Webinar: I Can’t Believe It’s Not Real Data! An Introduction to Synthetic Data
Easy access to relevant, safe data is a major bottleneck hindering developers and data scientists. But what if you could generate your own accurate, privacy-protected, shareable data? Synthetic data can provide an inexpensive alternative to real sets of data that can’t be used due to its sensitivity or regulations. Such data is used for training machine learning models, testing, and performing quality assurance. In this webinar with Mason Egger, we'll learn about using Synthetic Data, and we’ll learn how to get started creating our own Synthetic Data. Join us on July 2
New in Datalore: Chart Cells, Spelling and Grammar Checking, Initialization Scripts, and More
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 towards this idea and released a new type of cell – Chart cells. Chart cells allow you to create visualizations for any data frames you already have in your notebook with just a few clicks.
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 👇🏻
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 №1: Drag and drop files and folders Quickly add new files and folders in Datalore by opening the Attached files sidebar tab and dragging and dropping your files and folders there. Tip №2: Unzip files
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 that need more powerful computation hardware. Here is a comparison table for Datalore Community and Datalore Professional. Take a closer look, and decide which plan best fits your needs! Community
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.
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.