Intermediate Python: Slicing & Dicing Data with Pandas

Description

This workshop aims to help Python-users understand language features, packages, and programming strategies that will help them write more efficient code, be more productive when writing code, and debug code more effectively. This is not an introduction to Python and is appropriate for motivated intermediate to advanced users who want a better understanding of working with Python for their research. Researchers from all domains at UC Davis who meet the workshop prerequisites are welcome to enroll.

In order to satisfy the requirements for the GradPathways microcredential, students must attend all four Intermediate Python workshops scheduled for April 18, May 2, May 16, and June 6, 2022.

Learning Objectives

After completing all four Intermediate Python workshops, learners should be able to:

• Identify problems that can be solved efficiently with Pandas indexing
• Use Pandas indexes and multi-indexes to extract subsets of data
• Convert between columns and (multi-)indexes in a Pandas data frame
• Organize project files using modules and namespaces
• Install and remove Python, Python packages, and other software with conda
• Explain how Python’s iterators and generators work
• Determine the cause(s) of bugs in code using Python’s debugging tools.        

Prerequisites

Participants must have taken DataLab’s “Python Basics” workshop series and/or have prior experience using Python, be comfortable with basic Python syntax, and have it pre-installed and running on their laptops.

Software: Python          

Instructor: Nick Ulle

Instructor Bio

Nick Ulle is a statistician and computer scientist. Prior to DataLab he was a visiting assistant professor of Statistics at UC Berkeley, where he designed and taught courses in data science. During his PhD in Statistics at UC Davis, he developed source code analysis techniques for the R programming language. His research interests include statistical computing, programming languages, data visualization, and pedagogy.

TA: Arthur Koehl

Arthur Koehl is a research data scientist. He graduated from UC Davis with degrees in history, economics, and computer science. Prior to DataLab he worked for several years as a scientific computing intern at the Center for BioImaging Sciences at the National University of Singapore, where he learned the basics of Linux system administration. His interests include natural language processing, computer vision, and web programming. At DataLab he develops tools and provides technical expertise on interdisciplinary research projects with an emphasis in the humanities and social sciences.