MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Difficulty: Advanced | Genre: eLearning | Language: English | Duration: 5 Lectures (41m) | Size: 203 MB

Description
This course takes a look at some of the lesser-known but highly useful methods that can be used in Pandas for advanced data analytics. We'll explore the methods available to you in Pandas to make your code more efficient through evaluating expressions and conditional iterative statements.

We'll also look at methods for time series and windows operations and how these can be used for analyzing datetime objects.

This is a hands-on course that is full of real-world demonstrations in Pandas. If you want to follow along with the course, you can find everything you need in the GitHub repo below.

Learning Objectives
Perform iterative operations in Pandas to make your code more efficient
Learn about evaluation expressions and how to use them
Perform time series data analysis using a variety of methods

Intended Audience
Data scientists
Anyone looking to enhance their knowledge of Pandas for data analytics

Prerequisites
To the most out of this course, you should already have a good understanding of handling data using Pandas. We recommend taking our Data Wrangling with Pandas course before embarking on this one.

Resources
The GitHub repository for this course can be found here:


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