def test_cython_group_mean_wrong_min_count():
actual = np.zeros(shape=(1, 1), dtype="float64")
counts = np.zeros(1, dtype="int64")
data = np.zeros(1, dtype="float64")[:, None]
labels = np.zeros(1, dtype=np.intp)
with pytest.raises(AssertionError, match="min_count"):
group_mean(actual, counts, data, labels, is_datetimelike=True, min_count=0)
def test_cython_group_mean_not_datetimelike_but_has_NaT_values():
actual = np.zeros(shape=(1, 1), dtype="float64")
counts = np.array([0], dtype="int64")
data = (np.array(
[np.timedelta64("NaT"), np.timedelta64("NaT")],
dtype="m8[ns]",
)[:, None].view("int64").astype("float64"))
labels = np.zeros(len(data), dtype=np.intp)
group_mean(actual, counts, data, labels, is_datetimelike=False)
tm.assert_numpy_array_equal(
actual[:, 0],
np.array(np.divide(np.add(data[0], data[1]), 2), dtype="float64"))
def test_cython_group_mean_datetimelike():
actual = np.zeros(shape=(1, 1), dtype="float64")
counts = np.array([0], dtype="int64")
data = (np.array(
[
np.timedelta64(2, "ns"),
np.timedelta64(4, "ns"),
np.timedelta64("NaT")
],
dtype="m8[ns]",
)[:, None].view("int64").astype("float64"))
labels = np.zeros(len(data), dtype=np.intp)
group_mean(actual, counts, data, labels, is_datetimelike=True)
tm.assert_numpy_array_equal(actual[:, 0], np.array([3], dtype="float64"))