Time Series Analysis in Pandas
This lesson 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 lesson that is full of real-world demonstrations in Pandas. If you want to follow along with the lesson, you can find everything you need in the GitHub repo below.
If you have any feedback on this lesson, please write to us at support@cloudacademy.com.
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 lesson, you should already have a good understanding of handling data using Pandas. We recommend taking our Data Wrangling with Pandas lesson before embarking on this one.
Resources
The GitHub repository for this lesson can be found here: https://github.com/cloudacademy/advanced-pandas-for-data-analytics
Andrea is a Data Scientist at Cloud Academy. He is passionate about statistical modeling and machine learning algorithms, especially for solving business tasks.
He holds a PhD in Statistics, and he has published in several peer-reviewed academic journals. He is also the author of the book Applied Machine Learning with Python.