Pandas Bootcamp 2022: Complete Pandas Walkthrough

Pandas Bootcamp 2022: Complete Pandas Walkthrough, Analyze data quickly and easily with Python’s powerful pandas library!

Data Analysis with Pandas and Python offers 19+ hours of in-depth video tutorials on the most powerful data analysis toolkit available today. Lessons include:

• installing
• sorting
• filtering
• grouping
• aggregating
• de-duplicating
• pivoting
• munging
• deleting
• merging
• visualizing

and more!

Why learn pandas?

If you’ve spent time in a spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you!

Why should you learn Pandas?

The world is getting more and more data-driven. Data Scientists are gaining ground with $100k+ salaries. It´s time to switch from soapbox cars (spreadsheet software like Excel) to High Tuned Racing Cars (Pandas)!

Python is a great platform/environment for Data Science with powerful Tools for Science, Statistics, Finance, and Machine Learning. The Pandas Library is the Heart of Python Data Science. Pandas enables you to import, clean, join/merge/concatenate, manipulate, and deeply understand your Data and finally prepare/process Data for further Statistical Analysis, Machine Learning, or Data Presentation. In reality, all of these tasks require a high proficiency in Pandas! Data Scientists typically spend up to 85% of their time manipulating Data in Pandas.

Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language.

Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets — analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more!

I call it “Excel on steroids”!

Over the course of more than 19 hours, I’ll take you step-by-step through Pandas, from installation to visualization! We’ll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We’ll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package.