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Nov 26, 2020
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CLN: inconsistent namespace usage in tests.indexes #38076

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6 changes: 3 additions & 3 deletions pandas/tests/indexes/datetimes/test_astype.py
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Original file line number Diff line number Diff line change
Expand Up @@ -172,7 +172,7 @@ def test_astype_object(self):

@pytest.mark.parametrize("tz", [None, "Asia/Tokyo"])
def test_astype_object_tz(self, tz):
idx = pd.date_range(start="2013-01-01", periods=4, freq="M", name="idx", tz=tz)
idx = date_range(start="2013-01-01", periods=4, freq="M", name="idx", tz=tz)
expected_list = [
Timestamp("2013-01-31", tz=tz),
Timestamp("2013-02-28", tz=tz),
Expand Down Expand Up @@ -288,7 +288,7 @@ def test_dti_astype_period(self):
class TestAstype:
@pytest.mark.parametrize("tz", [None, "US/Central"])
def test_astype_category(self, tz):
obj = pd.date_range("2000", periods=2, tz=tz, name="idx")
obj = date_range("2000", periods=2, tz=tz, name="idx")
result = obj.astype("category")
expected = pd.CategoricalIndex(
[Timestamp("2000-01-01", tz=tz), Timestamp("2000-01-02", tz=tz)],
Expand All @@ -302,7 +302,7 @@ def test_astype_category(self, tz):

@pytest.mark.parametrize("tz", [None, "US/Central"])
def test_astype_array_fallback(self, tz):
obj = pd.date_range("2000", periods=2, tz=tz, name="idx")
obj = date_range("2000", periods=2, tz=tz, name="idx")
result = obj.astype(bool)
expected = Index(np.array([True, True]), name="idx")
tm.assert_index_equal(result, expected)
Expand Down
22 changes: 11 additions & 11 deletions pandas/tests/indexes/datetimes/test_constructors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@ def test_freq_validation_with_nat(self, dt_cls):
@pytest.mark.parametrize(
"index",
[
pd.date_range("2016-01-01", periods=5, tz="US/Pacific"),
date_range("2016-01-01", periods=5, tz="US/Pacific"),
pd.timedelta_range("1 Day", periods=5),
],
)
Expand Down Expand Up @@ -103,14 +103,14 @@ def test_construction_caching(self):

df = pd.DataFrame(
{
"dt": pd.date_range("20130101", periods=3),
"dttz": pd.date_range("20130101", periods=3, tz="US/Eastern"),
"dt": date_range("20130101", periods=3),
"dttz": date_range("20130101", periods=3, tz="US/Eastern"),
"dt_with_null": [
Timestamp("20130101"),
pd.NaT,
Timestamp("20130103"),
],
"dtns": pd.date_range("20130101", periods=3, freq="ns"),
"dtns": date_range("20130101", periods=3, freq="ns"),
}
)
assert df.dttz.dtype.tz.zone == "US/Eastern"
Expand All @@ -121,7 +121,7 @@ def test_construction_caching(self):
)
def test_construction_with_alt(self, kwargs, tz_aware_fixture):
tz = tz_aware_fixture
i = pd.date_range("20130101", periods=5, freq="H", tz=tz)
i = date_range("20130101", periods=5, freq="H", tz=tz)
kwargs = {key: attrgetter(val)(i) for key, val in kwargs.items()}
result = DatetimeIndex(i, **kwargs)
tm.assert_index_equal(i, result)
Expand All @@ -132,7 +132,7 @@ def test_construction_with_alt(self, kwargs, tz_aware_fixture):
)
def test_construction_with_alt_tz_localize(self, kwargs, tz_aware_fixture):
tz = tz_aware_fixture
i = pd.date_range("20130101", periods=5, freq="H", tz=tz)
i = date_range("20130101", periods=5, freq="H", tz=tz)
i = i._with_freq(None)
kwargs = {key: attrgetter(val)(i) for key, val in kwargs.items()}

Expand Down Expand Up @@ -754,7 +754,7 @@ def test_construction_int_rountrip(self, tz_naive_fixture):

def test_construction_from_replaced_timestamps_with_dst(self):
# GH 18785
index = pd.date_range(
index = date_range(
Timestamp(2000, 1, 1),
Timestamp(2005, 1, 1),
freq="MS",
Expand Down Expand Up @@ -804,15 +804,15 @@ def test_constructor_with_ambiguous_keyword_arg(self):
start = Timestamp(year=2020, month=11, day=1, hour=1).tz_localize(
timezone, ambiguous=False
)
result = pd.date_range(start=start, periods=2, ambiguous=False)
result = date_range(start=start, periods=2, ambiguous=False)
tm.assert_index_equal(result, expected)

# ambiguous keyword in end
timezone = "America/New_York"
end = Timestamp(year=2020, month=11, day=2, hour=1).tz_localize(
timezone, ambiguous=False
)
result = pd.date_range(end=end, periods=2, ambiguous=False)
result = date_range(end=end, periods=2, ambiguous=False)
tm.assert_index_equal(result, expected)

def test_constructor_with_nonexistent_keyword_arg(self):
Expand All @@ -824,7 +824,7 @@ def test_constructor_with_nonexistent_keyword_arg(self):
start = Timestamp("2015-03-29 02:30:00").tz_localize(
timezone, nonexistent="shift_forward"
)
result = pd.date_range(start=start, periods=2, freq="H")
result = date_range(start=start, periods=2, freq="H")
expected = DatetimeIndex(
[
Timestamp("2015-03-29 03:00:00+02:00", tz=timezone),
Expand All @@ -838,7 +838,7 @@ def test_constructor_with_nonexistent_keyword_arg(self):
end = Timestamp("2015-03-29 02:30:00").tz_localize(
timezone, nonexistent="shift_forward"
)
result = pd.date_range(end=end, periods=2, freq="H")
result = date_range(end=end, periods=2, freq="H")
expected = DatetimeIndex(
[
Timestamp("2015-03-29 01:00:00+01:00", tz=timezone),
Expand Down
28 changes: 14 additions & 14 deletions pandas/tests/indexes/datetimes/test_date_range.py
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Original file line number Diff line number Diff line change
Expand Up @@ -650,10 +650,10 @@ def test_timezone_comparaison_assert(self):
def test_negative_non_tick_frequency_descending_dates(self, tz_aware_fixture):
# GH 23270
tz = tz_aware_fixture
result = pd.date_range(start="2011-06-01", end="2011-01-01", freq="-1MS", tz=tz)
expected = pd.date_range(
end="2011-06-01", start="2011-01-01", freq="1MS", tz=tz
)[::-1]
result = date_range(start="2011-06-01", end="2011-01-01", freq="-1MS", tz=tz)
expected = date_range(end="2011-06-01", start="2011-01-01", freq="1MS", tz=tz)[
::-1
]
tm.assert_index_equal(result, expected)


Expand Down Expand Up @@ -739,10 +739,10 @@ def test_3(self):

def test_precision_finer_than_offset(self):
# GH#9907
result1 = pd.date_range(
result1 = date_range(
start="2015-04-15 00:00:03", end="2016-04-22 00:00:00", freq="Q"
)
result2 = pd.date_range(
result2 = date_range(
start="2015-04-15 00:00:03", end="2015-06-22 00:00:04", freq="W"
)
expected1_list = [
Expand Down Expand Up @@ -788,9 +788,9 @@ def test_mismatching_tz_raises_err(self, start, end):
# issue 18488
msg = "Start and end cannot both be tz-aware with different timezones"
with pytest.raises(TypeError, match=msg):
pd.date_range(start, end)
date_range(start, end)
with pytest.raises(TypeError, match=msg):
pd.date_range(start, end, freq=BDay())
date_range(start, end, freq=BDay())


class TestBusinessDateRange:
Expand Down Expand Up @@ -849,18 +849,18 @@ def test_bdays_and_open_boundaries(self, closed):
# GH 6673
start = "2018-07-21" # Saturday
end = "2018-07-29" # Sunday
result = pd.date_range(start, end, freq="B", closed=closed)
result = date_range(start, end, freq="B", closed=closed)

bday_start = "2018-07-23" # Monday
bday_end = "2018-07-27" # Friday
expected = pd.date_range(bday_start, bday_end, freq="D")
expected = date_range(bday_start, bday_end, freq="D")
tm.assert_index_equal(result, expected)
# Note: we do _not_ expect the freqs to match here

def test_bday_near_overflow(self):
# GH#24252 avoid doing unnecessary addition that _would_ overflow
start = Timestamp.max.floor("D").to_pydatetime()
rng = pd.date_range(start, end=None, periods=1, freq="B")
rng = date_range(start, end=None, periods=1, freq="B")
expected = DatetimeIndex([start], freq="B")
tm.assert_index_equal(rng, expected)

Expand All @@ -869,7 +869,7 @@ def test_bday_overflow_error(self):
msg = "Out of bounds nanosecond timestamp"
start = Timestamp.max.floor("D").to_pydatetime()
with pytest.raises(OutOfBoundsDatetime, match=msg):
pd.date_range(start, periods=2, freq="B")
date_range(start, periods=2, freq="B")


class TestCustomDateRange:
Expand Down Expand Up @@ -995,15 +995,15 @@ def test_all_custom_freq(self, freq):
def test_range_with_millisecond_resolution(self, start_end):
# https://github.com/pandas-dev/pandas/issues/24110
start, end = start_end
result = pd.date_range(start=start, end=end, periods=2, closed="left")
result = date_range(start=start, end=end, periods=2, closed="left")
expected = DatetimeIndex([start])
tm.assert_index_equal(result, expected)


def test_date_range_with_custom_holidays():
# GH 30593
freq = pd.offsets.CustomBusinessHour(start="15:00", holidays=["2020-11-26"])
result = pd.date_range(start="2020-11-25 15:00", periods=4, freq=freq)
result = date_range(start="2020-11-25 15:00", periods=4, freq=freq)
expected = DatetimeIndex(
[
"2020-11-25 15:00:00",
Expand Down
20 changes: 10 additions & 10 deletions pandas/tests/indexes/datetimes/test_datetime.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,7 @@ def test_time_loc(self): # GH8667
step = 24 * 3600

for n in ns:
idx = pd.date_range("2014-11-26", periods=n, freq="S")
idx = date_range("2014-11-26", periods=n, freq="S")
ts = pd.Series(np.random.randn(n), index=idx)
i = np.arange(start, n, step)

Expand All @@ -89,10 +89,10 @@ def test_time_overflow_for_32bit_machines(self):
# overflow.
periods = np.int_(1000)

idx1 = pd.date_range(start="2000", periods=periods, freq="S")
idx1 = date_range(start="2000", periods=periods, freq="S")
assert len(idx1) == periods

idx2 = pd.date_range(end="2000", periods=periods, freq="S")
idx2 = date_range(end="2000", periods=periods, freq="S")
assert len(idx2) == periods

def test_nat(self):
Expand Down Expand Up @@ -251,7 +251,7 @@ def test_ns_index(self):
index = DatetimeIndex(dt, freq=freq, name="time")
self.assert_index_parameters(index)

new_index = pd.date_range(start=index[0], end=index[-1], freq=index.freq)
new_index = date_range(start=index[0], end=index[-1], freq=index.freq)
self.assert_index_parameters(new_index)

def test_factorize(self):
Expand Down Expand Up @@ -304,7 +304,7 @@ def test_factorize(self):
def test_factorize_tz(self, tz_naive_fixture):
tz = tz_naive_fixture
# GH#13750
base = pd.date_range("2016-11-05", freq="H", periods=100, tz=tz)
base = date_range("2016-11-05", freq="H", periods=100, tz=tz)
idx = base.repeat(5)

exp_arr = np.arange(100, dtype=np.intp).repeat(5)
Expand All @@ -317,14 +317,14 @@ def test_factorize_tz(self, tz_naive_fixture):

def test_factorize_dst(self):
# GH 13750
idx = pd.date_range("2016-11-06", freq="H", periods=12, tz="US/Eastern")
idx = date_range("2016-11-06", freq="H", periods=12, tz="US/Eastern")

for obj in [idx, pd.Series(idx)]:
arr, res = obj.factorize()
tm.assert_numpy_array_equal(arr, np.arange(12, dtype=np.intp))
tm.assert_index_equal(res, idx)

idx = pd.date_range("2016-06-13", freq="H", periods=12, tz="US/Eastern")
idx = date_range("2016-06-13", freq="H", periods=12, tz="US/Eastern")

for obj in [idx, pd.Series(idx)]:
arr, res = obj.factorize()
Expand All @@ -350,7 +350,7 @@ def test_unique(self, arr, expected):

def test_asarray_tz_naive(self):
# This shouldn't produce a warning.
idx = pd.date_range("2000", periods=2)
idx = date_range("2000", periods=2)
# M8[ns] by default
result = np.asarray(idx)

Expand All @@ -365,7 +365,7 @@ def test_asarray_tz_naive(self):

def test_asarray_tz_aware(self):
tz = "US/Central"
idx = pd.date_range("2000", periods=2, tz=tz)
idx = date_range("2000", periods=2, tz=tz)
expected = np.array(["2000-01-01T06", "2000-01-02T06"], dtype="M8[ns]")
result = np.asarray(idx, dtype="datetime64[ns]")

Expand Down Expand Up @@ -393,7 +393,7 @@ def test_to_frame_datetime_tz(self):

def test_split_non_utc(self):
# GH 14042
indices = pd.date_range("2016-01-01 00:00:00+0200", freq="S", periods=10)
indices = date_range("2016-01-01 00:00:00+0200", freq="S", periods=10)
result = np.split(indices, indices_or_sections=[])[0]
expected = indices._with_freq(None)
tm.assert_index_equal(result, expected)
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