How to load only specific columns from csv file into a DataFrame
There's no default way to do this right now. I would suggest chunking the file and iterating over it and discarding the columns you don't want.
So something like pd.concat([x.ix[:, cols_to_keep] for x in pd.read_csv(..., chunksize=200)])
Suppose I have a csv file with 400 columns.  I cannot load the entire file into a DataFrame (won't fit in memory).  However, I only really want 50 columns, and this will fit in memory.  I don't see any built in Pandas way to do this.  What do you suggest?  I'm open to using the PyTables interface, or pandas.io.sql.  
The best-case scenario would be a function like:  pandas.read_csv(...., columns=['name', 'age',...,'income']).  I.e. we pass a list of column names (or numbers) that will be loaded.