DOC: Incorrect typing in `astype` for the `copy` argument

[Matt711]

From https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.astype.html

copy : bool, default True

But from the source, copy can be None

copy: bool_t | None = None

pandas/pandas/core/generic.py

Lines 6485 to 6682 in 9c8bc3e

	@final
	def astype(
	self, dtype, copy: bool_t | None = None, errors: IgnoreRaise = "raise"
	) -> Self:
	"""
	Cast a pandas object to a specified dtype ``dtype``.
	Parameters
	----------
	dtype : str, data type, Series or Mapping of column name -> data type
	Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to
	cast entire pandas object to the same type. Alternatively, use a
	mapping, e.g. {col: dtype, ...}, where col is a column label and dtype is
	a numpy.dtype or Python type to cast one or more of the DataFrame's
	columns to column-specific types.
	copy : bool, default True
	Return a copy when ``copy=True`` (be very careful setting
	``copy=False`` as changes to values then may propagate to other
	pandas objects).
	.. note::
	The `copy` keyword will change behavior in pandas 3.0.
	`Copy-on-Write
	<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
	will be enabled by default, which means that all methods with a
	`copy` keyword will use a lazy copy mechanism to defer the copy and
	ignore the `copy` keyword. The `copy` keyword will be removed in a
	future version of pandas.
	You can already get the future behavior and improvements through
	enabling copy on write ``pd.options.mode.copy_on_write = True``
	errors : {'raise', 'ignore'}, default 'raise'
	Control raising of exceptions on invalid data for provided dtype.
	- ``raise`` : allow exceptions to be raised
	- ``ignore`` : suppress exceptions. On error return original object.
	Returns
	-------
	same type as caller
	See Also
	--------
	to_datetime : Convert argument to datetime.
	to_timedelta : Convert argument to timedelta.
	to_numeric : Convert argument to a numeric type.
	numpy.ndarray.astype : Cast a numpy array to a specified type.
	Notes
	-----
	.. versionchanged:: 2.0.0
	Using ``astype`` to convert from timezone-naive dtype to
	timezone-aware dtype will raise an exception.
	Use :meth:`Series.dt.tz_localize` instead.
	Examples
	--------
	Create a DataFrame:
	>>> d = {'col1': [1, 2], 'col2': [3, 4]}
	>>> df = pd.DataFrame(data=d)
	>>> df.dtypes
	col1 int64
	col2 int64
	dtype: object
	Cast all columns to int32:
	>>> df.astype('int32').dtypes
	col1 int32
	col2 int32
	dtype: object
	Cast col1 to int32 using a dictionary:
	>>> df.astype({'col1': 'int32'}).dtypes
	col1 int32
	col2 int64
	dtype: object
	Create a series:
	>>> ser = pd.Series([1, 2], dtype='int32')
	>>> ser
	0 1
	1 2
	dtype: int32
	>>> ser.astype('int64')
	0 1
	1 2
	dtype: int64
	Convert to categorical type:
	>>> ser.astype('category')
	0 1
	1 2
	dtype: category
	Categories (2, int32): [1, 2]
	Convert to ordered categorical type with custom ordering:
	>>> from pandas.api.types import CategoricalDtype
	>>> cat_dtype = CategoricalDtype(
	... categories=[2, 1], ordered=True)
	>>> ser.astype(cat_dtype)
	0 1
	1 2
	dtype: category
	Categories (2, int64): [2 < 1]
	Create a series of dates:
	>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
	>>> ser_date
	0 2020-01-01
	1 2020-01-02
	2 2020-01-03
	dtype: datetime64[ns]
	"""
	if copy and using_copy_on_write():
	copy = False
	if is_dict_like(dtype):
	if self.ndim == 1: # i.e. Series
	if len(dtype) > 1 or self.name not in dtype:
	raise KeyError(
	"Only the Series name can be used for "
	"the key in Series dtype mappings."
	)
	new_type = dtype[self.name]
	return self.astype(new_type, copy, errors)
	# GH#44417 cast to Series so we can use .iat below, which will be
	# robust in case we
	from pandas import Series
	dtype_ser = Series(dtype, dtype=object)
	for col_name in dtype_ser.index:
	if col_name not in self:
	raise KeyError(
	"Only a column name can be used for the "
	"key in a dtype mappings argument. "
	f"'{col_name}' not found in columns."
	)
	dtype_ser = dtype_ser.reindex(self.columns, fill_value=None, copy=False)
	results = []
	for i, (col_name, col) in enumerate(self.items()):
	cdt = dtype_ser.iat[i]
	if isna(cdt):
	res_col = col.copy(deep=copy)
	else:
	try:
	res_col = col.astype(dtype=cdt, copy=copy, errors=errors)
	except ValueError as ex:
	ex.args = (
	f"{ex}: Error while type casting for column '{col_name}'",
	)
	raise
	results.append(res_col)
	elif is_extension_array_dtype(dtype) and self.ndim > 1:
	# TODO(EA2D): special case not needed with 2D EAs
	dtype = pandas_dtype(dtype)
	if isinstance(dtype, ExtensionDtype) and all(
	arr.dtype == dtype for arr in self._mgr.arrays
	):
	return self.copy(deep=copy)
	# GH 18099/22869: columnwise conversion to extension dtype
	# GH 24704: self.items handles duplicate column names
	results = [
	ser.astype(dtype, copy=copy, errors=errors) for _, ser in self.items()
	]
	else:
	# else, only a single dtype is given
	new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
	res = self._constructor_from_mgr(new_data, axes=new_data.axes)
	return res.__finalize__(self, method="astype")
	# GH 33113: handle empty frame or series
	if not results:
	return self.copy(deep=None)
	# GH 19920: retain column metadata after concat
	result = concat(results, axis=1, copy=False)
	# GH#40810 retain subclass
	# error: Incompatible types in assignment
	# (expression has type "Self", variable has type "DataFrame")
	result = self._constructor(result) # type: ignore[assignment]
	result.columns = self.columns
	result = result.__finalize__(self, method="astype")
	# https://github.com/python/mypy/issues/8354
	return cast(Self, result)

[rustamali9183]

I would like to work on this issue. Will open a PR shortly