REF/API: DataFrame.__setitem__ never operate in-place
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry

xref https://github.com/pandas-dev/pandas/issues/38896#issuecomment-761620727
getting this and #39163 in will make it easier to keep ArrayManager behavior in sync with non-ArrayManager @jreback gentle ping; some simplification in `iloc.__setitem__` becomes feasible after this, some of which I think may make #39578 easier  > Can you give some explanation what this PR is exactly changing? (it's hard to see that from the code changed in frame.py)

There are cases where `DataFrame.__setitem__` currently ends up dispatching to `iloc.__setitem__` (which in turn ends up iterating over columns).  This does the iterating-over-columns directly in the DataFrame method, avoiding the iloc dispatch.  This resolves the trouble in `iloc[:, foo]` of having to tell whether to write into existing data or insert a new array. I think we talked about this a few weeks back. I prepared a few tests back then but did not get around to push this forward.

The tests are checking, if we are really not operating inplace. Do you think it is worth adding them?

```
class TestDataFrameSetItemNotInplace:
    def test_setitem_duplicate_columns(self):
        cols = ["A", "B"] * 2
        df = DataFrame(0.0, index=[0], columns=cols)
        df_copy = df.copy()
        df_view = df[:]
        df["B"] = (2, 5)

        expected = DataFrame([[0.0, 2, 0.0, 5]], columns=cols)
        tm.assert_frame_equal(df_view, df_copy)
        tm.assert_frame_equal(df, expected)

    @pytest.mark.xfail(reason="Setitem with same dtype still changing inplace")
    @pytest.mark.parametrize("value", [1, np.array([[1], [1]]), [[1], [1]]])
    def test_setitem_same_dtype(self, value):
        cols = ["A", "B"]
        df = DataFrame(0, index=[0, 1], columns=cols)
        df_copy = df.copy()
        df_view = df[:]
        df[["B"]] = value

        expected = DataFrame([[0, 1], [0, 1]], columns=cols)
        tm.assert_frame_equal(df, expected)
        tm.assert_frame_equal(df_view, df_copy)

    @pytest.mark.parametrize("value", [1.0, np.array([[1.0], [1.0]]), [[1.0], [1.0]]])
    def test_setitem_listlike_key_scalar_value(self, value):
        cols = ["A", "B"]
        df = DataFrame(0, index=[0, 1], columns=cols)
        df_copy = df.copy()
        df_view = df[:]
        df[["B"]] = value

        expected = DataFrame([[0, 1.0], [0, 1.0]], columns=cols)
        tm.assert_frame_equal(df_view, df_copy)
        tm.assert_frame_equal(df, expected)
``` >  Do you think it is worth adding them?

absolutely.  will check that these pass on the branch and then update.  thanks @jreback gentle ping on this and #39163 Can you add a note as this has a user facing change. ping on green. ping > Can you clarify the whatsnew a bit more about what actually changed / what the impact is for the user? (this is not really a bug fix ..)

The only part to call a bugfix is the int32 changes (see affected tests).  The changed exception messages hardly seem worth noting The whatsnew note now says "some cases". Eg could make that more specific? To try to clarify what I mean. I didn't have time to review the *code* of this PR in enough detail to know what it changes, but I would still expect to get an idea about that based on the (whatsnew) description. But now neither the PR description nor the whatsnew gives me a clear idea of the (potential) changes.  > To try to clarify what I mean. I didn't have time to review the code of this PR in enough detail to know what it changes, but I would still expect to get an idea about that based on the (whatsnew) description. But now neither the PR description nor the whatsnew gives me a clear idea of the (potential) changes.


ATM `DataFrame.__setitem__` calls `self.iloc[:, indexer] = value`, which has different copy/view semantics than we want for `__setitem__` (see https://github.com/pandas-dev/pandas/issues/38896#issuecomment-761620727, also linked in the OP).  Big picture: this PR is avoiding that `self.iloc` dispatch.

Luckily some of the tests suggested by @phofl fails in master:

```
# test_setitem_listlike_key_scalar_value_not_inplace with value = 1.0
cols = ["A", "B"]
df = DataFrame(0, index=[0, 1], columns=cols)
df_copy = df.copy()
df_view = df[:]
df[["B"]] = 1.0

expected = DataFrame([[0, 1.0], [0, 1.0]], columns=cols)
tm.assert_frame_equal(df_view, df_copy)  # <-- raises in master, b/c df["B"]._values is written _into_
tm.assert_frame_equal(df, expected)

# test_setitem_duplicate_columns_not_inplace
cols = ["A", "B"] * 2
df = DataFrame(0.0, index=[0], columns=cols)
df_copy = df.copy()
df_view = df[:]
df["B"] = (2, 5)

expected = DataFrame([[0.0, 2, 0.0, 5]], columns=cols)
tm.assert_frame_equal(df_view, df_copy)  # <-- raises in master, b/c df["B"]._values is written _into_
tm.assert_frame_equal(df, expected)
```


 OK, so a consequence is that the resulting dtype can change compared to master when you are setting multiple columns. For example (using this branch):

```
In [4]: df = pd.DataFrame(np.random.randn(3, 5), columns=['a', 'b', 'c', 'd', 'e'])

In [5]: df[["b", "c"]] = 1

In [6]: df
Out[6]: 
          a  b  c         d         e
0  0.576266  1  1  0.115430  0.670362
1 -0.409783  1  1  1.824356  1.702370
2  1.168233  1  1 -0.708717  1.555301

In [7]: df.dtypes
Out[7]: 
a    float64
b      int64
c      int64
d    float64
e    float64
dtype: object
```

On master, the above preserves the all-float dtypes. Do you know if that is specific to that case? (multiple columns, and setting with a scalar) From quick testing, it seems that other cases (like setting multiple columns with a dataframe, or array, .. ) didn't try to preserve the dtype if possible. 
But where there other cases also going through the `self.iloc[:, indexer] = value` path? > But where there other cases also going through the self.iloc[:, indexer] = value path?

The only place where we go through `self.iloc[:, indexer] = value` is in `_setitem_array`, which is reached in two ways (note: a bunch of other cases are caught before going through _setitem_array, so these conditions are not sufficient to ensure we go through iloc)

`if isinstance(key, (Series, np.ndarray, list, Index)):`
`elif is_list_like(value) and 1 < len(self.columns.get_indexer_for([key])) == len(value):`

which is pretty well summed up by "multiple columns" I would personally then add a small subsection to the "Notable bug fixes" part showing the example of setting multiple columns now having different dtypes as result (there is already a subsection for iloc/loc changes (actually trying inplace instead of replacing), which is quite related (but opposite) change. rebased, green ex two issues on master being addressed elsewhere thanks @jbrockmendel nice! There is a perf regression between 1.2.5 and 1.3.x that points to this PR that is not shown on the asv dashboard https://pandas.pydata.org/speed/pandas/#indexing.InsertColumns.time_assign_list_like_with_setitem?python=3.8&Cython=0.29.21&jinja2=

```
(pandas-dev) simon@T3630:~/pandas/asv_bench (master)$ asv compare v1.2.5 1.3.x |grep time_assign_list_like_with_setitem
x     2.38±0.03ms       27.4±0.4ms    11.48  indexing.InsertColumns.time_assign_list_like_with_setitem
```

profiling gives

<details>

```
(pandas-dev) simon@T3630:~/pandas/asv_bench (master)$ asv profile indexing.InsertColumns.time_assign_list_like_with_setitem fefd9991658c4717fa2e6b37328be81158be4192|head -n 30
· Discovering benchmarks
· Profile data does not already exist. Running profiler now.
·· Benchmarking conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
··· Running (indexing.InsertColumns.time_assign_list_like_with_setitem--).
··· ...rtColumns.time_assign_list_like_with_setitem         24.5±0.2ms

Tue Jun 29 11:28:30 2021    /tmp/tmpa23qf3wg

         71979 function calls (70571 primitive calls) in 0.039 seconds

   Ordered by: cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    0.039    0.039 {built-in method builtins.exec}
        1    0.000    0.000    0.039    0.039 /home/simon/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py:540(method_caller)
        1    0.000    0.000    0.039    0.039 /home/simon/pandas/asv_bench/benchmarks/indexing.py:378(time_assign_list_like_with_setitem)
    101/1    0.000    0.000    0.038    0.038 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/frame.py:3516(__setitem__)
        1    0.000    0.000    0.038    0.038 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/frame.py:3548(_setitem_array)
        1    0.000    0.000    0.038    0.038 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/frame.py:3585(_iset_not_inplace)
      100    0.000    0.000    0.027    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/frame.py:3703(_set_item)
      100    0.000    0.000    0.025    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/frame.py:3678(_set_item_mgr)
      100    0.001    0.000    0.024    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/managers.py:1230(insert)
  401/301    0.003    0.000    0.016    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexes/base.py:334(__new__)
      100    0.001    0.000    0.015    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexes/base.py:5864(insert)
      100    0.000    0.000    0.010    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexes/base.py:5220(get_indexer_for)
  101/100    0.000    0.000    0.009    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexes/base.py:3378(get_indexer)
      101    0.000    0.000    0.005    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexes/base.py:6174(ensure_index)
      101    0.000    0.000    0.005    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/construction.py:455(sanitize_array)
      100    0.000    0.000    0.004    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/managers.py:1994(_fast_count_smallints)
 1200/800    0.001    0.000    0.004    0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}
```

</details>

before

<details>

```
(pandas-dev) simon@T3630:~/pandas/asv_bench (master)$ asv profile indexing.InsertColumns.time_assign_list_like_with_setitem fefd9991658c4717fa2e6b37328be81158be4192~1|head -n 30
· Discovering benchmarks
·· Uninstalling from conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
·· Building 203f9014 <v1.3.0rc0~819> for conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
·· Installing 203f9014 <v1.3.0rc0~819> into conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
· Profile data does not already exist. Running profiler now.
·· Benchmarking conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
··· Running (indexing.InsertColumns.time_assign_list_like_with_setitem--).
··· ...rtColumns.time_assign_list_like_with_setitem        2.31±0.01ms

Tue Jun 29 11:33:24 2021    /tmp/tmp7ioufpog

         2162 function calls (2141 primitive calls) in 0.003 seconds

   Ordered by: cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    0.003    0.003 {built-in method builtins.exec}
        1    0.000    0.000    0.003    0.003 /home/simon/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/asv/benchmark.py:540(method_caller)
        1    0.000    0.000    0.003    0.003 /home/simon/pandas/asv_bench/benchmarks/indexing.py:378(time_assign_list_like_with_setitem)
        1    0.002    0.002    0.002    0.002 {method 'randn' of 'numpy.random.mtrand.RandomState' objects}
        1    0.000    0.000    0.001    0.001 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/frame.py:3516(__setitem__)
        1    0.000    0.000    0.001    0.001 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/frame.py:3548(_setitem_array)
        1    0.000    0.000    0.001    0.001 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexing.py:670(_ensure_listlike_indexer)
        1    0.000    0.000    0.000    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexing.py:1257(_get_listlike_indexer)
      6/5    0.000    0.000    0.000    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexes/base.py:334(__new__)
      2/1    0.000    0.000    0.000    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexes/base.py:2784(union)
      3/2    0.000    0.000    0.000    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexes/base.py:3378(get_indexer)
        1    0.000    0.000    0.000    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexes/base.py:5220(get_indexer_for)
        1    0.000    0.000    0.000    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/internals/base.py:62(reindex_axis)
      3/2    0.000    0.000    0.000    0.000 /home/simon/pandas/asv_bench/env/11a1c20ede452de2525075dc4a15eb94/lib/python3.8/site-packages/pandas/core/indexes/base.py:3405(_get_indexer)
```

</details>

so spends some time in _iset_not_inplace

Is this expected?

 > Is this expected?

With the current implementation, yes.  I think a deep(ish) refactor can avoid the perf hit, but it's not a 1-day thing.  cc @phofl 

BTW is there an option you can pass to the asv profile command like strip_dirs?  (ideally just strip everything up through "site-packages/")