Pandas for Data Analysis

Pandas is one of the most important libraries in the Python ecosystem—especially for working with data. But to many beginners, it feels overwhelming. This workshop is designed to break that barrier.

In this fully hands-on workshop, you’ll learn how to load, explore, clean, and analyze data using Pandas. We’ll work inside Jupyter notebooks and use real-world datasets so you can apply everything you learn in practical ways. Whether you’re a beginner or someone who’s played with Excel, this session will help you confidently transition to using Python for data work.

We’ll start with the absolute basics: what a DataFrame is, how to read CSVs, and how to manipulate columns and rows. Then we’ll move on to filtering, sorting, grouping, and visualizing data. You’ll leave with enough knowledge to start doing real analysis on your own data—and the confidence to explore further using ChatGPT as your assistant.

Agenda: Pandas for Data Analytics (6 hours)

• Introduction & Setup
  What is Pandas? Why is it useful?
  Setting up Jupyter with uv, installing Pandas, and creating your first notebook.

• Understanding DataFrames
  Loading CSVs into Pandas.
  Viewing and inspecting data: .head(), .info(), .describe()
  Indexes and what makes Pandas different from lists or dicts.

• Basic Data Operations
  Selecting rows and columns
  Filtering data with conditions
  Sorting, renaming, and working with missing values

• Short Break

• GroupBy and Aggregations
  How to group data, compute aggregates, and summarize results.

• Exploratory Data Analysis
  Using built-in plotting features
  Combining Pandas with Matplotlib for quick visualizations

• Hands-On: Small Analysis Project
  We’ll work through a dataset together (e.g. movies, books, or sales data)
  You’ll perform a mini-analysis, create plots, and share findings

• Lunch Break

• ChatGPT for Learning Pandas
  Learn how to ask questions when you get stuck
  Using ChatGPT to get explanations, fix errors, and write Pandas code

• Where to Go From Here
  Additional libraries (Seaborn, Polars, DuckDB)
  How to keep practicing and exploring

Q&A + Wrap Up (30 minutes)

Prerequisites

• Familiarity with Python basics
• Python installed and uv set up (instructions here)
• Laptop running Linux or macOS (Windows supported with JupyterLab in browser)
• You must have permission to install software
• A personal Github account
  • Before the workshop, send me your ID so I can add you to the
    repo
    for access. This is necessary because we will be using gitter
    for the group chat during the workshop, and for sharing links.
  • Some familiarity to git is useful, but not required. If you’re
    interested here is a good
    intro to both Git and Github

Target Audience

• Beginners who want to explore data with Python
• Analysts and Excel users looking to move to code
• Students or researchers working with datasets
• Anyone curious about data science and wants to build a foundation

Slides

WIP

Exercises

WIP

About the Trainer

Vinay Keerthi is a self-taught developer who has spent the past decade building tools and systems across the software stack—from internal developer tools to large-scale data systems. He has worked at companies like Flipkart, Visa, and ChainSafe and has taught programming to developers, analysts, and tech-curious folks alike.

A firm believer in empowering beginners, Vinay uses ChatGPT and other modern tools to make learning programming more accessible and less intimidating.

Contact details

For inquiries and bulk bookings, contact Hasgeek on +91-7676332020 or email info@hasgeek.com