high temporary memory usage on creation of DataFrame from list
When I create a DataFrame from a list of arrays, it temporarily uses a lot more memory and is slower than if I convert the list to an array first (if you want to test this, make sure you have enough memory or change the size of `a`):

``` python
In [1]: import numpy as np
   ...: import pandas as pd
In [2]: a = np.random.rand(10000000, 10)
In [3]: print(a.nbytes / 1024 / 1024)
762.939453125
In [4]: a = list(a)
In [5]: %time b = pd.DataFrame(np.array(a))  # used memory goes up 840 MB
Wall time: 3.83 s
In [6]: %time c = pd.DataFrame(a)  # used memory goes up 5 GB, then drops back to 840 MB above previous value
Wall time: 26.6 s
```

The same problem exists when creating a DataFrame from a list of lists, but definitely not as bad:

``` python
In [1]: import numpy as np
   ...: import pandas as pd
In [2]: a = np.random.rand(10000000, 10)
In [3]: a = [list(i) for i in a]
In [4]: %time b = pd.DataFrame(np.array(a))  # used memory goes up 840 MB
Wall time: 9.73 s
In [5]: %time c = pd.DataFrame(a)  # used memory goes up 1.3 GB, then drops back to 840 MB above previous value
Wall time: 8.52 s
```

Versions:

``` text
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: 2.8
```
