Essential Guide to Python Pandas

Welcome to our Pandas crash course! This course is designed to provide you with a practical guide to using Pandas, the popular data manipulation library in Python. We've included real-life examples and reusable code snippets to help you quickly apply what you learn to your own data analysis projects.

Throughout this course, you will learn how to:

• Describe the Anatomy of Pandas Data Structures. This includes Pandas DataFrames, Series, and Indices.

• Implement several methods to get data into and from Pandas DataFrames. These methods include Python Native Data Structures,  Tabular data files, API queries and JSON format, web scraping, and more.

• Describe any information within a Pandas DataFrame. This will help you to identify data problems such as having missing values or using incorrect data types.

• Understand Pandas Data Types and the correct use case for each type.

• Perform Data manipulation and cleaning. This part includes fixing data types, handling missing values, removing duplicate records, and many more.

• Merge & Join multiple datasets into Pandas DataFrames

• Perform Data Summarization & Aggregation within any DataFrame

• Create different types of Data Visualization

• Update Pandas Styling Settings

• Conduct a Data Analysis Project using Pandas library to collect and investigate COVID-19 infection, and the consequent lockdown in different countries.

In addition to the course materials, you'll also have free access to a Jupyter Notebook with all of the code examples covered in this course, as well as a free e-book in PDF format. By the end of this course, you'll have a solid understanding of how to use Pandas to perform data manipulation tasks and analyze data.