Getting only particular columns in every row in a numpy array [duplicate]

Question

Possible Duplicate:
numpy: access an array by column

I have a numpy array (numpy is imported as np)

gona = np.array([[ 1,  2,  3],
       [ 4,  5,  6],
       [ 7,  8,  9],
       [10, 11, 12]])

I can get the values of entire column of 1th row by gona[1][:].

array([4, 5, 6])

But if I try to get all values of a particular column of all rows (say I want values of 1st column in every row) I would try the gona[:][1]. But the result I get from this is same as before.

What can be the reason for this? How do I do such a thing in numpy?

Answer 1

You actually want to do this:

>>> a
array([[ 1,  2,  3],
       [ 4,  5,  6],
       [ 7,  8,  9],
       [10, 11, 12]])
>>> a[:,1]
array([ 2,  5,  8, 11])

a[:] just returns the entire array, so then a[:][1] is returning the second row of a. I think that's where your confusion arises.

See this section of the Tentative Numpy Tutorial for more information on indexing multidimensional arrays.

Answer 2

There seems to be a slight confusion in terms of the positioning of the braces, gona[:][1] first selects everything from the array, and from that array then selects the second row. To select particular columns you put the indices within the same square brackets separated by a comma:

gona = np.array([[ 1,  2,  3],
       [ 4,  5,  6],
       [ 7,  8,  9],
       [10, 11, 12]])

gona[1,:]
Out[21]: array([4, 5, 6])

gona[:,1]
Out[22]: array([ 2,  5,  8, 11])

gona[:,0]
Out[23]: array([ 1,  4,  7, 10])

you can also just select a range of rows for instance

gona[0:2,0] # only take the two first rows of the first column
Out[24]: array([2, 5])

Answer 3

Like this:

gona = numpy.array([[ 1,  2,  3],
       [ 4,  5,  6],
       [ 7,  8,  9],
       [10, 11, 12]])

# List comprehension, just get each element in 'gona', and then get first element in that list
out = [x[0] for x in gona]

print out

Output:

>>> 
[1, 4, 7, 10]
>>>