[Tut] np.shape() - Printable Version

[Tut] np.shape() - Printable Version

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[Tut] np.shape() - xSicKxBot - 11-19-2020

np.shape()

This tutorial explains NumPy’s

Code:
shape()

function.

Code:
numpy.shape(a)

Return the shape of an array or array_like object

Code:
a

.

Argument |
Data Type |
Description

Code:
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

Code:
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:

Code:
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:

Code:
>>> 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:

Code:
>>> 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

Code:
(2, 4)

with two rows and four columns.

np.shape() vs array.shape

Note that the result of

Code:
np.shape(b)

and

Code:
b.shape

is the same if

Code:
b

is a NumPy array. If

Code:
b

isn’t a NumPy array but a list, you cannot use

Code:
b.shape

as lists don’t have the shape attribute. Let’s have a look at this example:

Code:
>>> b = [[1, 2, 3, 4], [5, 6, 7, 8]]
>>> np.shape(b)
(2, 4)

The

Code:
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:

Code:
>>> 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

Code:
np.shape()

and

Code:
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

Code:
shape

attribute.

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References

Implementation: https://github.com/numpy/numpy/blob/master/numpy/core/fromnumeric.py#L1926-L1969

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