264 lines
9.1 KiB
Python
264 lines
9.1 KiB
Python
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from datetime import datetime
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import numpy as np
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import pytest
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from pandas._libs.tslibs.offsets import MonthEnd
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from pandas import (
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DataFrame,
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DatetimeIndex,
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Series,
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date_range,
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period_range,
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to_datetime,
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)
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import pandas._testing as tm
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from pandas.tseries import offsets
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class TestAsFreq:
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@pytest.fixture(params=["s", "ms", "us", "ns"])
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def unit(self, request):
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return request.param
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def test_asfreq2(self, frame_or_series):
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ts = frame_or_series(
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[0.0, 1.0, 2.0],
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index=DatetimeIndex(
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[
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datetime(2009, 10, 30),
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datetime(2009, 11, 30),
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datetime(2009, 12, 31),
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],
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dtype="M8[ns]",
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freq="BME",
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),
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)
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daily_ts = ts.asfreq("B")
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monthly_ts = daily_ts.asfreq("BME")
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tm.assert_equal(monthly_ts, ts)
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daily_ts = ts.asfreq("B", method="pad")
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monthly_ts = daily_ts.asfreq("BME")
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tm.assert_equal(monthly_ts, ts)
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daily_ts = ts.asfreq(offsets.BDay())
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monthly_ts = daily_ts.asfreq(offsets.BMonthEnd())
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tm.assert_equal(monthly_ts, ts)
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result = ts[:0].asfreq("ME")
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assert len(result) == 0
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assert result is not ts
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if frame_or_series is Series:
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daily_ts = ts.asfreq("D", fill_value=-1)
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result = daily_ts.value_counts().sort_index()
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expected = Series(
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[60, 1, 1, 1], index=[-1.0, 2.0, 1.0, 0.0], name="count"
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).sort_index()
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tm.assert_series_equal(result, expected)
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def test_asfreq_datetimeindex_empty(self, frame_or_series):
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# GH#14320
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index = DatetimeIndex(["2016-09-29 11:00"])
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expected = frame_or_series(index=index, dtype=object).asfreq("h")
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result = frame_or_series([3], index=index.copy()).asfreq("h")
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tm.assert_index_equal(expected.index, result.index)
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@pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"])
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def test_tz_aware_asfreq_smoke(self, tz, frame_or_series):
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dr = date_range("2011-12-01", "2012-07-20", freq="D", tz=tz)
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obj = frame_or_series(
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np.random.default_rng(2).standard_normal(len(dr)), index=dr
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)
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# it works!
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obj.asfreq("min")
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def test_asfreq_normalize(self, frame_or_series):
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rng = date_range("1/1/2000 09:30", periods=20)
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norm = date_range("1/1/2000", periods=20)
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vals = np.random.default_rng(2).standard_normal((20, 3))
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obj = DataFrame(vals, index=rng)
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expected = DataFrame(vals, index=norm)
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if frame_or_series is Series:
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obj = obj[0]
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expected = expected[0]
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result = obj.asfreq("D", normalize=True)
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tm.assert_equal(result, expected)
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def test_asfreq_keep_index_name(self, frame_or_series):
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# GH#9854
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index_name = "bar"
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index = date_range("20130101", periods=20, name=index_name)
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obj = DataFrame(list(range(20)), columns=["foo"], index=index)
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obj = tm.get_obj(obj, frame_or_series)
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assert index_name == obj.index.name
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assert index_name == obj.asfreq("10D").index.name
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def test_asfreq_ts(self, frame_or_series):
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index = period_range(freq="Y", start="1/1/2001", end="12/31/2010")
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obj = DataFrame(
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np.random.default_rng(2).standard_normal((len(index), 3)), index=index
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)
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obj = tm.get_obj(obj, frame_or_series)
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result = obj.asfreq("D", how="end")
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exp_index = index.asfreq("D", how="end")
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assert len(result) == len(obj)
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tm.assert_index_equal(result.index, exp_index)
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result = obj.asfreq("D", how="start")
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exp_index = index.asfreq("D", how="start")
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assert len(result) == len(obj)
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tm.assert_index_equal(result.index, exp_index)
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def test_asfreq_resample_set_correct_freq(self, frame_or_series):
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# GH#5613
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# we test if .asfreq() and .resample() set the correct value for .freq
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dti = to_datetime(["2012-01-01", "2012-01-02", "2012-01-03"])
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obj = DataFrame({"col": [1, 2, 3]}, index=dti)
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obj = tm.get_obj(obj, frame_or_series)
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# testing the settings before calling .asfreq() and .resample()
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assert obj.index.freq is None
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assert obj.index.inferred_freq == "D"
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# does .asfreq() set .freq correctly?
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assert obj.asfreq("D").index.freq == "D"
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# does .resample() set .freq correctly?
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assert obj.resample("D").asfreq().index.freq == "D"
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def test_asfreq_empty(self, datetime_frame):
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# test does not blow up on length-0 DataFrame
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zero_length = datetime_frame.reindex([])
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result = zero_length.asfreq("BME")
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assert result is not zero_length
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def test_asfreq(self, datetime_frame):
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offset_monthly = datetime_frame.asfreq(offsets.BMonthEnd())
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rule_monthly = datetime_frame.asfreq("BME")
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tm.assert_frame_equal(offset_monthly, rule_monthly)
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rule_monthly.asfreq("B", method="pad")
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# TODO: actually check that this worked.
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# don't forget!
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rule_monthly.asfreq("B", method="pad")
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def test_asfreq_datetimeindex(self):
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df = DataFrame(
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{"A": [1, 2, 3]},
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index=[datetime(2011, 11, 1), datetime(2011, 11, 2), datetime(2011, 11, 3)],
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)
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df = df.asfreq("B")
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assert isinstance(df.index, DatetimeIndex)
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ts = df["A"].asfreq("B")
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assert isinstance(ts.index, DatetimeIndex)
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def test_asfreq_fillvalue(self):
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# test for fill value during upsampling, related to issue 3715
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# setup
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rng = date_range("1/1/2016", periods=10, freq="2s")
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# Explicit cast to 'float' to avoid implicit cast when setting None
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ts = Series(np.arange(len(rng)), index=rng, dtype="float")
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df = DataFrame({"one": ts})
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# insert pre-existing missing value
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df.loc["2016-01-01 00:00:08", "one"] = None
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actual_df = df.asfreq(freq="1s", fill_value=9.0)
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expected_df = df.asfreq(freq="1s").fillna(9.0)
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expected_df.loc["2016-01-01 00:00:08", "one"] = None
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tm.assert_frame_equal(expected_df, actual_df)
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expected_series = ts.asfreq(freq="1s").fillna(9.0)
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actual_series = ts.asfreq(freq="1s", fill_value=9.0)
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tm.assert_series_equal(expected_series, actual_series)
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def test_asfreq_with_date_object_index(self, frame_or_series):
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rng = date_range("1/1/2000", periods=20)
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ts = frame_or_series(np.random.default_rng(2).standard_normal(20), index=rng)
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ts2 = ts.copy()
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ts2.index = [x.date() for x in ts2.index]
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result = ts2.asfreq("4h", method="ffill")
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expected = ts.asfreq("4h", method="ffill")
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tm.assert_equal(result, expected)
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def test_asfreq_with_unsorted_index(self, frame_or_series):
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# GH#39805
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# Test that rows are not dropped when the datetime index is out of order
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index = to_datetime(["2021-01-04", "2021-01-02", "2021-01-03", "2021-01-01"])
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result = frame_or_series(range(4), index=index)
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expected = result.reindex(sorted(index))
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expected.index = expected.index._with_freq("infer")
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result = result.asfreq("D")
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tm.assert_equal(result, expected)
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def test_asfreq_after_normalize(self, unit):
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# https://github.com/pandas-dev/pandas/issues/50727
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result = DatetimeIndex(
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date_range("2000", periods=2).as_unit(unit).normalize(), freq="D"
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)
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expected = DatetimeIndex(["2000-01-01", "2000-01-02"], freq="D").as_unit(unit)
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tm.assert_index_equal(result, expected)
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@pytest.mark.parametrize(
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"freq, freq_half",
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[
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("2ME", "ME"),
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(MonthEnd(2), MonthEnd(1)),
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],
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)
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def test_asfreq_2ME(self, freq, freq_half):
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index = date_range("1/1/2000", periods=6, freq=freq_half)
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df = DataFrame({"s": Series([0.0, 1.0, 2.0, 3.0, 4.0, 5.0], index=index)})
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expected = df.asfreq(freq=freq)
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index = date_range("1/1/2000", periods=3, freq=freq)
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result = DataFrame({"s": Series([0.0, 2.0, 4.0], index=index)})
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"freq, freq_depr",
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[
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("2ME", "2M"),
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("2QE", "2Q"),
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("2QE-SEP", "2Q-SEP"),
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("1BQE", "1BQ"),
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("2BQE-SEP", "2BQ-SEP"),
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("1YE", "1Y"),
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("2YE-MAR", "2Y-MAR"),
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("1YE", "1A"),
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("2YE-MAR", "2A-MAR"),
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("2BYE-MAR", "2BA-MAR"),
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],
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)
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def test_asfreq_frequency_M_Q_Y_A_deprecated(self, freq, freq_depr):
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# GH#9586, #55978
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depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed "
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f"in a future version, please use '{freq[1:]}' instead."
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index = date_range("1/1/2000", periods=4, freq=f"{freq[1:]}")
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df = DataFrame({"s": Series([0.0, 1.0, 2.0, 3.0], index=index)})
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expected = df.asfreq(freq=freq)
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with tm.assert_produces_warning(FutureWarning, match=depr_msg):
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result = df.asfreq(freq=freq_depr)
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tm.assert_frame_equal(result, expected)
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