else:
out_coords[index] = c2
out_data[index] = finish_last_data[iter]
iter += 1
c2 = finish_last_coords[iter]
if iter < finish_last_len:
index += 1
end_index = index + finish_last_len - iter
out_coords[index:end_index] = finish_last_coords[iter:]
out_data[index:end_index] = finish_last_data[iter:]
return ret_out_coords[:, :end_index], out_data[:end_index]
SparseExtensionArray._add_arithmetic_ops()
SparseExtensionArray._add_comparison_ops()
registry.register(SparseArrayType)
#@jit(void(float64[:], int64[:], int64, float64), parallel=True, nogil=True, nopython=True)
@jit(cache=True, nogil=True, nopython=True)
def iter(data, coords, length, na_value):
coords_len = data.shape[0]
coords_iter = 0
for i in range(length):
if coords_iter >= coords_len:
yield na_value
elif coords[coords_iter] != i:
yield na_value
else:
val = data[coords_iter]
coords_iter += 1
return self._maybe_mask_result(result, mask, other, op_name)
name = '__{name}__'.format(name=op.__name__)
return set_function_name(integer_arithmetic_method, name, cls)
IntegerArray._add_arithmetic_ops()
IntegerArray._add_comparison_ops()
module = sys.modules[__name__]
# create the Dtype
_dtypes = {}
for dtype in [
'int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32',
'uint64'
]:
if dtype.startswith('u'):
name = "U{}".format(dtype[1:].capitalize())
else:
name = dtype.capitalize()
classname = "{}Dtype".format(name)
attributes_dict = {'type': getattr(np, dtype), 'name': name}
dtype_type = type(classname, (_IntegerDtype, ), attributes_dict)
setattr(module, classname, dtype_type)
# register
registry.register(dtype_type)
_dtypes[dtype] = dtype_type()
op_name = ops._get_op_name(op, True)
return set_function_name(_binop, op_name, cls)
@classmethod
def _create_arithmetic_method(cls, op):
return cls._create_method(op)
@classmethod
def _create_comparison_method(cls, op):
return cls._create_method(op, coerce_to_dtype=False)
PintArray._add_arithmetic_ops()
PintArray._add_comparison_ops()
# register
registry.register(PintType)
@register_dataframe_accessor("pint")
class PintDataFrameAccessor(object):
def __init__(self, pandas_obj):
self._obj = pandas_obj
def quantify(self, ureg, level=-1):
df = self._obj
Q_ = ureg.Quantity
df_columns = df.columns.to_frame()
unit_col_name = df_columns.columns[level]
units = df_columns[unit_col_name]
df_columns = df_columns.drop(columns=unit_col_name)
df_columns.values
name = '__{name}__'.format(name=op.__name__)
return set_function_name(integer_arithmetic_method, name, cls)
IntegerArray._add_arithmetic_ops()
IntegerArray._add_comparison_ops()
module = sys.modules[__name__]
# create the Dtype
_dtypes = {}
for dtype in ['int8', 'int16', 'int32', 'int64',
'uint8', 'uint16', 'uint32', 'uint64']:
if dtype.startswith('u'):
name = "U{}".format(dtype[1:].capitalize())
else:
name = dtype.capitalize()
classname = "{}Dtype".format(name)
attributes_dict = {'type': getattr(np, dtype),
'name': name}
dtype_type = type(classname, (_IntegerDtype, ), attributes_dict)
setattr(module, classname, dtype_type)
# register
registry.register(dtype_type)
_dtypes[dtype] = dtype_type()