The Anaconda Blog

Data Science Blog
Intake released on Conda-Forge

Intake is a package for cataloging, finding and loading your data. It has been developed recently by Anaconda, Inc., and continues to gain new features. To read general information about Intake and how to use…

Read More
Data Science Blog
RPM and Debian Repositories for Miniconda

Conda, the package manager from Anaconda, is now available as either a RedHat RPM or as a Debian package. The packages are the equivalent to the Miniconda installer which only contains Conda and its dependencies.…

Read More
Data Science Blog
Conda 4.6 Release

The latest set of major Conda improvements are here, with version 4.6.  This release has been stewing for a while and has the feature list to show for it.  Let’s walk through some of the…

Read More
Data Science Blog
Anaconda Distribution 2018.12 Released

We are changing versioning in Anaconda Distribution from a major/minor version scheme to a year.month scheme. We made this change to differentiate between the open source Anaconda Distribution and Anaconda Enterprise, our managed data science…

Read More
Data Science Blog
Python Data Visualization 2018: Where Do We Go From Here?

This post is the third in a three-part series on the current state of Python data visualization and the trends that emerged from SciPy 2018.   By James A. Bednar As we saw in Part…

Read More
Data Science Blog
Intake for Cataloging Spark

By: Martin Durant Intake is an open source project for providing easy pythonic access to a wide variety of data formats, and a simple cataloging system for these data sources. Intake is a new project,…

Read More
Data Science Blog
Using Pip in a Conda Environment

Unfortunately, issues can arise when conda and pip are used together to create an environment, especially when the tools are used back-to-back multiple times, establishing a state that can be hard to reproduce. Most of…

Read More
Data Science Blog
Python Data Visualization 2018: Moving Toward Convergence

This post is the second in a three-part series on the current state of Python data visualization and the trends that emerged from SciPy 2018. In my previous post, I provided an overview of the…

Read More
Data Science Blog
Understanding Conda and Pip

Conda and pip are often considered as being nearly identical. Although some of the functionality of these two tools overlap, they were designed and should be used for different purposes. Pip is the Python Packaging…

Read More
Data Science Blog
Deriving Business Value from Data Science Deployments

One of the biggest challenges facing organizations trying to derive value from data science and machine learning is deployment. In this post, we’ll take a look at three common approaches to deploying data science projects,…

Read More
Data Science Blog
Python Data Visualization 2018: Why So Many Libraries?

This post is the first in a three-part series on the state of Python data visualization tools and the trends that emerged from SciPy 2018. By James A. Bednar At a special session of SciPy…

Read More
Data Science Blog
Choose Your Anaconda IDE Adventure: Jupyter, JupyterLab, or Apache Zeppelin

As humans we are faced with multiple choices every day. Every person is different: some people prefer Firefox while others like Chrome; some people prefer Python while others like R. Here at Anaconda, we abstain…

Read More