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
New in Datalore: S3 Buckets Support, Workspace Files, Two Inspiring Research Posts and Other Updates
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.
We Downloaded 10,000,000 Jupyter Notebooks From Github – This Is What We Learned
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.
We Analyzed 495 AMD Radeon and Nvidia GPU Specifications and Shared the Dataset with Everyone
In Datalore, we've manually assembled a dataset with the technical specifications of 495 Nvidia and AMD Radeon GPU machines. Let's learn more about how this type of hardware has improved over time.
New in Datalore: Pro Plan Launch and Future Changes, Anaconda Collaboration, Dark Theme, Soft-wrap and Advanced Visualization Tutorial
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.
Datalore Pro: Online Jupyter Notebooks with GPU Access, Hosted by JetBrains
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.
New in Datalore: Onboarding, Code Formatting, Shortcuts for Moving Cells, and Neural Network for Art Generation
Our team has been working on a huge project this October, which we will be revealing in November. And we’ve still managed to add some small but long-awaited features to Datalore to make the UX even better.
New in Datalore: Sidebar, Startup Time Improvement, Stack Trace Navigation, Tensorflow Code Insight, and Two New tutorials
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!