This month brought a sense of “Community” to the forefront of all our projects. We wrapped up our first Python Madness Tournament, polling the community about their most valuable Python package. Congratulations to pandas! Inspired by user questions, our dev-rel team was particularly prolific, writing several general interest blogs on topics ranging from Google Cloud as an alternative to replacing your laptop, to how to choose the right file format for your data. We also hosted our first AMA on reddit, answering questions about data science, working with large datasets, Python programming, and even the caffeine source of choice of our team.
Highlights from our blog, YouTube channel, and user guide are discussed below.
Spotlight on Parquet
One of Deephaven’s reasons for being is to make working with massive datasets easy. There are a lot of tutorials in the wild about working with large CSV files and we felt we could enter the conversation to suggest Parquet for certain use cases.
- r/place is a social experiment where millions of users cooperate (or compete!) to carve out pixels on a shared artistic canvas. Devin Smith blogs about translating the data from CSV to Parquet. Reducing the 22 GB CSV dataset to a 1.5 GB Parquet file provides a significant advantage to your data analysis.
- Do you want to make pandas 60x faster? Parquet can help with that, too.
Plotting
Ok, you’ve got data, but you want to create slick visualizations.
General tutorials
Guest post
Finally, check out a guest post by Dylan Carter, a high school user who successfully completed his AP Research capstone project using Deephaven. Inspired by some of our example projects, Dylan wrote an AI model for analyzing Solana and Twitter sentiment.
If you missed our live stream of the reddit AMA, you can catch up on our YouTube channel.
While you’re there, check out our newest Learning Sessions:
We also continue to post our developer demos weekly. See how Deephaven Community Core continually evolves.
The Deephaven user guide is constantly updated. Of note:
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