BUG: Series.rename(scalar, copy=False) still copies data

[kandersolar]

Pandas version checks

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

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

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd

s1 = pd.Series([1])
s2 = s1.rename(None, copy=False)
s2.loc[:] = 100
print(s1)  # original series not modified

Issue Description

Series.rename does not consider the copy kwarg when passing scalar index:

pandas/pandas/core/series.py

Lines 4848 to 4865 in 478d340

	if axis is not None:
	# Make sure we raise if an invalid 'axis' is passed.
	axis = self._get_axis_number(axis)
	if callable(index) or is_dict_like(index):
	# error: Argument 1 to "_rename" of "NDFrame" has incompatible
	# type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
	# Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
	# Hashable], Callable[[Any], Hashable], None]"
	return super()._rename(
	index, # type: ignore[arg-type]
	copy=copy,
	inplace=inplace,
	level=level,
	errors=errors,
	)
	else:
	return self._set_name(index, inplace=inplace)

If inplace=False, the call to self._set_name eventually leads to a call to self.copy() which leaves the default deep=True, meaning the data gets copied.

Expected Behavior

I expect setting copy=False to prevent data from being copied regardless of which branch in Series.rename is taken.

Installed Versions

The versions below are with pandas 2.0.0, but I observe this issue in 1.x as well.

INSTALLED VERSIONS

commit : 478d340
python : 3.9.12.final.0
python-bits : 64
OS : Linux
OS-release : 6.1.11-76060111-generic
Version : #202302091138167597574922.04~f771a7f SMP PREEMPT_DYNAMIC Thu F
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.0.0
numpy : 1.22.4
pytz : 2022.1
dateutil : 2.8.2
setuptools : 61.2.0
pip : 22.1.2
Cython : 0.29.30
pytest : 7.1.2
hypothesis : None
sphinx : 4.5.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 8.4.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.5.2
numba : 0.55.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.1
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[dannyi96]

take

[dannyi96]

the gist of the issue is that _set_name ( in the else portion ) doesn't support copy param and solely relies on inplace param.
Hence the copy param supplied to rename is just ignored in this case.

Some possible solutions here would be -

1. In the else portion, determine inplace based on copy ( in the case where inplace is False ) and then invoke _set_name
2. Support copy and inplace in _set_name and change its implementation similar to rename

I guess approach 1 might work/be better ( subject to review ) and will raise a PR for the same.

[lithomas1]

Hi,
Thanks for reporting this. I believe that this is intended behavior. copy=False always returns a new pandas dataframe (same as copy=True).
The fact that renaming does a copy=False still makes a copy is a bug, though. I don't think there is a plan to fix it, as the copy keyword is going to be deprecated.

Instead you should turn on Copy on Write(read this to understand how it works https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html) if you want to save memory, and do something like this.

import pandas as pd
pd.options.mode.copy_on_write = True
s1 = pd.Series([1])
# s2 = s1.rename(None) # automatically same behavior as copy=False
# s2.loc[:] = 100 # Don't do this! This is not allowed by Copy on Write rules, so you will not be able to modify s1 (you will trigger a copy of s1's values in s2)
s1 = s1.rename(None)
s1.loc[:] = 100 # Only way of modifying s1 is through s1 under Copy on Write

If you did not want to modify s2, you could see that it should shares values with s1

import pandas as pd
import numpy as np
pd.options.mode.copy_on_write = True
s1 = pd.Series([1])
s2 = s1.rename(None)
assert np.shares_memory(s1.values, s2.values)

Unfortunately, this doesn't work right now due to the aformentioned bug and some other things.
I will raise a PR.