excel reader & skip row between data & header & docs
the merge conflict chaos was too hard ;-(

This is a continuation of:
https://github.com/pydata/pandas/pull/4404

Because:
- we hit aparently a GitHub bug
- the branches diverged too much.

I shall fix:
- in the excel.py there is a fix enabling reading xlsx files with both
  datemodes: (see #4332)
- in the parsers.py there is the fix for readinh the header even if there
  are additional rows (to be skipped) between a header and the data (see: #4340)
- a few doc improvements

@jreback @jtratner 

So Travis announces we could merge. I've done my best to clean the code.

What are the (last) steps to get this through?
 @timmie let me have a detailed look
 thanks.
 @timmie if you tell me the specific files that need to be included just for this (in your opinion) I'd be willing to rebase your branch for you - I know that can be a bit complicated to do.
 @jtratner I am almost done...
 @jreback ah, okay.
 @jtratner 

Thanks.

So this one shall just include all the *-py file changes:
io/excel.py
io/parsers.py
io/date_converters.py

& the respective tests.
 The docs could then go elsewhere.

The doc changes are:
- document the fix of this issues
- add shortcut links to handy references in the docs
- bring all integer indices methods together
- add section about working with the datetime index to timeseries.rst
 @jtratner 

privately, I prefer BZR. So with git I always feel of not having control on my files (accidently delete changes) for everthing except: pull, commit, push ;-(
But seems I have learned with you guy how to write simple tests. the rest may come with time.
 @timmie yeah, git can be complicated, you might want to check out scm_breeze, which might make it easier.

I'm sure @jreback will have additional comments, but you need to split out the date_parser changes from the row skipping changes and the doc changes.
 @timmie to be clear, I don't mean that this will (or will not) get accepted - I just mean that I'll help you take out all the changes that need to be separated from this PR
 uff, so it' on my desk again. though i was done ;-(
 https://github.com/jreback/pandas/compare/timmie_4634?expand=1

@timmie this needs an additional test with what happens if you have dates in the excel files that you don't want converted with offset_datetime (I think it will not work); which I guess is ok, you can always convert them later,
essentially what you want is to apply the date_parser to only certain columns (which you can do with ``parse_dates = ['A']), BUT you cannot then parse 'other' dates

any thoughts here?
 @jreback 
The excel file included is also a corner case counting hours 1-24.

In case the hours are counted 0-23 then there is no need for conversion.

Or do I misunderstand you here?
 @timmie your excel files were fine

add a column to one of them with regular dates; I am not sure what it will do with those (e.g. it SHOULD NOT apply the offset_datetime) to them, but no easy way to tell it not to (well you could make multiple passes on the file, in pass 1 say use the offset_datetime, in pass 2 do the other dates)
 > essentially what you want is to apply the date_parser to only certain columns (which you can do with >``parse_dates = ['A']), BUT you cannot then parse 'other' dates

Ah, mean like combining columns like date, hour into one?

yes, this is currently not foreseen.
would it be bad to covert that later?
we already assume that datetime / time is read in.

But we still have a problem with creating datetime index from 2 excel columns.

This was not the scope of my fix but still a deficiency of the excel reader.
 The issue I am getting at, is that the `offset_datetime` cannot 'know' a column is meant for timedelta conversion (rather than a plain ole datetime conversion). I am thinking that we need another set of keywords, e.g. parse_timedelta, timedelta_parser (which actually we need in another case)
 @jreback 

I agree. but how does the read_csv handle it?
Don't we want to have a similar behaviour?

I am going offline now -- apparently another timezone at your place. 
Thanks so far. Let's try to get this into main pandas until Saturday, latest. If not I will have to wait some weeks until I can look at it again.
 main read_csv doesn't handle this...have to think about it

@cpcloud @jtratner @hayd 

`parse_timedelta` and `timedelta_parser` keywords for `read_csv`?
 I think now we should have a test for both cases that you are aiming at.

Here's how I handled the same problem with CSV input (back then with scikits.timeseries):

```
def dc_date_time_1to24(date_str, time_str, freq='T'):
    """
    .. csv-table:: Normal Time Series: 00:00-23:59
           :header: "Date", "Time"
           :delim: ;

           01.10.2008;00:10
           01.10.2008;00:20

           [...];[...];[...];[...];[...]
           01.10.2008;23:50
           01.10.2008;00:00
           02.10.2008;00:10

    Note
    -----
    assumed datecols::

        datecols = (0,1)

    """
    date_dt = dt.datetime.strptime(date_str, "%d.%m.%Y")

    time_dt = dt.datetime.strptime(time_str, "%H:%M")
    time_dt = time_dt - dt.timedelta(minutes=10) 
    time_dt = time_dt.time()

    dt_concat = dt.datetime.combine(date_dt, time_dt)


    ts_date =  ts.Date(freq, datetime=(dt_concat))

    return ts_date
```

I would assume that this works with the pandas CSV reader as well. But not with the excel reader.
 @jreback 
I think a solution would be to pass this PR through to get the current situation fixed.

Meanwhile, we enter a new issue with the points raised and then improve the Excel reader generally to have a similar behaviour as the read_csv.

But I also think that the Excel reader was not equipped with that much magic because the basic assumtion is:
- The data provider would have formatted cells in Excel the right way
- entered the data according to standards (e.g. ISO 8601 for datetime)

So any further actions could be done with the data frame read in, e.g.:
- merge date and hour column to datetime
- set index

Now in my test case where the time is not entered in ISO (1-24 instead of 0-23) the reader fails to recognise the dates at all. This was main reason for the fix.

The dateconverters are actually only "by-products" of the tests. But they will be useful in similar situations.
 At 
https://travis-ci.org/timmie/pandas/builds/10491379
you may see that #4634 passes.

So there you have them separate.

@jreback 
I have changed my git workflow:
- keep master always in sync with upstream
- from master branch new to get a feature coded
- regularly rebase / pull

Maybe this will make things easier?
 @timmie try this

```
git remote add jreback https://github.com/jreback/pandas.git && git fetch jreback && git checkout -b timmie_4634 --track jreback/timmie_4634
```

this will give you a starting point of the branch I created
 @jreback 
- So you have taken out all general doc modifications.
- my comments on the python modifications are in https://github.com/jreback/pandas/commit/73fbd2162869ca9e240fed3efdb2afef9339b683#commitcomment-3921863
 @timmie 

as I said you are free to submit doc corrects, but as separate PR (unless the issue you are fixing actually needs a doc change).
 Ok, I'll do that.

we can add this do the development FAQ wiki.
 @timmie new gitworkflow sounds good. As jeff says you should do separate prs for each feature (and that's a good/git mentality to have in general), don't do lots of things in a PR, concentrate on one thing. (e.g. pr should include: test for a bug, fix for that bug, and release note about fixed bug, ideally all in one commit; e.g. pr should contain improvements to docs on certain thing).

@jreback is there any kind of standard for timedeltas (I can't recall coming across any) or are you suggesting only works with user defined parser?
 @hayd timedeltas not support right now for writing to csv (though can kind of hack it), nor reading at all (though again if you write it as an integer then coerce back in it works)

I am thinking that `read_csv` needs `parse_timedelta=` and `timedelta_parser=` in order to really support it
 @jreback seems reasonable, will more useful once we have Timestamp-like efficient timedeltas. Don't know if there is a market for it...

I wonder if it makes sense for users to put in multiple arbitrary parsers, i.e. not just for timedeltas.

(tbh I usually just munge after the fact as it's so easy..)
 @hayd that's a nice idea....sort of like the `dtype` keyword...in fact, why couldn't we hijack that? (accept a callable for the a dtype, which will result in it being parsed using that callable? from a string)?
