatibility Deep Dive

Hey everyone! Today, we're diving into a crucial compatibility issue concerning the [parquet] extra feature in Pandas, specifically how it interacts with different versions of the library and its implications for projects like Evidently AI. This is super important for anyone working with data analysis and machine learning in Python, so let's get started!

Understanding the Pandas `[parquet]` Feature

The Pandas library is a cornerstone of data manipulation and analysis in Python. One of its powerful features is the ability to read and write data in various formats, including the efficient Parquet format. The [parquet] extra feature simplifies the process of working with Parquet files by bundling the necessary dependencies, like pyarrow, directly with Pandas. This means you don't have to install pyarrow separately, making your environment setup cleaner and easier.

However, here's the catch: this convenient [parquet] extra feature isn't available in all versions of Pandas. It was introduced in version 2.0.0. This means if you're using an older version of Pandas (specifically, versions 1.x), you won't be able to use the pandas[parquet] syntax when specifying dependencies. Trying to do so will result in errors, as the system won't recognize the [parquet] extra.

This version-specific availability can lead to dependency resolution issues, especially when working with projects that rely on Pandas and Parquet functionality. For instance, if a project specifies pandas[parquet]>=1.3.5 as a dependency, it will work seamlessly with Pandas 2.0.0 and later. However, if someone tries to install this project with Pandas 1.x, the installation will fail because the [parquet] extra doesn't exist in those older versions. This is exactly the kind of problem we're going to explore in the context of Evidently AI.

To avoid these issues, it's crucial to be aware of the Pandas version you're using and how it affects your ability to utilize the [parquet] extra feature. When specifying dependencies, you might need to use conditional requirements to ensure compatibility across different Pandas versions. For example, you could specify pandas[parquet]>=2.0.0 for Pandas 2.0.0 and later, and a separate requirement for pandas>=1.3.5,<2 along with pyarrow for older versions. This ensures that the necessary dependencies are installed correctly, regardless of the Pandas version in use. By understanding these nuances, you can prevent installation errors and ensure a smooth experience when working with Pandas and Parquet data.

The Issue in Evidently AI's Dependencies

Recently, a user pointed out an issue in the dependency definitions for the Evidently AI library. In a previous update (#885), the Pandas dependency was changed from pandas>=1.3.5 to pandas[parquet]>=1.3.5. The intention behind this change was to streamline the installation process by including pyarrow (which is essential for Parquet support) as part of the Pandas installation, rather than requiring it as a separate dependency. This is a great idea in principle, as it simplifies the user experience and reduces the potential for dependency conflicts.

However, as we discussed earlier, the [parquet] extra feature is only available in Pandas versions 2.0.0 and later. This means that specifying pandas[parquet]>=1.3.5 as a dependency creates a compatibility problem for users who are using Pandas versions 1.x. When they try to install Evidently AI, the dependency resolver will look for the [parquet] extra in Pandas 1.x, which doesn't exist. This leads to an error during the installation process, preventing users from using Evidently AI.

The user who reported the issue provided a clear and concise explanation, along with a screenshot demonstrating the error. The screenshot clearly shows that the [parquet] extra is not recognized in Pandas version 1.5.3, which is the latest 1.x release. They also included links to the Pandas GitHub repository, showcasing the relevant code sections in setup.cfg (for version 1.5.3) and pyproject.toml (for version 2.0.0) that confirm the absence of the [parquet] extra in older versions. This level of detail is incredibly helpful for the Evidently AI team, as it provides them with all the necessary information to understand and address the issue effectively.

This situation highlights the importance of carefully considering version compatibility when defining dependencies. While the intention behind using the [parquet] extra was good, it inadvertently introduced a compatibility issue for users with older Pandas versions. To resolve this, the Evidently AI team needs to either revert the change or implement a more intelligent dependency management strategy. This could involve using conditional requirements, as we discussed earlier, to specify different dependencies based on the Pandas version. By addressing this issue promptly, the Evidently AI team can ensure a smooth and consistent installation experience for all users, regardless of their Pandas version.

Proposed Solutions and Handling the Issue

Okay, so we've identified the problem: specifying pandas[parquet]>=1.3.5 as a dependency in Evidently AI causes issues for users with Pandas versions 1.x. Now, let's explore some potential solutions and how the Evidently AI team can handle this situation effectively. There are a couple of main approaches they can take:

1. Reverting the Change

The simplest solution would be to revert the change that introduced the pandas[parquet]>=1.3.5 dependency. This means going back to the original dependency definition of pandas>=1.3.5. This would immediately resolve the installation errors for users with Pandas 1.x. However, it also means that users would need to manually install pyarrow if they want to work with Parquet files. This isn't ideal, as it adds an extra step to the installation process and could lead to confusion for some users.

Despite this drawback, reverting the change might be the most pragmatic approach in the short term, especially if the Evidently AI team wants to quickly address the issue and ensure that users can install the library without encountering errors. It buys them time to develop a more robust solution that addresses the underlying compatibility problem without introducing new issues.

2. Implementing Conditional Dependencies

A more sophisticated solution involves using conditional dependencies. This allows the Evidently AI team to specify different dependencies based on the Pandas version. In this case, they could use a syntax like this:

pandas[parquet]>=2.0.0
pandas>=1.3.5,<2; pyarrow

This effectively says: "If Pandas version is 2.0.0 or later, use pandas[parquet]. Otherwise, if Pandas version is between 1.3.5 and 2.0.0, require both pandas and pyarrow." This approach ensures that the correct dependencies are installed for all Pandas versions, providing a seamless experience for users.

Implementing conditional dependencies requires a bit more effort, as it involves understanding the syntax and capabilities of the dependency management tool being used (e.g., pip, conda). However, it's a more robust solution in the long run, as it addresses the root cause of the problem and avoids the need for manual intervention by users.

Handling the Transition

Regardless of which solution the Evidently AI team chooses, it's crucial to communicate the changes clearly to users. This could involve:

Updating the documentation: Clearly explain the Pandas version compatibility and how to install Evidently AI with different Pandas versions.
Providing informative error messages: If a user tries to install Evidently AI with an incompatible Pandas version, the error message should clearly explain the issue and suggest a solution.
Releasing a new version: Once the issue is resolved, release a new version of Evidently AI that includes the fix. This will ensure that new users don't encounter the problem.

By taking these steps, the Evidently AI team can ensure a smooth transition and maintain a positive user experience. The key is to be proactive, communicate clearly, and provide users with the information they need to successfully install and use Evidently AI.

Conclusion: Version Compatibility Matters!

Alright, guys, we've covered a lot today! We've seen how a seemingly small change in dependency management – switching to pandas[parquet] – can have significant consequences if version compatibility isn't carefully considered. This issue highlights the importance of understanding the nuances of library versions and how they interact with each other.

For the Evidently AI team, this is a valuable learning experience. By addressing this issue effectively, they can not only fix the immediate problem but also improve their overall dependency management strategy. This will lead to a more robust and user-friendly library in the long run. And for us as users, this serves as a reminder to always pay attention to version compatibility and to be mindful of the potential pitfalls when specifying dependencies.

Remember, software development is a collaborative effort. By reporting issues and providing clear and detailed information, we can all contribute to building better tools and libraries. So, keep exploring, keep learning, and keep those bug reports coming!