Instructors: Ted Petrou
2 sections • 27 lectures • 1h 16m total length
Video: MP4 1280×720 44 KHz | English + Sub
Updated 6/2022 | Size: 475 MB
Solve these data analysis challenges using the Python Pandas library
What you’ll learn
You will learn how to answer a wide variety of data analysis questions using the Pandas library
You will learn best practices for using the pandas library
You will learn how to develop the most efficient solutions using pandas
You will learn how to answer difficult pandas challenges
You need to have a solid grasp of the fundamentals of PythonYou need to have worked with the Pandas data analysis library previously
In this course you are presented with dozens of data analysis challenges requiring the Python Pandas library to solve. Each challenge is provided within a Jupyter Notebook and upon submission will get graded immediately. The challenges vary in difficulty and cover nearly all parts of the pandas library. Video solutions for each challenge are provided so that you can see exactly how Ted thinks about the problem.
Ted Petrou is a world-renowned pandas expert having written the books Pandas Cookbook and Master Data;Analysis with Python. Ted has also answered more than 400 pandas questions on Stack Overflow and taught thousands of students both in-person and online. With this experience, he has developed hundreds of exercises that aim to teach the most efficient and effective ways at using the pandas library.
The pandas library is one of the most powerful and popular tools today for analyzing data with Python. Although it is widely used, it takes a long time to master. There are often multiple ways of solving the same problem and unfortunately many of these solutions are poor and ineffective. Ted has developed these challenges to teach you the very best practices for doing data analysis with pandas.
Do you have what it takes to solve these challenges?
Who this course is for:Beginning, Intermediate, and Advanced users of Pandas looking for challenging exercises covering a wide variety of data analysis topics.