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120年奥运历史数据分析

一、数据概览

1.背景描述

该数据集整理了从1896年雅典奥运会至2016年里约热内卢奥运会120年的奥林匹克运动会的历史数据。

需要注意的是,在1896年-1992年期间,冬季奥运会与夏季奥运会都是在同一年举行的。在这之后,冬季与夏季的奥运会才被错开举办,冬季奥运会从1994年开始4年举办一次,夏季奥运会从1996开始4年举办一次。大家在分析这些数据时,经常会犯得一个错误就是认为夏季与冬季奥运会是一直错开举办的。

  • 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);

2.数据说明

  • 文件列表 该数据集包含两个文件:- athlete_events.csv :参赛运动员基本生物数据和奖牌结果- noc_regions.csv : 国家奥委会3个字母的代码与对应国家信息

3.属性描述

文件athlete_events.csv中包含15个字段,具体信息如下:

每一行代表的是一个参加个人比赛运动员
No属性数据类型字段描述1IDInteger给每个运动员的唯一ID2NameString运动员名字3SexInteger性别4AgeFloat年龄5HeightFloat身高6WeightFloat体重7TeamString所代表的国家队8NOCString国家奥委会3个字母的代码9GamesString年份与季节10YearInteger比赛年份11SeasonString比赛季节12CityString举办城市13SportString运动类别14EventString比赛项目15MedalSring奖牌
文件noc_regions.csv中包含3个字段,具体信息如下:
No属性数据类型字段描述1NOCString国家奥委会3个字母的代码2RegionString国家3NotesString地区

4.数据来源

数据集源自于kaggle平台用户分享,基于证书 CC0: Public Domain 发布,具体信息内容源自Sports Reference。

二、数据集可探索、研究的方向

可以从以下几个方面来探索奥林匹克运动会的演变历程:

  • 历年来 男女参赛运动员的表现如何?
  • 那不同地区?
  • 不同运动项目?
  • 不同比赛项目?

三、可视化分析

1.🏆各国累计奖牌数

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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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

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(r".\htmlRender\01_各国累计奖牌数(夏季奥运会).html")

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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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

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))

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(r".\htmlRender\02_各国累计奖牌数(冬季奥运会).html")

2.⚽️各项运动产生金牌数

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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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)

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(r".\htmlRender\03_2016夏2014年冬奥会各项运动金牌数.html")

3.⛳️运动员层面

①参赛人数趋势

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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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)
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(r".\htmlRender\04_历届奥运会参赛人数.html")

②女性参赛比例趋势


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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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)

# 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(r".\htmlRender\05_历届奥运会参赛女性占比趋势.html")

③获得金牌最多的运动员


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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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)

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(r".\htmlRender\06_获得金牌数量最多的运动员.html")

④获得奖牌/金牌比例


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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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)

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(r".\htmlRender\07_获得金牌奖牌比例.html")

⑤各项目运动员平均体质数据


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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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)

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0
        is_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(r".\htmlRender\08_运动员平均体质数据.html")

4.中国表现

①中国历届奥运会参赛人数


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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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)

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

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(r".\htmlRender\09_历届奥运会参赛人数.html")

②中国历届奥运会奖牌数


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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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)

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

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(r".\htmlRender\10_中国历届奥运会奖牌数.html")

③中国优势项目


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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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)

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

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(r".\htmlRender\11_中国优势项目.html")

5.💥被单个国家统治的奥运会项目


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

athlete_data = pd.read_csv('./data/athlete_events.csv')
noc_region = pd.read_csv('./data/noc_regions.csv')

# 关联代表国家
data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
print(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)

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 = 0

for _, row in t_data.iterrows():

    if idx % 2 == 0:
        x = 30
        y = int(idx / 2) * 22 + 18
    else:
        x = 70
        y = int(idx / 2) * 22 + 18
    idx += 1
    pos_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(r".\htmlRender\12_被单个国家统治的奥运会项目.html")

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