PERF: maybe_convert_numeric speedup
 xref #16043
 
Probably more to be done, but picks up some easy performance on the int path.  For some reason I'm having trouble running asv, here are some timings.

### setup
```
ints = np.array(list(range(1000000)), dtype=object)
floats = np.array([float(x) for x in range(1000000)], dtype=object)
ints_with_float = ints.copy()
ints_with_float[-1] = 1.2
```
### Master
```
In [2]: %timeit pd.to_numeric(floats)
10 loops, best of 3: 132 ms per loop

In [3]: %timeit pd.to_numeric(ints)
1 loop, best of 3: 437 ms per loop

In [4]: %timeit pd.to_numeric(ints_with_float)
1 loop, best of 3: 444 ms per loop
```

### PR
```
In [5]: %timeit pd.to_numeric(floats)
10 loops, best of 3: 88.9 ms per loop

In [6]: %timeit pd.to_numeric(ints)
10 loops, best of 3: 57.6 ms per loop

In [7]: %timeit pd.to_numeric(ints_with_float)
1 loop, best of 3: 392 ms per loop
```

the other thing is it *might* be worth it to actually try to cast once you have found an actual int (or a uint), 

```
In [13]: (ints.astype('int64', casting='unsafe') == ints).all()
Out[13]: True

In [14]: %timeit (ints.astype('int64', casting='unsafe') == ints).all()
55.5 ms ± 1.15 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```

At this point it *would* raise if you encounter a float so this is ok (you can do it in a try/except).

not sure if this is totally feasible though. OK, I've added some fastpath logic that will try and convert the whole array at once - have to see if anything breaks! This should go in 0.20, correct? I need to work through a couple more tests, but assuming I can get this to
work in a non-breaking way, yes I'd think this would be good for 0.20.
`to_numeric` performance with ints has regressed quite a bit since 0.19.1
(although is more functional now, to be fair)

On Mon, Apr 24, 2017 at 10:07 AM, Tom Augspurger <notifications@github.com>
wrote:

> This should go in 0.20, correct?
>
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 # [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/16104?src=pr&el=h1) Report
> Merging [#16104](https://codecov.io/gh/pandas-dev/pandas/pull/16104?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/e501e1d7d161da9596156e07201b1370ed2ee10e?src=pr&el=desc) will **decrease** coverage by `<.01%`.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/16104/graphs/tree.svg?token=eZ4WkYLtcO&src=pr&height=150&width=650)](https://codecov.io/gh/pandas-dev/pandas/pull/16104?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #16104      +/-   ##
==========================================
- Coverage   90.84%   90.84%   -0.01%     
==========================================
  Files         159      159              
  Lines       50775    50768       -7     
==========================================
- Hits        46126    46118       -8     
- Misses       4649     4650       +1
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `88.62% <ø> (-0.01%)` | :arrow_down: |
| #single | `40.33% <ø> (-0.01%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/16104?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/common.py](https://codecov.io/gh/pandas-dev/pandas/pull/16104?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvY29tbW9uLnB5) | `90.68% <0%> (-0.35%)` | :arrow_down: |
| [pandas/util/testing.py](https://codecov.io/gh/pandas-dev/pandas/pull/16104?src=pr&el=tree#diff-cGFuZGFzL3V0aWwvdGVzdGluZy5weQ==) | `79.07% <0%> (-0.16%)` | :arrow_down: |
| [pandas/api/types/\_\_init\_\_.py](https://codecov.io/gh/pandas-dev/pandas/pull/16104?src=pr&el=tree#diff-cGFuZGFzL2FwaS90eXBlcy9fX2luaXRfXy5weQ==) | `100% <0%> (ø)` | :arrow_up: |

------

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 nice! thanks @chris-b1  Yeah, I put the 0.21 milestone on it as it was about performance, but forgot it was for a perf regressiono in 0.20dev. So certainly fine. Thanks Chris!