Wrangling data in Pandas, when to use Pandas, Matplotlib or Seaborn, and why you should learn to create Python packages: Jon Krohn speaks with guest Stefanie Molin, author of Hands-On Data Analysis with Pandas.

This episode is brought to you by Posit, the open-source data science company (https://posit.co), and by AWS Inferentia (https://go.aws/3zWS0au). Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• The advantages of using pandas over other libraries [07:55]
• Why data wrangling in pandas is so helpful [12:05]
• Stefanie’s Data Morph library [24:27]
• When to use pandas, matplotlib, or seaborn [33:45]
• Understanding the ticker module in matplotlib [36:48]
• Where data analysts should start their learning journey [40:08]
• What it’s like being a software engineer at Bloomberg [51:19]

Additional materials: www.superdatascience.com/675

The Super Data Science Podcast: Machine Learning | AI | Success

675: Pandas for Data Analysis and Visualization

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

New tracks tagged #Wrangling Data