memory issues when doing inplace operations and dataframe wide operations
Hi,

Whenever I do inplace operations in pandas, especially when performing operations like replace or astype. The memory blows up close or greater than 8GB(esp on values).  

Same happens when using values or concatenation of larger dataframes. 

The workaroud is to do a per column operation, even on concatenation(using column addition). But since this is becoming a common issue (or at least that what I see), can algorithms for efficient memory management be added whole dataframe operations, e.g., checking the memory of the machine and then splitting the operation into chunks so that the user won't be less concerned in having memory bottlenecks when executing such pandas commands.

Thanks in advance.

Example,

``` python
allcols=allcols[0:400]
train=[eval('dftrain.'+allcols[0]).values]
del allcols[0]
for x in allcols:
    train=np.append(train,[eval('dftrain.'+x).values], axis=0)
del dftrain
```

if I used dftrain.values[x,y] command, the memory usage will blow up above 8GB, and the kernel will die.
