Python畫圖主要用到matplotlib這個庫。具體來說是pylab和pyplot這兩個子庫。這兩個庫可以滿足基本的畫圖需求，而條形圖，散點圖等特殊圖，下面再單獨具體介紹。

首先給出pylab神器鎮文：pylab.rcParams.update(params)。這個函數幾乎可以調節圖的一切屬性，包括但不限于：坐標范圍，axes標簽字號大小，xtick,ytick標簽字號，圖線寬，legend字號等。

具體參數參看官方文檔：http://matplotlib.org/users/customizing.html

首先給出一個Python3畫圖的例子。

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import?matplotlib.pyplot as plt import?matplotlib.pylab as pylab import?scipy.io import?numpy as np params={ ????'axes.labelsize':?'35',??????? ????'xtick.labelsize':'27', ????'ytick.labelsize':'27', ????'lines.linewidth':2?, ????'legend.fontsize':?'27', ????'figure.figsize'???:?'12, 9'????# set figure size } pylab.rcParams.update(params)????????????#set figure parameter #line_styles=['ro-','b^-','gs-','ro--','b^--','gs--']? #set line style ????????? #We give the coordinate date directly to give an example. x1?=?[-20,-15,-10,-5,0,0,5,10,15,20] y1?=?[0,0.04,0.1,0.21,0.39,0.74,0.78,0.80,0.82,0.85] y2?=?[0,0.014,0.03,0.16,0.37,0.78,0.81,0.83,0.86,0.92] y3?=?[0,0.001,0.02,0.14,0.34,0.77,0.82,0.85,0.90,0.96] y4?=?[0,0,0.02,0.12,0.32,0.77,0.83,0.87,0.93,0.98] y5?=?[0,0,0.02,0.11,0.32,0.77,0.82,0.90,0.95,1] plt.plot(x1,y1,'bo-',label='m=2, p=10%',markersize=20)?# in 'bo-', b is blue, o is O marker, - is solid line and so on plt.plot(x1,y2,'gv-',label='m=4, p=10%',markersize=20) plt.plot(x1,y3,'ys-',label='m=6, p=10%',markersize=20) plt.plot(x1,y4,'ch-',label='m=8, p=10%',markersize=20) plt.plot(x1,y5,'mD-',label='m=10, p=10%',markersize=20) fig1?=?plt.figure(1) axes?=?plt.subplot(111)?? #axes = plt.gca() axes.set_yticks([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]) axes.grid(True)??# add grid plt.legend(loc="lower right")??#set legend location plt.ylabel('Percentage')???# set ystick label plt.xlabel('Difference')??# set xstck label plt.savefig('D:\\commonNeighbors_CDF_snapshots.eps',dpi?=?1000,bbox_inches='tight') plt.show()

顯示效果如下：

　

代碼沒什么好說的，這里只說一下plt.subplot(111)這個函數。

plt.subplot(111)和plt.subplot(1,1,1)是等價的。意思是將區域分成1行1列，當前畫的是第一個圖（排序由行至列）。

plt.subplot(211)意思就是將區域分成2行1列，當前畫的是第一個圖（第一行，第一列）。以此類推，只要不超過10，逗號就可省去。

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python畫條形圖。代碼如下。

import scipy.io import numpy as np import matplotlib.pylab as pylab import matplotlib.pyplot as plt import matplotlib.ticker as mtick params={'axes.labelsize': '35','xtick.labelsize':'27','ytick.labelsize':'27','lines.linewidth':2 ,'legend.fontsize': '27','figure.figsize' : '24, 9' } pylab.rcParams.update(params)y1 = [9.79,7.25,7.24,4.78,4.20] y2 = [5.88,4.55,4.25,3.78,3.92] y3 = [4.69,4.04,3.84,3.85,4.0] y4 = [4.45,3.96,3.82,3.80,3.79] y5 = [3.82,3.89,3.89,3.78,3.77]ind = np.arange(5) # the x locations for the groups width = 0.15 plt.bar(ind,y1,width,color = 'blue',label = 'm=2') plt.bar(ind+width,y2,width,color = 'g',label = 'm=4') # ind+width adjusts the left start location of the bar. plt.bar(ind+2*width,y3,width,color = 'c',label = 'm=6') plt.bar(ind+3*width,y4,width,color = 'r',label = 'm=8') plt.bar(ind+4*width,y5,width,color = 'm',label = 'm=10') plt.xticks(np.arange(5) + 2.5*width, ('10%','15%','20%','25%','30%'))plt.xlabel('Sample percentage') plt.ylabel('Error rate')fmt = '%.0f%%' # Format you want the ticks, e.g. '40%' xticks = mtick.FormatStrFormatter(fmt) # Set the formatter axes = plt.gca() # get current axes axes.yaxis.set_major_formatter(xticks) # set % format to ystick. axes.grid(True) plt.legend(loc="upper right") plt.savefig('D:\\errorRate.eps', format='eps',dpi = 1000,bbox_inches='tight')plt.show()

結果如下：

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畫散點圖，主要是scatter這個函數，其他類似。

畫網絡圖，要用到networkx這個庫，下面給出一個實例：

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import?networkx as nx import?pylab as plt g?=?nx.Graph() g.add_edge(1,2,weight?=?4) g.add_edge(1,3,weight?=?7) g.add_edge(1,4,weight?=?8) g.add_edge(1,5,weight?=?3) g.add_edge(1,9,weight?=?3) g.add_edge(1,6,weight?=?6) g.add_edge(6,7,weight?=?7) g.add_edge(6,8,weight?=?7)? g.add_edge(6,9,weight?=?6) g.add_edge(9,10,weight?=?7) g.add_edge(9,11,weight?=?6) fixed_pos?=?{1:(1,1),2:(0.7,2.2),3:(0,1.8),4:(1.6,2.3),5:(2,0.8),6:(-0.6,-0.6),7:(-1.3,0.8),?8:(-1.5,-1),?9:(0.5,-1.5),?10:(1.7,-0.8),?11:(1.5,-2.3)}?#set fixed layout location #pos=nx.spring_layout(g) # or you can use other layout set in the module nx.draw_networkx_nodes(g,pos?=?fixed_pos,nodelist=[1,2,3,4,5], node_color?=?'g',node_size?=?600) nx.draw_networkx_edges(g,pos?=?fixed_pos,edgelist=[(1,2),(1,3),(1,4),(1,5),(1,9)],edge_color='g',width?=?[4.0,4.0,4.0,4.0,4.0],label?=?[1,2,3,4,5],node_size?=?600) nx.draw_networkx_nodes(g,pos?=?fixed_pos,nodelist=[6,7,8], node_color?=?'r',node_size?=?600) nx.draw_networkx_edges(g,pos?=?fixed_pos,edgelist=[(6,7),(6,8),(1,6)],width?=?[4.0,4.0,4.0],edge_color='r',node_size?=?600) nx.draw_networkx_nodes(g,pos?=?fixed_pos,nodelist=[9,10,11], node_color?=?'b',node_size?=?600) nx.draw_networkx_edges(g,pos?=?fixed_pos,edgelist=[(6,9),(9,10),(9,11)],width?=?[4.0,4.0,4.0],edge_color='b',node_size?=?600) plt.text(fixed_pos[1][0],fixed_pos[1][1]+0.2, s?=?'1',fontsize?=?40) plt.text(fixed_pos[2][0],fixed_pos[2][1]+0.2, s?=?'2',fontsize?=?40) plt.text(fixed_pos[3][0],fixed_pos[3][1]+0.2, s?=?'3',fontsize?=?40) plt.text(fixed_pos[4][0],fixed_pos[4][1]+0.2, s?=?'4',fontsize?=?40) plt.text(fixed_pos[5][0],fixed_pos[5][1]+0.2, s?=?'5',fontsize?=?40) plt.text(fixed_pos[6][0],fixed_pos[6][1]+0.2, s?=?'6',fontsize?=?40) plt.text(fixed_pos[7][0],fixed_pos[7][1]+0.2, s?=?'7',fontsize?=?40) plt.text(fixed_pos[8][0],fixed_pos[8][1]+0.2, s?=?'8',fontsize?=?40) plt.text(fixed_pos[9][0],fixed_pos[9][1]+0.2, s?=?'9',fontsize?=?40) plt.text(fixed_pos[10][0],fixed_pos[10][1]+0.2, s?=?'10',fontsize?=?40) plt.text(fixed_pos[11][0],fixed_pos[11][1]+0.2, s?=?'11',fontsize?=?40) plt.show()

結果如下：

分類:?python

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