Effective immediately in response to COVID-19, all Technology Training classes will be delivered online until further notice.
In advance of each session, Tech Training will provide you with a Zoom link to your class, along with any required class materials.
In this class, you will have the opportunity to develop Python scripts to create pivot tables in an automated way by leveraging the Pandas module. Real-life examples are presented to generate reports based on data aggregation that can be reused for dynamic datasets.
Prerequisite: Completion of the "Python for Beginners" class, or equivalent Python experience.
Topics in this session include:
- Introduction to Jupyter Notebook
- Learn the Pandas and Numpy modules to read, prepare, and write datasets
- Introduction to the "pivot_table", "pivot", "stacked", and "unstacked" functions
- Splitting, apply, and grouping datasets
- Grouping datasets by multiple columns
- Aggregate data with different statistical functions such as len, sum, min,max, unique, nunique, etc.
- Aggregate data by multiple columns
- Resetting hierarchical indices and columns
- Build aggregation automation and share results
About the Instructor: Arafat Mokhtar
Arafat Mokhtar is a Business Intel Engineer at Stanford School of Medicine, who supports the Human Resources Group with data collections, validation, cleansing, and analytics to provide actionable data insights used by leadership management to make data-driven decisions on the organization workforce. He develops code to automate data analytics processes, proposes data solutions, and develops measurable business metrics.
Arafat holds a Ph.D. in Particle Physics and spent several years as a postdoctoral fellow at SLAC National Accelerator Laboratory. He has 10+ years of teaching experience and conducted a number of Python and R training sessions for Stanford Technology Training Programs.
University IT Technology Training classes are only available to Stanford University staff, faculty, or students. A valid SUNet ID is needed in order to enroll in a class.