so they need to persist all the data in memory at once. They can't just persist the columns that are relevant to the query. Persisting all the columns causes certain queries to error out that wouldn't otherwise have issues if only 2 or 3 columns were persisted. ...
Polars’ high performance inspires me to explore how to speed up data processing of csv file. I prepare to compare Go and Rust if Polars can handle billion-rows with multiple dimensions for these big search commands: Distinct, Groupby, Filter, Sorting and JoinTable. Except filter, all other ...
Polars has anstr.extractmethod that can compare the above patterns to our text and (you guessed it) extract the matching groups. Here’s how you can apply it to theemails_plDataFrame. emails_pl = emails_pl.with_columns( # Extract the first match group as email pl.col("emails").str.e...
return df.with_columns(pl.col(pl.FLOAT_DTYPES).cast(pl.Float32)) @pytest.mark.parametrize("solve_method", ("qr", "svd", "chol", "lu", None)) def test_ols(solve_method: SolveMethod): df = _make_data() # compute OLS w/ polars-ols ...
The following two queries produce the same results def q1_polars_with_over(df): return df.with_columns( pl.col(TARGET).shift(lag).over("id").alias(f"{TARGET}_lag_{lag}") for lag in LAG_DAYS ) def q1_polars_with_explode(df): return df.group_by(TARGET).agg( pl.col(TARGET).shi...
.gitattributes feat: add performace compare Jun 5, 2024 .gitignore refactor: bench and clean Jan 1, 2024 Cargo.toml fix: f32 convert to f64 pass all func Nov 8, 2024 LICENSE Initial commit Dec 29, 2023 Makefile feat: init polars ta extension Dec 30, 2023 README.md feat: add perfor...