I’ve been a long-time Pandas user, relying on it heavily since the start of my data science career. However, up until the last couple of years, I struggled with certain issues, such as not being able to work with very large DataFrames or efficiently run heavy data processing tasks. I’d also often find my Jupyter notebooks cluttered with intermediate DataFrames after applying transformations, making it harder to read the code and keep my notebook tidy. For a long time, I had thought that these issues were just endemic to Pandas and accepted there wasn’t a better way; however, there is!
If you find yourself dealing with similar issues, join Matt and me as we discuss tips for making Pandas more memory-friendly, getting the best performance possible when applying operations to Series and DataFrames, and keeping your Pandas code as reproducible and tidy as possible. We’ll also be talking about using Pandas as a powerful basis for data visualization, and how using the right tooling can make working with Pandas easier.
Attendees of this webinar will receive a discount code for Matt’s book, Effective Pandas, which includes even more tips on how to get the most out of Pandas. In addition, join us on Twitter where we’re running a competition this week asking for your best Pandas tips. Five tips will be selected during the webinar and the winners will receive a free hard copy of the book!
Contents
When
Tuesday, June 215:00 pm (UTC)
Speaking to You
Matt Harrison runs MetaSnake, a Python and Data Science consultancy and corporate training shop. In the past, he has worked across the domains of search, build management and testing, business intelligence, and storage. He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON, as well as local user conferences. He has written a number of books on Python and Data Science, including Machine Learning Pocket Reference, Pandas 1.x Cookbook, Effective PyCharm, and Effective Pandas. He blogs at hairysun.com.
Dr. Jodie Burchell is a Developer Advocate for Data Science at JetBrains and was previously the Lead Data Scientist in audiences generation at Verve Group Europe. After finishing a PhD in psychology and a postdoc in biostatistics, she has worked in a range of data science and machine learning roles across search improvement, recommendation systems, natural language processing, and programmatic advertising. She is also the author of two books, The Hitchhiker’s Guide to Ggplot2 and The Hitchhiker’s Guide to Plotnine, and blogs at t-redactyl.io.