317 lines
11 KiB
Python
317 lines
11 KiB
Python
from datetime import (
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datetime,
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timedelta,
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)
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import numpy as np
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import pytest
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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.datetimes import date_range
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from pandas.core.indexes.period import (
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PeriodIndex,
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period_range,
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)
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from pandas.core.indexes.timedeltas import timedelta_range
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from pandas.tseries.offsets import (
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BDay,
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Minute,
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)
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DATE_RANGE = (date_range, "dti", datetime(2005, 1, 1), datetime(2005, 1, 10))
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PERIOD_RANGE = (period_range, "pi", datetime(2005, 1, 1), datetime(2005, 1, 10))
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TIMEDELTA_RANGE = (timedelta_range, "tdi", "1 day", "10 day")
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all_ts = pytest.mark.parametrize(
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"_index_factory,_series_name,_index_start,_index_end",
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[DATE_RANGE, PERIOD_RANGE, TIMEDELTA_RANGE],
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)
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@pytest.fixture()
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def _index_factory():
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return period_range
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@pytest.fixture
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def create_index(_index_factory):
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def _create_index(*args, **kwargs):
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"""return the _index_factory created using the args, kwargs"""
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return _index_factory(*args, **kwargs)
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return _create_index
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# new test to check that all FutureWarning are triggered
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def test_deprecating_on_loffset_and_base():
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# GH 31809
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idx = date_range("2001-01-01", periods=4, freq="T")
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df = DataFrame(data=4 * [range(2)], index=idx, columns=["a", "b"])
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with tm.assert_produces_warning(FutureWarning):
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pd.Grouper(freq="10s", base=0)
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with tm.assert_produces_warning(FutureWarning):
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pd.Grouper(freq="10s", loffset="0s")
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# not checking the stacklevel for .groupby().resample() because it's complicated to
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# reconcile it with the stacklevel for Series.resample() and DataFrame.resample();
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# see GH #37603
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with tm.assert_produces_warning(FutureWarning):
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df.groupby("a").resample("3T", base=0).sum()
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with tm.assert_produces_warning(FutureWarning):
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df.groupby("a").resample("3T", loffset="0s").sum()
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msg = "'offset' and 'base' cannot be present at the same time"
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with tm.assert_produces_warning(FutureWarning):
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with pytest.raises(ValueError, match=msg):
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df.groupby("a").resample("3T", base=0, offset=0).sum()
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with tm.assert_produces_warning(FutureWarning):
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df.resample("3T", base=0).sum()
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with tm.assert_produces_warning(FutureWarning):
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df.resample("3T", loffset="0s").sum()
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@all_ts
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@pytest.mark.parametrize("arg", ["mean", {"value": "mean"}, ["mean"]])
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def test_resample_loffset_arg_type(frame, create_index, arg):
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# GH 13218, 15002
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df = frame
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expected_means = [df.values[i : i + 2].mean() for i in range(0, len(df.values), 2)]
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expected_index = create_index(df.index[0], periods=len(df.index) / 2, freq="2D")
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# loffset coerces PeriodIndex to DateTimeIndex
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if isinstance(expected_index, PeriodIndex):
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expected_index = expected_index.to_timestamp()
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expected_index += timedelta(hours=2)
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expected = DataFrame({"value": expected_means}, index=expected_index)
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with tm.assert_produces_warning(FutureWarning):
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result_agg = df.resample("2D", loffset="2H").agg(arg)
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if isinstance(arg, list):
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expected.columns = pd.MultiIndex.from_tuples([("value", "mean")])
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tm.assert_frame_equal(result_agg, expected)
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@pytest.mark.parametrize(
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"loffset", [timedelta(minutes=1), "1min", Minute(1), np.timedelta64(1, "m")]
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)
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def test_resample_loffset(loffset):
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# GH 7687
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rng = date_range("1/1/2000 00:00:00", "1/1/2000 00:13:00", freq="min")
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s = Series(np.random.randn(14), index=rng)
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with tm.assert_produces_warning(FutureWarning):
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result = s.resample(
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"5min", closed="right", label="right", loffset=loffset
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).mean()
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idx = date_range("1/1/2000", periods=4, freq="5min")
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expected = Series(
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[s[0], s[1:6].mean(), s[6:11].mean(), s[11:].mean()],
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index=idx + timedelta(minutes=1),
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)
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tm.assert_series_equal(result, expected)
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assert result.index.freq == Minute(5)
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# from daily
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dti = date_range(start=datetime(2005, 1, 1), end=datetime(2005, 1, 10), freq="D")
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ser = Series(np.random.rand(len(dti)), dti)
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# to weekly
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result = ser.resample("w-sun").last()
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business_day_offset = BDay()
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with tm.assert_produces_warning(FutureWarning):
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expected = ser.resample("w-sun", loffset=-business_day_offset).last()
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assert result.index[0] - business_day_offset == expected.index[0]
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def test_resample_loffset_upsample():
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# GH 20744
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rng = date_range("1/1/2000 00:00:00", "1/1/2000 00:13:00", freq="min")
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s = Series(np.random.randn(14), index=rng)
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with tm.assert_produces_warning(FutureWarning):
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result = s.resample(
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"5min", closed="right", label="right", loffset=timedelta(minutes=1)
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).ffill()
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idx = date_range("1/1/2000", periods=4, freq="5min")
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expected = Series([s[0], s[5], s[10], s[-1]], index=idx + timedelta(minutes=1))
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tm.assert_series_equal(result, expected)
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def test_resample_loffset_count():
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# GH 12725
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start_time = "1/1/2000 00:00:00"
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rng = date_range(start_time, periods=100, freq="S")
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ts = Series(np.random.randn(len(rng)), index=rng)
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with tm.assert_produces_warning(FutureWarning):
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result = ts.resample("10S", loffset="1s").count()
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expected_index = date_range(start_time, periods=10, freq="10S") + timedelta(
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seconds=1
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)
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expected = Series(10, index=expected_index)
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tm.assert_series_equal(result, expected)
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# Same issue should apply to .size() since it goes through
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# same code path
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with tm.assert_produces_warning(FutureWarning):
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result = ts.resample("10S", loffset="1s").size()
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tm.assert_series_equal(result, expected)
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def test_resample_base():
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rng = date_range("1/1/2000 00:00:00", "1/1/2000 02:00", freq="s")
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ts = Series(np.random.randn(len(rng)), index=rng)
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with tm.assert_produces_warning(FutureWarning):
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resampled = ts.resample("5min", base=2).mean()
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exp_rng = date_range("12/31/1999 23:57:00", "1/1/2000 01:57", freq="5min")
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tm.assert_index_equal(resampled.index, exp_rng)
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def test_resample_float_base():
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# GH25161
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dt = pd.to_datetime(
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["2018-11-26 16:17:43.51", "2018-11-26 16:17:44.51", "2018-11-26 16:17:45.51"]
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)
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s = Series(np.arange(3), index=dt)
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base = 17 + 43.51 / 60
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with tm.assert_produces_warning(FutureWarning):
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result = s.resample("3min", base=base).size()
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expected = Series(
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3, index=pd.DatetimeIndex(["2018-11-26 16:17:43.51"], freq="3min")
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)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("kind", ["period", None, "timestamp"])
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@pytest.mark.parametrize("agg_arg", ["mean", {"value": "mean"}, ["mean"]])
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def test_loffset_returns_datetimeindex(frame, kind, agg_arg):
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# make sure passing loffset returns DatetimeIndex in all cases
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# basic method taken from Base.test_resample_loffset_arg_type()
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df = frame
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expected_means = [df.values[i : i + 2].mean() for i in range(0, len(df.values), 2)]
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expected_index = period_range(df.index[0], periods=len(df.index) / 2, freq="2D")
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# loffset coerces PeriodIndex to DateTimeIndex
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expected_index = expected_index.to_timestamp()
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expected_index += timedelta(hours=2)
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expected = DataFrame({"value": expected_means}, index=expected_index)
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with tm.assert_produces_warning(FutureWarning):
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result_agg = df.resample("2D", loffset="2H", kind=kind).agg(agg_arg)
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if isinstance(agg_arg, list):
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expected.columns = pd.MultiIndex.from_tuples([("value", "mean")])
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tm.assert_frame_equal(result_agg, expected)
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@pytest.mark.parametrize(
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"start,end,start_freq,end_freq,base,offset",
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[
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("19910905", "19910909 03:00", "H", "24H", 10, "10H"),
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("19910905", "19910909 12:00", "H", "24H", 10, "10H"),
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("19910905", "19910909 23:00", "H", "24H", 10, "10H"),
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("19910905 10:00", "19910909", "H", "24H", 10, "10H"),
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("19910905 10:00", "19910909 10:00", "H", "24H", 10, "10H"),
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("19910905", "19910909 10:00", "H", "24H", 10, "10H"),
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("19910905 12:00", "19910909", "H", "24H", 10, "10H"),
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("19910905 12:00", "19910909 03:00", "H", "24H", 10, "10H"),
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("19910905 12:00", "19910909 12:00", "H", "24H", 10, "10H"),
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("19910905 12:00", "19910909 12:00", "H", "24H", 34, "34H"),
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("19910905 12:00", "19910909 12:00", "H", "17H", 10, "10H"),
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("19910905 12:00", "19910909 12:00", "H", "17H", 3, "3H"),
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("19910905 12:00", "19910909 1:00", "H", "M", 3, "3H"),
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("19910905", "19910913 06:00", "2H", "24H", 10, "10H"),
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("19910905", "19910905 01:39", "Min", "5Min", 3, "3Min"),
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("19910905", "19910905 03:18", "2Min", "5Min", 3, "3Min"),
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],
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)
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def test_resample_with_non_zero_base(start, end, start_freq, end_freq, base, offset):
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# GH 23882
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s = Series(0, index=period_range(start, end, freq=start_freq))
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s = s + np.arange(len(s))
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with tm.assert_produces_warning(FutureWarning):
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result = s.resample(end_freq, base=base).mean()
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result = result.to_timestamp(end_freq)
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# test that the replacement argument 'offset' works
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result_offset = s.resample(end_freq, offset=offset).mean()
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result_offset = result_offset.to_timestamp(end_freq)
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tm.assert_series_equal(result, result_offset)
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# to_timestamp casts 24H -> D
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result = result.asfreq(end_freq) if end_freq == "24H" else result
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with tm.assert_produces_warning(FutureWarning):
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expected = s.to_timestamp().resample(end_freq, base=base).mean()
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if end_freq == "M":
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# TODO: is non-tick the relevant characteristic? (GH 33815)
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expected.index = expected.index._with_freq(None)
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tm.assert_series_equal(result, expected)
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def test_resample_base_with_timedeltaindex():
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# GH 10530
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rng = timedelta_range(start="0s", periods=25, freq="s")
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ts = Series(np.random.randn(len(rng)), index=rng)
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with tm.assert_produces_warning(FutureWarning):
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with_base = ts.resample("2s", base=5).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_interpolate_posargs_deprecation():
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# GH 41485
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idx = pd.to_datetime(["1992-08-27 07:46:48", "1992-08-27 07:46:59"])
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s = Series([1, 4], index=idx)
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msg = (
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r"In a future version of pandas all arguments of Resampler\.interpolate "
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r"except for the argument 'method' will be keyword-only"
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)
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = s.resample("3s").interpolate("linear", 0)
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idx = pd.to_datetime(
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[
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"1992-08-27 07:46:48",
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"1992-08-27 07:46:51",
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"1992-08-27 07:46:54",
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"1992-08-27 07:46:57",
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]
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)
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expected = Series([1.0, 1.0, 1.0, 1.0], index=idx)
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expected.index._data.freq = "3s"
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tm.assert_series_equal(result, expected)
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def test_pad_backfill_deprecation():
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# GH 33396
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s = Series([1, 2, 3], index=date_range("20180101", periods=3, freq="h"))
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with tm.assert_produces_warning(FutureWarning, match="backfill"):
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s.resample("30min").backfill()
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with tm.assert_produces_warning(FutureWarning, match="pad"):
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s.resample("30min").pad()
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