本项目将会从以下角度来呈现奥运会历史：

1、🏆各国累计奖牌数；
2、⚽️各项运动产生金牌数
3、⛳️运动员层面
- 参赛人数趋势
- 女性参赛比例趋势
- 获得金牌最多的运动员
- 获得奖牌/金牌比例
- 各项目运动员平均体质数据
4、主要国家表现
- 🇨🇳中国表现
- 🇺🇸美国表现
5、💥被单个国家统治的奥运会项目
6、🏅️2020东京奥运会金牌分布图
一、导入库

import pandas as pd
import numpy as np
import pyecharts
from pyecharts.charts import *
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode

athlete_data = pd.read_csv('athlete_events.csv')
noc_region = pd.read_csv('noc_regions.csv')# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
data.head()

medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']                                      
medal_data = medal_data.sort_values(by="Year" , ascending=True)

def medal_stat(year, season='Summer'):t_data = medal_data[(medal_data['Year'] <= year) & (medal_data['Season'] == season)]t_data = t_data.groupby(['region', 'Medal'])['Nums'].sum().reset_index()t_data = t_data.set_index(['region', 'Medal']).unstack().reset_index().fillna(0, inplace=False)t_data = sorted([(row['region'][0], int(row['Nums']['Gold']), int(row['Nums']['Silver']), int(row['Nums']['Bronze'])) for _, row in t_data.iterrows()], key=lambda x: x[1]+x[2]+x[3], reverse=True)[:20] return t_data

二、可视化展示

1、累计奖牌数
夏季奥运会 & 冬季奥运会分别统计
🏖️夏季奥运会开始于1896年雅典奥运会；
❄️冬季奥运会开始于1924年慕尼黑冬奥会；

medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']                                      
medal_data = medal_data.sort_values(by="Year" , ascending=True)

def medal_stat(year, season='Summer'):t_data = medal_data[(medal_data['Year'] <= year) & (medal_data['Season'] == season)]t_data = t_data.groupby(['region', 'Medal'])['Nums'].sum().reset_index()t_data = t_data.set_index(['region', 'Medal']).unstack().reset_index().fillna(0, inplace=False)t_data = sorted([(row['region'][0], int(row['Nums']['Gold']), int(row['Nums']['Silver']), int(row['Nums']['Bronze'])) for _, row in t_data.iterrows()], key=lambda x: x[1]+x[2]+x[3], reverse=True)[:20] return t_data

1.1各国夏奥会累计奖牌数

截止2016年夏季奥运会，美俄分别获得了2544和1577枚奖牌，位列一二位；
中国由于参加奥运会时间较晚，截止2016年累计获得了545枚奖牌，位列第七位；

year_list = sorted(list(set(medal_data['Year'].to_list())), reverse=True)tl = Timeline(init_opts=opts.InitOpts(theme='dark', width='1000px', height='1000px'))
tl.add_schema(is_timeline_show=True,is_rewind_play=True, is_inverse=False,label_opts=opts.LabelOpts(is_show=False))for year in year_list:t_data = medal_stat(year)[::-1]bar = (Bar(init_opts=opts.InitOpts()).add_xaxis([x[0] for x in t_data]).add_yaxis("铜牌🥉", [x[3] for x in t_data], stack='stack1',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(218,165,32)')).add_yaxis("银牌🥈", [x[2] for x in t_data], stack='stack1',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(192,192,192)')).add_yaxis("金牌🏅️", [x[1] for x in t_data], stack='stack1',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(255,215,0)')).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='insideRight',font_style='italic'),).set_global_opts(title_opts=opts.TitleOpts(title="各国累计奖牌数（夏季奥运会）"),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=True),graphic_opts=[opts.GraphicGroup(graphic_item=opts.GraphicItem(rotation=JsCode("Math.PI / 4"),bounding="raw",right=110,bottom=110,z=100),children=[opts.GraphicRect(graphic_item=opts.GraphicItem(left="center", top="center", z=100),graphic_shape_opts=opts.GraphicShapeOpts(width=400, height=50),graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(fill="rgba(0,0,0,0.3)"),),opts.GraphicText(graphic_item=opts.GraphicItem(left="center", top="center", z=100),graphic_textstyle_opts=opts.GraphicTextStyleOpts(text=year,font="bold 26px Microsoft YaHei",graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(fill="#fff"),),),],)],).reversal_axis())tl.add(bar, year)tl.render_notebook()

year_list = sorted(list(set(medal_data['Year'][medal_data.Season=='Winter'].to_list())), reverse=True)tl = Timeline(init_opts=opts.InitOpts(theme='dark', width='1000px', height='1000px'))
tl.add_schema(is_timeline_show=True,is_rewind_play=True, is_inverse=False,label_opts=opts.LabelOpts(is_show=False))for year in year_list:t_data = medal_stat(year, 'Winter')[::-1]bar = (Bar(init_opts=opts.InitOpts(theme='dark')).add_xaxis([x[0] for x in t_data]).add_yaxis("铜牌🥉", [x[3] for x in t_data], stack='stack1',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(218,165,32)')).add_yaxis("银牌🥈", [x[2] for x in t_data], stack='stack1',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(192,192,192)')).add_yaxis("金牌🏅️", [x[1] for x in t_data], stack='stack1',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(255,215,0)')).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='insideRight',font_style='italic'),).set_global_opts(title_opts=opts.TitleOpts(title="各国累计奖牌数（冬季奥运会）"),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=True),graphic_opts=[opts.GraphicGroup(graphic_item=opts.GraphicItem(rotation=JsCode("Math.PI / 4"),bounding="raw",right=110,bottom=110,z=100),children=[opts.GraphicRect(graphic_item=opts.GraphicItem(left="center", top="center", z=100),graphic_shape_opts=opts.GraphicShapeOpts(width=400, height=50),graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(fill="rgba(0,0,0,0.3)"),),opts.GraphicText(graphic_item=opts.GraphicItem(left="center", top="center", z=100),graphic_textstyle_opts=opts.GraphicTextStyleOpts(text='截止{}'.format(year),font="bold 26px Microsoft YaHei",graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(fill="#fff"),),),],)],).reversal_axis())tl.add(bar, year)tl.render_notebook()

2、各项运动产生金牌数

基于2016年夏奥会和2014年冬奥会统计；

🏃田径 & 游泳是大项，在2016年夏奥会上分别产生了47和34枚金牌；

background_color_js = """new echarts.graphic.RadialGradient(0.5, 0.5, 1, [{offset: 0,color: '#696969'}, {offset: 1,color: '#000000'}])"""tab = Tab()
temp = data[(data['Medal']=='Gold') & (data['Year']==2016) & (data['Season']=='Summer')]event_medal = temp.groupby(['Sport'])['Event'].nunique().reset_index()
event_medal.columns = ['Sport', 'Nums']                                      
event_medal = event_medal.sort_values(by="Nums" , ascending=False) pie = (Pie(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='800px')).add('金牌🏅️', [(row['Sport'], row['Nums']) for _, row in event_medal.iterrows()],radius=["30%", "75%"],rosetype="radius").set_global_opts(title_opts=opts.TitleOpts(title="2016年夏季奥运会各项运动产生金牌占比", pos_left="center",title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20),     ),legend_opts=opts.LegendOpts(is_show=False)).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"),tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),))
tab.add(pie, '2016年夏奥会')temp = data[(data['Medal']=='Gold') & (data['Year']==2014) & (data['Season']=='Winter')]event_medal = temp.groupby(['Sport'])['Event'].nunique().reset_index()
event_medal.columns = ['Sport', 'Nums']                                      
event_medal = event_medal.sort_values(by="Nums" , ascending=False) pie = (Pie(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='800px')).add('金牌🏅️', [(row['Sport'], row['Nums']) for _, row in event_medal.iterrows()],radius=["30%", "75%"],rosetype="radius").set_global_opts(title_opts=opts.TitleOpts(title="2014年冬季奥运会各项运动产生金牌占比", pos_left="center",title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20),     ),legend_opts=opts.LegendOpts(is_show=False)).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"),tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),))
tab.add(pie, '2014年冬奥会')
tab.render_notebook()
Out[7]:

3、运动员层面

3.1、历年参赛人数趋势

从人数来看，每届夏奥会参赛人数都是冬奥会的4-5倍；
整体参赛人数是上涨趋势，但由于历史原因也出现过波动，如1980年莫斯科奥运会层遭遇65个国家抵制；

athlete = data.groupby(['Year', 'Season'])['Name'].nunique().reset_index()
athlete.columns = ['Year', 'Season', 'Nums']                                      
athlete = athlete.sort_values(by="Year" , ascending=True) x_list, y1_list, y2_list = [], [], []for _, row in athlete.iterrows():x_list.append(str(row['Year']))if row['Season'] == 'Summer':y1_list.append(row['Nums'])y2_list.append(None)else:y2_list.append(row['Nums'])y1_list.append(None)background_color_js = ("new echarts.graphic.LinearGradient(1, 1, 0, 0, ""[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)line = (Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px')).add_xaxis(x_list).add_yaxis("夏季奥运会", y1_list, is_smooth=True, is_connect_nones=True,symbol="circle",symbol_size=6,linestyle_opts=opts.LineStyleOpts(color="#fff"),label_opts=opts.LabelOpts(is_show=False, position="top", color="white"),itemstyle_opts=opts.ItemStyleOpts(color="green", border_color="#fff", border_width=3),tooltip_opts=opts.TooltipOpts(is_show=True)).add_yaxis("冬季季奥运会", y2_list, is_smooth=True, is_connect_nones=True, symbol="circle",symbol_size=6,linestyle_opts=opts.LineStyleOpts(color="#FF4500"),label_opts=opts.LabelOpts(is_show=False, position="top", color="white"),itemstyle_opts=opts.ItemStyleOpts(color="red", border_color="#fff", border_width=3),tooltip_opts=opts.TooltipOpts(is_show=True)).set_series_opts(markarea_opts=opts.MarkAreaOpts(label_opts=opts.LabelOpts(is_show=True, position="bottom", color="white"),data=[opts.MarkAreaItem(name="第一次世界大战", x=(1914, 1918)),opts.MarkAreaItem(name="第二次世界大战", x=(1939, 1945)),])).set_global_opts(title_opts=opts.TitleOpts(title="历届奥运会参赛人数",pos_left="center",title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20),),legend_opts=opts.LegendOpts(is_show=True, pos_top='5%',textstyle_opts=opts.TextStyleOpts(color="white", font_size=12)),xaxis_opts=opts.AxisOpts(type_="value",min_=1904,max_=2016,boundary_gap=False,axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63",formatter=JsCode("""function (value) {return value+'年';}""")),axisline_opts=opts.AxisLineOpts(is_show=False),axistick_opts=opts.AxisTickOpts(is_show=True,length=25,linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),),splitline_opts=opts.SplitLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),),yaxis_opts=opts.AxisOpts(type_="value",position="right",axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),axistick_opts=opts.AxisTickOpts(is_show=True,length=15,linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),),splitline_opts=opts.SplitLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),),)
)line.render_notebook()

3.2、历年女性运动员占比趋势

一开始奥运会基本是「男人的运动」，女性运动员仅为个位数，到近几届奥运会男女参赛人数基本趋于相等；

# 历年男性运动员人数
m_data = data[data.Sex=='M'].groupby(['Year', 'Season'])['Name'].nunique().reset_index()
m_data.columns = ['Year', 'Season', 'M-Nums']                                      
m_data = m_data.sort_values(by="Year" , ascending=True) # 历年女性运动员人数
f_data = data[data.Sex=='F'].groupby(['Year', 'Season'])['Name'].nunique().reset_index()
f_data.columns = ['Year', 'Season', 'F-Nums']                                      
f_data = f_data.sort_values(by="Year" , ascending=True) t_data = pd.merge(m_data, f_data, on=['Year', 'Season'])
t_data['F-rate'] = round(t_data['F-Nums'] / (t_data['F-Nums']  + t_data['M-Nums'] ), 4)x_list, y1_list, y2_list = [], [], []for _, row in t_data.iterrows():x_list.append(str(row['Year']))if row['Season'] == 'Summer':y1_list.append(row['F-rate'])y2_list.append(None)else:y2_list.append(row['F-rate'])y1_list.append(None)background_color_js = ("new echarts.graphic.LinearGradient(0, 0, 0, 1, ""[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)line = (Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px')).add_xaxis(x_list).add_yaxis("夏季奥运会", y1_list, is_smooth=True, is_connect_nones=True,symbol="circle",symbol_size=6,linestyle_opts=opts.LineStyleOpts(color="#fff"),label_opts=opts.LabelOpts(is_show=False, position="top", color="white"),itemstyle_opts=opts.ItemStyleOpts(color="green", border_color="#fff", border_width=3),tooltip_opts=opts.TooltipOpts(is_show=True),).add_yaxis("冬季季奥运会", y2_list, is_smooth=True, is_connect_nones=True, symbol="circle",symbol_size=6,linestyle_opts=opts.LineStyleOpts(color="#FF4500"),label_opts=opts.LabelOpts(is_show=False, position="top", color="white"),itemstyle_opts=opts.ItemStyleOpts(color="red", border_color="#fff", border_width=3),tooltip_opts=opts.TooltipOpts(is_show=True),).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter=JsCode("""function (params) {return params.data[0]+ '年: ' + Number(params.data[1])*100 +'%';}""")),).set_global_opts(title_opts=opts.TitleOpts(title="历届奥运会参赛女性占比趋势",pos_left="center",title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20),),legend_opts=opts.LegendOpts(is_show=True, pos_top='5%',textstyle_opts=opts.TextStyleOpts(color="white", font_size=12)),xaxis_opts=opts.AxisOpts(type_="value",min_=1904,max_=2016,boundary_gap=False,axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63",formatter=JsCode("""function (value) {return value+'年';}""")),axisline_opts=opts.AxisLineOpts(is_show=False),axistick_opts=opts.AxisTickOpts(is_show=True,length=25,linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),),splitline_opts=opts.SplitLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),),yaxis_opts=opts.AxisOpts(type_="value",position="right",axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63",formatter=JsCode("""function (value) {return Number(value *100)+'%';}""")),axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),axistick_opts=opts.AxisTickOpts(is_show=True,length=15,linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),),splitline_opts=opts.SplitLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),),)
)line.render_notebook()

3.3、获得金牌/奖牌比例
整个奥运会（包括夏季，冬季奥运会）历史上参赛人数为134732，获得过金牌的运动员只有10413，占比7.7%;
获得过奖牌（包括金银铜）的运动员有28202人，占比20.93%；

total_athlete = len(set(data['Name']))
medal_athlete = len(set(data['Name'][data['Medal'].isin(['Gold', 'Silver', 'Bronze'])]))
gold_athlete = len(set(data['Name'][data['Medal']=='Gold']))l1 = Liquid(init_opts=opts.InitOpts(theme='dark', width='1000px', height='800px'))
l1.add("获得奖牌", [medal_athlete/total_athlete], center=["70%", "50%"],label_opts=opts.LabelOpts(font_size=50,formatter=JsCode("""function (param) {return (Math.floor(param.value * 10000) / 100) + '%';}"""),position="inside",))
l1.set_global_opts(title_opts=opts.TitleOpts(title="获得过奖牌比例", pos_left='62%', pos_top='8%'))
l1.set_series_opts(tooltip_opts=opts.TooltipOpts(is_show=False))l2 = Liquid(init_opts=opts.InitOpts(theme='dark', width='1000px', height='800px'))
l2.add("获得金牌",[gold_athlete/total_athlete],center=["25%", "50%"],label_opts=opts.LabelOpts(font_size=50,formatter=JsCode("""function (param) {return (Math.floor(param.value * 10000) / 100) + '%';}"""),position="inside",),)
l2.set_global_opts(title_opts=opts.TitleOpts(title="获得过金牌比例", pos_left='17%', pos_top='8%'))
l2.set_series_opts(tooltip_opts=opts.TooltipOpts(is_show=False))grid = Grid().add(l1, grid_opts=opts.GridOpts()).add(l2, grid_opts=opts.GridOpts())
grid.render_notebook()

运动员平均体质数据

根据不同的运动项目进行统计
运动员平均身高最高的项目是篮球，女子平均身高达182cm，男子平均身高达到194cm；
在男子项目中，运动员平均体重最大的项目是拔河，平均体重达到96kg（拔河自第七届奥运会后已取消）；
运动员平均年龄最大的项目是Art competition（自行百度这奇怪的项目），平均年龄46岁，除此之外便是马术和射击，男子平均年龄分别为34.4岁和34.2岁，女子平均年龄34.22岁和29.12岁；

tool_js = """function (param) {return param.data[2] +'<br/>' +'平均体重： '+Number(param.data[0]).toFixed(2)+' kg<br/>'+'平均身高： '+Number(param.data[1]).toFixed(2)+' cm<br/>'+'平均年龄： '+Number(param.data[3]).toFixed(2);}"""background_color_js = ("new echarts.graphic.LinearGradient(1, 0, 0, 1, ""[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)temp_data = data[data['Sex']=='M'].groupby(['Sport'])['Age', 'Height', 'Weight'].mean().reset_index().dropna(how='any')scatter = (Scatter(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px')).add_xaxis(temp_data['Weight'].tolist()).add_yaxis("男性", [[row['Height'], row['Sport'], row['Age']] for _, row in temp_data.iterrows()],# 渐变效果实现部分color=JsCode("""new echarts.graphic.RadialGradient(0.4, 0.3, 1, [{offset: 0,color: 'rgb(129, 227, 238)'}, {offset: 1,color: 'rgb(25, 183, 207)'}])""")).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="各项目运动员平均升高体重年龄",pos_left="center",title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20)),legend_opts=opts.LegendOpts(is_show=True, pos_top='5%',textstyle_opts=opts.TextStyleOpts(color="white", font_size=12)),tooltip_opts = opts.TooltipOpts(formatter=JsCode(tool_js)),xaxis_opts=opts.AxisOpts(name='体重/kg',# 设置坐标轴为数值类型type_="value", is_scale=True,# 显示分割线axislabel_opts=opts.LabelOpts(margin=30, color="white"),axisline_opts=opts.AxisLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),axistick_opts=opts.AxisTickOpts(is_show=True, length=25,linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),splitline_opts=opts.SplitLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"))),yaxis_opts=opts.AxisOpts(name='身高/cm',# 设置坐标轴为数值类型type_="value",# 默认为False表示起始为0is_scale=True,axislabel_opts=opts.LabelOpts(margin=30, color="white"),axisline_opts=opts.AxisLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),axistick_opts=opts.AxisTickOpts(is_show=True, length=25,linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),splitline_opts=opts.SplitLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"))),visualmap_opts=opts.VisualMapOpts(is_show=False, type_='size', range_size=[5,50], min_=10, max_=40)))temp_data = data[data['Sex']=='F'].groupby(['Sport'])['Age', 'Height', 'Weight'].mean().reset_index().dropna(how='any')scatter1 = (Scatter().add_xaxis(temp_data['Weight'].tolist()).add_yaxis("女性", [[row['Height'], row['Sport'], row['Age']] for _, row in temp_data.iterrows()],itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""new echarts.graphic.RadialGradient(0.4, 0.3, 1, [{offset: 0,color: 'rgb(251, 118, 123)'}, {offset: 1,color: 'rgb(204, 46, 72)'}])"""))).set_series_opts(label_opts=opts.LabelOpts(is_show=False)))
scatter.overlap(scatter1)
scatter.render_notebook()

🇨🇳中国奥运会表现

CN_data = data[data.region=='China']
CN_data.head()

历届奥运会参赛人数

background_color_js = ("new echarts.graphic.LinearGradient(1, 0, 0, 1, ""[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)athlete = CN_data.groupby(['Year', 'Season'])['Name'].nunique().reset_index()
athlete.columns = ['Year', 'Season', 'Nums']                                      
athlete = athlete.sort_values(by="Year" , ascending=False) s_bar = (Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px')).add_xaxis([row['Year'] for _, row in athlete[athlete.Season=='Summer'].iterrows()]).add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season=='Summer'].iterrows()],category_gap='40%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 1,color: '#00BFFF'}, {offset: 0,color: '#32CD32'}])"""))).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top',font_style='italic')).set_global_opts(title_opts=opts.TitleOpts(title="中国历年奥运会参赛人数-夏奥会", pos_left='center'),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=False),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),graphic_opts=[opts.GraphicImage(graphic_item=opts.GraphicItem(id_="logo", right=0, top=0, z=-10, bounding="raw", origin=[75, 75]),graphic_imagestyle_opts=opts.GraphicImageStyleOpts(image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",width=1000,height=600,opacity=0.6,),)],))w_bar = (Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='300px')).add_xaxis([row['Year'] for _, row in athlete[athlete.Season=='Winter'].iterrows()]).add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season=='Winter'].iterrows()],category_gap='50%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 1,color: '#00BFFF'}, {offset: 0.8,color: '#FFC0CB'}, {offset: 0,color: '#40E0D0'}])"""))).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top',font_style='italic')).set_global_opts(title_opts=opts.TitleOpts(title="中国历年奥运会参赛人数-冬奥会", pos_left='center'),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=False),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),graphic_opts=[opts.GraphicImage(graphic_item=opts.GraphicItem(id_="logo", right=0, top=-300, z=-10, bounding="raw", origin=[75, 75]),graphic_imagestyle_opts=opts.GraphicImageStyleOpts(image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",width=1000,height=600,opacity=0.6,),)],))page = (Page().add(s_bar,).add(w_bar,)
)
page.render_notebook()

历届奥运会获得金牌数🏅️

background_color_js = ("new echarts.graphic.LinearGradient(1, 0, 0, 1, ""[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)CN_medals = CN_data.groupby(['Year', 'Season', 'Medal'])['Event'].nunique().reset_index()
CN_medals.columns = ['Year', 'Season', 'Medal', 'Nums']                                      
CN_medals = CN_medals.sort_values(by="Year" , ascending=False) s_bar = (Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px')).add_xaxis(sorted(list(set([row['Year'] for _, row in CN_medals[CN_medals.Season=='Summer'].iterrows()])), reverse=True)).add_yaxis("金牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Summer') & (CN_medals.Medal=='Gold')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#FFD700'}, {offset: 1,color: '#FFFFF0'}])"""))).add_yaxis("银牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Summer') & (CN_medals.Medal=='Silver')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#C0C0C0'}, {offset: 1,color: '#FFFFF0'}])"""))).add_yaxis("铜牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Summer') & (CN_medals.Medal=='Bronze')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#DAA520'}, {offset: 1,color: '#FFFFF0'}])"""))).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top',font_style='italic')).set_global_opts(title_opts=opts.TitleOpts(title="中国历年奥运会获得奖牌数数-夏奥会", pos_left='center'),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=False),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),graphic_opts=[opts.GraphicImage(graphic_item=opts.GraphicItem(id_="logo", right=0, top=0, z=-10, bounding="raw", origin=[75, 75]),graphic_imagestyle_opts=opts.GraphicImageStyleOpts(image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",width=1000,height=600,opacity=0.6,),)],))w_bar = (Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px')).add_xaxis(sorted(list(set([row['Year'] for _, row in CN_medals[CN_medals.Season=='Winter'].iterrows()])), reverse=True)).add_yaxis("金牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Winter') & (CN_medals.Medal=='Gold')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#FFD700'}, {offset: 1,color: '#FFFFF0'}])"""))).add_yaxis("银牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Winter') & (CN_medals.Medal=='Silver')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#C0C0C0'}, {offset: 1,color: '#FFFFF0'}])"""))).add_yaxis("铜牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Winter') & (CN_medals.Medal=='Bronze')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#DAA520'}, {offset: 1,color: '#FFFFF0'}])"""))).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top',font_style='italic')).set_global_opts(title_opts=opts.TitleOpts(title="中国历年奥运会获得奖牌数-冬奥会", pos_left='center'),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=False),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),graphic_opts=[opts.GraphicImage(graphic_item=opts.GraphicItem(id_="logo", right=0, top=-300, z=-10, bounding="raw", origin=[75, 75]),graphic_imagestyle_opts=opts.GraphicImageStyleOpts(image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",width=1000,height=600,opacity=0.6,),)],))page = (Page().add(s_bar,).add(w_bar,)
)
page.render_notebook()

优势项目

跳水，体操，射击，举重，乒乓球，羽毛球

background_color_js = ("new echarts.graphic.LinearGradient(1, 0, 0, 1, ""[{offset: 0.5, color: '#FFC0CB'}, {offset: 1, color: '#F0FFFF'}, {offset: 0, color: '#EE82EE'}], false)"
)CN_events = CN_data[CN_data.Medal=='Gold'].groupby(['Year', 'Sport'])['Event'].nunique().reset_index()
CN_events = CN_events.groupby(['Sport'])['Event'].sum().reset_index()
CN_events.columns = ['Sport', 'Nums']                                      data_pair = [(row['Sport'], row['Nums']) for _, row in CN_events.iterrows()]wc = (WordCloud(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px')).add("", data_pair,word_size_range=[30, 80]).set_global_opts(title_opts=opts.TitleOpts(title="中国获得过金牌运动项目",pos_left="center",title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20)))
)wc.render_notebook()

🇺🇸美国奥运会表现

USA_data = data[data.region=='USA']
USA_data.head()

历届奥运会参加人数

background_color_js = ("new echarts.graphic.LinearGradient(1, 0, 0, 1, ""[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)athlete = USA_data.groupby(['Year', 'Season'])['Name'].nunique().reset_index()
athlete.columns = ['Year', 'Season', 'Nums']                                      
athlete = athlete.sort_values(by="Year" , ascending=False) s_bar = (Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px')).add_xaxis([row['Year'] for _, row in athlete[athlete.Season=='Summer'].iterrows()]).add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season=='Summer'].iterrows()],category_gap='40%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 1,color: '#00BFFF'}, {offset: 0,color: '#32CD32'}])"""))).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top',font_style='italic')).set_global_opts(title_opts=opts.TitleOpts(title="美国历年奥运会参赛人数-夏奥会", pos_left='center'),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=False),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),graphic_opts=[opts.GraphicImage(graphic_item=opts.GraphicItem(id_="logo", right=0, top=0, z=-10, bounding="raw", origin=[75, 75]),graphic_imagestyle_opts=opts.GraphicImageStyleOpts(image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",width=1000,height=600,opacity=0.6,),)],))w_bar = (Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='300px')).add_xaxis([row['Year'] for _, row in athlete[athlete.Season=='Winter'].iterrows()]).add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season=='Winter'].iterrows()],category_gap='50%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 1,color: '#00BFFF'}, {offset: 0.8,color: '#FFC0CB'}, {offset: 0,color: '#40E0D0'}])"""))).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top',font_style='italic')).set_global_opts(title_opts=opts.TitleOpts(title="美国历年奥运会参赛人数-冬奥会", pos_left='center'),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=False),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),graphic_opts=[opts.GraphicImage(graphic_item=opts.GraphicItem(id_="logo", right=0, top=-300, z=-10, bounding="raw", origin=[75, 75]),graphic_imagestyle_opts=opts.GraphicImageStyleOpts(image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",width=1000,height=600,opacity=0.6,),)],))page = (Page().add(s_bar,).add(w_bar,)
)
page.render_notebook()

历届奥运会获得奖牌数

background_color_js = ("new echarts.graphic.LinearGradient(1, 0, 0, 1, ""[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)medals = USA_data.groupby(['Year', 'Season', 'Medal'])['Event'].nunique().reset_index()
medals.columns = ['Year', 'Season', 'Medal', 'Nums']                                      
medals = medals.sort_values(by="Year" , ascending=False) s_bar = (Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px')).add_xaxis(sorted(list(set([row['Year'] for _, row in medals[medals.Season=='Summer'].iterrows()])), reverse=True)).add_yaxis("金牌", [row['Nums'] for _, row in medals[(medals.Season=='Summer') & (medals.Medal=='Gold')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#FFD700'}, {offset: 1,color: '#FFFFF0'}])"""))).add_yaxis("银牌", [row['Nums'] for _, row in medals[(medals.Season=='Summer') & (medals.Medal=='Silver')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#C0C0C0'}, {offset: 1,color: '#FFFFF0'}])"""))).add_yaxis("铜牌", [row['Nums'] for _, row in medals[(medals.Season=='Summer') & (medals.Medal=='Bronze')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#DAA520'}, {offset: 1,color: '#FFFFF0'}])"""))).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top',font_style='italic')).set_global_opts(title_opts=opts.TitleOpts(title="美国历年奥运会获得奖牌数数-夏奥会", pos_left='center'),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=False),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),graphic_opts=[opts.GraphicImage(graphic_item=opts.GraphicItem(id_="logo", right=0, top=0, z=-10, bounding="raw", origin=[75, 75]),graphic_imagestyle_opts=opts.GraphicImageStyleOpts(image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",width=1000,height=600,opacity=0.6,),)],))w_bar = (Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px')).add_xaxis(sorted(list(set([row['Year'] for _, row in medals[medals.Season=='Winter'].iterrows()])), reverse=True)).add_yaxis("金牌", [row['Nums'] for _, row in medals[(medals.Season=='Winter') & (medals.Medal=='Gold')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#FFD700'}, {offset: 1,color: '#FFFFF0'}])"""))).add_yaxis("银牌", [row['Nums'] for _, row in medals[(medals.Season=='Winter') & (medals.Medal=='Silver')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#C0C0C0'}, {offset: 1,color: '#FFFFF0'}])"""))).add_yaxis("铜牌", [row['Nums'] for _, row in medals[(medals.Season=='Winter') & (medals.Medal=='Bronze')].iterrows()],category_gap='20%',itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#DAA520'}, {offset: 1,color: '#FFFFF0'}])"""))).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top',font_style='italic')).set_global_opts(title_opts=opts.TitleOpts(title="美国历年奥运会获得奖牌数-冬奥会", pos_left='center'),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=False),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),graphic_opts=[opts.GraphicImage(graphic_item=opts.GraphicItem(id_="logo", right=0, top=-300, z=-10, bounding="raw", origin=[75, 75]),graphic_imagestyle_opts=opts.GraphicImageStyleOpts(image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",width=1000,height=600,opacity=0.6,),)],))page = (Page().add(s_bar,).add(w_bar,)
)
page.render_notebook()

优势项目

田径，游泳

background_color_js = ("new echarts.graphic.LinearGradient(1, 0, 0, 1, ""[{offset: 0.5, color: '#FFC0CB'}, {offset: 1, color: '#F0FFFF'}, {offset: 0, color: '#EE82EE'}], false)"
)events = USA_data[USA_data.Medal=='Gold'].groupby(['Year', 'Sport'])['Event'].nunique().reset_index()
events = events.groupby(['Sport'])['Event'].sum().reset_index()
events.columns = ['Sport', 'Nums']                                      data_pair = [(row['Sport'], row['Nums']) for _, row in events.iterrows()]wc = (WordCloud(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px')).add("", data_pair,word_size_range=[30, 80]).set_global_opts(title_opts=opts.TitleOpts(title="美国获得过金牌运动项目",pos_left="center",title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20)))
)wc.render_notebook()

被单个国家统治的奥运会项目

很多运动长期以来一直是被某个国家统治，譬如我们熟知的中国🇨🇳的乒乓球，美国🇺🇸的篮球；

此次筛选了近5届奥运会（2000年悉尼奥运会之后）上累计产生10枚金牌以上且存在单个国家「夺金率」超过50%的项目；

俄罗斯🇷🇺包揽了2000年以后花样游泳 & 艺术体操两个项目上所有的20枚金牌；
中国🇨🇳在乒乓球项目上获得了2000年之后10枚金牌中的9枚，丢失金牌的一次是在04年雅典奥运会男单项目上；
美国🇺🇸在篮球项目上同样获得了过去10枚金牌中的9枚，丢失金牌的一次同样在04年，男篮半决赛中输给了阿根廷，最终获得铜牌；
跳水项目上，中国🇨🇳获得了过去40枚金牌中的31枚，梦之队名不虚传；
射箭项目上，韩国🇰🇷获得了过去20枚金牌中的15枚；
羽毛球项目上，中国🇨🇳获得了过去25枚金牌中的17枚；
沙滩排球项目上，美国🇺🇸获得了过去10枚金牌中的5枚；

f1 = lambda x:max(x['Event']) / sum(x['Event'])
f2 = lambda x: x.sort_values('Event', ascending=False).head(1)t_data = data[(data.Medal=='Gold') & (data.Year>=2000) &(data.Season=='Summer')].groupby(['Year', 'Sport', 'region'])['Event'].nunique().reset_index()
t_data = t_data.groupby(['Sport', 'region'])['Event'].sum().reset_index()
t1 = t_data.groupby(['Sport']).apply(f2).reset_index(drop=True)
t2 = t_data.groupby(['Sport'])['Event'].sum().reset_index()
t_data = pd.merge(t1, t2, on='Sport', how='inner')
t_data['gold_rate'] = t_data.Event_x/ t_data.Event_y
t_data = t_data.sort_values('gold_rate', ascending=False).reset_index(drop=True)t_data = t_data[(t_data.gold_rate>=0.5) & (t_data.Event_y>=10)]background_color_js = ("new echarts.graphic.LinearGradient(1, 0, 0, 1, ""[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)fn = """function(params) {if(params.name == '其他国家')return '\\n\\n\\n' + params.name + ' : ' + params.value ;return params.seriesName+ '\\n' + params.name + ' : ' + params.value;}"""def new_label_opts():return opts.LabelOpts(formatter=JsCode(fn), position="center")pie = Pie(init_opts=opts.InitOpts(theme='dark', width='1000px', height='1000px'))
idx = 0for _, row in t_data.iterrows():if idx % 2 == 0:x = 30y = int(idx/2) * 22 + 18else:x = 70y = int(idx/2) * 22 + 18idx += 1pos_x = str(x)+'%'pos_y = str(y)+'%'pie.add(row['Sport'],[[row['region'], row['Event_x']], ['其他国家', row['Event_y']-row['Event_x']]],center=[pos_x, pos_y],radius=[70, 100],label_opts=new_label_opts(),)pie.set_global_opts(title_opts=opts.TitleOpts(title="被单个国家统治的项目",subtitle='统计周期：2000年悉尼奥运会起',pos_left="center",title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20)),legend_opts=opts.LegendOpts(is_show=False),)pie.render_notebook()

2020东京奥运会金牌分布🏅️

数据准备

import requests
import pandas as pddata_url = 'https://app-sc.miguvideo.com/vms-livedata/olympic\
-medal/total-table/15/110000004609'
# 请求数据
data = requests.get(data_url).json()
df = pd.DataFrame()for item in data['body']['allMedalData']:df = df.append([[item['countryName'],item['countryId'],item['rank'],item['goldMedalNum'],item['silverMedalNum'],item['bronzeMedalNum'],item['totalMedalNum']]])
# 修改列名
df.columns = ['国家', '国家id', '排名', '金牌', '银牌', '铜牌', '奖牌']
# 重置索引
df.reset_index(drop=True, inplace=True)
df.head()

data_url = 'https://app-sc.miguvideo.com/\
vms-livedata/olympic-medal/detail-total/15/110000004609'data = requests.get(data_url).json()
detail_df = pd.DataFrame()
# 请求数据
for item in data['body']['medalTableDetail']:detail_df = detail_df.append([[item['awardTime'],item['medalType'],item['sportsName'],item['countryId'],item['bigItemName']]])
# 修改列名
detail_df.columns = ['获奖时间', '奖牌类型', '运动员', '国家id', '运动类别']
# 重置索引
detail_df.reset_index(drop=True, inplace=True)
detail_df.head()

detail_df.loc[detail_df['奖牌类型'] == 1, '奖牌类型'] = '金牌'
detail_df.loc[detail_df['奖牌类型'] == 2, '奖牌类型'] = '银牌'
detail_df.loc[detail_df['奖牌类型'] == 3, '奖牌类型'] = '铜牌'courtry_df = df.loc[:, ['国家', '国家id']]
detail_df = pd.merge(detail_df, courtry_df, on='国家id', how = "inner")
detail_df.head()

df.to_csv('东京奥运会国家排名.csv', index=False)
detail_df.to_csv('东京奥运会获奖详情.csv', index=False)

分布图展示

data = [("United States", 113), ("China", 88), ("Japan", 58), ("United Kingdom", 65), ("United Kingdom", 65), ("Russia", 71),("Australia", 46),  ("Netherlands", 36),  ("France", 33),  ("Germany", 37),  ("Italy", 40),  ("Canada", 24),("Brazil", 21),  ("New Zealand", 20),  ("Cuba", 15),  ("Hungary", 20),  ("South Korea", 20),  ("Poland", 14),("Czech Republic", 11),  ("Kenya", 10),  ("Norway", 8),  ("Jamaica", 9),  ("Spain", 17),  ("Sweden", 9),("Switzerland", 13),  ("Denmark", 11),  ("Croatia", 8),  ("Iran", 7),  ("Serbia", 9),  ("Belgium", 7),("Bulgaria", 6),  ("Slovenia", 5),  ("Uzbekistan", 5),  ("Georgia", 8),  ("China Taibei", 12),  ("Turkey", 13),("Greece", 4),  ("Uganda", 4),  ("Ecuador", 3),  ("Ireland", 4),  ("Israel", 4),  ("Qatar", 3),("Bahamas", 2),  ("kosovo", 2),  ("Ukraine", 19),  ("Belarus", 7),  ("Romania", 4),  ("Venezuela", 4),("India", 7),  ("Hong Kong China", 6),  (" Philippine Islands", 4),  ("Slovakia", 4),  ("South Africa", 3),  ("Austria", 7),("Egypt", 6),  ("Indonesia", 5),  ("Ethiopia", 4),  ("Portugal", 4),  ("Tunisia", 2),  ("Estonia", 2),  ("Fiji", 2),  ("Latvia", 2),  ("Thailand", 2),  ("Bermuda", 1),  ("Morocco", 1),  ("Puerto Rico", 1),("Columbia", 5),  ("Azerbaijan", 7),  ("Dominican", 5),  ("Armenian", 4),  ("Kyrgyzstan", 3),  ("Mongolia", 4),("Argentina", 3),  ("San Marino", 3),  ("Jordan", 2),  ("Malaysia", 2),  ("Nigeria", 2),  ("Bahrain", 1),("Lithuania", 1),  ("Namibia", 1),  ("Northern Macedonia", 1),  ("Saudi Arabia", 1),  ("Turkmenistan", 1),  ("Kazakhstan", 8),("Mexico",4 ),  ("Finland", 2),  ("Botswana", 1),  ("burkina faso", 1),  ("Ghana", 1),  ("Grenada", 1),("Côte d'Ivoire", 1),  ("Kuwait", 1),  ("Moldova", 1),  ("Syria", 1)]
charts = (Map().add("奖牌",data,"world",is_map_symbol_show=False).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="2020东京奥运会各国金牌分布图"),visualmap_opts=opts.VisualMapOpts(max_=120,is_piecewise=True,split_number=3)))
charts.render_notebook()