Skip to content

Fixed bug #9733 where stat functions returned a python scalar for empty series #9829

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 1 commit into from
Closed

Fixed bug #9733 where stat functions returned a python scalar for empty series #9829

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 2 additions & 0 deletions doc/source/whatsnew/v0.17.0.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -701,3 +701,5 @@ Bug Fixes
- Bug in ``iloc`` allowing memory outside bounds of a Series to be accessed with negative integers (:issue:`10779`)
- Bug in ``read_msgpack`` where encoding is not respected (:issue:`10580`)
- Bug preventing access to the first index when using ``iloc`` with a list containing the appropriate negative integer (:issue:`10547`, :issue:`10779`)

- Bug in stat functions (``sum``, ``mean``, etc) returning a python scalar for empty series (:issue:`9733`)
7 changes: 7 additions & 0 deletions pandas/core/common.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2484,11 +2484,18 @@ def is_integer_dtype(arr_or_dtype):
return (issubclass(tipo, np.integer) and
not issubclass(tipo, (np.datetime64, np.timedelta64)))


def is_int64_dtype(arr_or_dtype):
tipo = _get_dtype_type(arr_or_dtype)
return issubclass(tipo, np.int64)


def is_unsigned_integer_dtype(arr_or_dtype):
tipo = _get_dtype_type(arr_or_dtype)
return (issubclass(tipo, np.unsignedinteger) and
not issubclass(tipo, (np.datetime64, np.timedelta64)))


def is_int_or_datetime_dtype(arr_or_dtype):
tipo = _get_dtype_type(arr_or_dtype)
return (issubclass(tipo, np.integer) or
Expand Down
41 changes: 16 additions & 25 deletions pandas/core/nanops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
is_float, is_integer, is_complex,
is_float_dtype,
is_complex_dtype, is_integer_dtype,
is_unsigned_integer_dtype,
is_bool_dtype, is_object_dtype,
is_datetime64_dtype, is_timedelta64_dtype,
is_datetime_or_timedelta_dtype, _get_dtype,
Expand Down Expand Up @@ -67,21 +68,7 @@ def f(values, axis=None, skipna=True, **kwds):
if k not in kwds:
kwds[k] = v
try:
if self.zero_value is not None and values.size == 0:
if values.ndim == 1:

# wrap the 0's if needed
if is_timedelta64_dtype(values):
return lib.Timedelta(0)
return 0
else:
result_shape = (values.shape[:axis] +
values.shape[axis + 1:])
result = np.empty(result_shape)
result.fill(0)
return result

if _USE_BOTTLENECK and skipna and _bn_ok_dtype(values.dtype,
if values.size != 0 and _USE_BOTTLENECK and skipna and _bn_ok_dtype(values.dtype,
bn_name):
result = bn_func(values, axis=axis, **kwds)

Expand Down Expand Up @@ -187,7 +174,10 @@ def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
# return a platform independent precision dtype
dtype_max = dtype
if is_integer_dtype(dtype) or is_bool_dtype(dtype):
dtype_max = np.int64
if is_unsigned_integer_dtype(dtype):
dtype_max = np.uint64
else:
dtype_max = np.int64
elif is_float_dtype(dtype):
dtype_max = np.float64

Expand Down Expand Up @@ -241,14 +231,14 @@ def nanall(values, axis=None, skipna=True):


@disallow('M8')
@bottleneck_switch(zero_value=0)
@bottleneck_switch()
def nansum(values, axis=None, skipna=True):
values, mask, dtype, dtype_max = _get_values(values, skipna, 0)
dtype_sum = dtype_max
if is_float_dtype(dtype):
dtype_sum = dtype
the_sum = values.sum(axis, dtype=dtype_sum)
the_sum = _maybe_null_out(the_sum, axis, mask)
the_sum = _maybe_null_out(the_sum, axis, mask, False)

return _wrap_results(the_sum, dtype)

Expand Down Expand Up @@ -414,7 +404,7 @@ def nanmin(values, axis=None, skipna=True):
result = values.min(axis)

result = _wrap_results(result, dtype)
return _maybe_null_out(result, axis, mask)
return _maybe_null_out(result, axis, mask, True)


@bottleneck_switch()
Expand Down Expand Up @@ -445,7 +435,7 @@ def nanmax(values, axis=None, skipna=True):
result = values.max(axis)

result = _wrap_results(result, dtype)
return _maybe_null_out(result, axis, mask)
return _maybe_null_out(result, axis, mask, True)


def nanargmax(values, axis=None, skipna=True):
Expand Down Expand Up @@ -554,7 +544,7 @@ def nanprod(values, axis=None, skipna=True):
values = values.copy()
values[mask] = 1
result = values.prod(axis)
return _maybe_null_out(result, axis, mask)
return _maybe_null_out(result, axis, mask, False)


def _maybe_arg_null_out(result, axis, mask, skipna):
Expand Down Expand Up @@ -588,9 +578,11 @@ def _get_counts(mask, axis, dtype=float):
return np.array(count, dtype=dtype)


def _maybe_null_out(result, axis, mask):
def _maybe_null_out(result, axis, mask, null_on_empty):
if axis is not None and getattr(result, 'ndim', False):
null_mask = (mask.shape[axis] - mask.sum(axis)) == 0
if not null_on_empty:
null_mask = null_mask & (mask.shape[axis] > 0)
if np.any(null_mask):
if np.iscomplexobj(result):
result = result.astype('c16')
Expand All @@ -599,9 +591,8 @@ def _maybe_null_out(result, axis, mask):
result[null_mask] = np.nan
else:
null_mask = mask.size - mask.sum()
if null_mask == 0:
result = np.nan

if null_mask == 0 and (mask.size > 0 or null_on_empty):
return np.nan
return result


Expand Down
43 changes: 36 additions & 7 deletions pandas/tests/test_series.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3616,15 +3616,44 @@ def test_ops_consistency_on_empty(self):
# GH 7869
# consistency on empty

# float
result = Series(dtype=float).sum()
self.assertEqual(result,0)
# Test type of empty Series

result = Series(dtype=float).mean()
self.assertTrue(isnull(result))
ops = ['median', 'mean', 'sum', 'prod']

result = Series(dtype=float).median()
self.assertTrue(isnull(result))
# First test numpy types
# Just make sure that numpy and pandas have the same return type
for dtype in ['int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64', 'float16', 'float32',
'float64', 'complex64', 'complex128']:
s = Series(dtype=dtype)
for op in ops:
result = getattr(s, op)()
np_type = getattr(np, dtype)
reference = getattr(np, op)(np_type([]))
if np.isnan(reference):
self.assertTrue(np.isnan(result),
msg="%s on empty %s Series: expecting nan, got %s" % (op, dtype, str(result)))
else:
self.assertEqual(result.dtype, reference.dtype,
msg="%s on empty %s Series: returned type %s, expected %s" %
(op, dtype, str(result.dtype), str(reference.dtype)))
self.assertEqual(result, reference,
msg='%s on empty %s Series: expected %s but received %s' %
(op, dtype, str(reference), str(result)))

# Test str/unicode types
str_series = Series(dtype='str')
unicode_series = Series(dtype='unicode')
for op in ['median', 'mean', 'prod']:
# TODO: these operations should raise type errors
# self.assertRaises(TypeError, getattr(str_series, op)(),
# msg="%s on empty str Series should raise TypeError" % op)
# self.assertRaises(TypeError, getattr(unicode_series, op)(),
# msg="%s on empty unicode Series should raise TypeError" % op)
pass

# TODO: these operations should return empty strings
# self.assertEqual('', str_series.sum())
# self.assertEqual('', unicode_series.sum())

# timedelta64[ns]
result = Series(dtype='m8[ns]').sum()
Expand Down