PERF: Fixes performance regression in DataFrame[bool_indexer] (#33924)
- [x] closes #33924
- [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry

Insted of calling `construction.array` in `check_array_indexer`, creates array with `dtype=bool` before calling `check_array_indexer`.

Fixes performance regression after commit b9bcdc307.

```
setup = """
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(100000, 5))
bool_indexer = [True] * 50000 + [False] * 50000
"""
import timeit
timeit.timeit("df[bool_indexer]",setup=setup, number=1000)

# master
# 27.29123323399108
# now
# 8.814320757053792
```

Hello @mproszewska! Thanks for updating this PR. We checked the lines you've touched for [PEP 8](https://www.python.org/dev/peps/pep-0008) issues, and found:





There are currently no PEP 8 issues detected in this Pull Request. Cheers! :beers: 

##### Comment last updated at 2020-05-31 23:38:07 UTC `indexing.DataFrameNumericIndexing.time_bool_indexer` looks smiliar to the case that linked issue describes, so I'm not sure if additional asv is needed can you run and post the indexing the asvs ```
[ 50.00%] · For pandas commit e0a34f8f <perf-bool> (round 2/2):
[ 50.00%] ·· Benchmarking conda-py3.6-Cython0.29.16-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 55.00%] ··· indexing.DataFrameNumericIndexing.time_bool_indexer                                                                                    1.29±0.3ms
[ 60.00%] ··· indexing.DataFrameNumericIndexing.time_iloc                                                                                              147±20μs
[ 65.00%] ··· indexing.DataFrameNumericIndexing.time_iloc_dups                                                                                         204±20μs
[ 70.00%] ··· indexing.DataFrameNumericIndexing.time_loc                                                                                               83.2±4μs
[ 75.00%] ··· indexing.DataFrameNumericIndexing.time_loc_dups                                                                                        4.73±0.3ms
[ 75.00%] · For pandas commit 5f26c342 <master^2> (round 2/2):
[ 75.00%] ·· Building for conda-py3.6-Cython0.29.16-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...
[ 75.00%] ·· Benchmarking conda-py3.6-Cython0.29.16-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 80.00%] ··· indexing.DataFrameNumericIndexing.time_bool_indexer                                                                                    3.63±0.1ms
[ 85.00%] ··· indexing.DataFrameNumericIndexing.time_iloc                                                                                               116±6μs
[ 90.00%] ··· indexing.DataFrameNumericIndexing.time_iloc_dups                                                                                         180±20μs
[ 95.00%] ··· indexing.DataFrameNumericIndexing.time_loc                                                                                               85.1±9μs
[100.00%] ··· indexing.DataFrameNumericIndexing.time_loc_dups                                                                                        5.86±0.8ms
``` I could change size of tested DataFrame, so that benchmarks would change more significantly. > I could change size of tested DataFrame, so that benchmarks would change more significantly.

sure as long as benchamarks are << 1s its ok (e.g. in ms). please post the actual asv run. otherwise looks fine. ping on green. After asv change
```
 before           after         ratio
     [5f26c342]       [e0a34f8f]
     <master^2>       <perf-bool>
-      26.3±0.3ms      7.27±0.03ms     0.28  indexing.DataFrameNumericIndexing.time_bool_indexer
``` lgtm ping on green > lgtm ping on green

ping thanks @mproszewska 