3. Agenda

• Questions about HW2?

• Overview of Pandas As aspiring quantitative finance professionals, Pandas is likely the most important Python package you will use. It is a powerful data manipulation library that is built on top of Numpy. It is especially useful for working with time series data. We will go over the basics of Pandas and then work through a series of exercises to practice using Pandas. I’ll skim through some introductory material from Python Data Science Handbook by Jake VanderPlas.

  • Introducing Pandas Objects

  • Data Indexing and Selection

  • Operations in Pandas

  • Missing Values

• Hands-On Example with Data in Pandas Demonstrate Pandas in the context of factor analysis/principal components analysis of a panel of economic and financial time series. ./src/factor_analysis_demo.ipynb

• Introduction to WRDS We will discuss the Center for Research in Security Prices (CRSP) database, which is a renowned financial research database, primarily recognized for its comprehensive historical data on securities traded in the United States. We will also discuss how to use the Wharton Research Data Services (WRDS) web query system. WRDS_intro_and_web_queries.md