I loved his Statistics for Hackers talk: https://speakerdeck.com/pycon2016/jake-vanderplas-statistics...
Python Data Science Handbook
11–20 of 67 posts
Re: Python Data Science Handbook
#12Interesting choice of Pandas in this day and age. Maybe he’s after imparting general concepts that you could apply to any tabular data manipulator rather than selecting for the latest shiny tool.
why? It's the industry standard as far as my reach goes. What other framework would you replace it with? No, polars or spark is not a good answer, those are optimized for data engineering performance, not a holistic approach to data science.
> No, polars or spark is not a good answer, those are optimized for data engineering performance, not a holistic approach to data science.
Can you expand on why Polars isn't optimised for a holistic approach to data science?
Re: Python Data Science Handbook
#13This is one of the few books that I read cover-to-cover when I was starting out learning Data Science in 2020/21. Will recommend.
Re: Python Data Science Handbook
#14He's a great writer and I miss his blog. He had an awesome post on pivot table that I think is now a part of this book.
He is also the creator of the Altair visualization library (Vega-Lite in Python https://altair-viz.github.io/). I really like using it.
Re: Python Data Science Handbook
#15Re: Python Data Science Handbook
#16Interesting choice of Pandas in this day and age. Maybe he’s after imparting general concepts that you could apply to any tabular data manipulator rather than selecting for the latest shiny tool.
The book is quite old actually, not sure if "this day and age" still applies to it
Re: Python Data Science Handbook
#17Earlier quoted context omitted.
What's wrong with Pandas?
I probably wouldn’t rewrite an entire data science stack that used pandas, but most people would use polars if starting a new project today.
R and Matlab workflows have been fairly stable for the past decade. Why is the Python ecosystem so... unstable? It puts me off investing any time in it.
Re: Python Data Science Handbook
#18Interesting choice of Pandas in this day and age. Maybe he’s after imparting general concepts that you could apply to any tabular data manipulator rather than selecting for the latest shiny tool.
why? It's the industry standard as far as my reach goes. What other framework would you replace it with? No, polars or spark is not a good answer, those are optimized for data engineering performance, not a holistic approach to data science.
You can assert whatever you want, but Polars is a great answer. The performance improvements are secondary to me compared to the dramatic improvement in interface.
Today all serious DS work will ultimately become data engineering work anyway. The time when DS can just fiddle around in notebooks all day has passed.
Re: Python Data Science Handbook
#19Re: Python Data Science Handbook
#20This book was absolute fire for getting started with data science in 2017-2018, Jake is a great teacher.