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.

Last year presented an unexpected challenge for the world, one we all had to rise to. In 2020, we managed to launch Datalore Professional and deliver lots of great new features for our users. Here we’ve assembled a description of some of the most notable ones. Datalore Professional In November we launched Datalore Professional. It was designed for solving more complex tasks with larger datasets t

Though this is our final blog post of 2020, we have a bunch of new Datalore updates we’d like to share, including the results of two major studies we recently conducted.

Greetings from the Datalore team! This November we released a major update: the Professional Plan is now available for Datalore users 🚀 Read on to learn more about the future of Datalore Pro.

We have some really exciting news for you – we’ve introduced a new Datalore plan, called Datalore Professional. This new plan comes with increased processing power and added storage for even faster calculation of bigger datasets.

This September has been quite productive for our Datalore team. We’ve performed a lot of user interviews and implemented some long-awaited features to improve the user experience. And now we are happy to share the new updates with you!

We hope your summer has been amazing. For us at Datalore, the upcoming autumn season brings a new beginning, and we are happy to announce some fresh updates. Logo update Perhaps you’ve already noticed that the Datalore logo has changed. This update is symbolic of a fresh start for the team, and we hope you like it as well. A huge thanks to the JetBrains Design team for working hard on it. The pre

Greetings from the Datalore team! In this blog post we’ll show you 10 tricks you can use to help you work more productively with data files in Datalore. Try Datalore! Before we start In Datalore, files are persistently attached to notebooks. After you create a notebook and upload some data, you will have access to the data even after you restart the kernel or close and reopen Datalore. Tip

Here’s how we used the hundreds of thousands of publicly accessible repos on GitHub to learn more about the current state of data science.

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.