373 lines
12 KiB
Python
373 lines
12 KiB
Python
from datetime import date, datetime, timedelta
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from dateutil import tz
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import DataFrame, Index, Series, Timestamp, date_range
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import pandas._testing as tm
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class TestDatetimeIndex:
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def test_setitem_with_datetime_tz(self):
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# 16889
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# support .loc with alignment and tz-aware DatetimeIndex
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mask = np.array([True, False, True, False])
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idx = date_range("20010101", periods=4, tz="UTC")
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df = DataFrame({"a": np.arange(4)}, index=idx).astype("float64")
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result = df.copy()
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result.loc[mask, :] = df.loc[mask, :]
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tm.assert_frame_equal(result, df)
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result = df.copy()
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result.loc[mask] = df.loc[mask]
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tm.assert_frame_equal(result, df)
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idx = date_range("20010101", periods=4)
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df = DataFrame({"a": np.arange(4)}, index=idx).astype("float64")
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result = df.copy()
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result.loc[mask, :] = df.loc[mask, :]
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tm.assert_frame_equal(result, df)
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result = df.copy()
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result.loc[mask] = df.loc[mask]
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tm.assert_frame_equal(result, df)
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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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# indexing
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result = df.iloc[1]
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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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tm.assert_series_equal(result, expected)
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result = df.loc[1]
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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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tm.assert_series_equal(result, expected)
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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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# indexing - setting an element
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df = DataFrame(
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data=pd.to_datetime(["2015-03-30 20:12:32", "2015-03-12 00:11:11"]),
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columns=["time"],
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)
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df["new_col"] = ["new", "old"]
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df.time = df.set_index("time").index.tz_localize("UTC")
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v = df[df.new_col == "new"].set_index("time").index.tz_convert("US/Pacific")
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# trying to set a single element on a part of a different timezone
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# this converts to object
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df2 = df.copy()
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df2.loc[df2.new_col == "new", "time"] = v
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expected = Series([v[0], df.loc[1, "time"]], name="time")
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tm.assert_series_equal(df2.time, expected)
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v = df.loc[df.new_col == "new", "time"] + pd.Timedelta("1s")
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df.loc[df.new_col == "new", "time"] = v
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tm.assert_series_equal(df.loc[df.new_col == "new", "time"], v)
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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):
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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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tm.assert_series_equal(ser[sel], ser)
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# setitem
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result = ser.copy()
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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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# .loc getitem
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tm.assert_series_equal(ser.loc[sel], ser)
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# .loc setitem
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result = ser.copy()
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result.loc[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 ser[index[1]] == 1
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# setitem
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result = ser.copy()
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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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# .loc getitem
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assert ser.loc[index[1]] == 1
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# .loc setitem
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result = ser.copy()
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result.loc[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_partial_setting_with_datetimelike_dtype(self):
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# GH9478
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# a datetimeindex alignment issue with partial setting
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df = DataFrame(
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np.arange(6.0).reshape(3, 2),
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columns=list("AB"),
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index=date_range("1/1/2000", periods=3, freq="1H"),
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)
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expected = df.copy()
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expected["C"] = [expected.index[0]] + [pd.NaT, pd.NaT]
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mask = df.A < 1
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df.loc[mask, "C"] = df.loc[mask].index
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tm.assert_frame_equal(df, expected)
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def test_loc_setitem_datetime(self):
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# GH 9516
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dt1 = Timestamp("20130101 09:00:00")
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dt2 = Timestamp("20130101 10:00:00")
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for conv in [
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lambda x: x,
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lambda x: x.to_datetime64(),
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lambda x: x.to_pydatetime(),
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lambda x: np.datetime64(x),
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]:
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df = DataFrame()
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df.loc[conv(dt1), "one"] = 100
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df.loc[conv(dt2), "one"] = 200
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expected = DataFrame({"one": [100.0, 200.0]}, index=[dt1, dt2])
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tm.assert_frame_equal(df, expected)
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def test_series_partial_set_datetime(self):
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# GH 11497
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idx = date_range("2011-01-01", "2011-01-02", freq="D", name="idx")
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ser = Series([0.1, 0.2], index=idx, name="s")
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result = ser.loc[[Timestamp("2011-01-01"), Timestamp("2011-01-02")]]
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exp = Series([0.1, 0.2], index=idx, name="s")
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tm.assert_series_equal(result, exp, check_index_type=True)
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keys = [
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Timestamp("2011-01-02"),
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Timestamp("2011-01-02"),
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Timestamp("2011-01-01"),
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]
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exp = Series(
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[0.2, 0.2, 0.1], index=pd.DatetimeIndex(keys, name="idx"), name="s"
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)
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tm.assert_series_equal(ser.loc[keys], exp, check_index_type=True)
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keys = [
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Timestamp("2011-01-03"),
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Timestamp("2011-01-02"),
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Timestamp("2011-01-03"),
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]
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with pytest.raises(KeyError, match="with any missing labels"):
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ser.loc[keys]
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def test_series_partial_set_period(self):
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# GH 11497
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idx = pd.period_range("2011-01-01", "2011-01-02", freq="D", name="idx")
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ser = Series([0.1, 0.2], index=idx, name="s")
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result = ser.loc[
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[pd.Period("2011-01-01", freq="D"), pd.Period("2011-01-02", freq="D")]
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]
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exp = Series([0.1, 0.2], index=idx, name="s")
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tm.assert_series_equal(result, exp, check_index_type=True)
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keys = [
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pd.Period("2011-01-02", freq="D"),
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pd.Period("2011-01-02", freq="D"),
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pd.Period("2011-01-01", freq="D"),
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]
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exp = Series([0.2, 0.2, 0.1], index=pd.PeriodIndex(keys, name="idx"), name="s")
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tm.assert_series_equal(ser.loc[keys], exp, check_index_type=True)
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keys = [
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pd.Period("2011-01-03", freq="D"),
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pd.Period("2011-01-02", freq="D"),
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pd.Period("2011-01-03", freq="D"),
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]
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with pytest.raises(KeyError, match="with any missing labels"):
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ser.loc[keys]
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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_loc_getitem_across_dst(self):
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# GH 21846
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idx = pd.date_range(
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"2017-10-29 01:30:00", tz="Europe/Berlin", periods=5, freq="30 min"
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)
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series2 = pd.Series([0, 1, 2, 3, 4], index=idx)
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t_1 = pd.Timestamp(
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"2017-10-29 02:30:00+02:00", tz="Europe/Berlin", freq="30min"
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)
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t_2 = pd.Timestamp(
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"2017-10-29 02:00:00+01:00", tz="Europe/Berlin", freq="30min"
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)
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result = series2.loc[t_1:t_2]
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expected = pd.Series([2, 3], index=idx[2:4])
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tm.assert_series_equal(result, expected)
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result = series2[t_1]
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expected = 2
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assert result == expected
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def test_loc_incremental_setitem_with_dst(self):
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# GH 20724
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base = datetime(2015, 11, 1, tzinfo=tz.gettz("US/Pacific"))
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idxs = [base + timedelta(seconds=i * 900) for i in range(16)]
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result = pd.Series([0], index=[idxs[0]])
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for ts in idxs:
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result.loc[ts] = 1
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expected = pd.Series(1, index=idxs)
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tm.assert_series_equal(result, expected)
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def test_loc_setitem_with_existing_dst(self):
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# GH 18308
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start = pd.Timestamp("2017-10-29 00:00:00+0200", tz="Europe/Madrid")
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end = pd.Timestamp("2017-10-29 03:00:00+0100", tz="Europe/Madrid")
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ts = pd.Timestamp("2016-10-10 03:00:00", tz="Europe/Madrid")
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idx = pd.date_range(start, end, closed="left", freq="H")
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result = pd.DataFrame(index=idx, columns=["value"])
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result.loc[ts, "value"] = 12
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expected = pd.DataFrame(
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[np.nan] * len(idx) + [12],
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index=idx.append(pd.DatetimeIndex([ts])),
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columns=["value"],
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dtype=object,
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)
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tm.assert_frame_equal(result, expected)
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def test_loc_str_slicing(self):
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ix = pd.period_range(start="2017-01-01", end="2018-01-01", freq="M")
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ser = ix.to_series()
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result = ser.loc[:"2017-12"]
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expected = ser.iloc[:-1]
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tm.assert_series_equal(result, expected)
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def test_loc_label_slicing(self):
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ix = pd.period_range(start="2017-01-01", end="2018-01-01", freq="M")
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ser = ix.to_series()
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result = ser.loc[: ix[-2]]
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expected = ser.iloc[:-1]
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"slice_, positions",
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[
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[slice(date(2018, 1, 1), None), [0, 1, 2]],
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[slice(date(2019, 1, 2), None), [2]],
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[slice(date(2020, 1, 1), None), []],
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[slice(None, date(2020, 1, 1)), [0, 1, 2]],
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[slice(None, date(2019, 1, 1)), [0]],
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],
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)
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def test_getitem_slice_date(self, slice_, positions):
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# https://github.com/pandas-dev/pandas/issues/31501
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s = pd.Series(
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[0, 1, 2],
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pd.DatetimeIndex(["2019-01-01", "2019-01-01T06:00:00", "2019-01-02"]),
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)
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result = s[slice_]
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expected = s.take(positions)
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tm.assert_series_equal(result, expected)
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