- \n
- Introduction<\/a><\/li>\n
- Syntax<\/a><\/li>\n
- Examples<\/a><\/li>\n
- Frequently Asked Questions<\/a><\/li>\n<\/ul>\n
<\/a><\/p>\n
A Quick Introduction to Numpy Shape<\/h2>\n
The Numpy Shape function is pretty straight forward.<\/p>\n
It retrieves the shape<\/em> of a Numpy array.<\/p>\n
Let me quickly explain.<\/p>\n
A Quick Review of Numpy Array Shapes<\/h3>\n
As you’re probably aware, Numpy is a toolkit in Python<\/a> for working with Numpy arrays.<\/p>\n
Numpy arrays are data structures that store numbers in a row-and-column format.<\/p>\n
![\"A](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/07\/numpy-array_2d-example_1-to-6.png\")
<\/p>\nSo structurally, they’re something like an Excel spreadsheet with numbers in it. Or like a matrix in linear algebra.<\/p>\n
Numpy arrays have a “shape”<\/h4>\n
Numpy arrays have a variety of properties, like size of the array and the number of dimensions.<\/p>\n
But one of the most important properties is the shape<\/em> of the array.<\/p>\n
What is array shape?<\/p>\n
The shape is the number of units along each dimension of the array.<\/p>\n
So let’s say that we have a 2D array with 2 rows and 3 columns.<\/p>\n
![\"An](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2099\/01\/numpy-array-shape-attribute-visual-explanation.png\")
<\/p>\nThe shape of the array then, is
(2,3)<\/code>.<\/p>\nThat’s the number of rows and the number of columns.<\/p>\n
np.shape returns the shape of an array<\/h4>\n
So now let’s go back to the Numpy shape function.<\/p>\n
The np.shape function simply returns the shape of an array.<\/p>\n
![\"An](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2099\/01\/numpy-shape-function_visual-explanation.png\")
<\/p>\nThat’s sort of it.<\/p>\n
It’s a pretty simple function.<\/p>\n
That being said, let’s take a look at the syntax of Numpy shape, so we can see exactly how it works.<\/p>\n
<\/a><\/p>\n
The Syntax of Numpy Shape<\/h2>\n
Ok. Here, we’ll look at the syntax of the Numpy shape function.<\/p>\n
A quick note<\/h4>\n
One quick reminder before we get started.<\/p>\n
Everything in this syntax section, and in the examples section, assumes that you’ve imported Numpy with the alias
np<\/code>.<\/p>\nYou can do that with the following code:<\/p>\n
\r\nimport numpy as np\r\n<\/pre>\n
Remember: this enables us to call Numpy functions with the prefix
np<\/code>. <\/p>\nThis obviously changes the syntax slightly, so it’s relevant to our syntax discusssion.<\/p>\n
np.shape syntax<\/h3>\n
The syntax of Numpy shape is extremely simple.<\/p>\n
You call the function with the name
np.shape()<\/code>.<\/p>\n![\"An](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2099\/01\/numpy-shape_syntax-explanation.png\")
<\/p>\nThen, inside the parenthesis, you provide the name of the Numpy array<\/a> that you want to operate on.<\/p>\n
<\/a><\/p>\n
The Parameters and Inputs of Numpy Shape<\/h3>\n
As shown above, there is only one input to the np.shape function: the array you want to operate on.<\/p>\n
input_array<\/code> (required)<\/h6>\nThe
input_array<\/code> is the input to the function.<\/p>\nThis input is required.<\/p>\n
Having said that, as is common with Numpy functions, the function will not only accept Numpy arrays, but also array-like objects, such as Python lists and tuples.<\/p>\n
The Output of np.shape<\/h3>\n
The output of np.shape() is a tuple of integers.<\/p>\n
These integers represent the shape of the input array.<\/p>\n
More specifically, the integers provide the number of elements along each axis of the the array<\/a>.<\/p>\n
So for example, if the function returns the tuple
(2,3)<\/code>, that indicates that the array has 2 units along axis-0, and 3 units along axis-1.<\/p>\n<\/a><\/p>\n
Examples of How to Use Numpy Shape<\/h2>\n
Now that we’ve looked at the syntax, let’s look at some examples.<\/p>\n
Examples:<\/strong><\/p>\n
- \n
- Get the shape of a 1D array<\/a><\/li>\n
- Get the shape of a 2D array<\/a><\/li>\n<\/ul>\n
Run this code first<\/h4>\n
Before you run the examples, make sure that you import Numpy.<\/p>\n
You can do that with the following code:<\/p>\n
\r\nimport numpy as np\r\n<\/pre>\n
<\/a><\/p>\n
EXAMPLE 1: Get the shape of a 1D array<\/h3>\n
First, we’ll get the shape of a 1D array.<\/p>\n
Create 1D array<\/h4>\n
The first step of this example, is to create our 1-dimensional array.<\/p>\n
To do this, we’ll use Numpy random randint to generate an array with random integers<\/a>.<\/p>\n
\r\nnp.random.seed(44)\r\nmy_1d_array = np.random.randint(size = 5, low = 0, high = 100)\r\n<\/pre>\n
And let’s print out the array.<\/p>\n
\r\nprint(my_1d_array)\r\n<\/pre>\n
OUT:<\/p>\n
\r\n[20 35 45 59 3]\r\n<\/pre>\n
As you can see, this array has 5 values, and 1 dimension.<\/p>\n
Get the Shape<\/h4>\n
Now, we’ll use Numpy shape to get the shape of the array.<\/p>\n
\r\nnp.shape(my_1d_array)\r\n<\/pre>\n
OUT:<\/p>\n
\r\n(5,)\r\n<\/pre>\n
Explanation<\/h5>\n
As you can see, the output is a tuple with only one value: 5.<\/p>\n
There’s only one value, because it’s a 1-dimensional array (there’s one value per dimension).<\/p>\n
And the integer value is 5, because there are five numbers inside the array.<\/p>\n
- Get the shape of a 2D array<\/a><\/li>\n<\/ul>\n
- Syntax<\/a><\/li>\n