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

Gaining perspective on stock prices


Investors who have purchased long stock positions would obviously like to sell stocks at or near their all-time highs. This, of course, is very difficult to do in practice, especially if a stock price has only spent a small portion of its history above a certain threshold. We can use boolean indexing to find all points in time that a stock has spent above or below a certain value. This exercise may help us gain perspective as to what a common range for some stock to be trading within.

Getting ready

In this recipe, we examine Schlumberger stock from the start of 2010 until mid-2017. We use boolean indexing to extract a Series of the lowest and highest ten percent of closing prices during this time period. We then plot all points and highlight those that are in the upper and lower ten percent.

How to do it...

  1. Read in the Schlumberger stock data, put the Date column into the index, and convert it to a DatetimeIndex:
>>> slb = pd.read_csv('data/slb_stock...