How to solve pandas (2.2.0) "FutureWarning: Downcasting behavior in `replace` is deprecated" on a Series?

[c.b...@posteo.jp]

Hello,

and please take my apologize for asking this way. My stackoverflow
question [1] was closed for IMHO no good reason. The linked duplicates
do not help me.

The example code below gives me this error using Pandas 2.2.0

FutureWarning: Downcasting behavior in `replace` is deprecated
and will be removed in a future version. To retain the old behavior,
explicitly call `result.infer_objects(copy=False)`.
To opt-in to the future behavior, set
`pd.set_option('future.no_silent_downcasting', True)`
s = s.replace(replace_dict)

I found several postings about this future warning. But my problem is I
don't understand why it happens and I also don't know how to solve it.

#!/usr/bin/python3
from pandas import Series
s = Series(['foo', 'bar'])
replace_dict = {'foo': 2, 'bar': 4}
s = s.replace(replace_dict)

I am aware of other questions and answers [2] but I don't know how to
apply them to my own code. The reason might be that I do not understand
the cause of the error.

The linked answers using `astype()` before replacement. But again: I
don't know how this could solve my problem.

Thanks in advance
Christian

[1] -- <https://stackoverflow.com/q/77995105/4865723>
[2] -- <https://stackoverflow.com/q/77900971/4865723>

[Chris Rodgers]

Also having this issue. The OP's minimal code example reproduces the problem by emitting an undesired warning.

from pandas import Series
s = Series(['foo', 'bar'])
replace_dict = {'foo': 2, 'bar': 4}
s = s.replace(replace_dict)

I believe the underlying issue is that the "replace" method thinks something is wrong when you replace all of the strings in a Series with integers. It downcasts the result to integer, but warns you this may cause problems, and that the behavior will go away in the future. The problem is that, in this type of scenario, we DO want to downcast, and there is no way (that I know of) to inform "replace" that this is the intended behavior. (The warning emits a suggestion involving "infer_objects" that I can't understand.) Suppressing the warnings is a work-around, but not that satisfactory given that it will probably become an error in the future. 

Here is the workaround I have found. Replace:

    s.replace(replace_dict)

with:

    s.map(pandas.Series(replace_dict))

This works if replace_dict is a dict. I'm not too sure how to generally fix the various other objects that "replace" can accept.