import numpy as np import pytest from pandas._libs.tslibs import IncompatibleFrequency from pandas.errors import Pandas4Warning import pandas as pd import pandas._testing as tm class TestSeriesAsof: def test_asof_nanosecond_index_access(self): ts = pd.Timestamp("20130101").as_unit("ns")._value dti = pd.DatetimeIndex([ts + 50 + i for i in range(100)]) ser = pd.Series(np.random.default_rng(2).standard_normal(100), index=dti) first_value = ser.asof(ser.index[0]) # GH#46903 previously incorrectly was "day" assert dti.resolution == "nanosecond" # this used to not work bc parsing was done by dateutil that didn't # handle nanoseconds assert first_value == ser["2013-01-01 00:00:00.000000050"] expected_ts = np.datetime64("2013-01-01 00:00:00.000000050", "ns") assert first_value == ser[pd.Timestamp(expected_ts)] def test_basic(self): # array or list or dates N = 50 rng = pd.date_range("1/1/1990", periods=N, freq="53s") ts = pd.Series(np.random.default_rng(2).standard_normal(N), index=rng) ts.iloc[15:30] = np.nan dates = pd.date_range("1/1/1990", periods=N * 3, freq="25s") result = ts.asof(dates) assert pd.notna(result).all() lb = ts.index[14] ub = ts.index[30] result = ts.asof(list(dates)) assert pd.notna(result).all() lb = ts.index[14] ub = ts.index[30] mask = (result.index >= lb) & (result.index < ub) rs = result[mask] assert (rs == ts[lb]).all() val = result[result.index[result.index >= ub][0]] assert ts[ub] == val def test_scalar(self): N = 30 rng = pd.date_range("1/1/1990", periods=N, freq="53s") # Explicit cast to float avoid implicit cast when setting nan ts = pd.Series(np.arange(N), index=rng, dtype="float") ts.iloc[5:10] = np.nan ts.iloc[15:20] = np.nan val1 = ts.asof(ts.index[7]) val2 = ts.asof(ts.index[19]) assert val1 == ts.iloc[4] assert val2 == ts.iloc[14] # accepts strings val1 = ts.asof(str(ts.index[7])) assert val1 == ts.iloc[4] # in there result = ts.asof(ts.index[3]) assert result == ts.iloc[3] # no as of value d = ts.index[0] - pd.offsets.BDay() assert np.isnan(ts.asof(d)) def test_with_nan(self): # basic asof test rng = pd.date_range("1/1/2000", "1/2/2000", freq="4h") s = pd.Series(np.arange(len(rng)), index=rng) r = s.resample("2h").mean() result = r.asof(r.index) expected = pd.Series( [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6.0], index=pd.date_range("1/1/2000", "1/2/2000", freq="2h"), ) tm.assert_series_equal(result, expected) r.iloc[3:5] = np.nan result = r.asof(r.index) expected = pd.Series( [0, 0, 1, 1, 1, 1, 3, 3, 4, 4, 5, 5, 6.0], index=pd.date_range("1/1/2000", "1/2/2000", freq="2h"), ) tm.assert_series_equal(result, expected) r.iloc[-3:] = np.nan result = r.asof(r.index) expected = pd.Series( [0, 0, 1, 1, 1, 1, 3, 3, 4, 4, 4, 4, 4.0], index=pd.date_range("1/1/2000", "1/2/2000", freq="2h"), ) tm.assert_series_equal(result, expected) def test_periodindex(self): # array or list or dates N = 50 rng = pd.period_range("1/1/1990", periods=N, freq="h") ts = pd.Series(np.random.default_rng(2).standard_normal(N), index=rng) ts.iloc[15:30] = np.nan dates = pd.date_range("1/1/1990", periods=N * 3, freq="37min") result = ts.asof(dates) assert pd.notna(result).all() lb = ts.index[14] ub = ts.index[30] result = ts.asof(list(dates)) assert pd.notna(result).all() lb = ts.index[14] ub = ts.index[30] pix = pd.PeriodIndex(result.index.values, freq="h") mask = (pix >= lb) & (pix < ub) rs = result[mask] assert (rs == ts[lb]).all() ts.iloc[5:10] = np.nan ts.iloc[15:20] = np.nan val1 = ts.asof(ts.index[7]) val2 = ts.asof(ts.index[19]) assert val1 == ts.iloc[4] assert val2 == ts.iloc[14] # accepts strings val1 = ts.asof(str(ts.index[7])) assert val1 == ts.iloc[4] # in there assert ts.asof(ts.index[3]) == ts.iloc[3] # no as of value d = ts.index[0].to_timestamp() - pd.offsets.BDay() assert pd.isna(ts.asof(d)) # Mismatched freq msg = "Input has different freq" with pytest.raises(IncompatibleFrequency, match=msg): ts.asof(rng.asfreq("D")) def test_errors(self): s = pd.Series( [1, 2, 3], index=[ pd.Timestamp("20130101"), pd.Timestamp("20130103"), pd.Timestamp("20130102"), ], ) # non-monotonic assert not s.index.is_monotonic_increasing with pytest.raises(ValueError, match="requires a sorted index"): s.asof(s.index[0]) # subset with Series N = 10 rng = pd.date_range("1/1/1990", periods=N, freq="53s") s = pd.Series(np.random.default_rng(2).standard_normal(N), index=rng) with pytest.raises(ValueError, match="not valid for Series"): s.asof(s.index[0], subset="foo") def test_all_nans(self): # GH 15713 # series is all nans # testing non-default indexes N = 50 rng = pd.date_range("1/1/1990", periods=N, freq="53s") dates = pd.date_range("1/1/1990", periods=N * 3, freq="25s") result = pd.Series(np.nan, index=rng).asof(dates) expected = pd.Series(np.nan, index=dates) tm.assert_series_equal(result, expected) # testing scalar input date = pd.date_range("1/1/1990", periods=N * 3, freq="25s")[0] result = pd.Series(np.nan, index=rng).asof(date) assert pd.isna(result) # test name is propagated result = pd.Series(np.nan, index=[1, 2, 3, 4], name="test").asof([4, 5]) expected = pd.Series(np.nan, index=[4, 5], name="test") tm.assert_series_equal(result, expected) def test_asof_quarterly_string_deprecated(): # GH#50907 asof converts a string `where` with Timestamp before casting it # to a Period, so a quarterly string warns even on a PeriodIndex -- as with # truncate, the parsed value is the one actually used pi = pd.period_range("2000Q1", periods=8, freq="Q") ser = pd.Series(range(len(pi)), index=pi) with tm.assert_produces_warning( Pandas4Warning, match="quarterly string is deprecated" ): result = ser.asof("2000Q2") assert result == 1 # following the deprecation message preserves the behavior assert result == ser.asof(pd.Period("2000Q2").to_timestamp())