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

Improving readability of boolean indexing with the query method


Boolean indexing is not necessarily the most pleasant syntax to read or write, especially when using a single line to write a complex filter. Pandas has an alternative string-based syntax through the DataFrame query method that can provide more clarity.

Note

The query DataFrame method is experimental and not as capable as boolean indexing and should not be used for production code.

Getting ready

This recipe replicates the earlier recipe in this chapter, Translating SQL WHERE clauses, but instead takes advantage of the query DataFrame method. The goal here is to filter the employee data for female employees from the police or fire departments that earn a salary between 80 and 120 thousand dollars.

How to do it...

  1. Read in the employee data, assign the chosen departments, and import columns to variables:
>>> employee = pd.read_csv('data/employee.csv')
>>> depts = ['Houston Police Department-HPD',
             'Houston...