a98c8ae57d
### What changes were proposed in this pull request? Introduce `Name` and `Label` type aliases to distinguish what is expected instead of `Any` or `Union[Any, Tuple]`. - `Label`: `Tuple[Any, ...]` Internal expression for name-like metadata, like `index_names`, `column_labels`, and `column_label_names` in `InternalFrame`, and similar internal structures. - `Name`: `Union[Any, Label]` External expression for user-facing names, which can be scalar values or tuples. ### Why are the changes needed? Currently `Any` or `Union[Any, Tuple]` is used for name-like types, but type aliases should be used to distinguish what is expected clearly. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Existing tests. Closes #33159 from ueshin/issues/SPARK-35944/name_and_label. Authored-by: Takuya UESHIN <ueshin@databricks.com> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
#
|
|
# Licensed to the Apache Software Foundation (ASF) under one or more
|
|
# contributor license agreements. See the NOTICE file distributed with
|
|
# this work for additional information regarding copyright ownership.
|
|
# The ASF licenses this file to You under the Apache License, Version 2.0
|
|
# (the "License"); you may not use this file except in compliance with
|
|
# the License. You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
import datetime
|
|
import decimal
|
|
from typing import Any, Tuple, TypeVar, Union, TYPE_CHECKING
|
|
|
|
import numpy as np
|
|
from pandas.api.extensions import ExtensionDtype
|
|
|
|
if TYPE_CHECKING:
|
|
from pyspark.pandas.base import IndexOpsMixin # noqa: F401 (SPARK-34943)
|
|
from pyspark.pandas.frame import DataFrame # noqa: F401 (SPARK-34943)
|
|
from pyspark.pandas.generic import Frame # noqa: F401 (SPARK-34943)
|
|
from pyspark.pandas.indexes.base import Index # noqa: F401 (SPARK-34943)
|
|
from pyspark.pandas.series import Series # noqa: F401 (SPARK-34943)
|
|
|
|
|
|
# TypeVars
|
|
T = TypeVar("T")
|
|
|
|
FrameLike = TypeVar("FrameLike", bound="Frame")
|
|
IndexOpsLike = TypeVar("IndexOpsLike", bound="IndexOpsMixin")
|
|
|
|
# Type aliases
|
|
Scalar = Union[
|
|
int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None
|
|
]
|
|
|
|
# TODO: use the actual type parameters.
|
|
Label = Tuple[Any, ...]
|
|
Name = Union[Any, Label]
|
|
|
|
Axis = Union[int, str]
|
|
Dtype = Union[np.dtype, ExtensionDtype]
|
|
|
|
DataFrameOrSeries = Union["DataFrame", "Series"]
|
|
SeriesOrIndex = Union["Series", "Index"]
|