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import numpy as np
import pytest
from pandas import (
NaT,
SparseDtype,
Timestamp,
isna,
)
from pandas.core.arrays.sparse import SparseArray
class TestReductions:
@pytest.mark.parametrize(
"data,pos,neg",
[
([True, True, True], True, False),
([1, 2, 1], 1, 0),
([1.0, 2.0, 1.0], 1.0, 0.0),
],
)
def test_all(self, data, pos, neg):
# GH#17570
out = SparseArray(data).all()
assert out
out = SparseArray(data, fill_value=pos).all()
assert out
data[1] = neg
out = SparseArray(data).all()
assert not out
out = SparseArray(data, fill_value=pos).all()
assert not out
@pytest.mark.parametrize(
"data,pos,neg",
[
([True, True, True], True, False),
([1, 2, 1], 1, 0),
([1.0, 2.0, 1.0], 1.0, 0.0),
],
)
def test_numpy_all(self, data, pos, neg):
# GH#17570
out = np.all(SparseArray(data))
assert out
out = np.all(SparseArray(data, fill_value=pos))
assert out
data[1] = neg
out = np.all(SparseArray(data))
assert not out
out = np.all(SparseArray(data, fill_value=pos))
assert not out
# raises with a different message on py2.
msg = "the 'out' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.all(SparseArray(data), out=np.array([]))
@pytest.mark.parametrize(
"data,pos,neg",
[
([False, True, False], True, False),
([0, 2, 0], 2, 0),
([0.0, 2.0, 0.0], 2.0, 0.0),
],
)
def test_any(self, data, pos, neg):
# GH#17570
out = SparseArray(data).any()
assert out
out = SparseArray(data, fill_value=pos).any()
assert out
data[1] = neg
out = SparseArray(data).any()
assert not out
out = SparseArray(data, fill_value=pos).any()
assert not out
@pytest.mark.parametrize(
"data,pos,neg",
[
([False, True, False], True, False),
([0, 2, 0], 2, 0),
([0.0, 2.0, 0.0], 2.0, 0.0),
],
)
def test_numpy_any(self, data, pos, neg):
# GH#17570
out = np.any(SparseArray(data))
assert out
out = np.any(SparseArray(data, fill_value=pos))
assert out
data[1] = neg
out = np.any(SparseArray(data))
assert not out
out = np.any(SparseArray(data, fill_value=pos))
assert not out
msg = "the 'out' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.any(SparseArray(data), out=out)
def test_sum(self):
data = np.arange(10).astype(float)
out = SparseArray(data).sum()
assert out == 45.0
data[5] = np.nan
out = SparseArray(data, fill_value=2).sum()
assert out == 40.0
out = SparseArray(data, fill_value=np.nan).sum()
assert out == 40.0
@pytest.mark.parametrize(
"arr",
[np.array([0, 1, np.nan, 1]), np.array([0, 1, 1])],
)
@pytest.mark.parametrize("fill_value", [0, 1, np.nan])
@pytest.mark.parametrize("min_count, expected", [(3, 2), (4, np.nan)])
def test_sum_min_count(self, arr, fill_value, min_count, expected):
# GH#25777
sparray = SparseArray(arr, fill_value=fill_value)
result = sparray.sum(min_count=min_count)
if np.isnan(expected):
assert np.isnan(result)
else:
assert result == expected
def test_bool_sum_min_count(self):
spar_bool = SparseArray([False, True] * 5, dtype=np.bool_, fill_value=True)
res = spar_bool.sum(min_count=1)
assert res == 5
res = spar_bool.sum(min_count=11)
assert isna(res)
def test_numpy_sum(self):
data = np.arange(10).astype(float)
out = np.sum(SparseArray(data))
assert out == 45.0
data[5] = np.nan
out = np.sum(SparseArray(data, fill_value=2))
assert out == 40.0
out = np.sum(SparseArray(data, fill_value=np.nan))
assert out == 40.0
msg = "the 'dtype' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.sum(SparseArray(data), dtype=np.int64)
msg = "the 'out' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.sum(SparseArray(data), out=out)
def test_mean(self):
data = np.arange(10).astype(float)
out = SparseArray(data).mean()
assert out == 4.5
data[5] = np.nan
out = SparseArray(data).mean()
assert out == 40.0 / 9
def test_numpy_mean(self):
data = np.arange(10).astype(float)
out = np.mean(SparseArray(data))
assert out == 4.5
data[5] = np.nan
out = np.mean(SparseArray(data))
assert out == 40.0 / 9
msg = "the 'dtype' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.mean(SparseArray(data), dtype=np.int64)
msg = "the 'out' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.mean(SparseArray(data), out=out)
class TestMinMax:
@pytest.mark.parametrize(
"raw_data,max_expected,min_expected",
[
(np.arange(5.0), [4], [0]),
(-np.arange(5.0), [0], [-4]),
(np.array([0, 1, 2, np.nan, 4]), [4], [0]),
(np.array([np.nan] * 5), [np.nan], [np.nan]),
(np.array([]), [np.nan], [np.nan]),
],
)
def test_nan_fill_value(self, raw_data, max_expected, min_expected):
arr = SparseArray(raw_data)
max_result = arr.max()
min_result = arr.min()
assert max_result in max_expected
assert min_result in min_expected
max_result = arr.max(skipna=False)
min_result = arr.min(skipna=False)
if np.isnan(raw_data).any():
assert np.isnan(max_result)
assert np.isnan(min_result)
else:
assert max_result in max_expected
assert min_result in min_expected
@pytest.mark.parametrize(
"fill_value,max_expected,min_expected",
[
(100, 100, 0),
(-100, 1, -100),
],
)
def test_fill_value(self, fill_value, max_expected, min_expected):
arr = SparseArray(
np.array([fill_value, 0, 1]), dtype=SparseDtype("int", fill_value)
)
max_result = arr.max()
assert max_result == max_expected
min_result = arr.min()
assert min_result == min_expected
def test_only_fill_value(self):
fv = 100
arr = SparseArray(np.array([fv, fv, fv]), dtype=SparseDtype("int", fv))
assert len(arr._valid_sp_values) == 0
assert arr.max() == fv
assert arr.min() == fv
assert arr.max(skipna=False) == fv
assert arr.min(skipna=False) == fv
@pytest.mark.parametrize("func", ["min", "max"])
@pytest.mark.parametrize("data", [np.array([]), np.array([np.nan, np.nan])])
@pytest.mark.parametrize(
"dtype,expected",
[
(SparseDtype(np.float64, np.nan), np.nan),
(SparseDtype(np.float64, 5.0), np.nan),
(SparseDtype("datetime64[ns]", NaT), NaT),
(SparseDtype("datetime64[ns]", Timestamp("2018-05-05")), NaT),
],
)
def test_na_value_if_no_valid_values(self, func, data, dtype, expected):
arr = SparseArray(data, dtype=dtype)
result = getattr(arr, func)()
if expected is NaT:
# TODO: pin down whether we wrap datetime64("NaT")
assert result is NaT or np.isnat(result)
else:
assert np.isnan(result)
class TestArgmaxArgmin:
@pytest.mark.parametrize(
"arr,argmax_expected,argmin_expected",
[
(SparseArray([1, 2, 0, 1, 2]), 1, 2),
(SparseArray([-1, -2, 0, -1, -2]), 2, 1),
(SparseArray([np.nan, 1, 0, 0, np.nan, -1]), 1, 5),
(SparseArray([np.nan, 1, 0, 0, np.nan, 2]), 5, 2),
(SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=-1), 5, 2),
(SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=0), 5, 2),
(SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=1), 5, 2),
(SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=2), 5, 2),
(SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=3), 5, 2),
(SparseArray([0] * 10 + [-1], fill_value=0), 0, 10),
(SparseArray([0] * 10 + [-1], fill_value=-1), 0, 10),
(SparseArray([0] * 10 + [-1], fill_value=1), 0, 10),
(SparseArray([-1] + [0] * 10, fill_value=0), 1, 0),
(SparseArray([1] + [0] * 10, fill_value=0), 0, 1),
(SparseArray([-1] + [0] * 10, fill_value=-1), 1, 0),
(SparseArray([1] + [0] * 10, fill_value=1), 0, 1),
],
)
def test_argmax_argmin(self, arr, argmax_expected, argmin_expected):
argmax_result = arr.argmax()
argmin_result = arr.argmin()
assert argmax_result == argmax_expected
assert argmin_result == argmin_expected
@pytest.mark.parametrize(
"arr,method",
[(SparseArray([]), "argmax"), (SparseArray([]), "argmin")],
)
def test_empty_array(self, arr, method):
msg = f"attempt to get {method} of an empty sequence"
with pytest.raises(ValueError, match=msg):
arr.argmax() if method == "argmax" else arr.argmin()
|
