8000 Series.add / Series.lt with fill_value by 1e-to · Pull Request #655 · IntelPython/sdc · GitHub
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Series.add / Series.lt with fill_value #655

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merged 22 commits into from
Apr 23, 2020
Merged

Series.add / Series.lt with fill_value #655

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d331183
Series.add / Series.lt with fill_value
Feb 28, 2020
eb4a728
Merge branch 'master' of https://github.com/IntelPython/sdc into oper…
Feb 28, 2020
722e966
del useless
Feb 28, 2020
52560c1
Add oprimization for fill_value=None
Mar 2, 2020
8839fc1
Merge branch 'master' of https://github.com/IntelPython/sdc into oper…
Mar 2, 2020
475cb58
pep
Mar 2, 2020
b483957
test and impl fix
Mar 13, 2020
1684822
non index impl fix
Mar 13, 2020
b2ec0a6
update script
Mar 17, 2020
c0ca1ad
fix
Mar 17, 2020
af3bbdd
Merge branch 'master' of https://github.com/IntelPython/sdc into oper…
Mar 17, 2020
92d0692
fix operators
Mar 20, 2020
15e2806
add doc
Mar 20, 2020
fc0510b
div fix
Mar 20, 2020
90e5647
Merge branch 'master' into oper_ser
1e-to Mar 20, 2020
ef8fe79
doc fix
Mar 21, 2020
c48f2e3
fix algo for scalars
Mar 21, 2020
70a5484
small fixes
8000 Apr 14, 2020
bf87286
Merge branch 'master' into oper_ser
1e-to Apr 14, 2020
8f76b36
del prange
Apr 14, 2020
e09a6fc
Fix script
Apr 20, 2020
542a59c
fix vars
Apr 20, 2020
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fix algo for scalars
  • Loading branch information
elena.totmenina committed Mar 21, 2020
commit c48f2e36a14caed8244762da82d38dccf7608c21
92 changes: 56 additions & 36 deletions sdc/sdc_autogenerated.py
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< 10000 td id="diff-e5a30b63234c4c98253cb380afedafc7ab94e37f52312788c5ecd647b56cf89fL467" data-line-number="467" class="blob-num blob-num-context js-linkable-line-number">
Original file line number Diff line number Diff line change
Expand Up @@ -126,14 +126,15 @@ def sdc_pandas_series_add(self, other, level=None, fill_value=None, axis=0):
# specializations for numeric series only
if not operands_are_series:
def _series_add_scalar_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)

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)
Expand Down Expand Up @@ -293,14 +294,15 @@ def sdc_pandas_series_div(self, other, level=None, fill_value=None, axis=0):
# specializations for numeric series only
if not operands_are_series:
def _series_div_scalar_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)

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)
Expand Down Expand Up @@ -460,14 +462,15 @@ def sdc_pandas_series_sub(self, other, level=None, fill_value=None, axis=0):
# specializations for numeric series only
if not operands_are_series:
def _series_sub_scalar_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)

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)
Expand Down Expand Up @@ -627,14 +630,15 @@ def sdc_pandas_series_mul(self, other, level=None, fill_value=None, axis=0):
# specializations for numeric series only
if not operands_are_series:
def _series_mul_scalar_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)

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)
Expand Down Expand Up @@ -794,14 +798,15 @@ def sdc_pandas_series_truediv(self, other, level=None, fill_value=None, axis=0):
# specializations for numeric series only
if not operands_are_series:
def _series_truediv_scalar_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)

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)
Expand Down Expand Up @@ -961,14 +966,15 @@ def sdc_pandas_series_floordiv(self, other, level=None, fill_value=None, axis=0)
# specializations for numeric series only
if not operands_are_series:
def _series_floordiv_scalar_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)

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)
Expand Down Expand Up @@ -1124,14 +1130,15 @@ def sdc_pandas_series_mod(self, other, level=None, fill_value=None, axis=0):
# specializations for numeric series only
if not operands_are_series:
def _series_mod_scalar_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)

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)
Expand Down Expand Up @@ -1291,14 +1298,15 @@ def sdc_pandas_series_pow(self, other, level=None, fill_value=None, axis=0):
# specializations for numeric series only
if not operands_are_series:
def _series_pow_scalar_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)

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)
Expand Down Expand Up @@ -1452,11 +1460,13 @@ def sdc_pandas_series_lt(self, other, level=None, fill_value=None, axis=0):
fill_value_is_none = isinstance(fill_value, (types.NoneType, types.Omitted)) or fill_value is None
if not operands_are_series:
def _series_lt_scalar_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)
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)

return _series_lt_scalar_impl
Expand Down Expand Up @@ -1577,11 +1587,13 @@ def sdc_pandas_series_gt(self, other, level=None, fill_value=None, axis=0):
fill_value_is_none = isinstance(fill_value, (types.NoneType, types.Omitted)) or fill_value is None
if not operands_are_series:
def _series_gt_scalar_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)
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)

return _series_gt_scalar_impl
Expand Down Expand Up @@ -1702,11 +1714,13 @@ def sdc_pandas_series_le(self, other, level=None, fill_value=None, axis=0):
fill_value_is_none = isinstance(fill_value, (types.NoneType, types.Omitted)) or fill_value is None
if not operands_are_series:
def _series_le_scalar_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)
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)

return _series_le_scalar_impl
Expand Down Expand Up @@ -1827,11 +1841,13 @@ def sdc_pandas_series_ge(self, other, level=None, fill_value=None, axis=0):
fill_value_is_none = isinstance(fill_value, (types.NoneType, types.Omitted)) or fill_value is None
if not operands_are_series:
def _series_ge_scalar_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)
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)

return _series_ge_scalar_impl
Expand Down Expand Up @@ -1952,11 +1968,13 @@ def sdc_pandas_series_ne(self, other, level=None, fill_value=None, axis=0):
fill_value_is_none = isinstance(fill_value, (types.NoneType, types.Omitted)) or fill_value is None
if not operands_are_series:
def _series_ne_scalar_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)
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)

return _series_ne_scalar_impl
Expand Down Expand Up @@ -2077,11 +2095,13 @@ def sdc_pandas_series_eq(self, other, level=None, fill_value=None, axis=0):
fill_value_is_none = isinstance(fill_value, (types.NoneType, types.Omitted)) or fill_value is None
if not operands_are_series:
def _series_eq_scalar_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)
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)

return _series_eq_scalar_impl
Expand Down
13 changes: 8 additions & 5 deletions sdc/sdc_methods_templates.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,14 +114,15 @@ def sdc_pandas_series_binop(self, other, level=None, fill_value=None, axis=0):
# specializations for numeric series only
if not operands_are_series:
def _series_binop_scalar_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)

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)
Expand Down Expand Up @@ -278,11 +279,13 @@ def sdc_pandas_series_comp_binop(self, other, level=None, fill_value=None, axis=
fill_value_is_none = isinstance(fill_value, (types.NoneType, types.Omitted)) or fill_value is None
if not operands_are_series:
def _series_comp_binop_scalar_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)
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)

return _series_comp_binop_scalar_impl
Expand Down
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