python:matplotlib

一，一般步骤：

• 只包含一个坐标轴

1. plt.figure()
2. plt.scatter(),plt.bar(),plt.hist(),plt.pie()，plt.plot()
3. plt.savefig() 可以保存pdf和png格式
4. plt.show()

• 包含多个坐标轴

1. fig=plt.figure()
2. ax1=fig.add_subplot(), ax1.hist()
3. ax2=fig.add_subplot(), ax2.scatter()
4. ax3=fig.add_subplot(),ax3.plt.pie()
5. plt.savefig() 可以保存pdf和png格
6. plt.show()

示例代码：

# coding=utf-8

from matplotlib import mpl

import matplotlib.pyplot as plt
import numpy as np


#添上这两句配置，才会显示中文
mpl.rcParams['axes.unicode_minus']=False
mpl.rcParams['font.sans-serif']=['SimHei']
def scatter_test():
    plt.figure(1,figsize=(5, 4))
    plt.scatter(np.random.rand(20), np.random.rand(20))
    plt.title(u'散列图')
    plt.show()

def _test():
    x=np.arange(0,10,0.1)
    y=np.random.randn(len(x))
    plt.figure()#创建一个图形
    plt.plot(x,y)#画图
    plt.title('random numbers')#设置标题
    plt.show()#显示
    
def bar_test():
    N = 5
    ind = np.arange(N)  # the x locations for the groups
    width = 0.35  # the width of the bars
 
    fig=plt.figure()
    ax=fig.add_subplot(111)#增加一个坐标轴
    menMeans = (20, 35, 30, 35, 27)
    rects1 = ax.bar(ind, menMeans, width, color='r', yerr=0.01)
 
    womenMeans = (25, 32, 34, 20, 25)
    rects2 = ax.bar(ind + width, womenMeans, width, color='y', yerr=0.01)
 
    # add some
    ax.set_ylabel('Scores')
    ax.set_title('Scores by group and gender')
    ax.set_xticks(ind + width)
    ax.set_xticklabels(tuple(np.arange(1,6)))
 
    ax.legend((rects1, rects2), ('Men', 'Women'))
    autolabel(rects1,ax)
    autolabel(rects2,ax)
    plt.show()
    
def autolabel(rects,ax):
    # attach some text labels
    for rect in rects:
        height = rect.get_height()
        ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height, '%d' % int(height),ha='center', va='bottom')
        
#一个界面包含多个图       
def test():
    fig=plt.figure()
    ax1=fig.add_subplot(2,2,1)
    ax1.hist(np.random.randn(100), bins=20, color='k', alpha=0.3) 
    ax2=fig.add_subplot(2,2,2)
    ax2.scatter(np.arange(30), np.arange(30) + 3 * np.random.randn(30)) 
    ax3=fig.add_subplot(2,2,3)
    plt.plot([1.5, 3.5, -2, 1.6])
    ax4=fig.add_subplot(2,2,4)
    plt.show()
    
    
def pie_test():
    labels   = []#标签
    quants   = []#对应的数值
    f=open('data.txt','r')
    # Read data
    for line in f:
        info = line.split()
        labels.append(info[0])
        quants.append(float(info[1]))

    for i in range(len(labels)):
        print labels[i],'------->',quants[i]/reduce(lambda x,y:x+y,quants)
        
    # make a square figure
    plt.figure(1, figsize=(6,6))
    
    #是否突出显示
    expl = map(explode,labels)
    # Colors used. Recycle if not enough.
    colors  = ["pink","coral","yellow","orange"]
    # Pie Plot
    # autopct: format of "percent" string;
    plt.pie(quants, explode=expl, colors=colors, labels=labels, autopct='%1.2f%%',pctdistance=0.8, shadow=True)
    plt.title('Top 10 GDP Countries', bbox={'facecolor':'0.8', 'pad':5})
    plt.show()

# For China, make the piece explode a bit
def explode(label, target='China'):
    if label == target: return 0.1
    else: return 0
    
    
def bar_test2():
    labels   = []#标签
    quants   = []#对应的数值
    f=open('data.txt','r')
    # Read data
    for line in f:
        info = line.split()
        labels.append(info[0])
        quants.append(float(info[1]))
    
    width=0.4
    ind=np.linspace(0.5, 9.5, 10)
    fig=plt.figure(1, figsize=(12,6))
    ax=fig.add_subplot(111)
    ax.bar(ind-width/2,quants,width,color='r')
    ax.set_ylabel('GDP (Billion US dollar)')
    ax.set_xlabel(u'国家')
    ax.set_title('Top 10 GDP Countries', bbox={'facecolor':'1.0', 'pad':5})
    ax.set_xticks(ind)
    ax.set_xticklabels(labels)
    plt.savefig('test.pdf')
    plt.savefig('picture.png')
    plt.show()
        
if __name__ == '__main__':
    bar_test2()