How to Upload a Picture to a Jupyter Notebook

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Information Science Jupyter Python Visualization

Final Updated

February 25, 2021

Introduction

The Jupyter Notebook (formerly IPython Notebooks) is a popular web-based interactive environment that was first started from the IPython project and is currently maintained by the nonprofit arrangement Project Jupyter . Information technology's a user-friendly tool to create and share documents that comprise codes, equations, texts, and visualizations. A Jupyter Notebook can exist easily converted to HTML, LaTeX, PDF, Markdown, Python, and other open standard formats [one] .

In this post, I volition nowadays 3 ways to add images to your notebook. The starting time two approaches are pretty standard that rely on external resources to illustrate the images, and those are to use the image URL or to load an image from a local file. Notwithstanding, both of these methods rely on external resources. To contain all images used in the notebook within itself without relying on whatever external source, we can utilize the Base64 encoding algorithm to encode our images and use those encoded data to illustrate them. So, we will briefly talk about the Base64 algorithm too.

Here, I will be using the Image  course from IPython'southward display  module to show all images.

Approach one: Add an image from a local file

We can add images from your local bulldoze past providing the path to the file.

              from IPython import display display.Image("./image.png")            

At that place are two downsides to this approach: 1) the local or absolute path provided may not work well on another arrangement. ii) you accept to make certain to include all images used in a notebook with anyone you desire to share. Y'all may end up compressing all files to a single zip file for convenience when sharing your notebook.

Approach 2: Add an image from a URL

You can too add together an image to your notebook using the URL link to the epitome, as shown beneath.

              from IPython import display display.Epitome("URL of the image")            

In this example, the image provider may remove the image or alter the image properties without knowing it. And so, let'southward say yous have an old notebook that has a broken epitome link. It might exist difficult to retrieve the original image. Even if y'all are taken the image from your website, you should exist careful not to change the image link or properties!

Approach 3: Embed an epitome by Base64 Encode-Decode

The starting time two approaches rely on external resources. In Approach one, we rely on a URL, and any modify in the original link will touch the image in the notebook. In Approach ii, we used the path to a file that is saved locally. Any change in the filename or path may impact the image in the notebook. Unlike the previous methods, Approach 3 embeds the image as a text using the Base64 encoding algorithm . This style, we will non be relying on any external resources for the embedded image. Hence, we can have all images embedded in the same notebook file.

Base64 is a binary-to-text encoding algorithm to catechumen information (including simply not limited to images) as plain text. It is one of the most popular binary-to-text encoding schemes (if not the most one). Information technology'southward widely used in text documents such equally HTML, JavaScript, CSS, or XML scripts [two] . However, technically speaking, you can even encode/decode audio or video files too!!

First, you need to encode your epitome. For this, you tin use the online tool Base64-Image . Later you upload your prototype, you tin can then click on the copy image, as shown below.

Screenshot of the uploaded epitome at base64-paradigm

Now y'all can paste the encoded image code into your notebook, but first, y'all should remove information:image/png;base64,  at the commencement. Don't forget to also remove the comma subsequently base64!

At present that we have the encoded image code, nosotros can apply the Python standard base64 library  to decode the base64 data, as shown below.

              from IPython import display from base64 import b64decode base64_data = "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" display.Image(b64decode(base64_data))            

Equally you lot may accept noticed by now, the main advantage of using Base64 to add together all images to your Notebook is the fact that do yo no longer need to worry about any external resources for your images every bit they are all self-contained in your Notebook. The other point to be aware of is that including the images in your notebook will increase your notebook'south file size depending on the epitome resolution.

Conclusion

In this postal service, we went over three means to add together an image to a Jupyter Notebook, and those are through 1) a URL, 2) a local file, or 3) by Base64 encoding the image information. I also provided a resource link that you can utilize to Base64 encode your image. The main do good of using the Base64 encoding scheme is to reduce (or even) remove any external images in your notebook.

📓

You tin can find the notebook for this postal service on GitHub .

Thanks for reading 🙏

If yous liked this post, you can join my mailing list to receive similar posts. You lot can follow me on LinkedIn , GitHub , Twitter and Medium .

And finally, you tin detect my knowledge forest 🌲 (raw digital notes) at notes.ealizadeh.com .

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References

[ane] Wikipedia, Project Jupyter  (Accessed on November sixteen, 2020)

[2] Wikipedia, Base64  (Accessed on November 16, 2020)

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Source: https://ealizadeh.com/blog/3-ways-to-add-images-to-your-jupyter-notebook