Python for Data Analysis: Python Fundamentals

1. Overview

Python is a popular general-purpose programing language. This 2-day workshop focuses on the basic concepts and features of Python, with an emphasis on the module pandas. These basic concepts will serve as building blocks for future data analysis tasks. The workshop is taught using JupyterLab in the Interactive Data Analytics Service (IDAS).

2. Prerequisites

This workshop is for learners who have no experience with Python and no experience with computer programming.

3. Eligibility

This workshop is available to current University of Iowa faculty, staff, and students, who are employed by the University or enrolled in a class at the University at the time of the workshop.

4. How to register

Click HERE then log in with your HawkID and password. Click “Register now” at the bottom of the page to register. After registering successfully, an automated email with a Zoom link will be sent to your University of Iowa email. Registration will close at 10 a.m. on Monday, Feb. 26, 2024.

5. Additional information         

If you have any questions, please see the workshop FAQs or contact research-computing@uiowa.edu.

6. Workshop agenda

This workshop is taught in 2 sessions over 2 days. The later session builds on the previous one. Participants are encouraged to attend all sessions to learn the complete contents of the workshop.

Again, note that we start by learning the basic concepts and features of Python that will be relevant for data analysis tasks later. If you are already familiar with the concepts below, please see the workshop FAQs for a list of additional, free learning resources.

Tentative topics to be covered:

Day 1:

• Introduction and log in to the workshop computing environment
• A brief overview of using JupyterLab and Jupyter Notebook documents
• Variable assignment
• Built-in functions
• Getting help in Python
• Getting and setting the current working directory
• Modules
• Using the pandas module to read and inspect tabular data

Day 2:

• Log in to the workshop computing environment
• Lists
• Select a subset of a DataFrame
• Booleans
• Comparison statements

• Summary statistics
• Create groups and get summary statistics by groups
• Sort values
• Resources for after the workshop