python画图代码⼤全-Python科学画图代码分享
Python画图主要⽤到matplotlib这个库。Matplotlib 是⼀个 Python 的 2D绘图库，它以各种硬拷贝格式和跨平台的交互式环境⽣成出版质量级别的图形。
这⾥有⼀本电⼦书供⼤家参考：《Python图表绘制：matplotlib绘图库⼊门》代码转换成16进制
具体来说是pylab和pyplot这两个⼦库。这两个库可以满⾜基本的画图需求，⽽条形图，散点图等特殊图，下⾯再单独具体介绍。
⾸先给出pylab神器镇⽂：Params.update(params)。这个函数⼏乎可以调节图的⼀切属性，包括但不限于：坐标范围，axes标签字号⼤⼩，xtick,ytick标签字号，图线宽，legend字号等。
⾸先给出⼀个Python3画图的例⼦。
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
}
#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 = a()
axes.set_yticks([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0])
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()
显⽰效果如下：
代码没什么好说的，这⾥只说⼀下plt.subplot(111)这个函数。
plt.subplot(111)和plt.subplot(1,1,1)是等价的。意思是将区域分成1⾏1列，当前画的是第⼀个图（排序由⾏⾄列）。
plt.subplot(211)意思就是将区域分成2⾏1列，当前画的是第⼀个图（第⼀⾏，第⼀列）。以此类推，只要不超过10，逗号就可省去。python画条形图。代码如下。
import scipy.iopython代码画图案
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',简述元组和列表的不同
'ytick.labelsize':'27',
'lines.linewidth':2 ,
'legend.fontsize': '27',
'figure.figsize' : '24, 9'
}vue和jsp的区别
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.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 = a() # get current axes
axes.yaxis.set_major_formatter(xticks) # set % format to ystick.
plt.legend(loc="upper right")
plt.savefig('D:\errorRate.eps', format='eps',dpi = 1000,bbox_inches='tight')
plt.show()
结果如下：
画散点图，主要是scatter这个函数，其他类似。
画⽹络图，要⽤到networkx这个库，下⾯给出⼀个实例：
hexo使用教程
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)
种植容器有哪些类型
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)
<(fixed_pos[1][0],fixed_pos[1][1]+0.2, s = '1',fontsize = 40)
<(fixed_pos[2][0],fixed_pos[2][1]+0.2, s = '2',fontsize = 40)
<(fixed_pos[3][0],fixed_pos[3][1]+0.2, s = '3',fontsize = 40)
<(fixed_pos[4][0],fixed_pos[4][1]+0.2, s = '4',fontsize = 40)
<(fixed_pos[5][0],fixed_pos[5][1]+0.2, s = '5',fontsize = 40)
<(fixed_pos[6][0],fixed_pos[6][1]+0.2, s = '6',fontsize = 40)
<(fixed_pos[7][0],fixed_pos[7][1]+0.2, s = '7',fontsize = 40)
<(fixed_pos[8][0],fixed_pos[8][1]+0.2, s = '8',fontsize = 40)
<(fixed_pos[9][0],fixed_pos[9][1]+0.2, s = '9',fontsize = 40)
<(fixed_pos[10][0],fixed_pos[10][1]+0.2, s = '10',fontsize = 40)
<(fixed_pos[11][0],fixed_pos[11][1]+0.2, s = '11',fontsize = 40)
plt.show()
结果如下：
总结
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