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

Selecting with booleans, integer location, and labels


Chapter 10, Selecting Subsets of Data, covered a wide range of recipes on selecting different subsets of data through the .iloc and .loc indexers. Both these indexers select rows and columns simultaneously by either integer location or label. Both these indexers can also do data selection through boolean indexing, even though booleans are not integers and not labels.

Getting ready

In this recipe, we will filter both rows and columns with boolean indexing for both the .iloc and .loc indexers.

How to do it...

  1. Read in the movie dataset, set the index as the title, and then create a boolean Series matching all movies with a content rating of G and an IMDB score less than 4:
>>> movie = pd.read_csv('data/movie.csv', index_col='movie_title')
>>> c1 = movie['content_rating'] == 'G'
>>> c2 = movie['imdb_score'] < 4
>>> criteria = c1 & c2
  1. Let's first pass these criteria to the .loc indexer to filter the rows...