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CLN: resolve isort mypy import confilict test_transform #35386

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Jul 29, 2020
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CLN: resolve isort mypy import confilict test_transform #35386

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12 changes: 6 additions & 6 deletions pandas/tests/groupby/transform/test_transform.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
import numpy as np
import pytest

from pandas._libs import groupby
from pandas._libs.groupby import group_cumprod_float64, group_cumsum

from pandas.core.dtypes.common import ensure_platform_int, is_timedelta64_dtype

Expand Down Expand Up @@ -545,14 +545,14 @@ def _check_cython_group_transform_cumulative(pd_op, np_op, dtype):
def test_cython_group_transform_cumsum(any_real_dtype):
# see gh-4095
dtype = np.dtype(any_real_dtype).type
pd_op, np_op = groupby.group_cumsum, np.cumsum
pd_op, np_op = group_cumsum, np.cumsum
_check_cython_group_transform_cumulative(pd_op, np_op, dtype)


def test_cython_group_transform_cumprod():
# see gh-4095
dtype = np.float64
pd_op, np_op = groupby.group_cumprod_float64, np.cumproduct
pd_op, np_op = group_cumprod_float64, np.cumproduct
_check_cython_group_transform_cumulative(pd_op, np_op, dtype)


Expand All @@ -567,21 +567,21 @@ def test_cython_group_transform_algos():
data = np.array([[1], [2], [3], [np.nan], [4]], dtype="float64")
actual = np.zeros_like(data)
actual.fill(np.nan)
groupby.group_cumprod_float64(actual, data, labels, ngroups, is_datetimelike)
group_cumprod_float64(actual, data, labels, ngroups, is_datetimelike)
expected = np.array([1, 2, 6, np.nan, 24], dtype="float64")
tm.assert_numpy_array_equal(actual[:, 0], expected)

actual = np.zeros_like(data)
actual.fill(np.nan)
groupby.group_cumsum(actual, data, labels, ngroups, is_datetimelike)
group_cumsum(actual, data, labels, ngroups, is_datetimelike)
expected = np.array([1, 3, 6, np.nan, 10], dtype="float64")
tm.assert_numpy_array_equal(actual[:, 0], expected)

# timedelta
is_datetimelike = True
data = np.array([np.timedelta64(1, "ns")] * 5, dtype="m8[ns]")[:, None]
actual = np.zeros_like(data, dtype="int64")
groupby.group_cumsum(actual, data.view("int64"), labels, ngroups, is_datetimelike)
group_cumsum(actual, data.view("int64"), labels, ngroups, is_datetimelike)
expected = np.array(
[
np.timedelta64(1, "ns"),
Expand Down