REF: import _libs.reduction as libreduction

pandas/core/apply.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from pandas._libs import reduction
+from pandas._libs import reduction as libreduction
 from pandas.util._decorators import cache_readonly
 
 from pandas.core.dtypes.common import (
@@ -221,7 +221,7 @@ def apply_raw(self):
         """ apply to the values as a numpy array """
 
         try:
-            result = reduction.compute_reduction(self.values, self.f, axis=self.axis)
+            result = libreduction.compute_reduction(self.values, self.f, axis=self.axis)
         except Exception:
             result = np.apply_along_axis(self.f, self.axis, self.values)
 
@@ -281,7 +281,7 @@ def apply_standard(self):
             dummy = Series(empty_arr, index=index, dtype=values.dtype)
 
             try:
-                result = reduction.compute_reduction(
+                result = libreduction.compute_reduction(
                     values, self.f, axis=self.axis, dummy=dummy, labels=labels
                 )
                 return self.obj._constructor_sliced(result, index=labels)

pandas/core/groupby/ops.py

@@ -12,7 +12,7 @@
 
 from pandas._libs import NaT, iNaT, lib
 import pandas._libs.groupby as libgroupby
-import pandas._libs.reduction as reduction
+import pandas._libs.reduction as libreduction
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import cache_readonly
 
@@ -207,7 +207,7 @@ def apply(self, f, data, axis=0):
                 if len(result_values) == len(group_keys):
                     return group_keys, result_values, mutated
 
-            except reduction.InvalidApply:
+            except libreduction.InvalidApply:
                 # Cannot fast apply on MultiIndex (_has_complex_internals).
                 # This Exception is also raised if `f` triggers an exception
                 # but it is preferable to raise the exception in Python.
@@ -678,7 +678,7 @@ def _aggregate_series_fast(self, obj, func):
         indexer = get_group_index_sorter(group_index, ngroups)
         obj = obj.take(indexer)
         group_index = algorithms.take_nd(group_index, indexer, allow_fill=False)
-        grouper = reduction.SeriesGrouper(obj, func, group_index, ngroups, dummy)
+        grouper = libreduction.SeriesGrouper(obj, func, group_index, ngroups, dummy)
         result, counts = grouper.get_result()
         return result, counts
 
@@ -852,7 +852,7 @@ def groupings(self):
 
     def agg_series(self, obj, func):
         dummy = obj[:0]
-        grouper = reduction.SeriesBinGrouper(obj, func, self.bins, dummy)
+        grouper = libreduction.SeriesBinGrouper(obj, func, self.bins, dummy)
         return grouper.get_result()
 
 
@@ -940,7 +940,7 @@ def fast_apply(self, f, names):
             return [], True
 
         sdata = self._get_sorted_data()
-        return reduction.apply_frame_axis0(sdata, f, names, starts, ends)
+        return libreduction.apply_frame_axis0(sdata, f, names, starts, ends)
 
     def _chop(self, sdata, slice_obj):
         if self.axis == 0:

pandas/tests/groupby/test_bin_groupby.py

@@ -2,7 +2,7 @@
 from numpy import nan
 import pytest
 
-from pandas._libs import groupby, lib, reduction
+from pandas._libs import groupby, lib, reduction as libreduction
 
 from pandas.core.dtypes.common import ensure_int64
 
@@ -18,7 +18,7 @@ def test_series_grouper():
 
     labels = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int64)
 
-    grouper = reduction.SeriesGrouper(obj, np.mean, labels, 2, dummy)
+    grouper = libreduction.SeriesGrouper(obj, np.mean, labels, 2, dummy)
     result, counts = grouper.get_result()
 
     expected = np.array([obj[3:6].mean(), obj[6:].mean()])
@@ -34,7 +34,7 @@ def test_series_bin_grouper():
 
     bins = np.array([3, 6])
 
-    grouper = reduction.SeriesBinGrouper(obj, np.mean, bins, dummy)
+    grouper = libreduction.SeriesBinGrouper(obj, np.mean, bins, dummy)
     result, counts = grouper.get_result()
 
     expected = np.array([obj[:3].mean(), obj[3:6].mean(), obj[6:].mean()])
@@ -120,31 +120,31 @@ class TestMoments:
 class TestReducer:
     def test_int_index(self):
         arr = np.random.randn(100, 4)
-        result = reduction.compute_reduction(arr, np.sum, labels=Index(np.arange(4)))
+        result = libreduction.compute_reduction(arr, np.sum, labels=Index(np.arange(4)))
         expected = arr.sum(0)
         assert_almost_equal(result, expected)
 
-        result = reduction.compute_reduction(
+        result = libreduction.compute_reduction(
             arr, np.sum, axis=1, labels=Index(np.arange(100))
         )
         expected = arr.sum(1)
         assert_almost_equal(result, expected)
 
         dummy = Series(0.0, index=np.arange(100))
-        result = reduction.compute_reduction(
+        result = libreduction.compute_reduction(
             arr, np.sum, dummy=dummy, labels=Index(np.arange(4))
         )
         expected = arr.sum(0)
         assert_almost_equal(result, expected)
 
         dummy = Series(0.0, index=np.arange(4))
-        result = reduction.compute_reduction(
+        result = libreduction.compute_reduction(
             arr, np.sum, axis=1, dummy=dummy, labels=Index(np.arange(100))
         )
         expected = arr.sum(1)
         assert_almost_equal(result, expected)
 
-        result = reduction.compute_reduction(
+        result = libreduction.compute_reduction(
             arr, np.sum, axis=1, dummy=dummy, labels=Index(np.arange(100))
         )
         assert_almost_equal(result, expected)