一、选题背景：
中超联赛作为中国顶级足球赛事，吸引了广泛的关注，其球员数据包含了丰富的信息，涵盖球员技术、表现和比赛策略等方面。随着数据科学技术的不断发展，对于足球俱乐部和教练来说，充分利用这些数据进行分析和挖掘，以制定更有效的战术和管理策略变得愈发重要。选题背景重点：1. 数据驱动的足球管理：中超俱乐部和教练需要通过深度分析球员数据来了解球员表现、评估战术，以及预测比赛结果，从而制定更有效的管理和竞技策略。2. 决策支持和智能化分析：利用大数据分析、机器学习和统计建模等技术，为决策者提供智能化的分析工具，辅助他们做出更准确的战术和球员管理决策。3. 培养数据科学与足球运动结合的跨学科能力：通过这门课程，学生将能够学习和应用数据科学技术，结合足球运动领域的实际问题进行数据分析和解决方案设计。4. 促进足球运动的科技创新：通过分析球员数据，发掘潜在的技术创新机会，为球队带来竞争优势和创新性的解决方案。这个选题背景将侧重于中超联赛球员数据的分析，强调数据科学在提升足球运动管理、战术决策和推动创新方面的作用。
二、目标实现设计方案：
1.数据获取：
数据来源：从官方网站、API或其他数据提供商获取中超联赛球员数据。数据类型：球员基本信息、比赛统计数据（进球数、助攻数、传球成功率等）、位置信息等。数据格式：采用 JSON、CSV 或其他常用数据格式。2. 数据处理与清洗：数据清洗：处理缺失值、重复值和异常值。数据整合：整合多个数据源，确保数据格式一致性。特征工程：构建新特征、转换数据类型，以支持后续的分析需求。3. 数据分析：基本统计分析：球员得分、助攻、传球成功率等基本指标的统计。比较分析：不同球队、位置或赛季的数据对比分析。预测分析：使用机器学习或统计模型预测球员未来表现或比赛结果。4.结果呈现与报告：数据解释与结论：解释分析结果，提出洞察和结论。数据分析报告：要求学生提交数据分析报告或进行展示，以呈现他们的分析发现和建议。三、主题页面的结构特征分析：
四、网络爬虫程序设计：
导入所需要的库，并验证获取信息的网站是否可靠
1 import urllib
2 import csv
3 from bs4 import BeautifulSoup
4 from lxml import etree
5
6 # 检查是否存在球员
7 def checkHtml(num):
8 url = "https://www.dongqiudi.com/player/%s.html" % num
9 html = askURL(url)
10 soup = BeautifulSoup(html, "html.parser")
11 name = soup.find('p', attrs={'class': 'china-name'})
12 if (name == None):
13 print('无效网站')
14 return 'none'
15 else:
16 return soup
17
18 # 获取数据，并储存
19 def getData(soup):
20 # url = "https://www.dongqiudi.com/player/%s.html" % num
21 # html = askURL(url)
22 # soup = BeautifulSoup(html, "html.parser")
获取球员详细信息
1 # 姓名
2 name = soup.find('p', attrs={'class': 'china-name'})
3 name = str(name)
4 con = etree.HTML(name)
5 namestr = con.xpath("//p/text()")
6 name = namestr[0]
7 print(name)
8
9
10 # 获取详细信息list
11 detail_list = []
12 detail_info_div = soup.find('div',attrs={'class': 'detail-info'})
13 # con2 = etree.HTML(detail_info_div)
14 detail_info_ul = detail_info_div.find_all('li')
15 for each in detail_info_ul:
16 detail = each.text.strip()
17 detail_list.append(detail)
18 # print(detail_list)
19
20 # 俱乐部
21 club = str(detail_list[0]).replace('俱乐部：' ,'')
22 # print('俱乐部', club)
23 # 国籍
24 contry = str(detail_list[1]).replace('国 籍：' ,'')
25 # print('国籍', contry)
26 # 身高
27 height = 0
28 heightstr = str(detail_list[2]).replace('CM', '')
29 heightstr = heightstr.replace('身 高：', '')
30 if heightstr != '':
31 height = int(heightstr)
32 # print('身高', height)
33 # 位置
34 location = str(detail_list[3]).replace('位 置：', '')
35 # print('位置', location)
36 # 年龄
37 age = 0
38 agestr = str(detail_list[4]).replace('年 龄：', '')
39 agestr = agestr.replace('岁', '')
40 if agestr != '':
41 age = int(agestr)
42 # print('年龄', age)
43 # 体重
44 weight = 0
45 weightstr = str(detail_list[5]).replace('体 重：', '')
46 weightstr = weightstr.replace('KG', '')
47 if weightstr != '':
48 weight = weightstr
49 # print('体重', weight)
50 # 号码
51 number = 0
52 numberstr = str(detail_list[6]).replace('号 码：', '')
53 numberstr = numberstr.replace('号', '')
54 if numberstr != '':
55 number = int(numberstr)
56 # print('号码', number)
57 # 生日
58 birth = str(detail_list[7]).replace('生 日：', '')
59 # print(birth)
60 # 惯用脚
61 foot = str(detail_list[8]).replace('惯用脚：', '')
62 # print(foot)
63
64 # 获取俱乐部比赛数据详细信息list
65 total_con_wrap_div = soup.find('div', attrs={'class': 'total-con-wrap'})
66 total_con_wrap_td = str(total_con_wrap_div.find_all('p', attrs={'class': 'td'}))
67 con3 = etree.HTML(total_con_wrap_td)
68 detail_info_list = con3.xpath("//p//span/text()")
69 detail_info_list_years = con3.xpath("//p")
70
71 # 一线队时间（年）
72 years = len(detail_info_list_years) - 1
73 # print('一线队时长', len(detail_info_list_years) - 1)
74
75 # 总计上场次数
76 total_session = 0
77 for i in range(2, len(detail_info_list), 9):
78 if detail_info_list[i] == '~':
79 detail_info_list[i] = 0
80 total_session = total_session+int(detail_info_list[i])
81 # print('累计出场数', total_session)
82
83 # 总计进球数
84 total_goals = 0
85 for i in range(4, len(detail_info_list), 9):
86 if detail_info_list[i] == '~':
87 detail_info_list[i] = 0
88 total_goals = total_goals + int(detail_info_list[i])
89 # print('累计进球数', total_goals)
90
91 # 总计助攻数
92 total_assist = 0
93 for i in range(5, len(detail_info_list), 9):
94 if detail_info_list[i] == '~':
95 detail_info_list[i] = 0
96 total_assist = total_assist + int(detail_info_list[i])
97 # print('累计助攻数', total_assist)
98
99 # 总计黄牌数
100 total_yellow_card = 0
101 for i in range(6, len(detail_info_list), 9):
102 if detail_info_list[i] == '~':
103 detail_info_list[i] = 0
104 total_yellow_card = total_yellow_card + int(detail_info_list[i])
105 # print('累计黄牌数', total_yellow_card)
106
107 # 总计红牌数
108 total_red_card = 0
109 for i in range(7, len(detail_info_list), 9):
110 if detail_info_list[i] == '~':
111 detail_info_list[i] = 0
112 total_red_card = total_red_card + int(detail_info_list[i])
113 # print('累计红牌数', total_red_card)
114
115
116 # 获取总评分
117 average = 0
118 speed = 0
119 power = 0
120 guard = 0
121 dribbling = 0
122 passing = 0
123 shooting = 0
124 grade_average = soup.find('p', attrs={'class': 'average'})
125 if grade_average != None:
126 con4 = etree.HTML(str(grade_average))
127 average = con4.xpath("//p//b/text()")
128 average = int(average[0])
129 # print('综合能力', average)
130 # 详细评分
131 grade_detail_div = soup.find('div', attrs={'class': 'box_chart'})
132 if grade_detail_div != None:
133 con5 = etree.HTML(str(grade_detail_div))
134 grade_detail = con5.xpath("//div//span/text()")
135 # 速度
136 speed = int(grade_detail[0])
137 # print(speed)
138 # 力量
139 power = int(grade_detail[1])
140 # print(power)
141 # 防守
142 guard = int(grade_detail[2])
143 # print(guard)
144 # 盘带
145 dribbling = int(grade_detail[3])
146 # print(dribbling)
147 # 传球
148 passing = int(grade_detail[4])
149 # print(passing)
150 # 射门
151 shooting = int(grade_detail[5])
152 # print(shooting)
将获取到的信息写入文件
1 csv.writer(f).writerow([name, club, contry, height, location, age, weight, number, birth, foot, years, total_session,
2 total_goals, total_assist, total_yellow_card, total_red_card, average, speed, power,
3 guard, dribbling, passing, shooting])
得到指定一个URL的网页内容
1 def askURL(url):
2 head = { # 模拟浏览器头部信息，向豆瓣服务器发送消息
3 "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36 Edg/96.0.1054.29"
4 }
5 # 用户代理，表示告诉豆瓣服务器，我们是什么类型的机器、浏览器（本质上是告诉浏览器，我们可以接收什么水平的文件内容）
6
7 request = urllib.request.Request(url, headers=head)
8 html = ""
9 try:
10 response = urllib.request.urlopen(request)
11 html = response.read().decode("utf-8")
12 except urllib.error.URLError as e:
13 if hasattr(e, "code"):
14 print(e.code)
15 if hasattr(e, "reason"):
16 print(e.reason)
17 return html
将数据写入csv文件
1 f = open("足球运动员.csv", mode="a", encoding='utf-8')
2 # csv.writer(f).writerow(["姓名","俱乐部","国籍","身高(CM)","位置","年龄(岁)","体重(KG)","号码","生日","惯用脚","职业生涯(年)",
3 # "累计出场","累计进球","累计助攻","累计黄牌","累计红牌","综合能力","速度","力量","防守","盘带","传球","射门"])
4 for num in range(50184113, 50184150):
5 print(num)
6 soup = checkHtml(num)
7 if soup != 'none':
8 getData(soup)
9 # getData(num)
结果截图
在获取我所需要的数据后，制作一个中超球员的年龄散点图
1 import csv
2 import matplotlib.pyplot as plt
3 import matplotlib
4
5 # 设置中文字体，确保中文显示正常
6 matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # 设置中文字体为黑体
7 matplotlib.rcParams['axes.unicode_minus'] = False # 解决坐标轴负号'-'显示问题
8
9 # 读取CSV文件并提取年龄数据
10 ages = []
11 with open('足球运动员.csv', mode='r', encoding='utf-8') as csv_file:
12 csv_reader = csv.reader(csv_file)
13 next(csv_reader) # 跳过标题行
14 for row in csv_reader:
15 age = int(row[5]) # 年龄在CSV文件的第6列（索引为5）
16 ages.append(age)
17
18 # 创建散点图
19 plt.figure(figsize=(8, 6))
20 plt.scatter(range(1, len(ages) + 1), ages, color='blue', alpha=0.5)
21 plt.title('年龄散点图')
22 plt.xlabel('球员编号')
23 plt.ylabel('年龄')
24 plt.grid(True)
25 plt.show()
我们都知道中国足球很大程度上依赖归化球员，所以一支球队往往有来自多个国家，不同国籍的球员，在知道了他们年龄的分布状态后，我还需要知道他们的国籍分布状态
1 import csv
2 import matplotlib.pyplot as plt
3 import matplotlib
4
5 # 设置中文字体，确保中文显示正常
6 matplotlib.rcParams['font.sans-serif'] = ['SimHei']
7 matplotlib.rcParams['axes.unicode_minus'] = False
8
9 # 读取CSV文件并统计各个国籍的球员数量
10 nationalities = {}
11 with open('足球运动员.csv', mode='r', encoding='utf-8') as csv_file:
12 csv_reader = csv.reader(csv_file)
13 next(csv_reader) # 跳过标题行
14 for row in csv_reader:
15 nationality = row[2] # 国籍在CSV文件的第3列（索引为2）
16 if nationality in nationalities:
17 nationalities[nationality] += 1
18 else:
19 nationalities[nationality] = 1
20
21 # 获取国籍和对应的球员数量
22 countries = list(nationalities.keys())
23 player_counts = list(nationalities.values())
24
25 # 创建散点图
26 plt.figure(figsize=(10, 6))
27 plt.scatter(countries, player_counts, color='red', alpha=0.7)
28 plt.title('球员各国籍散点图')
29 plt.xlabel('国籍')
30 plt.ylabel('球员数量')
31 plt.xticks(rotation=45) # 旋转x轴标签，防止重叠
32 plt.grid(True)
33 plt.tight_layout()
34 plt.show()
绘制球员的身高柱状图
1 import csv
2 import matplotlib.pyplot as plt
3
4 # 读取CSV文件并统计不同身高范围内的球员数量
5 height_ranges = {'150-160': 0, '161-170': 0, '171-180': 0, '181-190': 0, '191-200': 0, '200以上': 0}
6 with open('足球运动员.csv', mode='r', encoding='utf-8') as csv_file:
7 csv_reader = csv.reader(csv_file)
8 next(csv_reader) # 跳过标题行
9 for row in csv_reader:
10 height = int(row[3]) # 身高在CSV文件的第4列（索引为3）
11 if 150 <= height <= 160:
12 height_ranges['150-160'] += 1
13 elif 161 <= height <= 170:
14 height_ranges['161-170'] += 1
15 elif 171 <= height <= 180:
16 height_ranges['171-180'] += 1
17 elif 181 <= height <= 190:
18 height_ranges['181-190'] += 1
19 elif 191 <= height <= 200:
20 height_ranges['191-200'] += 1
21 else:
22 height_ranges['200以上'] += 1
23
24 # 获取身高范围和对应的球员数量
25 height_labels = list(height_ranges.keys())
26 player_counts = list(height_ranges.values())
27
28 # 创建柱状图
29 plt.figure(figsize=(10, 6))
30 plt.bar(height_labels, player_counts, color='blue')
31 plt.title('球员身高柱状图')
32 plt.xlabel('身高范围')
33 plt.ylabel('球员数量')
34 plt.xticks(rotation=45) # 旋转x轴标签，防止重叠
35 plt.tight_layout()
36 plt.show()
五、总代码：
1 import urllib
2 import csv
3 from bs4 import BeautifulSoup
4 from lxml import etree
5
6 # 检查是否存在球员
7 def checkHtml(num):
8 url = "https://www.dongqiudi.com/player/%s.html" % num
9 html = askURL(url)
10 soup = BeautifulSoup(html, "html.parser")
11 name = soup.find('p', attrs={'class': 'china-name'})
12 if (name == None):
13 print('无效网站')
14 return 'none'
15 else:
16 return soup
17
18 # 获取数据，并储存
19 def getData(soup):
20 # url = "https://www.dongqiudi.com/player/%s.html" % num
21 # html = askURL(url)
22 # soup = BeautifulSoup(html, "html.parser")
23
24 # 姓名
25 name = soup.find('p', attrs={'class': 'china-name'})
26 name = str(name)
27 con = etree.HTML(name)
28 namestr = con.xpath("//p/text()")
29 name = namestr[0]
30 print(name)
31
32
33 # 获取详细信息list
34 detail_list = []
35 detail_info_div = soup.find('div',attrs={'class': 'detail-info'})
36 # con2 = etree.HTML(detail_info_div)
37 detail_info_ul = detail_info_div.find_all('li')
38 for each in detail_info_ul:
39 detail = each.text.strip()
40 detail_list.append(detail)
41 # print(detail_list)
42
43 # 俱乐部
44 club = str(detail_list[0]).replace('俱乐部：' ,'')
45 # print('俱乐部', club)
46 # 国籍
47 contry = str(detail_list[1]).replace('国 籍：' ,'')
48 # print('国籍', contry)
49 # 身高
50 height = 0
51 heightstr = str(detail_list[2]).replace('CM', '')
52 heightstr = heightstr.replace('身 高：', '')
53 if heightstr != '':
54 height = int(heightstr)
55 # print('身高', height)
56 # 位置
57 location = str(detail_list[3]).replace('位 置：', '')
58 # print('位置', location)
59 # 年龄
60 age = 0
61 agestr = str(detail_list[4]).replace('年 龄：', '')
62 agestr = agestr.replace('岁', '')
63 if agestr != '':
64 age = int(agestr)
65 # print('年龄', age)
66 # 体重
67 weight = 0
68 weightstr = str(detail_list[5]).replace('体 重：', '')
69 weightstr = weightstr.replace('KG', '')
70 if weightstr != '':
71 weight = weightstr
72 # print('体重', weight)
73 # 号码
74 number = 0
75 numberstr = str(detail_list[6]).replace('号 码：', '')
76 numberstr = numberstr.replace('号', '')
77 if numberstr != '':
78 number = int(numberstr)
79 # print('号码', number)
80 # 生日
81 birth = str(detail_list[7]).replace('生 日：', '')
82 # print(birth)
83 # 惯用脚
84 foot = str(detail_list[8]).replace('惯用脚：', '')
85 # print(foot)
86
87 # 获取俱乐部比赛数据详细信息list
88 total_con_wrap_div = soup.find('div', attrs={'class': 'total-con-wrap'})
89 total_con_wrap_td = str(total_con_wrap_div.find_all('p', attrs={'class': 'td'}))
90 con3 = etree.HTML(total_con_wrap_td)
91 detail_info_list = con3.xpath("//p//span/text()")
92 detail_info_list_years = con3.xpath("//p")
93
94 # 一线队时间（年）
95 years = len(detail_info_list_years) - 1
96 # print('一线队时长', len(detail_info_list_years) - 1)
97
98 # 总计上场次数
99 total_session = 0
100 for i in range(2, len(detail_info_list), 9):
101 if detail_info_list[i] == '~':
102 detail_info_list[i] = 0
103 total_session = total_session+int(detail_info_list[i])
104 # print('累计出场数', total_session)
105
106 # 总计进球数
107 total_goals = 0
108 for i in range(4, len(detail_info_list), 9):
109 if detail_info_list[i] == '~':
110 detail_info_list[i] = 0
111 total_goals = total_goals + int(detail_info_list[i])
112 # print('累计进球数', total_goals)
113
114 # 总计助攻数
115 total_assist = 0
116 for i in range(5, len(detail_info_list), 9):
117 if detail_info_list[i] == '~':
118 detail_info_list[i] = 0
119 total_assist = total_assist + int(detail_info_list[i])
120 # print('累计助攻数', total_assist)
121
122 # 总计黄牌数
123 total_yellow_card = 0
124 for i in range(6, len(detail_info_list), 9):
125 if detail_info_list[i] == '~':
126 detail_info_list[i] = 0
127 total_yellow_card = total_yellow_card + int(detail_info_list[i])
128 # print('累计黄牌数', total_yellow_card)
129
130 # 总计红牌数
131 total_red_card = 0
132 for i in range(7, len(detail_info_list), 9):
133 if detail_info_list[i] == '~':
134 detail_info_list[i] = 0
135 total_red_card = total_red_card + int(detail_info_list[i])
136 # print('累计红牌数', total_red_card)
137
138
139 # 获取总评分
140 average = 0
141 speed = 0
142 power = 0
143 guard = 0
144 dribbling = 0
145 passing = 0
146 shooting = 0
147 grade_average = soup.find('p', attrs={'class': 'average'})
148 if grade_average != None:
149 con4 = etree.HTML(str(grade_average))
150 average = con4.xpath("//p//b/text()")
151 average = int(average[0])
152 # print('综合能力', average)
153 # 详细评分
154 grade_detail_div = soup.find('div', attrs={'class': 'box_chart'})
155 if grade_detail_div != None:
156 con5 = etree.HTML(str(grade_detail_div))
157 grade_detail = con5.xpath("//div//span/text()")
158 # 速度
159 speed = int(grade_detail[0])
160 # print(speed)
161 # 力量
162 power = int(grade_detail[1])
163 # print(power)
164 # 防守
165 guard = int(grade_detail[2])
166 # print(guard)
167 # 盘带
168 dribbling = int(grade_detail[3])
169 # print(dribbling)
170 # 传球
171 passing = int(grade_detail[4])
172 # print(passing)
173 # 射门
174 shooting = int(grade_detail[5])
175 # print(shooting)
176
177
178 # 写进文件
179
180 csv.writer(f).writerow([name, club, contry, height, location, age, weight, number, birth, foot, years, total_session,
181 total_goals, total_assist, total_yellow_card, total_red_card, average, speed, power,
182 guard, dribbling, passing, shooting])
183
184 # 得到指定一个URL的网页内容
185 def askURL(url):
186 head = { # 模拟浏览器头部信息，向豆瓣服务器发送消息
187 "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36 Edg/96.0.1054.29"
188 }
189 # 用户代理，表示告诉豆瓣服务器，我们是什么类型的机器、浏览器（本质上是告诉浏览器，我们可以接收什么水平的文件内容）
190
191 request = urllib.request.Request(url, headers=head)
192 html = ""
193 try:
194 response = urllib.request.urlopen(request)
195 html = response.read().decode("utf-8")
196 except urllib.error.URLError as e:
197 if hasattr(e, "code"):
198 print(e.code)
199 if hasattr(e, "reason"):
200 print(e.reason)
201 return html
202
203
204
205 f = open("足球运动员.csv", mode="a", encoding='utf-8')
206 # csv.writer(f).writerow(["姓名","俱乐部","国籍","身高(CM)","位置","年龄(岁)","体重(KG)","号码","生日","惯用脚","职业生涯(年)",
207 # "累计出场","累计进球","累计助攻","累计黄牌","累计红牌","综合能力","速度","力量","防守","盘带","传球","射门"])
208 for num in range(50184113, 50184150):
209 print(num)
210 soup = checkHtml(num)
211 if soup != 'none':
212 getData(soup)
213 # getData(num)
214
215
216
217
218 import csv
219 import matplotlib.pyplot as plt
220 import matplotlib
221
222 # 设置中文字体，确保中文显示正常
223 matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # 设置中文字体为黑体
224 matplotlib.rcParams['axes.unicode_minus'] = False # 解决坐标轴负号'-'显示问题
225
226 # 读取CSV文件并提取年龄数据
227 ages = []
228 with open('足球运动员.csv', mode='r', encoding='utf-8') as csv_file:
229 csv_reader = csv.reader(csv_file)
230 next(csv_reader) # 跳过标题行
231 for row in csv_reader:
232 age = int(row[5]) # 假设年龄在CSV文件的第6列（索引为5）
233 ages.append(age)
234
235 # 创建散点图
236 plt.figure(figsize=(8, 6))
237 plt.scatter(range(1, len(ages) + 1), ages, color='blue', alpha=0.5)
238 plt.title('年龄散点图')
239 plt.xlabel('球员编号')
240 plt.ylabel('年龄')
241 plt.grid(True)
242 plt.show()
243 import csv
244 import matplotlib.pyplot as plt
245 import matplotlib
246
247 # 设置中文字体，确保中文显示正常
248 matplotlib.rcParams['font.sans-serif'] = ['SimHei']
249 matplotlib.rcParams['axes.unicode_minus'] = False
250
251 # 读取CSV文件并统计各个国籍的球员数量
252 nationalities = {}
253 with open('足球运动员.csv', mode='r', encoding='utf-8') as csv_file:
254 csv_reader = csv.reader(csv_file)
255 next(csv_reader) # 跳过标题行
256 for row in csv_reader:
257 nationality = row[2] # 假设国籍在CSV文件的第3列（索引为2）
258 if nationality in nationalities:
259 nationalities[nationality] += 1
260 else:
261 nationalities[nationality] = 1
262
263 # 获取国籍和对应的球员数量
264 countries = list(nationalities.keys())
265 player_counts = list(nationalities.values())
266
267 # 创建散点图
268 plt.figure(figsize=(10, 6))
269 plt.scatter(countries, player_counts, color='red', alpha=0.7)
270 plt.title('球员各国籍散点图')
271 plt.xlabel('国籍')
272 plt.ylabel('球员数量')
273 plt.xticks(rotation=45) # 旋转x轴标签，防止重叠
274 plt.grid(True)
275 plt.tight_layout()
276 plt.show()
277 import csv
278 import matplotlib.pyplot as plt
279
280 # 读取CSV文件并统计不同身高范围内的球员数量
281 height_ranges = {'150-160': 0, '161-170': 0, '171-180': 0, '181-190': 0, '191-200': 0, '200以上': 0}
282 with open('足球运动员.csv', mode='r', encoding='utf-8') as csv_file:
283 csv_reader = csv.reader(csv_file)
284 next(csv_reader) # 跳过标题行
285 for row in csv_reader:
286 height = int(row[3]) # 假设身高在CSV文件的第4列（索引为3）
287 if 150 <= height <= 160:
288 height_ranges['150-160'] += 1
289 elif 161 <= height <= 170:
290 height_ranges['161-170'] += 1
291 elif 171 <= height <= 180:
292 height_ranges['171-180'] += 1
293 elif 181 <= height <= 190:
294 height_ranges['181-190'] += 1
295 elif 191 <= height <= 200:
296 height_ranges['191-200'] += 1
297 else:
298 height_ranges['200以上'] += 1
299
300 # 获取身高范围和对应的球员数量
301 height_labels = list(height_ranges.keys())
302 player_counts = list(height_ranges.values())
303
304 # 创建柱状图
305 plt.figure(figsize=(10, 6))
306 plt.bar(height_labels, player_counts, color='blue')
307 plt.title('球员身高柱状图')
308 plt.xlabel('身高范围')
309 plt.ylabel('球员数量')
310 plt.xticks(rotation=45) # 旋转x轴标签，防止重叠
311 plt.tight_layout()
312 plt.show()
六、总结：
中超足球联赛自诞生以来一直面临着诸多挑战和困难，这些问题导致了其在国际足球舞台上的落后。其中，影响最大的原因包括：1. 财政投入不足： 尽管中超俱乐部在引进外援和球员方面投入了大量资金，但整体对基础设施、青训体系以及联赛的长期发展投入不足。这导致了对足球整体发展的限制，与一些欧洲豪门相比，中国俱乐部在发展的长期规划和整体实力上仍有较大差距。2. 青训体系不完善：近年来有关注青训的努力，但中国足球的青训系统仍处于起步阶段。与一流足球国家相比，中国足球基层训练和青少年培养的体系和质量有较大差距。这使得在培养本土球星和提高整体水平方面存在难度。3.管理层面问题 ：中超联赛中的俱乐部管理、赛事组织、裁判水平等方面存在一定问题。这些问题可能导致比赛质量和整体联赛形象下降，也可能影响球员的发展和态度。4. 国内外援政策的调整： 针对外援政策的不断调整也影响了中超联赛的整体水平。过度依赖外援导致国内球员发展受限，而频繁的政策变化可能给球队战术体系和球员建设带来困扰。虽然中超面临着诸多问题，但也不乏着改善和发展的希望。需要各方共同努力，提高青训水平，完善联赛管理体系，加大对足球基础设施的投入，并长期持续地推动足球发展，这样才能逐渐缩小与国际顶级联赛之间的差距。最后，在完成该项目后，我深刻明白自己的水平实属不行，很多代码功能不能按照自己的设想去实现，作品距离真正的作为分析数据的工具是远远不够的，我还需要继续努力学习。