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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 FREE CHAPTER 2. Data Munging 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

Histograms


Data exploration after a basic understanding can also be done with the aid of visualizations. Plotting a histogram is one of the most common ways of data exploration through visualization. A histogram type is used to tabulate data over a real plane separated into regular intervals.

A histogram is created using the fit method:

julia> fit(Histogram, data[, weight][, edges])  

fit takes the following arguments:

  • data: Data is passed to the fit function in the form of a vector, which can either be one-dimensional or n-dimensional (tuple of vectors of equal length).

  • weight: This is the optional argument. A WeightVec type can be passed as an argument if values have different weights. The default weight of values is 1.

  • edges: This is a vector used to give the edges of the bins along each dimension.

It also takes a keyword argument, nbins, which is used to define the number of bins that the histogram should use along each side:

In this example, we used two random value generators...

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