POC: Use khash sets instead of maps for isin
This is a POC towards what @realead described in #39799

The IsIn benchmarks overall seemed a bit unreliable, but I could consistently get results of `algos.isin.IsinWithArangeSorted` that look like:

```sh
>>> asv continuous upstream/main HEAD -b algos.isin.IsinWithArangeSorted
       before           after         ratio
     [38881793]       [939529bb]
     <main>           <khash-set-poc>
-        47.3±1μs       42.9±0.3μs     0.91  algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 2000)
-        715±30μs          601±4μs     0.84  algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 100000)
-         107±5μs         89.6±1μs     0.84  algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 8000)
-      26.5±0.4ms       12.2±0.7ms     0.46  algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 1000000)

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```

The performance improvement on the largest dataset might be in line with @realead expectation that 

`For big datasets, the running time of the above algorithms is dominated by cache-misses. Thus having twice as many cache-misses, because also values are touched could mean a factor 2 slowdown`

Just found out about the perf tool to measure branch prediction misses. Just ran this for a benchmark:

`perf stat python -c "import pandas as pd; import numpy as np; N = 1_000_000; ser = pd.Series(np.arange(N)); vals = np.arange(N); ser.isin(vals)"`

On main I get the following:

```sh
 Performance counter stats for 'python -c import pandas as pd; import numpy as np; N = 1_000_000; ser = pd.Series(np.arange(N)); vals = np.arange(N); ser.isin(vals)':

          1,965.47 msec task-clock                       #    2.357 CPUs utilized             
               147      context-switches                 #   74.791 /sec                      
                 0      cpu-migrations                   #    0.000 /sec                      
            31,457      page-faults                      #   16.005 K/sec                     
     3,936,570,319      cpu_core/cycles/                 #    2.003 G/sec                     
     3,802,321,801      cpu_atom/cycles/                 #    1.935 G/sec                       (52.15%)
     7,214,481,236      cpu_core/instructions/           #    3.671 G/sec                     
     8,924,412,911      cpu_atom/instructions/           #    4.541 G/sec                       (52.15%)
     1,376,744,013      cpu_core/branches/               #  700.464 M/sec                     
     1,457,321,116      cpu_atom/branches/               #  741.460 M/sec                       (52.15%)
        23,755,435      cpu_core/branch-misses/          #   12.086 M/sec                     
            76,473      cpu_atom/branch-misses/          #   38.908 K/sec                       (52.15%)
    19,950,214,260      cpu_core/slots/                  #   10.150 G/sec                     
     6,988,756,509      cpu_core/topdown-retiring/       #     34.9% Retiring                 
     3,077,912,645      cpu_core/topdown-bad-spec/       #     15.4% Bad Speculation          
     5,841,369,682      cpu_core/topdown-fe-bound/       #     29.2% Frontend Bound           
     4,090,935,951      cpu_core/topdown-be-bound/       #     20.5% Backend Bound            
       607,579,968      cpu_core/topdown-heavy-ops/      #      3.0% Heavy Operations          #     31.9% Light Operations         
     2,935,493,211      cpu_core/topdown-br-mispredict/  #     14.7% Branch Mispredict         #      0.7% Machine Clears           
     2,838,977,426      cpu_core/topdown-fetch-lat/      #     14.2% Fetch Latency             #     15.0% Fetch Bandwidth          
     2,956,225,784      cpu_core/topdown-mem-bound/      #     14.8% Memory Bound              #      5.7% Core Bound               

       0.833991099 seconds time elapsed

       1.010541000 seconds user
       0.958761000 seconds sys
```

Versus this PR:

```sh
 Performance counter stats for 'python -c import pandas as pd; import numpy as np; N = 1_000_000; ser = pd.Series(np.arange(N)); vals = np.arange(N); ser.isin(vals)':

          1,954.42 msec task-clock                       #    2.370 CPUs utilized             
               215      context-switches                 #  110.007 /sec                      
                 2      cpu-migrations                   #    1.023 /sec                      
            26,302      page-faults                      #   13.458 K/sec                     
     3,927,968,672      cpu_core/cycles/                 #    2.010 G/sec                     
     3,786,117,128      cpu_atom/cycles/                 #    1.937 G/sec                       (52.56%)
     7,217,333,673      cpu_core/instructions/           #    3.693 G/sec                     
     8,849,613,601      cpu_atom/instructions/           #    4.528 G/sec                       (52.56%)
     1,378,602,285      cpu_core/branches/               #  705.376 M/sec                     
     1,443,771,308      cpu_atom/branches/               #  738.720 M/sec                       (52.56%)
        23,818,420      cpu_core/branch-misses/          #   12.187 M/sec                     
           299,996      cpu_atom/branch-misses/          #  153.496 K/sec                       (52.56%)
    19,880,779,164      cpu_core/slots/                  #   10.172 G/sec                     
     6,961,062,372      cpu_core/topdown-retiring/       #     35.0% Retiring                 
     3,064,425,117      cpu_core/topdown-bad-spec/       #     15.4% Bad Speculation          
     5,844,723,817      cpu_core/topdown-fe-bound/       #     29.4% Frontend Bound           
     4,026,716,906      cpu_core/topdown-be-bound/       #     20.2% Backend Bound            
       600,833,950      cpu_core/topdown-heavy-ops/      #      3.0% Heavy Operations          #     32.0% Light Operations         
     2,926,332,561      cpu_core/topdown-br-mispredict/  #     14.7% Branch Mispredict         #      0.7% Machine Clears           
     2,818,938,163      cpu_core/topdown-fetch-lat/      #     14.2% Fetch Latency             #     15.2% Fetch Bandwidth          
     2,943,103,050      cpu_core/topdown-mem-bound/      #     14.8% Memory Bound              #      5.4% Core Bound               

       0.824527840 seconds time elapsed

       1.034003000 seconds user
       0.924299000 seconds sys
```

So not sure this really makes a big difference for branch predictions. Will have to research more This pull request is stale because it has been open for thirty days with no activity. Please [update](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#updating-your-pull-request) and respond to this comment if you're still interested in working on this. Closing for now but would be nice to pick up again in the future Expanded this to other primitives aside from int64 / uint64. Across the board seems like this helps, although there are some regressions. Needs more investigation

<details>

```
| Change   | Before [daa9cdb1] <csv-perf>   | After [ed3f0467] <khash-set-poc>   |   Ratio | Benchmark (Parameter)                                                                    |
|----------|--------------------------------|------------------------------------|---------|------------------------------------------------------------------------------------------|
| +        | 2.26±0.2ms                     | 14.5±0.5ms                         |    6.44 | algos.isin.IsinWithArange.time_isin(<class 'numpy.uint64'>, 1000, -2)                    |
| +        | 1.36±0.04ms                    | 1.73±0.2ms                         |    1.27 | algos.isin.IsIn.time_isin_empty('category[object]')                                      |
| +        | 60.4±2μs                       | 74.8±2μs                           |    1.24 | algos.isin.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 2000, 'inside')        |
| +        | 1.39±0.06ms                    | 1.68±0.2ms                         |    1.2  | algos.isin.IsIn.time_isin_empty('category[int]')                                         |
| +        | 62.0±2μs                       | 74.0±3μs                           |    1.19 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.float64'>, 2000)                 |
| -        | 32.9±1μs                       | 29.9±1μs                           |    0.91 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.int64'>, 11, 'outside')   |
| -        | 33.1±0.2μs                     | 30.0±1μs                           |    0.91 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 11, 'inside')   |
| -        | 66.1±0.9ms                     | 59.7±0.4ms                         |    0.9  | algos.isin.IsInLongSeriesLookUpDominates.time_isin('object', 5, 'monotone_hits')         |
| -        | 424±10μs                       | 381±4μs                            |    0.9  | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 15, 'outside')  |
| -        | 142±2μs                        | 126±8μs                            |    0.89 | algos.isin.IsIn.time_isin('Int64')                                                       |
| -        | 3.32±0.2ms                     | 2.96±0.1ms                         |    0.89 | algos.isin.IsInForObjects.time_isin('long_floats', 'long_floats')                        |
| -        | 42.3±1μs                       | 37.7±0.4μs                         |    0.89 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 11, 'inside')  |
| -        | 42.5±0.7μs                     | 38.0±0.6μs                         |    0.89 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 11, 'outside') |
| -        | 328±10μs                       | 291±2μs                            |    0.89 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 15, 'inside')   |
| -        | 1.76±0.1ms                     | 1.57±0.02ms                        |    0.89 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 17, 'outside')  |
| -        | 33.0±1μs                       | 29.2±0.4μs                         |    0.88 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 11, 'outside')  |
| -        | 2.81±0.1ms                     | 2.48±0.07ms                        |    0.88 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 18, 'inside')   |
| -        | 41.4±0.3μs                     | 36.3±0.6μs                         |    0.88 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 2000)                   |
| -        | 2.53±0.4ms                     | 2.19±0.09ms                        |    0.87 | algos.isin.IsIn.time_isin_categorical('datetime64[ns]')                                  |
| -        | 62.9±3μs                       | 54.7±0.7μs                         |    0.87 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 12, 'inside')  |
| -        | 8.16±0.8ms                     | 7.13±0.07ms                        |    0.87 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.int64'>, 19, 'outside')   |
| -        | 1.37±0.03ms                    | 1.19±0.02ms                        |    0.87 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 17, 'inside')   |
| -        | 3.66±0.1ms                     | 3.17±0.03ms                        |    0.87 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 18, 'outside')  |
| -        | 2.55±0.1ms                     | 2.22±0.05ms                        |    0.87 | algos.isin.IsinWithArange.time_isin(<class 'numpy.uint64'>, 2000, 0)                     |
| -        | 40.1±0.4ms                     | 34.6±0.9ms                         |    0.86 | algos.isin.IsInLongSeriesValuesDominate.time_isin('int32', 'monotone')                   |
| -        | 16.1±0.9ms                     | 13.8±0.09ms                        |    0.86 | algos.isin.IsinWithArange.time_isin(<class 'numpy.uint64'>, 1000, 2)                     |
| -        | 41.0±2μs                       | 35.1±0.7μs                         |    0.86 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.uint64'>, 2000)                  |
| -        | 3.42±0.1ms                     | 2.89±0.05ms                        |    0.85 | algos.isin.IsInFloat64.time_isin(<class 'numpy.float64'>, 'only_nans_values')            |
| -        | 16.5±2ms                       | 14.0±0.1ms                         |    0.85 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 19, 'outside') |
| -        | 3.10±0.05ms                    | 2.64±0.1ms                         |    0.85 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.int64'>, 18, 'inside')    |
| -        | 6.37±0.6ms                     | 5.42±0.1ms                         |    0.85 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.int64'>, 19, 'inside')    |
| -        | 143±7μs                        | 121±3μs                            |    0.84 | algos.isin.IsIn.time_isin_categorical('Int64')                                           |
| -        | 102±1μs                        | 85.1±2μs                           |    0.84 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 8000)                   |
| -        | 202±100μs                      | 168±3μs                            |    0.83 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 13, 'outside') |
| -        | 222±1μs                        | 185±4μs                            |    0.83 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 14, 'outside')  |
| -        | 2.68±0.4ms                     | 2.22±0.04ms                        |    0.83 | algos.isin.IsinWithArange.time_isin(<class 'numpy.uint64'>, 1000, 0)                     |
| -        | 734±7μs                        | 610±10μs                           |    0.83 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 100000)                 |
| -        | 41.3±3ms                       | 33.9±2ms                           |    0.82 | algos.isin.IsInLongSeriesLookUpDominates.time_isin('float64', 5, 'random_hits')          |
| -        | 473±50μs                       | 390±2μs                            |    0.82 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 14, 'inside')  |
| -        | 19.0±2ms                       | 15.5±0.3ms                         |    0.82 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 19, 'inside')  |
| -        | 48.8±0.6μs                     | 39.4±0.8μs                         |    0.81 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 12, 'inside')   |
| -        | 141±3μs                        | 113±3μs                            |    0.8  | algos.isin.IsIn.time_isin_categorical('int64')                                           |
| -        | 8.04±0.6ms                     | 6.45±0.1ms                         |    0.8  | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 19, 'outside')  |
| -        | 51.8±8μs                       | 41.0±0.4μs                         |    0.79 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.int64'>, 12, 'inside')    |
| -        | 168±5μs                        | 132±2μs                            |    0.78 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 14, 'inside')   |
| -        | 18.2±2ms                       | 14.0±0.7ms                         |    0.77 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 20, 'outside')  |
| -        | 82.9±1ms                       | 63.6±6ms                           |    0.77 | algos.isin.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 900000, 'outside')     |
| -        | 3.51±0.06ms                    | 2.65±0.4ms                         |    0.75 | algos.isin.IsInFloat64.time_isin(<class 'numpy.float64'>, 'few_different_values')        |
| -        | 6.62±1ms                       | 4.99±0.05ms                        |    0.75 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 19, 'inside')   |
| -        | 15.8±2ms                       | 11.7±0.7ms                         |    0.74 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.int64'>, 20, 'inside')    |
| -        | 79.8±2μs                       | 58.9±0.7μs                         |    0.74 | algos.isin.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 2000, 'outside')       |
| -        | 81.3±5μs                       | 59.6±2μs                           |    0.73 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 12, 'outside') |
| -        | 101±9ms                        | 73.9±3ms                           |    0.73 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.float64'>, 1000000)              |
| -        | 115±8μs                        | 83.5±5μs                           |    0.72 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 13, 'outside')  |
| -        | 14.1±0.5ms                     | 10.1±0.06ms                        |    0.71 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 20, 'inside')   |
| -        | 103±3μs                        | 73.1±0.7μs                         |    0.71 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.uint64'>, 8000)                  |
| -        | 156±40μs                       | 106±9μs                            |    0.68 | algos.isin.IsIn.time_isin('int64')                                                       |
| -        | 54.6±1μs                       | 36.2±2μs                           |    0.66 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 12, 'outside')  |
| -        | 733±30μs                       | 448±3μs                            |    0.61 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.uint64'>, 100000)                |
| -        | 27.9±2ms                       | 13.0±1ms                           |    0.47 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 1000000)                |
| -        | 26.7±2ms                       | 7.49±0.2ms                         |    0.28 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.uint64'>, 1000000)               |

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
```

</details>

Haven't done the PyObject case yet as the naming conventions there don't follow the same conventions as described here. Might need to tackle that in a follow up OK got everything set up. Some of the prior regressions were due to improper macro use / declarations. Surprised those didn't throw compiler errors...but there are many layers of indirection between tempita / khash. Something to investigate another day...

Here are the results for a full run of the isin benchmarks - looks like this does help with scalability as larger datasets are showing 20-50% improvement

@mroeschke @jbrockmendel @realead for review

```
| Change   | Before [daa9cdb1] <csv-perf>   | After [5248e1aa] <khash-set-poc>   |   Ratio | Benchmark (Parameter)                                                                    |
|----------|--------------------------------|------------------------------------|---------|------------------------------------------------------------------------------------------|
| -        | 38.9±0.6μs                     | 35.4±0.3μs                         |    0.91 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 2000)                   |
| -        | 35.6±0.9ms                     | 32.3±0.6ms                         |    0.91 | algos.isin.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 750000, 'outside')     |
| -        | 86.2±1μs                       | 77.3±0.4μs                         |    0.9  | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.int64'>, 13, 'inside')    |
| -        | 30.7±0.7μs                     | 27.7±0.4μs                         |    0.9  | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 1000)                   |
| -        | 41.9±1μs                       | 37.3±1μs                           |    0.89 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 11, 'outside') |
| -        | 171±2μs                        | 152±3μs                            |    0.89 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 13, 'inside')  |
| -        | 317±30μs                       | 283±4μs                            |    0.89 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 15, 'inside')   |
| -        | 60.1±1μs                       | 53.5±1μs                           |    0.89 | algos.isin.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 2000, 'inside')        |
| -        | 34.5±2μs                       | 30.4±1μs                           |    0.88 | algos.isin.IsInIndexes.time_isin_index                                                   |
| -        | 96.2±4μs                       | 83.3±0.6μs                         |    0.87 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 8000)                   |
| -        | 138±6μs                        | 119±0.6μs                          |    0.86 | algos.isin.IsIn.time_isin('Int64')                                                       |
| -        | 38.0±0.6ms                     | 32.7±0.3ms                         |    0.86 | algos.isin.IsInLongSeriesValuesDominate.time_isin('int32', 'monotone')                   |
| -        | 164±2μs                        | 141±4μs                            |    0.86 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 14, 'inside')   |
| -        | 95.0±1μs                       | 81.6±1μs                           |    0.86 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.uint64'>, 8000)                  |
| -        | 138±4μs                        | 117±1μs                            |    0.85 | algos.isin.IsIn.time_isin_categorical('int64')                                           |
| -        | 51.5±1μs                       | 43.6±0.9μs                         |    0.85 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.int64'>, 12, 'outside')   |
| -        | 47.7±10μs                      | 40.0±1μs                           |    0.84 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.int64'>, 12, 'inside')    |
| -        | 42.5±3μs                       | 35.6±0.3μs                         |    0.84 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.uint64'>, 2000)                  |
| -        | 122±2μs                        | 99.6±1μs                           |    0.82 | algos.isin.IsIn.time_isin('int64')                                                       |
| -        | 46.9±0.5μs                     | 38.3±0.3μs                         |    0.82 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 12, 'inside')   |
| -        | 706±9μs                        | 582±5μs                            |    0.82 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.uint64'>, 100000)                |
| -        | 142±5μs                        | 115±1μs                            |    0.81 | algos.isin.IsIn.time_isin_categorical('Int64')                                           |
| -        | 721±10μs                       | 587±4μs                            |    0.81 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 100000)                 |
| -        | 87.4±2ms                       | 70.3±2ms                           |    0.8  | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.float64'>, 1000000)              |
| -        | 83.7±4ms                       | 64.7±2ms                           |    0.77 | algos.isin.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 900000, 'inside')      |
| -        | 84.9±1μs                       | 63.9±0.2μs                         |    0.75 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 13, 'inside')   |
| -        | 3.15±0.06ms                    | 2.30±0.1ms                         |    0.73 | algos.isin.IsInFloat64.time_isin('Float64', 'only_nans_values')                          |
| -        | 109±0.9μs                      | 79.4±1μs                           |    0.73 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 13, 'outside')  |
| -        | 51.5±0.9μs                     | 36.8±0.8μs                         |    0.72 | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 12, 'outside')  |
| -        | 79.0±3ms                       | 56.8±1ms                           |    0.72 | algos.isin.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 900000, 'outside')     |
| -        | 3.36±0.05ms                    | 2.35±0.07ms                        |    0.7  | algos.isin.IsInFloat64.time_isin(<class 'numpy.float64'>, 'few_different_values')        |
| -        | 77.6±3μs                       | 54.6±0.6μs                         |    0.7  | algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 12, 'outside') |
| -        | 3.16±0.1ms                     | 2.18±0.03ms                        |    0.69 | algos.isin.IsInFloat64.time_isin(<class 'numpy.float64'>, 'only_nans_values')            |
| -        | 25.5±2ms                       | 13.2±0.7ms                         |    0.52 | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.uint64'>, 1000000)               |
| -        | 25.7±2ms                       | 12.8±0.6ms                         |    0.5  | algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 1000000)                |

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```