Introducing Datalore Enterprise 2021.3: Database Connections, SQL Cells, Interactive Reports, and More

It’s already 2022, and we are here with the first newsletter of the year! Read on to learn what the Datalore team has been up to recently.

Greetings from the Datalore team! Exciting news for those of you who love more than just Python: Datalore now also supports R and Scala notebooks! Read on to learn more about the updates. 👇🏻 R and Scala support Although Python is the most popular language in data science, R and Scala also do a great job for data science tasks and have a rich user community. That’s why we a

Greetings from the Datalore team! Does the #Tesla stock price depend on Elon Musk’s mood? In this newsletter, we’ll share a new tutorial for sentiment analysis and talk about the new features that can improve your daily workflow in Datalore. Chart cells Could you imagine that working with visualizations in Python would be as easy as it is in spreadsheets? Last month we took a step further

Greetings from the Datalore team! Jupyter notebooks can get messy. Perhaps you have tried different things in one notebook, or maybe you have chunks of outdated code and variable declarations all over the place. This isn’t necessarily a sign that something is wrong. Rather, the tool was designed to allow you to work this way. For the last 3 years, our Datalore team has been experimenting wit

We have been busy this month laying the foundations for future features. This work will enable us to take some big steps with Datalore over the coming year.

We believe that Jypyter notebooks could become a great presentation and reporting tool. Read on to learn how Datalore makes notebooks more friendly for non-tech people.

Jupyter notebooks are arguably the most widely used tool in data science. However, when it comes to collaboration, resource management, and security...

Read this blog post to learn about how we improved the plotting workflow, integrated Terminal and Pip package manager, and adjusted the features on the Community plan.

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.

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