Understand the benefits of using the correct types
Write pandas code that is fit for EDA but also deployment
Master best practices to be more efficient
This section will focus on ingesting data and best practices for working with raw data in Pandas.
We will explore the different types and their pros and cons. Looking and speed and memory performance with strings, numbers, dates, and categories.
The pandas library encourages chaining operations, yet most articles completely neglect this. We will explore how to get the most out of chaining.
Function application is another thorny topic in Pandas. In general it is slow. We will talk about the reasons why, how to speed it up, and when to use application.
Grouping and Aggregation
One of the most powerful features of Pandas is the ability to group and pivot data. In this section we will show examples and explain how to understand and master this skill.
Access to live training and QA session with the Instructor
Access to the on-demand recording
Certificate of completion