【2020东京奥运会】奥运榜单以及各国参赛运动员数据可视化~ |
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【2020东京奥运会】奥运榜单以及各国参赛运动员数据可视化~
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项目
本文中的代码是基于notebook写的,可以访问https://www.heywhale.com/mw/project/61015e73aca24600179ec778获取完整notebook. 奖牌榜数据通过咪咕视频的接口获取奖牌榜单的数据，貌似也没做什么反爬虫，直接就可以获取到数据： import requests rank_url = 'https://app-sc.miguvideo.com/vms-livedata/olympic-medal/total-table/15/110000004609' data = requests.get(rank_url).json()数据处理，将json数据转化为dataframe： df = pd.DataFrame() for item in data['body']['allMedalData']: df = df.append([[ item['rank'], item['countryName'], item['goldMedalNum'], item['silverMedalNum'], item['bronzeMedalNum'], item['totalMedalNum']]]) df.columns = ['排名', '国家', '金牌', '银牌', '铜牌', '奖牌'] df = df.reset_index(drop=True)使用plotly展示榜单前30个国家： import plotly.graph_objects as go from plotly.colors import n_colors import numpy as np np.random.seed(1) colors = n_colors('rgb(225,255,255)', 'rgb(255,192,203)', 10, colortype='rgb') fig = go.Figure( data=[go.Table( columnwidth=[20, 80, 80, 80, 80], header=dict(values=["{}".format(c) for c in df.columns], line_color='darkslategray', fill_color='rgb(255,0,0)', align=['center'], # font=dict(color='white', size=13), height=40), cells=dict(values=df.head(30).T, line_color='darkslategray', fill=dict(color=[colors, 'white']), align=['center'], font_size=13, height=30)) ]) fig.update_layout( height=1200, title_text="2020东京奥运会奖牌榜更新时间：{}".format(update_time), ) fig.show() 运动员数据
2020年东京奥运会全部参赛人员名单数据来自奥委会官网（https://olympics.com/tokyo-2020/olympic-games/zh/results/all-sports/athletes.htm）；整体数据来看，总共206个国家或地区的代表团共11309名选手参赛，比2016年里约奥运会的11180人多出129人，为史上参赛人数最多的一届奥运会；
获取国家简称和项目的中文名称对应表： # 获取国家简称对应的中文名称 noc = 'https://olympics.com/tokyo-2020/olympic-games/zh/results/all-sports/nocs-list.htm' r = requests.get(noc) para = r'.*?country="(?P.*?)">.*?(?P.*?)' patterns = re.compile(para) noc_dict = {} for k, v in patterns.findall(r.text): noc_dict[k] = v # 获取项目简称对应的中文代码 sports = 'https://olympics.com/tokyo-2020/zh/sports/' r = requests.get(sports) para = r'\s+.*?title">\s+(.*?)\s+' patterns = re.compile(para) sports_dict = {} for k, v in patterns.findall(r.text): sports_dict[k.upper()] = v获取运动员数据 athlete_url = 'https://olympics.com/tokyo-2020/olympic-games/zh/results/all-sports/zzje001a.json' data = requests.get(athlete_url).json() athletes_df = pd.DataFrame(columns=['姓名', '国家', '项目']) for item in data['data']: athletes_df = athletes_df.append( [{'姓名': item['name'], '国家':noc_dict[item['noc']], '项目':sports_dict[item['dis']]}]) athletes_df = athletes_df.reset_index(drop=True)按国家维度聚合统计： df_t = athletes_df.groupby(['国家', '项目'])['姓名'].count().reset_index() df_t.columns = ['国家', '项目', '人数'] data = [] country = [] for idx, row in df_t.iterrows(): if row['国家'] in country: data[-1]['children'].append(dict(name=row['项目'], value=row['人数'])) else: data.append(dict(name=row['国家'], children=[dict(name=row['项目'], value=row['人数'])])) country.append(row['国家']) tree = TreeMap( init_opts=opts.InitOpts( theme='light', width='1000px', height='600px', # bg_color='rgb(0,0,0)' )) tree.add( "参赛人数", data, leaf_depth=1, label_opts=opts.LabelOpts(position="inside", formatter='{b}：{c}名'), levels=[ opts.TreeMapLevelsOpts( treemap_itemstyle_opts=opts.TreeMapItemStyleOpts( border_color="#555", border_width=4, gap_width=4 ) ), opts.TreeMapLevelsOpts( color_saturation=[0.3, 0.6], treemap_itemstyle_opts=opts.TreeMapItemStyleOpts( border_color_saturation=0.7, gap_width=2, border_width=2 ), ), opts.TreeMapLevelsOpts( color_saturation=[0.3, 0.5], treemap_itemstyle_opts=opts.TreeMapItemStyleOpts( border_color_saturation=0.6, gap_width=1 ), ), # opts.TreeMapLevelsOpts(color_saturation=[0.3, 0.5]), ], ) tree.set_global_opts( title_opts=opts.TitleOpts(title="2020东京奥运会参赛人数统计（国家/地区）", pos_left='center', title_textstyle_opts=opts.TextStyleOpts(color='#00BFFF', font_size=20)), legend_opts=opts.LegendOpts(is_show=False) ) tree.render_notebook()
按运动项目聚合统计： df_t = athletes_df.groupby(['项目', '国家'])['姓名'].count().reset_index() df_t.columns = ['项目', '国家', '人数'] data = [] event = [] for idx, row in df_t.iterrows(): if row['项目'] in event: data[-1]['children'].append(dict(name=row['国家'], value=row['人数'])) else: data.append(dict(name=row['项目'], children=[dict(name=row['国家'], value=row['人数'])])) event.append(row['项目']) tree = TreeMap( init_opts=opts.InitOpts( theme='light', width='1000px', height='600px', # bg_color='rgb(0,0,0)' )) tree.add( "参赛人数", data, leaf_depth=1, label_opts=opts.LabelOpts(position="inside", formatter='{b}：{c}名'), levels=[ opts.TreeMapLevelsOpts( treemap_itemstyle_opts=opts.TreeMapItemStyleOpts( border_color="#555", border_width=4, gap_width=4 ) ), opts.TreeMapLevelsOpts( color_saturation=[0.3, 0.6], treemap_itemstyle_opts=opts.TreeMapItemStyleOpts( border_color_saturation=0.7, gap_width=2, border_width=2 ), ), opts.TreeMapLevelsOpts( color_saturation=[0.3, 0.5], treemap_itemstyle_opts=opts.TreeMapItemStyleOpts( border_color_saturation=0.6, gap_width=1 ), ), # opts.TreeMapLevelsOpts(color_saturation=[0.3, 0.5]), ], ) tree.set_global_opts( title_opts=opts.TitleOpts( title="2020东京奥运会参赛人数统计（项目）", pos_left='center', title_textstyle_opts=opts.TextStyleOpts(color='#00BFFF', font_size=20) ), legend_opts=opts.LegendOpts( is_show=False ) ) tree.render_notebook()
主要国家各项目参数人数对比 pie = Pie( init_opts=opts.InitOpts( theme='light', width='1000px', height='800px', ) ) titles = [dict( text='2020东京奥运会各国主要项目参赛运动员比例', left='center', top='0%', textStyle=dict( color='#000', fontSize=20) ) ] for i, c in enumerate(country_list): d = df_t[df_t['国家'] == c].reset_index() data_pair = [] else_num = 0 for idx, row in d.iterrows(): if idx < 5: data_pair.append( opts.PieItem( name=row['项目'], value=row['人数'], label_opts=opts.LabelOpts( is_show=True, formatter='{b}:{d}%') ) ) else: else_num += row['人数'] data_pair.append( opts.PieItem( name='其他', value=else_num, label_opts=opts.LabelOpts( is_show=True, formatter='{b}:{d}%') ) ) pos_x = '{}%'.format(int(i / 4) * 33 + 16) pos_y = '{}%'.format(i % 4 * 24 + 15) titles.append( dict( text=c+' ', left=pos_x, top=pos_y, textAlign='center', textVerticalAlign='middle', textStyle=dict( color='#00BFFF', fontSize=12) ) ) pie.add( c, data_pair, center=[pos_x, pos_y], radius=['8%', '12%'] ) pie.set_global_opts( legend_opts=opts.LegendOpts(is_show=False), title_opts=titles ) pie.render_notebook()
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