Checking a Pandas pipeline before moving it to Polars
I have seen migration discussions start with a speed comparison. The harder question for an existing pipeline is often what the code already assumes about Pandas. An implicit index, row iteration, or a Python callback can shape the whole design. I built polars-ready to expose those calls before anyone starts replacing imports. The CLI accepts a Python file, notebook, or directory. It parses…
When evaluating the suitability of migrating an existing Pandas pipeline to Polars, it is crucial to thoroughly examine the code for any assumptions specific to the Pandas library. A new tool called polars-ready has been developed to assist in this process. This command-line interface scans a Python file, notebook, or directory, parsing the source code without importing the actual code. It generates a detailed report listing recognized operations, their source lines, categories, and suggested Polars alternatives.
The report provides valuable insights into the pipeline's structure, highlighting any explicit indexing, row iteration, or Python callbacks that may impact the migration process. For example, if the pipeline uses operations like set_index() followed by resample(), the report flags these actions for closer review. Polars operates differently than Pandas, as it keeps keys as columns and employs dynamic grouping for time windows, which may require adjustments to the original code.
While the percentage at the top of the report indicates the number of recognized calls classified as direct, it is essential to understand that the tool does not guarantee a seamless migration. The converted code may not produce identical outputs, nor does it ensure improved performance. The tool serves as a source inventory, providing a comprehensive list of Pandas-specific operations that need to be carefully considered during the migration.
The scanner used by polars-ready can sometimes miss certain pandas constructs, such as frames passed through functions or stored in containers. Additionally, it may report unrelated methods if a tracked variable name is reused. Notebook cells starting with shell commands or IPython magic are excluded from the scan, while other parse errors are included in the report.
To ensure accurate results, the tool includes a reproducible sales pipeline fixture and comprehensive tests for alias handling, chained calls, notebook locations, and unrelated method names. The project is licensed under MIT, making it freely available for use and modification. The repository can be found at https://github.com/Arthur031221/polars-ready.
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