Parsing dates using  `read_csv` is slower than parsing them manually later.
I'm sorry if this is the expected behavior, but I think this is odd and may be an issue.

Parsing the dates automatically using `read_csv` (with the keywords `parse_dates` and `date_parser`) is actually slower than reading the file without parsing any dates, manually putting the date columns together and then using `pandas.to_datetime` function on the newly created column to parse the dates.

Basically (assuming you have date and hour in two different columns in the csv) doing this

``` python
df = pd.read_csv("..\\file.csv",  parse_dates = [['YYYYMMDD', 'HH']], 
            index_col = 0, 
            date_parser=parse)
```

is 3 to 5 times slower than this

``` python
df = pd.read_csv("..\\file.csv")
format = "%Y%m%d %H"
times = pd.to_datetime(df.YYYYMMDD + ' ' + df.HH, format=format)
df.set_index(times, inplace=True)
df = df.drop(['YYYYMMDD','HH'], axis=1)
```

I first found this issue comparing the two answers of [this question](http://stackoverflow.com/questions/11615504/parse-dates-when-yyyymmdd-and-hh-are-in-separate-columns-using-pandas-in-python) and thought it was weird but OK, maybe it was due to some feature in his dataset. But while the person on the answer reported a 3 times time difference between both approaches, when I did the comparison I found that the difference is 5 times for me.

This seems to me like an issue, since using the function's internal options should be faster, or at the least take the same time.
