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test_period_index.py
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from datetime import datetime
import warnings
import dateutil
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs.ccalendar import (
DAYS,
MONTHS,
)
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.errors import InvalidIndexError
import pandas as pd
from pandas import (
DataFrame,
Series,
Timestamp,
)
import pandas._testing as tm
from pandas.core.indexes.datetimes import date_range
from pandas.core.indexes.period import (
Period,
PeriodIndex,
period_range,
)
from pandas.core.resample import _get_period_range_edges
from pandas.tseries import offsets
pytestmark = pytest.mark.filterwarnings(
"ignore:Resampling with a PeriodIndex is deprecated:FutureWarning"
)
@pytest.fixture
def simple_period_range_series():
"""
Series with period range index and random data for test purposes.
"""
def _simple_period_range_series(start, end, freq="D"):
with warnings.catch_warnings():
# suppress Period[B] deprecation warning
msg = "|".join(["Period with BDay freq", r"PeriodDtype\[B\] is deprecated"])
warnings.filterwarnings(
"ignore",
msg,
category=FutureWarning,
)
rng = period_range(start, end, freq=freq)
return Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
return _simple_period_range_series
class TestPeriodIndex:
@pytest.mark.parametrize("freq", ["2D", "1h", "2h"])
@pytest.mark.parametrize("kind", ["period", None, "timestamp"])
def test_asfreq(self, frame_or_series, freq, kind):
# GH 12884, 15944
# make sure .asfreq() returns PeriodIndex (except kind='timestamp')
obj = frame_or_series(range(5), index=period_range("2020-01-01", periods=5))
if kind == "timestamp":
expected = obj.to_timestamp().resample(freq).asfreq()
else:
start = obj.index[0].to_timestamp(how="start")
end = (obj.index[-1] + obj.index.freq).to_timestamp(how="start")
new_index = date_range(start=start, end=end, freq=freq, inclusive="left")
expected = obj.to_timestamp().reindex(new_index).to_period(freq)
msg = "The 'kind' keyword in (Series|DataFrame).resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = obj.resample(freq, kind=kind).asfreq()
tm.assert_almost_equal(result, expected)
def test_asfreq_fill_value(self):
# test for fill value during resampling, issue 3715
index = period_range(datetime(2005, 1, 1), datetime(2005, 1, 10), freq="D")
s = Series(range(len(index)), index=index)
new_index = date_range(
s.index[0].to_timestamp(how="start"),
(s.index[-1]).to_timestamp(how="start"),
freq="1h",
)
expected = s.to_timestamp().reindex(new_index, fill_value=4.0)
msg = "The 'kind' keyword in Series.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = s.resample("1h", kind="timestamp").asfreq(fill_value=4.0)
tm.assert_series_equal(result, expected)
frame = s.to_frame("value")
new_index = date_range(
frame.index[0].to_timestamp(how="start"),
(frame.index[-1]).to_timestamp(how="start"),
freq="1h",
)
expected = frame.to_timestamp().reindex(new_index, fill_value=3.0)
msg = "The 'kind' keyword in DataFrame.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = frame.resample("1h", kind="timestamp").asfreq(fill_value=3.0)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("freq", ["h", "12h", "2D", "W"])
@pytest.mark.parametrize("kind", [None, "period", "timestamp"])
@pytest.mark.parametrize("kwargs", [{"on": "date"}, {"level": "d"}])
def test_selection(self, freq, kind, kwargs):
# This is a bug, these should be implemented
# GH 14008
index = period_range(datetime(2005, 1, 1), datetime(2005, 1, 10), freq="D")
rng = np.arange(len(index), dtype=np.int64)
df = DataFrame(
{"date": index, "a": rng},
index=pd.MultiIndex.from_arrays([rng, index], names=["v", "d"]),
)
msg = (
"Resampling from level= or on= selection with a PeriodIndex is "
r"not currently supported, use \.set_index\(\.\.\.\) to "
"explicitly set index"
)
depr_msg = "The 'kind' keyword in DataFrame.resample is deprecated"
with pytest.raises(NotImplementedError, match=msg):
with tm.assert_produces_warning(FutureWarning, match=depr_msg):
df.resample(freq, kind=kind, **kwargs)
@pytest.mark.parametrize("month", MONTHS)
@pytest.mark.parametrize("meth", ["ffill", "bfill"])
@pytest.mark.parametrize("conv", ["start", "end"])
@pytest.mark.parametrize(
("offset", "period"), [("D", "D"), ("B", "B"), ("ME", "M"), ("QE", "Q")]
)
def test_annual_upsample_cases(
self, offset, period, conv, meth, month, simple_period_range_series
):
ts = simple_period_range_series("1/1/1990", "12/31/1991", freq=f"Y-{month}")
warn = FutureWarning if period == "B" else None
msg = r"PeriodDtype\[B\] is deprecated"
if warn is None:
msg = "Resampling with a PeriodIndex is deprecated"
warn = FutureWarning
with tm.assert_produces_warning(warn, match=msg):
result = getattr(ts.resample(period, convention=conv), meth)()
expected = result.to_timestamp(period, how=conv)
expected = expected.asfreq(offset, meth).to_period()
tm.assert_series_equal(result, expected)
def test_basic_downsample(self, simple_period_range_series):
ts = simple_period_range_series("1/1/1990", "6/30/1995", freq="M")
result = ts.resample("Y-DEC").mean()
expected = ts.groupby(ts.index.year).mean()
expected.index = period_range("1/1/1990", "6/30/1995", freq="Y-DEC")
tm.assert_series_equal(result, expected)
# this is ok
tm.assert_series_equal(ts.resample("Y-DEC").mean(), result)
tm.assert_series_equal(ts.resample("Y").mean(), result)
@pytest.mark.parametrize(
"rule,expected_error_msg",
[
("Y-DEC", "<YearEnd: month=12>"),
("Q-MAR", "<QuarterEnd: startingMonth=3>"),
("M", "<MonthEnd>"),
("w-thu", "<Week: weekday=3>"),
],
)
def test_not_subperiod(self, simple_period_range_series, rule, expected_error_msg):
# These are incompatible period rules for resampling
ts = simple_period_range_series("1/1/1990", "6/30/1995", freq="w-wed")
msg = (
"Frequency <Week: weekday=2> cannot be resampled to "
f"{expected_error_msg}, as they are not sub or super periods"
)
with pytest.raises(IncompatibleFrequency, match=msg):
ts.resample(rule).mean()
@pytest.mark.parametrize("freq", ["D", "2D"])
def test_basic_upsample(self, freq, simple_period_range_series):
ts = simple_period_range_series("1/1/1990", "6/30/1995", freq="M")
result = ts.resample("Y-DEC").mean()
msg = "The 'convention' keyword in Series.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
resampled = result.resample(freq, convention="end").ffill()
expected = result.to_timestamp(freq, how="end")
expected = expected.asfreq(freq, "ffill").to_period(freq)
tm.assert_series_equal(resampled, expected)
def test_upsample_with_limit(self):
rng = period_range("1/1/2000", periods=5, freq="Y")
ts = Series(np.random.default_rng(2).standard_normal(len(rng)), rng)
msg = "The 'convention' keyword in Series.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = ts.resample("M", convention="end").ffill(limit=2)
expected = ts.asfreq("M").reindex(result.index, method="ffill", limit=2)
tm.assert_series_equal(result, expected)
def test_annual_upsample(self, simple_period_range_series):
ts = simple_period_range_series("1/1/1990", "12/31/1995", freq="Y-DEC")
df = DataFrame({"a": ts})
rdf = df.resample("D").ffill()
exp = df["a"].resample("D").ffill()
tm.assert_series_equal(rdf["a"], exp)
def test_annual_upsample2(self):
rng = period_range("2000", "2003", freq="Y-DEC")
ts = Series([1, 2, 3, 4], index=rng)
result = ts.resample("M").ffill()
ex_index = period_range("2000-01", "2003-12", freq="M")
expected = ts.asfreq("M", how="start").reindex(ex_index, method="ffill")
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("month", MONTHS)
@pytest.mark.parametrize("convention", ["start", "end"])
@pytest.mark.parametrize(
("offset", "period"), [("D", "D"), ("B", "B"), ("ME", "M")]
)
def test_quarterly_upsample(
self, month, offset, period, convention, simple_period_range_series
):
freq = f"Q-{month}"
ts = simple_period_range_series("1/1/1990", "12/31/1995", freq=freq)
warn = FutureWarning if period == "B" else None
msg = r"PeriodDtype\[B\] is deprecated"
if warn is None:
msg = "Resampling with a PeriodIndex is deprecated"
warn = FutureWarning
with tm.assert_produces_warning(warn, match=msg):
result = ts.resample(period, convention=convention).ffill()
expected = result.to_timestamp(period, how=convention)
expected = expected.asfreq(offset, "ffill").to_period()
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("target", ["D", "B"])
@pytest.mark.parametrize("convention", ["start", "end"])
def test_monthly_upsample(self, target, convention, simple_period_range_series):
ts = simple_period_range_series("1/1/1990", "12/31/1995", freq="M")
warn = None if target == "D" else FutureWarning
msg = r"PeriodDtype\[B\] is deprecated"
if warn is None:
msg = "Resampling with a PeriodIndex is deprecated"
warn = FutureWarning
with tm.assert_produces_warning(warn, match=msg):
result = ts.resample(target, convention=convention).ffill()
expected = result.to_timestamp(target, how=convention)
expected = expected.asfreq(target, "ffill").to_period()
tm.assert_series_equal(result, expected)
def test_resample_basic(self):
# GH3609
s = Series(
range(100),
index=date_range("20130101", freq="s", periods=100, name="idx"),
dtype="float",
)
s[10:30] = np.nan
index = PeriodIndex(
[Period("2013-01-01 00:00", "min"), Period("2013-01-01 00:01", "min")],
name="idx",
)
expected = Series([34.5, 79.5], index=index)
msg = "The 'kind' keyword in Series.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = s.to_period().resample("min", kind="period").mean()
tm.assert_series_equal(result, expected)
with tm.assert_produces_warning(FutureWarning, match=msg):
result2 = s.resample("min", kind="period").mean()
tm.assert_series_equal(result2, expected)
@pytest.mark.parametrize(
"freq,expected_vals", [("M", [31, 29, 31, 9]), ("2M", [31 + 29, 31 + 9])]
)
def test_resample_count(self, freq, expected_vals):
# GH12774
series = Series(1, index=period_range(start="2000", periods=100))
result = series.resample(freq).count()
expected_index = period_range(
start="2000", freq=freq, periods=len(expected_vals)
)
expected = Series(expected_vals, index=expected_index)
tm.assert_series_equal(result, expected)
def test_resample_same_freq(self, resample_method):
# GH12770
series = Series(range(3), index=period_range(start="2000", periods=3, freq="M"))
expected = series
result = getattr(series.resample("M"), resample_method)()
tm.assert_series_equal(result, expected)
def test_resample_incompat_freq(self):
msg = (
"Frequency <MonthEnd> cannot be resampled to <Week: weekday=6>, "
"as they are not sub or super periods"
)
pi = period_range(start="2000", periods=3, freq="M")
ser = Series(range(3), index=pi)
rs = ser.resample("W")
with pytest.raises(IncompatibleFrequency, match=msg):
# TODO: should this raise at the resample call instead of at the mean call?
rs.mean()
@pytest.mark.parametrize(
"tz",
[
pytz.timezone("America/Los_Angeles"),
dateutil.tz.gettz("America/Los_Angeles"),
],
)
def test_with_local_timezone(self, tz):
# see gh-5430
local_timezone = tz
start = datetime(year=2013, month=11, day=1, hour=0, minute=0, tzinfo=pytz.utc)
# 1 day later
end = datetime(year=2013, month=11, day=2, hour=0, minute=0, tzinfo=pytz.utc)
index = date_range(start, end, freq="h", name="idx")
series = Series(1, index=index)
series = series.tz_convert(local_timezone)
msg = "The 'kind' keyword in Series.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = series.resample("D", kind="period").mean()
# Create the expected series
# Index is moved back a day with the timezone conversion from UTC to
# Pacific
expected_index = (
period_range(start=start, end=end, freq="D", name="idx") - offsets.Day()
)
expected = Series(1.0, index=expected_index)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"tz",
[
pytz.timezone("America/Los_Angeles"),
dateutil.tz.gettz("America/Los_Angeles"),
],
)
def test_resample_with_tz(self, tz, unit):
# GH 13238
dti = date_range("2017-01-01", periods=48, freq="h", tz=tz, unit=unit)
ser = Series(2, index=dti)
result = ser.resample("D").mean()
exp_dti = pd.DatetimeIndex(
["2017-01-01", "2017-01-02"], tz=tz, freq="D"
).as_unit(unit)
expected = Series(
2.0,
index=exp_dti,
)
tm.assert_series_equal(result, expected)
# Especially assert that the timezone is LMT for pytz
assert result.index.tz == tz
def test_resample_nonexistent_time_bin_edge(self):
# GH 19375
index = date_range("2017-03-12", "2017-03-12 1:45:00", freq="15min")
s = Series(np.zeros(len(index)), index=index)
expected = s.tz_localize("US/Pacific")
expected.index = pd.DatetimeIndex(expected.index, freq="900s")
result = expected.resample("900s").mean()
tm.assert_series_equal(result, expected)
def test_resample_nonexistent_time_bin_edge2(self):
# GH 23742
index = date_range(start="2017-10-10", end="2017-10-20", freq="1h")
index = index.tz_localize("UTC").tz_convert("America/Sao_Paulo")
df = DataFrame(data=list(range(len(index))), index=index)
result = df.groupby(pd.Grouper(freq="1D")).count()
expected = date_range(
start="2017-10-09",
end="2017-10-20",
freq="D",
tz="America/Sao_Paulo",
nonexistent="shift_forward",
inclusive="left",
)
tm.assert_index_equal(result.index, expected)
def test_resample_ambiguous_time_bin_edge(self):
# GH 10117
idx = date_range(
"2014-10-25 22:00:00",
"2014-10-26 00:30:00",
freq="30min",
tz="Europe/London",
)
expected = Series(np.zeros(len(idx)), index=idx)
result = expected.resample("30min").mean()
tm.assert_series_equal(result, expected)
def test_fill_method_and_how_upsample(self):
# GH2073
s = Series(
np.arange(9, dtype="int64"),
index=date_range("2010-01-01", periods=9, freq="QE"),
)
last = s.resample("ME").ffill()
both = s.resample("ME").ffill().resample("ME").last().astype("int64")
tm.assert_series_equal(last, both)
@pytest.mark.parametrize("day", DAYS)
@pytest.mark.parametrize("target", ["D", "B"])
@pytest.mark.parametrize("convention", ["start", "end"])
def test_weekly_upsample(self, day, target, convention, simple_period_range_series):
freq = f"W-{day}"
ts = simple_period_range_series("1/1/1990", "12/31/1995", freq=freq)
warn = None if target == "D" else FutureWarning
msg = r"PeriodDtype\[B\] is deprecated"
if warn is None:
msg = "Resampling with a PeriodIndex is deprecated"
warn = FutureWarning
with tm.assert_produces_warning(warn, match=msg):
result = ts.resample(target, convention=convention).ffill()
expected = result.to_timestamp(target, how=convention)
expected = expected.asfreq(target, "ffill").to_period()
tm.assert_series_equal(result, expected)
def test_resample_to_timestamps(self, simple_period_range_series):
ts = simple_period_range_series("1/1/1990", "12/31/1995", freq="M")
msg = "The 'kind' keyword in Series.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = ts.resample("Y-DEC", kind="timestamp").mean()
expected = ts.to_timestamp(how="start").resample("YE-DEC").mean()
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("month", MONTHS)
def test_resample_to_quarterly(self, simple_period_range_series, month):
ts = simple_period_range_series("1990", "1992", freq=f"Y-{month}")
quar_ts = ts.resample(f"Q-{month}").ffill()
stamps = ts.to_timestamp("D", how="start")
qdates = period_range(
ts.index[0].asfreq("D", "start"),
ts.index[-1].asfreq("D", "end"),
freq=f"Q-{month}",
)
expected = stamps.reindex(qdates.to_timestamp("D", "s"), method="ffill")
expected.index = qdates
tm.assert_series_equal(quar_ts, expected)
@pytest.mark.parametrize("how", ["start", "end"])
def test_resample_to_quarterly_start_end(self, simple_period_range_series, how):
# conforms, but different month
ts = simple_period_range_series("1990", "1992", freq="Y-JUN")
msg = "The 'convention' keyword in Series.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = ts.resample("Q-MAR", convention=how).ffill()
expected = ts.asfreq("Q-MAR", how=how)
expected = expected.reindex(result.index, method="ffill")
# FIXME: don't leave commented-out
# .to_timestamp('D')
# expected = expected.resample('Q-MAR').ffill()
tm.assert_series_equal(result, expected)
def test_resample_fill_missing(self):
rng = PeriodIndex([2000, 2005, 2007, 2009], freq="Y")
s = Series(np.random.default_rng(2).standard_normal(4), index=rng)
stamps = s.to_timestamp()
filled = s.resample("Y").ffill()
expected = stamps.resample("YE").ffill().to_period("Y")
tm.assert_series_equal(filled, expected)
def test_cant_fill_missing_dups(self):
rng = PeriodIndex([2000, 2005, 2005, 2007, 2007], freq="Y")
s = Series(np.random.default_rng(2).standard_normal(5), index=rng)
msg = "Reindexing only valid with uniquely valued Index objects"
with pytest.raises(InvalidIndexError, match=msg):
s.resample("Y").ffill()
@pytest.mark.parametrize("freq", ["5min"])
@pytest.mark.parametrize("kind", ["period", None, "timestamp"])
def test_resample_5minute(self, freq, kind):
rng = period_range("1/1/2000", "1/5/2000", freq="min")
ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
expected = ts.to_timestamp().resample(freq).mean()
if kind != "timestamp":
expected = expected.to_period(freq)
msg = "The 'kind' keyword in Series.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
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")
msg = "The 'convention' keyword in Series.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
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.default_rng(2).standard_normal(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, unit):
dr = date_range(start="2012-4-13", end="2012-5-1", unit=unit)
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 = ts_local_naive.index.tz_localize(None)
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()
def test_resample_tz_localized2(self):
# #2245
idx = date_range(
"2001-09-20 15:59", "2001-09-20 16:00", freq="min", 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
msg = "The 'kind' keyword in Series.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
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)
def test_resample_tz_localized3(self):
# 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.default_rng(2).standard_normal(len(rng))
ts["second"] = np.cumsum(np.random.default_rng(2).standard_normal(len(rng)))
expected = DataFrame(
{
"first": ts.resample("YE").sum()["first"],
"second": ts.resample("YE").mean()["second"],
},
columns=["first", "second"],
)
result = (
ts.resample("YE")
.agg({"first": "sum", "second": "mean"})
.reindex(columns=["first", "second"])
)
tm.assert_frame_equal(result, expected)
def test_closed_left_corner(self):
# #1465
s = Series(
np.random.default_rng(2).standard_normal(21),
index=date_range(start="1/1/2012 9:30", freq="1min", periods=21),
)
s.iloc[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("Y").mean()
exp = ts.to_timestamp().resample("YE").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")
.as_unit(index.unit)
)
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
timeseries = Series(
index=pd.bdate_range("20000101", "20000201"), dtype=np.float64
)
res1 = timeseries.resample("BMS").mean()
res2 = timeseries.resample("BMS").mean().resample("B").mean()
assert res1.index[0] == Timestamp("20000103")
assert res1.index[0] == res2.index[0]
@pytest.mark.xfail(reason="Commented out for more than 3 years. Should this work?")
def test_monthly_convention_span(self):
rng = period_range("2000-01", periods=3, freq="ME")
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").mean()
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"from_freq, to_freq", [("D", "ME"), ("QE", "YE"), ("ME", "QE"), ("D", "W")]
)
def test_default_right_closed_label(self, from_freq, to_freq):
idx = date_range(start="8/15/2012", periods=100, freq=from_freq)
df = DataFrame(np.random.default_rng(2).standard_normal((len(idx), 2)), idx)
resampled = df.resample(to_freq).mean()
tm.assert_frame_equal(
resampled, df.resample(to_freq, closed="right", label="right").mean()
)
@pytest.mark.parametrize(
"from_freq, to_freq",
[("D", "MS"), ("QE", "YS"), ("ME", "QS"), ("h", "D"), ("min", "h")],
)
def test_default_left_closed_label(self, from_freq, to_freq):
idx = date_range(start="8/15/2012", periods=100, freq=from_freq)
df = DataFrame(np.random.default_rng(2).standard_normal((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):
# GH#2070
index = period_range(start="2012-01-01", end="2012-12-31", freq="M")
ser = Series(np.random.default_rng(2).standard_normal(len(index)), index=index)
result = ser.resample("Y").mean()
tm.assert_almost_equal(result.iloc[0], ser.mean())
def test_evenly_divisible_with_no_extra_bins(self):
# GH#4076
# when the frequency is evenly divisible, sometimes extra bins
df = DataFrame(
np.random.default_rng(2).standard_normal((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")], dtype="M8[ns]", freq="5D"
)
tm.assert_frame_equal(result, expected)
def test_evenly_divisible_with_no_extra_bins2(self):
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)
msg = "The 'kind' keyword in Series.resample is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
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)
msg = "Resampling with a PeriodIndex is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
rs = frame.resample(freq)
result = rs.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", "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
pi = period_range(start, end, freq=start_freq)
ser = Series(np.arange(len(pi)), index=pi)
msg = "Resampling with a PeriodIndex is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
rs = ser.resample(end_freq, offset=offset)
result = rs.mean()
result = result.to_timestamp(end_freq)
expected = ser.to_timestamp().resample(end_freq, offset=offset).mean()
tm.assert_series_equal(result, expected)
def test_resample_with_offset_month(self):
# GH 23882 & 31809
pi = period_range("19910905 12:00", "19910909 1:00", freq="h")
ser = Series(np.arange(len(pi)), index=pi)
msg = "Resampling with a PeriodIndex is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
rs = ser.resample("M", offset="3h")
result = rs.mean()
result = result.to_timestamp("M")
expected = ser.to_timestamp().resample("ME", offset="3h").mean()
# 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,freq_to_offset,exp_first,exp_last",
[
("19910905", "19920406", "D", "D", "19910905", "19920406"),
("19910905 00:00", "19920406 06:00", "D", "D", "19910905", "19920406"),
(
"19910905 06:00",
"19920406 06:00",
"h",
"h",
"19910905 06:00",
"19920406 06:00",
),
("19910906", "19920406", "M", "ME", "1991-09", "1992-04"),
("19910831", "19920430", "M", "ME", "1991-08", "1992-04"),
("1991-08", "1992-04", "M", "ME", "1991-08", "1992-04"),
],
)
def test_get_period_range_edges(
self, first, last, freq, freq_to_offset, 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_to_offset)
result = _get_period_range_edges(first, last, freq)
expected = (exp_first, exp_last)
assert result == expected
def test_sum_min_count(self):
# GH 19974
index = date_range(start="2018", freq="ME", periods=6)
data = np.ones(6)
data[3:6] = np.nan
s = Series(data, index).to_period()
msg = "Resampling with a PeriodIndex is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
rs = s.resample("Q")
result = rs.sum(min_count=1)
expected = Series(
[3.0, np.nan], index=PeriodIndex(["2018Q1", "2018Q2"], freq="Q-DEC")
)
tm.assert_series_equal(result, expected)
def test_resample_t_l_deprecated(self):
# GH#52536
msg_t = "'T' is deprecated and will be removed in a future version."
msg_l = "'L' is deprecated and will be removed in a future version."
with tm.assert_produces_warning(FutureWarning, match=msg_l):
rng_l = period_range(
"2020-01-01 00:00:00 00:00", "2020-01-01 00:00:00 00:01", freq="L"
)
ser = Series(np.arange(len(rng_l)), index=rng_l)
rng = period_range(
"2020-01-01 00:00:00 00:00", "2020-01-01 00:00:00 00:01", freq="min"
)
expected = Series([29999.5, 60000.0], index=rng)
with tm.assert_produces_warning(FutureWarning, match=msg_t):
result = ser.resample("T").mean()
tm.assert_series_equal(result, expected)