mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-12-26 07:24:56 +01:00
263 lines
9.9 KiB
Python
263 lines
9.9 KiB
Python
from datetime import datetime, timedelta
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
from pandas import DataFrame, Series
|
|
import pandas._testing as tm
|
|
from pandas.core.indexes.datetimes import date_range
|
|
from pandas.core.indexes.period import PeriodIndex, period_range
|
|
from pandas.core.indexes.timedeltas import timedelta_range
|
|
|
|
from pandas.tseries.offsets import BDay, Minute
|
|
|
|
DATE_RANGE = (date_range, "dti", datetime(2005, 1, 1), datetime(2005, 1, 10))
|
|
PERIOD_RANGE = (period_range, "pi", datetime(2005, 1, 1), datetime(2005, 1, 10))
|
|
TIMEDELTA_RANGE = (timedelta_range, "tdi", "1 day", "10 day")
|
|
|
|
all_ts = pytest.mark.parametrize(
|
|
"_index_factory,_series_name,_index_start,_index_end",
|
|
[DATE_RANGE, PERIOD_RANGE, TIMEDELTA_RANGE],
|
|
)
|
|
|
|
|
|
@pytest.fixture()
|
|
def _index_factory():
|
|
return period_range
|
|
|
|
|
|
@pytest.fixture
|
|
def create_index(_index_factory):
|
|
def _create_index(*args, **kwargs):
|
|
""" return the _index_factory created using the args, kwargs """
|
|
return _index_factory(*args, **kwargs)
|
|
|
|
return _create_index
|
|
|
|
|
|
# new test to check that all FutureWarning are triggered
|
|
def test_deprecating_on_loffset_and_base():
|
|
# GH 31809
|
|
|
|
idx = pd.date_range("2001-01-01", periods=4, freq="T")
|
|
df = pd.DataFrame(data=4 * [range(2)], index=idx, columns=["a", "b"])
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
pd.Grouper(freq="10s", base=0)
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
pd.Grouper(freq="10s", loffset="0s")
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
df.groupby("a").resample("3T", base=0).sum()
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
df.groupby("a").resample("3T", loffset="0s").sum()
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
df.resample("3T", base=0).sum()
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
df.resample("3T", loffset="0s").sum()
|
|
msg = "'offset' and 'base' cannot be present at the same time"
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.groupby("a").resample("3T", base=0, offset=0).sum()
|
|
|
|
|
|
@all_ts
|
|
@pytest.mark.parametrize("arg", ["mean", {"value": "mean"}, ["mean"]])
|
|
def test_resample_loffset_arg_type(frame, create_index, arg):
|
|
# GH 13218, 15002
|
|
df = frame
|
|
expected_means = [df.values[i : i + 2].mean() for i in range(0, len(df.values), 2)]
|
|
expected_index = create_index(df.index[0], periods=len(df.index) / 2, freq="2D")
|
|
|
|
# loffset coerces PeriodIndex to DateTimeIndex
|
|
if isinstance(expected_index, PeriodIndex):
|
|
expected_index = expected_index.to_timestamp()
|
|
|
|
expected_index += timedelta(hours=2)
|
|
expected = DataFrame({"value": expected_means}, index=expected_index)
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result_agg = df.resample("2D", loffset="2H").agg(arg)
|
|
|
|
if isinstance(arg, list):
|
|
expected.columns = pd.MultiIndex.from_tuples([("value", "mean")])
|
|
|
|
tm.assert_frame_equal(result_agg, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"loffset", [timedelta(minutes=1), "1min", Minute(1), np.timedelta64(1, "m")]
|
|
)
|
|
def test_resample_loffset(loffset):
|
|
# GH 7687
|
|
rng = date_range("1/1/2000 00:00:00", "1/1/2000 00:13:00", freq="min")
|
|
s = Series(np.random.randn(14), index=rng)
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = s.resample(
|
|
"5min", closed="right", label="right", loffset=loffset
|
|
).mean()
|
|
idx = date_range("1/1/2000", periods=4, freq="5min")
|
|
expected = Series(
|
|
[s[0], s[1:6].mean(), s[6:11].mean(), s[11:].mean()],
|
|
index=idx + timedelta(minutes=1),
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
assert result.index.freq == Minute(5)
|
|
|
|
# from daily
|
|
dti = date_range(start=datetime(2005, 1, 1), end=datetime(2005, 1, 10), freq="D")
|
|
ser = Series(np.random.rand(len(dti)), dti)
|
|
|
|
# to weekly
|
|
result = ser.resample("w-sun").last()
|
|
business_day_offset = BDay()
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
expected = ser.resample("w-sun", loffset=-business_day_offset).last()
|
|
assert result.index[0] - business_day_offset == expected.index[0]
|
|
|
|
|
|
def test_resample_loffset_upsample():
|
|
# GH 20744
|
|
rng = date_range("1/1/2000 00:00:00", "1/1/2000 00:13:00", freq="min")
|
|
s = Series(np.random.randn(14), index=rng)
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = s.resample(
|
|
"5min", closed="right", label="right", loffset=timedelta(minutes=1)
|
|
).ffill()
|
|
idx = date_range("1/1/2000", periods=4, freq="5min")
|
|
expected = Series([s[0], s[5], s[10], s[-1]], index=idx + timedelta(minutes=1))
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_resample_loffset_count():
|
|
# GH 12725
|
|
start_time = "1/1/2000 00:00:00"
|
|
rng = date_range(start_time, periods=100, freq="S")
|
|
ts = Series(np.random.randn(len(rng)), index=rng)
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = ts.resample("10S", loffset="1s").count()
|
|
|
|
expected_index = date_range(start_time, periods=10, freq="10S") + timedelta(
|
|
seconds=1
|
|
)
|
|
expected = Series(10, index=expected_index)
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# Same issue should apply to .size() since it goes through
|
|
# same code path
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = ts.resample("10S", loffset="1s").size()
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_resample_base():
|
|
rng = date_range("1/1/2000 00:00:00", "1/1/2000 02:00", freq="s")
|
|
ts = Series(np.random.randn(len(rng)), index=rng)
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
resampled = ts.resample("5min", base=2).mean()
|
|
exp_rng = date_range("12/31/1999 23:57:00", "1/1/2000 01:57", freq="5min")
|
|
tm.assert_index_equal(resampled.index, exp_rng)
|
|
|
|
|
|
def test_resample_float_base():
|
|
# GH25161
|
|
dt = pd.to_datetime(
|
|
["2018-11-26 16:17:43.51", "2018-11-26 16:17:44.51", "2018-11-26 16:17:45.51"]
|
|
)
|
|
s = Series(np.arange(3), index=dt)
|
|
|
|
base = 17 + 43.51 / 60
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = s.resample("3min", base=base).size()
|
|
expected = Series(
|
|
3, index=pd.DatetimeIndex(["2018-11-26 16:17:43.51"], freq="3min")
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("kind", ["period", None, "timestamp"])
|
|
@pytest.mark.parametrize("agg_arg", ["mean", {"value": "mean"}, ["mean"]])
|
|
def test_loffset_returns_datetimeindex(frame, kind, agg_arg):
|
|
# make sure passing loffset returns DatetimeIndex in all cases
|
|
# basic method taken from Base.test_resample_loffset_arg_type()
|
|
df = frame
|
|
expected_means = [df.values[i : i + 2].mean() for i in range(0, len(df.values), 2)]
|
|
expected_index = period_range(df.index[0], periods=len(df.index) / 2, freq="2D")
|
|
|
|
# loffset coerces PeriodIndex to DateTimeIndex
|
|
expected_index = expected_index.to_timestamp()
|
|
expected_index += timedelta(hours=2)
|
|
expected = DataFrame({"value": expected_means}, index=expected_index)
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result_agg = df.resample("2D", loffset="2H", kind=kind).agg(agg_arg)
|
|
if isinstance(agg_arg, list):
|
|
expected.columns = pd.MultiIndex.from_tuples([("value", "mean")])
|
|
tm.assert_frame_equal(result_agg, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"start,end,start_freq,end_freq,base,offset",
|
|
[
|
|
("19910905", "19910909 03:00", "H", "24H", 10, "10H"),
|
|
("19910905", "19910909 12:00", "H", "24H", 10, "10H"),
|
|
("19910905", "19910909 23:00", "H", "24H", 10, "10H"),
|
|
("19910905 10:00", "19910909", "H", "24H", 10, "10H"),
|
|
("19910905 10:00", "19910909 10:00", "H", "24H", 10, "10H"),
|
|
("19910905", "19910909 10:00", "H", "24H", 10, "10H"),
|
|
("19910905 12:00", "19910909", "H", "24H", 10, "10H"),
|
|
("19910905 12:00", "19910909 03:00", "H", "24H", 10, "10H"),
|
|
("19910905 12:00", "19910909 12:00", "H", "24H", 10, "10H"),
|
|
("19910905 12:00", "19910909 12:00", "H", "24H", 34, "34H"),
|
|
("19910905 12:00", "19910909 12:00", "H", "17H", 10, "10H"),
|
|
("19910905 12:00", "19910909 12:00", "H", "17H", 3, "3H"),
|
|
("19910905 12:00", "19910909 1:00", "H", "M", 3, "3H"),
|
|
("19910905", "19910913 06:00", "2H", "24H", 10, "10H"),
|
|
("19910905", "19910905 01:39", "Min", "5Min", 3, "3Min"),
|
|
("19910905", "19910905 03:18", "2Min", "5Min", 3, "3Min"),
|
|
],
|
|
)
|
|
def test_resample_with_non_zero_base(start, end, start_freq, end_freq, base, offset):
|
|
# GH 23882
|
|
s = pd.Series(0, index=pd.period_range(start, end, freq=start_freq))
|
|
s = s + np.arange(len(s))
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = s.resample(end_freq, base=base).mean()
|
|
result = result.to_timestamp(end_freq)
|
|
|
|
# test that the replacement argument 'offset' works
|
|
result_offset = s.resample(end_freq, offset=offset).mean()
|
|
result_offset = result_offset.to_timestamp(end_freq)
|
|
tm.assert_series_equal(result, result_offset)
|
|
|
|
# to_timestamp casts 24H -> D
|
|
result = result.asfreq(end_freq) if end_freq == "24H" else result
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
expected = s.to_timestamp().resample(end_freq, base=base).mean()
|
|
if end_freq == "M":
|
|
# TODO: is non-tick the relevant characteristic? (GH 33815)
|
|
expected.index = expected.index._with_freq(None)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_resample_base_with_timedeltaindex():
|
|
# GH 10530
|
|
rng = timedelta_range(start="0s", periods=25, freq="s")
|
|
ts = Series(np.random.randn(len(rng)), index=rng)
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
with_base = ts.resample("2s", base=5).mean()
|
|
without_base = ts.resample("2s").mean()
|
|
|
|
exp_without_base = timedelta_range(start="0s", end="25s", freq="2s")
|
|
exp_with_base = timedelta_range(start="5s", end="29s", freq="2s")
|
|
|
|
tm.assert_index_equal(without_base.index, exp_without_base)
|
|
tm.assert_index_equal(with_base.index, exp_with_base)
|