8000 ERR: Boolean comparisons of a Series vs None will now be equivalent to null comparisons by jreback · Pull Request #10569 · pandas-dev/pandas · GitHub
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ERR: Boolean comparisons of a Series vs None will now be equivalent to null comparisons #10569

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Merged
merged 8 commits into from
Jul 17, 2015
Merged

ERR: Boolean comparisons of a Series vs None will now be equivalent to null comparisons #10569

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4fe7c68
ERR: Boolean comparisons of a Series vs None will now be equivalent o…
jreback Jul 11, 2015
dccf5eb
DEPR: remove numpy deprecation warnings for i8 vs integer comparisions
jreback Jul 16, 2015
8016a7f
DEPR: remove visible deprecation warning for slicing in test_internals
jreback Jul 16, 2015
b381327
DEPR: remove unordered types depreceation usage in core/index.py
jreback Jul 17, 2015
26ee43e
Align options in chunk and full file read
kshedden Jul 17, 2015
4def8e4
TST: fixes stata datetimelike comparisons for #10606
jreback Jul 17, 2015
0bd25ab
DOC: whatsnew changes
jreback Jul 17, 2015
effb676
misc import cleanups
jreback Jul 17, 2015
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misc import cleanups
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@jreback
jreback committed Jul 17, 2015
commit effb6761e1e7608c44e9bd1e02dfef2137c928fc
5 changes: 3 additions & 2 deletions doc/source/whatsnew/v0.17.0.txt
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Expand Up @@ -230,8 +230,6 @@ Other API Changes
- Enable serialization of lists and dicts to strings in ExcelWriter (:issue:`8188`)
- Allow passing `kwargs` to the interpolation methods (:issue:`10378`).
- Serialize metadata properties of subclasses of pandas objects (:issue:`10553`).
- Boolean comparisons of a ``Series`` vs ``None`` will now be equivalent to comparing with ``np.nan``, rather than raise ``TypeError``, xref (:issue:`1079`).
- Remove use of some deprecated numpy comparisons (:issue:`10569`)

.. _whatsnew_0170.deprecations:

Expand All @@ -243,6 +241,8 @@ Deprecations
Removal of prior version deprecations/changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

- Remove use of some deprecated numpy comparison operations, mainly in tests. (:issue:`10569`)

.. _dask: https://dask.readthedocs.org/en/latest/

.. _whatsnew_0170.gil:
Expand Down Expand Up @@ -285,6 +285,7 @@ Performance Improvements
Bug Fixes
~~~~~~~~~

- Boolean comparisons of a ``Series`` vs ``None`` will now be equivalent to comparing with ``np.nan``, rather than raise ``TypeError``, xref (:issue:`1079`).
- Bug in ``DataFrame.apply`` when function returns categorical series. (:issue:`9573`)
- Bug in ``to_datetime`` with invalid dates and formats supplied (:issue:`10154`)
- Bug in ``Index.drop_duplicates`` dropping name(s) (:issue:`10115`)
Expand Down
38 changes: 20 additions & 18 deletions pandas/core/ops.py
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Expand Up @@ -17,7 +17,9 @@
from pandas.tslib import iNaT
from pandas.core.common import(bind_method, is_list_like, notnull, isnull,
_values_from_object, _maybe_match_name,
needs_i8_conversion, is_datetimelike_v_numeric, is_integer_dtype)
needs_i8_conversion, is_datetimelike_v_numeric, 8000
is_integer_dtype, is_categorical_dtype, is_object_dtype,
is_timedelta64_dtype, is_datetime64_dtype, is_bool_dtype)

# -----------------------------------------------------------------------------
# Functions that add arithmetic methods to objects, given arithmetic factory
Expand Down Expand Up @@ -276,11 +278,11 @@ def __init__(self, left, right, name):
lvalues = self._convert_to_array(left, name=name)
rvalues = self._convert_to_array(right, name=name, other=lvalues)

self.is_timedelta_lhs = com.is_timedelta64_dtype(left)
self.is_datetime_lhs = com.is_datetime64_dtype(left)
self.is_timedelta_lhs = is_timedelta64_dtype(left)
self.is_datetime_lhs = is_datetime64_dtype(left)
self.is_integer_lhs = left.dtype.kind in ['i', 'u']
self.is_datetime_rhs = com.is_datetime64_dtype(rvalues)
self.is_timedelta_rhs = com.is_timedelta64_dtype(rvalues)
self.is_datetime_rhs = is_datetime64_dtype(rvalues)
self.is_timedelta_rhs = is_timedelta64_dtype(rvalues)
self.is_integer_rhs = rvalues.dtype.kind in ('i', 'u')

self._validate()
Expand Down Expand Up @@ -355,7 +357,7 @@ def _convert_to_array(self, values, name=None, other=None):
elif isinstance(values, pd.DatetimeIndex):
values = values.to_series()
elif not (isinstance(values, (np.ndarray, pd.Series)) and
com.is_datetime64_dtype(values)):
is_datetime64_dtype(values)):
values = tslib.array_to_datetime(values)
elif inferred_type in ('timedelta', 'timedelta64'):
# have a timedelta, convert to to ns here
Expand Down Expand Up @@ -448,8 +450,8 @@ def maybe_convert_for_time_op(cls, left, right, name):
that the data is not the right type for time ops.
"""
# decide if we can do it
is_timedelta_lhs = com.is_timedelta64_dtype(left)
is_datetime_lhs = com.is_datetime64_dtype(left)
is_timedelta_lhs = is_timedelta64_dtype(left)
is_datetime_lhs = is_datetime64_dtype(left)
if not (is_datetime_lhs or is_timedelta_lhs):
return None

Expand Down Expand Up @@ -547,17 +549,17 @@ def na_op(x, y):

# dispatch to the categorical if we have a categorical
# in either operand
if com.is_categorical_dtype(x):
if is_categorical_dtype(x):
return op(x,y)
elif com.is_categorical_dtype(y) and not lib.isscalar(y):
elif is_categorical_dtype(y) and not isscalar(y):
return op(y,x)

if com.is_object_dtype(x.dtype):
if is_object_dtype(x.dtype):
if isinstance(y, list):
y = lib.list_to_object_array(y)

if isinstance(y, (np.ndarray, pd.Series)):
if not com.is_object_dtype(y.dtype):
if not is_object_dtype(y.dtype):
result = lib.vec_compare(x, y.astype(np.object_), op)
else:
result = lib.vec_compare(x, y, op)
Expand All @@ -574,7 +576,7 @@ def na_op(x, y):
raise TypeError("invalid type comparison")

# numpy does not like comparisons vs None
if lib.isscalar(y) and isnull(y):
if isscalar(y) and isnull(y):
y = np.nan

# we have a datetime/timedelta and may need to convert
Expand Down Expand Up @@ -624,13 +626,13 @@ def wrapper(self, other, axis=None):
return self._constructor(na_op(self.values, np.asarray(other)),
index=self.index).__finalize__(self)
elif isinstance(other, pd.Categorical):
if not com.is_categorical_dtype(self):
if not is_categorical_dtype(self):
msg = "Cannot compare a Categorical for op {op} with Series of dtype {typ}.\n"\
"If you want to compare values, use 'series <op> np.asarray(other)'."
raise TypeError(msg.format(op=op,typ=self.dtype))


if com.is_categorical_dtype(self):
if is_categorical_dtype(self):
# cats are a special case as get_values() would return an ndarray, which would then
# not take categories ordering into account
# we can go directly to op, as the na_op would just test again and dispatch to it.
Expand All @@ -641,7 +643,7 @@ def wrapper(self, other, axis=None):
other = np.asarray(other)

res = na_op(values, other)
if lib.isscalar(res):
if isscalar(res):
raise TypeError('Could not compare %s type with Series'
% type(other))

Expand All @@ -667,7 +669,7 @@ def na_op(x, y):
y = lib.list_to_object_array(y)

if isinstance(y, (np.ndarray, pd.Series)):
if (com.is_bool_dtype(x.dtype) and com.is_bool_dtype(y.dtype)):
if (is_bool_dtype(x.dtype) and is_bool_dtype(y.dtype)):
result = op(x, y) # when would this be hit?
else:
x = com._ensure_object(x)
Expand Down Expand Up @@ -1069,7 +1071,7 @@ def na_op(x, y):

# work only for scalars
def f(self, other):
if not lib.isscalar(other):
if not isscalar(other):
raise ValueError('Simple arithmetic with %s can only be '
'done with scalar values' %
self._constructor.__name__)
Expand Down
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