from datetime import timedelta import numpy as np import pytest import pandas.util._test_decorators as td import pandas as pd from pandas import ( DataFrame, Series, ) import pandas._testing as tm from pandas.core.indexes.timedeltas import timedelta_range def test_asfreq_bug(): df = DataFrame(data=[1, 3], index=[timedelta(), timedelta(minutes=3)]) result = df.resample("1min").asfreq() expected = DataFrame( data=[1, np.nan, np.nan, 3], index=timedelta_range("0 day", periods=4, freq="1min"), ) tm.assert_frame_equal(result, expected) def test_resample_with_nat(): # GH 13223 index = pd.to_timedelta(["0s", pd.NaT, "2s"]) result = DataFrame({"value": [2, 3, 5]}, index).resample("1s").mean() expected = DataFrame( {"value": [2.5, np.nan, 5.0]}, index=timedelta_range("0 day", periods=3, freq="1s"), ) tm.assert_frame_equal(result, expected) def test_resample_as_freq_with_subperiod(): # GH 13022 index = timedelta_range("00:00:00", "00:10:00", freq="5min") df = DataFrame(data={"value": [1, 5, 10]}, index=index) result = df.resample("2min").asfreq() expected_data = {"value": [1, np.nan, np.nan, np.nan, np.nan, 10]} expected = DataFrame( data=expected_data, index=timedelta_range("00:00:00", "00:10:00", freq="2min") ) tm.assert_frame_equal(result, expected) def test_resample_with_timedeltas(): expected = DataFrame({"A": np.arange(1480)}) expected = expected.groupby(expected.index // 30).sum() expected.index = timedelta_range("0 days", freq="30min", periods=50) df = DataFrame( {"A": np.arange(1480)}, index=pd.to_timedelta(np.arange(1480), unit="min") ) result = df.resample("30min").sum() tm.assert_frame_equal(result, expected) s = df["A"] result = s.resample("30min").sum() tm.assert_series_equal(result, expected["A"]) def test_resample_single_period_timedelta(): s = Series(list(range(5)), index=timedelta_range("1 day", freq="s", periods=5)) result = s.resample("2s").sum() expected = Series([1, 5, 4], index=timedelta_range("1 day", freq="2s", periods=3)) tm.assert_series_equal(result, expected) def test_resample_timedelta_idempotency(): # GH 12072 index = timedelta_range("0", periods=9, freq="10ms") series = Series(range(9), index=index) result = series.resample("10ms").mean() expected = series.astype(float) tm.assert_series_equal(result, expected) def test_resample_offset_with_timedeltaindex(): # GH 10530 & 31809 rng = timedelta_range(start="0s", periods=25, freq="s") ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng) with_base = ts.resample("2s", offset="5s").mean() without_base = ts.resample("2s").mean() exp_without_base = timedelta_range(start="0s", end="25s", freq="2s") exp_with_base = timedelta_range(start="5s", end="29s", freq="2s") tm.assert_index_equal(without_base.index, exp_without_base) tm.assert_index_equal(with_base.index, exp_with_base) def test_resample_categorical_data_with_timedeltaindex(): # GH #12169 df = DataFrame({"Group_obj": "A"}, index=pd.to_timedelta(list(range(20)), unit="s")) df["Group"] = df["Group_obj"].astype("category") result = df.resample("10s").agg(lambda x: (x.value_counts().index[0])) exp_tdi = pd.TimedeltaIndex(np.array([0, 10], dtype="m8[s]"), freq="10s").as_unit( "ns" ) expected = DataFrame( {"Group_obj": ["A", "A"], "Group": ["A", "A"]}, index=exp_tdi, ) expected = expected.reindex(["Group_obj", "Group"], axis=1) expected["Group"] = expected["Group_obj"].astype("category") tm.assert_frame_equal(result, expected) def test_resample_timedelta_values(): # GH 13119 # check that timedelta dtype is preserved when NaT values are # introduced by the resampling times = timedelta_range("1 day", "6 day", freq="4D") df = DataFrame({"time": times}, index=times) times2 = timedelta_range("1 day", "6 day", freq="2D") exp = Series(times2, index=times2, name="time") exp.iloc[1] = pd.NaT res = df.resample("2D").first()["time"] tm.assert_series_equal(res, exp) res = df["time"].resample("2D").first() tm.assert_series_equal(res, exp) @pytest.mark.parametrize( "start, end, freq, resample_freq", [ ("8h", "21h59min50s", "10s", "3h"), # GH 30353 example ("3h", "22h", "1h", "5h"), ("527D", "5006D", "3D", "10D"), ("1D", "10D", "1D", "2D"), # GH 13022 example # tests that worked before GH 33498: ("8h", "21h59min50s", "10s", "2h"), ("0h", "21h59min50s", "10s", "3h"), ("10D", "85D", "D", "2D"), ], ) def test_resample_timedelta_edge_case(start, end, freq, resample_freq): # GH 33498 # check that the timedelta bins does not contains an extra bin idx = timedelta_range(start=start, end=end, freq=freq) s = Series(np.arange(len(idx)), index=idx) result = s.resample(resample_freq).min() expected_index = timedelta_range(freq=resample_freq, start=start, end=end) tm.assert_index_equal(result.index, expected_index) assert result.index.freq == expected_index.freq assert not np.isnan(result.iloc[-1]) @pytest.mark.parametrize("duplicates", [True, False]) def test_resample_with_timedelta_yields_no_empty_groups(duplicates): # GH 10603 df = DataFrame( np.random.default_rng(2).normal(size=(10000, 4)), index=timedelta_range(start="0s", periods=10000, freq="3906250ns"), ) if duplicates: # case with non-unique columns df.columns = ["A", "B", "A", "C"] result = df.loc["1s":, :].resample("3s").apply(lambda x: len(x)) expected = DataFrame( [[768] * 4] * 12 + [[528] * 4], index=timedelta_range(start="1s", periods=13, freq="3s"), ) expected.columns = df.columns tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"]) def test_resample_quantile_timedelta(unit): # GH: 29485 dtype = np.dtype(f"m8[{unit}]") df = DataFrame( {"value": pd.to_timedelta(np.arange(4), unit="s").astype(dtype)}, index=pd.date_range("20200101", periods=4, tz="UTC"), ) result = df.resample("2D").quantile(0.99) expected = DataFrame( { "value": [ pd.Timedelta("0 days 00:00:00.990000"), pd.Timedelta("0 days 00:00:02.990000"), ] }, index=pd.date_range("20200101", periods=2, tz="UTC", freq="2D"), ).astype(dtype) tm.assert_frame_equal(result, expected) def test_resample_closed_right(): # GH#45414 idx = pd.Index([pd.Timedelta(seconds=120 + i * 30) for i in range(10)]) ser = Series(range(10), index=idx) result = ser.resample("min", closed="right", label="right").sum() expected = Series( [0, 3, 7, 11, 15, 9], index=pd.TimedeltaIndex( [pd.Timedelta(seconds=120 + i * 60) for i in range(6)], freq="min" ), ) tm.assert_series_equal(result, expected) @td.skip_if_no("pyarrow") def test_arrow_duration_resample(): # GH 56371 idx = pd.Index(timedelta_range("1 day", periods=5), dtype="duration[ns][pyarrow]") expected = Series(np.arange(5, dtype=np.float64), index=idx) result = expected.resample("1D").mean() tm.assert_series_equal(result, expected)