345 lines
11 KiB
Python
345 lines
11 KiB
Python
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import numpy as np
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import pytest
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from pandas._libs import groupby as libgroupby
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from pandas._libs.groupby import (
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group_cumprod,
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group_cumsum,
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group_mean,
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group_sum,
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group_var,
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)
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from pandas.core.dtypes.common import ensure_platform_int
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from pandas import isna
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import pandas._testing as tm
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@pytest.mark.parametrize("dtype, rtol", [("float32", 1e-2), ("float64", 1e-5)])
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class TestGroupVar:
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def test_group_var_generic_1d(self, dtype, rtol):
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prng = np.random.default_rng(2)
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out = (np.nan * np.ones((5, 1))).astype(dtype)
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counts = np.zeros(5, dtype="int64")
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values = 10 * prng.random((15, 1)).astype(dtype)
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labels = np.tile(np.arange(5), (3,)).astype("intp")
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expected_out = (
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np.squeeze(values).reshape((5, 3), order="F").std(axis=1, ddof=1) ** 2
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)[:, np.newaxis]
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expected_counts = counts + 3
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group_var(out, counts, values, labels)
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assert np.allclose(out, expected_out, rtol)
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tm.assert_numpy_array_equal(counts, expected_counts)
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def test_group_var_generic_1d_flat_labels(self, dtype, rtol):
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prng = np.random.default_rng(2)
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out = (np.nan * np.ones((1, 1))).astype(dtype)
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counts = np.zeros(1, dtype="int64")
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values = 10 * prng.random((5, 1)).astype(dtype)
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labels = np.zeros(5, dtype="intp")
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expected_out = np.array([[values.std(ddof=1) ** 2]])
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expected_counts = counts + 5
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group_var(out, counts, values, labels)
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assert np.allclose(out, expected_out, rtol)
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tm.assert_numpy_array_equal(counts, expected_counts)
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def test_group_var_generic_2d_all_finite(self, dtype, rtol):
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prng = np.random.default_rng(2)
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out = (np.nan * np.ones((5, 2))).astype(dtype)
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counts = np.zeros(5, dtype="int64")
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values = 10 * prng.random((10, 2)).astype(dtype)
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labels = np.tile(np.arange(5), (2,)).astype("intp")
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expected_out = np.std(values.reshape(2, 5, 2), ddof=1, axis=0) ** 2
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expected_counts = counts + 2
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group_var(out, counts, values, labels)
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assert np.allclose(out, expected_out, rtol)
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tm.assert_numpy_array_equal(counts, expected_counts)
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def test_group_var_generic_2d_some_nan(self, dtype, rtol):
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prng = np.random.default_rng(2)
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out = (np.nan * np.ones((5, 2))).astype(dtype)
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counts = np.zeros(5, dtype="int64")
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values = 10 * prng.random((10, 2)).astype(dtype)
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values[:, 1] = np.nan
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labels = np.tile(np.arange(5), (2,)).astype("intp")
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expected_out = np.vstack(
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[
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values[:, 0].reshape(5, 2, order="F").std(ddof=1, axis=1) ** 2,
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np.nan * np.ones(5),
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]
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).T.astype(dtype)
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expected_counts = counts + 2
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group_var(out, counts, values, labels)
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tm.assert_almost_equal(out, expected_out, rtol=0.5e-06)
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tm.assert_numpy_array_equal(counts, expected_counts)
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def test_group_var_constant(self, dtype, rtol):
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# Regression test from GH 10448.
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out = np.array([[np.nan]], dtype=dtype)
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counts = np.array([0], dtype="int64")
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values = 0.832845131556193 * np.ones((3, 1), dtype=dtype)
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labels = np.zeros(3, dtype="intp")
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group_var(out, counts, values, labels)
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assert counts[0] == 3
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assert out[0, 0] >= 0
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tm.assert_almost_equal(out[0, 0], 0.0)
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def test_group_var_large_inputs():
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dtype = np.float64
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prng = np.random.default_rng(2)
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out = np.array([[np.nan]], dtype=dtype)
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counts = np.array([0], dtype="int64")
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values = (prng.random((10**6, 1)) + 10**12).astype(dtype)
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labels = np.zeros(10**6, dtype="intp")
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group_var(out, counts, values, labels)
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assert counts[0] == 10**6
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tm.assert_almost_equal(out[0, 0], 1.0 / 12, rtol=0.5e-3)
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@pytest.mark.parametrize("dtype", ["float32", "float64"])
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def test_group_ohlc(dtype):
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obj = np.array(np.random.default_rng(2).standard_normal(20), dtype=dtype)
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bins = np.array([6, 12, 20])
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out = np.zeros((3, 4), dtype)
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counts = np.zeros(len(out), dtype=np.int64)
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labels = ensure_platform_int(np.repeat(np.arange(3), np.diff(np.r_[0, bins])))
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func = libgroupby.group_ohlc
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func(out, counts, obj[:, None], labels)
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def _ohlc(group):
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if isna(group).all():
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return np.repeat(np.nan, 4)
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return [group[0], group.max(), group.min(), group[-1]]
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expected = np.array([_ohlc(obj[:6]), _ohlc(obj[6:12]), _ohlc(obj[12:])])
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tm.assert_almost_equal(out, expected)
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tm.assert_numpy_array_equal(counts, np.array([6, 6, 8], dtype=np.int64))
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obj[:6] = np.nan
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func(out, counts, obj[:, None], labels)
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expected[0] = np.nan
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tm.assert_almost_equal(out, expected)
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@pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float32, np.float64])
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@pytest.mark.parametrize(
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"pd_op, np_op",
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[
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(group_cumsum, np.cumsum),
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(group_cumprod, np.cumprod),
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],
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)
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def test_cython_group_transform(dtype, pd_op, np_op):
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# see gh-4095
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is_datetimelike = False
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data = np.array([[1], [2], [3], [4]], dtype=dtype)
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answer = np.zeros_like(data)
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labels = np.array([0, 0, 0, 0], dtype=np.intp)
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ngroups = 1
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pd_op(answer, data, labels, ngroups, is_datetimelike)
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tm.assert_numpy_array_equal(np_op(data), answer[:, 0], check_dtype=False)
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def test_cython_group_transform_algos():
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# see gh-4095
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is_datetimelike = False
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# with nans
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labels = np.array([0, 0, 0, 0, 0], dtype=np.intp)
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ngroups = 1
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data = np.array([[1], [2], [3], [np.nan], [4]], dtype="float64")
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actual = np.zeros_like(data)
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actual.fill(np.nan)
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group_cumprod(actual, data, labels, ngroups, is_datetimelike)
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expected = np.array([1, 2, 6, np.nan, 24], dtype="float64")
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tm.assert_numpy_array_equal(actual[:, 0], expected)
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actual = np.zeros_like(data)
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actual.fill(np.nan)
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group_cumsum(actual, data, labels, ngroups, is_datetimelike)
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expected = np.array([1, 3, 6, np.nan, 10], dtype="float64")
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tm.assert_numpy_array_equal(actual[:, 0], expected)
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# timedelta
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is_datetimelike = True
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data = np.array([np.timedelta64(1, "ns")] * 5, dtype="m8[ns]")[:, None]
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actual = np.zeros_like(data, dtype="int64")
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group_cumsum(actual, data.view("int64"), labels, ngroups, is_datetimelike)
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expected = np.array(
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[
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np.timedelta64(1, "ns"),
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np.timedelta64(2, "ns"),
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np.timedelta64(3, "ns"),
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np.timedelta64(4, "ns"),
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np.timedelta64(5, "ns"),
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]
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)
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tm.assert_numpy_array_equal(actual[:, 0].view("m8[ns]"), expected)
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def test_cython_group_mean_datetimelike():
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actual = np.zeros(shape=(1, 1), dtype="float64")
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counts = np.array([0], dtype="int64")
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data = (
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np.array(
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[np.timedelta64(2, "ns"), np.timedelta64(4, "ns"), np.timedelta64("NaT")],
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dtype="m8[ns]",
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)[:, None]
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.view("int64")
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.astype("float64")
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)
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labels = np.zeros(len(data), dtype=np.intp)
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group_mean(actual, counts, data, labels, is_datetimelike=True)
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tm.assert_numpy_array_equal(actual[:, 0], np.array([3], dtype="float64"))
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def test_cython_group_mean_wrong_min_count():
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actual = np.zeros(shape=(1, 1), dtype="float64")
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counts = np.zeros(1, dtype="int64")
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data = np.zeros(1, dtype="float64")[:, None]
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labels = np.zeros(1, dtype=np.intp)
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with pytest.raises(AssertionError, match="min_count"):
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group_mean(actual, counts, data, labels, is_datetimelike=True, min_count=0)
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def test_cython_group_mean_not_datetimelike_but_has_NaT_values():
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actual = np.zeros(shape=(1, 1), dtype="float64")
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counts = np.array([0], dtype="int64")
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data = (
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np.array(
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[np.timedelta64("NaT"), np.timedelta64("NaT")],
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dtype="m8[ns]",
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)[:, None]
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.view("int64")
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.astype("float64")
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)
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labels = np.zeros(len(data), dtype=np.intp)
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group_mean(actual, counts, data, labels, is_datetimelike=False)
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tm.assert_numpy_array_equal(
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actual[:, 0], np.array(np.divide(np.add(data[0], data[1]), 2), dtype="float64")
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)
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def test_cython_group_mean_Inf_at_beginning_and_end():
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# GH 50367
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actual = np.array([[np.nan, np.nan], [np.nan, np.nan]], dtype="float64")
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counts = np.array([0, 0], dtype="int64")
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data = np.array(
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[[np.inf, 1.0], [1.0, 2.0], [2.0, 3.0], [3.0, 4.0], [4.0, 5.0], [5, np.inf]],
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dtype="float64",
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)
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labels = np.array([0, 1, 0, 1, 0, 1], dtype=np.intp)
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group_mean(actual, counts, data, labels, is_datetimelike=False)
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expected = np.array([[np.inf, 3], [3, np.inf]], dtype="float64")
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tm.assert_numpy_array_equal(
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actual,
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expected,
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)
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@pytest.mark.parametrize(
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"values, out",
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[
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([[np.inf], [np.inf], [np.inf]], [[np.inf], [np.inf]]),
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([[np.inf], [np.inf], [-np.inf]], [[np.inf], [np.nan]]),
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([[np.inf], [-np.inf], [np.inf]], [[np.inf], [np.nan]]),
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([[np.inf], [-np.inf], [-np.inf]], [[np.inf], [-np.inf]]),
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],
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)
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def test_cython_group_sum_Inf_at_beginning_and_end(values, out):
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# GH #53606
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actual = np.array([[np.nan], [np.nan]], dtype="float64")
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counts = np.array([0, 0], dtype="int64")
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data = np.array(values, dtype="float64")
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labels = np.array([0, 1, 1], dtype=np.intp)
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group_sum(actual, counts, data, labels, None, is_datetimelike=False)
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expected = np.array(out, dtype="float64")
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tm.assert_numpy_array_equal(
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actual,
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expected,
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)
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@pytest.mark.parametrize(
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"values, expected_values",
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[
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(np.finfo(np.float64).max, [[np.inf]]),
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(np.finfo(np.float64).min, [[-np.inf]]),
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(
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np.complex128(np.finfo(np.float64).min + np.finfo(np.float64).max * 1j),
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[[complex(-np.inf, np.inf)]],
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),
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(
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np.complex128(np.finfo(np.float64).max + np.finfo(np.float64).min * 1j),
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[[complex(np.inf, -np.inf)]],
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),
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(
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np.complex128(np.finfo(np.float64).max + np.finfo(np.float64).max * 1j),
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[[complex(np.inf, np.inf)]],
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),
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(
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np.complex128(np.finfo(np.float64).min + np.finfo(np.float64).min * 1j),
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[[complex(-np.inf, -np.inf)]],
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),
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(
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np.complex128(3.0 + np.finfo(np.float64).min * 1j),
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[[complex(9.0, -np.inf)]],
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),
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(
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np.complex128(np.finfo(np.float64).max + 3 * 1j),
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[[complex(np.inf, 9.0)]],
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),
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],
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)
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def test_cython_group_sum_overflow(values, expected_values):
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# GH-60303
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data = np.array([[values] for _ in range(3)])
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labels = np.array([0, 0, 0], dtype=np.intp)
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||
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counts = np.array([0], dtype="int64")
|
||
|
|
|
||
|
|
expected = np.array(expected_values, dtype=values.dtype)
|
||
|
|
actual = np.zeros_like(expected)
|
||
|
|
|
||
|
|
group_sum(actual, counts, data, labels, None, is_datetimelike=False)
|
||
|
|
|
||
|
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tm.assert_numpy_array_equal(actual, expected)
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