TST/REF: collect _libs tests

pandas/tests/groupby/test_bin_groupby.py

@@ -1,12 +1,10 @@
 import numpy as np
 import pytest
 
-from pandas._libs import groupby, lib, reduction as libreduction
-
-from pandas.core.dtypes.common import ensure_int64
+from pandas._libs import lib, reduction as libreduction
 
 import pandas as pd
-from pandas import Series, isna
+from pandas import Series
 import pandas._testing as tm
 
 
@@ -103,36 +101,5 @@ def test_generate_bins(binner, closed, expected):
     tm.assert_numpy_array_equal(result, expected)
 
 
-def test_group_ohlc():
-    def _check(dtype):
-        obj = np.array(np.random.randn(20), dtype=dtype)
-
-        bins = np.array([6, 12, 20])
-        out = np.zeros((3, 4), dtype)
-        counts = np.zeros(len(out), dtype=np.int64)
-        labels = ensure_int64(np.repeat(np.arange(3), np.diff(np.r_[0, bins])))
-
-        func = getattr(groupby, f"group_ohlc_{dtype}")
-        func(out, counts, obj[:, None], labels)
-
-        def _ohlc(group):
-            if isna(group).all():
-                return np.repeat(np.nan, 4)
-            return [group[0], group.max(), group.min(), group[-1]]
-
-        expected = np.array([_ohlc(obj[:6]), _ohlc(obj[6:12]), _ohlc(obj[12:])])
-
-        tm.assert_almost_equal(out, expected)
-        tm.assert_numpy_array_equal(counts, np.array([6, 6, 8], dtype=np.int64))
-
-        obj[:6] = np.nan
-        func(out, counts, obj[:, None], labels)
-        expected[0] = np.nan
-        tm.assert_almost_equal(out, expected)
-
-    _check("float32")
-    _check("float64")
-
-
 class TestMoments:
     pass

pandas/tests/groupby/test_libgroupby.py

@@ -0,0 +1,237 @@
+import numpy as np
+
+from pandas._libs import groupby as libgroupby
+from pandas._libs.groupby import (
+    group_cumprod_float64,
+    group_cumsum,
+    group_var_float32,
+    group_var_float64,
+)
+
+from pandas.core.dtypes.common import ensure_int64
+
+from pandas import isna
+import pandas._testing as tm
+
+
+class GroupVarTestMixin:
+    def test_group_var_generic_1d(self):
+        prng = np.random.RandomState(1234)
+
+        out = (np.nan * np.ones((5, 1))).astype(self.dtype)
+        counts = np.zeros(5, dtype="int64")
+        values = 10 * prng.rand(15, 1).astype(self.dtype)
+        labels = np.tile(np.arange(5), (3,)).astype("int64")
+
+        expected_out = (
+            np.squeeze(values).reshape((5, 3), order="F").std(axis=1, ddof=1) ** 2
+        )[:, np.newaxis]
+        expected_counts = counts + 3
+
+        self.algo(out, counts, values, labels)
+        assert np.allclose(out, expected_out, self.rtol)
+        tm.assert_numpy_array_equal(counts, expected_counts)
+
+    def test_group_var_generic_1d_flat_labels(self):
+        prng = np.random.RandomState(1234)
+
+        out = (np.nan * np.ones((1, 1))).astype(self.dtype)
+        counts = np.zeros(1, dtype="int64")
+        values = 10 * prng.rand(5, 1).astype(self.dtype)
+        labels = np.zeros(5, dtype="int64")
+
+        expected_out = np.array([[values.std(ddof=1) ** 2]])
+        expected_counts = counts + 5
+
+        self.algo(out, counts, values, labels)
+
+        assert np.allclose(out, expected_out, self.rtol)
+        tm.assert_numpy_array_equal(counts, expected_counts)
+
+    def test_group_var_generic_2d_all_finite(self):
+        prng = np.random.RandomState(1234)
+
+        out = (np.nan * np.ones((5, 2))).astype(self.dtype)
+        counts = np.zeros(5, dtype="int64")
+        values = 10 * prng.rand(10, 2).astype(self.dtype)
+        labels = np.tile(np.arange(5), (2,)).astype("int64")
+
+        expected_out = np.std(values.reshape(2, 5, 2), ddof=1, axis=0) ** 2
+        expected_counts = counts + 2
+
+        self.algo(out, counts, values, labels)
+        assert np.allclose(out, expected_out, self.rtol)
+        tm.assert_numpy_array_equal(counts, expected_counts)
+
+    def test_group_var_generic_2d_some_nan(self):
+        prng = np.random.RandomState(1234)
+
+        out = (np.nan * np.ones((5, 2))).astype(self.dtype)
+        counts = np.zeros(5, dtype="int64")
+        values = 10 * prng.rand(10, 2).astype(self.dtype)
+        values[:, 1] = np.nan
+        labels = np.tile(np.arange(5), (2,)).astype("int64")
+
+        expected_out = np.vstack(
+            [
+                values[:, 0].reshape(5, 2, order="F").std(ddof=1, axis=1) ** 2,
+                np.nan * np.ones(5),
+            ]
+        ).T.astype(self.dtype)
+        expected_counts = counts + 2
+
+        self.algo(out, counts, values, labels)
+        tm.assert_almost_equal(out, expected_out, rtol=0.5e-06)
+        tm.assert_numpy_array_equal(counts, expected_counts)
+
+    def test_group_var_constant(self):
+        # Regression test from GH 10448.
+
+        out = np.array([[np.nan]], dtype=self.dtype)
+        counts = np.array([0], dtype="int64")
+        values = 0.832845131556193 * np.ones((3, 1), dtype=self.dtype)
+        labels = np.zeros(3, dtype="int64")
+
+        self.algo(out, counts, values, labels)
+
+        assert counts[0] == 3
+        assert out[0, 0] >= 0
+        tm.assert_almost_equal(out[0, 0], 0.0)
+
+
+class TestGroupVarFloat64(GroupVarTestMixin):
+    __test__ = True
+
+    algo = staticmethod(group_var_float64)
+    dtype = np.float64
+    rtol = 1e-5
+
+    def test_group_var_large_inputs(self):
+        prng = np.random.RandomState(1234)
+
+        out = np.array([[np.nan]], dtype=self.dtype)
+        counts = np.array([0], dtype="int64")
+        values = (prng.rand(10 ** 6) + 10 ** 12).astype(self.dtype)
+        values.shape = (10 ** 6, 1)
+        labels = np.zeros(10 ** 6, dtype="int64")
+
+        self.algo(out, counts, values, labels)
+
+        assert counts[0] == 10 ** 6
+        tm.assert_almost_equal(out[0, 0], 1.0 / 12, rtol=0.5e-3)
+
+
+class TestGroupVarFloat32(GroupVarTestMixin):
+    __test__ = True
+
+    algo = staticmethod(group_var_float32)
+    dtype = np.float32
+    rtol = 1e-2
+
+
+def test_group_ohlc():
+    def _check(dtype):
+        obj = np.array(np.random.randn(20), dtype=dtype)
+
+        bins = np.array([6, 12, 20])
+        out = np.zeros((3, 4), dtype)
+        counts = np.zeros(len(out), dtype=np.int64)
+        labels = ensure_int64(np.repeat(np.arange(3), np.diff(np.r_[0, bins])))
+
+        func = getattr(libgroupby, f"group_ohlc_{dtype}")
+        func(out, counts, obj[:, None], labels)
+
+        def _ohlc(group):
+            if isna(group).all():
+                return np.repeat(np.nan, 4)
+            return [group[0], group.max(), group.min(), group[-1]]
+
+        expected = np.array([_ohlc(obj[:6]), _ohlc(obj[6:12]), _ohlc(obj[12:])])
+
+        tm.assert_almost_equal(out, expected)
+        tm.assert_numpy_array_equal(counts, np.array([6, 6, 8], dtype=np.int64))
+
+        obj[:6] = np.nan
+        func(out, counts, obj[:, None], labels)
+        expected[0] = np.nan
+        tm.assert_almost_equal(out, expected)
+
+    _check("float32")
+    _check("float64")
+
+
+def _check_cython_group_transform_cumulative(pd_op, np_op, dtype):
+    """
+    Check a group transform that executes a cumulative function.
+
+    Parameters
+    ----------
+    pd_op : callable
+        The pandas cumulative function.
+    np_op : callable
+        The analogous one in NumPy.
+    dtype : type
+        The specified dtype of the data.
+    """
+    is_datetimelike = False
+
+    data = np.array([[1], [2], [3], [4]], dtype=dtype)
+    ans = np.zeros_like(data)
+
+    labels = np.array([0, 0, 0, 0], dtype=np.int64)
+    ngroups = 1
+    pd_op(ans, data, labels, ngroups, is_datetimelike)
+
+    tm.assert_numpy_array_equal(np_op(data), ans[:, 0], check_dtype=False)
+
+
+def test_cython_group_transform_cumsum(any_real_dtype):
+    # see gh-4095
+    dtype = np.dtype(any_real_dtype).type
+    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 = group_cumprod_float64, np.cumproduct
+    _check_cython_group_transform_cumulative(pd_op, np_op, dtype)
+
+
+def test_cython_group_transform_algos():
+    # see gh-4095
+    is_datetimelike = False
+
+    # with nans
+    labels = np.array([0, 0, 0, 0, 0], dtype=np.int64)
+    ngroups = 1
+
+    data = np.array([[1], [2], [3], [np.nan], [4]], dtype="float64")
+    actual = np.zeros_like(data)
+    actual.fill(np.nan)
+    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)
+    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")
+    group_cumsum(actual, data.view("int64"), labels, ngroups, is_datetimelike)
+    expected = np.array(
+        [
+            np.timedelta64(1, "ns"),
+            np.timedelta64(2, "ns"),
+            np.timedelta64(3, "ns"),
+            np.timedelta64(4, "ns"),
+            np.timedelta64(5, "ns"),
+        ]
+    )
+    tm.assert_numpy_array_equal(actual[:, 0].view("m8[ns]"), expected)

pandas/tests/groupby/transform/test_transform.py

@@ -4,8 +4,6 @@
 import numpy as np
 import pytest
 
-from pandas._libs.groupby import group_cumprod_float64, group_cumsum
-
 from pandas.core.dtypes.common import ensure_platform_int, is_timedelta64_dtype
 
 import pandas as pd
@@ -515,83 +513,6 @@ def f(group):
             tm.assert_frame_equal(res, result.loc[key])
 
 
-def _check_cython_group_transform_cumulative(pd_op, np_op, dtype):
-    """
-    Check a group transform that executes a cumulative function.
-
-    Parameters
-    ----------
-    pd_op : callable
-        The pandas cumulative function.
-    np_op : callable
-        The analogous one in NumPy.
-    dtype : type
-        The specified dtype of the data.
-    """
-    is_datetimelike = False
-
-    data = np.array([[1], [2], [3], [4]], dtype=dtype)
-    ans = np.zeros_like(data)
-
-    labels = np.array([0, 0, 0, 0], dtype=np.int64)
-    ngroups = 1
-    pd_op(ans, data, labels, ngroups, is_datetimelike)
-
-    tm.assert_numpy_array_equal(np_op(data), ans[:, 0], check_dtype=False)
-
-
-def test_cython_group_transform_cumsum(any_real_dtype):
-    # see gh-4095
-    dtype = np.dtype(any_real_dtype).type
-    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 = group_cumprod_float64, np.cumproduct
-    _check_cython_group_transform_cumulative(pd_op, np_op, dtype)
-
-
-def test_cython_group_transform_algos():
-    # see gh-4095
-    is_datetimelike = False
-
-    # with nans
-    labels = np.array([0, 0, 0, 0, 0], dtype=np.int64)
-    ngroups = 1
-
-    data = np.array([[1], [2], [3], [np.nan], [4]], dtype="float64")
-    actual = np.zeros_like(data)
-    actual.fill(np.nan)
-    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)
-    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")
-    group_cumsum(actual, data.view("int64"), labels, ngroups, is_datetimelike)
-    expected = np.array(
-        [
-            np.timedelta64(1, "ns"),
-            np.timedelta64(2, "ns"),
-            np.timedelta64(3, "ns"),
-            np.timedelta64(4, "ns"),
-            np.timedelta64(5, "ns"),
-        ]
-    )
-    tm.assert_numpy_array_equal(actual[:, 0].view("m8[ns]"), expected)
-
-
 @pytest.mark.parametrize(
     "op, args, targop",
     [