Datalore

This version brings native R package support for R users, and elevates your collaborative data science experience with notebook content search, environment variable management, and usability improvements for the Report builder.

Have you ever struggled to create presentations for your stakeholders with your data science or data analysis results? Take a look at how you can nail this task with Report builder in Datalore.

The Report builder is a flexible new way to turn your notebooks into beautiful data reports. In this blogpost we’ll give a short introduction to the new features and will share more details in upcoming newsletters!

Data science projects can be complex, consisting of many parts, such as notebooks, data, environments, and scripts, and it can be challenging for data science teams to effectively collaborate on them.

Schedule Jupyter notebooks with just a few clicks. Specify run intervals from the interface or use the cron string to configure a schedule.

Today we are happy to introduce the docker-based installation for Datalore Enterprise and a bunch of other ways to streamline your data science team work. Read on for the highlights of the release!

In the past few months, we’ve been working hard on improving the data exploration and collaboration workflows in Datalore. In this blog post, we’ll give you an overview of what’s new!

Check out the newest data exploration features and enjoy the upgraded interactive reporting experience in Datalore Enterprise 2022.1.

We talk and write a lot about sharing in Datalore as we truly believe in the collaborative nature of data workflows. Today we are happy to introduce a new feature for the result sharing workflow – publishing of Interactive reports.

For the last month, we’ve been working on implementing a frequent user request to export notebooks into different types of files and to share code on other platforms. You might have also already noticed that the look of the editor has changed a bit. Read on to learn more about the recent updates in Datalore 👇🏻

In January and February we introduced a lot of updates to Datalore: we integrated the UI for publishing notebooks, released the first version of Datalore’s documentation, updated the landing page, and introduced improvements for working with files from Python code.

Pandas is one of the first libraries you will learn about when you start working with Python for data analysis and data science. In this tutorial, we will answer 10 of the most frequently asked questions people have when working with pandas.