mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-11-27 01:08:12 +01:00
431 lines
14 KiB
Python
431 lines
14 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
from pandas.errors import PerformanceWarning
|
|
|
|
from pandas.core.dtypes.common import is_object_dtype
|
|
|
|
import pandas as pd
|
|
from pandas import SparseDtype
|
|
import pandas._testing as tm
|
|
from pandas.arrays import SparseArray
|
|
from pandas.tests.extension import base
|
|
|
|
|
|
def make_data(fill_value):
|
|
if np.isnan(fill_value):
|
|
data = np.random.uniform(size=100)
|
|
else:
|
|
data = np.random.randint(1, 100, size=100)
|
|
if data[0] == data[1]:
|
|
data[0] += 1
|
|
|
|
data[2::3] = fill_value
|
|
return data
|
|
|
|
|
|
@pytest.fixture
|
|
def dtype():
|
|
return SparseDtype()
|
|
|
|
|
|
@pytest.fixture(params=[0, np.nan])
|
|
def data(request):
|
|
"""Length-100 PeriodArray for semantics test."""
|
|
res = SparseArray(make_data(request.param), fill_value=request.param)
|
|
return res
|
|
|
|
|
|
@pytest.fixture
|
|
def data_for_twos(request):
|
|
return SparseArray(np.ones(100) * 2)
|
|
|
|
|
|
@pytest.fixture(params=[0, np.nan])
|
|
def data_missing(request):
|
|
"""Length 2 array with [NA, Valid]"""
|
|
return SparseArray([np.nan, 1], fill_value=request.param)
|
|
|
|
|
|
@pytest.fixture(params=[0, np.nan])
|
|
def data_repeated(request):
|
|
"""Return different versions of data for count times"""
|
|
|
|
def gen(count):
|
|
for _ in range(count):
|
|
yield SparseArray(make_data(request.param), fill_value=request.param)
|
|
|
|
yield gen
|
|
|
|
|
|
@pytest.fixture(params=[0, np.nan])
|
|
def data_for_sorting(request):
|
|
return SparseArray([2, 3, 1], fill_value=request.param)
|
|
|
|
|
|
@pytest.fixture(params=[0, np.nan])
|
|
def data_missing_for_sorting(request):
|
|
return SparseArray([2, np.nan, 1], fill_value=request.param)
|
|
|
|
|
|
@pytest.fixture
|
|
def na_value():
|
|
return np.nan
|
|
|
|
|
|
@pytest.fixture
|
|
def na_cmp():
|
|
return lambda left, right: pd.isna(left) and pd.isna(right)
|
|
|
|
|
|
@pytest.fixture(params=[0, np.nan])
|
|
def data_for_grouping(request):
|
|
return SparseArray([1, 1, np.nan, np.nan, 2, 2, 1, 3], fill_value=request.param)
|
|
|
|
|
|
class BaseSparseTests:
|
|
def _check_unsupported(self, data):
|
|
if data.dtype == SparseDtype(int, 0):
|
|
pytest.skip("Can't store nan in int array.")
|
|
|
|
@pytest.mark.xfail(reason="SparseArray does not support setitem")
|
|
def test_ravel(self, data):
|
|
super().test_ravel(data)
|
|
|
|
|
|
class TestDtype(BaseSparseTests, base.BaseDtypeTests):
|
|
def test_array_type_with_arg(self, data, dtype):
|
|
assert dtype.construct_array_type() is SparseArray
|
|
|
|
|
|
class TestInterface(BaseSparseTests, base.BaseInterfaceTests):
|
|
def test_no_values_attribute(self, data):
|
|
pytest.skip("We have values")
|
|
|
|
def test_copy(self, data):
|
|
# __setitem__ does not work, so we only have a smoke-test
|
|
data.copy()
|
|
|
|
def test_view(self, data):
|
|
# __setitem__ does not work, so we only have a smoke-test
|
|
data.view()
|
|
|
|
|
|
class TestConstructors(BaseSparseTests, base.BaseConstructorsTests):
|
|
pass
|
|
|
|
|
|
class TestReshaping(BaseSparseTests, base.BaseReshapingTests):
|
|
def test_concat_mixed_dtypes(self, data):
|
|
# https://github.com/pandas-dev/pandas/issues/20762
|
|
# This should be the same, aside from concat([sparse, float])
|
|
df1 = pd.DataFrame({"A": data[:3]})
|
|
df2 = pd.DataFrame({"A": [1, 2, 3]})
|
|
df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category")
|
|
dfs = [df1, df2, df3]
|
|
|
|
# dataframes
|
|
result = pd.concat(dfs)
|
|
expected = pd.concat(
|
|
[x.apply(lambda s: np.asarray(s).astype(object)) for x in dfs]
|
|
)
|
|
self.assert_frame_equal(result, expected)
|
|
|
|
def test_concat_columns(self, data, na_value):
|
|
self._check_unsupported(data)
|
|
super().test_concat_columns(data, na_value)
|
|
|
|
def test_concat_extension_arrays_copy_false(self, data, na_value):
|
|
self._check_unsupported(data)
|
|
super().test_concat_extension_arrays_copy_false(data, na_value)
|
|
|
|
def test_align(self, data, na_value):
|
|
self._check_unsupported(data)
|
|
super().test_align(data, na_value)
|
|
|
|
def test_align_frame(self, data, na_value):
|
|
self._check_unsupported(data)
|
|
super().test_align_frame(data, na_value)
|
|
|
|
def test_align_series_frame(self, data, na_value):
|
|
self._check_unsupported(data)
|
|
super().test_align_series_frame(data, na_value)
|
|
|
|
def test_merge(self, data, na_value):
|
|
self._check_unsupported(data)
|
|
super().test_merge(data, na_value)
|
|
|
|
@pytest.mark.xfail(reason="SparseArray does not support setitem")
|
|
def test_transpose(self, data):
|
|
super().test_transpose(data)
|
|
|
|
|
|
class TestGetitem(BaseSparseTests, base.BaseGetitemTests):
|
|
def test_get(self, data):
|
|
s = pd.Series(data, index=[2 * i for i in range(len(data))])
|
|
if np.isnan(s.values.fill_value):
|
|
assert np.isnan(s.get(4)) and np.isnan(s.iloc[2])
|
|
else:
|
|
assert s.get(4) == s.iloc[2]
|
|
assert s.get(2) == s.iloc[1]
|
|
|
|
def test_reindex(self, data, na_value):
|
|
self._check_unsupported(data)
|
|
super().test_reindex(data, na_value)
|
|
|
|
|
|
# Skipping TestSetitem, since we don't implement it.
|
|
|
|
|
|
class TestMissing(BaseSparseTests, base.BaseMissingTests):
|
|
def test_isna(self, data_missing):
|
|
expected_dtype = SparseDtype(bool, pd.isna(data_missing.dtype.fill_value))
|
|
expected = SparseArray([True, False], dtype=expected_dtype)
|
|
|
|
result = pd.isna(data_missing)
|
|
self.assert_equal(result, expected)
|
|
|
|
result = pd.Series(data_missing).isna()
|
|
expected = pd.Series(expected)
|
|
self.assert_series_equal(result, expected)
|
|
|
|
# GH 21189
|
|
result = pd.Series(data_missing).drop([0, 1]).isna()
|
|
expected = pd.Series([], dtype=expected_dtype)
|
|
self.assert_series_equal(result, expected)
|
|
|
|
def test_fillna_limit_pad(self, data_missing):
|
|
with tm.assert_produces_warning(PerformanceWarning):
|
|
super().test_fillna_limit_pad(data_missing)
|
|
|
|
def test_fillna_limit_backfill(self, data_missing):
|
|
with tm.assert_produces_warning(PerformanceWarning):
|
|
super().test_fillna_limit_backfill(data_missing)
|
|
|
|
def test_fillna_series_method(self, data_missing):
|
|
with tm.assert_produces_warning(PerformanceWarning):
|
|
super().test_fillna_limit_backfill(data_missing)
|
|
|
|
@pytest.mark.skip(reason="Unsupported")
|
|
def test_fillna_series(self):
|
|
# this one looks doable.
|
|
pass
|
|
|
|
def test_fillna_frame(self, data_missing):
|
|
# Have to override to specify that fill_value will change.
|
|
fill_value = data_missing[1]
|
|
|
|
result = pd.DataFrame({"A": data_missing, "B": [1, 2]}).fillna(fill_value)
|
|
|
|
if pd.isna(data_missing.fill_value):
|
|
dtype = SparseDtype(data_missing.dtype, fill_value)
|
|
else:
|
|
dtype = data_missing.dtype
|
|
|
|
expected = pd.DataFrame(
|
|
{
|
|
"A": data_missing._from_sequence([fill_value, fill_value], dtype=dtype),
|
|
"B": [1, 2],
|
|
}
|
|
)
|
|
|
|
self.assert_frame_equal(result, expected)
|
|
|
|
|
|
class TestMethods(BaseSparseTests, base.BaseMethodsTests):
|
|
def test_combine_le(self, data_repeated):
|
|
# We return a Series[SparseArray].__le__ returns a
|
|
# Series[Sparse[bool]]
|
|
# rather than Series[bool]
|
|
orig_data1, orig_data2 = data_repeated(2)
|
|
s1 = pd.Series(orig_data1)
|
|
s2 = pd.Series(orig_data2)
|
|
result = s1.combine(s2, lambda x1, x2: x1 <= x2)
|
|
expected = pd.Series(
|
|
SparseArray(
|
|
[a <= b for (a, b) in zip(list(orig_data1), list(orig_data2))],
|
|
fill_value=False,
|
|
)
|
|
)
|
|
self.assert_series_equal(result, expected)
|
|
|
|
val = s1.iloc[0]
|
|
result = s1.combine(val, lambda x1, x2: x1 <= x2)
|
|
expected = pd.Series(
|
|
SparseArray([a <= val for a in list(orig_data1)], fill_value=False)
|
|
)
|
|
self.assert_series_equal(result, expected)
|
|
|
|
def test_fillna_copy_frame(self, data_missing):
|
|
arr = data_missing.take([1, 1])
|
|
df = pd.DataFrame({"A": arr})
|
|
|
|
filled_val = df.iloc[0, 0]
|
|
result = df.fillna(filled_val)
|
|
|
|
assert df.values.base is not result.values.base
|
|
assert df.A._values.to_dense() is arr.to_dense()
|
|
|
|
def test_fillna_copy_series(self, data_missing):
|
|
arr = data_missing.take([1, 1])
|
|
ser = pd.Series(arr)
|
|
|
|
filled_val = ser[0]
|
|
result = ser.fillna(filled_val)
|
|
|
|
assert ser._values is not result._values
|
|
assert ser._values.to_dense() is arr.to_dense()
|
|
|
|
@pytest.mark.skip(reason="Not Applicable")
|
|
def test_fillna_length_mismatch(self, data_missing):
|
|
pass
|
|
|
|
def test_where_series(self, data, na_value):
|
|
assert data[0] != data[1]
|
|
cls = type(data)
|
|
a, b = data[:2]
|
|
|
|
ser = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype))
|
|
|
|
cond = np.array([True, True, False, False])
|
|
result = ser.where(cond)
|
|
|
|
new_dtype = SparseDtype("float", 0.0)
|
|
expected = pd.Series(
|
|
cls._from_sequence([a, a, na_value, na_value], dtype=new_dtype)
|
|
)
|
|
self.assert_series_equal(result, expected)
|
|
|
|
other = cls._from_sequence([a, b, a, b], dtype=data.dtype)
|
|
cond = np.array([True, False, True, True])
|
|
result = ser.where(cond, other)
|
|
expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
|
|
self.assert_series_equal(result, expected)
|
|
|
|
def test_combine_first(self, data):
|
|
if data.dtype.subtype == "int":
|
|
# Right now this is upcasted to float, just like combine_first
|
|
# for Series[int]
|
|
pytest.skip("TODO(SparseArray.__setitem__ will preserve dtype.")
|
|
super().test_combine_first(data)
|
|
|
|
def test_searchsorted(self, data_for_sorting, as_series):
|
|
with tm.assert_produces_warning(PerformanceWarning):
|
|
super().test_searchsorted(data_for_sorting, as_series)
|
|
|
|
def test_shift_0_periods(self, data):
|
|
# GH#33856 shifting with periods=0 should return a copy, not same obj
|
|
result = data.shift(0)
|
|
|
|
data._sparse_values[0] = data._sparse_values[1]
|
|
assert result._sparse_values[0] != result._sparse_values[1]
|
|
|
|
@pytest.mark.parametrize("method", ["argmax", "argmin"])
|
|
def test_argmin_argmax_all_na(self, method, data, na_value):
|
|
# overriding because Sparse[int64, 0] cannot handle na_value
|
|
self._check_unsupported(data)
|
|
super().test_argmin_argmax_all_na(method, data, na_value)
|
|
|
|
@pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame])
|
|
def test_equals(self, data, na_value, as_series, box):
|
|
self._check_unsupported(data)
|
|
super().test_equals(data, na_value, as_series, box)
|
|
|
|
|
|
class TestCasting(BaseSparseTests, base.BaseCastingTests):
|
|
def test_astype_object_series(self, all_data):
|
|
# Unlike the base class, we do not expect the resulting Block
|
|
# to be ObjectBlock
|
|
ser = pd.Series(all_data, name="A")
|
|
result = ser.astype(object)
|
|
assert is_object_dtype(result._data.blocks[0].dtype)
|
|
|
|
def test_astype_object_frame(self, all_data):
|
|
# Unlike the base class, we do not expect the resulting Block
|
|
# to be ObjectBlock
|
|
df = pd.DataFrame({"A": all_data})
|
|
|
|
result = df.astype(object)
|
|
assert is_object_dtype(result._data.blocks[0].dtype)
|
|
|
|
# FIXME: these currently fail; dont leave commented-out
|
|
# check that we can compare the dtypes
|
|
# comp = result.dtypes.equals(df.dtypes)
|
|
# assert not comp.any()
|
|
|
|
def test_astype_str(self, data):
|
|
result = pd.Series(data[:5]).astype(str)
|
|
expected_dtype = SparseDtype(str, str(data.fill_value))
|
|
expected = pd.Series([str(x) for x in data[:5]], dtype=expected_dtype)
|
|
self.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.xfail(raises=TypeError, reason="no sparse StringDtype")
|
|
def test_astype_string(self, data):
|
|
super().test_astype_string(data)
|
|
|
|
|
|
class TestArithmeticOps(BaseSparseTests, base.BaseArithmeticOpsTests):
|
|
series_scalar_exc = None
|
|
frame_scalar_exc = None
|
|
divmod_exc = None
|
|
series_array_exc = None
|
|
|
|
def _skip_if_different_combine(self, data):
|
|
if data.fill_value == 0:
|
|
# arith ops call on dtype.fill_value so that the sparsity
|
|
# is maintained. Combine can't be called on a dtype in
|
|
# general, so we can't make the expected. This is tested elsewhere
|
|
raise pytest.skip("Incorrected expected from Series.combine")
|
|
|
|
def test_error(self, data, all_arithmetic_operators):
|
|
pass
|
|
|
|
def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
|
|
self._skip_if_different_combine(data)
|
|
super().test_arith_series_with_scalar(data, all_arithmetic_operators)
|
|
|
|
def test_arith_series_with_array(self, data, all_arithmetic_operators):
|
|
self._skip_if_different_combine(data)
|
|
super().test_arith_series_with_array(data, all_arithmetic_operators)
|
|
|
|
|
|
class TestComparisonOps(BaseSparseTests, base.BaseComparisonOpsTests):
|
|
def _compare_other(self, s, data, op_name, other):
|
|
op = self.get_op_from_name(op_name)
|
|
|
|
# array
|
|
result = pd.Series(op(data, other))
|
|
# hard to test the fill value, since we don't know what expected
|
|
# is in general.
|
|
# Rely on tests in `tests/sparse` to validate that.
|
|
assert isinstance(result.dtype, SparseDtype)
|
|
assert result.dtype.subtype == np.dtype("bool")
|
|
|
|
with np.errstate(all="ignore"):
|
|
expected = pd.Series(
|
|
SparseArray(
|
|
op(np.asarray(data), np.asarray(other)),
|
|
fill_value=result.values.fill_value,
|
|
)
|
|
)
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# series
|
|
s = pd.Series(data)
|
|
result = op(s, other)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
class TestPrinting(BaseSparseTests, base.BasePrintingTests):
|
|
@pytest.mark.xfail(reason="Different repr", strict=True)
|
|
def test_array_repr(self, data, size):
|
|
super().test_array_repr(data, size)
|
|
|
|
|
|
class TestParsing(BaseSparseTests, base.BaseParsingTests):
|
|
@pytest.mark.parametrize("engine", ["c", "python"])
|
|
def test_EA_types(self, engine, data):
|
|
expected_msg = r".*must implement _from_sequence_of_strings.*"
|
|
with pytest.raises(NotImplementedError, match=expected_msg):
|
|
super().test_EA_types(engine, data)
|