首页 > 代码库 > 根据nba数据预测17-18总冠军（转）
根据nba数据预测17-18总冠军（转）
#coding=utf-8 import urllib import re import csv import sys #计数，初始化 count = 0 #以下定义的与之对应的是球员姓名、赛季、胜负、比赛、首发、时间、投篮命中率、投篮命中数、投篮出手数、三分命中率、三分命中数、三分出手数、罚球命中率、罚球命中数、罚球次数、总篮板数、前场篮板数、后场篮板数、助攻数、抢断数、盖帽数、失误数、犯规数、得分 list0 = [] list1 = [] list2 = [] list3 = [] list4 = [] list5 = [] list6 = [] list7 = [] list8 = [] list9 = [] list10 = [] list11 = [] list12 = [] list13 = [] list14 = [] list15 = [] list16 = [] list17 = [] list18 = [] list19 = [] list20 = [] list21 = [] list22 = [] list23 = [] list24 = [] list25 = [] list26 = [] #定义获取页面函数 def getHtml(url): page = urllib.urlopen(url) html = page.read() return html #获取数据并存入数据库中 for k in range(0,51): #获取当前页面，该页面只有LBJ的职业生涯常规赛的数据，截止到2016.12.26 html = getHtml( "http://www.stat-nba.com/query.php?QueryType=game&GameType=season&Player_id=1862&crtcol=season&order=1&page=" + str( k)) # 获取球员姓名、赛季、胜负、比赛、首发、时间、投篮命中率、投篮命中数、投篮出手数、三分命中率、三分命中数、三分出手数、罚球命中率、罚球命中数、罚球次数、总篮板数、前场篮板数、后场篮板数、助攻数、抢断数、盖帽数、失误数、犯规数、得分 #正则得到相对应的数值 playerdata = http://www.mamicode.com/re.findall(r‘<td class ="normal player_name_out change_color col1 row.+"><a.*>(.*)</a></td>‘ r‘\s*<td class ="current season change_color col2 row.+"><a.*>(.*)</a></td>‘ r‘\s*<td class ="normal wl change_color col3 row.+">(.*)</td>‘ r‘\s*<td class ="normal result_out change_color col4 row.+"><a.*>(\D*|76人)(\d+)-(\d+)(\D*)</a></td>‘ r‘\s*<td class ="normal gs change_color col5 row.+">(.*)</td>‘ r‘\s*<td class ="normal mp change_color col6 row.+">(.*)</td>‘ r‘\s*<td class ="normal fgper change_color col7 row.+">(.*%|\s*)</td>‘ r‘\s*<td class ="normal fg change_color col8 row.+">(.*)</td>‘ r‘\s*<td class ="normal fga change_color col9 row.+">(.*)</td>‘ r‘\s*<td class ="normal threepper change_color col10 row.+">(.*%|\s*)</td>‘ r‘\s*<td class ="normal threep change_color col11 row.+">(.*)</td>‘ r‘\s*<td class ="normal threepa change_color col12 row.+">(.*)</td>‘ r‘\s*<td class ="normal ftper change_color col13 row.+">(.*%|\s*)</td>‘ r‘\s*<td class ="normal ft change_color col14 row.+">(.*)</td>‘ r‘\s*<td class ="normal fta change_color col15 row.+">(.*)</td>‘ r‘\s*<td class ="normal trb change_color col16 row.+">(.*)</td>‘ r‘\s*<td class ="normal orb change_color col17 row.+">(.*)</td>‘ r‘\s*<td class ="normal drb change_color col18 row.+">(.*)</td>‘ r‘\s*<td class ="normal ast change_color col19 row.+">(.*)</td>‘ r‘\s*<td class ="normal stl change_color col20 row.+">(.*)</td>‘ r‘\s*<td class ="normal blk change_color col21 row.+">(.*)</td>‘ r‘\s*<td class ="normal tov change_color col22 row.+">(.*)</td>‘ r‘\s*<td class ="normal pf change_color col23 row.+">(.*)</td>‘ r‘\s*<td class ="normal pts change_color col24 row.+">(.*)</td>‘, html) #获取每条数据， for data in playerdata: #将元组数据复制给列表，进行修改，数据中有空值，和含有%号的值，进行处理，得到数值 data1 = [data[0], data[1], data[2], data[3], int(data[4]), data[5], data[6], data[7], data[8], data[9], data[10], data[11], data[12], data[13], data[14], data[15], data[16], data[17], data[18], data[19], data[20], data[21], data[22], data[23], data[24], data[25], data[26]] #将百分号去掉，只保留数值部分 if (data1[15] == ‘ ‘): data1[15] = 0 else: data1[15] = float("".join(re.findall(r‘(.*)%‘, data1[15]))) if (data1[9] == ‘ ‘): data1[9] = 0 else: data1[9] = float("".join(re.findall(r‘(.*)%‘, data1[9]))) if (data1[12] == ‘ ‘): data1[12] = 0 else: data1[12] = float("".join(re.findall(r‘(.*)%‘, data1[12]))) list0.append(data1[0]) list1.append(data1[1]) list2.append(data1[2]) list3.append(data1[3]) list4.append(data1[4]) list5.append(data1[5]) list6.append(data1[6]) list7.append(data1[7]) list8.append(data1[8]) list9.append(data1[9]) list10.append(data1[10]) list11.append(data1[11]) list12.append(data1[12]) list13.append(data1[13]) list14.append(data1[14]) list15.append(data1[15]) list16.append(data1[16]) list17.append(data1[17]) list18.append(data1[18]) list19.append(data1[19]) list20.append(data1[20]) list21.append(data1[21]) list22.append(data1[22]) list23.append(data1[23]) list24.append(data1[24]) list25.append(data1[25]) list26.append(data1[26]) # 记录数据数量 count += 1 #建立csv存储文件，wb写 a+追加模式 csvfile = file(‘nbadata.csv‘, ‘ab+‘) writer = csv.writer(csvfile) #将提取的数据合并 data2 = [] for i in range(0,count): data2.append((list0[i],list1[i],list2[i],list3[i],list4[i],list5[i],list6[i],list7[i],list8[i] ,list9[i],list10[i],list11[i],list12[i],list13[i],list14[i],list15[i],list16[i] ,list17[i],list18[i],list19[i],list20[i],list21[i],list22[i],list23[i],list24[i] , list25[i],list26[i])) #将合并的数据存入csv writer.writerows(data2) csvfile.close()
import csv #文件路径 srcFilePath = "c:/myflask/nbadata.csv" #读取cvs格式的数据文件 reader = csv.reader(file(srcFilePath,‘rb‘)) #csv中各列属性代表的含义（1）代表第一列 # 球员姓名(1)、赛季(2)、胜负(3)、对手球队名称(4)、对手球队总得分（5）、己方球队总得分（6） # 、己方球队名称（7）、首发（8）【1为首发，0为替补】、上场时间（9）、投篮命中率（10）、投篮命中数（11） # 、投篮出手数（12）、三分命中率（13）、三分命中数（14）、三分出手数（15）、罚球命中率（16） # 、罚球命中数（17）、罚球次数（18）、总篮板数（19）、前场篮板数（20）、后场篮板数（21）、助攻数（22） # 、抢断数（23）、盖帽数（24）、失误数（25）、犯规数（26）、得分（27） records = [line for line in reader] frame = DataFrame(records) #获取得分数对应的场次数目 pts_count = frame[26].value_counts() a = [] b = [] for i in pts_count.keys(): a.append(i) for i in pts_count: b.append(i) c = {} for i in range(0,len(a)): c[int(a[i])] = int(b[i]) d = sorted(c.items(), key=lambda c:c[0]) #存储得分分数 e = [] #存储相应分数的次数 f = [] for i in d: e.append(i[0]) f.append(i[1]) #15-16赛季球员得分助攻篮板抢断盖帽平均值 records_p1 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘03-04‘] records_p2 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘04-05‘] records_p3 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘05-06‘] records_p4 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘06-07‘] records_p5 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘07-08‘] records_p6 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘08-09‘] records_p7 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘09-10‘] records_p8 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘10-11‘] records_p9 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘11-12‘] records_p10 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘12-13‘] records_p11 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘13-14‘] records_p12 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘14-15‘] records_p13 = [(int(line[26]),int(line[21]),int(line[18]),int(line[22]),int(line[23])) for line in records if line[1] == ‘15-16‘] g1 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p1).mean()] g2 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p2).mean()] g3 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p3).mean()] g4 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p4).mean()] g5 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p5).mean()] g6 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p6).mean()] g7 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p7).mean()] g8 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p8).mean()] g9 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p9).mean()] g10 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p10).mean()] g11 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p11).mean()] g12 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p12).mean()] g13 = [float(‘%0.1f‘ % i) for i in DataFrame(records_p13).mean()] app = Flask(__name__) #引入bootstrap前端框架 bootstrap = Bootstrap(app) @app.route(‘/‘) def hello_world(): return render_template(‘index.html‘, a=e, b=f, c1=g1,c2=g2,c3=g3,c4=g4,c5=g5,c6=g6,c7=g7,c8=g8,c9=g9,c10=g10,c11=g11,c12=g12,c13=g13) if __name__ == ‘__main__‘: app.run(debug=True)
index.html {% extends "base.html" %} {% block title %}Flasky{% endblock %} {% block page_content %} <div class="page-header"> <h1>数据分析</h1> </div> <!-- 为ECharts准备一个具备大小（宽高）的Dom --> <div id="main" style="height:400px; width: auto"></div> <div id="main2" style="height:600px; width: auto; background-color: #333"> <div id="s1" style="height:600px; width: auto"> </div> </div> <!-- ECharts单文件引入 --> <script src=http://www.mamicode.com/"../static/echarts.js"></script> <!-- 主题文件引入 --> <script src=http://www.mamicode.com/"../static/dark.js"></script> <script type="text/javascript"> // 基于准备好的dom，初始化echarts图表 var myChart = echarts.init(document.getElementById(‘main‘)); var option = { title: { text: ‘得分次数图‘, subtext: ‘数据来源：www.stat-nba.com‘ }, tooltip: { trigger: ‘axis‘ }, legend: { data: [‘次数‘] }, calculable: true, xAxis: [ { type: ‘category‘, boundaryGap: false, axisLabel: { formatter: ‘{value}分‘, rotate: 45, }, data: {{ a }} } ], yAxis: [ { type: ‘value‘, axisLabel: { formatter: ‘{value} 次数‘ } } ], series: [ { name: ‘次数‘, type: ‘bar‘, data:{{ b }}, markPoint: { data: [ {type: ‘max‘, name: ‘最大次数‘}, {type: ‘min‘, name: ‘最小次数‘} ] } }, ] } // 为echarts对象加载数据 myChart.setOption(option); </script> <script type="text/javascript"> // 基于准备好的dom，初始化echarts图表 var myChart1 = echarts.init(document.getElementById(‘s1‘),‘dark‘); option = { legend: { data: [‘03-04赛季‘, ‘04-05赛季‘, ‘05-06赛季‘, ‘06-07赛季‘, ‘07-08赛季‘ , ‘08-09赛季‘, ‘09-10赛季‘, ‘10-11赛季‘, ‘11-12赛季‘, ‘12-13赛季‘, ‘13-14赛季‘ , ‘14-15赛季‘, ‘15-16赛季‘], textStyle:{ fontSize:8, } }, radar: [ //03-04赛季 { indicator: [ {name: ‘得分‘, max: 28.0}, {name: ‘助攻‘, max: 9.2}, {name: ‘篮板‘, max: 13.9}, {name: ‘抢断‘, max: 2.4}, {name: ‘盖帽‘, max: 3.6} ], center: [‘10%‘, ‘25%‘], radius: 80, name:{ textStyle: { color:‘#67d15d‘, fontSize: 6 } } }, //04-05赛季 { indicator: [ {name: ‘得分‘, max: 30.7}, {name: ‘助攻‘, max: 11.5}, {name: ‘篮板‘, max: 13.5}, {name: ‘抢断‘, max: 2.9}, {name: ‘盖帽‘, max: 3.0} ], center: [‘30%‘, ‘25%‘], radius: 80, name:{ textStyle: { color:‘#d1c373‘, fontSize: 6 } } }, //05-06赛季 { indicator: [ {name: ‘得分‘, max: 35.4}, {name: ‘助攻‘, max: 10.5}, {name: ‘篮板‘, max: 12.7}, {name: ‘抢断‘, max: 2.3}, {name: ‘盖帽‘, max: 3.2} ], center: [‘50%‘, ‘25%‘], radius: 80, name:{ textStyle: { color:‘#d16a62‘, fontSize: 6 } } }, //06-07赛季 { indicator: [ {name: ‘得分‘, max: 31.6}, {name: ‘助攻‘, max: 11.6}, {name: ‘篮板‘, max: 12.8}, {name: ‘抢断‘, max: 2.1}, {name: ‘盖帽‘, max: 3.3} ], center: [‘70%‘, ‘25%‘], radius: 80, name:{ textStyle: { color:‘#d170b6‘, fontSize: 6 } } }, //07-08赛季 { indicator: [ {name: ‘得分‘, max: 30.0}, {name: ‘助攻‘, max: 11.6}, {name: ‘篮板‘, max: 14.2}, {name: ‘抢断‘, max: 2.7}, {name: ‘盖帽‘, max: 3.6} ], center: [‘90%‘, ‘25%‘], radius: 80, name:{ textStyle: { color:‘#8f45d1‘, fontSize: 6 } } }, //08-09赛季 { indicator: [ {name: ‘得分‘, max: 30.2}, {name: ‘助攻‘, max: 11.0}, {name: ‘篮板‘, max: 13.8}, {name: ‘抢断‘, max: 2.8}, {name: ‘盖帽‘, max: 2.9} ], center: [‘10%‘, ‘55%‘], radius: 80, name:{ textStyle: { color:‘#4048d1‘, fontSize: 6 } } }, //09-10赛季 { indicator: [ {name: ‘得分‘, max: 30.1}, {name: ‘助攻‘, max: 11.0}, {name: ‘篮板‘, max: 13.2}, {name: ‘抢断‘, max: 2.3}, {name: ‘盖帽‘, max: 2.8} ], center: [‘30%‘, ‘55%‘], radius: 80, name:{ textStyle: { color:‘#d11872‘, fontSize: 6 } } }, //10-11赛季 { indicator: [ {name: ‘得分‘, max: 27.7}, {name: ‘助攻‘, max: 11.4}, {name: ‘篮板‘, max: 15.2}, {name: ‘抢断‘, max: 2.4}, {name: ‘盖帽‘, max: 2.6} ], center: [‘50%‘, ‘55%‘], radius: 80, name:{ textStyle: { color:‘#d1c80e‘, fontSize: 6 } } }, //11-12赛季 { indicator: [ {name: ‘得分‘, max: 28.0}, {name: ‘助攻‘, max: 11.7}, {name: ‘篮板‘, max: 14.5}, {name: ‘抢断‘, max: 2.5}, {name: ‘盖帽‘, max: 3.7} ], center: [‘70%‘, ‘55%‘], radius: 80, name:{ textStyle: { color:‘#09e8ac‘, fontSize: 6 } } }, //12-13赛季 { indicator: [ {name: ‘得分‘, max: 28.7}, {name: ‘助攻‘, max: 9.7}, {name: ‘篮板‘, max: 12.4}, {name: ‘抢断‘, max: 2.4}, {name: ‘盖帽‘, max: 3.0} ], center: [‘90%‘, ‘55%‘], radius: 80, name:{ textStyle: { color:‘#9c8eca‘, fontSize: 6 } } }, //13-14赛季 { indicator: [ {name: ‘得分‘, max: 32.0}, {name: ‘助攻‘, max: 10.7}, {name: ‘篮板‘, max: 13.7}, {name: ‘抢断‘, max: 2.5}, {name: ‘盖帽‘, max: 2.8} ], center: [‘10%‘, ‘85%‘], radius: 80, name:{ textStyle: { color:‘#a6fdaa‘, fontSize: 6 } } }, //14-15赛季 { indicator: [ {name: ‘得分‘, max: 28.1}, {name: ‘助攻‘, max: 10.2}, {name: ‘篮板‘, max: 15.0}, {name: ‘抢断‘, max: 2.3}, {name: ‘盖帽‘, max: 2.9} ], center: [‘30%‘, ‘85%‘], radius: 80, name:{ textStyle: { color:‘#faa60d‘, fontSize: 6 } } }, //15-16赛季 { indicator: [ {name: ‘得分‘, max: 30.1}, {name: ‘助攻‘, max: 11.7}, {name: ‘篮板‘, max: 14.8}, {name: ‘抢断‘, max: 2.1}, {name: ‘盖帽‘, max: 3.7} ], center: [‘50%‘, ‘85%‘], radius: 80, name:{ textStyle: { color:‘#72ACD1‘, fontSize: 6 } } } ], series: [ //03-04赛季 { name: ‘03-04赛季‘, type: ‘radar‘, radarIndex: 0, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c1 }}, name : ‘03-04赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //04-05 { name: ‘04-05赛季‘, type: ‘radar‘, radarIndex: 1, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c2 }}, name : ‘04-05赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //05-06 { name: ‘05-06赛季‘, type: ‘radar‘, radarIndex: 2, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c3 }}, name : ‘05-06赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //06-07 { name: ‘06-07赛季‘, type: ‘radar‘, radarIndex: 3, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c4 }}, name : ‘06-07赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //07-08 { name: ‘07-08赛季‘, type: ‘radar‘, radarIndex: 4, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c5 }}, name : ‘07-08赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //08-09 { name: ‘08-09赛季‘, type: ‘radar‘, radarIndex: 5, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c6 }}, name : ‘08-09赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //09-10 { name: ‘09-10赛季‘, type: ‘radar‘, radarIndex: 6, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c7 }}, name : ‘09-10赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //10-11 { name: ‘10-11赛季‘, type: ‘radar‘, radarIndex: 7, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c8 }}, name : ‘10-11赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //11-12 { name: ‘11-12赛季‘, type: ‘radar‘, radarIndex: 8, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c9 }}, name : ‘11-12赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //12-13 { name: ‘12-13赛季‘, type: ‘radar‘, radarIndex: 9, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c10 }}, name : ‘12-13赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //13-14 { name: ‘13-14赛季‘, type: ‘radar‘, radarIndex: 10, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c11 }}, name : ‘13-14赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //14-15 { name: ‘14-15赛季‘, type: ‘radar‘, radarIndex: 11, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c12 }}, name : ‘14-15赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, //15-16 { name: ‘15-16赛季‘, type: ‘radar‘, radarIndex: 12, textStyle:{ color:‘#fff‘ }, data : [ { value : {{ c13 }}, name : ‘15-16赛季‘, label: { normal: { show: true, textStyle:{ color:"#fff", fontSize:8 } } }, areaStyle: { normal: { color: ‘rgba(100, 100, 255, 0.5)‘, } }, } ] }, ] }; // 为echarts对象加载数据 myChart1.setOption(option); </script>
{% endblock %}
根据nba数据预测17-18总冠军（转）
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