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878 lines
33 KiB
878 lines
33 KiB
from datetime import datetime
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import dateutil
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import numpy as np
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import pytest
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import pytz
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from pandas._libs.tslibs.ccalendar import (
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DAYS,
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MONTHS,
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)
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from pandas._libs.tslibs.period import IncompatibleFrequency
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from pandas.errors import InvalidIndexError
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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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Timestamp,
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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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Period,
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PeriodIndex,
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period_range,
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)
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from pandas.core.resample import _get_period_range_edges
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import pandas.tseries.offsets as offsets
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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 _series_name():
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return "pi"
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class TestPeriodIndex:
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@pytest.mark.parametrize("freq", ["2D", "1H", "2H"])
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@pytest.mark.parametrize("kind", ["period", None, "timestamp"])
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def test_asfreq(self, series_and_frame, freq, kind):
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# GH 12884, 15944
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# make sure .asfreq() returns PeriodIndex (except kind='timestamp')
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obj = series_and_frame
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if kind == "timestamp":
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expected = obj.to_timestamp().resample(freq).asfreq()
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else:
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start = obj.index[0].to_timestamp(how="start")
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end = (obj.index[-1] + obj.index.freq).to_timestamp(how="start")
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new_index = date_range(start=start, end=end, freq=freq, inclusive="left")
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expected = obj.to_timestamp().reindex(new_index).to_period(freq)
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result = obj.resample(freq, kind=kind).asfreq()
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tm.assert_almost_equal(result, expected)
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def test_asfreq_fill_value(self, series):
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# test for fill value during resampling, issue 3715
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s = series
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new_index = date_range(
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s.index[0].to_timestamp(how="start"),
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(s.index[-1]).to_timestamp(how="start"),
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freq="1H",
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)
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expected = s.to_timestamp().reindex(new_index, fill_value=4.0)
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result = s.resample("1H", kind="timestamp").asfreq(fill_value=4.0)
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tm.assert_series_equal(result, expected)
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frame = s.to_frame("value")
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new_index = date_range(
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frame.index[0].to_timestamp(how="start"),
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(frame.index[-1]).to_timestamp(how="start"),
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freq="1H",
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)
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expected = frame.to_timestamp().reindex(new_index, fill_value=3.0)
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result = frame.resample("1H", kind="timestamp").asfreq(fill_value=3.0)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("freq", ["H", "12H", "2D", "W"])
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@pytest.mark.parametrize("kind", [None, "period", "timestamp"])
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@pytest.mark.parametrize("kwargs", [{"on": "date"}, {"level": "d"}])
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def test_selection(self, index, freq, kind, kwargs):
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# This is a bug, these should be implemented
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# GH 14008
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rng = np.arange(len(index), dtype=np.int64)
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df = DataFrame(
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{"date": index, "a": rng},
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index=pd.MultiIndex.from_arrays([rng, index], names=["v", "d"]),
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)
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msg = (
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"Resampling from level= or on= selection with a PeriodIndex is "
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r"not currently supported, use \.set_index\(\.\.\.\) to "
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"explicitly set index"
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)
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with pytest.raises(NotImplementedError, match=msg):
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df.resample(freq, kind=kind, **kwargs)
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@pytest.mark.parametrize("month", MONTHS)
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@pytest.mark.parametrize("meth", ["ffill", "bfill"])
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@pytest.mark.parametrize("conv", ["start", "end"])
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@pytest.mark.parametrize("targ", ["D", "B", "M"])
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def test_annual_upsample_cases(
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self, targ, conv, meth, month, simple_period_range_series
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):
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ts = simple_period_range_series("1/1/1990", "12/31/1991", freq=f"A-{month}")
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result = getattr(ts.resample(targ, convention=conv), meth)()
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expected = result.to_timestamp(targ, how=conv)
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expected = expected.asfreq(targ, meth).to_period()
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tm.assert_series_equal(result, expected)
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def test_basic_downsample(self, simple_period_range_series):
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ts = simple_period_range_series("1/1/1990", "6/30/1995", freq="M")
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result = ts.resample("a-dec").mean()
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expected = ts.groupby(ts.index.year).mean()
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expected.index = period_range("1/1/1990", "6/30/1995", freq="a-dec")
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tm.assert_series_equal(result, expected)
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# this is ok
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tm.assert_series_equal(ts.resample("a-dec").mean(), result)
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tm.assert_series_equal(ts.resample("a").mean(), result)
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@pytest.mark.parametrize(
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"rule,expected_error_msg",
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[
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("a-dec", "<YearEnd: month=12>"),
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("q-mar", "<QuarterEnd: startingMonth=3>"),
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("M", "<MonthEnd>"),
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("w-thu", "<Week: weekday=3>"),
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],
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)
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def test_not_subperiod(self, simple_period_range_series, rule, expected_error_msg):
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# These are incompatible period rules for resampling
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ts = simple_period_range_series("1/1/1990", "6/30/1995", freq="w-wed")
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msg = (
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"Frequency <Week: weekday=2> cannot be resampled to "
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f"{expected_error_msg}, as they are not sub or super periods"
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)
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with pytest.raises(IncompatibleFrequency, match=msg):
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ts.resample(rule).mean()
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@pytest.mark.parametrize("freq", ["D", "2D"])
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def test_basic_upsample(self, freq, simple_period_range_series):
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ts = simple_period_range_series("1/1/1990", "6/30/1995", freq="M")
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result = ts.resample("a-dec").mean()
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resampled = result.resample(freq, convention="end").ffill()
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expected = result.to_timestamp(freq, how="end")
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expected = expected.asfreq(freq, "ffill").to_period(freq)
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tm.assert_series_equal(resampled, expected)
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def test_upsample_with_limit(self):
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rng = period_range("1/1/2000", periods=5, freq="A")
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ts = Series(np.random.randn(len(rng)), rng)
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result = ts.resample("M", convention="end").ffill(limit=2)
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expected = ts.asfreq("M").reindex(result.index, method="ffill", limit=2)
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tm.assert_series_equal(result, expected)
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def test_annual_upsample(self, simple_period_range_series):
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ts = simple_period_range_series("1/1/1990", "12/31/1995", freq="A-DEC")
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df = DataFrame({"a": ts})
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rdf = df.resample("D").ffill()
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exp = df["a"].resample("D").ffill()
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tm.assert_series_equal(rdf["a"], exp)
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rng = period_range("2000", "2003", freq="A-DEC")
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ts = Series([1, 2, 3, 4], index=rng)
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result = ts.resample("M").ffill()
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ex_index = period_range("2000-01", "2003-12", freq="M")
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expected = ts.asfreq("M", how="start").reindex(ex_index, method="ffill")
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("month", MONTHS)
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@pytest.mark.parametrize("target", ["D", "B", "M"])
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@pytest.mark.parametrize("convention", ["start", "end"])
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def test_quarterly_upsample(
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self, month, target, convention, simple_period_range_series
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):
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freq = f"Q-{month}"
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ts = simple_period_range_series("1/1/1990", "12/31/1995", freq=freq)
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result = ts.resample(target, convention=convention).ffill()
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expected = result.to_timestamp(target, how=convention)
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expected = expected.asfreq(target, "ffill").to_period()
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("target", ["D", "B"])
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@pytest.mark.parametrize("convention", ["start", "end"])
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def test_monthly_upsample(self, target, convention, simple_period_range_series):
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ts = simple_period_range_series("1/1/1990", "12/31/1995", freq="M")
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result = ts.resample(target, convention=convention).ffill()
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expected = result.to_timestamp(target, how=convention)
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expected = expected.asfreq(target, "ffill").to_period()
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tm.assert_series_equal(result, expected)
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def test_resample_basic(self):
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# GH3609
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s = Series(
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range(100),
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index=date_range("20130101", freq="s", periods=100, name="idx"),
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dtype="float",
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)
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s[10:30] = np.nan
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index = PeriodIndex(
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[Period("2013-01-01 00:00", "T"), Period("2013-01-01 00:01", "T")],
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name="idx",
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)
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expected = Series([34.5, 79.5], index=index)
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result = s.to_period().resample("T", kind="period").mean()
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tm.assert_series_equal(result, expected)
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result2 = s.resample("T", kind="period").mean()
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tm.assert_series_equal(result2, expected)
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@pytest.mark.parametrize(
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"freq,expected_vals", [("M", [31, 29, 31, 9]), ("2M", [31 + 29, 31 + 9])]
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)
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def test_resample_count(self, freq, expected_vals):
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# GH12774
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series = Series(1, index=period_range(start="2000", periods=100))
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result = series.resample(freq).count()
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expected_index = period_range(
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start="2000", freq=freq, periods=len(expected_vals)
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)
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expected = Series(expected_vals, index=expected_index)
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tm.assert_series_equal(result, expected)
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def test_resample_same_freq(self, resample_method):
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# GH12770
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series = Series(range(3), index=period_range(start="2000", periods=3, freq="M"))
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expected = series
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result = getattr(series.resample("M"), resample_method)()
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tm.assert_series_equal(result, expected)
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def test_resample_incompat_freq(self):
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msg = (
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"Frequency <MonthEnd> cannot be resampled to <Week: weekday=6>, "
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"as they are not sub or super periods"
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)
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with pytest.raises(IncompatibleFrequency, match=msg):
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Series(
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range(3), index=period_range(start="2000", periods=3, freq="M")
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).resample("W").mean()
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def test_with_local_timezone_pytz(self):
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# see gh-5430
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local_timezone = pytz.timezone("America/Los_Angeles")
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start = datetime(year=2013, month=11, day=1, hour=0, minute=0, tzinfo=pytz.utc)
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# 1 day later
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end = datetime(year=2013, month=11, day=2, hour=0, minute=0, tzinfo=pytz.utc)
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index = date_range(start, end, freq="H")
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series = Series(1, index=index)
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series = series.tz_convert(local_timezone)
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result = series.resample("D", kind="period").mean()
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# Create the expected series
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# Index is moved back a day with the timezone conversion from UTC to
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# Pacific
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expected_index = period_range(start=start, end=end, freq="D") - offsets.Day()
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expected = Series(1.0, index=expected_index)
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tm.assert_series_equal(result, expected)
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def test_resample_with_pytz(self):
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# GH 13238
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s = Series(
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2, index=date_range("2017-01-01", periods=48, freq="H", tz="US/Eastern")
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)
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result = s.resample("D").mean()
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expected = Series(
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2.0,
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index=pd.DatetimeIndex(
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["2017-01-01", "2017-01-02"], tz="US/Eastern", freq="D"
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),
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)
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tm.assert_series_equal(result, expected)
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# Especially assert that the timezone is LMT for pytz
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assert result.index.tz == pytz.timezone("US/Eastern")
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def test_with_local_timezone_dateutil(self):
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# see gh-5430
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local_timezone = "dateutil/America/Los_Angeles"
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start = datetime(
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year=2013, month=11, day=1, hour=0, minute=0, tzinfo=dateutil.tz.tzutc()
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)
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# 1 day later
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end = datetime(
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year=2013, month=11, day=2, hour=0, minute=0, tzinfo=dateutil.tz.tzutc()
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)
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index = date_range(start, end, freq="H", name="idx")
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series = Series(1, index=index)
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series = series.tz_convert(local_timezone)
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result = series.resample("D", kind="period").mean()
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# Create the expected series
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# Index is moved back a day with the timezone conversion from UTC to
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# Pacific
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expected_index = (
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period_range(start=start, end=end, freq="D", name="idx") - offsets.Day()
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)
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expected = Series(1.0, index=expected_index)
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tm.assert_series_equal(result, expected)
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def test_resample_nonexistent_time_bin_edge(self):
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# GH 19375
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index = date_range("2017-03-12", "2017-03-12 1:45:00", freq="15T")
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s = Series(np.zeros(len(index)), index=index)
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expected = s.tz_localize("US/Pacific")
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expected.index = pd.DatetimeIndex(expected.index, freq="900S")
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result = expected.resample("900S").mean()
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tm.assert_series_equal(result, expected)
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# GH 23742
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index = date_range(start="2017-10-10", end="2017-10-20", freq="1H")
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index = index.tz_localize("UTC").tz_convert("America/Sao_Paulo")
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df = DataFrame(data=list(range(len(index))), index=index)
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result = df.groupby(pd.Grouper(freq="1D")).count()
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expected = date_range(
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start="2017-10-09",
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end="2017-10-20",
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freq="D",
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tz="America/Sao_Paulo",
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nonexistent="shift_forward",
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inclusive="left",
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)
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tm.assert_index_equal(result.index, expected)
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def test_resample_ambiguous_time_bin_edge(self):
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# GH 10117
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idx = date_range(
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"2014-10-25 22:00:00", "2014-10-26 00:30:00", freq="30T", tz="Europe/London"
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)
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expected = Series(np.zeros(len(idx)), index=idx)
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result = expected.resample("30T").mean()
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tm.assert_series_equal(result, expected)
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def test_fill_method_and_how_upsample(self):
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# GH2073
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s = Series(
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np.arange(9, dtype="int64"),
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index=date_range("2010-01-01", periods=9, freq="Q"),
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)
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last = s.resample("M").ffill()
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both = s.resample("M").ffill().resample("M").last().astype("int64")
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tm.assert_series_equal(last, both)
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@pytest.mark.parametrize("day", DAYS)
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@pytest.mark.parametrize("target", ["D", "B"])
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@pytest.mark.parametrize("convention", ["start", "end"])
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def test_weekly_upsample(self, day, target, convention, simple_period_range_series):
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freq = f"W-{day}"
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ts = simple_period_range_series("1/1/1990", "12/31/1995", freq=freq)
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result = ts.resample(target, convention=convention).ffill()
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expected = result.to_timestamp(target, how=convention)
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expected = expected.asfreq(target, "ffill").to_period()
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tm.assert_series_equal(result, expected)
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def test_resample_to_timestamps(self, simple_period_range_series):
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ts = simple_period_range_series("1/1/1990", "12/31/1995", freq="M")
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result = ts.resample("A-DEC", kind="timestamp").mean()
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expected = ts.to_timestamp(how="start").resample("A-DEC").mean()
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tm.assert_series_equal(result, expected)
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def test_resample_to_quarterly(self, simple_period_range_series):
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for month in MONTHS:
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ts = simple_period_range_series("1990", "1992", freq=f"A-{month}")
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quar_ts = ts.resample(f"Q-{month}").ffill()
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stamps = ts.to_timestamp("D", how="start")
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qdates = period_range(
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ts.index[0].asfreq("D", "start"),
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ts.index[-1].asfreq("D", "end"),
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freq=f"Q-{month}",
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)
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expected = stamps.reindex(qdates.to_timestamp("D", "s"), method="ffill")
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expected.index = qdates
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tm.assert_series_equal(quar_ts, expected)
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# conforms, but different month
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ts = simple_period_range_series("1990", "1992", freq="A-JUN")
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for how in ["start", "end"]:
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result = ts.resample("Q-MAR", convention=how).ffill()
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expected = ts.asfreq("Q-MAR", how=how)
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expected = expected.reindex(result.index, method="ffill")
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# .to_timestamp('D')
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# expected = expected.resample('Q-MAR').ffill()
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tm.assert_series_equal(result, expected)
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def test_resample_fill_missing(self):
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rng = PeriodIndex([2000, 2005, 2007, 2009], freq="A")
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s = Series(np.random.randn(4), index=rng)
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stamps = s.to_timestamp()
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filled = s.resample("A").ffill()
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expected = stamps.resample("A").ffill().to_period("A")
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tm.assert_series_equal(filled, expected)
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def test_cant_fill_missing_dups(self):
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rng = PeriodIndex([2000, 2005, 2005, 2007, 2007], freq="A")
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s = Series(np.random.randn(5), index=rng)
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msg = "Reindexing only valid with uniquely valued Index objects"
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with pytest.raises(InvalidIndexError, match=msg):
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s.resample("A").ffill()
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@pytest.mark.parametrize("freq", ["5min"])
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@pytest.mark.parametrize("kind", ["period", None, "timestamp"])
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def test_resample_5minute(self, freq, kind):
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rng = period_range("1/1/2000", "1/5/2000", freq="T")
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ts = Series(np.random.randn(len(rng)), index=rng)
|
|
expected = ts.to_timestamp().resample(freq).mean()
|
|
if kind != "timestamp":
|
|
expected = expected.to_period(freq)
|
|
result = ts.resample(freq, kind=kind).mean()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_upsample_daily_business_daily(self, simple_period_range_series):
|
|
ts = simple_period_range_series("1/1/2000", "2/1/2000", freq="B")
|
|
|
|
result = ts.resample("D").asfreq()
|
|
expected = ts.asfreq("D").reindex(period_range("1/3/2000", "2/1/2000"))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
ts = simple_period_range_series("1/1/2000", "2/1/2000")
|
|
result = ts.resample("H", convention="s").asfreq()
|
|
exp_rng = period_range("1/1/2000", "2/1/2000 23:00", freq="H")
|
|
expected = ts.asfreq("H", how="s").reindex(exp_rng)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_resample_irregular_sparse(self):
|
|
dr = date_range(start="1/1/2012", freq="5min", periods=1000)
|
|
s = Series(np.array(100), index=dr)
|
|
# subset the data.
|
|
subset = s[:"2012-01-04 06:55"]
|
|
|
|
result = subset.resample("10min").apply(len)
|
|
expected = s.resample("10min").apply(len).loc[result.index]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_resample_weekly_all_na(self):
|
|
rng = date_range("1/1/2000", periods=10, freq="W-WED")
|
|
ts = Series(np.random.randn(len(rng)), index=rng)
|
|
|
|
result = ts.resample("W-THU").asfreq()
|
|
|
|
assert result.isna().all()
|
|
|
|
result = ts.resample("W-THU").asfreq().ffill()[:-1]
|
|
expected = ts.asfreq("W-THU").ffill()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_resample_tz_localized(self):
|
|
dr = date_range(start="2012-4-13", end="2012-5-1")
|
|
ts = Series(range(len(dr)), index=dr)
|
|
|
|
ts_utc = ts.tz_localize("UTC")
|
|
ts_local = ts_utc.tz_convert("America/Los_Angeles")
|
|
|
|
result = ts_local.resample("W").mean()
|
|
|
|
ts_local_naive = ts_local.copy()
|
|
ts_local_naive.index = [
|
|
x.replace(tzinfo=None) for x in ts_local_naive.index.to_pydatetime()
|
|
]
|
|
|
|
exp = ts_local_naive.resample("W").mean().tz_localize("America/Los_Angeles")
|
|
exp.index = pd.DatetimeIndex(exp.index, freq="W")
|
|
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
# it works
|
|
result = ts_local.resample("D").mean()
|
|
|
|
# #2245
|
|
idx = date_range(
|
|
"2001-09-20 15:59", "2001-09-20 16:00", freq="T", tz="Australia/Sydney"
|
|
)
|
|
s = Series([1, 2], index=idx)
|
|
|
|
result = s.resample("D", closed="right", label="right").mean()
|
|
ex_index = date_range("2001-09-21", periods=1, freq="D", tz="Australia/Sydney")
|
|
expected = Series([1.5], index=ex_index)
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# for good measure
|
|
result = s.resample("D", kind="period").mean()
|
|
ex_index = period_range("2001-09-20", periods=1, freq="D")
|
|
expected = Series([1.5], index=ex_index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# GH 6397
|
|
# comparing an offset that doesn't propagate tz's
|
|
rng = date_range("1/1/2011", periods=20000, freq="H")
|
|
rng = rng.tz_localize("EST")
|
|
ts = DataFrame(index=rng)
|
|
ts["first"] = np.random.randn(len(rng))
|
|
ts["second"] = np.cumsum(np.random.randn(len(rng)))
|
|
expected = DataFrame(
|
|
{
|
|
"first": ts.resample("A").sum()["first"],
|
|
"second": ts.resample("A").mean()["second"],
|
|
},
|
|
columns=["first", "second"],
|
|
)
|
|
result = (
|
|
ts.resample("A")
|
|
.agg({"first": np.sum, "second": np.mean})
|
|
.reindex(columns=["first", "second"])
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_closed_left_corner(self):
|
|
# #1465
|
|
s = Series(
|
|
np.random.randn(21),
|
|
index=date_range(start="1/1/2012 9:30", freq="1min", periods=21),
|
|
)
|
|
s[0] = np.nan
|
|
|
|
result = s.resample("10min", closed="left", label="right").mean()
|
|
exp = s[1:].resample("10min", closed="left", label="right").mean()
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
result = s.resample("10min", closed="left", label="left").mean()
|
|
exp = s[1:].resample("10min", closed="left", label="left").mean()
|
|
|
|
ex_index = date_range(start="1/1/2012 9:30", freq="10min", periods=3)
|
|
|
|
tm.assert_index_equal(result.index, ex_index)
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
def test_quarterly_resampling(self):
|
|
rng = period_range("2000Q1", periods=10, freq="Q-DEC")
|
|
ts = Series(np.arange(10), index=rng)
|
|
|
|
result = ts.resample("A").mean()
|
|
exp = ts.to_timestamp().resample("A").mean().to_period()
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
def test_resample_weekly_bug_1726(self):
|
|
# 8/6/12 is a Monday
|
|
ind = date_range(start="8/6/2012", end="8/26/2012", freq="D")
|
|
n = len(ind)
|
|
data = [[x] * 5 for x in range(n)]
|
|
df = DataFrame(data, columns=["open", "high", "low", "close", "vol"], index=ind)
|
|
|
|
# it works!
|
|
df.resample("W-MON", closed="left", label="left").first()
|
|
|
|
def test_resample_with_dst_time_change(self):
|
|
# GH 15549
|
|
index = (
|
|
pd.DatetimeIndex([1457537600000000000, 1458059600000000000])
|
|
.tz_localize("UTC")
|
|
.tz_convert("America/Chicago")
|
|
)
|
|
df = DataFrame([1, 2], index=index)
|
|
result = df.resample("12h", closed="right", label="right").last().ffill()
|
|
|
|
expected_index_values = [
|
|
"2016-03-09 12:00:00-06:00",
|
|
"2016-03-10 00:00:00-06:00",
|
|
"2016-03-10 12:00:00-06:00",
|
|
"2016-03-11 00:00:00-06:00",
|
|
"2016-03-11 12:00:00-06:00",
|
|
"2016-03-12 00:00:00-06:00",
|
|
"2016-03-12 12:00:00-06:00",
|
|
"2016-03-13 00:00:00-06:00",
|
|
"2016-03-13 13:00:00-05:00",
|
|
"2016-03-14 01:00:00-05:00",
|
|
"2016-03-14 13:00:00-05:00",
|
|
"2016-03-15 01:00:00-05:00",
|
|
"2016-03-15 13:00:00-05:00",
|
|
]
|
|
index = pd.to_datetime(expected_index_values, utc=True).tz_convert(
|
|
"America/Chicago"
|
|
)
|
|
index = pd.DatetimeIndex(index, freq="12h")
|
|
expected = DataFrame(
|
|
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],
|
|
index=index,
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_resample_bms_2752(self):
|
|
# GH2753
|
|
foo = Series(index=pd.bdate_range("20000101", "20000201"), dtype=np.float64)
|
|
res1 = foo.resample("BMS").mean()
|
|
res2 = foo.resample("BMS").mean().resample("B").mean()
|
|
assert res1.index[0] == Timestamp("20000103")
|
|
assert res1.index[0] == res2.index[0]
|
|
|
|
# def test_monthly_convention_span(self):
|
|
# rng = period_range('2000-01', periods=3, freq='M')
|
|
# ts = Series(np.arange(3), index=rng)
|
|
|
|
# # hacky way to get same thing
|
|
# exp_index = period_range('2000-01-01', '2000-03-31', freq='D')
|
|
# expected = ts.asfreq('D', how='end').reindex(exp_index)
|
|
# expected = expected.fillna(method='bfill')
|
|
|
|
# result = ts.resample('D', convention='span').mean()
|
|
|
|
# tm.assert_series_equal(result, expected)
|
|
|
|
def test_default_right_closed_label(self):
|
|
end_freq = ["D", "Q", "M", "D"]
|
|
end_types = ["M", "A", "Q", "W"]
|
|
|
|
for from_freq, to_freq in zip(end_freq, end_types):
|
|
idx = date_range(start="8/15/2012", periods=100, freq=from_freq)
|
|
df = DataFrame(np.random.randn(len(idx), 2), idx)
|
|
|
|
resampled = df.resample(to_freq).mean()
|
|
tm.assert_frame_equal(
|
|
resampled, df.resample(to_freq, closed="right", label="right").mean()
|
|
)
|
|
|
|
def test_default_left_closed_label(self):
|
|
others = ["MS", "AS", "QS", "D", "H"]
|
|
others_freq = ["D", "Q", "M", "H", "T"]
|
|
|
|
for from_freq, to_freq in zip(others_freq, others):
|
|
idx = date_range(start="8/15/2012", periods=100, freq=from_freq)
|
|
df = DataFrame(np.random.randn(len(idx), 2), idx)
|
|
|
|
resampled = df.resample(to_freq).mean()
|
|
tm.assert_frame_equal(
|
|
resampled, df.resample(to_freq, closed="left", label="left").mean()
|
|
)
|
|
|
|
def test_all_values_single_bin(self):
|
|
# 2070
|
|
index = period_range(start="2012-01-01", end="2012-12-31", freq="M")
|
|
s = Series(np.random.randn(len(index)), index=index)
|
|
|
|
result = s.resample("A").mean()
|
|
tm.assert_almost_equal(result[0], s.mean())
|
|
|
|
def test_evenly_divisible_with_no_extra_bins(self):
|
|
# 4076
|
|
# when the frequency is evenly divisible, sometimes extra bins
|
|
|
|
df = DataFrame(np.random.randn(9, 3), index=date_range("2000-1-1", periods=9))
|
|
result = df.resample("5D").mean()
|
|
expected = pd.concat([df.iloc[0:5].mean(), df.iloc[5:].mean()], axis=1).T
|
|
expected.index = pd.DatetimeIndex(
|
|
[Timestamp("2000-1-1"), Timestamp("2000-1-6")], freq="5D"
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
index = date_range(start="2001-5-4", periods=28)
|
|
df = DataFrame(
|
|
[
|
|
{
|
|
"REST_KEY": 1,
|
|
"DLY_TRN_QT": 80,
|
|
"DLY_SLS_AMT": 90,
|
|
"COOP_DLY_TRN_QT": 30,
|
|
"COOP_DLY_SLS_AMT": 20,
|
|
}
|
|
]
|
|
* 28
|
|
+ [
|
|
{
|
|
"REST_KEY": 2,
|
|
"DLY_TRN_QT": 70,
|
|
"DLY_SLS_AMT": 10,
|
|
"COOP_DLY_TRN_QT": 50,
|
|
"COOP_DLY_SLS_AMT": 20,
|
|
}
|
|
]
|
|
* 28,
|
|
index=index.append(index),
|
|
).sort_index()
|
|
|
|
index = date_range("2001-5-4", periods=4, freq="7D")
|
|
expected = DataFrame(
|
|
[
|
|
{
|
|
"REST_KEY": 14,
|
|
"DLY_TRN_QT": 14,
|
|
"DLY_SLS_AMT": 14,
|
|
"COOP_DLY_TRN_QT": 14,
|
|
"COOP_DLY_SLS_AMT": 14,
|
|
}
|
|
]
|
|
* 4,
|
|
index=index,
|
|
)
|
|
result = df.resample("7D").count()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame(
|
|
[
|
|
{
|
|
"REST_KEY": 21,
|
|
"DLY_TRN_QT": 1050,
|
|
"DLY_SLS_AMT": 700,
|
|
"COOP_DLY_TRN_QT": 560,
|
|
"COOP_DLY_SLS_AMT": 280,
|
|
}
|
|
]
|
|
* 4,
|
|
index=index,
|
|
)
|
|
result = df.resample("7D").sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("freq, period_mult", [("H", 24), ("12H", 2)])
|
|
@pytest.mark.parametrize("kind", [None, "period"])
|
|
def test_upsampling_ohlc(self, freq, period_mult, kind):
|
|
# GH 13083
|
|
pi = period_range(start="2000", freq="D", periods=10)
|
|
s = Series(range(len(pi)), index=pi)
|
|
expected = s.to_timestamp().resample(freq).ohlc().to_period(freq)
|
|
|
|
# timestamp-based resampling doesn't include all sub-periods
|
|
# of the last original period, so extend accordingly:
|
|
new_index = period_range(start="2000", freq=freq, periods=period_mult * len(pi))
|
|
expected = expected.reindex(new_index)
|
|
result = s.resample(freq, kind=kind).ohlc()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"periods, values",
|
|
[
|
|
(
|
|
[
|
|
pd.NaT,
|
|
"1970-01-01 00:00:00",
|
|
pd.NaT,
|
|
"1970-01-01 00:00:02",
|
|
"1970-01-01 00:00:03",
|
|
],
|
|
[2, 3, 5, 7, 11],
|
|
),
|
|
(
|
|
[
|
|
pd.NaT,
|
|
pd.NaT,
|
|
"1970-01-01 00:00:00",
|
|
pd.NaT,
|
|
pd.NaT,
|
|
pd.NaT,
|
|
"1970-01-01 00:00:02",
|
|
"1970-01-01 00:00:03",
|
|
pd.NaT,
|
|
pd.NaT,
|
|
],
|
|
[1, 2, 3, 5, 6, 8, 7, 11, 12, 13],
|
|
),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"freq, expected_values",
|
|
[
|
|
("1s", [3, np.NaN, 7, 11]),
|
|
("2s", [3, (7 + 11) / 2]),
|
|
("3s", [(3 + 7) / 2, 11]),
|
|
],
|
|
)
|
|
def test_resample_with_nat(self, periods, values, freq, expected_values):
|
|
# GH 13224
|
|
index = PeriodIndex(periods, freq="S")
|
|
frame = DataFrame(values, index=index)
|
|
|
|
expected_index = period_range(
|
|
"1970-01-01 00:00:00", periods=len(expected_values), freq=freq
|
|
)
|
|
expected = DataFrame(expected_values, index=expected_index)
|
|
result = frame.resample(freq).mean()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_resample_with_only_nat(self):
|
|
# GH 13224
|
|
pi = PeriodIndex([pd.NaT] * 3, freq="S")
|
|
frame = DataFrame([2, 3, 5], index=pi, columns=["a"])
|
|
expected_index = PeriodIndex(data=[], freq=pi.freq)
|
|
expected = DataFrame(index=expected_index, columns=["a"], dtype="float64")
|
|
result = frame.resample("1s").mean()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"start,end,start_freq,end_freq,offset",
|
|
[
|
|
("19910905", "19910909 03:00", "H", "24H", "10H"),
|
|
("19910905", "19910909 12:00", "H", "24H", "10H"),
|
|
("19910905", "19910909 23:00", "H", "24H", "10H"),
|
|
("19910905 10:00", "19910909", "H", "24H", "10H"),
|
|
("19910905 10:00", "19910909 10:00", "H", "24H", "10H"),
|
|
("19910905", "19910909 10:00", "H", "24H", "10H"),
|
|
("19910905 12:00", "19910909", "H", "24H", "10H"),
|
|
("19910905 12:00", "19910909 03:00", "H", "24H", "10H"),
|
|
("19910905 12:00", "19910909 12:00", "H", "24H", "10H"),
|
|
("19910905 12:00", "19910909 12:00", "H", "24H", "34H"),
|
|
("19910905 12:00", "19910909 12:00", "H", "17H", "10H"),
|
|
("19910905 12:00", "19910909 12:00", "H", "17H", "3H"),
|
|
("19910905 12:00", "19910909 1:00", "H", "M", "3H"),
|
|
("19910905", "19910913 06:00", "2H", "24H", "10H"),
|
|
("19910905", "19910905 01:39", "Min", "5Min", "3Min"),
|
|
("19910905", "19910905 03:18", "2Min", "5Min", "3Min"),
|
|
],
|
|
)
|
|
def test_resample_with_offset(self, start, end, start_freq, end_freq, offset):
|
|
# GH 23882 & 31809
|
|
s = Series(0, index=period_range(start, end, freq=start_freq))
|
|
s = s + np.arange(len(s))
|
|
result = s.resample(end_freq, offset=offset).mean()
|
|
result = result.to_timestamp(end_freq)
|
|
|
|
expected = s.to_timestamp().resample(end_freq, offset=offset).mean()
|
|
if end_freq == "M":
|
|
# TODO: is non-tick the relevant characteristic? (GH 33815)
|
|
expected.index = expected.index._with_freq(None)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"first,last,freq,exp_first,exp_last",
|
|
[
|
|
("19910905", "19920406", "D", "19910905", "19920406"),
|
|
("19910905 00:00", "19920406 06:00", "D", "19910905", "19920406"),
|
|
(
|
|
"19910905 06:00",
|
|
"19920406 06:00",
|
|
"H",
|
|
"19910905 06:00",
|
|
"19920406 06:00",
|
|
),
|
|
("19910906", "19920406", "M", "1991-09", "1992-04"),
|
|
("19910831", "19920430", "M", "1991-08", "1992-04"),
|
|
("1991-08", "1992-04", "M", "1991-08", "1992-04"),
|
|
],
|
|
)
|
|
def test_get_period_range_edges(self, first, last, freq, exp_first, exp_last):
|
|
first = Period(first)
|
|
last = Period(last)
|
|
|
|
exp_first = Period(exp_first, freq=freq)
|
|
exp_last = Period(exp_last, freq=freq)
|
|
|
|
freq = pd.tseries.frequencies.to_offset(freq)
|
|
result = _get_period_range_edges(first, last, freq)
|
|
expected = (exp_first, exp_last)
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assert result == expected
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def test_sum_min_count(self):
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# GH 19974
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index = date_range(start="2018", freq="M", periods=6)
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data = np.ones(6)
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data[3:6] = np.nan
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|
s = Series(data, index).to_period()
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|
result = s.resample("Q").sum(min_count=1)
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expected = Series(
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[3.0, np.nan], index=PeriodIndex(["2018Q1", "2018Q2"], freq="Q-DEC")
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|
)
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|
tm.assert_series_equal(result, expected)
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|
|