BUG: DataFrame.convert_dtypes() drops the columns' name

[stasstepanov]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

Code Sample, a copy-pastable example

import pandas as pd


df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
df.columns.name = 'cols'
print(df)
# drops the columns' name 'cols'
print(df.convert_dtypes())

Problem description

DataFrame.convert_dtypes() drops the columns' name.
This is unexpected.

Expected Output

convert_dtypes() should keep the columns' name.
It does so for the index name.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 2cb9652
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-50-generic
Version : #56~20.04.1-Ubuntu SMP Mon Apr 12 21:46:35 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.0
pyxlsb : None
s3fs : 0.6.0
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None

[pablodz]

Docs:

pandas/pandas/core/generic.py

Lines 6095 to 6250 in 2cb9652

	@final
	def convert_dtypes(
	self: FrameOrSeries,
	infer_objects: bool_t = True,
	convert_string: bool_t = True,
	convert_integer: bool_t = True,
	convert_boolean: bool_t = True,
	convert_floating: bool_t = True,
	) -> FrameOrSeries:
	"""
	Convert columns to best possible dtypes using dtypes supporting ``pd.NA``.
	.. versionadded:: 1.0.0
	Parameters
	----------
	infer_objects : bool, default True
	Whether object dtypes should be converted to the best possible types.
	convert_string : bool, default True
	Whether object dtypes should be converted to ``StringDtype()``.
	convert_integer : bool, default True
	Whether, if possible, conversion can be done to integer extension types.
	convert_boolean : bool, defaults True
	Whether object dtypes should be converted to ``BooleanDtypes()``.
	convert_floating : bool, defaults True
	Whether, if possible, conversion can be done to floating extension types.
	If `convert_integer` is also True, preference will be give to integer
	dtypes if the floats can be faithfully casted to integers.
	.. versionadded:: 1.2.0
	Returns
	-------
	Series or DataFrame
	Copy of input object with new dtype.
	See Also
	--------
	infer_objects : Infer dtypes of objects.
	to_datetime : Convert argument to datetime.
	to_timedelta : Convert argument to timedelta.
	to_numeric : Convert argument to a numeric type.
	Notes
	-----
	By default, ``convert_dtypes`` will attempt to convert a Series (or each
	Series in a DataFrame) to dtypes that support ``pd.NA``. By using the options
	``convert_string``, ``convert_integer``, ``convert_boolean`` and
	``convert_boolean``, it is possible to turn off individual conversions
	to ``StringDtype``, the integer extension types, ``BooleanDtype``
	or floating extension types, respectively.
	For object-dtyped columns, if ``infer_objects`` is ``True``, use the inference
	rules as during normal Series/DataFrame construction. Then, if possible,
	convert to ``StringDtype``, ``BooleanDtype`` or an appropriate integer
	or floating extension type, otherwise leave as ``object``.
	If the dtype is integer, convert to an appropriate integer extension type.
	If the dtype is numeric, and consists of all integers, convert to an
	appropriate integer extension type. Otherwise, convert to an
	appropriate floating extension type.
	.. versionchanged:: 1.2
	Starting with pandas 1.2, this method also converts float columns
	to the nullable floating extension type.
	In the future, as new dtypes are added that support ``pd.NA``, the results
	of this method will change to support those new dtypes.
	Examples
	--------
	>>> df = pd.DataFrame(
	... {
	... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
	... "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
	... "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
	... "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
	... "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
	... "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
	... }
	... )
	Start with a DataFrame with default dtypes.
	>>> df
	a b c d e f
	0 1 x True h 10.0 NaN
	1 2 y False i NaN 100.5
	2 3 z NaN NaN 20.0 200.0
	>>> df.dtypes
	a int32
	b object
	c object
	d object
	e float64
	f float64
	dtype: object
	Convert the DataFrame to use best possible dtypes.
	>>> dfn = df.convert_dtypes()
	>>> dfn
	a b c d e f
	0 1 x True h 10 <NA>
	1 2 y False i <NA> 100.5
	2 3 z <NA> <NA> 20 200.0
	>>> dfn.dtypes
	a Int32
	b string
	c boolean
	d string
	e Int64
	f Float64
	dtype: object
	Start with a Series of strings and missing data represented by ``np.nan``.
	>>> s = pd.Series(["a", "b", np.nan])
	>>> s
	0 a
	1 b
	2 NaN
	dtype: object
	Obtain a Series with dtype ``StringDtype``.
	>>> s.convert_dtypes()
	0 a
	1 b
	2 <NA>
	dtype: string
	"""
	if self.ndim == 1:
	return self._convert_dtypes(
	infer_objects,
	convert_string,
	convert_integer,
	convert_boolean,
	convert_floating,
	)
	else:
	results = [
	col._convert_dtypes(
	infer_objects,
	convert_string,
	convert_integer,
	convert_boolean,
	convert_floating,
	)
	for col_name, col in self.items()
	]
	result = pd.concat(results, axis=1, copy=False)
	return result