Fixing Pandas 2.2.3 Misbehavior With Python 3.11 And Spack-Stack 1.9.2

Aug 2, 2025 by ADMIN 71 views

Pandas 2.2.3 Misbehavior Under Python 3.11 and Spack-Stack 1.9.2

Hey everyone! Let's dive into an interesting issue we've encountered with Pandas 2.2.3 when running under Python 3.11 within the Spack-Stack 1.9.2 environment. It seems like there's an incompatibility that's causing some headaches, especially with existing JEDI IODA converters. Let's break down the problem, how to reproduce it, and the solution we've found.

The Issue: Pandas 2.2.3 and Python 3.11 Incompatibility

So, what's the deal? The core issue is that pandas 2.2.3 isn't playing nicely with Python 3.11. Python 3.11 brought some awesome performance and safety enhancements, but these improvements also expose some latent index inconsistencies in libraries like Pandas. Specifically, mask-based indexing, which you might know as df.loc[boolean_mask], has become stricter. Now, any misalignment or invalid entries—think leftover references to non-existent indices—will throw an IndexError: indices are out-of-bounds error. The thing is, pandas was more forgiving about this in Python 3.10, so this is a new challenge we need to address.

To put it simply, the enhanced checks in Python 3.11 are revealing underlying issues in how Pandas 2.2.3 handles indexing in certain scenarios. This often manifests when dealing with operations that involve boolean masks and index alignment. Think of it like this: Python 3.11 is a stricter teacher, and it's pointing out some areas where Pandas 2.2.3 needs to improve its indexing game. The good news is, this increased strictness ultimately leads to more robust and reliable code, but we need to make the necessary adjustments to take advantage of it. We need to ensure that our dataframes are squeaky clean and our indexing operations are precise.

One common scenario where this issue pops up is when you're working with data that has undergone transformations or filtering, which can sometimes lead to index misalignment. For example, dropping duplicate rows or merging dataframes can inadvertently create situations where the index is no longer perfectly aligned with the data. This is where the stricter validation in Python 3.11 comes into play, catching these subtle inconsistencies that might have slipped under the radar in earlier versions. The key takeaway here is that we need to be extra vigilant about ensuring index integrity when working with Pandas in Python 3.11, and upgrading to a more compatible Pandas version is a crucial step in that direction. This upgrade isn't just about fixing a bug; it's about embracing a more robust and reliable approach to data manipulation.

Digging Deeper: Why is this happening?

Let’s get a little more technical about why this pandas and Python 3.11 issue is occurring. At its heart, this incompatibility stems from changes in how Python 3.11 handles memory and indexing operations, particularly when it comes to boolean masks. Boolean masks are a fundamental part of Pandas, allowing us to select subsets of data based on conditions. They are incredibly powerful, but they also rely on precise alignment between the mask and the data's index.

In Python 3.11, the internal mechanisms for handling these masks have been tightened up. This means that if a boolean mask doesn't perfectly align with the DataFrame's index, or if the mask contains any invalid references (for example, an index that no longer exists), Python 3.11 will raise an IndexError. This is a deliberate design choice to prevent potential data corruption or unexpected behavior that could arise from misaligned indexing. Think of it as a safety net that prevents you from accidentally operating on the wrong data.

Pandas 2.2.3, while a solid library in its own right, was not fully designed with these stricter checks in mind. As a result, certain operations that involve mask-based indexing, such as dropping duplicates or selecting subsets of data, can trigger the IndexError in Python 3.11. This doesn't necessarily mean that Pandas 2.2.3 is inherently buggy; it simply means that it was built under a slightly different set of assumptions about how indexing should behave. The key difference lies in the level of validation performed during indexing operations.

To illustrate this, imagine you have a DataFrame with an index that has some gaps or missing values. In older versions of Python, Pandas might have been more lenient and allowed you to perform operations that referenced these missing indices. However, in Python 3.11, the stricter checks will catch these inconsistencies and raise an error. This can be a bit frustrating at first, but it ultimately leads to more robust code because it forces you to address these indexing issues explicitly. The upgrade to Pandas 2.3.1 resolves this by incorporating these stricter checks internally and handling index misalignment more gracefully.

The Culprit: JEDI IODA Converter

One specific area where this issue reared its head is with the JEDI IODA converter, particularly this file: https://github.com/JCSDA-internal/ioda-converters/blob/develop/src/compo/airnow2ioda_nc.py. This converter, which is crucial for our data processing pipeline, started throwing errors when running under the Spack-Stack 1.9.2 environment due to the Pandas 2.2.3 and Python 3.11 clash.

Decoding the Error Message

The error message itself is pretty telling, but let's break it down so everyone's on the same page. The traceback you'll see looks something like this:

df = df.drop_duplicates()
         ^^^^^^^^^^^^^^^^^^^^
  File "/autofs/ncrc-svm1_proj/epic/spack-stack/c6/spack-stack-1.9.2/envs/ue-intel-2023.2.0/install/intel/2023.2.0/py-pandas-2.2.3-4bngjxo/lib/python3.11/site-packages/pandas/core/frame.py", line 6818, in drop_duplicates
    result = self[-self.duplicated(subset, keep=keep)]
             ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/autofs/ncrc-svm1_proj/epic/spack-stack/c6/spack-stack-1.9.2/envs/ue-intel-2023.2.0/install/intel/2023.2.0/py-pandas-2.2.3-4bngjxo/lib/python3.11/site-packages/pandas/core/frame.py", line 4093, in __getitem__
    return self._getitem_bool_array(key)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/autofs/ncrc-svm1_proj/epic/spack-stack/c6/spack-stack-1.9.2/envs/ue-intel-2023.2.0/install/intel/2023.2.0/py-pandas-2.2.3-4bngjxo/lib/python3.11/site-packages/pandas/core/frame.py", line 4155, in _getitem_bool_array
    return self._take_with_is_copy(indexer, axis=0)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/autofs/ncrc-svm1_proj/epic/spack-stack/c6/spack-stack-1.9.2/envs/ue-intel-2023.2.0/install/intel/2023.2.0/py-pandas-2.2.3-4bngjxo/lib/python3.11/site-packages/pandas/core/generic.py", line 4153, in _take_with_is_copy
    result = self.take(indices=indices, axis=axis)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/autofs/ncrc-svm1_proj/epic/spack-stack/c6/spack-stack-1.9.2/envs/ue-intel-2023.2.0/install/intel/2023.2.0/py-pandas-2.2.3-4bngjxo/lib/python3.11/site-packages/pandas/core/generic.py", line 4133, in take
    new_data = self._mgr.take(
               ^^^^^^^^^^^^^^^
  File "/autofs/ncrc-svm1_proj/epic/spack-stack/c6/spack-stack-1.9.2/envs/ue-intel-2023.2.0/install/intel/2023.2.0/py-pandas-2.2.3-4bngjxo/lib/python3.11/site-packages/pandas/core/internals/managers.py", line 891, in take
    indexer = maybe_convert_indices(indexer, n, verify=verify)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/autofs/ncrc-svm1_proj/epic/spack-stack/c6/spack-stack-1.9.2/envs/ue-intel-2023.2.0/install/intel/2023.2.0/py-pandas-2.2.3-4bngjxo/lib/python3.11/site-packages/pandas/core/indexers/utils.py", line 282, in maybe_convert_indices
    raise IndexError("indices are out-of-bounds")
IndexError: indices are out-of-bounds

See that IndexError: indices are out-of-bounds at the end? That's our culprit! This error specifically arises during the df.drop_duplicates() operation, which is a common task in data cleaning and preprocessing. When you drop duplicates, Pandas uses boolean masking under the hood to identify and remove the redundant rows. However, in this case, the stricter index validation in Python 3.11 flags an issue during this process.

Looking at the traceback, you can see that the error originates deep within Pandas' internal indexing mechanisms. The chain of calls leads from drop_duplicates() to __getitem__, _getitem_bool_array, _take_with_is_copy, and finally to maybe_convert_indices, where the IndexError is raised. This intricate path highlights how fundamental indexing is to Pandas operations and how a seemingly simple task like dropping duplicates can trigger these underlying issues.

The important thing to note is that this error isn't necessarily a bug in your code or the JEDI IODA converter itself. Instead, it's a symptom of the incompatibility between Pandas 2.2.3 and Python 3.11's stricter indexing behavior. This means that the solution isn't to rewrite the converter's logic but rather to address the underlying Pandas version.

How to Reproduce the Error

Want to see this in action yourself? Here's how you can reproduce the error:

Environment: You'll need access to an environment similar to gaea-c6, which is where this issue was initially observed.
Spack-Stack 1.9.2: Ensure you're using Spack-Stack version 1.9.2, as this version includes the problematic Pandas 2.2.3 and Python 3.11 combination.
Regression Test: Run the regression test for the airnow2ioda_nc.py converter script. This script is located here: https://github.com/JCSDA-internal/ioda-converters/blob/develop/src/compo/airnow2ioda_nc.py.

When you run the test, you should see the IndexError: indices are out-of-bounds error pop up, confirming the incompatibility we've been discussing. This reproduction step is crucial because it allows us to verify that the issue is indeed present and that our proposed solution effectively resolves it. It's also a good practice to have a reproducible test case for any bug or issue you encounter, as this makes it easier to debug, fix, and prevent regressions in the future.

By following these steps, you can create a controlled environment where the error is consistently triggered, giving you a clear understanding of the problem and allowing you to validate the effectiveness of the fix. This is especially important in complex scientific workflows where subtle changes in libraries or environments can have significant impacts on the results. Being able to reproduce the error reliably is the first step towards a robust and reliable solution.

The Solution: Upgrade Pandas and Include pyresample

Okay, so we've identified the problem and how to reproduce it. Now, let's talk solutions! The key fix here is to upgrade Pandas to version 2.3.1 or later. In our testing, we found that Pandas 2.3.1 resolves this incompatibility with Python 3.11, effectively squashing the IndexError.

The reason this works is that the Pandas team was aware of these issues with Python 3.11 and made the necessary adjustments in the 2.3.x release line. These adjustments involve changes to how Pandas handles indexing and boolean masking, making it more robust and compatible with the stricter checks in Python 3.11. By upgrading, you're essentially bringing in these fixes and ensuring that Pandas plays nicely with the underlying Python environment. Think of it as a tune-up for your data manipulation engine, ensuring smooth and reliable operation.

But that's not all! While we're at it, there's another opportunity to improve our environment. We also need to include the pyresample package in the new Spack-Stack update. pyresample is a fantastic library for handling geospatial resampling, and it's a valuable tool for many of our data processing tasks. By adding it to the stack, we'll make it readily available to everyone, streamlining workflows and reducing the need for individual installations. This is all about making our lives easier and our workflows more efficient.

Why pyresample Matters

For those who aren't familiar with pyresample, it's worth taking a moment to appreciate what it brings to the table. In the world of geospatial data, we often deal with datasets that are on different grids or resolutions. Resampling is the process of transforming data from one grid to another, and it's a crucial step in many scientific workflows. pyresample makes this process much easier and more efficient by providing a high-level interface for various resampling techniques. It can handle a wide range of projections and grid types, making it a versatile tool for working with diverse geospatial datasets. By including pyresample in our Spack-Stack, we're empowering our users with a powerful tool that can significantly simplify their data processing pipelines.

Expected Behavior After the Upgrade

So, what should you expect once you've upgraded to Pandas 2.3.1 and included pyresample? The most immediate and noticeable change will be the disappearance of the IndexError. The JEDI IODA converter, along with any other code that was previously triggering this error, should now run smoothly under Python 3.11. This is a huge win because it unblocks our workflows and allows us to continue processing data without interruption. But the benefits go beyond just fixing a bug.

With the Pandas upgrade, you're also getting access to the performance improvements and new features that come with version 2.3.1. These enhancements can make your data manipulation tasks faster and more efficient, further boosting your productivity. And with pyresample now readily available, you'll have a powerful tool for geospatial resampling at your fingertips, simplifying your work with gridded data. This is a big step forward in terms of both stability and functionality.

In essence, this update is about more than just fixing a bug; it's about enhancing our overall data processing capabilities. By addressing the Pandas incompatibility and including pyresample, we're creating a more robust, efficient, and user-friendly environment for everyone. This means less time spent troubleshooting errors and more time focused on the science.

To Reproduce (the fix):

After the upgrade, you can verify the fix by following the same reproduction steps outlined earlier. The regression test for the airnow2ioda_nc.py converter should now pass without any errors. This is the ultimate confirmation that the issue has been resolved and that our environment is back on track. It's always a good practice to have a clear and repeatable way to verify fixes, as this gives us confidence that we've truly addressed the problem and haven't introduced any new issues in the process.

In addition to running the regression test, you should also consider testing any other code or workflows that were previously affected by the Pandas incompatibility. This will help ensure that the fix is comprehensive and that all affected areas are working as expected. The more thorough you are in your testing, the more confident you can be in the stability and reliability of the updated environment.

System and Context

This issue was observed on gaea-c6, which is our primary testing environment. This highlights the importance of having dedicated testing environments where we can catch these kinds of incompatibilities before they impact production systems. Testing in a controlled environment allows us to isolate issues, reproduce them reliably, and validate fixes effectively. It's a crucial part of our development process and helps us ensure the quality and stability of our software stack.

The context here is that we're using Spack-Stack to manage our software dependencies, which is a fantastic tool for ensuring reproducibility and consistency across different environments. However, even with Spack-Stack, it's important to stay on top of library updates and potential incompatibilities, as we've seen with this Pandas issue. This is why it's so valuable to have a community that's actively engaged in testing, reporting issues, and collaborating on solutions. Together, we can ensure that our software stack remains robust and reliable.

Shout out to @benkozi for bringing this to our attention and helping us get it resolved! Your contributions are greatly appreciated. This collaborative approach is what makes our community so strong and effective. By sharing our experiences and working together, we can tackle these challenges head-on and build a better environment for everyone.

In Summary

Alright, let's recap what we've covered: We've identified an incompatibility between Pandas 2.2.3 and Python 3.11 within the Spack-Stack 1.9.2 environment. This incompatibility manifests as an IndexError: indices are out-of-bounds error, particularly when running the JEDI IODA converter. We've shown how to reproduce the error and, most importantly, we've outlined the solution: upgrade Pandas to version 2.3.1 and include the pyresample package in the new Spack-Stack update.

This update will not only fix the immediate issue but also provide us with a more robust and feature-rich environment for data processing. By staying proactive and addressing these kinds of issues promptly, we can ensure that our workflows remain smooth and efficient. Remember, the key to a healthy scientific computing environment is continuous monitoring, testing, and collaboration.

So, let's get this upgrade rolling and keep pushing the boundaries of what's possible with our data!