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## You're reading fromPython Machine Learning (Wiley)

Product type Book
Published in Apr 2019
Publisher Wiley
ISBN-13 9781119545637
Pages 320 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Wei-Meng Lee

1. Cover
2. Introduction
3. CHAPTER 1: Introduction to Machine Learning 4. CHAPTER 2: Extending Python Using NumPy 5. CHAPTER 3: Manipulating Tabular Data Using Pandas 6. CHAPTER 4: Data Visualization Using matplotlib 7. CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning 8. CHAPTER 6: Supervised Learning—Linear Regression 9. CHAPTER 7: Supervised Learning—Classification Using Logistic Regression 10. CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines 11. CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN) 12. CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means 13. CHAPTER 11: Using Azure Machine Learning Studio 14. CHAPTER 12: Deploying Machine Learning Models 15. Index

# Plotting Line Charts

To see how easy it is to use matplotlib, let's plot a line chart using Jupyter Notebook. Here is a code snippet that plots a line chart:

%matplotlib inline
import matplotlib.pyplot as plt

plt.plot(
[1,2,3,4,5,6,7,8,9,10],
[2,4.5,1,2,3.5,2,1,2,3,2]
)

Figure 4.1 shows the line chart plotted.

The first statement tells matplotlib to display the output of the plotting commands in line within front‐ends likes Jupyter Notebook. In short, it means display the chart within the same page as your Jupyter Notebook:

%matplotlib inline

To use matplotlib, you import the pyplot module and name it plt (its commonly used alias):

import matplotlib.pyplot as plt

To plot a line chart, you use the plot() function from the pyplot module, supplying it with two arguments as follows:

1. A list of values representing the x‐axis
2. A list of values representing the y‐axis
[1,2,3,4,5,6,7,8,9,10],
[2,4.5,1...
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