PERF: indexing
- [ ] 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

Added a check as suggested in https://github.com/pandas-dev/pandas/pull/39280#issuecomment-764753400

```python
In [1]: from asv_bench.benchmarks.indexing import InsertColumns

In [2]: self = InsertColumns()

In [3]: self.setup()

In [4]: %timeit self.time_assign_with_setitem()
27.6 ms ± 68.1 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) #master

In [4]: %timeit self.time_assign_with_setitem()
8.6 ms ± 6.43 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) #PR
```
Edit:
```
       before           after         ratio
     [2b3a9b16]       [fa9c7140]
     <master>         <indexing.InsertColumns.time_assign_with_setitem>
-         179±4μs        163±0.6μs     0.91  indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
-        838±50μs          759±7μs     0.90  indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'nonunique_monotonic_inc')
-        37.7±4μs       34.0±0.5μs     0.90  indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-     2.91±0.09ms       2.61±0.1ms     0.90  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
-        60.1±2μs       54.0±0.5μs     0.90  indexing.NumericSeriesIndexing.time_iloc_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-     1.56±0.03ms      1.40±0.04ms     0.90  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-        183±20μs          164±1μs     0.89  indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-     2.39±0.05ms         2.14±0ms     0.89  indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-        879±30μs         780±50μs     0.89  indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_incr')
-        184±20μs          163±1μs     0.88  indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-         131±7μs          115±4μs     0.87  indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
-     3.02±0.03ms      2.63±0.09ms     0.87  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
-     3.19±0.04ms      2.78±0.04ms     0.87  indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
-      11.7±0.2ms       10.2±0.2ms     0.87  indexing.NumericSeriesIndexing.time_loc_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
-     1.89±0.04ms         1.65±0ms     0.87  indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-      4.63±0.1ms      3.99±0.02ms     0.86  indexing.NumericSeriesIndexing.time_getitem_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-     1.82±0.04ms      1.56±0.01ms     0.86  indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-      3.69±0.1ms       3.14±0.1ms     0.85  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
-     4.08±0.06ms      3.43±0.02ms     0.84  indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
-        150±30μs          120±3μs     0.80  indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
-        961±30μs         762±20μs     0.79  indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_decr')
-        295±30μs          231±3μs     0.78  indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-      1.11±0.3ms         825±20μs     0.74  indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.UInt8Engine'>, <class 'numpy.uint8'>), 'monotonic_incr')
-        741±80μs          288±9μs     0.39  indexing.AssignTimeseriesIndex.time_frame_assign_timeseries_index
-        49.3±2ms       18.2±0.2ms     0.37  indexing.InsertColumns.time_assign_with_setitem
-       54.3±20ms         18.5±1ms     0.34  indexing.InsertColumns.time_assign_list_like_with_setitem
```

nice cc @phofl can u run the indexing asvs and report here Really nice, good catch. also pls add a whatsnew note for 1.4 Could also go in 1.3.3, looks like this helps top regression here: https://simonjayhawkins.github.io/fantastic-dollop/#regressions?sort=3&dir=desc&branch=1.3.x Yeah good point, this check was added in 1.3 should I put the whatsnew in 1.3.3 fixed regression or under others?
@jreback - I have added the indexing asvs report in the top comment great yeah 1.3.3 is fine (add a perf section) > Could also go in 1.3.3, looks like this helps top regression here: https://simonjayhawkins.github.io/fantastic-dollop/#regressions?sort=3&dir=desc&branch=1.3.x

side note: `class InsertColumns` was updated in #42998. Changing the code or setup code invalidates previous results for that benchmark. So that regression is not currently reported. > side note: `class InsertColumns` was updated in #42998. Changing the code or setup code invalidates previous results for that benchmark. So that regression is not currently reported.

benchmarks have been re-run and gh-pages updated. thx @debnathshoham 