Creating plots that are publication-ready is essential, simply because we must be able to share our knowledge. In this section, we're going to take a look at plotting multiple variables on a single plot, adding a legend to that plot, and then saving our plots to a file. Let's go back to our notebook and check the last plot that we created in the previous section, which was the 200-day moving average of the stock price of Apple since the beginning. I want to point out a few things about our previous plot. First, we have a legend at the top that just says plot1.dat, and, if you've never seen this before, currently that doesn't really mean anything to anyone. Also, there's no way in this mechanism to save to a file the output that you see. We could definitely get some screenshot software and then create our own screenshot...
Getting Started with Haskell Data Analysis
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Getting Started with Haskell Data Analysis
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Overview of this book
Every business and organization that collects data is capable of tapping into its own data to gain insights how to improve. Haskell is a purely functional and lazy programming language, well-suited to handling large data analysis problems. This book will take you through the more difficult problems of data analysis in a hands-on manner.
This book will help you get up-to-speed with the basics of data analysis and approaches in the Haskell language. You'll learn about statistical computing, file formats (CSV and SQLite3), descriptive statistics, charts, and progress to more advanced concepts such as understanding the importance of normal distribution. While mathematics is a big part of data analysis, we've tried to keep this course simple and approachable so that you can apply what you learn to the real world.
By the end of this book, you will have a thorough understanding of data analysis, and the different ways of analyzing data. You will have a mastery of all the tools and techniques in Haskell for effective data analysis.
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Current Title:
Getting Started with Haskell Data Analysis
Table of Contents (8 chapters)
Preface
Free Chapter
Descriptive Statistics
SQLite3
Regular Expressions
Visualizations
Kernel Density Estimation
Course Review
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Customer Reviews