This tutorial explains NumPy’s
Return the shape of an array or array_like object
|array_like||NumPy array or Python list for which the shape should be returned. If it is a NumPy array, it returns the attribute |
shape — a tuple of integers that are set to the lengths of the corresponding array dimensions.
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
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)
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
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
Recap NumPy Shape
The shape attribute always returns a tuple that tells us the length of each dimension. The one-dimensional array is a row vector and its shape is a single value iterable followed by a comma. One-dimensional arrays don’t have rows and columns, so the shape attribute returns a single value tuple.
Let’s look at an example:
import numpy as np #an one-dimensional NumPy array arr = np.arange(10) #print an array print(arr) # [0 1 2 3 4 5 6 7 8 9] print(arr.shape) # (10, )
The code snippet also uses the NumPy
The shape attribute of a two-dimensional array (also called a matrix) gives us a tuple. The shape returns the number of elements along each dimension, which is the number of rows and columns in the two-dimensional array.
# A two-dimensional NumPy array import numpy as np arr = np.array([[1,2,3,4,5], [5,4,3,2,1]]) print(arr.shape) # (2, 5)
The following example is for the shape of three-dimensional arrays.
# A three-dimensional array import numpy as np arr = np.array([ [ [0, 11, 15, 16], [3, 7, 10, 34], [44, 99, 5, 67] ],[ [52, 8, 11, 13], [0, 4, 5, 6], [4, 4, 4, 4] ] ]) print(arr.shape) # (2, 3, 4)
It takes some practice to understand the shape tuple for multidimensional arrays. The dimensions represented by a tuple are read from the outside-in. If you observe the brackets, the outmost bracket is a part of the basic syntax for the whole array. In the shape tuple 2 represents the second set of brackets. If you count them you will see that there are 2 elements in this dimension.
[ [0, 11, 15, 16], [3, 7, 10, 34], [44, 99, 5, 67] ]
[ [52, 8, 11, 13], [0, 4, 5, 6], [4, 4, 4, 4] ]
Each element contains 3 more elements in the second dimension. If you think about nested lists, you can draw the analogy. These elements are:
[0, 11, 15, 16]
[3, 7, 10, 34]
3rd element [
44, 99, 5, 67]
Finally, number 4 represents the number of elements in the third dimension. Those are the innermost elements. For example 0, 11, 15 and 16.
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