Book Image

Data Analysis and Exploration with Pandas [Video]

By : Theodore Petrou
Book Image

Data Analysis and Exploration with Pandas [Video]

By: Theodore Petrou

Overview of this book

<p>Are you looking for a gigantic boost in your productivity? Are you searching for some interesting and fun tricks to solve your data problems? If so, then this course is indeed a perfect choice for you. This course provides you with unique, idiomatic, and amazing solutions for both fundamental and advanced data manipulation tasks with pandas.</p> <p>Some solutions focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. A few others will delve into a particular dataset, and let you uncover new and unexpected insights along the way.</p> <p>The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This course guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced solutions combine several different features across the pandas library to generate results.</p> <p>The code bundle for the video course is available at - <a style="color: #fa8d11;" href="https://github.com/PacktPublishing/Data-Analysis-and-Exploration-with-Pandas" target="blank">https://github.com/PacktPublishing/Data-Analysis-and-Exploration-with-Pandas</a></p> <h2><span class="sugar_field">Style and Approach</span></h2> <p>This course includes interesting and illustrative examples and delivers very detailed explanations for each line of code in all of the examples. All code and dataset explanations exist in Jupyter Notebooks, an excellent interface for exploring data. In other words, this is an easy guide with a problem/solution approach for real-world datasets.</p>
Table of Contents (9 chapters)
Chapter 6
Index Alignment
Content Locked
Section 8
Finding the Most Common Maximum
The college dataset contains the undergraduate population percentage of eight different races for over 7,500 colleges. It would be interesting to find the race with the highest undergrad population for each school. In this video, we will find the race with the highest percentage of the undergraduate population for each school with the idxmax method and then find the distribution of these maximums. - Read in the college dataset and select just those columns with undergraduate race percentage information - Use the idxmax method to get the column name with the highest race percentage for each row - Use the value_counts method to return the distribution of maximum occurrences