Hacking productivity in data science notebooks

The tools we use don’t just influence how fast we work, but also the kind of results we deliver.

The potential of data science teams is often limited by the engineering support that’s available to them. Managing Python packages, researching connectors to disparate data sources, and maintaining data pipelines is particularly difficult. The learning curve is steep and the experience is often exasperating. Setting up a development environment and maintaining its consistency takes up valuable time, and it can end up being an expensive and frustrating task.

First, Deepnote makes dependencies management easy. When you pip-install a package in the cell of a notebook, we prompt you to move it into requirements.txt and append a specific version of the used package. Second, you can create Teams in Deepnote, which allows you to share datasets, integrations, projects, and environment configurations. This way, when your colleague shares a project with you, it includes the environment it runs in, not just the .ipynb. To learn more, take a look at my previous article discussing how Deepnote fosters collaboration in notebooks.

Whether you are transforming your data, exploring, or building ML models, Deepnote helps with advanced code assistance. An IDE-style autocomplete system lets you work faster, and configurable linting tools point out bugs before they break your long training jobs.

Deepnote has a built-in variable explorer so that you can instantly review the contents of your variables without having to print them. It contains additional information, including histograms for each column of a data frame so that you can quickly get an overview of the current state. Discovering patterns is also made easier with the help of interactive plots.

As data scientists, we need interfaces that help us explore data efficiently, prototype quickly, and move towards actionable insights. With Deepnote, we’ve introduced a bunch of features that save your time and help you iterate on your experiments faster:

We’ve also introduced a powerful Command palette, as well as shortcuts that provide quick access to all your files and the most popular actions. Simply press Ctrl+P (or ⌘+P if you’re on Mac) and start typing to switch to another file, open a terminal, or execute an action.

This post is Part II in a series on how Deepnote tackles the common challenges of data science notebooks. Check out our currently released articles below:

There are more ways to learn from Deepnote and we’re always happy to share:

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