CLN: Remove special handling of nans in the float64-case of isin
It is no longer needed because the hash-table handles the nans correctly out of the box (see #21866)

Not having to scan the values via isna(...).any()  will improve the performance.


- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`

There are no changes visible for the user (others than slightly better performance).

# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/22117?src=pr&el=h1) Report
> Merging [#22117](https://codecov.io/gh/pandas-dev/pandas/pull/22117?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/475e391eaa0c85c4615ad6da5f81dee9b890635b?src=pr&el=desc) will **decrease** coverage by `<.01%`.
> The diff coverage is `100%`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/22117/graphs/tree.svg?width=650&src=pr&token=eZ4WkYLtcO&height=150)](https://codecov.io/gh/pandas-dev/pandas/pull/22117?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #22117      +/-   ##
==========================================
- Coverage   92.08%   92.08%   -0.01%     
==========================================
  Files         169      169              
  Lines       50704    50703       -1     
==========================================
- Hits        46691    46690       -1     
  Misses       4013     4013
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.49% <100%> (-0.01%)` | :arrow_down: |
| #single | `42.33% <0%> (ø)` | :arrow_up: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/22117?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/algorithms.py](https://codecov.io/gh/pandas-dev/pandas/pull/22117/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvYWxnb3JpdGhtcy5weQ==) | `94.68% <100%> (-0.01%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/22117?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/22117?src=pr&el=footer). Last update [475e391...567e7bb](https://codecov.io/gh/pandas-dev/pandas/pull/22117?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 can you run the asv suite for value_counts, isin, factorize and report any changes.
 I had to add some performance tests for `Series.isin` and  float64, because there weren't any.

For `asv continuous -f 1.1 upstream/master HEAD -b ^series_methods -b ^algorithms` the result was:

> BENCHMARKS NOT SIGNIFICANTLY CHANGED.
 Hello @realead! Thanks for updating the PR.







Cheers ! There are no PEP8 issues in this Pull Request. :beers: 

##### Comment last updated on August 09, 2018 at 21:45 Hours UTC @jreback to bump it and to show, that there is really a performance benefit for some scenarios:

```
>>> asv continuous -f 1.00001 measure cleanup_nans_in_isin_float64  -b ^series_methods.IsIn
...
       before           after         ratio
     [8c973f48]       [a94d8c34]
-      45.0±0.2ms       37.8±0.8ms     0.84  series_methods.IsInFloat64.time_isin_few_different
-      45.1±0.3ms       37.4±0.2ms     0.83  series_methods.IsInFloat64.time_isin_nan_values

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
wiih `measured = master+cherry-picked test-commits`

The strange thing: when I put all tests into `IsIn` class and just add `float64` to `params` no difference is reported. However, when I test it via `%timeit` or in a separate class (like now) I can see an improvement of around 15% for some scenarios. I don't know enough about `asv` to claim this is a bug in `asv 0.3.dev1327+8bce115f`, but at least a behavior I wouldn't expect.
 can you rebase Performance test:

`asv continuous -f 1.01 upstream/master HEAD -b ^series_methods -b ^algorithms -b ^categorial`

<details>

```
· Creating environments
· Discovering benchmarks
·· Uninstalling from conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
·· Installing into conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..
· Running 66 total benchmarks (2 commits * 1 environments * 33 benchmarks)
[  0.00%] · For pandas commit hash 567e7bb8:
[  0.00%] ·· Building for conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...
[  0.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[  0.00%] ··· Running benchmarks............................
[  0.00%] ··· Setting up algorithms.py:95
[  0.00%] ··· Running benchmarks.........
[  1.52%] ··· algorithms.Duplicated.time_duplicated_float                                                  16.4±0.7ms;...
[  3.03%] ··· algorithms.Duplicated.time_duplicated_int                                                    10.3±0.2ms;...
[  4.55%] ··· algorithms.Duplicated.time_duplicated_string                                                 10.5±0.2μs;...
[  6.06%] ··· algorithms.DuplicatedUniqueIndex.time_duplicated_unique_int                                     30.6±0.08μs
[  7.58%] ··· algorithms.Factorize.time_factorize_float                                                      62.6±2ms;...
[  9.09%] ··· algorithms.Factorize.time_factorize_int                                                        21.8±2ms;...
[ 10.61%] ··· algorithms.Factorize.time_factorize_string                                                      154±8ms;...
[ 12.12%] ··· algorithms.Match.time_match_string                                                                  373±5μs
[ 13.64%] ··· series_methods.Clip.time_clip                                                                     121±0.8μs
[ 15.15%] ··· series_methods.Dir.time_dir_strings                                                             1.98±0.02ms
[ 16.67%] ··· series_methods.Dropna.time_dropna                                                            2.87±0.1ms;...
[ 18.18%] ··· series_methods.IsIn.time_isin                                                               1.60±0.03ms;...
[ 19.70%] ··· series_methods.IsInFloat64.time_isin_few_different                                               37.9±0.4ms
[ 21.21%] ··· series_methods.IsInFloat64.time_isin_many_different                                              41.9±0.9ms
[ 22.73%] ··· series_methods.IsInFloat64.time_isin_nan_values                                                  38.6±0.5ms
[ 24.24%] ··· series_methods.IsInForObjects.time_isin_long_series_long_values                                 3.50±0.08ms
[ 25.76%] ··· series_methods.IsInForObjects.time_isin_long_series_long_values_floats                           6.08±0.1ms
[ 27.27%] ··· series_methods.IsInForObjects.time_isin_long_series_short_values                                2.20±0.01ms
[ 28.79%] ··· series_methods.IsInForObjects.time_isin_nans                                                        660±7μs
[ 30.30%] ··· series_methods.IsInForObjects.time_isin_short_series_long_values                                1.16±0.02ms
[ 31.82%] ··· series_methods.Map.time_map                                                                 1.02±0.03ms;...
[ 33.33%] ··· series_methods.NSort.time_nlargest                                                           2.93±0.1ms;...
[ 34.85%] ··· series_methods.NSort.time_nsmallest                                                          2.35±0.2ms;...
[ 36.36%] ··· series_methods.SeriesConstructor.time_constructor                                               164±2μs;...
[ 37.88%] ··· series_methods.SeriesGetattr.time_series_datetimeindex_repr                                     3.09±0.03μs
[ 39.39%] ··· series_methods.ValueCounts.time_value_counts                                                2.25±0.07ms;...
[ 40.91%] ··· algorithms.Hashing.time_frame                                                                    21.1±0.3ms
[ 42.42%] ··· algorithms.Hashing.time_series_categorical                                                      5.17±0.08ms
[ 43.94%] ··· algorithms.Hashing.time_series_dates                                                            3.26±0.06ms
[ 45.45%] ··· algorithms.Hashing.time_series_float                                                            3.24±0.04ms
[ 46.97%] ··· algorithms.Hashing.time_series_int                                                              3.20±0.02ms
[ 48.48%] ··· algorithms.Hashing.time_series_string                                                            14.3±0.7ms
[ 50.00%] ··· algorithms.Hashing.time_series_timedeltas                                                        3.30±0.1ms
[ 50.00%] · For pandas commit hash 475e391e:
[ 50.00%] ·· Building for conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...
[ 50.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 50.00%] ··· Running benchmarks............................
[ 50.00%] ··· Setting up algorithms.py:95
[ 50.00%] ··· Running benchmarks.........
[ 51.52%] ··· algorithms.Duplicated.time_duplicated_float                                                  15.3±0.7ms;...
[ 53.03%] ··· algorithms.Duplicated.time_duplicated_int                                                    9.54±0.2ms;...
[ 54.55%] ··· algorithms.Duplicated.time_duplicated_string                                                 10.5±0.1μs;...
[ 56.06%] ··· algorithms.DuplicatedUniqueIndex.time_duplicated_unique_int                                      30.7±0.1μs
[ 57.58%] ··· algorithms.Factorize.time_factorize_float                                                      57.2±2ms;...
[ 59.09%] ··· algorithms.Factorize.time_factorize_int                                                        21.5±3ms;...
[ 60.61%] ··· algorithms.Factorize.time_factorize_string                                                      153±8ms;...
[ 62.12%] ··· algorithms.Match.time_match_string                                                                  374±4μs
[ 63.64%] ··· series_methods.Clip.time_clip                                                                       120±2μs
[ 65.15%] ··· series_methods.Dir.time_dir_strings                                                             2.05±0.05ms
[ 66.67%] ··· series_methods.Dropna.time_dropna                                                           2.73±0.05ms;...
[ 68.18%] ··· series_methods.IsIn.time_isin                                                               1.60±0.02ms;...
[ 69.70%] ··· series_methods.IsInFloat64.time_isin_few_different                                               45.6±0.6ms
[ 71.21%] ··· series_methods.IsInFloat64.time_isin_many_different                                                43.3±1ms
[ 72.73%] ··· series_methods.IsInFloat64.time_isin_nan_values                                                  45.2±0.7ms
[ 74.24%] ··· series_methods.IsInForObjects.time_isin_long_series_long_values                                  3.44±0.1ms
[ 75.76%] ··· series_methods.IsInForObjects.time_isin_long_series_long_values_floats                          5.82±0.05ms
[ 77.27%] ··· series_methods.IsInForObjects.time_isin_long_series_short_values                                2.09±0.03ms
[ 78.79%] ··· series_methods.IsInForObjects.time_isin_nans                                                        642±3μs
[ 80.30%] ··· series_methods.IsInForObjects.time_isin_short_series_long_values                                 1.18±0.1ms
[ 81.82%] ··· series_methods.Map.time_map                                                                  1.13±0.1ms;...
[ 83.33%] ··· series_methods.NSort.time_nlargest                                                           2.95±0.1ms;...
[ 84.85%] ··· series_methods.NSort.time_nsmallest                                                         2.33±0.08ms;...
[ 86.36%] ··· series_methods.SeriesConstructor.time_constructor                                             163±0.9μs;...
[ 87.88%] ··· series_methods.SeriesGetattr.time_series_datetimeindex_repr                                     3.21±0.02μs
[ 89.39%] ··· series_methods.ValueCounts.time_value_counts                                                2.24±0.02ms;...
[ 90.91%] ··· algorithms.Hashing.time_frame                                                                    23.2±0.7ms
[ 92.42%] ··· algorithms.Hashing.time_series_categorical                                                       5.36±0.2ms
[ 93.94%] ··· algorithms.Hashing.time_series_dates                                                             3.35±0.2ms
[ 95.45%] ··· algorithms.Hashing.time_series_float                                                            3.49±0.08ms
[ 96.97%] ··· algorithms.Hashing.time_series_int                                                              3.23±0.05ms
[ 98.48%] ··· algorithms.Hashing.time_series_string                                                            15.5±0.5ms
[100.00%] ··· algorithms.Hashing.time_series_timedeltas                                                        3.43±0.2ms
```
</details>

```
       before           after         ratio
     [475e391e]       [567e7bb8]
-      45.2±0.7ms       38.6±0.5ms     0.85  series_methods.IsInFloat64.time_isin_nan_values
-      45.6±0.6ms       37.9±0.4ms     0.83  series_methods.IsInFloat64.time_isin_few_different

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
``` looks fine. ping on green. @jreback Done thanks @realead nice patch, keep em coming!