CLN: use dispatch_to_series where possible
A bunch of PRs touching DataFrame ops have gone through recently.  This does some follow-up cleanup to unify the way things are done across a few different methods.
Hello @jbrockmendel! Thanks for updating the PR.

 - There are no PEP8 issues in the file [`pandas/core/frame.py`](https://github.com/jbrockmendel/pandas/blob/f9e21ad8aedf189cb82794c05b7a73a35b5e1cf8/pandas/core/frame.py) !

 - In the file [`pandas/core/ops.py`](https://github.com/jbrockmendel/pandas/blob/f9e21ad8aedf189cb82794c05b7a73a35b5e1cf8/pandas/core/ops.py), following                are the PEP8 issues :
> [Line 1646:13](https://github.com/jbrockmendel/pandas/blob/f9e21ad8aedf189cb82794c05b7a73a35b5e1cf8/pandas/core/ops.py#L1646): [W504](https://duckduckgo.com/?q=pep8%20W504) line break after binary operator




##### Comment last updated on September 07, 2018 at 16:19 Hours UTC can you check perf. rebase as well. > can you check perf.

First attempt to check perf turned up a bug in master:

```
dti = pd.date_range('2016-01-01', periods=10000)
tdi = pd.timedelta_range('1', periods=10000)
tser = pd.Series(tdi)
df = pd.DataFrame({0: dti, 1: tdi})
>>> df.add(tser, axis=0)
```

Expected (which the PR gets right):
```
                              0                      1
0 2016-01-01 00:00:00.000000001 0 days 00:00:00.000000
1 2016-01-03 00:00:00.000000001 2 days 00:00:00.000000
2 2016-01-05 00:00:00.000000001 4 days 00:00:00.000000
```

master raises
```
ValueError: operands could not be broadcast together with shapes (20000,) (10000,) 
```

I'll add a test for this.

Non-broken cases, first a many-column case where we expect master to perform well:
```
df = pd.DataFrame(np.random.randn(100000, 60))
df[10:20] = df[10:20].astype('f4')
df[20:30] = df[20:30].astype('i8')
df[30:40] = df[30:40].astype('i4')
df[40:50] = df[40:50].astype('u8')
df[50:60] = df[50:60].astype('u4')

In [29]: %timeit out = df.add(df[0], axis=0)
100 loops, best of 3: 18.1 ms per loop   <-- master
100 loops, best of 3: 18.3 ms per loop  <-- PR
```

And a few-column case where we expect master to do poorly:
```
df = pd.DataFrame(np.random.randn(10000000, 6))
df[1] = df[1].astype('f4')
df[2] = df[2].astype('i8')
df[3] = df[3].astype('i4')
df[4] = df[4].astype('u8')
df[5] = df[5].astype('u4')

%timeit out = df.add(df[0], axis=0)
1 loop, best of 3: 903 ms per loop   <-- master
1 loop, best of 3: 582 ms per loop   <-- PR
```

As elsewhere, I expect perf to improve after #22284. this duplicates #22572 a lot. is this the original? > this duplicates #22572 a lot. is this the original?

This is original.  In the course of profiling this I found that this fixes a previously-unknown bug.  So this now needs tests and whatsnew etc.  #22572 splits off the still-easy part of this. To fully finish this off will require resolution to #22614. # [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/22534?src=pr&el=h1) Report
> :exclamation: No coverage uploaded for pull request base (`master@5eb9988`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).
> The diff coverage is `100%`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/22534/graphs/tree.svg?width=650&token=eZ4WkYLtcO&height=150&src=pr)](https://codecov.io/gh/pandas-dev/pandas/pull/22534?src=pr&el=tree)

```diff
@@            Coverage Diff            @@
##             master   #22534   +/-   ##
=========================================
  Coverage          ?   92.05%           
=========================================
  Files             ?      169           
  Lines             ?    50787           
  Branches          ?        0           
=========================================
  Hits              ?    46753           
  Misses            ?     4034           
  Partials          ?        0
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.46% <100%> (?)` | |
| #single | `42.29% <0%> (?)` | |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/22534?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/ops.py](https://codecov.io/gh/pandas-dev/pandas/pull/22534/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvb3BzLnB5) | `96.91% <100%> (ø)` | |
| [pandas/core/frame.py](https://codecov.io/gh/pandas-dev/pandas/pull/22534/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZnJhbWUucHk=) | `97.2% <100%> (ø)` | |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/22534?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/22534?src=pr&el=footer). Last update [5eb9988...8f0cdbc](https://codecov.io/gh/pandas-dev/pandas/pull/22534?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 can you rebase > can you rebase

Sure.  Big evening for merging.  Note comment above about #22614. Heh, looks like the other PRs merged this evening already cover this.  Closing.  Will need to follow-up with a test for the [bug](https://github.com/pandas-dev/pandas/pull/22534#issuecomment-417707933) that was accidentally fixed. great! I am not really sure about #22614, none of the options are really palatable. > I am not really sure about #22614, none of the options are really palatable.

Well we've de-facto been going down the path of option 1.  I actually prefer option 2 longer-term (better to discuss there), but for the time being correctness-first seems to favor option 1, and #22284 should take some of the pain out of it.