import numpy as np
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import pytest
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from pandas._libs.tslibs import IncompatibleFrequency
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from pandas import (
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DataFrame,
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Period,
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Series,
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Timestamp,
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date_range,
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period_range,
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to_datetime,
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)
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import pandas._testing as tm
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@pytest.fixture
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def date_range_frame():
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"""
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Fixture for DataFrame of ints with date_range index
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Columns are ['A', 'B'].
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"""
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N = 50
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rng = date_range("1/1/1990", periods=N, freq="53s")
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return DataFrame({"A": np.arange(N), "B": np.arange(N)}, index=rng)
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class TestFrameAsof:
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def test_basic(self, date_range_frame):
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# Explicitly cast to float to avoid implicit cast when setting np.nan
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df = date_range_frame.astype({"A": "float"})
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N = 50
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df.loc[df.index[15:30], "A"] = np.nan
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dates = date_range("1/1/1990", periods=N * 3, freq="25s")
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result = df.asof(dates)
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assert result.notna().all(1).all()
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lb = df.index[14]
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ub = df.index[30]
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dates = list(dates)
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result = df.asof(dates)
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assert result.notna().all(1).all()
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mask = (result.index >= lb) & (result.index < ub)
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rs = result[mask]
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assert (rs == 14).all(1).all()
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def test_subset(self, date_range_frame):
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N = 10
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# explicitly cast to float to avoid implicit upcast when setting to np.nan
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df = date_range_frame.iloc[:N].copy().astype({"A": "float"})
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df.loc[df.index[4:8], "A"] = np.nan
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dates = date_range("1/1/1990", periods=N * 3, freq="25s")
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# with a subset of A should be the same
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result = df.asof(dates, subset="A")
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expected = df.asof(dates)
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tm.assert_frame_equal(result, expected)
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# same with A/B
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result = df.asof(dates, subset=["A", "B"])
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expected = df.asof(dates)
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tm.assert_frame_equal(result, expected)
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# B gives df.asof
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result = df.asof(dates, subset="B")
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expected = df.resample("25s", closed="right").ffill().reindex(dates)
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expected.iloc[20:] = 9
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# no "missing", so "B" can retain int dtype (df["A"].dtype platform-dependent)
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expected["B"] = expected["B"].astype(df["B"].dtype)
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tm.assert_frame_equal(result, expected)
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def test_missing(self, date_range_frame):
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# GH 15118
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# no match found - `where` value before earliest date in index
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N = 10
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df = date_range_frame.iloc[:N].copy()
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result = df.asof("1989-12-31")
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expected = Series(
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index=["A", "B"], name=Timestamp("1989-12-31"), dtype=np.float64
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)
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tm.assert_series_equal(result, expected)
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result = df.asof(to_datetime(["1989-12-31"]))
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expected = DataFrame(
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index=to_datetime(["1989-12-31"]), columns=["A", "B"], dtype="float64"
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)
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tm.assert_frame_equal(result, expected)
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# Check that we handle PeriodIndex correctly, dont end up with
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# period.ordinal for series name
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df = df.to_period("D")
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result = df.asof("1989-12-31")
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assert isinstance(result.name, Period)
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def test_asof_all_nans(self, frame_or_series):
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# GH 15713
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# DataFrame/Series is all nans
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result = frame_or_series([np.nan]).asof([0])
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expected = frame_or_series([np.nan])
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tm.assert_equal(result, expected)
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def test_all_nans(self, date_range_frame):
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# GH 15713
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# DataFrame is all nans
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# testing non-default indexes, multiple inputs
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N = 150
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rng = date_range_frame.index
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dates = date_range("1/1/1990", periods=N, freq="25s")
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result = DataFrame(np.nan, index=rng, columns=["A"]).asof(dates)
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expected = DataFrame(np.nan, index=dates, columns=["A"])
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tm.assert_frame_equal(result, expected)
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# testing multiple columns
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dates = date_range("1/1/1990", periods=N, freq="25s")
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result = DataFrame(np.nan, index=rng, columns=["A", "B", "C"]).asof(dates)
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expected = DataFrame(np.nan, index=dates, columns=["A", "B", "C"])
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tm.assert_frame_equal(result, expected)
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# testing scalar input
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result = DataFrame(np.nan, index=[1, 2], columns=["A", "B"]).asof([3])
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expected = DataFrame(np.nan, index=[3], columns=["A", "B"])
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tm.assert_frame_equal(result, expected)
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result = DataFrame(np.nan, index=[1, 2], columns=["A", "B"]).asof(3)
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expected = Series(np.nan, index=["A", "B"], name=3)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"stamp,expected",
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[
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(
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Timestamp("2018-01-01 23:22:43.325+00:00"),
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Series(2, name=Timestamp("2018-01-01 23:22:43.325+00:00")),
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),
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(
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Timestamp("2018-01-01 22:33:20.682+01:00"),
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Series(1, name=Timestamp("2018-01-01 22:33:20.682+01:00")),
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),
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],
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)
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def test_time_zone_aware_index(self, stamp, expected):
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# GH21194
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# Testing awareness of DataFrame index considering different
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# UTC and timezone
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df = DataFrame(
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data=[1, 2],
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index=[
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Timestamp("2018-01-01 21:00:05.001+00:00"),
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Timestamp("2018-01-01 22:35:10.550+00:00"),
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],
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)
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result = df.asof(stamp)
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tm.assert_series_equal(result, expected)
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def test_is_copy(self, date_range_frame):
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# GH-27357, GH-30784: ensure the result of asof is an actual copy and
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# doesn't track the parent dataframe / doesn't give SettingWithCopy warnings
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df = date_range_frame.astype({"A": "float"})
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N = 50
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df.loc[df.index[15:30], "A"] = np.nan
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dates = date_range("1/1/1990", periods=N * 3, freq="25s")
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result = df.asof(dates)
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with tm.assert_produces_warning(None):
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result["C"] = 1
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def test_asof_periodindex_mismatched_freq(self):
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N = 50
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rng = period_range("1/1/1990", periods=N, freq="H")
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df = DataFrame(np.random.randn(N), index=rng)
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# Mismatched freq
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msg = "Input has different freq"
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with pytest.raises(IncompatibleFrequency, match=msg):
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df.asof(rng.asfreq("D"))
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def test_asof_preserves_bool_dtype(self):
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# GH#16063 was casting bools to floats
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dti = date_range("2017-01-01", freq="MS", periods=4)
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ser = Series([True, False, True], index=dti[:-1])
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ts = dti[-1]
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res = ser.asof([ts])
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expected = Series([True], index=[ts])
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tm.assert_series_equal(res, expected)
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