HyukjinKwon commented on a change in pull request #34176:
URL: https://github.com/apache/spark/pull/34176#discussion_r721862446
##########
File path: python/pyspark/pandas/typedef/typehints.py
##########
@@ -696,98 +702,160 @@ 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)]
+ return Tuple[_extract_types(params)]
-# TODO(SPARK-36708): numpy.typing (numpy 1.21+) support for nested types.
-def extract_types(params: Any) -> Tuple:
+def _extract_types(params: Any) -> Tuple:
origin = params
- if isinstance(params, zip):
- # Example:
- # DataFrame[zip(pdf.columns, pdf.dtypes)]
- params = tuple(slice(name, tpe) for name, tpe in params) # type:
ignore[misc, has-type]
- if isinstance(params, Iterable):
- params = tuple(params)
- else:
- params = (params,)
+ params = _to_tuple_of_params(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_named_params(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 = _address_named_type_hoders(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]
+ # DataFrame[[("index", int), ("index-2", int)], [("id", int), ("A",
int)]]
+ # DataFrame[zip(pdf.index.names, pdf.index.dtypes), zip(pdf.columns,
pdf.dtypes)]
- index_param = params[0]
- index_type = type(
- "IndexNameType", (IndexNameTypeHolder,), {}
- ) # type: Type[IndexNameTypeHolder]
- if isinstance(index_param, tuple):
- if len(index_param) != 2:
- raise TypeError(
- "Type hints for index should be specified as "
- "DataFrame[('name', type), ...]; however, got %s" %
index_param
- )
- name, tpe = index_param
- else:
- name, tpe = None, index_param
+ index_params = params[0]
+
+ if isinstance(index_params, tuple) and len(index_params) == 2:
+ index_params = tuple([slice(*index_params)])
+
+ index_params = _convert_tuples_to_zip(index_params)
+ index_params = _to_tuple_of_params(index_params)
- index_type.name = name
- if isinstance(tpe, ExtensionDtype):
- index_type.tpe = tpe
+ if _is_named_params(index_params):
+ # Example:
+ # DataFrame[[("id", int), ("A", int)], [int, int]]
+ new_index_params = _address_named_type_hoders(index_params,
is_index=True)
+ index_types = tuple(new_index_params)
else:
- index_type.tpe = tpe.type if isinstance(tpe, np.dtype) else tpe
+ # Exaxmples:
+ # DataFrame[[float, float], [int, int]]
+ # DataFrame[pdf.dtypes, [int, int]]
+ index_types = _address_unnamed_type_holders(index_params, origin,
is_index=True)
data_types = params[1]
- if (
- isinstance(data_types, list)
- and len(data_types) >= 1
- and isinstance(data_types[0], tuple)
- ):
- # Example:
- # DataFrame[("index", int), [("id", int), ("A", int)]]
- data_types = zip((name for name, _ in data_types), (tpe for _, tpe
in data_types))
- return (index_type,) + extract_types(data_types)
- elif all(not isinstance(param, slice) and not isinstance(param, Iterable)
for param in params):
+ data_types = _convert_tuples_to_zip(data_types)
+
+ return index_types + _extract_types(data_types)
+
+ else:
# Exaxmples:
# DataFrame[float, float]
# DataFrame[pdf.dtypes]
+ return _address_unnamed_type_holders(params, origin, is_index=False)
+
+
+def _is_named_params(params: Any) -> Any:
+ return all(
+ isinstance(param, slice) and param.step is None and param.stop is not
None
+ for param in params
+ )
+
+
+def _address_named_type_hoders(params: Any, is_index: bool) -> Any:
+ # Example:
+ # params = (slice("id", int, None), slice("A", int, None))
+ new_params = []
+ for param in params:
Review comment:
can we add an assert on the type of `param` and throw an exception like
`_address_unnamed_type_holders`?
##########
File path: python/pyspark/pandas/typedef/typehints.py
##########
@@ -696,98 +702,160 @@ 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)]
+ return Tuple[_extract_types(params)]
-# TODO(SPARK-36708): numpy.typing (numpy 1.21+) support for nested types.
-def extract_types(params: Any) -> Tuple:
+def _extract_types(params: Any) -> Tuple:
origin = params
- if isinstance(params, zip):
- # Example:
- # DataFrame[zip(pdf.columns, pdf.dtypes)]
- params = tuple(slice(name, tpe) for name, tpe in params) # type:
ignore[misc, has-type]
- if isinstance(params, Iterable):
- params = tuple(params)
- else:
- params = (params,)
+ params = _to_tuple_of_params(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_named_params(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 = _address_named_type_hoders(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]
+ # DataFrame[[("index", int), ("index-2", int)], [("id", int), ("A",
int)]]
+ # DataFrame[zip(pdf.index.names, pdf.index.dtypes), zip(pdf.columns,
pdf.dtypes)]
- index_param = params[0]
- index_type = type(
- "IndexNameType", (IndexNameTypeHolder,), {}
- ) # type: Type[IndexNameTypeHolder]
- if isinstance(index_param, tuple):
- if len(index_param) != 2:
- raise TypeError(
- "Type hints for index should be specified as "
- "DataFrame[('name', type), ...]; however, got %s" %
index_param
- )
- name, tpe = index_param
- else:
- name, tpe = None, index_param
+ index_params = params[0]
+
+ if isinstance(index_params, tuple) and len(index_params) == 2:
+ index_params = tuple([slice(*index_params)])
+
+ index_params = _convert_tuples_to_zip(index_params)
+ index_params = _to_tuple_of_params(index_params)
- index_type.name = name
- if isinstance(tpe, ExtensionDtype):
- index_type.tpe = tpe
+ if _is_named_params(index_params):
+ # Example:
+ # DataFrame[[("id", int), ("A", int)], [int, int]]
+ new_index_params = _address_named_type_hoders(index_params,
is_index=True)
+ index_types = tuple(new_index_params)
else:
- index_type.tpe = tpe.type if isinstance(tpe, np.dtype) else tpe
+ # Exaxmples:
+ # DataFrame[[float, float], [int, int]]
+ # DataFrame[pdf.dtypes, [int, int]]
+ index_types = _address_unnamed_type_holders(index_params, origin,
is_index=True)
data_types = params[1]
- if (
- isinstance(data_types, list)
- and len(data_types) >= 1
- and isinstance(data_types[0], tuple)
- ):
- # Example:
- # DataFrame[("index", int), [("id", int), ("A", int)]]
- data_types = zip((name for name, _ in data_types), (tpe for _, tpe
in data_types))
- return (index_type,) + extract_types(data_types)
- elif all(not isinstance(param, slice) and not isinstance(param, Iterable)
for param in params):
+ data_types = _convert_tuples_to_zip(data_types)
+
+ return index_types + _extract_types(data_types)
+
+ else:
# Exaxmples:
# DataFrame[float, float]
# DataFrame[pdf.dtypes]
+ return _address_unnamed_type_holders(params, origin, is_index=False)
+
+
+def _is_named_params(params: Any) -> Any:
+ return all(
+ isinstance(param, slice) and param.step is None and param.stop is not
None
+ for param in params
+ )
+
+
+def _address_named_type_hoders(params: Any, is_index: bool) -> Any:
Review comment:
Can we pass `IndexNameTypeHolder` or `NameTypeHolder` explicitly instead
of the bool `is_index`?
##########
File path: python/pyspark/pandas/typedef/typehints.py
##########
@@ -696,98 +702,160 @@ 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)]
+ return Tuple[_extract_types(params)]
-# TODO(SPARK-36708): numpy.typing (numpy 1.21+) support for nested types.
-def extract_types(params: Any) -> Tuple:
+def _extract_types(params: Any) -> Tuple:
origin = params
- if isinstance(params, zip):
- # Example:
- # DataFrame[zip(pdf.columns, pdf.dtypes)]
- params = tuple(slice(name, tpe) for name, tpe in params) # type:
ignore[misc, has-type]
- if isinstance(params, Iterable):
- params = tuple(params)
- else:
- params = (params,)
+ params = _to_tuple_of_params(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_named_params(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 = _address_named_type_hoders(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]
+ # DataFrame[[("index", int), ("index-2", int)], [("id", int), ("A",
int)]]
+ # DataFrame[zip(pdf.index.names, pdf.index.dtypes), zip(pdf.columns,
pdf.dtypes)]
- index_param = params[0]
- index_type = type(
- "IndexNameType", (IndexNameTypeHolder,), {}
- ) # type: Type[IndexNameTypeHolder]
- if isinstance(index_param, tuple):
- if len(index_param) != 2:
- raise TypeError(
- "Type hints for index should be specified as "
- "DataFrame[('name', type), ...]; however, got %s" %
index_param
- )
- name, tpe = index_param
- else:
- name, tpe = None, index_param
+ index_params = params[0]
+
+ if isinstance(index_params, tuple) and len(index_params) == 2:
+ index_params = tuple([slice(*index_params)])
+
+ index_params = _convert_tuples_to_zip(index_params)
+ index_params = _to_tuple_of_params(index_params)
- index_type.name = name
- if isinstance(tpe, ExtensionDtype):
- index_type.tpe = tpe
+ if _is_named_params(index_params):
+ # Example:
+ # DataFrame[[("id", int), ("A", int)], [int, int]]
+ new_index_params = _address_named_type_hoders(index_params,
is_index=True)
+ index_types = tuple(new_index_params)
else:
- index_type.tpe = tpe.type if isinstance(tpe, np.dtype) else tpe
+ # Exaxmples:
+ # DataFrame[[float, float], [int, int]]
+ # DataFrame[pdf.dtypes, [int, int]]
+ index_types = _address_unnamed_type_holders(index_params, origin,
is_index=True)
data_types = params[1]
- if (
- isinstance(data_types, list)
- and len(data_types) >= 1
- and isinstance(data_types[0], tuple)
- ):
- # Example:
- # DataFrame[("index", int), [("id", int), ("A", int)]]
- data_types = zip((name for name, _ in data_types), (tpe for _, tpe
in data_types))
- return (index_type,) + extract_types(data_types)
- elif all(not isinstance(param, slice) and not isinstance(param, Iterable)
for param in params):
+ data_types = _convert_tuples_to_zip(data_types)
+
+ return index_types + _extract_types(data_types)
+
+ else:
# Exaxmples:
# DataFrame[float, float]
# DataFrame[pdf.dtypes]
+ return _address_unnamed_type_holders(params, origin, is_index=False)
+
+
+def _is_named_params(params: Any) -> Any:
+ return all(
+ isinstance(param, slice) and param.step is None and param.stop is not
None
+ for param in params
+ )
+
+
+def _address_named_type_hoders(params: Any, is_index: bool) -> Any:
+ # Example:
+ # params = (slice("id", int, None), slice("A", int, None))
+ new_params = []
+ for param in params:
+ new_param = (
+ type("IndexNameType", (IndexNameTypeHolder,), {})
+ if is_index
+ else type("NameType", (NameTypeHolder,), {})
+ ) # type: Union[Type[IndexNameTypeHolder], Type[NameTypeHolder]]
+ new_param.name = param.start
+ if isinstance(param.stop, ExtensionDtype):
+ new_param.tpe = param.stop
+ else:
+ # 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)
+ return new_params
+
+
+def _to_tuple_of_params(params: Any) -> Any:
+ """
+ >>> _to_tuple_of_params(int)
+ (<class 'int'>,)
+
+ >>> _to_tuple_of_params([int, int, int])
+ (<class 'int'>, <class 'int'>, <class 'int'>)
+
+ >>> arrays = [[1, 1, 2], ['red', 'blue', 'red']]
+ >>> idx = pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))
+ >>> pdf = pd.DataFrame([[1, 2], [2, 3], [4, 5]], index=idx, columns=["a",
"b"])
+
+ >>> _to_tuple_of_params(zip(pdf.columns, pdf.dtypes))
+ (slice('a', dtype('int64'), None), slice('b', dtype('int64'), None))
+ >>> _to_tuple_of_params(zip(pdf.index.names, pdf.index.dtypes))
+ (slice('number', dtype('int64'), None), slice('color', dtype('O'), None))
+ """
+ if isinstance(params, zip):
+ params = tuple(slice(name, tpe) for name, tpe in params) # type:
ignore[misc, has-type]
+
+ if isinstance(params, Iterable):
+ params = tuple(params)
+ else:
+ params = (params,)
+ return params
+
+
+def _convert_tuples_to_zip(params: Any) -> Any:
+ if isinstance(params, list) and len(params) >= 1 and isinstance(params[0],
tuple):
+ return zip((name for name, _ in params), (tpe for _, tpe in params))
+ return params
+
+
+def _address_unnamed_type_holders(params: Any, origin: Any, is_index: bool) ->
Any:
Review comment:
```suggestion
def _convert_unnamed_params_to_holders(params: Any, origin: Any, is_index:
bool) -> Any:
```
##########
File path: python/pyspark/pandas/typedef/typehints.py
##########
@@ -696,98 +702,160 @@ 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)]
+ return Tuple[_extract_types(params)]
-# TODO(SPARK-36708): numpy.typing (numpy 1.21+) support for nested types.
-def extract_types(params: Any) -> Tuple:
+def _extract_types(params: Any) -> Tuple:
origin = params
- if isinstance(params, zip):
- # Example:
- # DataFrame[zip(pdf.columns, pdf.dtypes)]
- params = tuple(slice(name, tpe) for name, tpe in params) # type:
ignore[misc, has-type]
- if isinstance(params, Iterable):
- params = tuple(params)
- else:
- params = (params,)
+ params = _to_tuple_of_params(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_named_params(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 = _address_named_type_hoders(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]
+ # DataFrame[[("index", int), ("index-2", int)], [("id", int), ("A",
int)]]
+ # DataFrame[zip(pdf.index.names, pdf.index.dtypes), zip(pdf.columns,
pdf.dtypes)]
- index_param = params[0]
- index_type = type(
- "IndexNameType", (IndexNameTypeHolder,), {}
- ) # type: Type[IndexNameTypeHolder]
- if isinstance(index_param, tuple):
- if len(index_param) != 2:
- raise TypeError(
- "Type hints for index should be specified as "
- "DataFrame[('name', type), ...]; however, got %s" %
index_param
- )
- name, tpe = index_param
- else:
- name, tpe = None, index_param
+ index_params = params[0]
+
+ if isinstance(index_params, tuple) and len(index_params) == 2:
+ index_params = tuple([slice(*index_params)])
+
+ index_params = _convert_tuples_to_zip(index_params)
+ index_params = _to_tuple_of_params(index_params)
- index_type.name = name
- if isinstance(tpe, ExtensionDtype):
- index_type.tpe = tpe
+ if _is_named_params(index_params):
+ # Example:
+ # DataFrame[[("id", int), ("A", int)], [int, int]]
+ new_index_params = _address_named_type_hoders(index_params,
is_index=True)
+ index_types = tuple(new_index_params)
else:
- index_type.tpe = tpe.type if isinstance(tpe, np.dtype) else tpe
+ # Exaxmples:
+ # DataFrame[[float, float], [int, int]]
+ # DataFrame[pdf.dtypes, [int, int]]
+ index_types = _address_unnamed_type_holders(index_params, origin,
is_index=True)
data_types = params[1]
- if (
- isinstance(data_types, list)
- and len(data_types) >= 1
- and isinstance(data_types[0], tuple)
- ):
- # Example:
- # DataFrame[("index", int), [("id", int), ("A", int)]]
- data_types = zip((name for name, _ in data_types), (tpe for _, tpe
in data_types))
- return (index_type,) + extract_types(data_types)
- elif all(not isinstance(param, slice) and not isinstance(param, Iterable)
for param in params):
+ data_types = _convert_tuples_to_zip(data_types)
+
+ return index_types + _extract_types(data_types)
+
+ else:
# Exaxmples:
# DataFrame[float, float]
# DataFrame[pdf.dtypes]
+ return _address_unnamed_type_holders(params, origin, is_index=False)
+
+
+def _is_named_params(params: Any) -> Any:
+ return all(
+ isinstance(param, slice) and param.step is None and param.stop is not
None
+ for param in params
+ )
+
+
+def _address_named_type_hoders(params: Any, is_index: bool) -> Any:
Review comment:
```suggestion
def _convert_named_params_to_holders(params: Any, is_index: bool) -> Any:
```
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