New in Datalore: 2021 Recap, Visualization Improvements, 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.
Introducing Datalore Enterprise 2021.3: Database Connections, SQL Cells, Interactive Reports, and More
The focus of this release was to make it easy for data scientists to share their work and to facilitate fetching data from databases with SQL. Read on for the highlights of this release!
Revamped Reactive Mode and How It Makes Your Notebooks Reproducible
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 Perhaps you have tried different things in one notebook, or maybe you have chunks of outdated code and variable declarations all over the place.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
New in Datalore: Kernel Changes, New Automatic plots, Tutorials, and More
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.
🚀 New in Datalore: Interactive Controls, Hidden Cells, Collapsing Inputs and Outputs, and Bug Fixes
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.
New in Datalore: Cell Redesign, PDF and Python File Export, Embedly Support, Twitter Challenge, and More
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 👇🏻
New in Datalore: Publishing Update, Documentation, Pandas Tutorial and More
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 Tutorial: 10 Popular Questions for Python Data Frames
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.
10 Tips for Working With Data in Datalore
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
Datalore Improvements in 2020: Datalore Professional, a Better Coding and UI Experience, and More
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