The Anaconda Blog

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
Data Science Blog
Who You Gonna Call? Halloween Tips & Treats to Protect You from Ghosts, Gremlins…and Software Vulnerabilities

Happy Halloween, readers. At Anaconda, we’re not too scared about things that go bump in the night. We’ve examined the data and concluded that it’s just the cleaning staff upstairs. We are, however, kept awake…

Read More
Data Science Blog
Patching Source Code to Conda Build Recipes

If you are a developer who relies upon conda, we hope to encourage you to begin building your own packages so that your projects can be used just like all of the other packages you…

Read More
Company Blog
Anaconda Enterprise 5.2.2: Now With Apache Zeppelin and GPU improvements

Anaconda Enterprise 5.2 introduced exciting features such as GPU-acceleration, scalable machine learning, and cloud-native model management in July. Today we’re releasing Anaconda Enterprise 5.2.2 with a number of enhancements in IDEs (Integrated Development Environments), GPU resource…

Read More
Company Blog
Bringing Dataframe Acceleration to the GPU with RAPIDS Open-Source Software from NVIDIA

Today we are excited to talk about the RAPIDS GPU dataframe release along with our partners in this effort: NVIDIA, BlazingDB, and Quansight. RAPIDS is the culmination of 18 months of open source development to…

Read More
Data Science Blog
Intake: Parsing Data from Filenames and Paths

Motivation Do you have data in collections of files, where information is encoded both in the contents and the file/directory names? Perhaps something like ‘{year}/{month}/{day}/{site}/measurement.csv’? This is a very common problem for which people build custom…

Read More
Company Blog
Anaconda Distribution 5.3.0 Released

We’re excited to announce the release of Anaconda Distribution 5.3.0! Anaconda Distribution is the world’s most popular and easiest way to learn and perform data science and machine learning. Here’s a rundown of new features.…

Read More
Data Science Blog
Open Source Model Management Roundup: Polyaxon, Argo, and Seldon

One of the most common questions the Anaconda Enterprise team receives is something along the lines of: “But really, how difficult is it to build this using open source tools?” This is certainly a fair…

Read More
Data Science Blog
Intake: Caching Data on First Read Makes Future Analysis Faster

By Mike McCarty Intake provides easy access data sources from remote/cloud storage. However, for large files, the cost of downloading files every time data is read can be extremely high. To overcome this obstacle, we…

Read More