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 Series data


Series and DataFrames are complex data containers that have multiple attributes that use the indexing operator to select data in different ways. In addition to the indexing operator itself, the .iloc and .locattributes are available and use the indexing operator in their own unique ways. Collectively, these attributes are called the indexers.

Note

The indexing terminology can get confusing. The term indexing operator is used here to distinguish it from the other indexers. It refers to the brackets, [] directly after a Series or DataFrame. For instance, given a Series s, you can select data in the following ways: s[item] and s.loc[item]. The first uses the indexing operator. The second uses the .loc indexer.

Series and DataFrame indexers allow selection by integer location (like Python lists) and by label (like Python dictionaries). The .ilocindexer selects only by integer location and works similarly to Python lists. The .locindexer selects only by index label, which is...