作图首先要进行数据的输入，matplotlib包只提供作图相关功能，本身并没有数据读入、输出函数，针对各种试验或统计文本数据输入可以使用numpy提供的数据输入函数。
#-*- coding: gbk -*-"""Created on Sun Jan 11 11:17:42 2015
@author: zhang"""importnumpy as npimportmatplotlib.pyplot as pltimportmatplotlib as mpl
mpl.rcParams['font.family'] ='sans-serif'mpl.rcParams['font.sans-serif'] = [u'SimHei']#生成数据dataOut = np.arange(24).reshape(4, 6)PRint(dataOut)#保存数据np.savetxt('data.txt', dataOut, fmt ='%.1f')#读取数据data = np.loadtxt('data.txt')print(data)
plot 和 bar 函数
#-*- coding: gbk -*-"""Created on Sun Jan 11 11:33:14 2015
@author: zhang"""importnumpy as npimportmatplotlib.pyplot as pltimportmatplotlib as mpl
mpl.rcParams['font.family'] ='sans-serif'mpl.rcParams['font.sans-serif'] = [u'SimHei']
data= np.random.randint(1, 11, 5)
x=np.arange(len(data))
plt.plot(x, data, color='r')
plt.bar(x, data, alpha= .5, color ='g')
plt.show()
结果图片
饼图
#-*- coding: gbk -*-"""Created on Sun Jan 11 11:33:14 2015
@author: zhang"""importnumpy as npimportmatplotlib.pyplot as pltimportmatplotlib as mpl
mpl.rcParams['font.family'] ='sans-serif'mpl.rcParams['font.sans-serif'] = [u'SimHei']
data= np.random.randint(1, 11, 5)
x=np.arange(len(data))#plt.plot(x, data, color = 'r')#plt.bar(x, data, alpha = .5, color = 'g')plt.pie(data, explode = [0,0,.2, 0, 0])
plt.show
在实际工作中经常要对多组数据进行对比分析，这样需要在一个图表里表示出多个数据集。plot函数多数据集表示方法：
#-*- coding: gbk -*-"""Created on Sun Jan 11 11:51:41 2015
@author: zhang"""importnumpy as npimportmatplotlib.pyplot as pltimportmatplotlib as mpl
mpl.rcParams['font.family'] ='sans-serif'mpl.rcParams['font.sans-serif'] = [u'SimHei']
data= np.random.randint(1, 5, (5, 2))
x=np.arange(len(data))
plt.plot(x, data[:, 0],'--', color ='m')
plt.plot(x, data[:,1],'-.', color ='c')
plt.show()
这里用到了matplotlib中defered rendering的概念，它是指在绘图过程中，只有你调用到plt.plot函数是其它的绘图指令才会起效。
也可以通过对条形图的定制实现数据对比，主要有这几种类型 multy bar chart；stack bar chart和back to back bar chart
#-*- coding: gbk -*-"""Created on Sun Jan 11 12:03:57 2015
@author: zhang"""importnumpy as npimportmatplotlib.pyplot as pltimportmatplotlib as mpl
mpl.rcParams['font.family'] ='sans-serif'mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] =False
data= np.random.randint(1, 5, [3, 4])
index= np.arange(data.shape[1])
color_index= ['r','g','b']
fig, (ax1, ax2, ax3)= plt.subplots(3, 1, figsize = (5, 12))foriinrange(data.shape[0]):
ax1.bar(index+ i*.25 + .1, data[i], width = .25, color =color_index[i],\
alpha= .5)foriinrange(data.shape[0]):
ax2.bar(index+ .25, data[i], width = .5, color =color_index[i],\
bottom= np.sum(data[:i], axis = 0), alpha = .7)
ax3.barh(index, data[0], color='r', alpha = .5)
ax3.barh(index,-data[1], color ='b', alpha = .5)
plt.show()
plt.savefig('complex_bar_chart')
统计中常用的两种图标是直方图和盒须图，matplotlib中有针对这两种图表的专门函数：hist和boxplot
#-*- coding: gbk -*-"""Created on Sun Jan 11 12:29:34 2015
@author: zhang"""importnumpy as npimportmatplotlib.pyplot as pltimportmatplotlib as mpl
mpl.rcParams['font.family'] ='sans-serif'mpl.rcParams['font.sans-serif'] = [u'SimHei']
data= np.random.randn(100)
fig, (ax1, ax2)= plt.subplots(1, 2, figsize = (8, 4))
ax1.hist(data)
ax2.boxplot(data)
plt.savefig('hist_boxplot')
plt.show()
本文讲到的所有matplotlib命令都有非常丰富的定制参数，我会在后面文章中讲到，你也可以查看帮助文档学习。