projektAI/venv/Lib/site-packages/pandas/tests/resample/test_base.py

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2021-06-06 22:13:05 +02:00
from datetime import datetime
import numpy as np
import pytest
from pandas import DataFrame, Series
import pandas._testing as tm
from pandas.core.groupby.groupby import DataError
from pandas.core.groupby.grouper import Grouper
from pandas.core.indexes.datetimes import date_range
from pandas.core.indexes.period import period_range
from pandas.core.indexes.timedeltas import timedelta_range
from pandas.core.resample import _asfreq_compat
# a fixture value can be overridden by the test parameter value. Note that the
# value of the fixture can be overridden this way even if the test doesn't use
# it directly (doesn't mention it in the function prototype).
# see https://docs.pytest.org/en/latest/fixture.html#override-a-fixture-with-direct-test-parametrization # noqa
# in this module we override the fixture values defined in conftest.py
# tuples of '_index_factory,_series_name,_index_start,_index_end'
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 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
@pytest.mark.parametrize("freq", ["2D", "1H"])
@pytest.mark.parametrize(
"_index_factory,_series_name,_index_start,_index_end", [DATE_RANGE, TIMEDELTA_RANGE]
)
def test_asfreq(series_and_frame, freq, create_index):
obj = series_and_frame
result = obj.resample(freq).asfreq()
new_index = create_index(obj.index[0], obj.index[-1], freq=freq)
expected = obj.reindex(new_index)
tm.assert_almost_equal(result, expected)
@pytest.mark.parametrize(
"_index_factory,_series_name,_index_start,_index_end", [DATE_RANGE, TIMEDELTA_RANGE]
)
def test_asfreq_fill_value(series, create_index):
# test for fill value during resampling, issue 3715
s = series
result = s.resample("1H").asfreq()
new_index = create_index(s.index[0], s.index[-1], freq="1H")
expected = s.reindex(new_index)
tm.assert_series_equal(result, expected)
frame = s.to_frame("value")
frame.iloc[1] = None
result = frame.resample("1H").asfreq(fill_value=4.0)
new_index = create_index(frame.index[0], frame.index[-1], freq="1H")
expected = frame.reindex(new_index, fill_value=4.0)
tm.assert_frame_equal(result, expected)
@all_ts
def test_resample_interpolate(frame):
# # 12925
df = frame
tm.assert_frame_equal(
df.resample("1T").asfreq().interpolate(), df.resample("1T").interpolate()
)
def test_raises_on_non_datetimelike_index():
# this is a non datetimelike index
xp = DataFrame()
msg = (
"Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, "
"but got an instance of 'Index'"
)
with pytest.raises(TypeError, match=msg):
xp.resample("A").mean()
@all_ts
@pytest.mark.parametrize("freq", ["M", "D", "H"])
def test_resample_empty_series(freq, empty_series_dti, resample_method):
# GH12771 & GH12868
if resample_method == "ohlc":
pytest.skip("need to test for ohlc from GH13083")
s = empty_series_dti
result = getattr(s.resample(freq), resample_method)()
expected = s.copy()
expected.index = _asfreq_compat(s.index, freq)
tm.assert_index_equal(result.index, expected.index)
assert result.index.freq == expected.index.freq
tm.assert_series_equal(result, expected, check_dtype=False)
@all_ts
@pytest.mark.parametrize("freq", ["M", "D", "H"])
@pytest.mark.parametrize("resample_method", ["count", "size"])
def test_resample_count_empty_series(freq, empty_series_dti, resample_method):
# GH28427
result = getattr(empty_series_dti.resample(freq), resample_method)()
index = _asfreq_compat(empty_series_dti.index, freq)
expected = Series([], dtype="int64", index=index, name=empty_series_dti.name)
tm.assert_series_equal(result, expected)
@all_ts
@pytest.mark.parametrize("freq", ["M", "D", "H"])
def test_resample_empty_dataframe(empty_frame_dti, freq, resample_method):
# GH13212
df = empty_frame_dti
# count retains dimensions too
result = getattr(df.resample(freq), resample_method)()
if resample_method != "size":
expected = df.copy()
else:
# GH14962
expected = Series([], dtype=object)
expected.index = _asfreq_compat(df.index, freq)
tm.assert_index_equal(result.index, expected.index)
assert result.index.freq == expected.index.freq
tm.assert_almost_equal(result, expected, check_dtype=False)
# test size for GH13212 (currently stays as df)
@all_ts
@pytest.mark.parametrize("freq", ["M", "D", "H"])
def test_resample_count_empty_dataframe(freq, empty_frame_dti):
# GH28427
empty_frame_dti["a"] = []
result = empty_frame_dti.resample(freq).count()
index = _asfreq_compat(empty_frame_dti.index, freq)
expected = DataFrame({"a": []}, dtype="int64", index=index)
tm.assert_frame_equal(result, expected)
@all_ts
@pytest.mark.parametrize("freq", ["M", "D", "H"])
def test_resample_size_empty_dataframe(freq, empty_frame_dti):
# GH28427
empty_frame_dti["a"] = []
result = empty_frame_dti.resample(freq).size()
index = _asfreq_compat(empty_frame_dti.index, freq)
expected = Series([], dtype="int64", index=index)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("index", tm.all_timeseries_index_generator(0))
@pytest.mark.parametrize("dtype", [float, int, object, "datetime64[ns]"])
def test_resample_empty_dtypes(index, dtype, resample_method):
# Empty series were sometimes causing a segfault (for the functions
# with Cython bounds-checking disabled) or an IndexError. We just run
# them to ensure they no longer do. (GH #10228)
empty_series_dti = Series([], index, dtype)
try:
getattr(empty_series_dti.resample("d"), resample_method)()
except DataError:
# Ignore these since some combinations are invalid
# (ex: doing mean with dtype of np.object)
pass
@all_ts
def test_apply_to_empty_series(empty_series_dti):
# GH 14313
s = empty_series_dti
for freq in ["M", "D", "H"]:
result = s.resample(freq).apply(lambda x: 1)
expected = s.resample(freq).apply(np.sum)
tm.assert_series_equal(result, expected, check_dtype=False)
@all_ts
def test_resampler_is_iterable(series):
# GH 15314
freq = "H"
tg = Grouper(freq=freq, convention="start")
grouped = series.groupby(tg)
resampled = series.resample(freq)
for (rk, rv), (gk, gv) in zip(resampled, grouped):
assert rk == gk
tm.assert_series_equal(rv, gv)
@all_ts
def test_resample_quantile(series):
# GH 15023
s = series
q = 0.75
freq = "H"
result = s.resample(freq).quantile(q)
expected = s.resample(freq).agg(lambda x: x.quantile(q)).rename(s.name)
tm.assert_series_equal(result, expected)