Essential Guide to Python Pandas

A Python Pandas crash course to teach you all the essentials to get started with data analytics

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Description

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.

What You Will Learn!

  • Describe the Anatomy and main components of Pandas Data Structures. Understand Pandas Data Types and the correct use case for each type.
  • Implement several methods to get data into and from Pandas DataFrames. These methods include Python Native Data Structures, Tabular data files, API queries etc
  • 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
  • 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
  • Apply all the Pandas knowledge you have learned in this course to a real-world Data Analysis Project to investigate COVID-19 infection

Who Should Attend!

  • This course is for aspiring data professionals and Python developers who want to learn how to process data in Pandas.