PERF: speed up concat on Series by skipping unnecessary DataFrame creation
Removes an unnecessary `DataFrame` creation when dealing solely with `Series` objects, which reduces runtime of `concat`.

- [ ] xref #23362
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry

### Performance Comparison
```python
import pandas as pd
import numpy as np
a = np.random.randint(2000, 2100, size=100000)  # larger n than #23362 
b = np.random.randint(2000, 2100, size=100000)

x = pd.core.arrays.period_array(a, freq='B')
y = pd.core.arrays.period_array(b, freq='B')

s = pd.Series(x)
t = pd.Series(y)
```

#### Baseline
```python
%timeit x._concat_same_type([x, y])
202 µs ± 10.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit pd.concat([s, t], ignore_index=True)
839 µs ± 32.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

#### After
```python
%timeit pd.concat([s, t], ignore_index=True)
396 µs ± 12.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
And the excised code itself:
```python
In [13]: pd.DataFrame()._get_axis_number(0)
Out[13]: 0

In [14]: %timeit pd.DataFrame()._get_axis_number(0)
Out[14]: 312 µs ± 23.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
So roughly 40% of the runtime was being spent mapping `axis=0 -> axis=0`
Hello @qwhelan! Thanks for updating the PR.

 - There are no PEP8 issues in the file [`pandas/core/generic.py`](https://github.com/qwhelan/pandas/blob/486d3b86a2bde411d7e29ae1867eb08ed64e9a58/pandas/core/generic.py) !

 - There are no PEP8 issues in the file [`pandas/core/reshape/concat.py`](https://github.com/qwhelan/pandas/blob/486d3b86a2bde411d7e29ae1867eb08ed64e9a58/pandas/core/reshape/concat.py) !

 - There are no PEP8 issues in the file [`pandas/tests/generic/test_generic.py`](https://github.com/qwhelan/pandas/blob/486d3b86a2bde411d7e29ae1867eb08ed64e9a58/pandas/tests/generic/test_generic.py) !



##### Comment last updated on November 02, 2018 at 00:32 Hours UTC # [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/23404?src=pr&el=h1) Report
> Merging [#23404](https://codecov.io/gh/pandas-dev/pandas/pull/23404?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/93aba79e4fa822b4d029bda795c61d62fe5998e3?src=pr&el=desc) will **not change** coverage.
> The diff coverage is `100%`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/23404/graphs/tree.svg?width=650&token=eZ4WkYLtcO&height=150&src=pr)](https://codecov.io/gh/pandas-dev/pandas/pull/23404?src=pr&el=tree)

```diff
@@           Coverage Diff           @@
##           master   #23404   +/-   ##
=======================================
  Coverage   92.22%   92.22%           
=======================================
  Files         161      161           
  Lines       51191    51191           
=======================================
  Hits        47210    47210           
  Misses       3981     3981
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.6% <100%> (ø)` | :arrow_up: |
| #single | `42.26% <100%> (ø)` | :arrow_up: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/23404?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/reshape/concat.py](https://codecov.io/gh/pandas-dev/pandas/pull/23404/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvcmVzaGFwZS9jb25jYXQucHk=) | `97.6% <ø> (ø)` | :arrow_up: |
| [pandas/core/generic.py](https://codecov.io/gh/pandas-dev/pandas/pull/23404/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZ2VuZXJpYy5weQ==) | `96.81% <100%> (ø)` | :arrow_up: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/23404?src=pr&el=continue).
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> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/23404?src=pr&el=footer). Last update [93aba79...486d3b8](https://codecov.io/gh/pandas-dev/pandas/pull/23404?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 @jreback I was waiting for `asv` to finish before posting benchmarks, but I've updated the initial post with some basic benchmarks as that's taking longer than anticipated. @qwhelan doing this just on Py3 also feels hacky. I think everything in `_get_axis_number` is static, so you can do

```diff
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index db10494f0..14edc1d7f 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -358,18 +358,19 @@ class NDFrame(PandasObject, SelectionMixin):
             d.update(kwargs)
             return cls(data, **d)
 
-    def _get_axis_number(self, axis):
-        axis = self._AXIS_ALIASES.get(axis, axis)
+    @classmethod
+    def _get_axis_number(cls, axis):
+        axis = cls._AXIS_ALIASES.get(axis, axis)
         if is_integer(axis):
-            if axis in self._AXIS_NAMES:
+            if axis in cls._AXIS_NAMES:
                 return axis
         else:
             try:
-                return self._AXIS_NUMBERS[axis]
+                return cls._AXIS_NUMBERS[axis]
             except KeyError:
                 pass
         raise ValueError('No axis named {0} for object type {1}'
-                         .format(axis, type(self)))
+                         .format(axis, cls))
 
     def _get_axis_name(self, axis):
         axis = self._AXIS_ALIASES.get(axis, axis)
```

Will you see if the same change can be made to

- `_get_axis_name`
- `_get_axis`
- `_get_block_manager_axis`

Then, will you ensure that we have basic tests for using all those methods on classes (as well as instances). @jreback Yeah, I'm aware this change is trivial but just using it as an excuse to get my dev setup fully working for some more substantial perf patches that will be coming soon.

@TomAugspurger Thanks, I'll check into it. @TomAugspurger I implemented the `@classmethod` change for all but `_get_axis()` (which is used to get an axis instance, so shouldn't be a `@classmethod`).

There's decent existing test coverage but I added an explicit test of class vs instance invocations for all valid possible calls for good measure. Thanks, LGTM, though you have a linting error: 
https://github.com/pandas-dev/pandas/pull/23404#issuecomment-433784500 @TomAugspurger Thanks, resolved. lgtm. ping on green. @jreback All green, thanks! ```
In [4]: %timeit x._concat_same_type([x, y])
   ...:
99.8 µs ± 2.25 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

In [5]: %timeit pd.concat([s, t], ignore_index=True, copy=False)
   ...:
   ...:
194 µs ± 3.98 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```

so we're down to 2x. Let me know if you're interested in pushing this further @qwhelan (in a separate PR). thanks @qwhelan  @TomAugspurger Yeah, I'm looking at some related issues if you'd like to keep that issue open for now.