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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

Counting functions


In data exploration, counting over a range is often done. It helps to find out the most/least occurring value. Julia provides the counts function to count over a range. Let's say we have an array of values. For our convenience, we will now use the random function to create an array:

We have created an array of 30 values ranging from 1 to 5. Now we want to know how many times they occur in the dataset:

Using the count function, we found that 1(7), 2(1), 3(5), 4(11), and 5(6). counts take different arguments to suit the use case.

The proportions() function is used to compute the proportions of the values in the dataset and  Julia provides the function:

We calculated proportions on the same dataset that we used in the previous examples. It shows that the ratio of value 1 in the dataset is 0.23333. This can also be seen as the probability of finding the value in the dataset.

Other count functions include:

  • countmap(arr): This is a map function that maps the values to the number...

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