This works:
>>> a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
>>> a[: , 2]
array([ 3, 7, 11])
This doesn't
>>> a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11]])
>>> a[:,2]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: too many indices for array
Why so ?
Numpy ndarrays are meant for all elements to have the same length. In this case, your second array doesn't contain lists of the same length, so it ends up being a 1-D array of lists, as opposed to a "proper" 2-D array.
From the Numpy docs on N-dimensional arrays:
An ndarray is a (usually fixed-size) multidimensional container of items of the same type and size.
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
a.shape # (3,4)
a.ndim # 2
b = np.array([[1,2,3,4], [5,6,7,8], [9,10,11]])
b.shape # (3,)
b.ndim # 1
This discussion may be useful.
The first array has shape (3,4) and the second has shape (3,). The second array is missing a second dimension. np.array is unable to use this input to construct a matrix (or array of similarly-lengthed arrays). It is only able to make an array of lists.
>>> a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
>>> print(a)
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
>>> print(type(a))
<class 'numpy.ndarray'>
>>> b = np.array([[1,2,3,4], [5,6,7,8], [9,10,11]])
>>> print(b)
[list([1, 2, 3, 4]) list([5, 6, 7, 8]) list([9, 10, 11])]
>>> print(type(b))
<class 'numpy.ndarray'>
So they are both Numpy arrays, but only the first can be treated as a matrix with two dimensions.
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