Poor read_csv performance on file with many columns
A student came to me for help using PyData tools for processing data in many-column CSV files. The files have 2740 rows; the longer file has 50k columns of floats, the smaller has 2740 columns of 0/1 binary data (defining a graph).

She currently is using numpy.loadtxt to read the files; the float file takes ~10 - 15 min to read on various Macs we've benchmarked. I was aware of benchmarks such as these indicating poor performance of loadtxt vs. various alternatives, including Pandas:

Fastest Python library to read a CSV file
http://softwarerecs.stackexchange.com/questions/7463/fastest-python-library-to-read-a-csv-file

A new high performance, memory-efficient file parser engine for pandas
http://wesmckinney.com/blog/a-new-high-performance-memory-efficient-file-parser-engine-for-pandas/

I suggested she try Pandas; she reported it performed _worse_ than loadtxt.  This puzzled me, so I did some benchmarks. I duplicated the benchmarks in the SO post linked above, showing Pandas greatly outperforming loadtxt on a many-row, few-column CSV file; this likely rules out a problem with our installations (we are both using current Anaconda Python 2.7). I also duplicated her poor Pandas performance on the large float file.

I've put a MWE online here:

https://dl.dropboxusercontent.com/u/40075366/BDA2015/CSV_MWE.zip

That unpacks to a benchmark script and two large data files; it's a 500 kB .zip archive but it unpacks to 59 MB (otherwise I would have just posted a Gist). The data files are truncated to 10% of full size. The results on my 2009 quad-core Mac Pro (48 GB RAM) are:

```
loadtxt, float file laps:
  * CSV:  8.576581 s
  * csr_matrix:  180.887938 ms
Total elapsed time = 8.757631 s

loadtxt, graph file laps:
  * CSV:  386.301041 ms
  * csr_matrix:  22.848845 ms
Total elapsed time = 409.296036 ms

pandas, float file elapsed time = 17.595026 s

iopro, float file elapsed time = 473.067179 s
```

Notably, IOPro performs abysmally with the float file.

Perhaps this is just not a use case Pandas was designed to address. Any advice on the fastest way to read such wide CSV files in Python would be appreciated.
