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。