Comments (2)
I think your %timeit
example will only time 1 cast as it is modifying the Series in-place?
Just timing the casts, Polars is faster for me:
import pandas as pd
df = pd.read_parquet("15477.parquet")
%timeit df["group"].astype("category")
# 1.81 s ± 25.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
import polars as pl
df = pl.read_parquet("15477.parquet")
%timeit df.with_columns(pl.col("group").cast(pl.Categorical))
# 1.02 s ± 4.78 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
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@cmdlineluser Youre right. I dont know why my tests showed otherwise
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Related Issues (20)
- Transpose option for `DataFrame.describe()` HOT 1
- Adding “Rounding half to even”
- `the name 'literal' is duplicate` when selecting a multi-element NumPy array or list
- Using pl.write_parquet() gives wrong results for values inside lists. HOT 12
- Concat of columns with lists of objects raises error HOT 3
- Append scalar column to list column HOT 2
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- Make `int_range()` and `int_ranges()` work with no inputs and default to `int_range(0, pl.len())` HOT 7
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- `scan_csv`does not support a list of datatypes in `schema_overrides` HOT 1
- `read_csv` and `read_ipc` do not use native `storage_options` configuration keys
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- Arithmetic with nested arrays gives wrong results HOT 2
- Rename: Support for ignore_missing parameter HOT 1
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- `pl.col(col).last()` example shows both "max" and "last" behaviour HOT 1
- `any_horizontal` and `all_horizontal` treat null values differently than their vertical counterpart HOT 2
- Allow `os.PathLike` objects in `read_*` and `scan_*` functions HOT 1
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