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keshe/.venv/Lib/site-packages/pandas/tests/tseries/frequencies/test_inference.py

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from datetime import (
datetime,
timedelta,
)
import numpy as np
import pytest
from pandas._libs.tslibs.ccalendar import (
DAYS,
MONTHS,
)
from pandas._libs.tslibs.offsets import _get_offset
from pandas._libs.tslibs.period import INVALID_FREQ_ERR_MSG
from pandas.compat import is_platform_windows
from pandas import (
DatetimeIndex,
Index,
RangeIndex,
Series,
Timestamp,
date_range,
period_range,
)
import pandas._testing as tm
from pandas.core.arrays import (
DatetimeArray,
TimedeltaArray,
)
from pandas.core.tools.datetimes import to_datetime
from pandas.tseries import (
frequencies,
offsets,
)
@pytest.fixture(
params=[
(timedelta(1), "D"),
(timedelta(hours=1), "h"),
(timedelta(minutes=1), "min"),
(timedelta(seconds=1), "s"),
(np.timedelta64(1, "ns"), "ns"),
(timedelta(microseconds=1), "us"),
(timedelta(microseconds=1000), "ms"),
]
)
def base_delta_code_pair(request):
return request.param
freqs = (
[f"QE-{month}" for month in MONTHS]
+ [f"{annual}-{month}" for annual in ["YE", "BYE"] for month in MONTHS]
+ ["ME", "BME", "BMS"]
+ [f"WOM-{count}{day}" for count in range(1, 5) for day in DAYS]
+ [f"W-{day}" for day in DAYS]
)
@pytest.mark.parametrize("freq", freqs)
@pytest.mark.parametrize("periods", [5, 7])
def test_infer_freq_range(periods, freq):
freq = freq.upper()
gen = date_range("1/1/2000", periods=periods, freq=freq)
index = DatetimeIndex(gen.values)
if not freq.startswith("QE-"):
assert frequencies.infer_freq(index) == gen.freqstr
else:
inf_freq = frequencies.infer_freq(index)
is_dec_range = inf_freq == "QE-DEC" and gen.freqstr in (
"QE",
"QE-DEC",
"QE-SEP",
"QE-JUN",
"QE-MAR",
)
is_nov_range = inf_freq == "QE-NOV" and gen.freqstr in (
"QE-NOV",
"QE-AUG",
"QE-MAY",
"QE-FEB",
)
is_oct_range = inf_freq == "QE-OCT" and gen.freqstr in (
"QE-OCT",
"QE-JUL",
"QE-APR",
"QE-JAN",
)
assert is_dec_range or is_nov_range or is_oct_range
def test_raise_if_period_index():
index = period_range(start="1/1/1990", periods=20, freq="M")
msg = "Check the `freq` attribute instead of using infer_freq"
with pytest.raises(TypeError, match=msg):
frequencies.infer_freq(index)
def test_raise_if_too_few():
index = DatetimeIndex(["12/31/1998", "1/3/1999"])
msg = "Need at least 3 dates to infer frequency"
with pytest.raises(ValueError, match=msg):
frequencies.infer_freq(index)
def test_business_daily():
index = DatetimeIndex(["01/01/1999", "1/4/1999", "1/5/1999"])
assert frequencies.infer_freq(index) == "B"
def test_business_daily_look_alike():
# see gh-16624
#
# Do not infer "B when "weekend" (2-day gap) in wrong place.
index = DatetimeIndex(["12/31/1998", "1/3/1999", "1/4/1999"])
assert frequencies.infer_freq(index) is None
def test_day_corner():
index = DatetimeIndex(["1/1/2000", "1/2/2000", "1/3/2000"])
assert frequencies.infer_freq(index) == "D"
def test_non_datetime_index():
dates = to_datetime(["1/1/2000", "1/2/2000", "1/3/2000"])
assert frequencies.infer_freq(dates) == "D"
def test_fifth_week_of_month_infer():
# see gh-9425
#
# Only attempt to infer up to WOM-4.
index = DatetimeIndex(["2014-03-31", "2014-06-30", "2015-03-30"])
assert frequencies.infer_freq(index) is None
def test_week_of_month_fake():
# All of these dates are on same day
# of week and are 4 or 5 weeks apart.
index = DatetimeIndex(["2013-08-27", "2013-10-01", "2013-10-29", "2013-11-26"])
assert frequencies.infer_freq(index) != "WOM-4TUE"
def test_fifth_week_of_month():
# see gh-9425
#
# Only supports freq up to WOM-4.
msg = (
"Of the four parameters: start, end, periods, "
"and freq, exactly three must be specified"
)
with pytest.raises(ValueError, match=msg):
date_range("2014-01-01", freq="WOM-5MON")
def test_monthly_ambiguous():
rng = DatetimeIndex(["1/31/2000", "2/29/2000", "3/31/2000"])
assert rng.inferred_freq == "ME"
def test_annual_ambiguous():
rng = DatetimeIndex(["1/31/2000", "1/31/2001", "1/31/2002"])
assert rng.inferred_freq == "YE-JAN"
@pytest.mark.parametrize("count", range(1, 5))
def test_infer_freq_delta(base_delta_code_pair, count):
b = Timestamp(datetime.now())
base_delta, code = base_delta_code_pair
inc = base_delta * count
index = DatetimeIndex([b + inc * j for j in range(3)])
exp_freq = f"{count:d}{code}" if count > 1 else code
assert frequencies.infer_freq(index) == exp_freq
@pytest.mark.parametrize(
"constructor",
[
lambda now, delta: DatetimeIndex(
[now + delta * 7] + [now + delta * j for j in range(3)]
),
lambda now, delta: DatetimeIndex(
[now + delta * j for j in range(3)] + [now + delta * 7]
),
],
)
def test_infer_freq_custom(base_delta_code_pair, constructor):
b = Timestamp(datetime.now())
base_delta, _ = base_delta_code_pair
index = constructor(b, base_delta)
assert frequencies.infer_freq(index) is None
@pytest.mark.parametrize(
"freq,expected", [("Q", "QE-DEC"), ("Q-NOV", "QE-NOV"), ("Q-OCT", "QE-OCT")]
)
def test_infer_freq_index(freq, expected):
rng = period_range("1959Q2", "2009Q3", freq=freq)
with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
rng = Index(rng.to_timestamp("D", how="e").astype(object))
assert rng.inferred_freq == expected
@pytest.mark.parametrize(
"expected,dates",
list(
{
"YS-JAN": ["2009-01-01", "2010-01-01", "2011-01-01", "2012-01-01"],
"QE-OCT": ["2009-01-31", "2009-04-30", "2009-07-31", "2009-10-31"],
"ME": ["2010-11-30", "2010-12-31", "2011-01-31", "2011-02-28"],
"W-SAT": ["2010-12-25", "2011-01-01", "2011-01-08", "2011-01-15"],
"D": ["2011-01-01", "2011-01-02", "2011-01-03", "2011-01-04"],
"h": [
"2011-12-31 22:00",
"2011-12-31 23:00",
"2012-01-01 00:00",
"2012-01-01 01:00",
],
}.items()
),
)
@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
def test_infer_freq_tz(tz_naive_fixture, expected, dates, unit):
# see gh-7310, GH#55609
tz = tz_naive_fixture
idx = DatetimeIndex(dates, tz=tz).as_unit(unit)
assert idx.inferred_freq == expected
def test_infer_freq_tz_series(tz_naive_fixture):
# infer_freq should work with both tz-naive and tz-aware series. See gh-52456
tz = tz_naive_fixture
idx = date_range("2021-01-01", "2021-01-04", tz=tz)
series = idx.to_series().reset_index(drop=True)
inferred_freq = frequencies.infer_freq(series)
assert inferred_freq == "D"
@pytest.mark.parametrize(
"date_pair",
[
["2013-11-02", "2013-11-5"], # Fall DST
["2014-03-08", "2014-03-11"], # Spring DST
["2014-01-01", "2014-01-03"], # Regular Time
],
)
@pytest.mark.parametrize(
"freq",
["h", "3h", "10min", "3601s", "3600001ms", "3600000001us", "3600000000001ns"],
)
def test_infer_freq_tz_transition(tz_naive_fixture, date_pair, freq):
# see gh-8772
tz = tz_naive_fixture
idx = date_range(date_pair[0], date_pair[1], freq=freq, tz=tz)
assert idx.inferred_freq == freq
def test_infer_freq_tz_transition_custom():
index = date_range("2013-11-03", periods=5, freq="3h").tz_localize(
"America/Chicago"
)
assert index.inferred_freq is None
@pytest.mark.parametrize(
"data,expected",
[
# Hourly freq in a day must result in "h"
(
[
"2014-07-01 09:00",
"2014-07-01 10:00",
"2014-07-01 11:00",
"2014-07-01 12:00",
"2014-07-01 13:00",
"2014-07-01 14:00",
],
"h",
),
(
[
"2014-07-01 09:00",
"2014-07-01 10:00",
"2014-07-01 11:00",
"2014-07-01 12:00",
"2014-07-01 13:00",
"2014-07-01 14:00",
"2014-07-01 15:00",
"2014-07-01 16:00",
"2014-07-02 09:00",
"2014-07-02 10:00",
"2014-07-02 11:00",
],
"bh",
),
(
[
"2014-07-04 09:00",
"2014-07-04 10:00",
"2014-07-04 11:00",
"2014-07-04 12:00",
"2014-07-04 13:00",
"2014-07-04 14:00",
"2014-07-04 15:00",
"2014-07-04 16:00",
"2014-07-07 09:00",
"2014-07-07 10:00",
"2014-07-07 11:00",
],
"bh",
),
(
[
"2014-07-04 09:00",
"2014-07-04 10:00",
"2014-07-04 11:00",
"2014-07-04 12:00",
"2014-07-04 13:00",
"2014-07-04 14:00",
"2014-07-04 15:00",
"2014-07-04 16:00",
"2014-07-07 09:00",
"2014-07-07 10:00",
"2014-07-07 11:00",
"2014-07-07 12:00",
"2014-07-07 13:00",
"2014-07-07 14:00",
"2014-07-07 15:00",
"2014-07-07 16:00",
"2014-07-08 09:00",
"2014-07-08 10:00",
"2014-07-08 11:00",
"2014-07-08 12:00",
"2014-07-08 13:00",
"2014-07-08 14:00",
"2014-07-08 15:00",
"2014-07-08 16:00",
],
"bh",
),
],
)
def test_infer_freq_business_hour(data, expected):
# see gh-7905
idx = DatetimeIndex(data)
assert idx.inferred_freq == expected
def test_not_monotonic():
rng = DatetimeIndex(["1/31/2000", "1/31/2001", "1/31/2002"])
rng = rng[::-1]
assert rng.inferred_freq == "-1YE-JAN"
def test_non_datetime_index2():
rng = DatetimeIndex(["1/31/2000", "1/31/2001", "1/31/2002"])
vals = rng.to_pydatetime()
result = frequencies.infer_freq(vals)
assert result == rng.inferred_freq
@pytest.mark.parametrize(
"idx",
[
Index(np.arange(5), dtype=np.int64),
Index(np.arange(5), dtype=np.float64),
period_range("2020-01-01", periods=5),
RangeIndex(5),
],
)
def test_invalid_index_types(idx):
# see gh-48439
msg = "|".join(
[
"cannot infer freq from a non-convertible",
"Check the `freq` attribute instead of using infer_freq",
]
)
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 datetime string format"
with pytest.raises(ValueError, match=msg):
frequencies.infer_freq(Index(["ZqgszYBfuL"]))
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(using_infer_string):
# see gh-6407
if using_infer_string:
msg = "cannot infer freq from"
with pytest.raises(TypeError, match=msg):
frequencies.infer_freq(Series(["foo", "bar"]))
else:
msg = "Unknown datetime string format"
with pytest.raises(ValueError, match=msg):
frequencies.infer_freq(Series(["foo", "bar"]))
@pytest.mark.parametrize("freq", [None, "ms"])
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", ["ME", "ms", "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",
[
_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",
"QE@JAN",
"QE@FEB",
"QE@MAR",
"YE@JAN",
"YE@FEB",
"YE@MAR",
"YE@APR",
"YE@MAY",
"YE@JUN",
"YE@JUL",
"YE@AUG",
"YE@SEP",
"YE@OCT",
"YE@NOV",
"YE@DEC",
"YE@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 = _get_offset("ms")
right = _get_offset("MS")
assert left == offsets.Milli()
assert right == offsets.MonthBegin()
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 == "ms"
def test_infer_freq_non_nano_tzaware(tz_aware_fixture):
tz = tz_aware_fixture
dti = date_range("2016-01-01", periods=365, freq="B", tz=tz)
dta = dti._data.as_unit("s")
res = frequencies.infer_freq(dta)
assert res == "B"
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