| Current File : //usr/local/lib64/python3.6/site-packages/pandas/tests/arrays/test_datetimes.py |
"""
Tests for DatetimeArray
"""
import operator
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import DatetimeTZDtype
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import DatetimeArray
from pandas.core.arrays.datetimes import sequence_to_dt64ns
class TestDatetimeArrayConstructor:
def test_from_sequence_invalid_type(self):
mi = pd.MultiIndex.from_product([np.arange(5), np.arange(5)])
with pytest.raises(TypeError, match="Cannot create a DatetimeArray"):
DatetimeArray._from_sequence(mi)
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"):
# 3-dim, we allow 2D to sneak in for ops purposes GH#29853
DatetimeArray(arr.reshape(2, 2, 1))
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_bool_dtype_raises(self):
arr = np.array([1, 2, 3], dtype="bool")
with pytest.raises(
ValueError, match="The dtype of 'values' is incorrect.*bool"
):
DatetimeArray(arr)
msg = r"dtype bool cannot be converted to datetime64\[ns\]"
with pytest.raises(TypeError, match=msg):
DatetimeArray._from_sequence(arr)
with pytest.raises(TypeError, match=msg):
sequence_to_dt64ns(arr)
with pytest.raises(TypeError, match=msg):
pd.DatetimeIndex(arr)
with pytest.raises(TypeError, match=msg):
pd.to_datetime(arr)
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", ["datetime64[ns]", "datetime64[ns, UTC]"])
@pytest.mark.parametrize(
"other", ["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, CET]"]
)
def test_astype_copies(self, dtype, other):
# https://github.com/pandas-dev/pandas/pull/32490
s = pd.Series([1, 2], dtype=dtype)
orig = s.copy()
t = s.astype(other)
t[:] = pd.NaT
tm.assert_series_equal(s, orig)
@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(TypeError, match="Cannot compare tz-naive and tz-aware"):
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
@pytest.mark.parametrize(
"obj",
[
pd.Timestamp.now(),
pd.Timestamp.now().to_datetime64(),
pd.Timestamp.now().to_pydatetime(),
],
)
def test_setitem_objects(self, obj):
# make sure we accept datetime64 and datetime in addition to Timestamp
dti = pd.date_range("2000", periods=2, freq="D")
arr = dti._data
arr[0] = obj
assert arr[0] == obj
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)
@pytest.mark.parametrize("index", [True, False])
def test_searchsorted_different_tz(self, index):
data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
arr = DatetimeArray(data, freq="D").tz_localize("Asia/Tokyo")
if index:
arr = pd.Index(arr)
expected = arr.searchsorted(arr[2])
result = arr.searchsorted(arr[2].tz_convert("UTC"))
assert result == expected
expected = arr.searchsorted(arr[2:6])
result = arr.searchsorted(arr[2:6].tz_convert("UTC"))
tm.assert_equal(result, expected)
@pytest.mark.parametrize("index", [True, False])
def test_searchsorted_tzawareness_compat(self, index):
data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
arr = DatetimeArray(data, freq="D")
if index:
arr = pd.Index(arr)
mismatch = arr.tz_localize("Asia/Tokyo")
msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
with pytest.raises(TypeError, match=msg):
arr.searchsorted(mismatch[0])
with pytest.raises(TypeError, match=msg):
arr.searchsorted(mismatch)
with pytest.raises(TypeError, match=msg):
mismatch.searchsorted(arr[0])
with pytest.raises(TypeError, match=msg):
mismatch.searchsorted(arr)
@pytest.mark.parametrize(
"other",
[
1,
np.int64(1),
1.0,
np.timedelta64("NaT"),
pd.Timedelta(days=2),
"invalid",
np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9,
np.arange(10).view("timedelta64[ns]") * 24 * 3600 * 10 ** 9,
pd.Timestamp.now().to_period("D"),
],
)
@pytest.mark.parametrize("index", [True, False])
def test_searchsorted_invalid_types(self, other, index):
data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
arr = DatetimeArray(data, freq="D")
if index:
arr = pd.Index(arr)
msg = "|".join(
[
"searchsorted requires compatible dtype or scalar",
"Unexpected type for 'value'",
]
)
with pytest.raises(TypeError, match=msg):
arr.searchsorted(other)
def test_shift_fill_value(self):
dti = pd.date_range("2016-01-01", periods=3)
dta = dti._data
expected = DatetimeArray(np.roll(dta._data, 1))
fv = dta[-1]
for fill_value in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
result = dta.shift(1, fill_value=fill_value)
tm.assert_datetime_array_equal(result, expected)
dta = dta.tz_localize("UTC")
expected = expected.tz_localize("UTC")
fv = dta[-1]
for fill_value in [fv, fv.to_pydatetime()]:
result = dta.shift(1, fill_value=fill_value)
tm.assert_datetime_array_equal(result, expected)
def test_shift_value_tzawareness_mismatch(self):
dti = pd.date_range("2016-01-01", periods=3)
dta = dti._data
fv = dta[-1].tz_localize("UTC")
for invalid in [fv, fv.to_pydatetime()]:
with pytest.raises(TypeError, match="Cannot compare"):
dta.shift(1, fill_value=invalid)
dta = dta.tz_localize("UTC")
fv = dta[-1].tz_localize(None)
for invalid in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
with pytest.raises(TypeError, match="Cannot compare"):
dta.shift(1, fill_value=invalid)
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