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()