GH13347 ENH Allow true_values and false_values options in read_excel
- [ X] closes #13347 
- [ X] tests added / passed
- [ X] passes `git diff upstream/master | flake8 --diff`
- [ X] whatsnew entry
  <img width="1016" alt="asv_tests" src="https://cloud.githubusercontent.com/assets/609873/17653246/3a3f842a-6260-11e6-8ed2-1d21cc2fccc8.png">

## [Current coverage](https://codecov.io/gh/pydata/pandas/pull/14002?src=pr) is 85.24% (diff: 100%)

> Merging [#14002](https://codecov.io/gh/pydata/pandas/pull/14002?src=pr) into [master](https://codecov.io/gh/pydata/pandas/branch/master?src=pr) will decrease coverage by **<.01%**

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- Hits          43103      43102     -1   
- Misses         7460       7461     +1   
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 @WillAyd ok, the issue is for some other things like this we accept scalar and lists. We need validation on this (in the parser code) to turn a scalar to a list (and a test for this). 
 Thanks Jeff.  I did however notice that the keyword arguments in read_csv don't do anything to turn scalars into a list.  For instance, if I had a line in test.csv that was simply "foo,bar" then running the below would not convert the record foo to a bool.

`df = pd.read_csv('test.csv', true_values='foo')`

I have to specify the argument as:

`df = pd.read_csv('test.csv', true_values=['foo'])`

to make it work.  I am thinking of two options:
1. Add the aforementioned validation to read_excel or
2. Have read_excel call the same function for bool checking as read_csv (maybe_convert_bool from pandas.lib)

The former would obviously fix the problem at hand but make the two read functions behave slightly differently.  The second one would keep them consistent but require more work or an additional change to add the validation you are describing.  Let me know which route you prefer.
 they should behave the same. You can for now mimic read_csv and we can come back in a later issue and fix this (as its a bug in read_csv). Or could do it all here.
 Latest commit should have fixed this to add the appropriate keyword arguments of true_values and false_values to the TextParser object referenced in the excel.py file.  This keeps it consistent with read_csv.  Updated asv_bench screenshot attached
<img width="913" alt="asv_read_excel" src="https://cloud.githubusercontent.com/assets/609873/17720937/12b42dbc-63f4-11e6-847c-6b42dff5c1ee.png">
 Thanks for the callout on mixing strings / floats, etc... into the testing scenario.  I realized my code isn't working when you mix in floats into the file and seems to be replacing them with np.nan.  I'll dig further back in and try to see what's going on
 can you rebase / update?
 Rebase should now be complete
 @WillAyd Thanks!
 @willayd, note that 'foo' is not a scalar, but a string, which is a list of characters. I think your code converts 'f' and 'o' to booleans
Just flagging this in case it's useful
 @adrivsh you would have to show an example
strings are normally treated similarly to scalars
 The example is 

import pandas as pd
pd.DataFrame(["f","o","o"]).to_csv("foo.csv")
pd.read_csv("foo.csv", false_values="foo")

The result is:

Unnamed: 0  0
0   0   False
1   1   False
2   2   False

What i am saying is that python duck-types strings as list of characters, so that false_values= "foo" and false_values = ["f","o","o"] will produce the same result, unless some code prevents that
 Interestingly,

import pandas as pd
pd.DataFrame(["f","o","o","b","a","r"]).to_csv("foo.csv")
pd.read_csv("foo.csv", false_values="foo")

does not transform "f"s and "o"s to False, so I actually have no idea what i am talking about.
 @adrivsh I think that is possibly correct, but is a bit a pitfall (and is not documented very well). The string is regarded as a list of characters, if you pass it a single element list it works as expected:

```
In [15]: pd.read_csv("foo.csv", false_values=["foo"])
Out[15]: 
   Unnamed: 0  0
0           0  f
1           1  o
2           2  o
```

@adrivsh do you want to open a new issue for that?
