projektAI/venv/Lib/site-packages/pandas/tests/window/test_apply.py

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2021-06-06 22:13:05 +02:00
import numpy as np
import pytest
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range
import pandas._testing as tm
@pytest.mark.parametrize("bad_raw", [None, 1, 0])
def test_rolling_apply_invalid_raw(bad_raw):
with pytest.raises(ValueError, match="raw parameter must be `True` or `False`"):
Series(range(3)).rolling(1).apply(len, raw=bad_raw)
def test_rolling_apply_out_of_bounds(engine_and_raw):
# gh-1850
engine, raw = engine_and_raw
vals = Series([1, 2, 3, 4])
result = vals.rolling(10).apply(np.sum, engine=engine, raw=raw)
assert result.isna().all()
result = vals.rolling(10, min_periods=1).apply(np.sum, engine=engine, raw=raw)
expected = Series([1, 3, 6, 10], dtype=float)
tm.assert_almost_equal(result, expected)
@pytest.mark.parametrize("window", [2, "2s"])
def test_rolling_apply_with_pandas_objects(window):
# 5071
df = DataFrame(
{"A": np.random.randn(5), "B": np.random.randint(0, 10, size=5)},
index=date_range("20130101", periods=5, freq="s"),
)
# we have an equal spaced timeseries index
# so simulate removing the first period
def f(x):
if x.index[0] == df.index[0]:
return np.nan
return x.iloc[-1]
result = df.rolling(window).apply(f, raw=False)
expected = df.iloc[2:].reindex_like(df)
tm.assert_frame_equal(result, expected)
with pytest.raises(AttributeError):
df.rolling(window).apply(f, raw=True)
def test_rolling_apply(engine_and_raw):
engine, raw = engine_and_raw
expected = Series([], dtype="float64")
result = expected.rolling(10).apply(lambda x: x.mean(), engine=engine, raw=raw)
tm.assert_series_equal(result, expected)
# gh-8080
s = Series([None, None, None])
result = s.rolling(2, min_periods=0).apply(lambda x: len(x), engine=engine, raw=raw)
expected = Series([1.0, 2.0, 2.0])
tm.assert_series_equal(result, expected)
result = s.rolling(2, min_periods=0).apply(len, engine=engine, raw=raw)
tm.assert_series_equal(result, expected)
def test_all_apply(engine_and_raw):
engine, raw = engine_and_raw
df = (
DataFrame(
{"A": date_range("20130101", periods=5, freq="s"), "B": range(5)}
).set_index("A")
* 2
)
er = df.rolling(window=1)
r = df.rolling(window="1s")
result = r.apply(lambda x: 1, engine=engine, raw=raw)
expected = er.apply(lambda x: 1, engine=engine, raw=raw)
tm.assert_frame_equal(result, expected)
def test_ragged_apply(engine_and_raw):
engine, raw = engine_and_raw
df = DataFrame({"B": range(5)})
df.index = [
Timestamp("20130101 09:00:00"),
Timestamp("20130101 09:00:02"),
Timestamp("20130101 09:00:03"),
Timestamp("20130101 09:00:05"),
Timestamp("20130101 09:00:06"),
]
f = lambda x: 1
result = df.rolling(window="1s", min_periods=1).apply(f, engine=engine, raw=raw)
expected = df.copy()
expected["B"] = 1.0
tm.assert_frame_equal(result, expected)
result = df.rolling(window="2s", min_periods=1).apply(f, engine=engine, raw=raw)
expected = df.copy()
expected["B"] = 1.0
tm.assert_frame_equal(result, expected)
result = df.rolling(window="5s", min_periods=1).apply(f, engine=engine, raw=raw)
expected = df.copy()
expected["B"] = 1.0
tm.assert_frame_equal(result, expected)
def test_invalid_engine():
with pytest.raises(ValueError, match="engine must be either 'numba' or 'cython'"):
Series(range(1)).rolling(1).apply(lambda x: x, engine="foo")
def test_invalid_engine_kwargs_cython():
with pytest.raises(ValueError, match="cython engine does not accept engine_kwargs"):
Series(range(1)).rolling(1).apply(
lambda x: x, engine="cython", engine_kwargs={"nopython": False}
)
def test_invalid_raw_numba():
with pytest.raises(
ValueError, match="raw must be `True` when using the numba engine"
):
Series(range(1)).rolling(1).apply(lambda x: x, raw=False, engine="numba")
@pytest.mark.parametrize("args_kwargs", [[None, {"par": 10}], [(10,), None]])
def test_rolling_apply_args_kwargs(args_kwargs):
# GH 33433
def foo(x, par):
return np.sum(x + par)
df = DataFrame({"gr": [1, 1], "a": [1, 2]})
idx = Index(["gr", "a"])
expected = DataFrame([[11.0, 11.0], [11.0, 12.0]], columns=idx)
result = df.rolling(1).apply(foo, args=args_kwargs[0], kwargs=args_kwargs[1])
tm.assert_frame_equal(result, expected)
midx = MultiIndex.from_tuples([(1, 0), (1, 1)], names=["gr", None])
expected = Series([11.0, 12.0], index=midx, name="a")
gb_rolling = df.groupby("gr")["a"].rolling(1)
result = gb_rolling.apply(foo, args=args_kwargs[0], kwargs=args_kwargs[1])
tm.assert_series_equal(result, expected)