How to Upload an Image in Anaconda

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Data Science Jupyter Python Visualization

Terminal Updated

February 25, 2021

Introduction

The Jupyter Notebook (formerly IPython Notebooks) is a popular web-based interactive surroundings that was first started from the IPython project and is currently maintained past the nonprofit organization 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 be easily converted to HTML, LaTeX, PDF, Markdown, Python, and other open standard formats [ane] .

In this mail service, I will present three ways to add images to your notebook. The first 2 approaches are pretty standard that rely on external resource to illustrate the images, and those are to use the epitome URL or to load an image from a local file. Yet, both of these methods rely on external resources. To contain all images used in the notebook within itself without relying on whatever external source, nosotros tin use 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 Paradigm  form from IPython'southward display  module to show all images.

Arroyo 1: Add an image from a local file

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

              from IPython import brandish brandish.Image("./paradigm.png")            

There are two downsides to this approach: 1) the local or absolute path provided may not work well on another organization. ii) you have to brand sure to include all images used in a notebook with anyone you want to share. You may finish up compressing all files to a single zip file for convenience when sharing your notebook.

Approach ii: Add an image from a URL

You can also add an image to your notebook using the URL link to the image, equally shown beneath.

              from IPython import display display.Image("URL of the prototype")            

In this case, the image provider may remove the image or change the image properties without knowing it. So, let'southward say you take an onetime notebook that has a cleaved image link. Information technology might be difficult to retrieve the original image. Fifty-fifty if you are taken the image from your website, you should be careful not to change the image link or properties!

Approach iii: Embed an image by Base64 Encode-Decode

The showtime two approaches rely on external resource. In Approach 1, nosotros rely on a URL, and whatever change in the original link will bear upon the image in the notebook. In Arroyo 2, 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. Different the previous methods, Approach 3 embeds the image as a text using the Base64 encoding algorithm . This way, nosotros will not be relying on whatsoever external resource for the embedded epitome. Hence, nosotros can have all images embedded in the aforementioned notebook file.

Base64 is a binary-to-text encoding algorithm to convert data (including but not limited to images) as obviously text. It is ane of the most popular binary-to-text encoding schemes (if not the most i). Information technology'southward widely used in text documents such as HTML, JavaScript, CSS, or XML scripts [2] . However, technically speaking, you tin even encode/decode audio or video files likewise!!

First, you demand to encode your image. For this, you tin use the online tool Base64-Epitome . Later on you upload your image, yous can so click on the re-create prototype, as shown below.

Screenshot of the uploaded epitome at base64-image

Now y'all can paste the encoded epitome lawmaking into your notebook, merely first, y'all should remove data:prototype/png;base64,  at the starting time. Don't forget to as well remove the comma after base64!

Now that we accept the encoded epitome code, we tin can use 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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" brandish.Epitome(b64decode(base64_data))            

Equally you may have noticed by now, the main reward of using Base64 to add together all images to your Notebook is the fact that do yo no longer demand to worry almost any external resources for your images as 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's file size depending on the paradigm resolution.

Conclusion

In this post, nosotros went over three ways to add an paradigm to a Jupyter Notebook, and those are through 1) a URL, 2) a local file, or 3) by Base64 encoding the image data. I also provided a resource link that you can use to Base64 encode your image. The master benefit of using the Base64 encoding scheme is to reduce (or even) remove any external images in your notebook.

📓

You can find the notebook for this post on GitHub .

Thanks for reading 🙏

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References

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

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

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

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