ENH/BUG/DOC: allow propogation and coexistance of numeric dtypes
Support for numeric dtype propogation and coexistance in DataFrames. Prior to 0.10.2, numeric dtypes passed to DataFrames were always casted to `int64` or `float64`. Now, if a dtype is passed (either directly via the `dtype` keyword, a passed `ndarray`, or a passed `Series`, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will **NOT** be combined. The following example will give you a taste. This closes GH #622

other changes introduced in this PR (i removed all datetime like issues to PR # 2752 - should be merged first)

ENH:
- validated get_numeric_data returns correct dtypes
- added `blocks` attribute (and as_blocks()) method that returns a 
  dict of dtype -> homogeneous dtyped DataFrame, analagous to `values` attribute
- added keyword 'raise_on_error' to astype, which can be set to false to exluded non-numeric columns
- changed `get_dtype_counts()` to use `blocks` attribute
- changed `convert_objects()` to use the internals method convert (which is block operated)
  - added option to `convert_numeric=False` to `convert_objects` to force numeric conversion (or set to `np.nan`, turned off by default)
  - added option to `convert_dates='coerce'` to `convert_objects` to force datetimelike conversions (or set to `NaT`) for invalid values, turned off by default, returns datetime64[ns] dtype
- groupby operations to respect dtype inputs wherever possible, even if intermediate casting is required (obviously if the input are ints and nans are resulting, this is casted),
  all cython functions are implemented
- auto generation of most groupby functions by type is now in `generated_code.py`
  e.g. (group_add,group_mean)
- added full `float32/int16/int8` support for all numeric operations, including (`diff, backfill, pad, take`)
- added `dtype` display to show on Series as a default

BUG:
- fixed up tests from from_records to use record arrays directly
  NOTE: using tuples will remove dtype info from the input stream (using a record array is ok though!)
- fixed merging to correctly merge on multiple dtypes with blocks (e.g. float64 and float32 in other merger)
- (#2623 can be fixed, but is dependent on #2752)
- fixed #2778, bug in pad/backfill with 0-len frame
- fixed very obscure bug in DataFrame.from_records with dictionary and columns passed and hash randomization is on!
- integer upcasts will now happend on `where` when using inplace ops (#2793)

TST:
- tests added for merging changes, astype, convert
- fixes for test_excel on 32-bit
- fixed test_resample_median_bug_1688
- separated out test_from_records_dictlike
- added tests for (GH #797)
- added lots of tests for`where`

DOC:
- added DataTypes section in Data Structres intro
- whatsnew examples

_It would be really helpful if some users could give this a test run before merging. I have put in test cases for numeric operations, combining with DataFrame and Series, but I am sure there are some corner cases that were missed_

```
In [17]: df1 = DataFrame(randn(8, 1), columns = ['A'], dtype = 'float32')

In [18]: df1
Out[18]: 
          A
0 -0.007220
1 -0.236432
2  2.427172
3 -0.998639
4 -1.039410
5  0.336029
6  0.832988
7 -0.413241

In [19]: df1.dtypes
Out[19]: A    float32

In [20]: df2 = DataFrame(dict( A = Series(randn(8),dtype='float16'), 
                                                B = Series(randn(8)), 
                                                C = Series(randn(8),dtype='uint8') ))

In [22]: df2
Out[22]: 
          A         B    C
0  1.150391 -1.033296    0
1  0.123047  1.915564    0
2  0.151367 -0.489826    0
3 -0.565430 -0.734238    0
4 -0.352295 -0.451430    0
5 -0.618164  0.673102  255
6  1.554688  0.322035    0
7  0.160767  0.420718    0


In [23]: df2.dtypes
Out[23]: 
A    float16
B    float64
C      uint8

In [24]: # here you get some upcasting

In [25]: df3 = df1.reindex_like(df2).fillna(value=0.0) + df2

In [26]: df3
Out[26]: 
          A         B    C
0  1.143170 -1.033296    0
1 -0.113385  1.915564    0
2  2.578539 -0.489826    0
3 -1.564069 -0.734238    0
4 -1.391705 -0.451430    0
5 -0.282135  0.673102  255
6  2.387676  0.322035    0
7 -0.252475  0.420718    0

In [27]: df3.dtypes
Out[27]: 
A    float32
B    float64
C    float64
```

the example from #622

```
In [23]: a = np.array(np.random.randint(10, size=1e6),dtype='int32')

In [24]: b = np.array(np.random.randint(10, size=1e6),dtype='int64')

In [25]: df = pandas.DataFrame(dict(a = a, b = b))

In [26]: df.dtypes
Out[26]: 
a    int32
b    int64
```

Conversion examples

```
# conversion of dtypes
In [81]: df3.astype('float32').dtypes
Out[81]: 
A    float32
B    float32
C    float32

# mixed type conversions
In [82]: df3['D'] = '1.'

In [83]: df3['E'] = '1'

In [84]: df3.convert_objects(convert_numeric=True).dtypes
Out[84]: 
A    float32
B    float64
C    float64
D    float64
E      int64

# same, but specific dtype conversion
In [85]: df3['D'] = df3['D'].astype('float16')

In [86]: df3['E'] = df3['E'].astype('int32')

In [87]: df3.dtypes
Out[87]: 
A    float32
B    float64
C    float64
D    float16
E      int32

# forcing date coercion
In [18]: s = Series([datetime(2001,1,1,0,0), 'foo', 1.0, 1,
   ....:             Timestamp('20010104'), '20010105'],dtype='O')
   ....:

In [19]: s.convert_objects(convert_dates='coerce')
Out[19]: 
0   2001-01-01 00:00:00
1                   NaT
2                   NaT
3                   NaT
4   2001-01-04 00:00:00
5   2001-01-05 00:00:00
Dtype: datetime64[ns]
```
