pd.concat() from list of DataFrames read with pd.read_hdf() without .copy() results in 100X slower concat!
```
pandas: 0.17.1
numpy: 1.9.2
```

I have two example DataFrames written to hdf5 files on disk. I use pd.read_hdf() to I/O them into a list. When I attempt to pd.concat([list,of,frames]) it takes 100X longer if I don't explicitly call .copy() right after the pd.read_hdf(). :confused:

Did something change in the pd.read_hdf() implementation to only reference data on disk instead of actually reading it into memory? This was a really subtle performance issue that threw a wrench in my data flow on the latest pandas stack. Profiling shows the bottleneck is in method 'ravel' of 'numpy.ndarray'  (when you don't call copy().) This does not happen in pandas 0.13.1 (I know very old). :anguished: 

``` python
import os
import pandas as pd
import numpy as np

rows, cols, skip, frames = 777, 10000, 250, 2

def create_df():
    ncols = cols - np.random.randint(0,skip,1,)
    nrows = rows - np.random.randint(0,skip,1,)
    df = pd.DataFrame(np.random.randn(nrows, ncols), columns=[('SUPER_LONG_COLUMN_NAME_NUMBER_THAT_IS_VERY_LONG_'+ str(i))*2 for i in range(ncols)]).astype(np.float32)
    df.iloc[100:25:500,1000:100:2000] = np.nan
    df.iloc[:,-10:] = 'TEST_STRING_COL_VALUE'
    df.iloc[1:,:100] = np.nan
    df.iloc[10:, 100:200] = np.nan
    return df[np.random.choice(df.columns, ncols-skip, replace=False)]

%time dfs = [create_df() for i in range(frames)]
Wall time: 508 ms

%%time
for i, df in enumerate(dfs):
    df.to_hdf(r'./dfs/{}.h5'.format(i), 'df', mode='w', complib='lzo', complevel=9)
Wall time: 508 ms

%time dfs_read_hdf = [pd.read_hdf(r'./dfs/{}'.format(fn), mode='r') for fn in os.listdir(r'./dfs/')]
Wall time: 247 ms

%time dfs_read_hdf_copy = [pd.read_hdf(r'./dfs/{}'.format(fn), mode='r').copy() for fn in os.listdir(r'./dfs/')]
Wall time: 337 ms

%time dfa = pd.concat(dfs_read_hdf, ignore_index=True)
Wall time: 34.6 s

%time dfa = pd.concat(dfs_read_hdf_copy, ignore_index=True)
Wall time: 304 ms

%prun pd.concat(dfs_read_hdf, ignore_index=True)
```

Here is the %prun of the slow concat. Notice that a whopping 30 seconds is being spent in np.ravel() :exclamation: 

```
298220 function calls (298178 primitive calls) in 34.758 seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     1431   30.263    0.021   30.263    0.021 {method 'ravel' of 'numpy.ndarray' objects}
      955    3.619    0.004   34.144    0.036 internals.py:4461(get_empty_dtype_and_na)
     1419    0.277    0.000    0.277    0.000 {pandas.algos.take_2d_axis1_float32_float32}
     1423    0.118    0.000    0.144    0.000 common.py:230(_isnull_ndarraylike)
     3337    0.031    0.000    0.031    0.000 {method 'reduce' of 'numpy.ufunc' objects}
     1910    0.027    0.000   30.476    0.016 internals.py:4767(is_null)
    62628    0.024    0.000    0.049    0.000 {isinstance}
     1427    0.024    0.000    0.361    0.000 common.py:697(take_nd)
  ...
```

```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 2008ServerR2
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 17.1.1
Cython: 0.23
numpy: 1.9.2
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.1
numexpr: 2.4.3
matplotlib: 1.5.0
openpyxl: 2.2.5
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.4.4
bs4: 4.4.0
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: None
Jinja2: None
```
