How to Plot Images with Plotly Imshow

In this tutorial, I’ll show you how to use the Plotly imshow function to display images in Python.

So I’ll explain the syntax of px.imshow. I’ll also show you a clear, step-by-step example of how to display an image with Plotly.

The tutorial has a few sections, and if you need something specific, just click on one of the following links.

Table of Contents:

Ok. Let’s get to it.

A Quick Introduction to the Plotly imshow Function

The Plotly imshow function has multiple uses.

For example, you can use the function to plot heatmaps and to plot numbers visually as colors.

But one of the best ways to use the function is to display images. (As the name implies, its fundamental purpose is for “image show”.)

An example showing how Plotly imshow can plot images.

So you can use Plotly imshow to visualize images as part of an image processing workflow.

In this tutorial, we’ll strictly stick to plotting images like .jpg and .png files, and we’ll cover heatmaps and other techniques in a separate tutorial.

The syntax of px.imshow

Now that I’ve given you a quick overview of what the Plotly imshow function does, let’s take a look at the syntax.

The syntax of px.imshow is very simple.

Typically, we call the function as px.imshow().

An image that explains the basic syntax of px.imshow.

Remember: this syntax assumes that you’ve imported Plotly express with the nickname px. You can do that with the code import as px.

Input to px.imshow

Let’s talk quickly about the input to the function.

In the syntax explanation above, you’ll notice the input that I’ve called your_image. This is often an .jpg or a .png file.

However, typically, before you can plot the image with px.imshow, you need to read it in with a separate function. Specifically, you need to read in the image with a function like the imread() function from scikit-image. I’ll show you how to do this in the examples section.

Examples: how to visualize images with Plotly Express

Now that we’ve looked at the syntax, let’s look at some examples of how to plot images and pictures with Plotly Express.


Run this code first

Before you run the examples, you’ll need to import some packages and run some preliminary code.

Import packages

First we need to import a few Python packages.

import as px
import as skio

We obviously need to run the px.imshow function.

We also need skimage to import our image and read it into our Python environment.

If you don’t have those packages, then you’ll need to install them first. Typically, these days, I install all of my Python packages via Anaconda. You can get more information here about installing skimage via Anaconda. You can get more information about installing Plotly here.

Set Up Image Rendering

By default, Plotly is set up to render images in browser windows.

So, if you’re using an Integrated Development Environment like Spyder to do your data science work, you’ll need to set it up to render plots from Plotly. (I use Spyder, so I need to do this step myself.)

Note: if you’re using Jupyter, you can skip this code!

To set up Plotly to render your plots as svg images in your IDE, you can run the following code:

import as pio
pio.renderers.default = 'svg'
Get Image (i.e., read in the image with scikit-image)

Next, let’s get an image that we can plot.

The image is in a folder online at, and we’ll use the imread() function from skimage to read it in.

ron = skio.imread('')

Once you run that code, you should be ready to go.

(If you have any problems getting the image, leave a comment in the comments section near the bottom of the page.)

EXAMPLE 1: Plot an image with px.imshow

Here, we’re going to “plot” the image with the Plotly imshow function.

This is fairly straightforward.

Here’s the code:


And here’s the output:

An image of Ron Burgundy, smirking, made with the Plotly imshow function.

“It’s science.”


Ok. This is pretty simple.

We called the Plotly imshow function with the syntax px.imshow().

Inside the parenthesis, we’ve passed in an image. The image is the “ron-smirk.png” file. The file is being stored with the variable name ron.

As you can see from the file ending, this is a .png file. But you could also use a .jpg file, and several other image types should work.

EXAMPLE 2: Remove the tick labels from the x and y axes

Now, we’ll remove the tick labels from the x-axis and y-axis.

In example 1, you probably noticed the numbers along the x and y axes. These are the “tick” labels of the axes.

For some applications, it may be fine to include them, but in some cases you may want to remove them.

To remove them, you need to use the update_xaxes() method and the update_yaxes() method. Importantly, inside the parenthesis, we need to set the showticklabels parameter to showticklabels = False.

Let’s take a look:

   .update_xaxes(showticklabels = False)
   .update_yaxes(showticklabels = False)


And image plotted with Python's Plotly imshow function, with the axis ticks removed.


Obviously, the output here is almost exactly the same as the output for example 1.

The only difference here is that we’ve removed the axis ticks (i.e., the numbers along the axis) by using update_xaxes and update_yaxes.

Notice as well that I used a special technique to call these methods. I’ve “chained” these methods together, one after another, but they’re on multiple lines.

To do this, the whole expression is enclosed inside parenthesis.

This is very similar to the “secret” pandas chaining syntax, which allows you to chain together multiple pandas methods to do advanced data manipulation in a single block of code.

I strongly recommend that you learn this technique to chain together Python methods. It’s very powerful once you learn how to use it properly.

Leave your other questions in the comments below

Do you still have questions about plotting images with the Plotly imshow function?

If so, just leave your questions in the comments section below.

If you want to master Plotly, join our course

In this blog post, I’ve shown you how plot an image in Python using the Plotly imshow function (AKA px.imshow). But to really master Python data visualization with Plotly, there’s a lot more to learn.

That said, if you’re serious about learning Plotly and if you want to master data visualization in Python, you should join our premium online course, Plotly Mastery.

Plotly Mastery is an online course that will teach you everything you need to know about Python data visualization with the Plotly package.

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Additionally, when you enroll, you’ll get access to our unique practice system that will enable you to memorize all of the syntax you learn. If you practice like we show you, you’ll memorize Plotly syntax and become “fluent” in data visualization.

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Joshua Ebner

Joshua Ebner is the founder, CEO, and Chief Data Scientist of Sharp Sight. Prior to founding the company, Josh worked as a Data Scientist at Apple. He has a degree in Physics from Cornell University.

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