Description

In this tutorial, you’ll dive into one of the most essential tools in data processing — the pandas library. Widely used by data analysts, data scientists, and engineers, pandas provides powerful and flexible tools for working with structured (tabular) data.

You’ll start by learning how to import datasets from various file formats such as CSV, Excel, and JSON. Once the data is loaded into a DataFrame — the core data structure in pandas — you’ll explore its shape, structure, column types, and statistical summaries. Understanding the layout of your data is a critical first step in any data analysis workflow.

Next, you’ll learn how to manipulate and explore your data effectively. You’ll filter rows and columns based on conditions, sort values, rename columns, and create new ones. You’ll group data by one or more fields to calculate aggregate statistics such as counts, averages, and sums — a common practice in reporting and dashboarding.

The tutorial will also cover useful methods like .apply() for applying custom functions to your data, as well as handling categorical values and date-time fields. You’ll practice reshaping data using pivot tables and the melt()/pivot() methods, which are essential when preparing data for analysis or visualization.

In the hands-on portion, you’ll work with a real-world dataset (e.g. product sales, customer orders, or weather data) and perform end-to-end table manipulations: from loading and cleaning to grouping, aggregating, and exporting the processed table. This will give you a strong foundation for using pandas in practical scenarios.

By the end of the tutorial, you’ll be confident in your ability to navigate and manipulate tabular data using pandas, and you’ll be ready to move on to more advanced topics like data cleaning, time series analysis, or merging datasets from multiple sources.

You’ll also get a Jupyter Notebook with code examples, a PDF summary of key commands and functions, and review questions to help solidify your knowledge.