315 lines
11 KiB
Python
315 lines
11 KiB
Python
"""
|
|
Tests for DatetimeArray
|
|
"""
|
|
import operator
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from pandas.core.dtypes.dtypes import DatetimeTZDtype
|
|
|
|
import pandas as pd
|
|
from pandas.core.arrays import DatetimeArray
|
|
from pandas.core.arrays.datetimes import sequence_to_dt64ns
|
|
import pandas.util.testing as tm
|
|
|
|
|
|
class TestDatetimeArrayConstructor:
|
|
def test_only_1dim_accepted(self):
|
|
arr = np.array([0, 1, 2, 3], dtype="M8[h]").astype("M8[ns]")
|
|
|
|
with pytest.raises(ValueError, match="Only 1-dimensional"):
|
|
# 2-dim
|
|
DatetimeArray(arr.reshape(2, 2))
|
|
|
|
with pytest.raises(ValueError, match="Only 1-dimensional"):
|
|
# 0-dim
|
|
DatetimeArray(arr[[0]].squeeze())
|
|
|
|
def test_freq_validation(self):
|
|
# GH#24623 check that invalid instances cannot be created with the
|
|
# public constructor
|
|
arr = np.arange(5, dtype=np.int64) * 3600 * 10 ** 9
|
|
|
|
msg = (
|
|
"Inferred frequency H from passed values does not "
|
|
"conform to passed frequency W-SUN"
|
|
)
|
|
with pytest.raises(ValueError, match=msg):
|
|
DatetimeArray(arr, freq="W")
|
|
|
|
@pytest.mark.parametrize(
|
|
"meth",
|
|
[
|
|
DatetimeArray._from_sequence,
|
|
sequence_to_dt64ns,
|
|
pd.to_datetime,
|
|
pd.DatetimeIndex,
|
|
],
|
|
)
|
|
def test_mixing_naive_tzaware_raises(self, meth):
|
|
# GH#24569
|
|
arr = np.array([pd.Timestamp("2000"), pd.Timestamp("2000", tz="CET")])
|
|
|
|
msg = (
|
|
"Cannot mix tz-aware with tz-naive values|"
|
|
"Tz-aware datetime.datetime cannot be converted "
|
|
"to datetime64 unless utc=True"
|
|
)
|
|
|
|
for obj in [arr, arr[::-1]]:
|
|
# check that we raise regardless of whether naive is found
|
|
# before aware or vice-versa
|
|
with pytest.raises(ValueError, match=msg):
|
|
meth(obj)
|
|
|
|
def test_from_pandas_array(self):
|
|
arr = pd.array(np.arange(5, dtype=np.int64)) * 3600 * 10 ** 9
|
|
|
|
result = DatetimeArray._from_sequence(arr, freq="infer")
|
|
|
|
expected = pd.date_range("1970-01-01", periods=5, freq="H")._data
|
|
tm.assert_datetime_array_equal(result, expected)
|
|
|
|
def test_mismatched_timezone_raises(self):
|
|
arr = DatetimeArray(
|
|
np.array(["2000-01-01T06:00:00"], dtype="M8[ns]"),
|
|
dtype=DatetimeTZDtype(tz="US/Central"),
|
|
)
|
|
dtype = DatetimeTZDtype(tz="US/Eastern")
|
|
with pytest.raises(TypeError, match="Timezone of the array"):
|
|
DatetimeArray(arr, dtype=dtype)
|
|
|
|
def test_non_array_raises(self):
|
|
with pytest.raises(ValueError, match="list"):
|
|
DatetimeArray([1, 2, 3])
|
|
|
|
def test_other_type_raises(self):
|
|
with pytest.raises(
|
|
ValueError, match="The dtype of 'values' is incorrect.*bool"
|
|
):
|
|
DatetimeArray(np.array([1, 2, 3], dtype="bool"))
|
|
|
|
def test_incorrect_dtype_raises(self):
|
|
with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):
|
|
DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="category")
|
|
|
|
def test_freq_infer_raises(self):
|
|
with pytest.raises(ValueError, match="Frequency inference"):
|
|
DatetimeArray(np.array([1, 2, 3], dtype="i8"), freq="infer")
|
|
|
|
def test_copy(self):
|
|
data = np.array([1, 2, 3], dtype="M8[ns]")
|
|
arr = DatetimeArray(data, copy=False)
|
|
assert arr._data is data
|
|
|
|
arr = DatetimeArray(data, copy=True)
|
|
assert arr._data is not data
|
|
|
|
|
|
class TestDatetimeArrayComparisons:
|
|
# TODO: merge this into tests/arithmetic/test_datetime64 once it is
|
|
# sufficiently robust
|
|
|
|
def test_cmp_dt64_arraylike_tznaive(self, all_compare_operators):
|
|
# arbitrary tz-naive DatetimeIndex
|
|
opname = all_compare_operators.strip("_")
|
|
op = getattr(operator, opname)
|
|
|
|
dti = pd.date_range("2016-01-1", freq="MS", periods=9, tz=None)
|
|
arr = DatetimeArray(dti)
|
|
assert arr.freq == dti.freq
|
|
assert arr.tz == dti.tz
|
|
|
|
right = dti
|
|
|
|
expected = np.ones(len(arr), dtype=bool)
|
|
if opname in ["ne", "gt", "lt"]:
|
|
# for these the comparisons should be all-False
|
|
expected = ~expected
|
|
|
|
result = op(arr, arr)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
for other in [right, np.array(right)]:
|
|
# TODO: add list and tuple, and object-dtype once those
|
|
# are fixed in the constructor
|
|
result = op(arr, other)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
result = op(other, arr)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
|
|
class TestDatetimeArray:
|
|
def test_astype_to_same(self):
|
|
arr = DatetimeArray._from_sequence(["2000"], tz="US/Central")
|
|
result = arr.astype(DatetimeTZDtype(tz="US/Central"), copy=False)
|
|
assert result is arr
|
|
|
|
@pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
|
|
def test_astype_int(self, dtype):
|
|
arr = DatetimeArray._from_sequence([pd.Timestamp("2000"), pd.Timestamp("2001")])
|
|
result = arr.astype(dtype)
|
|
|
|
if np.dtype(dtype).kind == "u":
|
|
expected_dtype = np.dtype("uint64")
|
|
else:
|
|
expected_dtype = np.dtype("int64")
|
|
expected = arr.astype(expected_dtype)
|
|
|
|
assert result.dtype == expected_dtype
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_tz_setter_raises(self):
|
|
arr = DatetimeArray._from_sequence(["2000"], tz="US/Central")
|
|
with pytest.raises(AttributeError, match="tz_localize"):
|
|
arr.tz = "UTC"
|
|
|
|
def test_setitem_different_tz_raises(self):
|
|
data = np.array([1, 2, 3], dtype="M8[ns]")
|
|
arr = DatetimeArray(data, copy=False, dtype=DatetimeTZDtype(tz="US/Central"))
|
|
with pytest.raises(ValueError, match="None"):
|
|
arr[0] = pd.Timestamp("2000")
|
|
|
|
with pytest.raises(ValueError, match="US/Central"):
|
|
arr[0] = pd.Timestamp("2000", tz="US/Eastern")
|
|
|
|
def test_setitem_clears_freq(self):
|
|
a = DatetimeArray(pd.date_range("2000", periods=2, freq="D", tz="US/Central"))
|
|
a[0] = pd.Timestamp("2000", tz="US/Central")
|
|
assert a.freq is None
|
|
|
|
def test_repeat_preserves_tz(self):
|
|
dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central")
|
|
arr = DatetimeArray(dti)
|
|
|
|
repeated = arr.repeat([1, 1])
|
|
|
|
# preserves tz and values, but not freq
|
|
expected = DatetimeArray(arr.asi8, freq=None, dtype=arr.dtype)
|
|
tm.assert_equal(repeated, expected)
|
|
|
|
def test_value_counts_preserves_tz(self):
|
|
dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central")
|
|
arr = DatetimeArray(dti).repeat([4, 3])
|
|
|
|
result = arr.value_counts()
|
|
|
|
# Note: not tm.assert_index_equal, since `freq`s do not match
|
|
assert result.index.equals(dti)
|
|
|
|
arr[-2] = pd.NaT
|
|
result = arr.value_counts()
|
|
expected = pd.Series([1, 4, 2], index=[pd.NaT, dti[0], dti[1]])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("method", ["pad", "backfill"])
|
|
def test_fillna_preserves_tz(self, method):
|
|
dti = pd.date_range("2000-01-01", periods=5, freq="D", tz="US/Central")
|
|
arr = DatetimeArray(dti, copy=True)
|
|
arr[2] = pd.NaT
|
|
|
|
fill_val = dti[1] if method == "pad" else dti[3]
|
|
expected = DatetimeArray._from_sequence(
|
|
[dti[0], dti[1], fill_val, dti[3], dti[4]], freq=None, tz="US/Central"
|
|
)
|
|
|
|
result = arr.fillna(method=method)
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
# assert that arr and dti were not modified in-place
|
|
assert arr[2] is pd.NaT
|
|
assert dti[2] == pd.Timestamp("2000-01-03", tz="US/Central")
|
|
|
|
def test_array_interface_tz(self):
|
|
tz = "US/Central"
|
|
data = DatetimeArray(pd.date_range("2017", periods=2, tz=tz))
|
|
result = np.asarray(data)
|
|
|
|
expected = np.array(
|
|
[
|
|
pd.Timestamp("2017-01-01T00:00:00", tz=tz),
|
|
pd.Timestamp("2017-01-02T00:00:00", tz=tz),
|
|
],
|
|
dtype=object,
|
|
)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
result = np.asarray(data, dtype=object)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
result = np.asarray(data, dtype="M8[ns]")
|
|
|
|
expected = np.array(
|
|
["2017-01-01T06:00:00", "2017-01-02T06:00:00"], dtype="M8[ns]"
|
|
)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_array_interface(self):
|
|
data = DatetimeArray(pd.date_range("2017", periods=2))
|
|
expected = np.array(
|
|
["2017-01-01T00:00:00", "2017-01-02T00:00:00"], dtype="datetime64[ns]"
|
|
)
|
|
|
|
result = np.asarray(data)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
result = np.asarray(data, dtype=object)
|
|
expected = np.array(
|
|
[pd.Timestamp("2017-01-01T00:00:00"), pd.Timestamp("2017-01-02T00:00:00")],
|
|
dtype=object,
|
|
)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
|
|
class TestSequenceToDT64NS:
|
|
def test_tz_dtype_mismatch_raises(self):
|
|
arr = DatetimeArray._from_sequence(["2000"], tz="US/Central")
|
|
with pytest.raises(TypeError, match="data is already tz-aware"):
|
|
sequence_to_dt64ns(arr, dtype=DatetimeTZDtype(tz="UTC"))
|
|
|
|
def test_tz_dtype_matches(self):
|
|
arr = DatetimeArray._from_sequence(["2000"], tz="US/Central")
|
|
result, _, _ = sequence_to_dt64ns(arr, dtype=DatetimeTZDtype(tz="US/Central"))
|
|
tm.assert_numpy_array_equal(arr._data, result)
|
|
|
|
|
|
class TestReductions:
|
|
@pytest.mark.parametrize("tz", [None, "US/Central"])
|
|
def test_min_max(self, tz):
|
|
arr = DatetimeArray._from_sequence(
|
|
[
|
|
"2000-01-03",
|
|
"2000-01-03",
|
|
"NaT",
|
|
"2000-01-02",
|
|
"2000-01-05",
|
|
"2000-01-04",
|
|
],
|
|
tz=tz,
|
|
)
|
|
|
|
result = arr.min()
|
|
expected = pd.Timestamp("2000-01-02", tz=tz)
|
|
assert result == expected
|
|
|
|
result = arr.max()
|
|
expected = pd.Timestamp("2000-01-05", tz=tz)
|
|
assert result == expected
|
|
|
|
result = arr.min(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
result = arr.max(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
@pytest.mark.parametrize("tz", [None, "US/Central"])
|
|
@pytest.mark.parametrize("skipna", [True, False])
|
|
def test_min_max_empty(self, skipna, tz):
|
|
arr = DatetimeArray._from_sequence([], tz=tz)
|
|
result = arr.min(skipna=skipna)
|
|
assert result is pd.NaT
|
|
|
|
result = arr.max(skipna=skipna)
|
|
assert result is pd.NaT
|