[ Python 套件 ] Matplotlib - Basic Introduction For ML/DataScience

Line Chart 
- test1.py 

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1. #!/usr/bin/env python3  
2. import matplotlib.pyplot as plt  
3. import numpy as np  
4.   
5. x = np.linspace(0, 10, 10)  
6. y = np.sin(x)  
7.   
8. plt.plot(x, y)  
9. plt.xlabel('time')  
10. plt.ylabel('Some function of time')  
11. plt.title("My cool chart")  
12. plt.show()  

ScatterPlot 
- test2.py 

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1. #!/usr/bin/env python3  
2. import matplotlib.pyplot as plt  
3. import numpy as np  
4. import pandas as pd  
5.   
6. A = pd.read_csv('data_1d.csv', header=None).as_matrix()  
7.   
8. x = A[:,0]  
9. y = A[:,1]  
10.   
11. plt.scatter(x, y)  
12.   
13. x_line = np.linspace(0, 100, 100)  
14. y_line = 2*x_line + 1  
15.   
16. plt.plot(x_line, y_line)  
17. plt.show()  

Histogram 
Load data from CSV file "data_1d.csv": 
- test3.py 

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1. #!/usr/bin/env python3  
2. import matplotlib.pyplot as plt  
3. import numpy as np  
4. import pandas as pd  
5.   
6. A = pd.read_csv('data_1d.csv', header=None).as_matrix()  
7.   
8. x = A[:,0]  
9. y = A[:,1]  
10.   
11. y_actual = 2*x + 1  
12. residuals = y - y_actual  
13. plt.hist(residuals)  
14. plt.show()  

Plotting Images 
Download train.csv for testing script below: 
- test4.py 

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1. #!/usr/bin/env python3  
2. import matplotlib.pyplot as plt  
3. import numpy as np  
4. import pandas as pd  
5.   
6. df = pd.read_csv('train.csv')  
7. print("Input data shape=%s" % str(df.shape))  
8.   
9. M = df.as_matrix()  
10. im = M[0, 1:]  # Remove label  
11. im = im.reshape(28, 28)  
12. print("The shape of image=%s" % (str(im.shape)))  
13.   
14. print("The image with label=%s" % (M[0,0]))  
15. plt.imshow(255 - im, cmap='gray')  
16. plt.show()