Event box

Visualization Is a key part of exploring and understanding patterns within data sets. This session introduces matplotlib, a popular library used to make scientific plots in Python. The purpose of this session is to provide a simple introduction to the matplotlib interface and to create two simple plots to visualize univariate data. This course is to provide hands-on, interactive experience in building scientific visualizations. The code that is developed in this course can be used on data in other contexts.

At the end of this class, participants will be able to:

• Open and work with tabular data using pandas library
• Create a bar plot to visualize the distribution of discrete variables
• Create a histogram to visualize the distribution of continuous variables
• Save figures in vector graphics or raster formats for scientific use

Basic Python knowledge is required for this class. The library's two-session Python course covers the required material.

This class is in person, please bring your own laptop. If you are planning to attend Part 2 (next week), please make sure you register for each class separately.

Accreditation Statement
In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.
Credit Designation
American Medical Association (AMA)
Stanford Medicine designates this live activity for a maximum of 1.5 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.