Add initial Arrow PyCapsule support #517
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jonmmease
commented
Oct 12, 2024
| for i in range(20): | ||
| # Break cache by removing one row each iteration | ||
| movies_inner = movies.iloc[i:] | ||
| movies["Title"] = movies["Title"].astype(str) |
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wish polars/pyarrow could handle loading mixed str/num columns from pandas
jonmmease
commented
Oct 12, 2024
| func: Arc::new((*STR_TO_UTC_TIMESTAMP_UDF).clone()), | ||
| args: vec![field_col, lit(default_input_tz)], | ||
| }) | ||
| } |
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I'm sure there are a lot more of these that will need to be updated, but just did this one to get tests passing
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Supersedes #501 now that everything needed has been updated.
Adopts arro3 / pyo3-arrow / narwhals for managing DataFrames in Python and the passing to Rust in arrow format with zero copy.
Uses
get_column_usageto determine which columns are needed for each input inline dataset, and then uses narwhals to downproject prior to wrapping in anarro3table to pass to rust. This approach removes the need for the Python data source framework, so I removed this all together.Performance of this workflow is improved from prior handling of pandas/polars, and is faster than the duckdb code path in my limited testing. We'll want to do more comprehensive benchmarks before release.