Pandas is a powerful library for data processing. In this course, Up and Running with Pandas, you’ll learn how to take advantage of the Pandas library to integrate data processing in your Python application. First, you’ll explore how to fetch data from a medium and programmatically represent it as a data frame object as well as create your own data frame from scratch using standard tools for portability and simplicity, specifically, running it in a Jupyter notebook. Next, you’ll discover the different properties of a data frame to know your way around its various sections, observing all sorts of information from it. Finally, you’ll learn how to do all sorts of operations against a data frame that are commonly used in most data processing scenarios such as getting statistical properties, performing arithmetic against the entire dataset and basic filtering. When you’re finished with this course, you’ll have the skills and knowledge of the 'FREE' aspect of Pandas: fundamentals, representation, exploration, and evaluation.
Overview
Pandas is a powerful library for data processing. In this course, Up and Running with Pandas, you’ll learn how to take advantage of the Pandas library to integrate data processing in your Python application. First, you’ll explore how to fetch data from a medium and programmatically represent it as a data frame object as well as create your own data frame from scratch using standard tools for portability and simplicity, specifically, running it in a Jupyter notebook. Next, you’ll discover the different properties of a data frame to know your way around its various sections, observing all sorts of information from it. Finally, you’ll learn how to do all sorts of operations against a data frame that are commonly used in most data processing scenarios such as getting statistical properties, performing arithmetic against the entire dataset and basic filtering. When you’re finished with this course, you’ll have the skills and knowledge of the 'FREE' aspect of Pandas: fundamentals, representation, exploration, and evaluation.
Syllabus
- Course Overview 1min
- Introduction 15mins
- Understanding Data Fundamentals with Pandas 25mins
- Programmatically Representing Data with Pandas 12mins
- Exploring and Evaluating Data with Pandas 20mins
Taught by
Pluralsight