How Do You Know The Shape and Size Of a Numpy Array ?
Table Of Contents:
- ndarray.ndim
- ndarray.size
- ndarray.shape
(1) ndarray.ndim
ndarray.ndim
will tell you the number of axes, or dimensions, of the array.
Example-1:
a = np.array([1, 2, 3])
a
array([1, 2, 3])
a.ndim
Output:
1
Example-2:
b = np.array([[1,4],[3,2]])
b
array([[1, 4],
[3, 2]])
b.ndim
Output:
2
Example-3:
c = np.array([[[1,4],[3,2]]])
c
array([[[1, 4],
[3, 2]]])
c.ndim
Output:
3
Example-4:
y = np.zeros((2, 3, 4))
y
array([[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]]])
y.ndim
Output:
3
(2) ndarray.size
ndarray.size
will tell you the total number of elements of the array. This is the product of the elements of the array’s shape.
Example-1:
a = np.array([1, 2, 3])
a
array([1, 2, 3])
a.size
Output:
3
Example-2:
b = np.array([[1,4],[3,2]])
b
array([[1, 4],
[3, 2]])
b.size
Output:
4
Example-3:
c = np.array([[[1,4],[3,2]]])
c
array([[[1, 4],
[3, 2]]])
c.size
Output:
4
Example-4:
y = np.zeros((2, 3, 4))
y
array([[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]]])
y.size
Output:
24
(3) ndarray.shape
ndarray.shape
will display a tuple of integers that indicate the number of elements stored along each dimension of the array. If, for example, you have a 2-D array with 2 rows and 3 columns, the shape of your array is(2, 3)
.
Example-1:
a = np.array([1, 2, 3])
a
array([1, 2, 3])
a.shape
Output:
(3,)
Example-2:
b = np.array([[1,4],[3,2]])
b
array([[1, 4],
[3, 2]])
b.shape
Output:
(2, 2)
Example-3:
c = np.array([[[1,4],[3,2]]])
c
array([[[1, 4],
[3, 2]]])
c.shape
Output:
(1, 2, 2)
Example-4:
y = np.zeros((2, 3, 4))
y
array([[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]]])
y.shape
Output:
(2, 3, 4)