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Small ops cleanups #19972

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Mar 7, 2018
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Small ops cleanups #19972

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98 changes: 49 additions & 49 deletions pandas/core/ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -971,9 +971,9 @@ def _arith_method_SERIES(cls, op, special):
code duplication.
"""
str_rep = _get_opstr(op, cls)
name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(name)
fill_zeros = _gen_fill_zeros(name)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)
construct_result = (_construct_divmod_result
if op is divmod else _construct_result)

Expand All @@ -996,7 +996,7 @@ def na_op(x, y):

result, changed = maybe_upcast_putmask(result, ~mask, np.nan)

result = missing.fill_zeros(result, x, y, name, fill_zeros)
result = missing.fill_zeros(result, x, y, op_name, fill_zeros)
return result

def safe_na_op(lvalues, rvalues):
Expand All @@ -1009,7 +1009,7 @@ def safe_na_op(lvalues, rvalues):
lambda x: op(x, rvalues))
raise

def wrapper(left, right, name=name, na_op=na_op):
def wrapper(left, right):

if isinstance(right, ABCDataFrame):
return NotImplemented
Expand Down Expand Up @@ -1100,8 +1100,8 @@ def _comp_method_SERIES(cls, op, special):
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
name = _get_op_name(op, special)
masker = _gen_eval_kwargs(name).get('masker', False)
op_name = _get_op_name(op, special)
masker = _gen_eval_kwargs(op_name).get('masker', False)

def na_op(x, y):

Expand Down Expand Up @@ -1133,7 +1133,7 @@ def na_op(x, y):
y = y.view('i8')
x = x.view('i8')

method = getattr(x, name, None)
method = getattr(x, op_name, None)
if method is not None:
with np.errstate(all='ignore'):
result = method(y)
Expand Down Expand Up @@ -1217,7 +1217,7 @@ def wrapper(self, other, axis=None):
else:
res_values = np.zeros(len(self), dtype=bool)
return self._constructor(res_values, index=self.index,
name=self.name, dtype='bool')
name=res_name, dtype='bool')

else:
values = self.get_values()
Expand All @@ -1232,8 +1232,8 @@ def wrapper(self, other, axis=None):

# always return a full value series here
res_values = com._values_from_object(res)
return pd.Series(res_values, index=self.index,
name=res_name, dtype='bool')
return self._constructor(res_values, index=self.index,
name=res_name, dtype='bool')

return wrapper

Expand Down Expand Up @@ -1430,10 +1430,10 @@ def to_series(right):

def _arith_method_FRAME(cls, op, special):
str_rep = _get_opstr(op, cls)
name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(name)
fill_zeros = _gen_fill_zeros(name)
default_axis = _get_frame_op_default_axis(name)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)
default_axis = _get_frame_op_default_axis(op_name)

def na_op(x, y):
import pandas.core.computation.expressions as expressions
Expand All @@ -1443,7 +1443,7 @@ def na_op(x, y):
except TypeError:
xrav = x.ravel()
if isinstance(y, (np.ndarray, ABCSeries)):
dtype = np.find_common_type([x.dtype, y.dtype], [])
dtype = find_common_type([x.dtype, y.dtype])
result = np.empty(x.size, dtype=dtype)
yrav = y.ravel()
mask = notna(xrav) & notna(yrav)
Expand Down Expand Up @@ -1471,20 +1471,20 @@ def na_op(x, y):
else:
raise TypeError("cannot perform operation {op} between "
"objects of type {x} and {y}"
.format(op=name, x=type(x), y=type(y)))
.format(op=op_name, x=type(x), y=type(y)))

result, changed = maybe_upcast_putmask(result, ~mask, np.nan)
result = result.reshape(x.shape)

result = missing.fill_zeros(result, x, y, name, fill_zeros)
result = missing.fill_zeros(result, x, y, op_name, fill_zeros)

return result

if name in _op_descriptions:
if op_name in _op_descriptions:
# i.e. include "add" but not "__add__"
doc = _make_flex_doc(name, 'dataframe')
doc = _make_flex_doc(op_name, 'dataframe')
else:
doc = _arith_doc_FRAME % name
doc = _arith_doc_FRAME % op_name

@Appender(doc)
def f(self, other, axis=default_axis, level=None, fill_value=None):
Expand All @@ -1503,15 +1503,15 @@ def f(self, other, axis=default_axis, level=None, fill_value=None):

return self._combine_const(other, na_op, try_cast=True)

f.__name__ = name
f.__name__ = op_name

return f


def _flex_comp_method_FRAME(cls, op, special):
str_rep = _get_opstr(op, cls)
name = _get_op_name(op, special)
default_axis = _get_frame_op_default_axis(name)
op_name = _get_op_name(op, special)
default_axis = _get_frame_op_default_axis(op_name)

def na_op(x, y):
try:
Expand All @@ -1522,7 +1522,7 @@ def na_op(x, y):
return result

@Appender('Wrapper for flexible comparison methods {name}'
.format(name=name))
.format(name=op_name))
def f(self, other, axis=default_axis, level=None):

other = _align_method_FRAME(self, other, axis)
Expand All @@ -1541,16 +1541,16 @@ def f(self, other, axis=default_axis, level=None):
else:
return self._combine_const(other, na_op, try_cast=False)

f.__name__ = name
f.__name__ = op_name

return f


def _comp_method_FRAME(cls, func, special):
str_rep = _get_opstr(func, cls)
name = _get_op_name(func, special)
op_name = _get_op_name(func, special)

@Appender('Wrapper for comparison method {name}'.format(name=name))
@Appender('Wrapper for comparison method {name}'.format(name=op_name))
def f(self, other):
if isinstance(other, ABCDataFrame):
# Another DataFrame
Expand All @@ -1572,7 +1572,7 @@ def f(self, other):
try_cast=False)
return res.fillna(True).astype(bool)

f.__name__ = name
f.__name__ = op_name

return f

Expand All @@ -1582,7 +1582,7 @@ def f(self, other):

def _arith_method_PANEL(cls, op, special):
# work only for scalars
name = _get_op_name(op, special)
op_name = _get_op_name(op, special)

def f(self, other):
if not is_scalar(other):
Expand All @@ -1592,13 +1592,13 @@ def f(self, other):

return self._combine(other, op)

f.__name__ = name
f.__name__ = op_name
return f


def _comp_method_PANEL(cls, op, special):
str_rep = _get_opstr(op, cls)
name = _get_op_name(op, special)
op_name = _get_op_name(op, special)

def na_op(x, y):
import pandas.core.computation.expressions as expressions
Expand All @@ -1609,7 +1609,7 @@ def na_op(x, y):
result = mask_cmp_op(x, y, op, np.ndarray)
return result

@Appender('Wrapper for comparison method {name}'.format(name=name))
@Appender('Wrapper for comparison method {name}'.format(name=op_name))
def f(self, other, axis=None):
# Validate the axis parameter
if axis is not None:
Expand All @@ -1624,16 +1624,16 @@ def f(self, other, axis=None):
else:
return self._combine_const(other, na_op, try_cast=False)

f.__name__ = name
f.__name__ = op_name

return f


def _flex_method_PANEL(cls, op, special):
str_rep = _get_opstr(op, cls)
name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(name)
fill_zeros = _gen_fill_zeros(name)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)

def na_op(x, y):
import pandas.core.computation.expressions as expressions
Expand All @@ -1648,20 +1648,20 @@ def na_op(x, y):
# handles discrepancy between numpy and numexpr on division/mod
# by 0 though, given that these are generally (always?)
# non-scalars, I'm not sure whether it's worth it at the moment
result = missing.fill_zeros(result, x, y, name, fill_zeros)
result = missing.fill_zeros(result, x, y, op_name, fill_zeros)
return result

if name in _op_descriptions:
doc = _make_flex_doc(name, 'panel')
if op_name in _op_descriptions:
doc = _make_flex_doc(op_name, 'panel')
else:
# doc strings substitors
doc = _agg_doc_PANEL.format(op_name=name)
doc = _agg_doc_PANEL.format(op_name=op_name)

@Appender(doc)
def f(self, other, axis=0):
return self._combine(other, na_op, axis=axis)

f.__name__ = name
f.__name__ = op_name
return f


Expand Down Expand Up @@ -1703,15 +1703,15 @@ def _arith_method_SPARSE_SERIES(cls, op, special):
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
name = _get_op_name(op, special)
op_name = _get_op_name(op, special)

def wrapper(self, other):
if isinstance(other, ABCDataFrame):
return NotImplemented
elif isinstance(other, ABCSeries):
if not isinstance(other, ABCSparseSeries):
other = other.to_sparse(fill_value=self.fill_value)
return _sparse_series_op(self, other, op, name)
return _sparse_series_op(self, other, op, op_name)
elif is_scalar(other):
with np.errstate(all='ignore'):
new_values = op(self.values, other)
Expand All @@ -1722,7 +1722,7 @@ def wrapper(self, other):
raise TypeError('operation with {other} not supported'
.format(other=type(other)))

wrapper.__name__ = name
wrapper.__name__ = op_name
return wrapper


Expand All @@ -1742,7 +1742,7 @@ def _arith_method_SPARSE_ARRAY(cls, op, special):
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
name = _get_op_name(op, special)
op_name = _get_op_name(op, special)

def wrapper(self, other):
from pandas.core.sparse.array import (
Expand All @@ -1755,16 +1755,16 @@ def wrapper(self, other):
dtype = getattr(other, 'dtype', None)
other = SparseArray(other, fill_value=self.fill_value,
dtype=dtype)
return _sparse_array_op(self, other, op, name)
return _sparse_array_op(self, other, op, op_name)
elif is_scalar(other):
with np.errstate(all='ignore'):
fill = op(_get_fill(self), np.asarray(other))
result = op(self.sp_values, other)

return _wrap_result(name, result, self.sp_index, fill)
return _wrap_result(op_name, result, self.sp_index, fill)
else: # pragma: no cover
raise TypeError('operation with {other} not supported'
.format(other=type(other)))

wrapper.__name__ = name
wrapper.__name__ = op_name
return wrapper