matplotlib (4) - related to coverage data processing coordinate axes, Scatter scatter plot, bar bar

. 1  Import matplotlib.pyplot AS PLT
 2  Import numpy AS NP
 . 3  
. 4 X = np.linspace (-3,. 3, 50 )
 . 5 Y1 = X + 2 *. 1
 . 6  
. 7  # Figure. 1 
. 8  plt.figure ()
 . 9 plt.plot (X, Y1, as linewidth = 10, = ZOrder. 1 )
 10  
. 11  
12 is  # cross-ordinate axis shows the range setting 
13 is plt.xlim ((- 2, 2 ))
 14 plt.ylim ((- 10, 10 ))
 15  
16  # mobile gca axis = "GET Current axis" 
. 17 AX = plt.gca ()
 18 is ax.spines [ "right"].set_color("none")
19 ax.spines["top"].set_color("none")
20 ax.xaxis.set_ticks_position("bottom")
21 ax.yaxis.set_ticks_position("left")
22 ax.spines["bottom"].set_position(("data", 0))   #Set the X and Y coordinates of the sprite simultaneously
23 ax.spines["left" ] .Set_position (( " Data " , 0))
 24  
25  # case when the coordinate value data overlay appears: 
26 is  
27  # Method a: direct plt.plot (x, y1, linewidth = 10) modified to plt.plot (x, y1, linewidth = 10 , zorder = 1) to 
28  # method two: each of coordinate values taken out, as the correlation process on the data so as to cover, in order to achieve visualization 
29  for label in ax.get_xticklabels () + ax.get_yticklabels ():
 30      label.set_fontsize (10 )
 31 is      label.set_zorder (. 1 )
 32      label.set_bbox (dict (facecolor = " White " , edgecolor = " None ", alpha = 0.1))
33 
34 plt.show()

 

. 1  Import matplotlib.pyplot AS PLT
 2  Import numpy AS NP
 . 3  
. 4 n-= 1024
 . 5 X-np.random.normal = (0,. 1 , n-)
 . 6 the Y np.random.normal = (0,. 1 , n-)
 . 7 T = np.arctan2 (the Y, X-)
 . 8  
. 9  # on scatter parameters related description, with reference to Bowen https://blog.csdn.net/qiu931110/article/details/68130199 
10 plt.scatter (X-, the Y, S = 75, T = C, Alpha = 0.5 )
 . 11  
12 is plt.xlim ((- for 1.5, +1.5 ))
 13 is plt.ylim ((- for 1.5, +1.5 ))
 14  
15 plt.show ()

 

. 1  Import matplotlib.pyplot AS PLT
 2  Import numpy AS NP
 . 3  
. 4 n-= 1024
 . 5 X-np.random.normal = (0,. 1 , n-)
 . 6 the Y np.random.normal = (0,. 1 , n-)
 . 7 T = np.arctan2 (the Y, X-)
 . 8  
. 9  # on scatter parameters related description, with reference to Bowen https://blog.csdn.net/qiu931110/article/details/68130199 
10 plt.scatter (X-, the Y, S = 75, T = C, Alpha = 0.5 )
 . 11 plt.xlim ((- for 1.5, +1.5 ))
 12 is plt.ylim ((- for 1.5, +1.5 ))
 13 is  
14  # coordinate scale hide 
15  plt.xticks (())
16 plt.yticks(())
17 
18 plt.show()

 

. 1  Import matplotlib.pyplot AS PLT
 2  Import numpy AS NP
 . 3  
. 4 n-= 12 is
 . 5 X-= np.arange (n-)
 . 6 Yl = (. 1 - X-/ a float (n-)) * np.random.uniform (0.5, 1.0 , n-)
 . 7 Y2 = (. 1 - X-/ a float (n-)) * np.random.uniform (0.5, 1.0 , n-)
 . 8  
. 9  # plt.bar () of parameters, reference Bowen https://www.cnblogs.com /shine-rainbow/p/10742952.html 
10  # draw bar 
. 11 plt.bar (X-, + Yl, facecolor = " # 9999FF " , edgecolor = " White " )
 12 isplt.bar (X-, -Y2, facecolor = " # FF9999 " , edgecolor = " White " )
 13 is  
14  # text () of parameters 
15  # ################## ## 
16  # (X, Y, String, fontSize = 15, VerticalAlignment = "Top", HorizontalAlignment = "right") plt.text 
. 17  # parameters: 
18 is  # X, Y: represents a value on the coordinate values 
. 19  # String: explanatory text 
20 is  # fontSize: the font size represents 
21 is  # VerticalAlignment: vertical alignment parameters: [ 'Center' | 'Top' | 'bottom' | 'Baseline'] 
22 is  # horizontalalignment：水平对齐方式 ，参数：[ ‘center’ | ‘right’ | ‘left’ ]
23 
24 for x,y in zip(X, Y1):
25     plt.text(x, y, "%.2f"%y, ha = "center", va = "bottom")
26 
27 for x,y in zip(X, -Y2):
28     plt.text(x, y, "%.2f"%y, ha = "center", va = "top")
29 
30 plt.xticks(())
31 plt.yticks (())
 32  
33 plt.show ()