485 lines
16 KiB
Python
485 lines
16 KiB
Python
from datetime import datetime
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import numpy as np
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import pytest
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from pandas.core.dtypes.common import is_extension_array_dtype
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import pandas as pd
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from pandas import (
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DataFrame,
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DatetimeIndex,
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MultiIndex,
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NaT,
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PeriodIndex,
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Series,
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TimedeltaIndex,
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)
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import pandas._testing as tm
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from pandas.core.groupby.groupby import DataError
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from pandas.core.groupby.grouper import Grouper
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from pandas.core.indexes.datetimes import date_range
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from pandas.core.indexes.period import period_range
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from pandas.core.indexes.timedeltas import timedelta_range
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from pandas.core.resample import _asfreq_compat
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# a fixture value can be overridden by the test parameter value. Note that the
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# value of the fixture can be overridden this way even if the test doesn't use
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# it directly (doesn't mention it in the function prototype).
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# see https://docs.pytest.org/en/latest/fixture.html#override-a-fixture-with-direct-test-parametrization # noqa: E501
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# in this module we override the fixture values defined in conftest.py
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# tuples of '_index_factory,_series_name,_index_start,_index_end'
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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 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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@pytest.mark.parametrize("freq", ["2D", "1h"])
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@pytest.mark.parametrize(
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"_index_factory,_series_name,_index_start,_index_end", [DATE_RANGE, TIMEDELTA_RANGE]
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)
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def test_asfreq(series_and_frame, freq, create_index):
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obj = series_and_frame
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result = obj.resample(freq).asfreq()
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new_index = create_index(obj.index[0], obj.index[-1], freq=freq)
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expected = obj.reindex(new_index)
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tm.assert_almost_equal(result, expected)
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@pytest.mark.parametrize(
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"_index_factory,_series_name,_index_start,_index_end", [DATE_RANGE, TIMEDELTA_RANGE]
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)
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def test_asfreq_fill_value(series, create_index):
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# test for fill value during resampling, issue 3715
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ser = series
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result = ser.resample("1h").asfreq()
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new_index = create_index(ser.index[0], ser.index[-1], freq="1h")
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expected = ser.reindex(new_index)
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tm.assert_series_equal(result, expected)
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# Explicit cast to float to avoid implicit cast when setting None
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frame = ser.astype("float").to_frame("value")
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frame.iloc[1] = None
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result = frame.resample("1h").asfreq(fill_value=4.0)
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new_index = create_index(frame.index[0], frame.index[-1], freq="1h")
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expected = frame.reindex(new_index, fill_value=4.0)
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tm.assert_frame_equal(result, expected)
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@all_ts
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def test_resample_interpolate(frame):
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# GH#12925
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df = frame
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warn = None
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if isinstance(df.index, PeriodIndex):
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warn = FutureWarning
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msg = "Resampling with a PeriodIndex is deprecated"
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with tm.assert_produces_warning(warn, match=msg):
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result = df.resample("1min").asfreq().interpolate()
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expected = df.resample("1min").interpolate()
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tm.assert_frame_equal(result, expected)
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def test_raises_on_non_datetimelike_index():
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# this is a non datetimelike index
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xp = DataFrame()
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msg = (
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"Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, "
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"but got an instance of 'RangeIndex'"
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)
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with pytest.raises(TypeError, match=msg):
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xp.resample("YE")
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@all_ts
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@pytest.mark.parametrize("freq", ["ME", "D", "h"])
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def test_resample_empty_series(freq, empty_series_dti, resample_method):
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# GH12771 & GH12868
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ser = empty_series_dti
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if freq == "ME" and isinstance(ser.index, TimedeltaIndex):
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msg = (
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"Resampling on a TimedeltaIndex requires fixed-duration `freq`, "
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"e.g. '24h' or '3D', not <MonthEnd>"
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)
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with pytest.raises(ValueError, match=msg):
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ser.resample(freq)
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return
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elif freq == "ME" and isinstance(ser.index, PeriodIndex):
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# index is PeriodIndex, so convert to corresponding Period freq
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freq = "M"
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warn = None
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if isinstance(ser.index, PeriodIndex):
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warn = FutureWarning
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msg = "Resampling with a PeriodIndex is deprecated"
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with tm.assert_produces_warning(warn, match=msg):
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rs = ser.resample(freq)
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result = getattr(rs, resample_method)()
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if resample_method == "ohlc":
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expected = DataFrame(
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[], index=ser.index[:0].copy(), columns=["open", "high", "low", "close"]
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)
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expected.index = _asfreq_compat(ser.index, freq)
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tm.assert_frame_equal(result, expected, check_dtype=False)
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else:
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expected = ser.copy()
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expected.index = _asfreq_compat(ser.index, freq)
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tm.assert_series_equal(result, expected, check_dtype=False)
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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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@pytest.mark.parametrize("min_count", [0, 1])
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def test_resample_empty_sum_string(string_dtype_no_object, min_count):
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# https://github.com/pandas-dev/pandas/issues/60229
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dtype = string_dtype_no_object
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ser = Series(
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pd.NA,
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index=DatetimeIndex(
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[
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"2000-01-01 00:00:00",
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"2000-01-01 00:00:10",
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"2000-01-01 00:00:20",
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"2000-01-01 00:00:30",
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]
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),
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dtype=dtype,
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)
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rs = ser.resample("20s")
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result = rs.sum(min_count=min_count)
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value = "" if min_count == 0 else pd.NA
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index = date_range(start="2000-01-01", freq="20s", periods=2)
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expected = Series(value, index=index, dtype=dtype)
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tm.assert_series_equal(result, expected)
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@all_ts
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@pytest.mark.parametrize(
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"freq",
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[
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pytest.param("ME", marks=pytest.mark.xfail(reason="Don't know why this fails")),
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"D",
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"h",
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],
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)
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def test_resample_nat_index_series(freq, series, resample_method):
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# GH39227
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ser = series.copy()
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ser.index = PeriodIndex([NaT] * len(ser), freq=freq)
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msg = "Resampling with a PeriodIndex is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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rs = ser.resample(freq)
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result = getattr(rs, resample_method)()
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if resample_method == "ohlc":
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expected = DataFrame(
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[], index=ser.index[:0].copy(), columns=["open", "high", "low", "close"]
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)
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tm.assert_frame_equal(result, expected, check_dtype=False)
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else:
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expected = ser[:0].copy()
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tm.assert_series_equal(result, expected, check_dtype=False)
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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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@all_ts
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@pytest.mark.parametrize("freq", ["ME", "D", "h"])
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@pytest.mark.parametrize("resample_method", ["count", "size"])
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def test_resample_count_empty_series(freq, empty_series_dti, resample_method):
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# GH28427
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ser = empty_series_dti
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if freq == "ME" and isinstance(ser.index, TimedeltaIndex):
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msg = (
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"Resampling on a TimedeltaIndex requires fixed-duration `freq`, "
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"e.g. '24h' or '3D', not <MonthEnd>"
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)
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with pytest.raises(ValueError, match=msg):
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ser.resample(freq)
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return
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elif freq == "ME" and isinstance(ser.index, PeriodIndex):
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# index is PeriodIndex, so convert to corresponding Period freq
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freq = "M"
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warn = None
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if isinstance(ser.index, PeriodIndex):
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warn = FutureWarning
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msg = "Resampling with a PeriodIndex is deprecated"
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with tm.assert_produces_warning(warn, match=msg):
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rs = ser.resample(freq)
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result = getattr(rs, resample_method)()
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index = _asfreq_compat(ser.index, freq)
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expected = Series([], dtype="int64", index=index, name=ser.name)
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tm.assert_series_equal(result, expected)
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@all_ts
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@pytest.mark.parametrize("freq", ["ME", "D", "h"])
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def test_resample_empty_dataframe(empty_frame_dti, freq, resample_method):
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# GH13212
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df = empty_frame_dti
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# count retains dimensions too
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if freq == "ME" and isinstance(df.index, TimedeltaIndex):
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msg = (
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"Resampling on a TimedeltaIndex requires fixed-duration `freq`, "
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"e.g. '24h' or '3D', not <MonthEnd>"
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)
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with pytest.raises(ValueError, match=msg):
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df.resample(freq, group_keys=False)
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return
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elif freq == "ME" and isinstance(df.index, PeriodIndex):
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# index is PeriodIndex, so convert to corresponding Period freq
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freq = "M"
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warn = None
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if isinstance(df.index, PeriodIndex):
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warn = FutureWarning
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msg = "Resampling with a PeriodIndex is deprecated"
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with tm.assert_produces_warning(warn, match=msg):
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rs = df.resample(freq, group_keys=False)
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result = getattr(rs, resample_method)()
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if resample_method == "ohlc":
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# TODO: no tests with len(df.columns) > 0
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mi = MultiIndex.from_product([df.columns, ["open", "high", "low", "close"]])
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expected = DataFrame(
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[], index=df.index[:0].copy(), columns=mi, dtype=np.float64
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)
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expected.index = _asfreq_compat(df.index, freq)
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elif resample_method != "size":
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expected = df.copy()
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else:
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# GH14962
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expected = Series([], dtype=np.int64)
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expected.index = _asfreq_compat(df.index, freq)
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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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tm.assert_almost_equal(result, expected)
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# test size for GH13212 (currently stays as df)
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@all_ts
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@pytest.mark.parametrize("freq", ["ME", "D", "h"])
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def test_resample_count_empty_dataframe(freq, empty_frame_dti):
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# GH28427
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empty_frame_dti["a"] = []
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if freq == "ME" and isinstance(empty_frame_dti.index, TimedeltaIndex):
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msg = (
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"Resampling on a TimedeltaIndex requires fixed-duration `freq`, "
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"e.g. '24h' or '3D', not <MonthEnd>"
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)
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with pytest.raises(ValueError, match=msg):
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empty_frame_dti.resample(freq)
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return
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elif freq == "ME" and isinstance(empty_frame_dti.index, PeriodIndex):
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# index is PeriodIndex, so convert to corresponding Period freq
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freq = "M"
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warn = None
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if isinstance(empty_frame_dti.index, PeriodIndex):
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warn = FutureWarning
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msg = "Resampling with a PeriodIndex is deprecated"
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with tm.assert_produces_warning(warn, match=msg):
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rs = empty_frame_dti.resample(freq)
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result = rs.count()
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index = _asfreq_compat(empty_frame_dti.index, freq)
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expected = DataFrame(dtype="int64", index=index, columns=["a"])
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tm.assert_frame_equal(result, expected)
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@all_ts
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@pytest.mark.parametrize("freq", ["ME", "D", "h"])
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def test_resample_size_empty_dataframe(freq, empty_frame_dti):
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# GH28427
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empty_frame_dti["a"] = []
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if freq == "ME" and isinstance(empty_frame_dti.index, TimedeltaIndex):
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msg = (
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"Resampling on a TimedeltaIndex requires fixed-duration `freq`, "
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"e.g. '24h' or '3D', not <MonthEnd>"
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)
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with pytest.raises(ValueError, match=msg):
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empty_frame_dti.resample(freq)
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return
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elif freq == "ME" and isinstance(empty_frame_dti.index, PeriodIndex):
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# index is PeriodIndex, so convert to corresponding Period freq
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freq = "M"
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msg = "Resampling with a PeriodIndex"
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warn = None
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if isinstance(empty_frame_dti.index, PeriodIndex):
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warn = FutureWarning
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with tm.assert_produces_warning(warn, match=msg):
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rs = empty_frame_dti.resample(freq)
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result = rs.size()
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index = _asfreq_compat(empty_frame_dti.index, freq)
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expected = Series([], dtype="int64", index=index)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"index",
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[
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PeriodIndex([], freq="M", name="a"),
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DatetimeIndex([], name="a"),
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TimedeltaIndex([], name="a"),
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],
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)
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@pytest.mark.parametrize("dtype", [float, int, object, "datetime64[ns]"])
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@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
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def test_resample_empty_dtypes(index, dtype, resample_method):
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# Empty series were sometimes causing a segfault (for the functions
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# with Cython bounds-checking disabled) or an IndexError. We just run
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# them to ensure they no longer do. (GH #10228)
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warn = None
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if isinstance(index, PeriodIndex):
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# GH#53511
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index = PeriodIndex([], freq="B", name=index.name)
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warn = FutureWarning
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msg = "Resampling with a PeriodIndex is deprecated"
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empty_series_dti = Series([], index, dtype)
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with tm.assert_produces_warning(warn, match=msg):
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rs = empty_series_dti.resample("d", group_keys=False)
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try:
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getattr(rs, resample_method)()
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except DataError:
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# Ignore these since some combinations are invalid
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# (ex: doing mean with dtype of np.object_)
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pass
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@all_ts
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@pytest.mark.parametrize("freq", ["ME", "D", "h"])
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def test_apply_to_empty_series(empty_series_dti, freq):
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# GH 14313
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ser = empty_series_dti
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if freq == "ME" and isinstance(empty_series_dti.index, TimedeltaIndex):
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msg = (
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"Resampling on a TimedeltaIndex requires fixed-duration `freq`, "
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"e.g. '24h' or '3D', not <MonthEnd>"
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)
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with pytest.raises(ValueError, match=msg):
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empty_series_dti.resample(freq)
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return
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elif freq == "ME" and isinstance(empty_series_dti.index, PeriodIndex):
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# index is PeriodIndex, so convert to corresponding Period freq
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freq = "M"
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msg = "Resampling with a PeriodIndex"
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warn = None
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if isinstance(empty_series_dti.index, PeriodIndex):
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warn = FutureWarning
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with tm.assert_produces_warning(warn, match=msg):
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rs = ser.resample(freq, group_keys=False)
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result = rs.apply(lambda x: 1)
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with tm.assert_produces_warning(warn, match=msg):
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expected = ser.resample(freq).apply("sum")
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tm.assert_series_equal(result, expected, check_dtype=False)
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@all_ts
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def test_resampler_is_iterable(series):
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# GH 15314
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freq = "h"
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tg = Grouper(freq=freq, convention="start")
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msg = "Resampling with a PeriodIndex"
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warn = None
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if isinstance(series.index, PeriodIndex):
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warn = FutureWarning
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with tm.assert_produces_warning(warn, match=msg):
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grouped = series.groupby(tg)
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with tm.assert_produces_warning(warn, match=msg):
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resampled = series.resample(freq)
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for (rk, rv), (gk, gv) in zip(resampled, grouped):
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assert rk == gk
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tm.assert_series_equal(rv, gv)
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@all_ts
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def test_resample_quantile(series):
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# GH 15023
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ser = series
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q = 0.75
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freq = "h"
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msg = "Resampling with a PeriodIndex"
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warn = None
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if isinstance(series.index, PeriodIndex):
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warn = FutureWarning
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with tm.assert_produces_warning(warn, match=msg):
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result = ser.resample(freq).quantile(q)
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expected = ser.resample(freq).agg(lambda x: x.quantile(q)).rename(ser.name)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("how", ["first", "last"])
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def test_first_last_skipna(any_real_nullable_dtype, skipna, how):
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# GH#57019
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if is_extension_array_dtype(any_real_nullable_dtype):
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na_value = Series(dtype=any_real_nullable_dtype).dtype.na_value
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else:
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na_value = np.nan
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df = DataFrame(
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{
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"a": [2, 1, 1, 2],
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"b": [na_value, 3.0, na_value, 4.0],
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"c": [na_value, 3.0, na_value, 4.0],
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},
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index=date_range("2020-01-01", periods=4, freq="D"),
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dtype=any_real_nullable_dtype,
|
|
)
|
|
rs = df.resample("ME")
|
|
method = getattr(rs, how)
|
|
result = method(skipna=skipna)
|
|
|
|
gb = df.groupby(df.shape[0] * [pd.to_datetime("2020-01-31")])
|
|
expected = getattr(gb, how)(skipna=skipna)
|
|
expected.index.freq = "ME"
|
|
tm.assert_frame_equal(result, expected)
|