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194 changes: 100 additions & 94 deletions pandas/tests/series/test_operators.py
Original file line number Diff line number Diff line change
Expand Up @@ -595,77 +595,85 @@ def test_divide_decimal(self):

assert_series_equal(expected, s)

def test_div(self):
@pytest.mark.parametrize('dtype2', [np.int64, np.int32, np.int16, np.int8,
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wouldn't this be better as a fixture

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Maybe eventually. ATM it is only used once so there's no upside.

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as a style note, generally I would write this like

@pytest.mark.parameterize(
    'dtype2',
     [
        np.int64, ......])

as it makes it shorter and more readable

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i'd like this changed

np.float64, np.float32, np.float16,
np.uint64, np.uint32,
np.uint16, np.uint8])
@pytest.mark.parametrize('dtype1', [np.int64, np.float64, np.uint64])
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pls clean the parameterize up here as I suggested

def test_ser_div_ser(self, dtype1, dtype2):
# no longer do integer div for any ops, but deal with the 0's
first = Series([3, 4, 5, 8], name='first').astype(dtype1)
second = Series([0, 0, 0, 3], name='second').astype(dtype2)

with np.errstate(all='ignore'):
# no longer do integer div for any ops, but deal with the 0's
p = DataFrame({'first': [3, 4, 5, 8], 'second': [0, 0, 0, 3]})
result = p['first'] / p['second']
expected = Series(
p['first'].values.astype(float) / p['second'].values,
dtype='float64')
expected.iloc[0:3] = np.inf
assert_series_equal(result, expected)
expected = Series(first.values.astype(np.float64) / second.values,
dtype='float64')
expected.iloc[0:3] = np.inf

result = p['first'] / 0
expected = Series(np.inf, index=p.index, name='first')
assert_series_equal(result, expected)
result = first / second
assert_series_equal(result, expected)

p = p.astype('float64')
result = p['first'] / p['second']
expected = Series(p['first'].values / p['second'].values)
assert_series_equal(result, expected)
def test_ser_div_ser_name_propagation(self):
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why is this a separate test and not in the div test?

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Not really sure what the original motivation was here. TBH I was just guessing that the point was name propagation, since it wasn't clear what else it could be for.

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this is a dupe of the test right above

first = Series([3, 4, 5, 8], name='first')
second = Series([1, 1, 1, 1], name='second')

p = DataFrame({'first': [3, 4, 5, 8], 'second': [1, 1, 1, 1]})
result = p['first'] / p['second']
assert_series_equal(result, p['first'].astype('float64'),
check_names=False)
assert result.name is None
assert not result.equals(p['second'] / p['first'])

# inf signing
s = Series([np.nan, 1., -1.])
result = s / 0
expected = Series([np.nan, np.inf, -np.inf])
assert_series_equal(result, expected)
expected = Series([3, 4, 5, 8], dtype='float64', name=None)
result = first / second
assert_series_equal(result, expected)
assert not result.equals(second / first)

# float/integer issue
# GH 7785
p = DataFrame({'first': (1, 0), 'second': (-0.01, -0.02)})
expected = Series([-0.01, -np.inf])
def test_div_equiv_binop(self):
# Test Series.div as well as Series.__div__
# float/integer issue
# GH#7785
first = pd.Series([1, 0], name='first')
second = pd.Series([-0.01, -0.02], name='second')
expected = Series([-0.01, -np.inf])

result = p['second'].div(p['first'])
assert_series_equal(result, expected, check_names=False)
result = second.div(first)
assert_series_equal(result, expected, check_names=False)

result = p['second'] / p['first']
assert_series_equal(result, expected)
result = second / first
assert_series_equal(result, expected)

# GH 9144
s = Series([-1, 0, 1])
def test_rdiv_zero_compat(self):
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parametrie over 0 with various int/uint dtypes

# GH#8674
zero_array = np.array([0] * 5)
data = np.random.randn(5)
expected = pd.Series([0.] * 5)

result = 0 / s
expected = Series([0.0, nan, 0.0])
assert_series_equal(result, expected)
result = zero_array / pd.Series(data)
assert_series_equal(result, expected)

result = s / 0
expected = Series([-inf, nan, inf])
assert_series_equal(result, expected)
result = pd.Series(zero_array) / data
assert_series_equal(result, expected)

result = s // 0
expected = Series([-inf, nan, inf])
assert_series_equal(result, expected)
result = pd.Series(zero_array) / pd.Series(data)
assert_series_equal(result, expected)

# GH 8674
zero_array = np.array([0] * 5)
data = np.random.randn(5)
expected = pd.Series([0.] * 5)
result = zero_array / pd.Series(data)
assert_series_equal(result, expected)
def test_div_zero_inf_signs(self):
# GH#9144, inf signing
ser = Series([-1, 0, 1], name='first')
expected = Series([-np.inf, np.nan, np.inf], name='first')

result = pd.Series(zero_array) / data
assert_series_equal(result, expected)
result = ser / 0
assert_series_equal(result, expected)

result = pd.Series(zero_array) / pd.Series(data)
assert_series_equal(result, expected)
def test_rdiv_zero(self):
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parametrize

# GH#9144
ser = Series([-1, 0, 1], name='first')
expected = Series([0.0, np.nan, 0.0], name='first')

result = 0 / ser
assert_series_equal(result, expected)

def test_floordiv_div(self):
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same. see the pattern :>

need to create a zero fixture

# GH#9144
ser = Series([-1, 0, 1], name='first')

result = ser // 0
expected = Series([-inf, nan, inf], name='first')
assert_series_equal(result, expected)


class TestTimedeltaSeriesArithmeticWithIntegers(object):
Expand Down Expand Up @@ -1574,33 +1582,36 @@ def test_dt64_series_add_intlike(self, tz):


class TestSeriesOperators(TestData):
def test_op_method(self):
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can you add a comment to describe what tests this class does

def check(series, other, check_reverse=False):
simple_ops = ['add', 'sub', 'mul', 'floordiv', 'truediv', 'pow']
if not compat.PY3:
simple_ops.append('div')

for opname in simple_ops:
op = getattr(Series, opname)

if op == 'div':
alt = operator.truediv
else:
alt = getattr(operator, opname)

result = op(series, other)
expected = alt(series, other)
assert_almost_equal(result, expected)
if check_reverse:
rop = getattr(Series, "r" + opname)
result = rop(series, other)
expected = alt(other, series)
assert_almost_equal(result, expected)
@pytest.mark.parametrize('ts', [lambda x: (x, x * 2, False),
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much nicer. if you'd format as I suggest this become very readable.

lambda x: (x, x[::2], False),
lambda x: (x, 5, True),
lambda x: (tm.makeFloatSeries(),
tm.makeFloatSeries(),
True)])
@pytest.mark.parametrize('opname', ['add', 'sub', 'mul', 'floordiv',
'truediv', 'div', 'pow'])
def test_op_method(self, opname, ts):
# check that Series.{opname} behaves like Series.__{opname}__,
series, other, check_reverse = ts(self.ts)

if opname == 'div' and compat.PY3:
pytest.skip('div test only for Py3')

op = getattr(Series, opname)

if op == 'div':
alt = operator.truediv
else:
alt = getattr(operator, opname)

check(self.ts, self.ts * 2)
check(self.ts, self.ts[::2])
check(self.ts, 5, check_reverse=True)
check(tm.makeFloatSeries(), tm.makeFloatSeries(), check_reverse=True)
result = op(series, other)
expected = alt(series, other)
assert_almost_equal(result, expected)
if check_reverse:
rop = getattr(Series, "r" + opname)
result = rop(series, other)
expected = alt(other, series)
assert_almost_equal(result, expected)

def test_neg(self):
assert_series_equal(-self.series, -1 * self.series)
Expand Down Expand Up @@ -1992,20 +2003,15 @@ def test_operators_corner(self):
index=self.ts.index[:-5], name='ts')
tm.assert_series_equal(added[:-5], expected)

def test_operators_reverse_object(self):
@pytest.mark.parametrize('op', [operator.add, operator.sub, operator.mul,
operator.truediv, operator.floordiv])
def test_operators_reverse_object(self, op):
# GH 56
arr = Series(np.random.randn(10), index=np.arange(10), dtype=object)

def _check_op(arr, op):
result = op(1., arr)
expected = op(1., arr.astype(float))
assert_series_equal(result.astype(float), expected)

_check_op(arr, operator.add)
_check_op(arr, operator.sub)
_check_op(arr, operator.mul)
_check_op(arr, operator.truediv)
_check_op(arr, operator.floordiv)
result = op(1., arr)
expected = op(1., arr.astype(float))
assert_series_equal(result.astype(float), expected)

def test_arith_ops_df_compat(self):
# GH 1134
Expand Down