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import re
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
Series,
Timestamp,
date_range,
)
import pandas._testing as tm
class TestDatetimeIndex:
def test_get_loc_naive_dti_aware_str_deprecated(self):
# GH#46903
ts = Timestamp("20130101")._value
dti = pd.DatetimeIndex([ts + 50 + i for i in range(100)])
ser = Series(range(100), index=dti)
key = "2013-01-01 00:00:00.000000050+0000"
msg = re.escape(repr(key))
with pytest.raises(KeyError, match=msg):
ser[key]
with pytest.raises(KeyError, match=msg):
dti.get_loc(key)
def test_indexing_with_datetime_tz(self):
# GH#8260
# support datetime64 with tz
idx = Index(date_range("20130101", periods=3, tz="US/Eastern"), name="foo")
dr = date_range("20130110", periods=3)
df = DataFrame({"A": idx, "B": dr})
df["C"] = idx
df.iloc[1, 1] = pd.NaT
df.iloc[1, 2] = pd.NaT
expected = Series(
[Timestamp("2013-01-02 00:00:00-0500", tz="US/Eastern"), pd.NaT, pd.NaT],
index=list("ABC"),
dtype="object",
name=1,
)
# indexing
result = df.iloc[1]
tm.assert_series_equal(result, expected)
result = df.loc[1]
tm.assert_series_equal(result, expected)
def test_indexing_fast_xs(self):
# indexing - fast_xs
df = DataFrame({"a": date_range("2014-01-01", periods=10, tz="UTC")})
result = df.iloc[5]
expected = Series(
[Timestamp("2014-01-06 00:00:00+0000", tz="UTC")],
index=["a"],
name=5,
dtype="M8[ns, UTC]",
)
tm.assert_series_equal(result, expected)
result = df.loc[5]
tm.assert_series_equal(result, expected)
# indexing - boolean
result = df[df.a > df.a[3]]
expected = df.iloc[4:]
tm.assert_frame_equal(result, expected)
def test_consistency_with_tz_aware_scalar(self):
# xef gh-12938
# various ways of indexing the same tz-aware scalar
df = Series([Timestamp("2016-03-30 14:35:25", tz="Europe/Brussels")]).to_frame()
df = pd.concat([df, df]).reset_index(drop=True)
expected = Timestamp("2016-03-30 14:35:25+0200", tz="Europe/Brussels")
result = df[0][0]
assert result == expected
result = df.iloc[0, 0]
assert result == expected
result = df.loc[0, 0]
assert result == expected
result = df.iat[0, 0]
assert result == expected
result = df.at[0, 0]
assert result == expected
result = df[0].loc[0]
assert result == expected
result = df[0].at[0]
assert result == expected
def test_indexing_with_datetimeindex_tz(self, indexer_sl):
# GH 12050
# indexing on a series with a datetimeindex with tz
index = date_range("2015-01-01", periods=2, tz="utc")
ser = Series(range(2), index=index, dtype="int64")
# list-like indexing
for sel in (index, list(index)):
# getitem
result = indexer_sl(ser)[sel]
expected = ser.copy()
if sel is not index:
expected.index = expected.index._with_freq(None)
tm.assert_series_equal(result, expected)
# setitem
result = ser.copy()
indexer_sl(result)[sel] = 1
expected = Series(1, index=index)
tm.assert_series_equal(result, expected)
# single element indexing
# getitem
assert indexer_sl(ser)[index[1]] == 1
# setitem
result = ser.copy()
indexer_sl(result)[index[1]] = 5
expected = Series([0, 5], index=index)
tm.assert_series_equal(result, expected)
def test_nanosecond_getitem_setitem_with_tz(self):
# GH 11679
data = ["2016-06-28 08:30:00.123456789"]
index = pd.DatetimeIndex(data, dtype="datetime64[ns, America/Chicago]")
df = DataFrame({"a": [10]}, index=index)
result = df.loc[df.index[0]]
expected = Series(10, index=["a"], name=df.index[0])
tm.assert_series_equal(result, expected)
result = df.copy()
result.loc[df.index[0], "a"] = -1
expected = DataFrame(-1, index=index, columns=["a"])
tm.assert_frame_equal(result, expected)
def test_getitem_str_slice_millisecond_resolution(self, frame_or_series):
# GH#33589
keys = [
"2017-10-25T16:25:04.151",
"2017-10-25T16:25:04.252",
"2017-10-25T16:50:05.237",
"2017-10-25T16:50:05.238",
]
obj = frame_or_series(
[1, 2, 3, 4],
index=[Timestamp(x) for x in keys],
)
result = obj[keys[1] : keys[2]]
expected = frame_or_series(
[2, 3],
index=[
Timestamp(keys[1]),
Timestamp(keys[2]),
],
)
tm.assert_equal(result, expected)
def test_getitem_pyarrow_index(self, frame_or_series):
# GH 53644
pytest.importorskip("pyarrow")
obj = frame_or_series(
range(5),
index=date_range("2020", freq="D", periods=5).astype(
"timestamp[us][pyarrow]"
),
)
result = obj.loc[obj.index[:-3]]
expected = frame_or_series(
range(2),
index=date_range("2020", freq="D", periods=2).astype(
"timestamp[us][pyarrow]"
),
)
tm.assert_equal(result, expected)
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