In this course, we are going to explore techniques to permit advanced data exploration and analysis using Python. We will focus on the Pandas library, focusing on real-life case scenarios to help you to better understand how data can be processed using Pandas.
In particular, we will explore the concept of tidy datasets, the concept of multi-index, and its impact on real datasets, and the concept of concatenating and merging different Pandas objects, with a focus on DataFrames. We’ll look at how to transform a DataFrame, and how to plot results with Pandas.
If you have any feedback relating to this course, feel free to reach out to us at firstname.lastname@example.org.
- Learn what a tidy dataset is and how to tidy data
- Merge and concatenate tidy data
- Learn about multi-level indexing and how to use it
- Transform datasets using advanced data reshape operations with Pandas
- Plot results with Pandas
This course is intended for data scientists, data engineers, or anyone looking to perform data exploration and analysis with Pandas.
To get the most out of this course, you should have some knowledge of Python and the Pandas library. If you want to brush up on Pandas, we recommend taking our Working with Pandas course.
The GitHub repo for this course can be found here.
Congratulations! You have now finished the course `Data Wrangling with Pandas`. Now, you are ready to go into the wild and apply those techniques to solve your own business challenges.
In this course, you learned what a tidy dataset is, and why it is important you start your analysis from that structure. You also learned how to manipulate and transform tidy datasets with different techniques, using Pandas, namely pivoting, melting and grouping, and we also used Pandas to perform standard data analysis techniques, such as the application of data transformation to get insights from the transformed dataset.
I hope you enjoyed the course, if you have any feedback relating to this course, please reach out to us at email@example.com. Thanks for watching!
Course Introduction - Tidying a Dataset - Merging and Concatenating Tidy Data - Multi-Level Indexing - Merging Tidy Data - Transformation of a Dataset - Plotting Results with Pandas
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.