Matplotlib for Python Developers

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• Matplotlib for Python Developers

• By: Sandro Tosi

• Publisher: Packt Publishing

• Pub. Date: November 9, 2009

• Print ISBN-13: 978-1-847197-90-0

• Pages in Print Edition: 308

• Subscriber Rating: [5 Ratings] Subscriber Reviews

• 
  
  

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关键词：Developers Matplotlib developer matplot Develop

1. Multiline plots
   
2. 
   
3. It's fun to plot a line, but it's even more fun when we can plot more than one line on the same figure. This is really easy with Matplotlib as we can simply plot all the lines that we want before calling show(). Have a look at the following code and screenshot:
   
4. 
   
5. In [1]: import matplotlib.pyplot as plt
   
6. In [2]: x = range(1, 5)
   
7. In [3]: plt.plot(x, [xi*1.5 for xi in x])
   
8. Out[3]: [<matplotlib.lines.Line2D object at 0x2076ed0>]
   
9. In [4]: plt.plot(x, [xi*3.0 for xi in x])
   
10. Out[4]: [<matplotlib.lines.Line2D object at 0x1e544d0>]
    
11. In [5]: plt.plot(x, [xi/3.0 for xi in x])
    
12. Out[5]: [<matplotlib.lines.Line2D object at 0x20864d0>]
    
13. In [6]: plt.show()

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1. A complete example
   
2. 
   
3. Let's now group together all that we've seen so far and create a complete example as follows:
   
4. 
   
5. In [1]: import matplotlib.pyplot as plt
   
6. In [2]: import numpy as np
   
7. In [3]: x = np.arange(1, 5)
   
8. In [4]: plt.plot(x, x*1.5, label='Normal')
   
9. Out[4]: [<matplotlib.lines.Line2D object at 0x2ab5f50>]
   
10. In [5]: plt.plot(x, x*3.0, label='Fast')
    
11. Out[5]: [<matplotlib.lines.Line2D object at 0x2ac5210>]
    
12. In [6]: plt.plot(x, x/3.0, label='Slow')
    
13. Out[6]: [<matplotlib.lines.Line2D object at 0x2ac5650>]
    
14. In [7]: plt.grid(True)
    
15. In [8]: plt.title('Sample Growth of a Measure')
    
16. Out[8]: <matplotlib.text.Text object at 0x2aa8890>
    
17. In [9]: plt.xlabel('Samples')
    
18. Out[9]: <matplotlib.text.Text object at 0x2aa6150>
    
19. In [10]: plt.ylabel('Values Measured')
    
20. Out[10]: <matplotlib.text.Text object at 0x2aa6d10>
    
21. In [11]: plt.legend(loc='upper left')
    
22. Out[11]: <matplotlib.legend.Legend object at 0x2ac5c50>
    
23. In [12]: plt.show()

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1. Control colors
   
2. 
   
3. We've already seen that in a multiline plot, Matplotlib automatically chooses different colors for different lines. We are also free to choose them by ourselves:
   
4. 
   
5. In [1]: import matplotlib.pyplot as plt
   
6. In [2]: import numpy as np
   
7. In [3]: y = np.arange(1, 3)
   
8. In [4]: plt.plot(y, 'y');
   
9. In [5]: plt.plot(y+1, 'm');
   
10. In [6]: plt.plot(y+2, 'c');
    
11. In [7]: plt.show()

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1. In [1]: import matplotlib.pyplot as plt
   
2. In [2]: x = [5, 3, 7, 2, 4, 1]
   
3. In [3]: plt.plot(x);
   
4. In [4]: plt.xticks(range(len(x)), ['a', 'b', 'c', 'd', 'e', 'f']);
   
5. In [5]: plt.yticks(range(1, 8, 2));
   
6. In [6]: plt.show()
   
7. 
   
8. In this code snippet, we used xticks() to specify both, locations and labels and yticks() to only show ticks at odd numbered locations.

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1. In [1]: import matplotlib.pyplot as plt
   
2. In [2]: import numpy as np
   
3. In [3]: y = np.random.randn(1000)
   
4. In [4]: plt.hist(y);
   
5. In [5]: plt.show()

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1. Error bar charts
   
2. 
   
3. In experimental sciences, we know that all the measurements that we take lack perfect precision. This leads to repeating the measurements, which results in obtaining a set of values. The expected result is that all those measures group up around the true value that we want to measure.
   
4. 
   
5. The representation of this distribution of data values is done by plotting a single data point, (commonly) the mean value of dataset, and an error bar to represent the overall distribution of data. This helps us to get a general idea of how accurate a measurement is (or how far the reported value could be from the error-free value).
   
6. 
   
7. Using the errorbar() function, Matplotlib allows us to create such a graph type.
   
8. 
   
9. Let's take a look at the next example:
   
10. 
    
11. In [1]: import matplotlib.pyplot as plt
    
12. In [2]: import numpy as np
    
13. In [3]: x = np.arange(0, 4, 0.2)
    
14. In [4]: y = np.exp(-x)
    
15. In [5]: e1 = 0.1 * np.abs(np.random.randn(len(y)))
    
16. In [6]: plt.errorbar(x, y, yerr=e1, fmt='.-');
    
17. In [7]: plt.show()

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1. Bar charts
   
2. 
   
3. Bar charts display rectangular bars (either vertical or horizontal) with their length proportional to the values they represent. They are commonly used to visually compare two or more values.
   
4. 
   
5. The bar() function is used to generate bar charts in Matplotlib. The function expects two lists of values: the X coordinates that are the positions of the bar's left margin and the heights of the bars:
   
6. 
   
7. In [1]: import matplotlib.pyplot as plt
   
8. In [2]: plt.bar([1, 2, 3], [3, 2, 5]);
   
9. In [3]: plt.show()

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1. Pie charts
   
2. 
   
3. Pie charts are circular representations, divided into sectors (also called wedges). The arc length of each sector is proportional to the quantity we're describing. It's an effective way to represent information when we are interested mainly in comparing the wedge against the whole pie, instead of wedges against each other.
   
4. 
   
5. Matplotlib provides the pie() function to plot pie charts from an array X. Wedges are created proportionally, so that each value x of array X generates a wedge proportional to x/sum(X).
   
6. 
   
7. Please note that if sum(X) is less than 1, then the pie is drawn using X values directly and no normalization is done, resulting in a pie with discontinuity.
   
8. 
   
9. Pie charts look best if the figure and axes are in a square format (if not, with the common rectangular figure, they look like ellipses).
   
10. 
    
11. In the next example, we are going to plot a simple pie, using the legend keyword argument to give names to the wedges:
    
12. 
    
13. In [1]: import matplotlib.pyplot as plt
    
14. In [2]: plt.figure(figsize=(3,3));
    
15. In [3]: x = [45, 35, 20]
    
16. In [4]: labels = ['Cats', 'Dogs', 'Fishes']
    
17. In [5]: plt.pie(x, labels=labels);
    
18. In [6]: plt.show()

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thanks you work