I'm trying to access each item in a numpy 2D array.
I'm used to something like this in Python [[...], [...], [...]]
for row in data:
for col in data:
print(data[row][col])
but now, I have a data_array = np.array(features)
How can I iterate through it the same way?
Try np.ndenumerate:
>>> a =numpy.array([[1,2],[3,4]])
>>> for (i,j), value in np.ndenumerate(a):
... print(i, j, value)
...
0 0 1
0 1 2
1 0 3
1 1 4
Make a small 2d array, and a nested list from it:
In [241]: A=np.arange(6).reshape(2,3)
In [242]: alist= A.tolist()
In [243]: alist
Out[243]: [[0, 1, 2], [3, 4, 5]]
One way of iterating on the list:
In [244]: for row in alist:
...: for item in row:
...: print(item)
...:
0
1
2
3
4
5
works just same for the array
In [245]: for row in A:
...: for item in row:
...: print(item)
...:
0
1
2
3
4
5
Now neither is good if you want to modify elements. But for crude iteration over all elements this works.
WIth the array I can easily treat it was a 1d
In [246]: [i for i in A.flat]
Out[246]: [0, 1, 2, 3, 4, 5]
I could also iterate with nested indices
In [247]: [A[i,j] for i in range(A.shape[0]) for j in range(A.shape[1])]
Out[247]: [0, 1, 2, 3, 4, 5]
In general it is better to work with arrays without iteration. I give these iteration examples to clearup some confusion.
If you want to access an item in a numpy 2D array features, you can use features[row_index, column_index]. If you wanted to iterate through a numpy array, you could just modify your script to
for row in data:
for col in data:
print(data[row, col])
row is a 1d array. You can use that as an index for data. And the 2nd iteration is just the same. You probably had for item in row: in mind.
for row in data.shape[0] and for col in data.shape[1], or alternatively, for row in data: for col in row: print(col).
for row in data: for elem in row: print(elem). This would work for both nested lists and 2D arrays