Beispiel #1
0
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)
Beispiel #2
0
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)

    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, 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, 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,
                         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)
Beispiel #3
0
    def test_cython_group_transform_algos(self):
        # GH 4095
        dtypes = [
            np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint32,
            np.uint64, np.float32, np.float64
        ]

        ops = [(groupby.group_cumprod_float64, np.cumproduct, [np.float64]),
               (groupby.group_cumsum, np.cumsum, dtypes)]

        is_datetimelike = False
        for pd_op, np_op, dtypes in ops:
            for dtype in dtypes:
                data = np.array([[1], [2], [3], [4]], dtype=dtype)
                ans = np.zeros_like(data)
                labels = np.array([0, 0, 0, 0], dtype=np.int64)
                pd_op(ans, data, labels, is_datetimelike)
                tm.assert_numpy_array_equal(np_op(data),
                                            ans[:, 0],
                                            check_dtype=False)

        # with nans
        labels = np.array([0, 0, 0, 0, 0], dtype=np.int64)

        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, 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, 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,
                             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)
Beispiel #4
0
    def test_cython_group_transform_algos(self):
        # GH 4095
        dtypes = [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint32,
                  np.uint64, np.float32, np.float64]

        ops = [(groupby.group_cumprod_float64, np.cumproduct, [np.float64]),
               (groupby.group_cumsum, np.cumsum, dtypes)]

        is_datetimelike = False
        for pd_op, np_op, dtypes in ops:
            for dtype in dtypes:
                data = np.array([[1], [2], [3], [4]], dtype=dtype)
                ans = np.zeros_like(data)
                labels = np.array([0, 0, 0, 0], dtype=np.int64)
                pd_op(ans, data, labels, is_datetimelike)
                tm.assert_numpy_array_equal(np_op(data), ans[:, 0],
                                            check_dtype=False)

        # with nans
        labels = np.array([0, 0, 0, 0, 0], dtype=np.int64)

        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, 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, 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,
                             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)