How to convert a subset of pandas DataFrame columns AND rows into a numpy array?

Question

I am wondering if there is a simpler, memory efficient way to select a subset of rows and columns from a pandas DataFrame.

For example, given this data frame:

 df = DataFrame (np.random.rand (4,5), columns = list ('abcde'))
 print df

           abcde
 0 0.945686 0.000710 0.909158 0.892892 0.326670
 1 0.919359 0.667057 0.462478 0.008204 0.473096
 2 0.976163 0.621712 0.208423 0.980471 0.048334
 3 0.459039 0.788318 0.309892 0.100539 0.753992

I only need rows where the value for column 'c' is greater than 0.5, but I only need columns 'b' and 'e' for these rows.

This is the method I came up with - maybe there is a better way to pandas?

 locs = [df.columns.get_loc (_) for _ in ['a', 'd']]
 print df [df.c> 0.5] [locs]

           ad
 0 0.945686 0.892892

My ultimate goal is to convert the result to a numpy array to go into the sklearn regression algorithm, so I will use the code above:

 training_set = array (df [df.c> 0.5] [locs])

... and it annoys me, as I get a huge copy of the array in memory. Maybe there is a better way to do this?

Answer 1

.loc accept row and column selectors at the same time (like .ix/.iloc FYI) This is done in a single pass.

 In [1]: df = DataFrame(np.random.rand(4,5), columns = list('abcde')) In [2]: df Out[2]: abcde 0 0.669701 0.780497 0.955690 0.451573 0.232194 1 0.952762 0.585579 0.890801 0.643251 0.556220 2 0.900713 0.790938 0.952628 0.505775 0.582365 3 0.994205 0.330560 0.286694 0.125061 0.575153 In [5]: df.loc[df['c']>0.5,['a','d']] Out[5]: ad 0 0.669701 0.451573 1 0.952762 0.643251 2 0.900713 0.505775 

And if you want values ​​(although this should go directly to sklearn as is); frames support an array interface

 In [6]: df.loc[df['c']>0.5,['a','d']].values Out[6]: array([[ 0.66970138, 0.45157274], [ 0.95276167, 0.64325143], [ 0.90071271, 0.50577509]]) 

Answer 2

Use its value directly:

 In [79]: df[df.c > 0.5][['b', 'e']].values Out[79]: array([[ 0.98836259, 0.82403141], [ 0.337358 , 0.02054435], [ 0.29271728, 0.37813099], [ 0.70033513, 0.69919695]]) 

Answer 3

Perhaps something similar for the first problem, you can just access the columns by their names:

 >>> df = pd.DataFrame(np.random.rand(4,5), columns = list('abcde')) >>> df[df['c']>.5][['b','e']] be 1 0.071146 0.132145 2 0.495152 0.420219 

For the second problem:

 >>> df[df['c']>.5][['b','e']].values array([[ 0.07114556, 0.13214495], [ 0.49515157, 0.42021946]])