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[Tut] np.shape()

#1
np.shape()

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.


Do you want to become a NumPy master? Check out our interactive puzzle book Coffee Break NumPy and boost your data science skills! (Amazon link opens in new tab.)

Coffee Break NumPy

References


The post np.shape() first appeared on Finxter.



https://www.sickgaming.net/blog/2020/11/18/np-shape/
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