525 lines
14 KiB
Python
525 lines
14 KiB
Python
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from datetime import (
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datetime,
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timedelta,
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)
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import numpy as np
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import pytest
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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 INVALID_FREQ_ERR_MSG
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from pandas.compat import is_platform_windows
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from pandas import (
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DatetimeIndex,
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Index,
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Series,
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Timestamp,
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date_range,
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period_range,
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)
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import pandas._testing as tm
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from pandas.core.arrays import (
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DatetimeArray,
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TimedeltaArray,
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)
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from pandas.core.tools.datetimes import to_datetime
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import pandas.tseries.frequencies as frequencies
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import pandas.tseries.offsets as offsets
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@pytest.fixture(
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params=[
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(timedelta(1), "D"),
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(timedelta(hours=1), "H"),
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(timedelta(minutes=1), "T"),
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(timedelta(seconds=1), "S"),
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(np.timedelta64(1, "ns"), "N"),
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(timedelta(microseconds=1), "U"),
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(timedelta(microseconds=1000), "L"),
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]
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)
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def base_delta_code_pair(request):
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return request.param
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freqs = (
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[f"Q-{month}" for month in MONTHS]
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+ [f"{annual}-{month}" for annual in ["A", "BA"] for month in MONTHS]
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+ ["M", "BM", "BMS"]
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+ [f"WOM-{count}{day}" for count in range(1, 5) for day in DAYS]
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+ [f"W-{day}" for day in DAYS]
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)
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@pytest.mark.parametrize("freq", freqs)
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@pytest.mark.parametrize("periods", [5, 7])
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def test_infer_freq_range(periods, freq):
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freq = freq.upper()
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gen = date_range("1/1/2000", periods=periods, freq=freq)
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index = DatetimeIndex(gen.values)
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if not freq.startswith("Q-"):
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assert frequencies.infer_freq(index) == gen.freqstr
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else:
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inf_freq = frequencies.infer_freq(index)
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is_dec_range = inf_freq == "Q-DEC" and gen.freqstr in (
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"Q",
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"Q-DEC",
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"Q-SEP",
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"Q-JUN",
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"Q-MAR",
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)
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is_nov_range = inf_freq == "Q-NOV" and gen.freqstr in (
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"Q-NOV",
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"Q-AUG",
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"Q-MAY",
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"Q-FEB",
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)
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is_oct_range = inf_freq == "Q-OCT" and gen.freqstr in (
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"Q-OCT",
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"Q-JUL",
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"Q-APR",
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"Q-JAN",
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)
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assert is_dec_range or is_nov_range or is_oct_range
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def test_raise_if_period_index():
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index = period_range(start="1/1/1990", periods=20, freq="M")
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msg = "Check the `freq` attribute instead of using infer_freq"
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with pytest.raises(TypeError, match=msg):
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frequencies.infer_freq(index)
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def test_raise_if_too_few():
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index = DatetimeIndex(["12/31/1998", "1/3/1999"])
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msg = "Need at least 3 dates to infer frequency"
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with pytest.raises(ValueError, match=msg):
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frequencies.infer_freq(index)
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def test_business_daily():
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index = DatetimeIndex(["01/01/1999", "1/4/1999", "1/5/1999"])
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assert frequencies.infer_freq(index) == "B"
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def test_business_daily_look_alike():
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# see gh-16624
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#
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# Do not infer "B when "weekend" (2-day gap) in wrong place.
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index = DatetimeIndex(["12/31/1998", "1/3/1999", "1/4/1999"])
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assert frequencies.infer_freq(index) is None
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def test_day_corner():
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index = DatetimeIndex(["1/1/2000", "1/2/2000", "1/3/2000"])
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assert frequencies.infer_freq(index) == "D"
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def test_non_datetime_index():
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dates = to_datetime(["1/1/2000", "1/2/2000", "1/3/2000"])
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assert frequencies.infer_freq(dates) == "D"
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def test_fifth_week_of_month_infer():
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# see gh-9425
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#
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# Only attempt to infer up to WOM-4.
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index = DatetimeIndex(["2014-03-31", "2014-06-30", "2015-03-30"])
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assert frequencies.infer_freq(index) is None
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def test_week_of_month_fake():
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# All of these dates are on same day
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# of week and are 4 or 5 weeks apart.
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index = DatetimeIndex(["2013-08-27", "2013-10-01", "2013-10-29", "2013-11-26"])
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assert frequencies.infer_freq(index) != "WOM-4TUE"
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def test_fifth_week_of_month():
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# see gh-9425
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#
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# Only supports freq up to WOM-4.
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msg = (
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"Of the four parameters: start, end, periods, "
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"and freq, exactly three must be specified"
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)
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with pytest.raises(ValueError, match=msg):
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date_range("2014-01-01", freq="WOM-5MON")
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def test_monthly_ambiguous():
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rng = DatetimeIndex(["1/31/2000", "2/29/2000", "3/31/2000"])
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assert rng.inferred_freq == "M"
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def test_annual_ambiguous():
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rng = DatetimeIndex(["1/31/2000", "1/31/2001", "1/31/2002"])
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assert rng.inferred_freq == "A-JAN"
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@pytest.mark.parametrize("count", range(1, 5))
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def test_infer_freq_delta(base_delta_code_pair, count):
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b = Timestamp(datetime.now())
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base_delta, code = base_delta_code_pair
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inc = base_delta * count
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index = DatetimeIndex([b + inc * j for j in range(3)])
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exp_freq = f"{count:d}{code}" if count > 1 else code
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assert frequencies.infer_freq(index) == exp_freq
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@pytest.mark.parametrize(
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"constructor",
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[
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lambda now, delta: DatetimeIndex(
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[now + delta * 7] + [now + delta * j for j in range(3)]
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),
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lambda now, delta: DatetimeIndex(
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[now + delta * j for j in range(3)] + [now + delta * 7]
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),
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],
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)
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def test_infer_freq_custom(base_delta_code_pair, constructor):
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b = Timestamp(datetime.now())
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base_delta, _ = base_delta_code_pair
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index = constructor(b, base_delta)
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assert frequencies.infer_freq(index) is None
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@pytest.mark.parametrize(
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"freq,expected", [("Q", "Q-DEC"), ("Q-NOV", "Q-NOV"), ("Q-OCT", "Q-OCT")]
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)
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def test_infer_freq_index(freq, expected):
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rng = period_range("1959Q2", "2009Q3", freq=freq)
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rng = Index(rng.to_timestamp("D", how="e").astype(object))
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assert rng.inferred_freq == expected
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@pytest.mark.parametrize(
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"expected,dates",
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list(
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{
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"AS-JAN": ["2009-01-01", "2010-01-01", "2011-01-01", "2012-01-01"],
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"Q-OCT": ["2009-01-31", "2009-04-30", "2009-07-31", "2009-10-31"],
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"M": ["2010-11-30", "2010-12-31", "2011-01-31", "2011-02-28"],
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"W-SAT": ["2010-12-25", "2011-01-01", "2011-01-08", "2011-01-15"],
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"D": ["2011-01-01", "2011-01-02", "2011-01-03", "2011-01-04"],
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"H": [
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"2011-12-31 22:00",
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"2011-12-31 23:00",
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"2012-01-01 00:00",
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"2012-01-01 01:00",
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],
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}.items()
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),
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)
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def test_infer_freq_tz(tz_naive_fixture, expected, dates):
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# see gh-7310
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tz = tz_naive_fixture
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idx = DatetimeIndex(dates, tz=tz)
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assert idx.inferred_freq == expected
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@pytest.mark.parametrize(
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"date_pair",
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[
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["2013-11-02", "2013-11-5"], # Fall DST
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["2014-03-08", "2014-03-11"], # Spring DST
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["2014-01-01", "2014-01-03"], # Regular Time
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],
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)
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@pytest.mark.parametrize(
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"freq", ["H", "3H", "10T", "3601S", "3600001L", "3600000001U", "3600000000001N"]
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)
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def test_infer_freq_tz_transition(tz_naive_fixture, date_pair, freq):
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# see gh-8772
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tz = tz_naive_fixture
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idx = date_range(date_pair[0], date_pair[1], freq=freq, tz=tz)
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assert idx.inferred_freq == freq
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def test_infer_freq_tz_transition_custom():
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index = date_range("2013-11-03", periods=5, freq="3H").tz_localize(
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"America/Chicago"
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)
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assert index.inferred_freq is None
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@pytest.mark.parametrize(
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"data,expected",
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[
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# Hourly freq in a day must result in "H"
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(
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[
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"2014-07-01 09:00",
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"2014-07-01 10:00",
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"2014-07-01 11:00",
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"2014-07-01 12:00",
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"2014-07-01 13:00",
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"2014-07-01 14:00",
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],
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"H",
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),
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(
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[
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"2014-07-01 09:00",
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"2014-07-01 10:00",
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"2014-07-01 11:00",
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"2014-07-01 12:00",
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"2014-07-01 13:00",
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"2014-07-01 14:00",
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"2014-07-01 15:00",
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"2014-07-01 16:00",
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"2014-07-02 09:00",
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"2014-07-02 10:00",
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"2014-07-02 11:00",
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],
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"BH",
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),
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(
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[
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"2014-07-04 09:00",
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"2014-07-04 10:00",
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"2014-07-04 11:00",
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"2014-07-04 12:00",
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"2014-07-04 13:00",
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"2014-07-04 14:00",
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"2014-07-04 15:00",
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"2014-07-04 16:00",
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"2014-07-07 09:00",
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"2014-07-07 10:00",
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"2014-07-07 11:00",
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],
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"BH",
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),
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(
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[
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"2014-07-04 09:00",
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"2014-07-04 10:00",
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"2014-07-04 11:00",
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"2014-07-04 12:00",
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"2014-07-04 13:00",
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"2014-07-04 14:00",
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"2014-07-04 15:00",
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"2014-07-04 16:00",
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"2014-07-07 09:00",
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"2014-07-07 10:00",
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"2014-07-07 11:00",
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"2014-07-07 12:00",
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"2014-07-07 13:00",
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"2014-07-07 14:00",
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"2014-07-07 15:00",
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"2014-07-07 16:00",
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"2014-07-08 09:00",
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"2014-07-08 10:00",
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"2014-07-08 11:00",
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"2014-07-08 12:00",
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"2014-07-08 13:00",
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"2014-07-08 14:00",
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"2014-07-08 15:00",
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"2014-07-08 16:00",
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],
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"BH",
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),
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],
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)
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def test_infer_freq_business_hour(data, expected):
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# see gh-7905
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idx = DatetimeIndex(data)
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assert idx.inferred_freq == expected
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def test_not_monotonic():
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rng = DatetimeIndex(["1/31/2000", "1/31/2001", "1/31/2002"])
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rng = rng[::-1]
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assert rng.inferred_freq == "-1A-JAN"
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def test_non_datetime_index2():
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rng = DatetimeIndex(["1/31/2000", "1/31/2001", "1/31/2002"])
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vals = rng.to_pydatetime()
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result = frequencies.infer_freq(vals)
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assert result == rng.inferred_freq
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@pytest.mark.parametrize(
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"idx", [tm.makeIntIndex(10), tm.makeFloatIndex(10), tm.makePeriodIndex(10)]
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)
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def test_invalid_index_types(idx):
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msg = "|".join(
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[
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"cannot infer freq from a non-convertible",
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"Check the `freq` attribute instead of using infer_freq",
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]
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)
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|
|
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
frequencies.infer_freq(idx)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.skipif(is_platform_windows(), reason="see gh-10822: Windows issue")
|
||
|
|
def test_invalid_index_types_unicode():
|
||
|
|
# see gh-10822
|
||
|
|
#
|
||
|
|
# Odd error message on conversions to datetime for unicode.
|
||
|
|
msg = "Unknown string format"
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
frequencies.infer_freq(tm.makeStringIndex(10))
|
||
|
|
|
||
|
|
|
||
|
|
def test_string_datetime_like_compat():
|
||
|
|
# see gh-6463
|
||
|
|
data = ["2004-01", "2004-02", "2004-03", "2004-04"]
|
||
|
|
|
||
|
|
expected = frequencies.infer_freq(data)
|
||
|
|
result = frequencies.infer_freq(Index(data))
|
||
|
|
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
|
||
|
|
def test_series():
|
||
|
|
# see gh-6407
|
||
|
|
s = Series(date_range("20130101", "20130110"))
|
||
|
|
inferred = frequencies.infer_freq(s)
|
||
|
|
assert inferred == "D"
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("end", [10, 10.0])
|
||
|
|
def test_series_invalid_type(end):
|
||
|
|
# see gh-6407
|
||
|
|
msg = "cannot infer freq from a non-convertible dtype on a Series"
|
||
|
|
s = Series(np.arange(end))
|
||
|
|
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
frequencies.infer_freq(s)
|
||
|
|
|
||
|
|
|
||
|
|
def test_series_inconvertible_string():
|
||
|
|
# see gh-6407
|
||
|
|
msg = "Unknown string format"
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
frequencies.infer_freq(Series(["foo", "bar"]))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("freq", [None, "L"])
|
||
|
|
def test_series_period_index(freq):
|
||
|
|
# see gh-6407
|
||
|
|
#
|
||
|
|
# Cannot infer on PeriodIndex
|
||
|
|
msg = "cannot infer freq from a non-convertible dtype on a Series"
|
||
|
|
s = Series(period_range("2013", periods=10, freq=freq))
|
||
|
|
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
frequencies.infer_freq(s)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("freq", ["M", "L", "S"])
|
||
|
|
def test_series_datetime_index(freq):
|
||
|
|
s = Series(date_range("20130101", periods=10, freq=freq))
|
||
|
|
inferred = frequencies.infer_freq(s)
|
||
|
|
assert inferred == freq
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"offset_func",
|
||
|
|
[
|
||
|
|
frequencies._get_offset,
|
||
|
|
lambda freq: date_range("2011-01-01", periods=5, freq=freq),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"freq",
|
||
|
|
[
|
||
|
|
"WEEKDAY",
|
||
|
|
"EOM",
|
||
|
|
"W@MON",
|
||
|
|
"W@TUE",
|
||
|
|
"W@WED",
|
||
|
|
"W@THU",
|
||
|
|
"W@FRI",
|
||
|
|
"W@SAT",
|
||
|
|
"W@SUN",
|
||
|
|
"Q@JAN",
|
||
|
|
"Q@FEB",
|
||
|
|
"Q@MAR",
|
||
|
|
"A@JAN",
|
||
|
|
"A@FEB",
|
||
|
|
"A@MAR",
|
||
|
|
"A@APR",
|
||
|
|
"A@MAY",
|
||
|
|
"A@JUN",
|
||
|
|
"A@JUL",
|
||
|
|
"A@AUG",
|
||
|
|
"A@SEP",
|
||
|
|
"A@OCT",
|
||
|
|
"A@NOV",
|
||
|
|
"A@DEC",
|
||
|
|
"Y@JAN",
|
||
|
|
"WOM@1MON",
|
||
|
|
"WOM@2MON",
|
||
|
|
"WOM@3MON",
|
||
|
|
"WOM@4MON",
|
||
|
|
"WOM@1TUE",
|
||
|
|
"WOM@2TUE",
|
||
|
|
"WOM@3TUE",
|
||
|
|
"WOM@4TUE",
|
||
|
|
"WOM@1WED",
|
||
|
|
"WOM@2WED",
|
||
|
|
"WOM@3WED",
|
||
|
|
"WOM@4WED",
|
||
|
|
"WOM@1THU",
|
||
|
|
"WOM@2THU",
|
||
|
|
"WOM@3THU",
|
||
|
|
"WOM@4THU",
|
||
|
|
"WOM@1FRI",
|
||
|
|
"WOM@2FRI",
|
||
|
|
"WOM@3FRI",
|
||
|
|
"WOM@4FRI",
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_legacy_offset_warnings(offset_func, freq):
|
||
|
|
with pytest.raises(ValueError, match=INVALID_FREQ_ERR_MSG):
|
||
|
|
offset_func(freq)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ms_vs_capital_ms():
|
||
|
|
left = frequencies._get_offset("ms")
|
||
|
|
right = frequencies._get_offset("MS")
|
||
|
|
|
||
|
|
assert left == offsets.Milli()
|
||
|
|
assert right == offsets.MonthBegin()
|
||
|
|
|
||
|
|
|
||
|
|
def test_infer_freq_warn_deprecated():
|
||
|
|
with tm.assert_produces_warning(FutureWarning):
|
||
|
|
frequencies.infer_freq(date_range(2022, periods=3), warn=False)
|
||
|
|
|
||
|
|
|
||
|
|
def test_infer_freq_non_nano():
|
||
|
|
arr = np.arange(10).astype(np.int64).view("M8[s]")
|
||
|
|
dta = DatetimeArray._simple_new(arr, dtype=arr.dtype)
|
||
|
|
res = frequencies.infer_freq(dta)
|
||
|
|
assert res == "S"
|
||
|
|
|
||
|
|
arr2 = arr.view("m8[ms]")
|
||
|
|
tda = TimedeltaArray._simple_new(arr2, dtype=arr2.dtype)
|
||
|
|
res2 = frequencies.infer_freq(tda)
|
||
|
|
assert res2 == "L"
|