def _box_series_data(dtype, data_typ, val, c): if isinstance(dtype, types.BaseTuple): np_dtype = np.dtype(','.join(str(t) for t in dtype.types), align=True) dtype = numba.numpy_support.from_dtype(np_dtype) if dtype == string_type: arr = box_str_arr(string_array_type, val, c) elif dtype == datetime_date_type: arr = box_datetime_date_array(data_typ, val, c) elif isinstance(dtype, PDCategoricalDtype): arr = box_categorical_array(data_typ, val, c) elif data_typ == string_array_split_view_type: arr = box_str_arr_split_view(data_typ, val, c) elif dtype == types.List(string_type): arr = box_list(list_string_array_type, val, c) else: arr = box_array(data_typ, val, c) if isinstance(dtype, types.Record): o_str = c.context.insert_const_string(c.builder.module, "O") o_str = c.pyapi.string_from_string(o_str) arr = c.pyapi.call_method(arr, "astype", (o_str, )) return arr
def box_dataframe(typ, val, c): context = c.context builder = c.builder n_cols = len(typ.columns) col_names = typ.columns arr_typs = typ.data dtypes = [a.dtype for a in arr_typs] # TODO: check Categorical dataframe = cgutils.create_struct_proxy(typ)(context, builder, value=val) col_arrs = [ builder.extract_value(dataframe.data, i) for i in range(n_cols) ] # df unboxed from Python has_parent = cgutils.is_not_null(builder, dataframe.parent) pyapi = c.pyapi # gil_state = pyapi.gil_ensure() # acquire GIL mod_name = context.insert_const_string(c.builder.module, "pandas") class_obj = pyapi.import_module_noblock(mod_name) df_obj = pyapi.call_method(class_obj, "DataFrame", ()) for i, cname, arr, arr_typ, dtype in zip(range(n_cols), col_names, col_arrs, arr_typs, dtypes): # df['cname'] = boxed_arr # TODO: datetime.date, DatetimeIndex? name_str = context.insert_const_string(c.builder.module, cname) cname_obj = pyapi.string_from_string(name_str) if dtype == string_type: arr_obj = box_str_arr(arr_typ, arr, c) elif isinstance(dtype, PDCategoricalDtype): arr_obj = box_categorical_array(arr_typ, arr, c) # context.nrt.incref(builder, arr_typ, arr) elif arr_typ == string_array_split_view_type: arr_obj = box_str_arr_split_view(arr_typ, arr, c) elif dtype == types.List(string_type): arr_obj = box_list(list_string_array_type, arr, c) # context.nrt.incref(builder, arr_typ, arr) # TODO required? # pyapi.print_object(arr_obj) else: arr_obj = box_array(arr_typ, arr, c) # TODO: is incref required? # context.nrt.incref(builder, arr_typ, arr) pyapi.object_setitem(df_obj, cname_obj, arr_obj) # pyapi.decref(arr_obj) pyapi.decref(cname_obj) # set df.index if necessary if typ.index != types.none: arr_obj = _box_series_data(typ.index.dtype, typ.index, dataframe.index, c) pyapi.object_setattr_string(df_obj, 'index', arr_obj) pyapi.decref(class_obj) # pyapi.gil_release(gil_state) # release GIL return df_obj
def _box_index_data(index_typ, val, c): """ Boxes native value used to represent Pandas index into appropriate Python object. Params: index_typ: Numba type of native value val: native value c: LLVM context object Returns: Python object native value is boxed into """ assert isinstance(index_typ, (RangeIndexType, StringArrayType, types.Array, types.NoneType)) if isinstance(index_typ, RangeIndexType): index = box_range_index(index_typ, val, c) elif isinstance(index_typ, types.Array): index = box_array(index_typ, val, c) elif isinstance(index_typ, StringArrayType): index = box_str_arr(string_array_type, val, c) else: # index_typ is types.none index = c.pyapi.make_none() return index