This tutorial explains NumPy’s shape() function.
numpy.shape(a)
Return the shape of an array or array_like object a.
| Argument | Data Type | Description |
|---|---|---|
a |
array_like | NumPy array or Python list for which the shape should be returned. If it is a NumPy array, it returns the attribute a.shape. If it is a Python list, it returns a tuple of integer values defining the number of elements in each dimension if you would’ve created a NumPy array from it. |
Return Value: shape — a tuple of integers that are set to the lengths of the corresponding array dimensions.
Examples
The straightforward example is when applied to a NumPy array:
>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> np.shape(a)
(2, 2)
You import the NumPy library and create a two-dimensional array from a list of lists. If you pass the NumPy array into the shape function, it returns a tuple with two values (=dimensions). Each dimension stores the number of elements in this dimension (=axis). As it is a 2×2 quadratic matrix, the result is (2,2).
The following shape is another example of a multi-dimensional array:
>>> b = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
>>> b
array([[1, 2, 3, 4], [5, 6, 7, 8]])
>>> b.shape
(2, 4)
>>> np.shape(b)
(2, 4)
The shape is now (2, 4) with two rows and four columns.
np.shape() vs array.shape
Note that the result of np.shape(b) and b.shape is the same if b is a NumPy array. If b isn’t a NumPy array but a list, you cannot use b.shape as lists don’t have the shape attribute. Let’s have a look at this example:
>>> b = [[1, 2, 3, 4], [5, 6, 7, 8]]
>>> np.shape(b)
(2, 4)
The np.shape() function returns the same shape tuple—even if you pass a nested list into the function instead of a NumPy array.
But if you try to access the list.shape attribute, NumPy throws the following error:
>>> b.shape
Traceback (most recent call last): File "<pyshell#9>", line 1, in <module> b.shape
AttributeError: 'list' object has no attribute 'shape'
So, the difference between np.shape() and array.shape is that the former can be used for all kinds of array_like objects while the latter can only be used for NumPy arrays with the shape attribute.
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References
The post np.shape() first appeared on Finxter.
https://www.sickgaming.net/blog/2020/11/18/np-shape/