DEPR: accepting Manager objects in DataFrame/Series
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@jorisvandenbossche looks like this deprecation would affect pyarrow. Any thoughts? That would certainly be a problem for pyarrow. There is also https://github.com/pandas-dev/pandas/pull/52132 which adds a separate method to construct from a manager. I would assume we would need to do something like that first?

@jbrockmendel it would help if you could add some more context in the top post of your PRs, short explainer, link to relevant PRs, or open an issue about this that can be linked to from the different PRs >  I would assume we would need to do something like [implement _from_mgr] first?

We'd need to have that by the time we enforce the deprecation, yes. +1 here

I agree that we still need a way for a power user to construct from collections of 2D arrays of homogenized dtypes. So that begs the question why don't we just do this? 

we have from_arrays why not from_2darrays (or better name)? this pretty much solves the downstream problem with efficiency 

 Mothballing until after #52132 So after 2.1, we can transition pyarrow to use `_from_mgr` and hopefully move forward with this > So after 2.1, we can transition pyarrow to use _from_mgr and hopefully move forward with this

I don't see why this would need to wait for 2.1.  Long-term pyarrow et al need to move to use public APIs and avoid Managers entirely. should use DeprecationWarning instead of FutureWarning? On main, I'm now seeing:

```
df = pd.DataFrame({'a': [1, 1, 2]})
df.to_parquet('temp.parquet')
pd.read_parquet('temp.parquet')
# DeprecationWarning: Passing a BlockManager to DataFrame is deprecated and will raise in a future version. Use public APIs instead.
``` Yes, pyarrow needs to update their usage. I think this is way too noisy if every user sees this. I would propose to revert this for now, and only put it back in when there is a released version of pyarrow that doesn't trigger those warnings. Without this deprecation, will pyarrow ever actually start using public APIs?

Isn't the point of using a DeprecationWarning instead of FutureWarning to assure appropriate visibility?

If we do revert, it should be just before release, not immediate. > Isn't the point of using a DeprecationWarning instead of FutureWarning to assure appropriate visibility?

Yeah, that's true, but for some reason it doesn't seem to be working here. See Richard's snippet above, which I can reproduce on main without any special filterwarnings settings. So something we should investigate why this is happening.

> Without this deprecation, will pyarrow ever actually start using public APIs?

I am planning to simply move pyarrow to whatever new private API is needed to achieve the same (I suppose `DataFrame._from_mgr`?).  
As mentioned above, I think there are valid use cases to allow power users to access those APIs to be able create a DataFrame from a Manager (also subclasses will need to be able to do this, BTW. And especially for something like pyarrow we want that the integration can be as optimal as possible). I fully agree that this API shouldn't be the main DataFrame constructor, so I am fully on board with this deprecation. I am only assuming we will provide an alternative. 
 > > Isn't the point of using a DeprecationWarning instead of FutureWarning to assure appropriate visibility?
> 
> Yeah, that's true, but for some reason it doesn't seem to be working here. See Richard's snippet above, which I can reproduce on main without any special filterwarnings settings. So something we should investigate why this is happening.

So it's actually quite simple ;) We need to set the stacklevel correctly for how this is used, i.e. we want to warn if someone passes a manager to a DataFrame themselves, and so for that use case, the stacklevel is always two (this deprecation doesn't live deeply nested where it can be reached in multiple ways, it's simply in `DataFrame/Series.__init__`, and only when calling this init we should show the warning)

PR to fix this -> https://github.com/pandas-dev/pandas/pull/55591 I also wasn't aware of this drawback of using `find_stack_level`, I think this would be good to know.

cc @pandas-dev/pandas-core see @jorisvandenbossche's comment immediately above. Yes, we will need to re-evaluate other DeprecationWarnings as well  After further working on https://github.com/pandas-dev/pandas/pull/55591, I discovered that correcting the stacklevel unfortunately does not fix all cases (eg calling `pyarrow_table.to_pandas()`  still incorrectly triggers the warning). 
I _think_ this is related to the DataFrame constructor being called from cython on the pyarrow side, and cython might mess up the stack? (I am moving this construction from cython to python in my PR to update pyarrow (https://github.com/apache/arrow/pull/38374), but that of course doesn't help for released versions)

Although this usage won't be as widespread as read_parquet, this might still be a reason to revert it temporarily .. Another option is maybe to, initially, set the stacklevel to 1. That would circumvent the cython issue (and avoid the question of reverting temporarily), and ensure the warning isn't seen by pandas/pyarrow users. It would also result in not seeing the warning when doing it manually (i.e. user doing `pd.DataFrame(mgr)`), but that's 1) the less likely use case, and 2) then in a next step we can bump the stacklevel back to 2. I implemented the stacklevel=1 idea in my PR at https://github.com/pandas-dev/pandas/pull/55591 I'm also seeing this warning hit by subclassing a pandas DataFrame:

https://github.com/pandas-dev/pandas/blob/2d2d67db48413fde356bce5c8d376d5b9dd5d0c2/pandas/core/frame.py#L648-L654

It's triggered by the test `test_groupby_preserves_subclass`.