Currently, if you create a Series from another Series, you get a new object, but because it shares the manager, it also shares the index:
>>> s = pd.Series([1, 2, 3])
>>> s2 = pd.Series(s)
>>> s2._mgr is s._mgr
True
# changing the index of one also updates the other
>>> s2.index = ["a", "b", "c"]
>>> s
a 1
b 2
c 3
dtype: int64
Is this the desired / expected behaviour?
When doing an explicit shallow copy, this doesn't happen:
>>> s = pd.Series([1, 2, 3])
>>> s2 = s.copy(deep=False)
>>> s2._mgr is s._mgr
False
>>> s2.index = ["a", "b", "c"]
>>> s
0 1
1 2
2 3
dtype: int64
In both cases, changing an element of either Series updates the other (eg s[0] = 10), which is currently expected with a Series(.., copy=False) / shallow copy. But so it is not about sharing values, but whether it should also share the underlying manager (with the result that changing attributes of that manager (the axes), also updates the other)
(running with current main, which is '2.0.0.dev0+566.g57d8d3a7cc')
Comment From: topper-123
I agree @jorisvandenbossche , a shallow copy is more reasonable to expect, and current behaviour could lead to surprises.
It's also important to be consistent, so IMO Dataframe(frame) should behave in the same way (haven't checked if they're similar).
Comment From: jorisvandenbossche
Yes, DataFrame() has currently the same issue:
In [41]: df = pd.DataFrame({'a': [1, 1, 2]})
In [42]: df2 = pd.DataFrame(df)
In [43]: df2._mgr is df._mgr
Out[43]: True
In [44]: df2.index = ['a', 'b', 'c']
In [45]: df
Out[45]:
a
a 1
b 1
c 2
Comment From: jorisvandenbossche
Opened a PR to change this for both Series and DataFrame: https://github.com/pandas-dev/pandas/pull/50539