ASV: add benchmarks for groupby cython aggregations
I noticed that we currently don't really have a groupby benchmark that specifically targets the cython aggregations. We have of course benchmarks that call those, but eg `GroupByMethods` is setup in such a way that it's mostly benchmarking the factorization and post-processing (also useful of course), while eg for sum only 7% is spent in the actual `groupby_add` algorithm.

So therefore this PR is adding an additional benchmark more targetted at the cython aggregation (where 30-50% of the time is spent in the actual aggregation function, so we will more easily catch regressions/improvements there)

For now I only added "float64". I could also add "float32", but not sure how useful that is (since they are all using fused types, it's the same implementation for both dtypes.
And the output of a run of those benchmarks (to give an idea of timings):

```
$ asv dev -b GroupByCythonAgg
· Discovering benchmarks
· Running 1 total benchmarks (1 commits * 1 environments * 1 benchmarks)
[  0.00%] ·· Benchmarking existing
[ 50.00%] ··· groupby.GroupByCythonAgg.time_frame_agg                                                                                         ok
[ 50.00%] ··· ========= ========== ========== ========== ========== ========== ========= ========== ========== ==========
              --                                                      method                                             
              --------- -------------------------------------------------------------------------------------------------
                dtype      sum        prod       min        max        mean      median     var       first       last   
              ========= ========== ========== ========== ========== ========== ========= ========== ========== ==========
               float64   73.6±0ms   66.8±0ms   63.8±0ms   62.5±0ms   61.1±0ms   301±0ms   78.0±0ms   90.2±0ms   80.1±0ms 
              ========= ========== ========== ========== ========== ========== ========= ========== ========== ==========
```

Only `median` is quite a bit slower (300ms) (so if needed I can make the dataframe smaller just for this case if desired) Do you see any way to refactor GroupByMethods to make it more useful? The benchmarks in this file take a very long time to run as is (in real time) so wondering if we can streamline them The issues with fitting it into GroupByMethods: it's also benchmarking different dtypes of the key column, which I am not interested in here. It also benchmarks more methods than just the cython aggregations. It also has a different number of groups / size of groups trade-off, and it is generally useful to have a bit varying benchmarks on this aspect.

To be clear, I agree that we should be careful about adding benchmarks as they already take a long time (that's also the reason that for now I didn't add float32, because I don't think it would add much value in addition to float64, as there is already a specific benchmark for float32 for a single case)
 What might be more useful to trim down the time to run the groupby benchmarks is to look a bit more into `GroupByMethods` itself (regardless of this PR), because I notice some of its parameter combinations are taking much longer than others. To be more specific, this is the quick `asv dev` run (so timings not super reliable, but a good general idea) of that class:

```
[ 50.00%] ··· ========== ============== ========== ================
              --                                application        
              ------------------------- ---------------------------
                dtype        method       direct    transformation 
              ========== ============== ========== ================
                 int          all        1.22±0ms      1.22±0ms    
                 int          any        1.17±0ms      1.48±0ms    
                 int         bfill       1.27±0ms      1.34±0ms    
                 int         count       1.05±0ms      990±0μs     
                 int        cumcount     2.41±0ms      1.50±0ms    
                 int         cummax      1.62±0ms      1.23±0ms    
                 int         cummin      1.11±0ms      1.09±0ms    
                 int        cumprod      1.24±0ms      1.49±0ms    
                 int         cumsum      1.13±0ms      2.10±0ms    
                 int        describe     1.47±0s       1.50±0s     
                 int         ffill       1.23±0ms      1.19±0ms    
                 int         first       1.36±0ms      1.59±0ms    
                 int          head       1.10±0ms      1.28±0ms    
                 int          last       1.40±0ms      1.80±0ms    
                 int          mad        453±0ms       433±0ms     
                 int          max        1.82±0ms      1.62±0ms    
                 int          min        1.84±0ms      2.39±0ms    
                 int         median      1.98±0ms      2.17±0ms    
                 int          mean       2.20±0ms      2.77±0ms    
                 int        nunique      2.61±0ms      2.64±0ms    
                 int       pct_change    4.84±0ms      3.59±0ms    
                 int          prod       1.74±0ms      1.94±0ms    
                 int        quantile     1.73±0ms      3.59±0ms    
                 int          rank       1.78±0ms      1.65±0ms    
                 int          sem        6.12±0ms      2.09±0ms    
                 int         shift       1.15±0ms      2.45±0ms    
                 int          size       1.68±0ms      1.30±0ms    
                 int          skew       120±0ms       112±0ms     
                 int          std        1.22±0ms      2.34±0ms    
                 int          sum        2.07±0ms      2.17±0ms    
                 int          tail       1.23±0ms      1.30±0ms    
                 int         unique      74.8±0ms      70.7±0ms    
                 int      value_counts   1.93±0ms      4.07±0ms    
                 int          var        1.61±0ms      1.36±0ms    
                float         all        1.44±0ms      1.64±0ms    
                float         any        1.18±0ms      1.29±0ms    
                float        bfill       1.48±0ms      1.63±0ms    
                float        count       1.23±0ms      1.19±0ms    
                float       cumcount     3.46±0ms      1.23±0ms    
                float        cummax      1.62±0ms      2.50±0ms    
                float        cummin      1.39±0ms      2.28±0ms    
                float       cumprod      3.61±0ms      1.87±0ms    
                float        cumsum      2.43±0ms      1.44±0ms    
                float       describe     2.30±0s       2.77±0s     
                float        ffill       1.54±0ms      1.56±0ms    
                float        first       1.62±0ms      1.80±0ms    
                float         head       1.44±0ms      1.46±0ms    
                float         last       1.59±0ms      2.07±0ms    
                float         mad        965±0ms       893±0ms     
                float         max        1.73±0ms      3.33±0ms    
                float         min        1.41±0ms      2.58±0ms    
                float        median      3.02±0ms      1.71±0ms    
                float         mean       3.47±0ms      4.26±0ms    
                float       nunique      1.45±0ms      4.04±0ms    
                float      pct_change    2.89±0ms      2.74±0ms    
                float         prod       2.08±0ms      4.08±0ms    
                float       quantile     2.53±0ms      1.83±0ms    
                float         rank       2.95±0ms      4.36±0ms    
                float         sem        2.87±0ms      3.34±0ms    
                float        shift       1.91±0ms      1.09±0ms    
                float         size       1.33±0ms      1.99±0ms    
                float         skew       222±0ms       211±0ms     
                float         std        1.42±0ms      1.67±0ms    
                float         sum        1.70±0ms      2.15±0ms    
                float         tail       1.41±0ms      2.64±0ms    
                float        unique      105±0ms       103±0ms     
                float     value_counts   1.91±0ms      2.45±0ms    
                float         var        2.69±0ms      1.48±0ms    
                object        all        2.01±0ms      1.34±0ms    
                object        any        1.89±0ms      884±0μs     
                object       bfill       1.36±0ms      2.23±0ms    
                object       count       1.63±0ms      842±0μs     
                object      cumcount     1.41±0ms      1.35±0ms    
                object       cummax        n/a           n/a       
                object       cummin        n/a           n/a       
                object      cumprod        n/a           n/a       
                object       cumsum        n/a           n/a       
                object      describe       n/a           n/a       
                object       ffill       1.48±0ms      1.03±0ms    
                object       first       4.86±0ms      1.56±0ms    
                object        head       2.53±0ms      1.40±0ms    
                object        last       3.28±0ms      1.83±0ms    
                object        mad          n/a           n/a       
                object        max          n/a           n/a       
                object        min          n/a           n/a       
                object       median        n/a           n/a       
                object        mean         n/a           n/a       
                object      nunique      1.08±0ms      1.49±0ms    
                object     pct_change      n/a           n/a       
                object        prod         n/a           n/a       
                object      quantile       n/a           n/a       
                object        rank       3.51±0ms      1.69±0ms    
                object        sem          n/a           n/a       
                object       shift       970±0μs       2.50±0ms    
                object        size       2.39±0ms      1.12±0ms    
                object        skew         n/a           n/a       
                object        std          n/a           n/a       
                object        sum          n/a           n/a       
                object        tail       2.66±0ms      1.29±0ms    
                object       unique      1.71±0ms      3.25±0ms    
                object    value_counts   1.65±0ms      1.91±0ms    
                object        var          n/a           n/a       
               datetime       all        2.28±0ms      1.10±0ms    
               datetime       any        1.24±0ms      1.40±0ms    
               datetime      bfill       1.16±0ms      1.98±0ms    
               datetime      count       1.30±0ms      1.43±0ms    
               datetime     cumcount     1.27±0ms      2.19±0ms    
               datetime      cummax        n/a           n/a       
               datetime      cummin      1.38±0ms      2.66±0ms    
               datetime     cumprod        n/a           n/a       
               datetime      cumsum        n/a           n/a       
               datetime     describe       n/a           n/a       
               datetime      ffill       979±0μs       1.45±0ms    
               datetime      first       2.38±0ms      1.35±0ms    
               datetime       head       2.54±0ms      1.16±0ms    
               datetime       last       1.46±0ms      1.57±0ms    
               datetime       mad          n/a           n/a       
               datetime       max        1.71±0ms      2.12±0ms    
               datetime       min        1.58±0ms      1.52±0ms    
               datetime      median        n/a           n/a       
               datetime       mean         n/a           n/a       
               datetime     nunique      1.48±0ms      1.65±0ms    
               datetime    pct_change      n/a           n/a       
               datetime       prod         n/a           n/a       
               datetime     quantile     1.41±0ms      1.39±0ms    
               datetime       rank       1.51±0ms      1.31±0ms    
               datetime       sem          n/a           n/a       
               datetime      shift       887±0μs       901±0μs     
               datetime       size       1.06±0ms      1.01±0ms    
               datetime       skew         n/a           n/a       
               datetime       std          n/a           n/a       
               datetime       sum          n/a           n/a       
               datetime       tail       1.52±0ms      2.11±0ms    
               datetime      unique      121±0ms       105±0ms     
               datetime   value_counts   1.91±0ms      3.78±0ms    
               datetime       var          n/a           n/a       
              ========== ============== ========== ================
```
So most are small benchmarks, but eg the `describe` ones take more than a second. And also the `mad` ones take 500ms to 1 second. 
Reducing the time for those two cases will probably already help a lot to get overall benchmark run time down.
 > So most are small benchmarks, but eg the describe ones take more than a second. And also the mad ones take 500ms to 1 second.
Reducing the time for those two cases will probably already help a lot to get overall benchmark run time down

+1 thanks @jorisvandenbossche (happy to have a followup for fixing those identified slow benchmarks). maybe even create an issue to generally reduce time on longish benchmarks (as a good first issue).