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fix(rust, python)!: return float dtype in interpolate (for method="li…
…near") for numeric dtypes (#11624)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,134 @@ | ||
| from __future__ import annotations | ||
|
|
||
| from datetime import date, datetime, time, timedelta | ||
| from typing import TYPE_CHECKING, Any | ||
|
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||
| import pytest | ||
|
|
||
| import polars as pl | ||
| from polars.testing import assert_frame_equal | ||
|
|
||
| if TYPE_CHECKING: | ||
| from polars.type_aliases import PolarsDataType, PolarsTemporalType | ||
|
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|
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| @pytest.mark.parametrize( | ||
| ("input_dtype", "output_dtype"), | ||
| [ | ||
| (pl.Int8, pl.Float64), | ||
| (pl.Int16, pl.Float64), | ||
| (pl.Int32, pl.Float64), | ||
| (pl.Int64, pl.Float64), | ||
| (pl.UInt8, pl.Float64), | ||
| (pl.UInt16, pl.Float64), | ||
| (pl.UInt32, pl.Float64), | ||
| (pl.UInt64, pl.Float64), | ||
| (pl.Float32, pl.Float32), | ||
| (pl.Float64, pl.Float64), | ||
| ], | ||
| ) | ||
| def test_interpolate_linear( | ||
| input_dtype: PolarsDataType, output_dtype: PolarsDataType | ||
| ) -> None: | ||
| df = pl.LazyFrame({"a": [1, None, 2, None, 3]}, schema={"a": input_dtype}) | ||
| result = df.with_columns(pl.all().interpolate(method="linear")) | ||
| assert result.schema["a"] == output_dtype | ||
| expected = pl.DataFrame( | ||
| {"a": [1.0, 1.5, 2.0, 2.5, 3.0]}, schema={"a": output_dtype} | ||
| ) | ||
| assert_frame_equal(result.collect(), expected) | ||
|
|
||
|
|
||
| @pytest.mark.parametrize( | ||
| ("input", "input_dtype", "output"), | ||
| [ | ||
| ( | ||
| [date(2020, 1, 1), None, date(2020, 1, 2)], | ||
| pl.Date, | ||
| [date(2020, 1, 1), date(2020, 1, 1), date(2020, 1, 2)], | ||
| ), | ||
| ( | ||
| [datetime(2020, 1, 1), None, datetime(2020, 1, 2)], | ||
| pl.Datetime("ms"), | ||
| [datetime(2020, 1, 1), datetime(2020, 1, 1, 12), datetime(2020, 1, 2)], | ||
| ), | ||
| ( | ||
| [datetime(2020, 1, 1), None, datetime(2020, 1, 2)], | ||
| pl.Datetime("us", "Asia/Kathmandu"), | ||
| [datetime(2020, 1, 1), datetime(2020, 1, 1, 12), datetime(2020, 1, 2)], | ||
| ), | ||
| ([time(1), None, time(2)], pl.Time, [time(1), time(1, 30), time(2)]), | ||
| ( | ||
| [timedelta(1), None, timedelta(2)], | ||
| pl.Duration("ms"), | ||
| [timedelta(1), timedelta(1, hours=12), timedelta(2)], | ||
| ), | ||
| ], | ||
| ) | ||
| def test_interpolate_temporal_linear( | ||
| input: list[Any], input_dtype: PolarsTemporalType, output: list[Any] | ||
| ) -> None: | ||
| df = pl.LazyFrame({"a": input}, schema={"a": input_dtype}) | ||
| result = df.with_columns(pl.all().interpolate(method="linear")) | ||
| assert result.schema["a"] == input_dtype | ||
| expected = pl.DataFrame({"a": output}, schema={"a": input_dtype}) | ||
| assert_frame_equal(result.collect(), expected) | ||
|
|
||
|
|
||
| @pytest.mark.parametrize( | ||
| "input_dtype", | ||
| [ | ||
| pl.Int8, | ||
| pl.Int16, | ||
| pl.Int32, | ||
| pl.Int64, | ||
| pl.UInt8, | ||
| pl.UInt16, | ||
| pl.UInt32, | ||
| pl.UInt64, | ||
| pl.Float32, | ||
| pl.Float64, | ||
| ], | ||
| ) | ||
| def test_interpolate_nearest(input_dtype: PolarsDataType) -> None: | ||
| df = pl.LazyFrame({"a": [1, None, 2, None, 3]}, schema={"a": input_dtype}) | ||
| result = df.with_columns(pl.all().interpolate(method="nearest")) | ||
| assert result.schema["a"] == input_dtype | ||
| expected = pl.DataFrame({"a": [1, 2, 2, 3, 3]}, schema={"a": input_dtype}) | ||
| assert_frame_equal(result.collect(), expected) | ||
|
|
||
|
|
||
| @pytest.mark.parametrize( | ||
| ("input", "input_dtype", "output"), | ||
| [ | ||
| ( | ||
| [date(2020, 1, 1), None, date(2020, 1, 2)], | ||
| pl.Date, | ||
| [date(2020, 1, 1), date(2020, 1, 2), date(2020, 1, 2)], | ||
| ), | ||
| ( | ||
| [datetime(2020, 1, 1), None, datetime(2020, 1, 2)], | ||
| pl.Datetime("ms"), | ||
| [datetime(2020, 1, 1), datetime(2020, 1, 2), datetime(2020, 1, 2)], | ||
| ), | ||
| ( | ||
| [datetime(2020, 1, 1), None, datetime(2020, 1, 2)], | ||
| pl.Datetime("us", "Asia/Kathmandu"), | ||
| [datetime(2020, 1, 1), datetime(2020, 1, 2), datetime(2020, 1, 2)], | ||
| ), | ||
| ([time(1), None, time(2)], pl.Time, [time(1), time(2), time(2)]), | ||
| ( | ||
| [timedelta(1), None, timedelta(2)], | ||
| pl.Duration("ms"), | ||
| [timedelta(1), timedelta(2), timedelta(2)], | ||
| ), | ||
| ], | ||
| ) | ||
| def test_interpolate_temporal_nearest( | ||
| input: list[Any], input_dtype: PolarsTemporalType, output: list[Any] | ||
| ) -> None: | ||
| df = pl.LazyFrame({"a": input}, schema={"a": input_dtype}) | ||
| result = df.with_columns(pl.all().interpolate(method="nearest")) | ||
| assert result.schema["a"] == input_dtype | ||
| expected = pl.DataFrame({"a": output}, schema={"a": input_dtype}) | ||
| assert_frame_equal(result.collect(), expected) |