def test_concatenate_blocks():
v1 = si_unit_pandas.TemperatureArray([1, 2, 3])
s = pandas.Series(v1, index=pandas.RangeIndex(3), fastpath=True)
result = pandas.concat([s, s], ignore_index=True)
expected = pandas.Series(
si_unit_pandas.TemperatureArray([1, 2, 3, 1, 2, 3]))
tm.assert_series_equal(result, expected)
def test_ops(tup):
a, b = tup
v1 = si_unit_pandas.TemperatureArray(a)
v2 = si_unit_pandas.TemperatureArray(b)
r1 = v1 <= v2
r2 = v2 >= v1
assert_numpy_array_equal(r1, r2)
def test_factorize():
arr = si_unit_pandas.TemperatureArray([1, 1, 10, 10])
labels, uniques = pandas.factorize(arr)
expected_labels = numpy.array([0, 0, 1, 1])
assert_numpy_array_equal(labels, expected_labels)
expected_uniques = si_unit_pandas.TemperatureArray([1, 10])
assert uniques.equals(expected_uniques)
def test_reindex(frame):
result = frame.reindex([0, 10])
expected = pandas.DataFrame({
'A': si_unit_pandas.TemperatureArray([0, numpy.nan]),
'B': [0, numpy.nan],
'C': si_unit_pandas.TemperatureArray([0, numpy.nan])
},
index=[0, 10])
tm.assert_frame_equal(result, expected)
def test_dropna(): missing = pandas.Series(si_unit_pandas.TemperatureArray([1, numpy.nan])) result = missing.dropna() expected = pandas.Series(si_unit_pandas.TemperatureArray([1])) tm.assert_series_equal(result, expected) result = missing.to_frame().dropna() expected = expected.to_frame() tm.assert_frame_equal(result, expected)
def test_dataframe_from_series_no_dict():
s = pandas.Series(si_unit_pandas.TemperatureArray([1, 2, 3]))
result = pandas.DataFrame(s)
expected = pandas.DataFrame({0: s})
tm.assert_frame_equal(result, expected)
s = pandas.Series(si_unit_pandas.TemperatureArray([1, 2, 3]), name='A')
result = pandas.DataFrame(s)
expected = pandas.DataFrame({'A': s})
tm.assert_frame_equal(result, expected)
def test_comparison_raises(op):
arr = si_unit_pandas.TemperatureArray([0, 1, 2])
with pytest.raises(TypeError):
op(arr, 'a')
with pytest.raises(TypeError):
op('a', arr)
def test_repr_works():
values = si_unit_pandas.TemperatureArray([0, 1, 2, 3, 2**32, 2**64 + 1])
result = repr(values)
expected = (
'TemperatureArray([0.0\u205f℃, 1.0\u205f℃, 2.0\u205f℃, 3.0\u205f℃, '
'4294967296.0\u205f℃, 1.8446744073709552e+19\u205f℃])')
assert result == expected
def test_output(file_regression: FileRegressionFixture):
s = pandas.Series(si_unit_pandas.TemperatureArray([1, 2, 3]))
result = pandas.DataFrame(s)
expected = pandas.DataFrame({0: s})
assert_frame_equal(result, expected)
check_file_regression(str(expected), file_regression)
def test_unique():
arr = si_unit_pandas.TemperatureArray([3, 3, 1, 2, 3])
result = arr.unique()
assert isinstance(result, si_unit_pandas.TemperatureArray)
result = result.astype(object)
expected = pandas.unique(arr.astype(object))
assert_numpy_array_equal(result, expected)
def test_isna():
v = si_unit_pandas.TemperatureArray([0, 2, 2**64, 2**64 + 1, 2**64 + 2])
r1 = v.isna()
r2 = pandas.isna(v)
expected = numpy.array([False, False, False, False, False])
numpy.testing.assert_array_equal(r1, expected)
numpy.testing.assert_array_equal(r2, expected)
def test_iter_works():
x = si_unit_pandas.TemperatureArray([0, 1, 2])
result = list(x)
expected = [
Celsius(0),
Celsius(1),
Celsius(2),
]
assert result == expected
def test_array():
v = si_unit_pandas.TemperatureArray([1, 2, 3])
result = numpy.array(v)
expected = numpy.array([
Celsius(1),
Celsius(2),
Celsius(3),
])
assert_numpy_array_equal(result, expected)
def test_factorize():
arr = si_unit_pandas.TemperatureArray([3, 3, 1, 2, 3])
labels, uniques = arr.factorize()
expected_labels, expected_uniques = pandas.factorize(arr.astype(object))
assert isinstance(uniques, si_unit_pandas.TemperatureArray)
uniques = uniques.astype(object)
assert_numpy_array_equal(labels, expected_labels)
assert_numpy_array_equal(uniques, expected_uniques)
def test_groupby_make_grouper():
df = pandas.DataFrame({
'A': [1, 1, 2, 2],
'B': si_unit_pandas.TemperatureArray([1, 1, 2, 2])
})
print(df)
print(df.to_string())
gr = df.groupby('B')
result = gr.grouper.groupings[0].grouper
print(result)
print(df.B.values)
assert result.equals(df.B.values)
def test_groupby_make_grouper_groupings():
df = pandas.DataFrame({
'A': [1, 1, 2, 2],
'B': si_unit_pandas.TemperatureArray([1, 1, 2, 2])
})
p1 = df.groupby('A').grouper.groupings[0]
p2 = df.groupby('B').grouper.groupings[0]
result = {int(k): v for k, v in p2.groups.items()}
assert result.keys() == p1.groups.keys()
for k in result.keys():
assert result[k].equals(p1.groups[k])
def frame():
return pandas.DataFrame({
'A': si_unit_pandas.TemperatureArray([0, 1, 2]),
'B': [0, 1, 2],
'C': si_unit_pandas.TemperatureArray([0, 1, 2])
})
def test_make_container():
values = si_unit_pandas.TemperatureArray([1, 2, 3])
npt.assert_array_equal(
values.data, numpy.array([1, 2, 3], dtype=values.dtype._record_type))
def test_from_ndarray(values):
result = si_unit_pandas.TemperatureArray(numpy.asarray(values))
expected = si_unit_pandas.TemperatureArray(values)
assert result.equals(expected)
def test_series_constructor():
v = si_unit_pandas.TemperatureArray([1, 2, 3])
result = pandas.Series(v)
assert result.dtype == v.dtype
assert isinstance(result._data.blocks[0], ExtensionBlock)
def test_setitem_array():
ser = si_unit_pandas.TemperatureArray([0, 1, 2])
ser[[1, 2]] = [10, 20]
expected = si_unit_pandas.TemperatureArray([0, 10, 20])
assert ser.equals(expected)
def test_setitem_scalar(value):
ser = si_unit_pandas.TemperatureArray([0, 1, 2])
ser[1] = Celsius(value)
expected = si_unit_pandas.TemperatureArray([0, 10, 2])
assert ser.equals(expected)
def test_getitem_slice():
ser = si_unit_pandas.TemperatureArray([0, 1, 2])
result = ser[1:]
expected = si_unit_pandas.TemperatureArray([1, 2])
assert result.equals(expected)
def test_getitem_scalar():
ser = si_unit_pandas.TemperatureArray([0, 1, 2])
result = ser[1]
assert result == Celsius(1)
def test_dataframe_constructor():
v = si_unit_pandas.TemperatureArray([1, 2, 3])
df = pandas.DataFrame({'A': v})
assert isinstance(df.dtypes['A'], si_unit_pandas.CelsiusType)
assert df.shape == (3, 1)
str(df)
def test_dataframe_from_series():
s = pandas.Series(si_unit_pandas.TemperatureArray([0, 1, 2]))
c = pandas.Series(pandas.Categorical(['a', 'b']))
result = pandas.DataFrame({'A': s, 'B': c})
assert isinstance(result.dtypes['A'], si_unit_pandas.CelsiusType)
def test_getitem_slice():
ser = pandas.Series(si_unit_pandas.TemperatureArray([0, 1, 2]))
result = ser[1:]
expected = pandas.Series(si_unit_pandas.TemperatureArray([1, 2]),
index=range(1, 3))
tm.assert_series_equal(result, expected)
def test_setitem_scalar():
ser = pandas.Series(si_unit_pandas.TemperatureArray([0, 1, 2]))
ser[1] = Celsius(10)
expected = pandas.Series(si_unit_pandas.TemperatureArray([0, 10, 2]))
tm.assert_series_equal(ser, expected)
def test_tolist():
v = si_unit_pandas.TemperatureArray([1, 2, 3])
result = v.tolist()
expected = [1.0, 2.0, 3.0]
assert result == expected
def test_topyints():
values = [0, 1, 2**32]
arr = si_unit_pandas.TemperatureArray(values)
result = [int(x) for x in arr]
assert result == values
