1 #提取数据
2 import requests
3 from bs4 import BeautifulSoup
4 import json
5 #
6 url='https://china.nba.cn/stats2/league/playerstats.json?conference=All&country=All&individual=All&locale=zh_CN&pageIndex=0&position=All&qualified=false&season=2021&seasonType=2&split=All+Team&statType=points&team=All&total=perGame'
7 #
8 def getHTMLText(url,timeout=30):
9 try:
10 r=requests.get(url,timeout=30) #
11 r.raise_for_status()
12 r.encoding=r.apparent_encoding
13 return r.text
14 except:
15 return'产生异常'
16 爵士赛事推荐
17 #html.parser表示用BeautifulSoup库解析网页
18 html=getHTMLText(url)
19 soup=BeautifulSoup(html,'html.parser')
20 print(soup.prettify())
21
22 #创建空表
23 pointsPg_list=[]
24 assistsPg_list=[]
25 rebsPg_list=[]
26 name_list=[]
27 data1=[]
28 tppct=[]
29 ftpct=[]
30 fgpct=[]
31 stealsPg_list=[]
32 blocksPg_list=[]
33 offRebsPg_list=[]
34 defRebsPg_list=[]
35 rank=[]
36 html=getHTMLText(url)
37 data=json.loads(html)
38 a=data['payload']['players']
39 data1.append(name_list)
40 data1.append(assistsPg_list)
41 b=1
42 for i in a:
43 rank.append(b)
44 name_list.append(i['playerProfile']['displayName'])
45 pointsPg_list.append(i['statAverage'][ 'pointsPg'])
46 rebsPg_list.append(i['statAverage'][ 'rebsPg'])
47 assistsPg_list.append(i['statAverage'][ 'assistsPg'])
48 stealsPg_list.append(i['statAverage'][ 'stealsPg'])
49 blocksPg_list.append(i['statAverage'][ 'blocksPg'])
50 offRebsPg_list.append(i['statAverage'][ 'offRebsPg'])
51 defRebsPg_list.append(i['statAverage'][ 'defRebsPg'])
52 tppct.append(i['statAverage']['tppct'])
53 ftpct.append(i['statAverage']['ftpct'])
54 fgpct.append(i['statAverage']['fgpct'])
55 b=b+1
56 list_1=['排名']
57
58 #导出球员的各项数据
59 import pandas as pd
60 df=pd.DataFrame(columns=list_1)
61 df['排名']=rank
62 df['NAME']=name_list
63 df['场均得分']=pointsPg_list
64 df['场均篮板']=rebsPg_list
65 df['场均助攻']=assistsPg_list
66 df['投篮命中率']=fgpct
67 df['三分命中率']=tppct
68 df['罚球命中率']=ftpct
69 df['进攻效率']=offRebsPg_list
70 df['防守效率']=defRebsPg_list
71 df['场均抢断']=stealsPg_list
72 df['场均盖帽']=blocksPg_list
73 df
74
75 #将dataframe写入csv
76 df.to_csv('D:/Python/NBA数据.csv',index=False)
77 df.to_csv('D:/Python/NBA.csv',index=False)
78
79 #检查并显示重复值
80 print(df.duplicated())
81
82 #删除重复值
83 df = df.drop_duplicates()
84 df.head()
85
86 #异常值处理
87 df.describe()
88
89 #检查是否有空值
90 print(df['排名'].isnull().value_counts())
91
92 #查看统计信息
93 print(df.describe())
94 df
95
96 #求取回归系数
97 from sklearn.linear_model import LinearRegression
98 X=df.drop('NAME',axis=1)
99 predict_model=LinearRegression()
100 predict_model.fit(X,df['排名'])
101 print('回归系数为：',predict_model.coef_)
102
103 #绘制回归图
104 import seaborn as sns
105 import matplotlib.pyplot as plt
106 plt.rcParams['font.sans-serif']=['SimHei']#用来正常显示中文标签
107 X=df.drop('NAME',axis=1)
108 sns.regplot(df['排名'],df['进攻效率'])
109 sns.regplot(df['排名'],df['防守效率'])
110 plt.title('排名与进攻、防守效率图')
111
112 #绘制柱状图
113 import pandas as pd
114 import numpy as np
115 import matplotlib.pyplot as plt
116 plt.rcParams['font.sans-serif']=['SimHei']
117 plt.bar(df.排名, df.投篮命中率, color='b')
118 plt.xlabel("排名")
119 plt.ylabel("投篮命中率")
120 plt.title('排名与投篮命中率柱状图')
121 plt.show()
122
123 #绘制散点图
124 import pandas as pd
125 import numpy as np
126 import matplotlib.pyplot as plt
127 plt.rcParams['font.sans-serif']=['SimHei']
128 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
129 size=30
130 plt.scatter(df.排名, df.场均得分,size, color='b',alpha=0.6,marker='o')
131 plt.xlabel("排名")
132 plt.ylabel("场均得分")
133 plt.title('排名与场均得分柱状图')
134 plt.show()
135
136 #罚球命中率与排名
137 import pandas as pd
138 import numpy as np
139 import matplotlib.pyplot as plt
140 plt.rcParams['font.sans-serif']=['SimHei']
141 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
142 plt.stackplot(df.排名, df.罚球命中率, color=['b',])
143 plt.xlabel("排名")
144 plt.ylabel("罚球命中率")
145 plt.title('排名与罚球命中率堆叠图')
146 plt.show()
147
148 #绘制折线图
149 import pandas as pd
150 import numpy as np
151 import matplotlib.pyplot as plt
152 plt.rcParams['font.sans-serif']=['SimHei']
153 plt.rcParams['axes.unicode_minus'] = False
154 plt.plot(df.排名, df.三分命中率, color='b')
155 plt.xlabel("排名")
156 plt.ylabel("三分命中率")
157 plt.title('排名与三分命中率折线图')
158 plt.show()
159
160 #绘制拟合曲线
161 import matplotlib.pyplot as plt
162 import matplotlib
163 import numpy as np
164 import scipy.optimize as opt
165 import csv
166 x0=df['进攻效率']
167 y0=df['场均得分']
168 def func(x,c):
169 k,a=c
170 return k*x+a
171 def errfc(c,x,y):
172 return y-func(x,c)
173 c0=(100,20)
174 #调用拟合曲线
175 print(opt.leastsq(errfc,c0,args=(x0,y0)))
176 #s设置画布
177 chinese=matplotlib.font_manager.FontProperties(fname='C:WindowsFontssimsun.ttc')
178
179 plt.plot(x0,y0,"o",label=u"进攻效率")
180
181 plt.plot(x0,func(x0,opt.leastsq(errfc,c0,args=(x0,y0))[0]),label=u"场均得分")
182 plt.title('进攻效率与场均得分拟合曲线图')
183 plt.legend(loc=3,prop=chinese)
184
185 plt.show()
186
187 #绘制拟合曲线
188 import matplotlib.pyplot as plt
189 import matplotlib
190 import numpy as np
191 import scipy.optimize as opt
192 import csv
193 x0=df['防守效率']
194 y0=df['场均抢断']
195 #
196 def func(x,c):
197 k,a=c
198 return k*x+a
199 def errfc(c,x,y):
200 return y-func(x,c)
201 c0=(100,20)
202 #调用拟合曲线
203 print(opt.leastsq(errfc,c0,args=(x0,y0)))
204 #s设置画布
205 chinese=matplotlib.font_manager.FontProperties(fname='C:WindowsFontssimsun.ttc')
206
207 plt.plot(x0,y0,"o",label=u"防守效率")
208
209 plt.plot(x0,func(x0,opt.leastsq(errfc,c0,args=(x0,y0))[0]),label=u"场均抢断")
210 plt.title('防守效率与场均抢断拟合曲线图')
211 plt.legend(loc=3,prop=chinese)
212
213 plt.show()
214
215 #数据持久化
216 df = pd.DataFrame(df,columns=['排名','NAME','场均得分','场均篮板','场均助攻','投篮命中率','罚球命中率','三分命中率','进攻效率','防守效率','场均抢断','场均盖帽'])
217 df.to_csv('NBA.csv',encoding = 'gbk') #保存文件，数据持久化
