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 Perhaps you have tried different things in one notebook, or maybe you have chunks of outdated code and variable declarations all over the place.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.
Announcing Datalore Enterprise – The Smart and Secure Jupyter Environment for Data Science Teams
Jupyter notebooks are arguably the most widely used tool in data science. However, when it comes to collaboration, resource management, and security...
New in Datalore: Automatic plots, Terminal, Pip package manager, and Community plan changes
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.
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 👇🏻
Interactive Visualizations in PyCharm and Datalore
The Lets-Plot library is an open-sourced interactive plotting library developed by JetBrains for Python and Kotlin. Its architecture was inspired by the ggplot library for the R language, and is built with layered graphic principles in mind. But what sets Lets-Plot apart from the well-known Matplotlib and Seaborn Python libraries? With Lets-Plot you can produce interactive visualizations, an
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