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Julia for Data Science

You're reading from   Julia for Data Science high-performance computing simplified

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Table of Contents (12) Chapters Close

Preface 1. The Groundwork – Julia's Environment 2. Data Munging FREE CHAPTER 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

Rankings


When a dataset is sorted in ascending order, a rank is assigned to each value. Ranking is a process where the dataset is transformed and values are replaced by their ranks. Julia provides functions for various types of rankings.

In ordinal ranking, all items in the dataset are assigned a distinct value. Items that have equal values are assigned a ranking arbitrarily. In Julia, this is done using the ordinalrank function.

Suppose this is our dataset and we want to do ordinal ranking:

Using the ordinalrank(arr) function, we've got the ordinal ranking. Similarly, StatsBase also provides functions to find other types of rankings, such as competerank(), denserank(), and tiedrank().

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