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207 lines
5.9 KiB
207 lines
5.9 KiB
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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DatetimeIndex,
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Series,
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Timestamp,
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date_range,
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isna,
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notna,
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offsets,
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)
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import pandas._testing as tm
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class TestSeriesAsof:
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def test_asof_nanosecond_index_access(self):
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ts = Timestamp("20130101").value
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dti = DatetimeIndex([ts + 50 + i for i in range(100)])
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ser = Series(np.random.randn(100), index=dti)
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first_value = ser.asof(ser.index[0])
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# this used to not work bc parsing was done by dateutil that didn't
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# handle nanoseconds
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assert first_value == ser["2013-01-01 00:00:00.000000050+0000"]
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expected_ts = np.datetime64("2013-01-01 00:00:00.000000050", "ns")
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assert first_value == ser[Timestamp(expected_ts)]
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def test_basic(self):
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# array or list or dates
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N = 50
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rng = date_range("1/1/1990", periods=N, freq="53s")
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ts = Series(np.random.randn(N), index=rng)
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ts.iloc[15:30] = np.nan
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dates = date_range("1/1/1990", periods=N * 3, freq="25s")
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result = ts.asof(dates)
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assert notna(result).all()
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lb = ts.index[14]
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ub = ts.index[30]
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result = ts.asof(list(dates))
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assert notna(result).all()
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lb = ts.index[14]
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ub = ts.index[30]
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mask = (result.index >= lb) & (result.index < ub)
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rs = result[mask]
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assert (rs == ts[lb]).all()
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val = result[result.index[result.index >= ub][0]]
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assert ts[ub] == val
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def test_scalar(self):
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N = 30
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rng = date_range("1/1/1990", periods=N, freq="53s")
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ts = Series(np.arange(N), index=rng)
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ts.iloc[5:10] = np.NaN
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ts.iloc[15:20] = np.NaN
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val1 = ts.asof(ts.index[7])
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val2 = ts.asof(ts.index[19])
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assert val1 == ts[4]
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assert val2 == ts[14]
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# accepts strings
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val1 = ts.asof(str(ts.index[7]))
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assert val1 == ts[4]
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# in there
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result = ts.asof(ts.index[3])
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assert result == ts[3]
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# no as of value
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d = ts.index[0] - offsets.BDay()
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assert np.isnan(ts.asof(d))
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def test_with_nan(self):
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# basic asof test
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rng = date_range("1/1/2000", "1/2/2000", freq="4h")
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s = Series(np.arange(len(rng)), index=rng)
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r = s.resample("2h").mean()
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result = r.asof(r.index)
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expected = Series(
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[0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6.0],
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index=date_range("1/1/2000", "1/2/2000", freq="2h"),
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)
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tm.assert_series_equal(result, expected)
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r.iloc[3:5] = np.nan
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result = r.asof(r.index)
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expected = Series(
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[0, 0, 1, 1, 1, 1, 3, 3, 4, 4, 5, 5, 6.0],
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index=date_range("1/1/2000", "1/2/2000", freq="2h"),
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)
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tm.assert_series_equal(result, expected)
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r.iloc[-3:] = np.nan
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result = r.asof(r.index)
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expected = Series(
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[0, 0, 1, 1, 1, 1, 3, 3, 4, 4, 4, 4, 4.0],
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index=date_range("1/1/2000", "1/2/2000", freq="2h"),
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)
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tm.assert_series_equal(result, expected)
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def test_periodindex(self):
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from pandas import (
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PeriodIndex,
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period_range,
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)
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# array or list or dates
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N = 50
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rng = period_range("1/1/1990", periods=N, freq="H")
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ts = Series(np.random.randn(N), index=rng)
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ts.iloc[15:30] = np.nan
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dates = date_range("1/1/1990", periods=N * 3, freq="37min")
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result = ts.asof(dates)
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assert notna(result).all()
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lb = ts.index[14]
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ub = ts.index[30]
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result = ts.asof(list(dates))
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assert notna(result).all()
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lb = ts.index[14]
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ub = ts.index[30]
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pix = PeriodIndex(result.index.values, freq="H")
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mask = (pix >= lb) & (pix < ub)
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rs = result[mask]
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assert (rs == ts[lb]).all()
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ts.iloc[5:10] = np.nan
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ts.iloc[15:20] = np.nan
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val1 = ts.asof(ts.index[7])
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val2 = ts.asof(ts.index[19])
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assert val1 == ts[4]
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assert val2 == ts[14]
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# accepts strings
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val1 = ts.asof(str(ts.index[7]))
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assert val1 == ts[4]
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# in there
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assert ts.asof(ts.index[3]) == ts[3]
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# no as of value
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d = ts.index[0].to_timestamp() - offsets.BDay()
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assert isna(ts.asof(d))
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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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ts.asof(rng.asfreq("D"))
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def test_errors(self):
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s = Series(
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[1, 2, 3],
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index=[Timestamp("20130101"), Timestamp("20130103"), Timestamp("20130102")],
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)
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# non-monotonic
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assert not s.index.is_monotonic
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with pytest.raises(ValueError, match="requires a sorted index"):
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s.asof(s.index[0])
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# subset with Series
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N = 10
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rng = date_range("1/1/1990", periods=N, freq="53s")
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s = Series(np.random.randn(N), index=rng)
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with pytest.raises(ValueError, match="not valid for Series"):
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s.asof(s.index[0], subset="foo")
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def test_all_nans(self):
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# GH 15713
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# series is all nans
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# testing non-default indexes
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N = 50
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rng = date_range("1/1/1990", periods=N, freq="53s")
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dates = date_range("1/1/1990", periods=N * 3, freq="25s")
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result = Series(np.nan, index=rng).asof(dates)
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expected = Series(np.nan, index=dates)
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tm.assert_series_equal(result, expected)
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# testing scalar input
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date = date_range("1/1/1990", periods=N * 3, freq="25s")[0]
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result = Series(np.nan, index=rng).asof(date)
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assert isna(result)
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# test name is propagated
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result = Series(np.nan, index=[1, 2, 3, 4], name="test").asof([4, 5])
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expected = Series(np.nan, index=[4, 5], name="test")
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tm.assert_series_equal(result, expected)
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