DOC: Improve reindex examples and docstring
Fixes https://github.com/pydata/pandas/issues/10995

Done/waiting for feedback.

lmk when you are finsihed
 how's this coming?
 Working to finish this right now. ETA in 2 hours (depending on how fast the coffee kicks in...)
 hi @jreback , just trying to flesh out some doc for the filing methods in reindex and running into this situation

Pandas version 0.17.0

``` python
# create original dataframe
index2 = pd.date_range('1/1/2010', periods=5, freq='D')
sample_df2 = pd.DataFrame({"prices":[100, 101, np.nan, np.nan, 89]}, index=index2)
print sample_df2

>>>
            prices
2010-01-01     100
2010-01-02     101
2010-01-03     NaN
2010-01-04     NaN
2010-01-05      89

#create new index, reindex and use backfill
index_new = pd.date_range('12/29/2009', periods=8, freq='D')
sample_df2.reindex(index_new, method='bfill')

>>>
            prices
2009-12-29     100
2009-12-30     100
2009-12-31     100
2010-01-01     100
2010-01-02     101
2010-01-03     NaN
2010-01-04     NaN
2010-01-05      89
```

Question: Why isn't '89' being propagated backwards to fill the NaN in 2010-01-03 and 2010-01-04?
It seems that we only fill NaN's if they were introduced by the reindexing and not if they were present in the original dataframe before reindexing.
Just want to confirm that this is desired behavior and I'll make a note of it in the examples. 
 ```
In [5]: sample_df2.reindex(index_new, method='bfill')
Out[5]: 
            prices
2009-12-29     100
2009-12-30     100
2009-12-31     100
2010-01-01     100
2010-01-02     101
2010-01-03     NaN
2010-01-04     NaN
2010-01-05      89

In [6]: sample_df2.reindex(index_new).bfill()        
Out[6]: 
            prices
2009-12-29     100
2009-12-30     100
2009-12-31     100
2010-01-01     100
2010-01-02     101
2010-01-03      89
2010-01-04      89
2010-01-05      89
```

see the issue here: https://github.com/pydata/pandas/issues/5669

I still don't buy that these should be different. Filling WHILE reindexing is IMHO just confusing. These should be separate operations.
 Filling while reindexing does not look at the data values when deciding which should be propagated. That's a nice property, for a few reasons:
1. it preserves NaNs in the original dataset
2. If means you unambiguously preserve records when you reindex a dataframe -- what would you do otherwise if a row had a missing value in one column but not another?
3. it means you can do the operation more efficiently, in a single take from the original array
 @shoyer well, I disagree. Reindexing is completely different that setting the index, which is the point the other issue trying to make. Yes under certainly limited circumstances this makes sense, though I completely discount point (3) (it doesn't matter mucho IMHO).

Much more important is obvious semantics. This differs from `df.reindex(....).ffill()` and causes _some_ confusion.
 It certainly is a source of confusion, but it _is_ following a sensible rule: the values reindexing looks at are only in the index. It's certainly worth clarifying when feasible in the docs.

I agree that efficiency does not matter here for pandas when the data is already all in memory, but it does make a difference for downstream libraries that copy the pandas API for out-of-core data. Namely, it's useful in xray when we define operations on dask arrays and also for dask.dataframe -- we never need to look at the array values to determine what the reindexing result looks like.
 @shoyer exactly my point. Reindexing is hard enough for users. The rule is sensible, but not to the average user. This should NOT be advertised as how to fill thing at all. Rather `.fillna` and cousins.
 Thanks @jreback and @shoyer for the clarification.
I'll do my best to clarify/make note of it in the examples.
 since this is long (yeh!) do we need to paginate somehow? @jorisvandenbossche @shoyer 
 @Winterflower can you update according to comments
 @jreback apologies for the delay!
added the fixes.
 thanks @Winterflower 
