本文实例讲述了Python数据分析之双色球统计单个红和蓝球哪个比例高的方法。分享给大家供大家参考，具体如下：
统计单个红球和蓝球，哪个组合最多，显示前19组数据
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#!/usr/bin/python# -*- coding:UTF-8 -*-import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport operatordf = pd.read_table('newdata.txt',header=None,sep=',')tdate = sorted(df.loc[:,0])# print tdateh1 = df.loc[:,1:7:6].values #取第一列红球和蓝球# print h1h2 = df.loc[:,2:7:5].values #取第二列红球和蓝球h3 = df.loc[:,3:7:4].valuesh4 = df.loc[:,4:7:3].valuesh5 = df.loc[:,5:7:2].valuesh6 = df.loc[:,6:7:1].values# tblue = df.loc[:,7]#将上方切分的所有数据组合到一起data = np.append(h1, h2, axis = 0)data = np.append(data, h3, axis = 0)data = np.append(data, h4, axis = 0)data = np.append(data, h5, axis = 0)data = np.append(data, h6, axis = 0)# print datadata1 = pd.DataFrame(data)# print data1#写入到一个文件中data1.to_csv('hldata.csv',index=None,header=None)#读取文件，将组合进行统计并从大到小排序f = open("hldata.csv")count_dict = {}for line in f.readlines(): line = line.strip() count = count_dict.setdefault(line, 0) count += 1 count_dict[line] = countsorted_count_dict = sorted(count_dict.iteritems(), key=operator.itemgetter(1), reverse=True)# for item in sorted_count_dict:# print "%s,%d" % (item[0], item[1])# print sorted_count_dictfenzu = pd.DataFrame(sorted_count_dict).set_index([0])#print fenzu#分别从第一列和第二列取前19个数据放到x y中x = list(fenzu.index[:19])y = list(fenzu.values[:19])print xprint y#将x对应数值，不然画图报错s = pd.Series(range(1,len(x)+1), index=x)#设置画图属性plt.figure(figsize=(12,6),dpi=80)plt.legend(loc='best')# plt.plot(fenzu,color='red')plt.bar(s,y,alpha=.5, color='r',width=0.8)plt.title('The one red and one blue ball number')plt.xlabel('one red and one blue number')plt.ylabel('times')#可以在图中放置标签字符# for i in range(0,19):# plt.text(int(i+1.4),25,x[i],color='b',size=10)# plt.text(1.4,20,x[0],color='g',ha='center')#将['1,12', '26,9', '5,13']这样的字符放到图中plt.xticks(s,x, rotation=10,size=10,ha='left')plt.show() |
结果如下：
可以看出红球1和蓝球12出现过的次数最多，其次是红球26和蓝球9
参考：
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import matplotlib.pyplot as pltimport numpy as npplt.rc('font', family='SimHei', size=13)num = np.array([13325, 9403, 9227, 8651])ratio = np.array([0.75, 0.76, 0.72, 0.75])men = num * ratiowomen = num * (1-ratio)x = ['聊天','支付','团购\n优惠券','在线视频']width = 0.5idx = np.arange(len(x))plt.bar(idx, men, width, color='red', label='男性用户')plt.bar(idx, women, width, bottom=men, color='yellow', label='女性用户')plt.xlabel('应用类别')plt.ylabel('男女分布')plt.xticks(idx+width/2, x, rotation=40)plt.legend() |
希望本文所述对大家Python程序设计有所帮助。
原文链接：http://blog.csdn.net/levy_cui/article/details/51446476
