Less blocks in groupby
This isn't complete (2 tests fail) but this is arguably controversial, so putting up for review.

With #29124 we *could* get rid of the DataFrame aggregation methods that go by blocks. This would hopefully make our code easier to grok and move towards only "one way of doing things" in groupby.

This causes a definite regression for very wide DataFrames with a small number of rows. If you look at the new benchmark created, when testing Frames of N rows x 10_000 columns where N is one of 100, 1000 or 10_000 here are the results:

```sh
       before           after         ratio
     [6b3ba986]       [124a135a]
     <categorical-regression>       <easier-groupby>
+      3.94±0.2ms        2.55±0.1s   646.23  groupby.GroupByWide.time_wide(100)
+        38.3±6ms       2.49±0.02s    64.91  groupby.GroupByWide.time_wide(1000)
```

So drastically slower at 100 rows, but we've already reached a break-even point by the time we get to 10,000 rows.

The rest of the benchmarks don't cover wide data frames that well but here are the results for posterity (I think these are noise)

```sh
       before           after         ratio
     [6b3ba986]       [124a135a]
     <categorical-regression>       <easier-groupby>
+        862±30μs       1.15±0.1ms     1.34  groupby.GroupByMethods.time_dtype_as_field('int', 'cummin', 'direct')
+         412±7μs         533±50μs     1.29  groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'direct')
+        300±10μs         366±10μs     1.22  groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'direct')
+        375±10ms          455±6ms     1.21  groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'transformation')
+         287±6μs         335±20μs     1.17  groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'direct')
+         252±2ms         290±10ms     1.15  groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'direct')
+         414±3μs         469±10μs     1.13  groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'transformation')
+         398±9ms          450±8ms     1.13  groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'direct')
```

@jbrockmendel 
Hello @WillAyd! 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 2019-11-21 04:54:58 UTC Here are graphs for the cProfile output using both methods and the below code:

```python
>>> import pandas as pd
>>> import numpy as np
>>> import cProfile
>>> grp = pd.DataFrame(np.ones((1, 100_00))).groupby([1])
>>> cProfile.run('grp.sum()', 'old_grpbystats')
```

![old_output](https://user-images.githubusercontent.com/609873/69270159-4d160280-0b87-11ea-82c5-b6d331e21e37.png)
![new_output](https://user-images.githubusercontent.com/609873/69270244-6dde5800-0b87-11ea-881f-ee6545b69627.png)
 We absolutely do not want to remove the blocks. Just make the code simpler / easier. Yea making things simpler / easier is the point of this as well. Just wasn't sure if performance of frames mostly composed of numpy types that are wider than they are larger was a huge sticking point to optimize on

I'll check out some alternates; I still think this method isn't required and adds a lot of unnecessary complexity / exception catching. It *might* make more sense if we allow `_iterate_slices` in the DataFrameGroupBy class to return an Iterable of Blocks / Series objects instead of just the latter i would lie to see as a first step moving this code to internals as an iterator
would make things a lot simpler Cool makes sense. Thanks for the feedback On the cProfile graphs are there any particular points of interest? @WillAyd i need to pivot to work on arithmetic for a bit, but if you're interested in taking taking my part of the groupby baton, the functions libreduction.check_result_array and libreduction._extract_result have analogues in the non-fast methods, but they behave slightly differently.  I think its worthwhile to get those in alignment or at least document why they are slightly different. Cool yea will do @jbrockmendel  are the asvs here worth salvaging?