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