Transforming Data in Python with Pandas (Part 1 and 2)

Description

Prerequisites: Participants should be familiar with basic programming concepts, including variable assignment, data types, function calls, and installing packages or libraries. Introductory experience in Python or R will be especially helpful for this workshop.?

This beginner-to-intermediate workshop by Johns Hopkins Data Services is part one of a two-part workshop series on the pandas library, a popular Python library for data cleaning, data wrangling, and data analysis. Participants in this interactive class will use Jupyter Notebooks software and Python code to import, understand, and prepare a dataset for further analysis or visualization.

By the end of this workshop series, participants will be able to:

• Identify and use the two primary data structures of the pandas library: Series and DataFrame
• Implement functions from the pandas library to explore and analyze a dataset, including: 1) handling missing data, 2) filtering and sorting data, 3) grouping data, and 4) calculating basic summary statistics
• Find documentation for the pandas library to troubleshoot errors and apply new functions to analyze a dataset