Datalore

Today, we’re introducing a redesign that aims to make Datalore notebooks appear simpler, cleaner, and ultimately more zen.

Reading data from a MySQL database to a pandas dataframe can be intimidating. Establishing a connection, keeping the credentials safe, creating an SQL query within a string variable, and saving the result to pandas is not a trivial task.

Working with financial data is not a trivial task, as you can’t just access a production database or a data lake, download the data, and work on it. You have to ensure secure access to the data and produce insights that are easy to share as well.

Learn about data preparation for machine learning and analysis, and avoid some of the most common problems real-world data can throw at you.

Find out how to conduct ad hoc analysis using Python and Datalore.

How to use Exploratory Data Analysis to improve your data analytics? Definition, best practices, and more.

Learn how to connect your Jupyter Notebook to Google BigQuery in Datalore and try SSH tunneling to connect to remote databases!

Today we are happy to introduce the docker-based installation for Datalore Enterprise and a bunch of other ways to streamline your data science team work. Read on for the highlights of the release!

In this blog post we’ll share a few updates to database support and SQL cells, announce the first Datalore webinar, and tell you how Datalore has become even more collaborative.

In the past few months, we’ve been working hard on improving the data exploration and collaboration workflows in Datalore. In this blog post, we’ll give you an overview of what’s new!