Today's episode is jam-packed with practical tips on using the Pandas library in Python for data analysis and visualization. Super-sharp Stefanie Molin — a bestselling author and sought-after instructor on these topics — is our guide.

Stefanie:
• Is the author of the bestselling book "Hands-On Data Analysis with Pandas".
• Provides hands-on pandas and data viz tutorials at top industry conferences.
• Is a software engineer and data scientist at Bloomberg, the financial data giant, where she tackles problems revolving around data wrangling/visualization and building tools for gathering data.
• Holds a degree in operations research from Columbia University as well as a masters in computer science, with an ML specialization, from Georgia Tech.

Today’s episode is intended primarily for hands-on practitioners like data analysts, data scientists, and ML engineers — or anyone that would like to be in a technical data role like these in the future.

In this episode, Stefanie details:
• Her top tips for wrangling data in pandas.
• In what data viz circumstances you should use pandas, matplotlib, or Seaborn.
• Why everyone who codes, including data scientists, should develop expertise in Python package creation as well as contribute to open-source projects.
• The tech stack she uses in her role at Bloomberg.
• The productivity tips she honed by simultaneously working full-time, completing a masters degree and writing a bestselling book.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.