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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 Pie Charts

Another chart that is popular is the pie chart. A pie chart is a circular statistical graphic divided into slices to illustrate numerical proportions. A pie chart is useful when showing percentage or proportions of data. Consider the following sets of data representing the various browser market shares:

````labels      = ["Chrome", "Internet Explorer", "Firefox",`
`               "Edge","Safari", "Sogou Explorer","Opera","Others"]`
`marketshare = [61.64, 11.98, 11.02, 4.23, 3.79, 1.63, 1.52, 4.19]` ```

In this case, it would be really beneficial to be able to represent the total market shares as a complete circle, with each slice representing the percentage held by each browser.

The following code snippet shows how you can plot a pie chart using the data that we have:

````%matplotlib inline`
`import matplotlib.pyplot as plt`
` `
`labels      = ["Chrome", "Internet Explorer",`
`            ...````
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