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

Constructing multiple boolean conditions


In Python, boolean expressions use the built-in logical operators and, or, and not. These keywords do not work with boolean indexing in pandas and are respectively replaced with &, |, and ~. Additionally, each expression must be wrapped in parentheses or an error will be raised.

Getting ready

Constructing a precise filter for your dataset might have you combining multiple boolean expressions together to extract an exact subset. In this recipe, we construct multiple boolean expressions before combining them together to find all the movies that have an imdb_score greater than 8, a content_rating of PG-13, and a title_year either before 2000 or after 2009.

 

How to do it...

  1. Load in the movie dataset and set the index as the title:
>>> movie = pd.read_csv('data/movie.csv', index_col='movie_title')
  1. Create a variable to hold each set of criteria independently as a boolean Series:
>>> criteria1 = movie.imdb_score > 8
>>> criteria2...