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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 Bar Charts

Besides plotting line charts, you can also plot bar charts using matplotlib. Bar charts are useful for comparing data. For example, you want to be able to compare the grades of a student over a number of semesters.

Using the same dataset that you used in the previous section, you can plot a bar chart using the `bar()` function as follows:

````%matplotlib inline`
`import matplotlib.pyplot as plt`
`from matplotlib import style`
` `
`style.use("ggplot")`
` `
`plt.bar(`
`    [1,2,3,4,5,6,7,8,9,10],`
`    [2,4.5,1,2,3.5,2,1,2,3,2],`
`    label = "Jim",`
`    color = "m",                    # m for magenta`
`    align = "center"`
`)`
` `
`plt.title("Results")`
`plt.xlabel("Semester")`
`plt.ylabel("Grade")`
` `
`plt.legend()`
`plt.grid(True, color="y")` ```

Figure 4.7 shows the bar chart plotted using the preceding code snippet.