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Table 4500. Characters for markers and colors.

 Figure 4500a. Components of a Matplotlib figure. [1]

Others:

• matplotlib.pyplot: code.

• ScaleBar()

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Annotate Matplotlib Chart:
               import matplotlib.patches as patches
          A. plt.add_patch or ax1.add_patch( patches.Rectangle( (15, 35), # (x,y)
               40, # width
               60, # height
               # Rotation as well with 'angle'
               alpha=0.3, facecolor="red", edgecolor="black", linewidth=3, linestyle='solid'))
          B. ax1.add_patch(patches.Circle((30, 55), # (x,y)
              430, # radius
              alpha=0.4, facecolor="green", edgecolor="black", linewidth=1, linestyle='solid'))
          C. ax1.add_patch(patches.Ellipse((50, 65), # (x,y)
              50, # width
              80, # height
              545, # radius
              alpha=0.4, facecolor="green", edgecolor="black", linewidth=1, linestyle='solid'))
          D. Segment annotation:
               plt.plot([60, 30], [25, 80], color="skyblue", lw=5, linestyle='solid', label="_not in legend")
          E. Vertical and Horizontal Lines:
               plt.axvline(50, color='r')
               plt.axhline(60, color='green')
          F.
               plt.text(20, 0.5, 'Text') # Coordinates and text
               plt.text(0.5, 0.5, 'Center', transform = ax.transAxes)
               plt.text(0.05, 0.05, 'Bottom-left', transform = ax.transAxes)
               plt.text(0.8, 0.85, 'Top-right', transform = ax.transAxes)
               plt.text(0.8, 0.05, 'Bottom-right', transform = ax.transAxes)
               plt.text(0.05, 0.85, 'Top-left', transform = ax.transAxes)
               plt.text(10, 0.5, 'Text', color = "green")
               plt.text(10, 0.5, 'Text', fontsize = 16)
               plt.text(10, 0.5, 'Text', verticalalignment = 'bottom', horizontalalignment = 'right')
               plt.text(10, 0.5, 'Text', rotation = 45)
               plt.text(10, 0.5, 'Text', bbox = {'facecolor': 'oldlace', 'alpha': 0.5, 'pad': 8})
               plt.text(10, 0.5, 'Text inside box', bbox = {'facecolor': 'oldlace', 'alpha': 0.5, 'boxstyle': "rarrow,pad=0.3", 'ec': 'red'})
               plt.annotate('', xy = (13, -0.8), xytext = (13, 0.15), arrowprops = dict(facecolor = 'red', width = 0.4, headwidth = 8))

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Pass multiple lists to plot a function with numpy and matplotlib.pyplot: code:
         
Output (the text is shown at the x- and y-coordinates in the code above):         
         

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Plot with ordered dates and texts is shown in the image: code:
         
Output:         
         
         
         

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Plot data from multiple sets of data in a csv files: code:
         
Input:         
         
Output:         
         

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Plot images from data, with names, with pie in a csv files: code:
         
Input:         
         
Output:         
         

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Pie with specific colors: code:
         
Input:         
         
Output:         
         

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Plot images from data, with names, with scatter in a csv files: code:
         
Input:         
         
Output:         
         

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Plot images from data, with names, in a csv files: code:
         
Input:         
         
Output:         
         

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Plot images from data, with names, in a csv files (see details at page4806): code:
         
          With the same code above, but there is an error showing if the csv file has only two columns as shown below:
          <_csv.reader object at 0x000001A5BCA70640>
          ['Mike', '41']
          Traceback (most recent call last):
          File "C:\GlobalSino2\ICs\page4853WithoutAxis.py", line 21, in <module>
          y.append(int(float(row[2])))
          IndexError: list index out of range
Input:         
                             

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The "X" samples and "Y" samples are clustered on the left and right sides, suggesting that they are correlated with each other in each group.The separation of the two clusters along the x-axis suggests that "X" samples are very different from "Y" samples.The loading scores for PC1 to determine which N had the largest influence on the separating the two clusters along the x-axis.
Apply training data to PCA: code:    
         
         
Output:         
         
         

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Plot pixel intensities along a line of an image: code:
         
Input:         
         
Output:         
         

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Plot can be blurry if the setting is not correct, for instance below.          
Code:         
         
Output:         
         
Code:         
         
Output:         
         
Code:         
         
Output:         
         
Code:         
         
Output:         
         

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Plot date related data in the re-ordered dates with annotation: code:
         
Output:         
         
         

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Plot date related data in the re-ordered dates with annotation: code:
         
Output:         
         
         

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Plot image with annotation in re-ordered dates: code:
         
         
Output:         
         
         

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[1] https://matplotlib.org.