Foreword to Second Edition

As the data science domain and educational landscape continues to evolve, there is an increasing need to train individuals to critically consider data both holistically and logically. Each year, given the advancement in computational power, magnitude of data, and data-informed decisions to make, more and more individuals are dipping their toes in the water of data science—and most are not aware of how messy their data sets are. Working with messy data is challenging, confusing, and not necessarily exciting, especially for newcomers. To continue to use data for informed decision-making, it is important to introduce concepts in data logic, planning, and purpose early in the stages of training best practices. The how, why, and lessons learned of teaching data science represent huge areas of exploration given the exponential increase in learners. There are numerous resources, MOOCs, Twitter threads, packages, cheat-sheets, and more out there for individuals to learn data science, either on their own or in a class. However, what is effective and what pathways are best for certain learner personas? Moreover, how does someone new to the field choose which educational resources mesh with their needs and background familiarity?

While spending many years as an educator for RStudio and The Carpentries, Dr. Daniel Chen recognized this need, and it has become his passion to introduce learners to core concepts to work with their data in more effective, reproducible, and reliable methods in an environment matching their comfort level with the field. I met Dan by semi-random chance and after a few conversations, we were well on our way with a dissertation topic stemming from these interests. With a shared passion in educating others in foundational data science methods and looking into those “hows” and “whys” of the ways in which we were teaching, we sought to understand our learners first and then create materials. It was a pleasure to work with Dan on his dissertation—and to see those insights incorporated here in Pandas for Everyone, Second Edition.

In the second edition, Dan takes learners step-by-step through practical scratch code examples for using Pandas. Using Pandas helps demystify Python data analysis, create organized manageable data sets, and, most importantly, have tidy data sets! It takes a special educator to get individuals (myself included!) excited about cleaning data, but that is what Dan does for his learners in Pandas for Everyone. Visualizing and modeling data are taught in easy-to-interpret style once learners become comfortable with manipulating and transforming their data sets, all of which is covered in sequential order. It is this mindset and presentation of materials that really makes this book for everyone—and aids the learner in best practices while working with example data sets that mimic data sets they might use in real life. Pandas for Everyone, Second Edition, is a quick but detailed foray for new data scientists, instructors, and more to experience best practices and the massive potential of Pandas in a clear-cut format.

–Anne M. Brown, PhD (she/her)

Assistant Professor

Data Services—University Libraries

Department of Biochemistry

Virginia Tech, Blacksburg, VA 24061