Book Image

Numerical Computing with Python

By : Pratap Dangeti, Allen Yu, Claire Chung, Aldrin Yim, Theodore Petrou
Book Image

Numerical Computing with Python

By: Pratap Dangeti, Allen Yu, Claire Chung, Aldrin Yim, Theodore Petrou

Overview of this book

Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining. You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models. By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following Packt products: • Statistics for Machine Learning by Pratap Dangeti • Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim • Pandas Cookbook by Theodore Petrou
Table of Contents (21 chapters)
Title Page
Contributors
About Packt
Preface
Index

Filtering for states with a minority majority


In Chapter 10, Selecting Subsets of Data, we marked every row as True or False before filtering out the False rows. In a similar fashion, it is possible to mark entire groups of data as either True or False before filtering out the False groups. To do this, we first form groups with the groupby method and then apply the filter method. The filter method accepts a function that must return either True or False to indicate whether a group is kept or not.

Note

This filter method applied after a call to the groupby method is completely different than the DataFrame filter method.

Getting ready

In this recipe, we use the college dataset to find all the states that have more non-white undergraduate students than white. As this is a dataset from the US, whites form the majority and therefore, we are looking for states with a minority majority.

How to do it...

  1. Read in the college dataset, group by state, and display the total number of groups. This should equal...