This tutorial offers a comprehensive introduction to the powerful pandas library for data analysis built on top of the Python programming language. Pandas represents a great step forward for graphical spreadsheet users looking to grow their data manipulation skills. I like to call it "Excel on steroids".

By completing this workshop, you'll have a strong foundation for using Pandas in your day-to-day data analysis needs. We'll start out with the basics -- importing datasets, selecting rows and columns, filtering rows by criteria -- and progress to advanced concepts like grouping values, joining multiple datasets together, and cleaning text.

Students will be exposed to diverse data sets across different disciplines  -- sports, finance, entertainment, and more. The training is open to all industries and is targeted for beginners --- basic Python knowledge is preferred but not required.


Tutorial Overview

  • 1

    Getting Started with Pandas for Data Analysis

    • Abstract and Author Bio

    • Before you get started: Prerequisites and Resources

  • 2

    Foundations of Pandas

    • Introduction, Who am I, and What is Pandas?

    • Jupyter Notebook, Importing Pandas, and Series

    • Previewing, Sorting and Selecting Series

  • 3

    Working with Data of Different Types

    • The DataFrame, Attributes and Methods

    • Selecting DataFrames and Sorting

    • Custom Indexing and Selecting by Row and Column

    • Filtering Methods

  • 4

    Aggregating and Joining Datasets

    • Pivot Tables and Melting

    • Grouping Data

    • Joining Datasets: Concatenation

Instructor Bio:

Boris Paskhaver

Software Engineer | Stride Consulting

Boris Paskhaver

Boris Paskhaver is a full-stack web developer based in New York City with experience building apps in React / Redux and Ruby on Rails. His favorite part of programming is the never-ending sense that there's always something new to master --- a secret language feature, a popular design pattern, an emerging library or -- most importantly -- a different way of looking at a problem.