from typing import Type

import numpy as np
import pytest

import polars as pl


@pytest.mark.parametrize("dtype", [pl.Float32, pl.Float64, pl.Int32])
def test_std(dtype: Type[pl.DataType]) -> None:
    if dtype == pl.Int32:
        df = pl.DataFrame(
            [
                pl.Series("groups", ["a", "a", "b", "b"]),
                pl.Series("values", [1, 2, 3, 4], dtype=dtype),
            ]
        )
    else:
        df = pl.DataFrame(
            [
                pl.Series("groups", ["a", "a", "b", "b"]),
                pl.Series("values", [1.0, 2.0, 3.0, 4.0], dtype=dtype),
            ]
        )

    out = df.select(pl.col("values").std().over("groups"))
    assert np.isclose(out["values"][0], 0.7071067690849304)

    out = df.select(pl.col("values").var().over("groups"))
    assert np.isclose(out["values"][0], 0.5)
    out = df.select(pl.col("values").mean().over("groups"))
    assert np.isclose(out["values"][0], 1.5)

19263f67aa2d02e344f1f7b5c3014295ce712c48
<mask token>

<mask token>


@pytest.mark.parametrize('dtype', [pl.Float32, pl.Float64, pl.Int32])
def test_std(dtype: Type[pl.DataType]) ->None:
    if dtype == pl.Int32:
        df = pl.DataFrame([pl.Series('groups', ['a', 'a', 'b', 'b']), pl.
            Series('values', [1, 2, 3, 4], dtype=dtype)])
    else:
        df = pl.DataFrame([pl.Series('groups', ['a', 'a', 'b', 'b']), pl.
            Series('values', [1.0, 2.0, 3.0, 4.0], dtype=dtype)])
    out = df.select(pl.col('values').std().over('groups'))
    assert np.isclose(out['values'][0], 0.7071067690849304)
    out = df.select(pl.col('values').var().over('groups'))
    assert np.isclose(out['values'][0], 0.5)
    out = df.select(pl.col('values').mean().over('groups'))
    assert np.isclose(out['values'][0], 1.5)

from typing import Type
import numpy as np
import pytest
import polars as pl


@pytest.mark.parametrize('dtype', [pl.Float32, pl.Float64, pl.Int32])
def test_std(dtype: Type[pl.DataType]) ->None:
    if dtype == pl.Int32:
        df = pl.DataFrame([pl.Series('groups', ['a', 'a', 'b', 'b']), pl.
            Series('values', [1, 2, 3, 4], dtype=dtype)])
    else:
        df = pl.DataFrame([pl.Series('groups', ['a', 'a', 'b', 'b']), pl.
            Series('values', [1.0, 2.0, 3.0, 4.0], dtype=dtype)])
    out = df.select(pl.col('values').std().over('groups'))
    assert np.isclose(out['values'][0], 0.7071067690849304)
    out = df.select(pl.col('values').var().over('groups'))
    assert np.isclose(out['values'][0], 0.5)
    out = df.select(pl.col('values').mean().over('groups'))
    assert np.isclose(out['values'][0], 1.5)

from typing import Type

import numpy as np
import pytest

import polars as pl


@pytest.mark.parametrize("dtype", [pl.Float32, pl.Float64, pl.Int32])
def test_std(dtype: Type[pl.DataType]) -> None:
    if dtype == pl.Int32:
        df = pl.DataFrame(
            [
                pl.Series("groups", ["a", "a", "b", "b"]),
                pl.Series("values", [1, 2, 3, 4], dtype=dtype),
            ]
        )
    else:
        df = pl.DataFrame(
            [
                pl.Series("groups", ["a", "a", "b", "b"]),
                pl.Series("values", [1.0, 2.0, 3.0, 4.0], dtype=dtype),
            ]
        )

    out = df.select(pl.col("values").std().over("groups"))
    assert np.isclose(out["values"][0], 0.7071067690849304)

    out = df.select(pl.col("values").var().over("groups"))
    assert np.isclose(out["values"][0], 0.5)
    out = df.select(pl.col("values").mean().over("groups"))
    assert np.isclose(out["values"][0], 1.5)
