Python Pandas - Deleting multiple series from a data frame in one command
Question: In short ... I have a Python Pandas data frame that is read in from an Excel
file using 'read_table'. I would like to keep a handful of the series from the
data, and purge the rest. I know that I can just delete what I don't want one-
by-one using 'del data['SeriesName']', but what I'd rather do is specify what
to keep instead of specifying what to delete.

If the simplest answer is to copy the existing data frame into a new data
frame that only contains the series I want, and then delete the existing frame
in its entirety, I would satisfied with that solution ... but if that is
indeed the best way, can someone walk me through it?

TIA ... I'm a newb to Pandas. :)


Answer: You can use the `DataFrame` `drop` function to remove columns. You have to
pass the `axis=1` option for it to work on columns and not rows. Note that it
returns a copy so you have to assign the result to a new `DataFrame`:

    
    
    In [1]: from pandas import *
    
    In [2]: df = DataFrame(dict(x=[0,0,1,0,1], y=[1,0,1,1,0], z=[0,0,1,0,1]))
    
    In [3]: df
    Out[3]:
       x  y  z
    0  0  1  0
    1  0  0  0
    2  1  1  1
    3  0  1  0
    4  1  0  1
    
    In [4]: df = df.drop(['x','y'], axis=1)
    
    In [5]: df
    Out[5]:
       z
    0  0
    1  0
    2  1
    3  0
    4  1
    

