def _series_binop_common_impl(self, other, level=None, fill_value=None, axis=0):
left_index, right_index = self.index, other.index
_fill_value = numpy.nan if fill_value_is_none == True else fill_value # noqa
if not (fill_value is None or numpy.isnan(fill_value)):
numpy_like.fillna(self._data, inplace=True, value=fill_value)
numpy_like.fillna(other._data, inplace=True, value=fill_value)
# check if indexes are equal and series don't have to be aligned
if sdc_check_indexes_equal(left_index, right_index):
result_data = numpy.empty(len(self._data), dtype=numpy.float64)
result_data[:] = self._data + other._data
if none_or_numeric_indexes == True: # noqa
result_index = numpy_like.astype(left_index, numba_index_common_dtype)
else:
result_index = self._index
return pandas.Series(result_data, index=result_index)
# TODO: replace below with core join(how='outer', return_indexers=True) when implemented
joined_index, left_indexer, right_indexer = sdc_join_series_indexes(left_index, right_index)
result_size = len(joined_index)
left_values = numpy.empty(result_size, dtype=numpy.float64)
right_values = numpy.empty(result_size, dtype=numpy.float64)
for i in range(result_size):
left_pos, right_pos = left_indexer[i], right_indexer[i]
left_values[i] = self._data[left_pos] if left_pos != -1 else _fill_value
right_values[i] = other._data[right_pos] if right_pos != -1 else _fill_value
result_data = left_values + right_values
return pandas.Series(result_data, joined_index)
def _series_binop_none_indexes_impl(self,
other,
level=None,
fill_value=None,
axis=0):
numpy_like.fillna(self._data, inplace=True, value=fill_value)
numpy_like.fillna(other._data, inplace=True, value=fill_value)
if (len(self._data) == len(other._data)):
result_data = numpy_like.astype(self._data, numpy.float64)
result_data = result_data + other._data
return pandas.Series(result_data)
else:
left_size, right_size = len(self._data), len(other._data)
min_data_size = min(left_size, right_size)
max_data_size = max(left_size, right_size)
result_data = numpy.empty(max_data_size,
dtype=numpy.float64)
_fill_value = numpy.nan if fill_value_is_none == True else fill_value # noqa
if (left_size == min_data_size):
result_data[:min_data_size] = self._data
for i in range(min_data_size, len(result_data)):
result_data[i] = _fill_value
result_data = result_data + other._data
else:
result_data[:min_data_size] = other._data
for i in range(min_data_size, len(result_data)):
result_data[i] = _fill_value
result_data = self._data + result_data
return pandas.Series(result_data)
def _series_comp_binop_none_indexes_impl(self, other, level=None, fill_value=None, axis=0):
if not (fill_value is None or numpy.isnan(fill_value)):
numpy_like.fillna(self._data, inplace=True, value=fill_value)
numpy_like.fillna(other._data, inplace=True, value=fill_value)
left_size, right_size = len(self._data), len(other._data)
if (left_size == right_size):
return pandas.Series(self._data < other._data)
else:
raise ValueError("Can only compare identically-labeled Series objects")
def _series_comp_binop_scalar_impl(self, other, level=None, fill_value=None, axis=0):
if self_is_series == True: # noqa
if not (fill_value is None or numpy.isnan(fill_value)):
numpy_like.fillna(self._data, inplace=True, value=fill_value)
return pandas.Series(self._data < other, index=self._index, name=self._name)
else:
if not (fill_value is None or numpy.isnan(fill_value)):
numpy_like.fillna(other._data, inplace=True, value=fill_value)
return pandas.Series(self < other._data, index=other._index, name=other._name)
def _series_binop_scalar_impl(self, other, level=None, fill_value=None, axis=0):
if self_is_series == True: # noqa
if not (fill_value is None or numpy.isnan(fill_value)):
numpy_like.fillna(self._data, inplace=True, value=fill_value)
result_data = numpy.empty(len(self._data), dtype=numpy.float64)
result_data[:] = self._data + numpy.float64(other)
return pandas.Series(result_data, index=self._index, name=self._name)
else:
if not (fill_value is None or numpy.isnan(fill_value)):
numpy_like.fillna(other._data, inplace=True, value=fill_value)
result_data = numpy.empty(len(other._data), dtype=numpy.float64)
result_data[:] = numpy.float64(self) + other._data
return pandas.Series(result_data, index=other._index, name=other._name)
def _series_comp_binop_common_impl(self, other, level=None, fill_value=None, axis=0):
if not (fill_value is None or numpy.isnan(fill_value)):
numpy_like.fillna(self._data, inplace=True, value=fill_value)
numpy_like.fillna(other._data, inplace=True, value=fill_value)
left_index, right_index = self.index, other.index
if sdc_check_indexes_equal(left_index, right_index):
if none_or_numeric_indexes == True: # noqa
new_index = numpy_like.astype(left_index, numba_index_common_dtype)
else:
new_index = self._index
return pandas.Series(self._data < other._data,
new_index)
else:
raise ValueError("Can only compare identically-labeled Series objects")
def _series_comp_binop_scalar_impl(self, other, fill_value=None):
_self = numpy_like.fillna(self._data,
inplace=False,
value=fill_value)
return pandas.Series(_self < other,
index=self._index,
name=self._name)
def _series_comp_binop_common_impl(self,
other,
level=None,
fill_value=None,
axis=0):
numpy_like.fillna(self._data, inplace=True, value=fill_value)
numpy_like.fillna(other._data, inplace=True, value=fill_value)
left_index, right_index = self.index, other.index
if (left_index is right_index
or numpy_like.array_equal(left_index, right_index)):
if index_dtypes_match == False: # noqa
new_index = numpy_like.astype(
left_index, numba_index_common_dtype)
else:
new_index = left_index.values if left_index_is_range == True else left_index # noqa
return pandas.Series(self._data < other._data, new_index)
else:
raise ValueError(
"Can only compare identically-labeled Series objects")
def _series_binop_common_impl(self,
other,
level=None,
fill_value=None,
axis=0):
left_index, right_index = self.index, other.index
numpy_like.fillna(self._data, inplace=True, value=fill_value)
numpy_like.fillna(other._data, inplace=True, value=fill_value)
if check_index_equal == True: # noqa
equal_indexes = numpy_like.array_equal(
left_index, right_index)
else:
equal_indexes = False
if (left_index is right_index or equal_indexes):
result_data = numpy.empty(len(self._data),
dtype=numpy.float64)
result_data[:] = self._data + other._data
if index_dtypes_match == False: # noqa
result_index = numpy_like.astype(
left_index, numba_index_common_dtype)
else:
result_index = left_index.values if left_index_is_range == True else left_index # noqa
return pandas.Series(result_data, index=result_index)
# TODO: replace below with core join(how='outer', return_indexers=True) when implemented
joined_index, left_indexer, right_indexer = sdc_join_series_indexes(
left_index, right_index)
result_size = len(joined_index)
left_values = numpy.empty(result_size, dtype=numpy.float64)
right_values = numpy.empty(result_size, dtype=numpy.float64)
_fill_value = numpy.nan if fill_value_is_none == True else fill_value # noqa
for i in range(result_size):
left_pos, right_pos = left_indexer[i], right_indexer[i]
left_values[i] = self._data[
left_pos] if left_pos != -1 else _fill_value
right_values[i] = other._data[
right_pos] if right_pos != -1 else _fill_value
result_data = left_values + right_values
return pandas.Series(result_data, joined_index)
def sdc_binop_impl(self, other, fill_value=None):
series = self if self_is_series == True else other # noqa
result_data = numpy.empty(len(series._data), dtype=numpy.float64)
series_data = numpy_like.fillna(series._data, inplace=False, value=fill_value)
if self_is_series == True: # noqa
_self, _other = series_data, numpy.float64(other)
else:
_self, _other = numpy.float64(self), series_data
result_data[:] = _self + _other
return pandas.Series(result_data, index=series._index, name=series._name)
def sdc_impl(S, value):
str_arr = S.values
return numpy_like.fillna(str_arr, inplace=False, value=value)
def sdc_impl(a, value):
return numpy_like.fillna(a, inplace=True, value=value)
