mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-12-26 23:41:47 +01:00
305 lines
9.6 KiB
Python
305 lines
9.6 KiB
Python
|
from collections import deque
|
||
|
|
import string
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
import pandas._testing as tm
|
||
|
|
from pandas.arrays import SparseArray
|
||
|
|
|
||
|
|
UNARY_UFUNCS = [np.positive, np.floor, np.exp]
|
||
|
|
BINARY_UFUNCS = [np.add, np.logaddexp] # dunder op
|
||
|
|
SPARSE = [True, False]
|
||
|
|
SPARSE_IDS = ["sparse", "dense"]
|
||
|
|
SHUFFLE = [True, False]
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture
|
||
|
|
def arrays_for_binary_ufunc():
|
||
|
|
"""
|
||
|
|
A pair of random, length-100 integer-dtype arrays, that are mostly 0.
|
||
|
|
"""
|
||
|
|
a1 = np.random.randint(0, 10, 100, dtype="int64")
|
||
|
|
a2 = np.random.randint(0, 10, 100, dtype="int64")
|
||
|
|
a1[::3] = 0
|
||
|
|
a2[::4] = 0
|
||
|
|
return a1, a2
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("ufunc", UNARY_UFUNCS)
|
||
|
|
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
|
||
|
|
def test_unary_ufunc(ufunc, sparse):
|
||
|
|
# Test that ufunc(Series) == Series(ufunc)
|
||
|
|
array = np.random.randint(0, 10, 10, dtype="int64")
|
||
|
|
array[::2] = 0
|
||
|
|
if sparse:
|
||
|
|
array = SparseArray(array, dtype=pd.SparseDtype("int64", 0))
|
||
|
|
|
||
|
|
index = list(string.ascii_letters[:10])
|
||
|
|
name = "name"
|
||
|
|
series = pd.Series(array, index=index, name=name)
|
||
|
|
|
||
|
|
result = ufunc(series)
|
||
|
|
expected = pd.Series(ufunc(array), index=index, name=name)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
|
||
|
|
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
|
||
|
|
@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
|
||
|
|
def test_binary_ufunc_with_array(flip, sparse, ufunc, arrays_for_binary_ufunc):
|
||
|
|
# Test that ufunc(Series(a), array) == Series(ufunc(a, b))
|
||
|
|
a1, a2 = arrays_for_binary_ufunc
|
||
|
|
if sparse:
|
||
|
|
a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
|
||
|
|
a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
|
||
|
|
|
||
|
|
name = "name" # op(Series, array) preserves the name.
|
||
|
|
series = pd.Series(a1, name=name)
|
||
|
|
other = a2
|
||
|
|
|
||
|
|
array_args = (a1, a2)
|
||
|
|
series_args = (series, other) # ufunc(series, array)
|
||
|
|
|
||
|
|
if flip:
|
||
|
|
array_args = reversed(array_args)
|
||
|
|
series_args = reversed(series_args) # ufunc(array, series)
|
||
|
|
|
||
|
|
expected = pd.Series(ufunc(*array_args), name=name)
|
||
|
|
result = ufunc(*series_args)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
|
||
|
|
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
|
||
|
|
@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
|
||
|
|
def test_binary_ufunc_with_index(flip, sparse, ufunc, arrays_for_binary_ufunc):
|
||
|
|
# Test that
|
||
|
|
# * func(Series(a), Series(b)) == Series(ufunc(a, b))
|
||
|
|
# * ufunc(Index, Series) dispatches to Series (returns a Series)
|
||
|
|
a1, a2 = arrays_for_binary_ufunc
|
||
|
|
if sparse:
|
||
|
|
a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
|
||
|
|
a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
|
||
|
|
|
||
|
|
name = "name" # op(Series, array) preserves the name.
|
||
|
|
series = pd.Series(a1, name=name)
|
||
|
|
other = pd.Index(a2, name=name).astype("int64")
|
||
|
|
|
||
|
|
array_args = (a1, a2)
|
||
|
|
series_args = (series, other) # ufunc(series, array)
|
||
|
|
|
||
|
|
if flip:
|
||
|
|
array_args = reversed(array_args)
|
||
|
|
series_args = reversed(series_args) # ufunc(array, series)
|
||
|
|
|
||
|
|
expected = pd.Series(ufunc(*array_args), name=name)
|
||
|
|
result = ufunc(*series_args)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
|
||
|
|
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
|
||
|
|
@pytest.mark.parametrize("shuffle", [True, False], ids=["unaligned", "aligned"])
|
||
|
|
@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
|
||
|
|
def test_binary_ufunc_with_series(
|
||
|
|
flip, shuffle, sparse, ufunc, arrays_for_binary_ufunc
|
||
|
|
):
|
||
|
|
# Test that
|
||
|
|
# * func(Series(a), Series(b)) == Series(ufunc(a, b))
|
||
|
|
# with alignment between the indices
|
||
|
|
a1, a2 = arrays_for_binary_ufunc
|
||
|
|
if sparse:
|
||
|
|
a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
|
||
|
|
a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
|
||
|
|
|
||
|
|
name = "name" # op(Series, array) preserves the name.
|
||
|
|
series = pd.Series(a1, name=name)
|
||
|
|
other = pd.Series(a2, name=name)
|
||
|
|
|
||
|
|
idx = np.random.permutation(len(a1))
|
||
|
|
|
||
|
|
if shuffle:
|
||
|
|
other = other.take(idx)
|
||
|
|
if flip:
|
||
|
|
index = other.align(series)[0].index
|
||
|
|
else:
|
||
|
|
index = series.align(other)[0].index
|
||
|
|
else:
|
||
|
|
index = series.index
|
||
|
|
|
||
|
|
array_args = (a1, a2)
|
||
|
|
series_args = (series, other) # ufunc(series, array)
|
||
|
|
|
||
|
|
if flip:
|
||
|
|
array_args = tuple(reversed(array_args))
|
||
|
|
series_args = tuple(reversed(series_args)) # ufunc(array, series)
|
||
|
|
|
||
|
|
expected = pd.Series(ufunc(*array_args), index=index, name=name)
|
||
|
|
result = ufunc(*series_args)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
|
||
|
|
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
|
||
|
|
@pytest.mark.parametrize("flip", [True, False])
|
||
|
|
def test_binary_ufunc_scalar(ufunc, sparse, flip, arrays_for_binary_ufunc):
|
||
|
|
# Test that
|
||
|
|
# * ufunc(Series, scalar) == Series(ufunc(array, scalar))
|
||
|
|
# * ufunc(Series, scalar) == ufunc(scalar, Series)
|
||
|
|
array, _ = arrays_for_binary_ufunc
|
||
|
|
if sparse:
|
||
|
|
array = SparseArray(array)
|
||
|
|
other = 2
|
||
|
|
series = pd.Series(array, name="name")
|
||
|
|
|
||
|
|
series_args = (series, other)
|
||
|
|
array_args = (array, other)
|
||
|
|
|
||
|
|
if flip:
|
||
|
|
series_args = tuple(reversed(series_args))
|
||
|
|
array_args = tuple(reversed(array_args))
|
||
|
|
|
||
|
|
expected = pd.Series(ufunc(*array_args), name="name")
|
||
|
|
result = ufunc(*series_args)
|
||
|
|
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("ufunc", [np.divmod]) # any others?
|
||
|
|
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
|
||
|
|
@pytest.mark.parametrize("shuffle", SHUFFLE)
|
||
|
|
@pytest.mark.filterwarnings("ignore:divide by zero:RuntimeWarning")
|
||
|
|
def test_multiple_output_binary_ufuncs(ufunc, sparse, shuffle, arrays_for_binary_ufunc):
|
||
|
|
# Test that
|
||
|
|
# the same conditions from binary_ufunc_scalar apply to
|
||
|
|
# ufuncs with multiple outputs.
|
||
|
|
if sparse and ufunc is np.divmod:
|
||
|
|
pytest.skip("sparse divmod not implemented.")
|
||
|
|
|
||
|
|
a1, a2 = arrays_for_binary_ufunc
|
||
|
|
# work around https://github.com/pandas-dev/pandas/issues/26987
|
||
|
|
a1[a1 == 0] = 1
|
||
|
|
a2[a2 == 0] = 1
|
||
|
|
|
||
|
|
if sparse:
|
||
|
|
a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
|
||
|
|
a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
|
||
|
|
|
||
|
|
s1 = pd.Series(a1)
|
||
|
|
s2 = pd.Series(a2)
|
||
|
|
|
||
|
|
if shuffle:
|
||
|
|
# ensure we align before applying the ufunc
|
||
|
|
s2 = s2.sample(frac=1)
|
||
|
|
|
||
|
|
expected = ufunc(a1, a2)
|
||
|
|
assert isinstance(expected, tuple)
|
||
|
|
|
||
|
|
result = ufunc(s1, s2)
|
||
|
|
assert isinstance(result, tuple)
|
||
|
|
tm.assert_series_equal(result[0], pd.Series(expected[0]))
|
||
|
|
tm.assert_series_equal(result[1], pd.Series(expected[1]))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
|
||
|
|
def test_multiple_output_ufunc(sparse, arrays_for_binary_ufunc):
|
||
|
|
# Test that the same conditions from unary input apply to multi-output
|
||
|
|
# ufuncs
|
||
|
|
array, _ = arrays_for_binary_ufunc
|
||
|
|
|
||
|
|
if sparse:
|
||
|
|
array = SparseArray(array)
|
||
|
|
|
||
|
|
series = pd.Series(array, name="name")
|
||
|
|
result = np.modf(series)
|
||
|
|
expected = np.modf(array)
|
||
|
|
|
||
|
|
assert isinstance(result, tuple)
|
||
|
|
assert isinstance(expected, tuple)
|
||
|
|
|
||
|
|
tm.assert_series_equal(result[0], pd.Series(expected[0], name="name"))
|
||
|
|
tm.assert_series_equal(result[1], pd.Series(expected[1], name="name"))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
|
||
|
|
@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
|
||
|
|
def test_binary_ufunc_drops_series_name(ufunc, sparse, arrays_for_binary_ufunc):
|
||
|
|
# Drop the names when they differ.
|
||
|
|
a1, a2 = arrays_for_binary_ufunc
|
||
|
|
s1 = pd.Series(a1, name="a")
|
||
|
|
s2 = pd.Series(a2, name="b")
|
||
|
|
|
||
|
|
result = ufunc(s1, s2)
|
||
|
|
assert result.name is None
|
||
|
|
|
||
|
|
|
||
|
|
def test_object_series_ok():
|
||
|
|
class Dummy:
|
||
|
|
def __init__(self, value):
|
||
|
|
self.value = value
|
||
|
|
|
||
|
|
def __add__(self, other):
|
||
|
|
return self.value + other.value
|
||
|
|
|
||
|
|
arr = np.array([Dummy(0), Dummy(1)])
|
||
|
|
ser = pd.Series(arr)
|
||
|
|
tm.assert_series_equal(np.add(ser, ser), pd.Series(np.add(ser, arr)))
|
||
|
|
tm.assert_series_equal(np.add(ser, Dummy(1)), pd.Series(np.add(ser, Dummy(1))))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"values",
|
||
|
|
[
|
||
|
|
pd.array([1, 3, 2], dtype="int64"),
|
||
|
|
pd.array([1, 10, 0], dtype="Sparse[int]"),
|
||
|
|
pd.to_datetime(["2000", "2010", "2001"]),
|
||
|
|
pd.to_datetime(["2000", "2010", "2001"]).tz_localize("CET"),
|
||
|
|
pd.to_datetime(["2000", "2010", "2001"]).to_period(freq="D"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_reduce(values):
|
||
|
|
a = pd.Series(values)
|
||
|
|
assert np.maximum.reduce(a) == values[1]
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("type_", [list, deque, tuple])
|
||
|
|
def test_binary_ufunc_other_types(type_):
|
||
|
|
a = pd.Series([1, 2, 3], name="name")
|
||
|
|
b = type_([3, 4, 5])
|
||
|
|
|
||
|
|
result = np.add(a, b)
|
||
|
|
expected = pd.Series(np.add(a.to_numpy(), b), name="name")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_object_dtype_ok():
|
||
|
|
class Thing:
|
||
|
|
def __init__(self, value):
|
||
|
|
self.value = value
|
||
|
|
|
||
|
|
def __add__(self, other):
|
||
|
|
other = getattr(other, "value", other)
|
||
|
|
return type(self)(self.value + other)
|
||
|
|
|
||
|
|
def __eq__(self, other) -> bool:
|
||
|
|
return type(other) is Thing and self.value == other.value
|
||
|
|
|
||
|
|
def __repr__(self) -> str:
|
||
|
|
return f"Thing({self.value})"
|
||
|
|
|
||
|
|
s = pd.Series([Thing(1), Thing(2)])
|
||
|
|
result = np.add(s, Thing(1))
|
||
|
|
expected = pd.Series([Thing(2), Thing(3)])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_outer():
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/27186
|
||
|
|
s = pd.Series([1, 2, 3])
|
||
|
|
o = np.array([1, 2, 3])
|
||
|
|
|
||
|
|
with pytest.raises(NotImplementedError):
|
||
|
|
np.subtract.outer(s, o)
|