BENCH: asv csv reading benchmarks now rewind StringIO objects
In short, the `asv` benchmarks used for timing `read_csv` are actually reading in empty file-like objects most of the time at the moment. This is because the `setup()` method of the benchmarks is only called between **repeats** and not between **iterations** of repeats--see [writing benchmarks docs](http://asv.readthedocs.io/en/latest/writing_benchmarks.html#timing).

You can confirm this by simply printing the `size` of the dataframes you get from `read_csv` in any StringIO-dependent benchmarks--the **first** iteration of a repeat should have the correct size, but the rest will all be 0. You could also just print the memory address (StringIO object)--they'll all be the same after the first iteration of a repeat. I did this with @stefanv and @mattip in the NumPy context.

This can be quite confusing--a bit different than the paradigm you might expect in i.e, a unit test context--for more gory details see [my related comment in NumPy loadtxt() asv benchmarks](https://github.com/numpy/numpy/pull/11422#issuecomment-403247951). As noted there, one could probably avoid this by using an actual file object instead / if preferred, but realistically this just boils down to [how timeit works](https://stackoverflow.com/questions/8220801/how-to-use-timeit-module#comment11535453_8220943). Confounding the results with the seek is unfortunate, so working with file objects may be preferred if dumping files in the bench env is acceptable.

# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/21807?src=pr&el=h1) Report
> Merging [#21807](https://codecov.io/gh/pandas-dev/pandas/pull/21807?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/7a2fbce899aad302891ff9a95aeb1bd55efe533a?src=pr&el=desc) will **not change** coverage.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/21807/graphs/tree.svg?token=eZ4WkYLtcO&height=150&src=pr&width=650)](https://codecov.io/gh/pandas-dev/pandas/pull/21807?src=pr&el=tree)

```diff
@@           Coverage Diff           @@
##           master   #21807   +/-   ##
=======================================
  Coverage   92.05%   92.05%           
=======================================
  Files         170      170           
  Lines       50708    50708           
=======================================
  Hits        46677    46677           
  Misses       4031     4031
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.45% <ø> (ø)` | :arrow_up: |
| #single | `42.36% <ø> (ø)` | :arrow_up: |


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> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/21807?src=pr&el=footer). Last update [7a2fbce...90d3dda](https://codecov.io/gh/pandas-dev/pandas/pull/21807?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 Hello @tylerjereddy! Thanks for updating the PR.

 - In the file [`asv_bench/benchmarks/io/csv.py`](https://github.com/pandas-dev/pandas/blob/90d3dda04b9423a39223dab0103c80ee0124603b/asv_bench/benchmarks/io/csv.py), following are the PEP8 issues :

> [Line 184:80](https://github.com/pandas-dev/pandas/blob/90d3dda04b9423a39223dab0103c80ee0124603b/asv_bench/benchmarks/io/csv.py#L184): [E501](https://duckduckgo.com/?q=pep8%20E501) line too long (87 > 79 characters)



##### Comment last updated on July 26, 2018 at 20:11 Hours UTC Revised accordingly with before / after log files from `asv run -e -b "ReadCSV*" >& bench_results.txt` below. Note that this won't scrape in all of the affected benchmarks because not all CSV reading benchmarks follow a consistent naming scheme for regular expression selection, but should pull in many of them. Not sure if pandas CI does a quick (`--quick`) check of asv suite to make sure there are no bench errors on PRs (proposals to do this are in progress for NumPy / SciPy).

Before rewinding `StringIO` objects in csv reading benchmarks (commit hash: 7829ad32): [old_bench_results.txt](https://github.com/pandas-dev/pandas/files/2189026/old_bench_results.txt)

After rewinding `StringIO` objects in csv reading benchmarks: [bench_results.txt](https://github.com/pandas-dev/pandas/files/2188990/bench_results.txt)

I agree that using the property approach may alleviate future forgetting-to-rewind issues, though you'll likely notice that one of the classes where two `data1/2` objects are used instead of parametrization becomes slightly more complicated to think about.

Most `csv` reading benchmarks are now a bit slower (based on visual inspection of above logs), but this is obviously going to happen when you don't read in empty file objects by mistake for most of the iterations, and there's also the conflated time from adding in rewinds now too.

There's another alternative here if you prefer--setting `number = 1 ` in each of the class objects will force `setup()` to run between each iteration; I've preferred not to do that and instead allow the decisions about appropriate number of repeats & iterations to be offloaded to `asv` and `timeit` under the hood. Travis failure looks unrelated.  @tylerjereddy this is almost over the finish line. Revised / squashed / rebased / force pushed. Hopefully better now. thanks @tylerjereddy 