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