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Python 3 and Data Visualization

You're reading from   Python 3 and Data Visualization Mastering Graphics and Data Manipulation with Python

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Product type Paperback
Published in Aug 2024
Publisher Mercury_Learning
ISBN-13 9781836645719
Length 281 pages
Edition 1st Edition
Languages
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Authors (2):
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Mercury Learning and Information Mercury Learning and Information
Author Profile Icon Mercury Learning and Information
Mercury Learning and Information
Oswald Campesato Oswald Campesato
Author Profile Icon Oswald Campesato
Oswald Campesato
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Table of Contents (9) Chapters Close

Preface
1. Chapter 1: Introduction to Python 3 2. Chapter 2: NumPy and Data Visualization FREE CHAPTER 3. Chapter 3: Pandas and Data Visualization 4. Chapter 4: Pandas and SQL 5. Chapter 5: Matplotlib for Data Visualization 6. Chapter 6: Seaborn for Data Visualization 7. Index
Appendix: SVG and D3

RANDOMIZED DATA POINTS IN Matplotlib

Listing 5.13 displays the contents of lin_reg_plot.py that illustrates how to plot a graph of random points.

LISTING 5.13: lin_plot_reg.py

import numpy as np
import matplotlib.pyplot as plt

trX = np.linspace(-1, 1, 101) # Linear space of 101 and [-1,1]

#Create the y function based on the x axis
trY = 2*trX + np.random.randn(*trX.shape)*0.4+0.2

#create figure and scatter plot of the random points
plt.figure()
plt.scatter(trX,trY)

# Draw one line with the line function
plt.plot (trX, .2 + 2 * trX)
plt.show()  

Listing 5.13 defines the NumPy variable trX that contains 101 equally spaced numbers that are between -1 and 1 (inclusive). The variable trY is defined in two parts: the first part is 2*trX and the second part is a random value that is partially based on the length of the one-dimensional array trX. The variable trY is the sum of these two “parts,” which creates a “fuzzy” line segment.

The next portion of Listing...

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