基于python的世界杯数据可视化分析，高分课程设计+源代码+详细注释

# 导入数据分析包：numpy（科学计算）、pandas（处理数据框）和 matplotlib/seaborn(可视化) import numpy as np import pandas as pd #表示调用Matplotlib子类pyplot绘图，并as重命名为plt，方便代码调用。 import matplotlib.pyplot as plt import seaborn as sns # 导入数据：世界杯成绩汇总表 worldcup_chengji = pd.read_csv('WorldCupsSummary.csv') #print(worldcup_chengji) # 统一"Germany FR"和"Germany" 归一化 worldcup_chengji = worldcup_chengji.replace(['Germany FR'],'Germany') #print(worldcup_chengji) # 设置全局绘图参数 font = { 'weight': 'bold', 'size': '20'}#字体为加粗、大小为20 plt.rc('font', **font) fig, ax= plt.subplots(figsize=(12,8)) #figure图片大小 #世界杯参加人数绘制： plt.title('Attendance Number') #表名称 worldcup_chengji.plot.scatter(x='Attendance',c="red",y='Year',ax=ax,zorder=2,s=100) #绘制散点图 设置标签获取表格参加人数与年份，设置s散点大小 #去掉上下左右边框 ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) #设置坐标轴标签 例如年份和人数 year #ax.set_ylabel(None) #ax.set_xlabel(None) #ax.grid()添加网格线。颜色为绿 ax.grid(visible=True,color='blue') #设置x与y主刻度 ax.tick_params(axis='both',which='major',labelsize=15,colors='gold') ax.set_yticks(worldcup_chengji['Year'].tolist())#获取表格年份信息并设置y刻度 ax.set_xticks([500000,1000000,1500000,2000000,2500000,3000000,3500000,4000000])#设置x的刻度 #禁止科学计数 ax.ticklabel_format(style='plain') #设置绘图区四个边框线上的的刻度线是否显示，此处不显示刻度 #plt.tick_params(bottom=False, left=False) #plt.show()#打印在屏幕 plt.savefig('观众人数散点图')#表格的储存 fig, ax= plt.subplots(figsize=(12,8)) plt.title('Goals Number') worldcup_chengji.plot.scatter(x='GoalsScored',y='Year',ax=ax,zorder=2,s=100) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.set_ylabel(None) ax.set_xlabel(None) ax.grid(visible=True) ax.tick_params(axis='both', which='major', labelsize=15) ax.set_yticks(worldcup_chengji['Year'].tolist()) ax.set_xticks([50,75,100,125,150,175,200]) plt.tick_params(bottom=False, left=False) #plt.savefig("进球总数散点图") plt.show() #国家冠军数 palette=['yellow','red','red','blue','purple','yellow','yellow','purple'] #调色板 fig, ax= plt.subplots(figsize=(16,8))#设置画布大小 plt.title('总冠军数') sns.countplot(x = worldcup_chengji['Winner'], palette=palette,linewidth=2.5, edgecolor=".2")#调用成绩汇总表绘图 #去除上下左右边框 ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) #设置x，y轴标签 ax.set_ylabel("冠军数",fontsize=14) ax.set_xlabel("国家",fontsize=14) plt.rcParams['font.sans-serif']=["SimHei"]#防止中文乱码 #plt.tick_params(labelleft=False, left=False,labelsize=14) #提取并显示条形图中的条形值 for i in ax.containers: plt.bar_label(i, fontsize=15) #plt.savefig("获得冠军的国家") plt.show() #夺冠队伍所在大洲分布 index1 = worldcup_chengji['WinnerContinent'].value_counts().index.tolist()#标签文本，value_count用于数据表的计数 value1 = worldcup_chengji['WinnerContinent'].value_counts().values.tolist()#饼图的比例，格式为数组或占比的序列，决定扇形面积 palette = ['yellow', 'blue'] fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 8)) sns.countplot(ax=ax[0], x=worldcup_chengji['WinnerContinent'], palette=palette, linewidth=2.5, edgecolor=".2") ax[0].set_title('Champion Continent Numbers') ax[0].spines['right'].set_visible(False) ax[0].spines['top'].set_visible(False) ax[0].spines['left'].set_visible(False) ax[0].spines['bottom'].set_visible(False) ax[0].set_ylabel(None) ax[0].set_xlabel(None) ax[0].tick_params(labelleft=False, left=False, labelsize=14) for i in ax[0].containers: ax[0].bar_label(i, fontsize=15); plt.pie(value1, labels=index1, autopct='%.0f%%', colors=['blue', 'yellow'], wedgeprops={"edgecolor": "0", 'linewidth': 2.5, 'antialiased': True}, startangle=90, textprops={'fontsize': 20}) ax[1].set_title('Champion Continent Ratios', size=20, weight='bold'); #plt.imsave("夺冠大洲分布") plt.show() # 东道主夺冠概率 worldcup_chengji['HostWinner']= worldcup_chengji['HostCountry']== worldcup_chengji['Winner']#将winner和hostcountry作比较，赋值ture和false index = worldcup_chengji['HostWinner'].value_counts().index.tolist()#标签文本，value_count用于数据表的计数 value = worldcup_chengji['HostWinner'].value_counts().values.tolist()#饼图的比例，格式为数组或占比的序列，决定扇形面积 palette = ['blue', 'yellow'] fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 8)) sns.countplot(ax=ax[0], x=worldcup_chengji['HostWinner'], palette=palette, linewidth=2.5, edgecolor=".2") ax[0].set_title('Champion Number', size=20, weight='bold') ax[0].spines['right'].set_visible(False) ax[0].spines['top'].set_visible(False) ax[0].spines['left'].set_visible(False) ax[0].spines['bottom'].set_visible(False) ax[0].set_ylabel(None) ax[0].set_xlabel(None) ax[0].tick_params(labelleft=False, left=False, labelsize=25) for i in ax[0].containers: ax[0].bar_label(i, fontsize=15); plt.pie(value, labels=index, autopct='%.0f%%', colors=['blue', 'yellow'], wedgeprops={"edgecolor": "0", 'linewidth': 2.5, 'antialiased': True}, startangle=90, textprops={'fontsize': 20}) ax[1].set_title('Champion Probability', size=20, weight='bold'); #plt.savefig("东道主夺冠概率") plt.show() # 导入数据：比赛信息表 matches = pd.read_csv('WorldCupMatches.csv') #print(matches) # 中国队参加的比赛 #matches[(matches['Away Team Name'] == 'China PR') | (matches['Home Team Name'] == 'China PR')] # 统一“联邦德国”和“德国” matches = matches.replace(['Germany FR'],'Germany') # 类型转化 matches['Home Team Goals']= matches['Home Team Goals'].astype(int)#astype()函数可用于转化dateframe某一列的数据类型 matches['Away Team Goals']= matches['Away Team Goals'].astype(int) # 格式化比赛结果，如 3-2 matches['result'] = matches['Home Team Goals'].astype(str)+"-"+matches['Away Team Goals'].astype(str) #比赛进球数分析 matches['total_goals'] = matches['Home Team Goals'] + matches['Away Team Goals']#总进球数 matches['VS'] = matches['Home Team Name'] + " VS " + matches['Away Team Name']#哪两个队比赛 top10_goals = matches.sort_values(by='total_goals', ascending=False)[:10]#排序函数，将total_goals排序 #print(top10_goals) top10_goals['VS'] = top10_goals['Home Team Name'] + " VS " + top10_goals['Away Team Name'] top10_goals['total_goals_str'] = top10_goals['total_goals'].astype(str) + " goals scored" #总进球数 top10_goals['Home Team Goals'] = top10_goals['Home Team Goals'].astype(int)#主队进球数 top10_goals['Away Team Goals'] = top10_goals['Away Team Goals'].astype(int)#客队进球数 top10_goals['result'] = top10_goals['Home Team Goals'].astype(str) + "-" + top10_goals['Away Team Goals'].astype(str)#主客队的比分 plt.figure(figsize=(30, 30)) ax = sns.barplot(y=top10_goals['VS'], x=top10_goals['total_goals'])#绘制柱状图，调用vs数据并显示在y轴上 sns.despine(right=True) plt.ylabel('Match',fontsize=25) plt.xlabel('Goals',fontsize=25) plt.yticks(size=15) plt.xticks(size=15) plt.title('Top10 Goals Match', size=20) for i, s in enumerate("Stadium " + top10_goals['Stadium'] + ", Date: " + top10_goals['Datetime'] + "\