def test_groupby_bool_first() -> None:
df = pd.DataFrame({"x": pd.Series([True, True], dtype=RLEDtype(bool)), "g": 1})
series = df.groupby("g")["x"].first()
assert series.dtype == RLEDtype(bool)
expected = RLEArray._from_sequence([True])
npt.assert_array_equal(series.array, expected)
def test_unary_operator(
rle_series: pd.Series,
uncompressed_series: pd.Series,
unary_operator: FUnaryOperator,
) -> None:
actual = unary_operator(rle_series)
assert actual.dtype == RLEDtype(float)
expected = unary_operator(uncompressed_series).astype(RLEDtype(float))
pd.testing.assert_series_equal(actual, expected)
def test_binary_bool_operator_scalar(
rle_bool_series: pd.Series,
uncompressed_bool_series: pd.Series,
bool_scalar: bool,
binary_bool_operator: FBinaryBoolOperator,
) -> None:
actual = binary_bool_operator(rle_bool_series, bool_scalar)
assert actual.dtype == RLEDtype(bool)
expected = binary_bool_operator(uncompressed_bool_series,
bool_scalar).astype(RLEDtype(bool))
pd.testing.assert_series_equal(actual, expected)
def test_groupby_bool_sum() -> None:
# Cython routines for integer addition are not available, so we need to accept floats here.
df = pd.DataFrame({"x": pd.Series([True, True], dtype=RLEDtype(bool)), "g": 1})
series = df.groupby("g")["x"].sum()
assert series.dtype == np.float64
expected = np.array([2], dtype=np.float64)
npt.assert_array_equal(series.to_numpy(), expected)
def test_round(data_orig: pd.Series, data_rle: pd.Series, decimals: int) -> None:
result_orig = data_orig.round(decimals=decimals)
result_rle = data_rle.round(decimals=decimals)
assert result_rle.dtype == RLEDtype(result_orig.dtype)
result_converted = result_rle.astype(result_rle.dtype._dtype)
pdt.assert_series_equal(result_orig, result_converted)
def test_shift(
data_orig: pd.Series, data_rle: pd.Series, periods: int, fill_value: Any
) -> None:
result_orig = data_orig.shift(periods=periods, fill_value=fill_value)
result_rle = data_rle.shift(periods=periods, fill_value=fill_value)
assert result_rle.dtype == RLEDtype(result_orig.dtype)
result_converted = result_rle.astype(result_rle.dtype._dtype)
pdt.assert_series_equal(result_orig, result_converted)
def test_unary_bool_operator_array(
rle_bool_series: pd.Series,
uncompressed_bool_series: pd.Series,
unary_bool_operator: FUnaryBoolOperator,
) -> None:
actual = unary_bool_operator(rle_bool_series.array)
assert actual.dtype == RLEDtype(bool)
expected = unary_bool_operator(uncompressed_bool_series.array)
npt.assert_array_equal(actual, expected)
def test_factorize_int() -> None:
array = RLEArray._from_sequence([42, -10, -10], dtype=RLEDtype(np.int32))
codes_actual, uniques_actual = array.factorize()
codes_expected = np.array([0, 1, 1], dtype=np.int64)
assert codes_actual.dtype == codes_expected.dtype
npt.assert_array_equal(codes_actual, codes_expected)
uniques_expected = RLEArray._from_sequence([42, -10], dtype=np.int32)
assert uniques_actual.dtype == uniques_expected.dtype
npt.assert_array_equal(uniques_actual, uniques_expected)
def test_from_sequence_bool() -> None:
array = RLEArray._from_sequence(
np.array([0, 1], dtype=np.int64), dtype=RLEDtype(bool)
)
npt.assert_array_equal(array, np.array([False, True]))
array = RLEArray._from_sequence(
np.array([0.0, 1.0], dtype=np.float64), dtype=RLEDtype(bool)
)
npt.assert_array_equal(array, np.array([False, True]))
with pytest.raises(TypeError, match="Need to pass bool-like values"):
RLEArray._from_sequence(np.array([1, 2], dtype=np.int64), dtype=RLEDtype(bool))
with pytest.raises(TypeError, match="Need to pass bool-like values"):
RLEArray._from_sequence(np.array([-1, 1], dtype=np.int64), dtype=RLEDtype(bool))
with pytest.raises(TypeError, match="Masked booleans are not supported"):
RLEArray._from_sequence(
np.array([np.nan, 1.0], dtype=np.float64), dtype=RLEDtype(bool)
)
def test_binary_operator_scalar(
rle_series: pd.Series,
uncompressed_series: pd.Series,
scalar: float,
binary_operator: FBinaryOperator,
) -> None:
actual = binary_operator(rle_series, scalar)
assert actual.dtype == RLEDtype(float)
expected = binary_operator(uncompressed_series,
scalar).astype("RLEDtype[float]")
pd.testing.assert_series_equal(actual, expected)
def test_compare_scalar(
rle_series: pd.Series,
uncompressed_series: pd.Series,
scalar: float,
compare_operator: FCompareOperator,
) -> None:
actual = compare_operator(rle_series, scalar)
assert actual.dtype == RLEDtype(bool)
expected = compare_operator(uncompressed_series,
scalar).astype("RLEDtype[bool]")
pd.testing.assert_series_equal(actual, expected)
def test_no_copy(series: pd.Series) -> None:
series2 = series.astype(series.dtype, copy=False)
assert series2.values is series.values
assert series2.dtype == RLEDtype(int)
def series() -> pd.Series:
return pd.Series([1, 1, 2]).astype(RLEDtype(int))
import numpy as np
import pytest
from rle_array import RLEDtype
@pytest.mark.parametrize(
"a,b,expected",
[
(
# a
RLEDtype(int),
# b
RLEDtype(int),
# expected
True,
),
(
# a
RLEDtype(int),
# b
RLEDtype(float),
# expected
False,
),
(
# a
RLEDtype(int),
# b
RLEDtype(np.int64),
# expected
def test_append_mixed() -> None:
actual = pd.concat(
[pd.Series([1], dtype=np.int8), pd.Series([1], dtype=RLEDtype(np.int8))]
)
assert actual.dtype == np.int8
def data_rle(data_orig: pd.Series) -> pd.Series:
return data_orig.astype(RLEDtype(data_orig.dtype))
def data_rle_bool(data_orig_bool):
return data_orig_bool.astype(RLEDtype(data_orig_bool.dtype))
def test_copy_different_dtype(series: pd.Series) -> None:
series2 = series.astype(RLEDtype(float), copy=False)
assert series2.values is not series.values
assert series2.dtype == RLEDtype(float)
def test_get_common_dtype(dtype: RLEDtype, dtypes: List[Any], expected: Any) -> None:
actual = dtype._get_common_dtype(dtypes)
assert actual == expected
def dtype():
"""A fixture providing the ExtensionDtype to validate."""
return RLEDtype(np.float32)
def data_rle_bool(data_orig_bool: pd.Series) -> pd.Series:
return data_orig_bool.astype(RLEDtype(data_orig_bool.dtype))
from typing import Any, List
import numpy as np
import pytest
from rle_array import RLEDtype
@pytest.mark.parametrize(
"a, b, expected",
[
(
# a
RLEDtype(int),
# b
RLEDtype(int),
# expected
True,
),
(
# a
RLEDtype(int),
# b
RLEDtype(float),
# expected
False,
),
(
# a
RLEDtype(int),
# b
def data_rle(data_orig):
return data_orig.astype(RLEDtype(data_orig.dtype))
def series_rle(series_orig: pd.Series) -> pd.Series:
return series_orig.astype(RLEDtype(series_orig.dtype))
def series_rle(series_orig):
return series_orig.astype(RLEDtype(series_orig.dtype))