Here’s our 8-minute guide showing you how to move from Jupyter notebooks to Einblick canvases, and get much more productive.
Stop: Just scrolling down (Start: Building in 2D)
Canvases take a little bit of time to get used to, but this is because you now have access to more dimensions. In order to optimize your newfound freedom, you need to get used to working in a more complex workspace.
For instance, you can lay out charts and content side-by-side for easy viewing and dissection:
Or go ahead and try two versions of the same model training code to see how differences in hyperparameters might change results:
Stop: Disorganization (Start: Organizing with Zones)
The biggest advantage of a canvas setting is that you can lay out all your analysis visually and in two dimensions. Zones are how you can go ahead and section off portions of the canvas to organize it, and make the analysis even easier to consume visually. Simply select Zone, draw over the area you want to highlight, and release. (Or you can use our hotkeys. Just click Z on your keyboard–for Zone–and then draw out the size you want)
You can use multiple, including nested zones in order to represent your analysis. Typically, you might have different areas for data ingestion, EDA, transformations, any charts to present, etc.
Stop: Losing your place (Start: Using bookmarks to save and navigate)
Einblick bookmarks can be created with a single click, and they will save your location and zoom level.
- Edit the name of the bookmark to something you can remember
- Copy the link if you want to share that spot
- Just click on the bookmark to jump to the spot.
Stop: Putting all comments in-line (Start: Using rich text comments)
While Jupyter notebook markdown cells also have the ability to use rich text, Einblick lets you have much greater control over how you want to annotate code blocks and sections. Use rich text, and provide commentary by overlaying them on top of cells, structuring it side-by-side, or using them as section headers.
# Heading 1 ## Heading 2 ### Heading 3 #### Heading 4 *Italic Text* **Bold Text** Math: $$E=mc^2$$ Link: [Link text](https://www.example.com) Bullets - Item 1 - Item 2 Numbers 1. Step 1 2. Step 2 Image: ![Alt text](https://upload.wikimedia.org/wikipedia/commons/thumb/c/c3/Python-logo-notext.svg/1200px-Python-logo-notext.svg.png)
Every data scientist has struggled with the issue of figuring out how to get a notebook into the right state. This is because as you “Run All” cells from top to bottom of the notebook, there might be a) cells you don’t need to run, b) code that is just leftover debugging/exploration, and c) sometimes the code is out of order and you need chunks from further down the notebook to run first.
Einblick solves this by explicitly allowing you to generate execution graphs (specifically, directed acyclic graphs, aka DAGS). The platform automatically tries to create linkages for you, but you can also apply your own by going into dependencies.
To see this in action, we can watch a short clip of how x, y, and z can be linked together.
And there you have it--a crash course in how to transition your data workflows from notebooks to canvases, so your work can reach new heights.
Einblick is an AI-native data science platform that provides data teams with an agile workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.