[GitHub] [spark] dgd-contributor commented on a change in pull request #34058: [SPARK-36711][PYTHON] Support multi-index in new syntax

[GitBox <gi...@apache.org>]

dgd-contributor commented on a change in pull request #34058:
URL: https://github.com/apache/spark/pull/34058#discussion_r714427614



##########
File path: python/pyspark/pandas/typedef/typehints.py
##########
@@ -673,98 +673,146 @@ def create_tuple_for_frame_type(params: Any) -> object:
     Typing data columns with an index:
 
         >>> ps.DataFrame[int, [int, int]]  # doctest: +ELLIPSIS
-        typing.Tuple[...IndexNameType, int, int]
+        typing.Tuple[...IndexNameType, ...NameType, ...NameType]
         >>> ps.DataFrame[pdf.index.dtype, pdf.dtypes]  # doctest: +ELLIPSIS
-        typing.Tuple[...IndexNameType, numpy.int64]
+        typing.Tuple[...IndexNameType, ...NameType]
         >>> ps.DataFrame[("index", int), [("id", int), ("A", int)]]  # doctest: +ELLIPSIS
         typing.Tuple[...IndexNameType, ...NameType, ...NameType]
         >>> ps.DataFrame[(pdf.index.name, pdf.index.dtype), zip(pdf.columns, pdf.dtypes)]
         ... # doctest: +ELLIPSIS
         typing.Tuple[...IndexNameType, ...NameType]
+
+    Typing data columns with an Multi-index:
+        >>> arrays = [[1, 1, 2], ['red', 'blue', 'red']]
+        >>> idx = pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))
+        >>> pdf = pd.DataFrame({'a': range(3)}, index=idx)
+        >>> ps.DataFrame[[int, int], [int, int]]  # doctest: +ELLIPSIS
+        typing.Tuple[...IndexNameType, ...IndexNameType, ...NameType, ...NameType]
+        >>> ps.DataFrame[pdf.index.dtypes, pdf.dtypes]  # doctest: +ELLIPSIS
+        typing.Tuple[...IndexNameType, ...NameType]
+        >>> ps.DataFrame[[("index-1", int), ("index-2", int)], [("id", int), ("A", int)]]
+        ... # doctest: +ELLIPSIS
+        typing.Tuple[...IndexNameType, ...IndexNameType, ...NameType, ...NameType]
+        >>> ps.DataFrame[zip(pdf.index.names, pdf.index.dtypes), zip(pdf.columns, pdf.dtypes)]
+        ... # doctest: +ELLIPSIS
+        typing.Tuple[...IndexNameType, ...NameType]
     """
     return Tuple[extract_types(params)]
 
 
 # TODO(SPARK-36708): numpy.typing (numpy 1.21+) support for nested types.
 def extract_types(params: Any) -> Tuple:
     origin = params
-    if isinstance(params, zip):  # type: ignore
-        # Example:
-        #   DataFrame[zip(pdf.columns, pdf.dtypes)]
-        params = tuple(slice(name, tpe) for name, tpe in params)  # type: ignore
 
-    if isinstance(params, Iterable):
-        params = tuple(params)
-    else:
-        params = (params,)
+    params = _prepare_a_tuple(params)
 
-    if all(
-        isinstance(param, slice)
-        and param.start is not None
-        and param.step is None
-        and param.stop is not None
-        for param in params
-    ):
+    if _is_valid_slices(params):
         # Example:
         #   DataFrame["id": int, "A": int]
-        new_params = []
-        for param in params:
-            new_param = type("NameType", (NameTypeHolder,), {})  # type: Type[NameTypeHolder]
-            new_param.name = param.start
-            # When the given argument is a numpy's dtype instance.
-            new_param.tpe = param.stop.type if isinstance(param.stop, np.dtype) else param.stop
-            new_params.append(new_param)
-
+        new_params = _convert_slices_to_holders(params, is_index=False)
         return tuple(new_params)
     elif len(params) == 2 and isinstance(params[1], (zip, list, pd.Series)):
         # Example:
         #   DataFrame[int, [int, int]]
         #   DataFrame[pdf.index.dtype, pdf.dtypes]
         #   DataFrame[("index", int), [("id", int), ("A", int)]]
         #   DataFrame[(pdf.index.name, pdf.index.dtype), zip(pdf.columns, pdf.dtypes)]
+        #
+        #   DataFrame[[int, int], [int, int]]
+        #   DataFrame[pdf.index.dtypes, pdf.dtypes]

Review comment:
       Thanks for reviewing,
   
   above comment is ```DataFrame[pdf.index.dtype, pdf.dtypes]```
   this one is ```DataFrame[pdf.index.dtypes, pdf.dtypes]```
   
   This one is used for :
   ``` python
   >>> arrays = [[1, 1, 2], ['red', 'blue', 'red']]
   >>> idx = pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))
   >>> pdf = pd.DataFrame([[1,2,3],[2,3,4],[4,5,6]], index=idx, columns=["a", "b", "c"])
   >>> ps.DataFrame[pdf.index.dtypes, pdf.dtypes]
   typing.Tuple[pyspark.pandas.typedef.typehints.IndexNameType, pyspark.pandas.typedef.typehints.IndexNameType, pyspark.pandas.typedef.typehints.NameType, pyspark.pandas.typedef.typehints.NameType, pyspark.pandas.typedef.typehints.NameType]
   ```




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