使用Pyecharts进行奥运会可视化分析！

受疫情影响，2020东京奥运会将延期至2021年举行；
虽然延期，但此次奥运会依旧会沿用「2020东京奥运会」这个名称；
这也将是奥运会历史上首次延期（1916年、1940年、1944年曾因一战，二战停办）；

既然奥运会延期了，那我们就来回顾下整个奥运会的历史吧:tada::tada:～

本项目将会从以下角度来呈现奥运会历史：

:trophy:各国累计奖牌数；
:soccer:️各项运动产生金牌数
:golf:️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
主要国家表现:cn:中国表现:us:美国表现
:boom: 被单个国家统治的奥运会项目

导入库 & 数据

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

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；
:snowflake: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) 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)

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()

各国冬奥会累计奖牌数

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()

各项运动产生金牌数

基于2016年夏奥会和2014年冬奥会统计；

:runner: 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()

运动员层面

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()

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()

获得金牌最多的运动员

排在第一的是美国泳坛名将「 菲尔普斯 」，截止2016年奥运会总共获得了 23 枚金牌；
博尔特累计获得8枚奥运会金牌；

temp = data[(data['Medal']=='Gold')]athlete = temp.groupby(['Name'])['Medal'].count().reset_index()
athlete.columns = ['Name', 'Nums']
athlete = athlete.sort_values(by="Nums" , ascending=True)background_color_js = ("new echarts.graphic.LinearGradient(0, 0, 1, 1, ""[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)pb = (PictorialBar(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='800px')).add_xaxis([x.replace(' ','\n') for x in athlete['Name'].tail(10).tolist()]).add_yaxis("",athlete['Nums'].tail(10).tolist(),label_opts=opts.LabelOpts(is_show=False),symbol_size=25,symbol_repeat='fixed',symbol_offset=[0, 0],is_symbol_clip=True,symbol='image://https://cdn.kesci.com/upload/image/q8f8otrlfc.png').reversal_axis().set_global_opts(title_opts=opts.TitleOpts(title="获得金牌数量最多的运动员", pos_left='center',title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20),),xaxis_opts=opts.AxisOpts(is_show=False,),yaxis_opts=opts.AxisOpts(axistick_opts=opts.AxisTickOpts(is_show=False),axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(opacity=0)),),))pb.render_notebook()

获得金牌/奖牌比例

看菲尔普斯拿金牌拿到手软，但实际上想获得一块金牌的难度高吗？

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29.12s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()

:cn: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()

历届奥运会奖牌数

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()

源码获取加群：850591259

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