CLN: resolve isort mypy import conflict (#35134) (#35386)

Author: fangchenli

pandas/tests/groupby/transform/test_transform.py

@@ -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
 
@@ -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)
 
 
@@ -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"),