Comments (4)
I believe the issue is not the equality operation but the filter
operation. I think @orlp is working on this.
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I don't know, running test_lazy.with_columns(pl.col("carrier") == "N802UA").collect()
instead of test_lazy.filter(pl.col("carrier") == "N802UA").collect()
also takes ~30sec
from polars.
You have't isolated the reading form the performance comparison. I suspect the filter
and comparisons
are red herrings and it is the reading of string data.
This should be fixed by: #14705
When making examples (on performance) it is best to isolate the operation you are benchmarking.
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Confirmed that #14705 fixes the pathological regression.
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Related Issues (20)
- rename π to selected_columns and σ to filters in logal graph dot? HOT 4
- Add `polars.Expr.list.drop_nans()` HOT 2
- `read_parquet` don't recognize OSS url scheme
- Not clear if Parquet statistics are used when filter applied
- `bottom_k` should not include nulls if the column contains at least `k` valid elements
- Construct CsvReader from bytestream using CsvReadOptions
- segfault when reading msql using arrow-odbc HOT 1
- `implode` results in extra level of nesting when run within a `group_by(...).agg` HOT 2
- Support reading directly from zipfile.Path objects.
- Read_json panics when infer_schema_length = 0
- `explain(streaming=True)` isn't showing correct plan
- Data in csv files with less columns than schema shifts data. HOT 4
- Add the argument `ignore_nulls` in `.arr.all()`, `.arr.any()`, `.list.all()` and `.list.any()`
- read_database_uri panics for dates beyond 2262.04.11 HOT 2
- Move streaming engine original plan to separate field on the `IRPlan`
- Write upgrade guide for 1.0.0
- Polars is unable to parse dates beyond 2262.04.11 HOT 1
- Make a ParquetWriter context handler and/or more control over row group creation
- Casting to float32, int32, int16 and int8 in polars is slower than pandas in larger dfs HOT 4
- Interpolate based on other Float64 column HOT 3
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