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Mastering Numerical Computing with NumPy

You're reading from   Mastering Numerical Computing with NumPy Master scientific computing and perform complex operations with ease

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788993357
Length 248 pages
Edition 1st Edition
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Authors (3):
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Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
Mert Cuhadaroglu Mert Cuhadaroglu
Author Profile Icon Mert Cuhadaroglu
Mert Cuhadaroglu
Umit Mert Cakmak Umit Mert Cakmak
Author Profile Icon Umit Mert Cakmak
Umit Mert Cakmak
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Table of Contents (11) Chapters Close

Preface 1. Working with NumPy Arrays 2. Linear Algebra with NumPy FREE CHAPTER 3. Exploratory Data Analysis of Boston Housing Data with NumPy Statistics 4. Predicting Housing Prices Using Linear Regression 5. Clustering Clients of a Wholesale Distributor Using NumPy 6. NumPy, SciPy, Pandas, and Scikit-Learn 7. Advanced Numpy 8. Overview of High-Performance Numerical Computing Libraries 9. Performance Benchmarks 10. Other Books You May Enjoy

Computing histograms

A histogram is a visual representation of the distribution of numerical data. Karl Pearson first introduced this concept more than a century ago. A histogram is a kind of bar chart that is used for continuous data, while a bar chart visually represents categorical variables. As a first step, you need to divide your entire range of values into a series of intervals (bins). Each bin has to be adjacent and none of them can overlap. In general, bin sizes are equal, and the rule of thumb for the number of bins to include is 5–20. This means that if you have more than 20 bins, your graph will be hard to read. On the contrary, if you have fewer than 5 bins, then your graph will give very little insight into the distribution of your data:

In [48]: %matplotlib notebook
%matplotlib notebook
import matplotlib.pyplot as plt
NOX = samples...
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