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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 FREE CHAPTER 2. Linear Algebra with NumPy 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

Box plots

Another important visual in exploratory data analysis is the box plot, also known as the box-and-whisker plot. It's built based on the five-number summary, which is the minimum, first quartile, median, third quartile, and maximum values. In a standard box plot, these values are represented as follows:

It's a very convenient way of comparing several distributions. In general, the whiskers of the plot generally extend to the extreme points. Alternatively, you can cut them with the 1.5 interquartile range. Let's check our CRIM and RM features:

In [60]: %matplotlib notebook
%matplotlib notebook
import matplotlib.pyplot as plt
from scipy import stats
samples = dataset.data
fig, (ax1,ax2) = plt.subplots(1,2, figsize =(8,3))
axs = [ax1, ax2]
list_features = ['CRIM', 'RM']
ax1...
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