可视化|历届奥运会数据可视化

文章目录

1 数据来源
2 数据可视化
- 2.1 数量可视化
- - 1. 男性运动员年龄分布直方图
  - 2. 女性运动员年龄分布直方图
  - 3. 全体运动员年龄分布直方图
  - 4. 男性运动员身高体重分布散点图
  - 5. 女性运动员身高体重分布散点图
  - 6. 全体运动员身高体重分布散点图
- 2.2 地理可视化
- - 1. 历届奥运会各国运动员分布轮播地图
  - 2. 历届夏奥会各国运动员分布轮播地图
  - 3. 历届冬奥会各国运动员分布轮播地图
  - 4. 历届夏奥会主办城市地图
  - 5. 历届冬奥会主办城市地图
  - 6. 历届夏奥会主办城市轨迹地图
  - 7. 历届冬奥会主办城市轨迹地图
  - 8. 历届夏奥会主办城市轨迹3D地图
  - 9. 历届冬奥会主办城市轨迹3D地图
  - 10. 历届夏奥会获奖国家分布时间线地图
  - 11. 历届冬奥会获奖国家分布时间线地图
  - 12. 历届夏奥会参赛国家分布时间线地图
  - 13. 历届冬奥会参赛国家分布时间线地图
- 2.3 趋势可视化
- - 1. 男性｜女性｜全体历届奥运会中国参赛人数折线图
  - 2. 男性｜女性｜全体历届奥运会中国参赛人数柱状折线图
  - 3. 历届夏奥会参赛国家数量时间线折线图
  - 4. 历届冬奥会参赛国家数量时间线折线图
  - 5. 历届夏奥会获奖国家数量时间线折线图
  - 6. 历届冬奥会获奖国家数量时间线折线图
  - 7. 历届夏奥会或冬奥会参赛｜获奖国家数量时间线折线图
- 2.4 比例可视化
- - 1. 历届夏奥会参赛国家获奖比例堆叠柱状折线图
  - 2. 历届冬奥会参赛国家获奖比例堆叠柱状折线图

1 数据来源

Kaggle奥运会数据集，包括从1896年雅典奥运会到2016年里约热内卢的所有奥运赛事和运动员数据，可用于历届奥运会数据可视化。

链接：https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results

2 数据可视化

2.1 数量可视化

1. 男性运动员年龄分布直方图

#[可视化]男性运动员年龄分布
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode# 将数据存储到列表中
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
df=athlete_df.groupby(['Sex','Age'])['ID'].count().reset_index()
m_age=df[df['Sex']=='M']['Age'].values.tolist()
m_age_num=df[df['Sex']=='M']['ID'].values.tolist()c = (Bar().add_xaxis(m_age).add_yaxis("", m_age_num, category_gap=0).set_series_opts(itemstyle_opts={"normal": {"color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: 'rgba(0, 244, 255, 1)'}, {offset: 1,color: 'rgba(0, 77, 167, 1)'}], false)"""),"shadowColor": "rgb(0, 160, 221)",}},label_opts=opts.LabelOpts(is_show=False),markline_opts=opts.MarkLineOpts()).set_global_opts(title_opts=opts.TitleOpts(title="男性运动员年龄分布",pos_left='center'),yaxis_opts=opts.AxisOpts(name='数量/个'),xaxis_opts=opts.AxisOpts(name='年龄/岁')).render("./Visual/[直方图]男性运动员年龄分布.html")
)

2. 女性运动员年龄分布直方图

#[可视化]女性运动员年龄分布
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker# 将数据存储到列表中
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
df=athlete_df.groupby(['Sex','Age'])['ID'].count().reset_index()
w_age=df[df['Sex']=='F']['Age'].values.tolist()
w_age_num=df[df['Sex']=='F']['ID'].values.tolist()c = (Bar().add_xaxis(w_age).add_yaxis("", w_age_num, category_gap=0).set_series_opts(itemstyle_opts={"normal": {"color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: 'rgba(0, 244, 255, 1)'}, {offset: 1,color: 'rgba(0, 77, 167, 1)'}], false)"""),"shadowColor": "rgb(0, 160, 221)",}},label_opts=opts.LabelOpts(is_show=False),markline_opts=opts.MarkLineOpts()).set_global_opts(title_opts=opts.TitleOpts(title="女性运动员年龄分布",pos_left='center'),yaxis_opts=opts.AxisOpts(name='数量/个'),xaxis_opts=opts.AxisOpts(name='年龄/岁')).render("./Visual/[直方图]女性运动员年龄分布.html")
)

3. 全体运动员年龄分布直方图

#[可视化]全体运动员年龄分布
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker# 将数据存储到列表中
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
df=athlete_df.groupby(['Age'])['ID'].count().reset_index()
age=df['Age'].values.tolist()
age_num=df['ID'].values.tolist()c = (Bar().add_xaxis(age).add_yaxis("", age_num, category_gap=0).set_series_opts(itemstyle_opts={"normal": {"color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: 'rgba(0, 244, 255, 1)'}, {offset: 1,color: 'rgba(0, 77, 167, 1)'}], false)"""),"shadowColor": "rgb(0, 160, 221)",}},label_opts=opts.LabelOpts(is_show=False),markline_opts=opts.MarkLineOpts()).set_global_opts(title_opts=opts.TitleOpts(title="运动员年龄分布",pos_left='center'),yaxis_opts=opts.AxisOpts(name='数量/个'),xaxis_opts=opts.AxisOpts(name='年龄/岁')).render("./Visual/[直方图]运动员年龄分布.html")
)

4. 男性运动员身高体重分布散点图

#[可视化]男性运动员身高体重
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Scatter# 将数据存储到列表中
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
athlete_df=athlete_df[athlete_df['Sex']=='M']
df=athlete_df[['Height','Weight']].drop_duplicates().dropna()
df=df.sort_values(by=['Weight'])
height=df['Height'].values.tolist()
weight=df['Weight'].values.tolist()c = (Scatter().add_xaxis(weight).add_yaxis("", height,symbol_size=7).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="男性运动员身高体重分布",pos_left='center'),xaxis_opts=opts.AxisOpts(min_=35,max_=60,name='Weight'),yaxis_opts=opts.AxisOpts(max_=220,min_=100,name='Height')).render("./Visual/[散点图]男性运动员身高体重.html")
)

5. 女性运动员身高体重分布散点图

#[可视化]女性运动员身高体重
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Scatter# 将数据存储到列表中
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
athlete_df=athlete_df[athlete_df['Sex']=='F']
df=athlete_df[['Height','Weight']].drop_duplicates().dropna()
df=df.sort_values(by=['Weight'])
height=df['Height'].values.tolist()
weight=df['Weight'].values.tolist()c = (Scatter().add_xaxis(weight).add_yaxis("", height,symbol_size=7).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="女性运动员身高体重分布",pos_left='center'),xaxis_opts=opts.AxisOpts(max_=50, min_=30, name='Weight'),yaxis_opts=opts.AxisOpts(max_=220,min_=100,name='Height')).render("./Visual/[散点图]女性运动员身高体重.html")
)

6. 全体运动员身高体重分布散点图

#[可视化]全体运动员身高体重
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Scatter# 将数据存储到列表中
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
df=athlete_df[['Height','Weight']].drop_duplicates().dropna()
df=df.sort_values(by=['Weight'])
height=df['Height'].values.tolist()
weight=df['Weight'].values.tolist()c = (Scatter().add_xaxis(weight).add_yaxis("", height,symbol_size=7).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="全体运动员身高体重分布",pos_left='center'),xaxis_opts=opts.AxisOpts(max_=80, min_=28, name='Weight'),yaxis_opts=opts.AxisOpts(max_=220,min_=100,name='Height')).render("./Visual/[散点图]全体运动员身高体重.html")
)

2.2 地理可视化

1. 历届奥运会各国运动员分布轮播地图

利用Pandas将历届运动员数据按照年份和国家聚类：

以1896年为例，筛选1896年的各国家参赛运动员数据：

利用Pandas统计奥运会举办年份数量：

利用Timeline和Map图表进行可视化：

#[可视化]历届奥运会各国运动员分布自动轮播地图
from pyecharts import options as opts
from pyecharts.charts import Map,Timeline# 统计年份数量
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
data=athlete_df.groupby(by=['Year','Team'])['ID'].count().reset_index()
count=len(athlete_df['Year'].unique())
years=athlete_df['Year'].sort_values().unique().tolist()
timeline =(Timeline().add_schema(is_auto_play=True, is_loop_play=True,is_timeline_show=True, play_interval=500)
)
for i in range(count):year=years[i]data_list=data[data['Year']==year][['Team','ID']].values.tolist()map=(Map().add("", data_list, "world",is_map_symbol_show=False,).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="历届奥运会各国运动员分布图————{}年".format(years[i])),visualmap_opts=opts.VisualMapOpts(max_=500,range_color=['#feeeed','#d71345'])))
timeline.add(map,'{}'.format(years[i]))timeline.render("./Visual/[时间线地图]各国运动员分布图.html")

2. 历届夏奥会各国运动员分布轮播地图

利用Pandas筛选季节为Summer的数据：

#[可视化]历届夏奥会各国运动员分布自动轮播地图
from pyecharts import options as opts
from pyecharts.charts import Map,Timeline# 统计年份数量
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
athlete_df=athlete_df[athlete_df['Season']=='Summer']
data=athlete_df.groupby(by=['Year','Team'])['ID'].count().reset_index()
count=len(athlete_df['Year'].unique())
years=athlete_df['Year'].sort_values().unique().tolist()timeline =(Timeline().add_schema(is_auto_play=True, is_loop_play=True,is_timeline_show=True, play_interval=500,checkpointstyle_opts=opts.TimelineCheckPointerStyle(color='#ef4136',border_color='#ffce7b'))
)
for i in range(count):year=years[i]data_list=data[data['Year']==year][['Team','ID']].values.tolist()map=(Map().add("", data_list, "world",is_map_symbol_show=False,).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="历届夏奥会各国运动员分布图————{}年".format(years[i])),visualmap_opts=opts.VisualMapOpts(max_=500,range_color=['#ffce7b','#ef4136'])))timeline.add(map,'{}'.format(years[i]))
timeline.render("./Visual/[时间线地图]夏奥会各国运动员分布图.html")

3. 历届冬奥会各国运动员分布轮播地图

#[可视化]历届冬奥会各国运动员分布自动轮播地图
from pyecharts import options as opts
from pyecharts.charts import Map,Timeline
import pandas as pd# 统计年份数量
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
athlete_df=athlete_df[athlete_df['Season']=='Winter']
data=athlete_df.groupby(by=['Year','Team'])['ID'].count().reset_index()
count=len(athlete_df['Year'].unique())
years=athlete_df['Year'].sort_values().unique().tolist()timeline =(Timeline().add_schema(is_auto_play=True, is_loop_play=True,is_timeline_show=True, play_interval=500,checkpointstyle_opts=opts.TimelineCheckPointerStyle(color='#6950a1',border_color='#9b95c9'))
)
for i in range(count):year=years[i]data_list=data[data['Year']==year][['Team','ID']].values.tolist()map=(Map().add("", data_list, "world",is_map_symbol_show=False,).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="历届冬奥会各国运动员分布图————{}年".format(years[i])),visualmap_opts=opts.VisualMapOpts(max_=500,range_color=['#afb4db','#6f599c'])))timeline.add(map,'{}'.format(years[i]))
timeline.render("./Visual/[时间线地图]冬奥会各国运动员分布图.html")

4. 历届夏奥会主办城市地图

利用Pandas获取历届夏奥会主办城市：

收集各个主办城市的经纬度：

将城市经纬度数据转换为json格式并写入文件：

#[可视化]历届夏奥会主办城市地图
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType, SymbolTypeathlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
athlete_df=athlete_df[athlete_df['Season']=='Summer']
city_df=athlete_df[['Year','City']].drop_duplicates().sort_values(by=['Year'])
# 获取城市
cities=city_df[['City','Year']].values.tolist()
locs=[]
for city in cities:locs.append(tuple(city))
# print(locs)
c = (Geo().add_schema(maptype="world",# itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"),).add_coordinate_json("./DataSet/Location/Summer_City.json").add("",locs,type_=ChartType.EFFECT_SCATTER,color="red",).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title='历届夏奥会主办城市地图',pos_left='center')).render("./Visual/[地理坐标图]历届夏奥会主办城市地图.html")
)

5. 历届冬奥会主办城市地图

#[可视化]历届冬奥会主办城市地图
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType, SymbolTypeathlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
athlete_df=athlete_df[athlete_df['Season']=='Winter']
city_df=athlete_df[['Year','City']].drop_duplicates().sort_values(by=['Year'])# 获取城市
cities=city_df[['City','Year']].values.tolist()
locs=[]
for city in cities:locs.append(tuple(city))
# print(locs)c = (Geo().add_schema(maptype="world",# itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"),).add_coordinate_json("./DataSet/Location/Winter_City.json").add("",locs,type_=ChartType.EFFECT_SCATTER,color="#4e72b8",).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title='历届冬奥会主办城市地图',pos_left='center')).render("./Visual/[地理坐标图]历届冬奥会主办城市地图.html")
)

6. 历届夏奥会主办城市轨迹地图

#[可视化]历届夏奥会主办城市轨迹地图
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType, SymbolTypeathlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
athlete_df=athlete_df[athlete_df['Season']=='Summer']
city_df=athlete_df[['Year','City']].drop_duplicates().sort_values(by=['Year'])# 获取城市
cities=city_df[['City','Year']].values.tolist()
locs=[]
for city in cities:locs.append(tuple(city))
# print(locs)
# 获取轨迹
city_list=list(city_df['City'])
lines=[]
for i in range(len(city_list)-1):lines.append(tuple([city_list[i],city_list[i+1]]))
# print(lines)c = (Geo().add_schema(maptype="world",itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"),).add_coordinate_json("./DataSet/Location/Summer_City.json").add("",locs,type_=ChartType.EFFECT_SCATTER,color="white",).add("",lines,type_=ChartType.LINES,effect_opts=opts.EffectOpts(symbol=SymbolType.ARROW, symbol_size=6, color="blue"),linestyle_opts=opts.LineStyleOpts(curve=0.2),).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title='历届夏奥会主办城市轨迹地图',pos_left='center')).render("./Visual/[地理坐标图]历届夏奥会主办城市轨迹地图.html")
)

7. 历届冬奥会主办城市轨迹地图

#[可视化]历届冬奥会主办城市轨迹地图
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType, SymbolTypeathlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
athlete_df=athlete_df[athlete_df['Season']=='Winter']
city_df=athlete_df[['Year','City']].drop_duplicates().sort_values(by=['Year'])# 获取城市
cities=city_df[['City','Year']].values.tolist()
locs=[]
for city in cities:locs.append(tuple(city))
# print(locs)
# 获取轨迹
city_list=list(city_df['City'])
lines=[]
for i in range(len(city_list)-1):lines.append(tuple([city_list[i],city_list[i+1]]))
# print(lines)c = (Geo().add_schema(maptype="world",itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"),).add_coordinate_json("./DataSet/Location/Winter_City.json").add("",locs,type_=ChartType.EFFECT_SCATTER,color="white",).add("",lines,type_=ChartType.LINES,effect_opts=opts.EffectOpts(symbol=SymbolType.ARROW, symbol_size=6, color="yellow"),linestyle_opts=opts.LineStyleOpts(curve=0.2),).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title='历届冬奥会主办城市轨迹地图',pos_left='center')).render("./Visual/[地理坐标图]历届冬奥会主办城市轨迹地图.html")
)

8. 历届夏奥会主办城市轨迹3D地图

#[可视化]历届夏奥会主办城市轨迹3D地图
from pyecharts import options as opts
from pyecharts.charts import Map3D
from pyecharts.globals import ChartType
import pandas as pdsummer_city=pd.read_csv("./DataSet/Location/Summer_City.csv",header=None)
cities_location=summer_city[[1,2]].values.tolist()
data=[]
for i in range(len(cities_location)-1):data.append([cities_location[i],cities_location[i+1]])c = (Map3D(init_opts=opts.InitOpts(width='1000px',height='700px')).add_schema(maptype="world",box_depth=70,itemstyle_opts=opts.ItemStyleOpts(color="rgb(5,101,123)",opacity=1,border_width=0.8,border_color="rgb(62,215,213)",),light_opts=opts.Map3DLightOpts(main_color="#fff",main_intensity=1.2,is_main_shadow=False,main_alpha=55,main_beta=10,ambient_intensity=0.3,),post_effect_opts=opts.Map3DPostEffectOpts(is_enable=True),).add(series_name="",data_pair=data,type_=ChartType.LINES3D,effect=opts.Lines3DEffectOpts(is_show=True,period=4,trail_width=3,trail_length=0.5,trail_color="#f00",trail_opacity=1,),linestyle_opts=opts.LineStyleOpts(is_show=False, color="#fff", opacity=0),).set_global_opts(title_opts=opts.TitleOpts(title="历届夏奥会主办城市轨迹3D地图")).render("./Visual/[地理坐标图]历届夏奥会主办城市轨迹3D地图.html")
)

9. 历届冬奥会主办城市轨迹3D地图

#[可视化]历届冬奥会主办城市轨迹3D地图
from pyecharts import options as opts
from pyecharts.charts import Map3D
from pyecharts.globals import ChartType
import pandas as pdwinter_city=pd.read_csv("./DataSet/Location/Winter_city.csv",header=None)
cities_location=winter_city[[1,2]].values.tolist()
data=[]
for i in range(len(cities_location)-1):data.append([cities_location[i],cities_location[i+1]])c = (Map3D(init_opts=opts.InitOpts(width='1000px',height='700px')).add_schema(maptype="world",box_depth=70,itemstyle_opts=opts.ItemStyleOpts(color="rgb(5,101,123)",opacity=1,border_width=0.8,border_color="rgb(62,215,213)",),light_opts=opts.Map3DLightOpts(main_color="#fff",main_intensity=1.2,is_main_shadow=False,main_alpha=55,main_beta=10,ambient_intensity=0.3,),post_effect_opts=opts.Map3DPostEffectOpts(is_enable=True),).add(series_name="",data_pair=data,type_=ChartType.LINES3D,effect=opts.Lines3DEffectOpts(is_show=True,period=4,trail_width=3,trail_length=0.5,trail_color="#f00",trail_opacity=1,),linestyle_opts=opts.LineStyleOpts(is_show=False, color="#fff", opacity=0),).set_global_opts(title_opts=opts.TitleOpts(title="历届冬奥会主办城市轨迹3D地图")).render("./Visual/[地理坐标图]历届冬奥会主办城市轨迹3D地图.html")
)

10. 历届夏奥会获奖国家分布时间线地图

#[可视化]历届夏奥会获奖国家分布自动轮播地图
from pyecharts import options as opts
from pyecharts.charts import Map,Timeline# 统计年份数量
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
count_df=athletes_df[athletes_df['Season']=='Summer'].groupby(['Year','Team'])['Medal'].count().reset_index()
count_df['value']=1
count=len(count_df['Year'].unique())
years=count_df['Year'].sort_values().unique().tolist()
timeline =(Timeline().add_schema(is_auto_play=True, is_loop_play=True,is_timeline_show=True, play_interval=500,checkpointstyle_opts=opts.TimelineCheckPointerStyle(color='#6950a1',border_color='#9b95c9'))
)
for i in range(count):year=years[i]data_list=count_df.loc[(count_df['Year']==year)&(count_df['Medal']!=0),['Team','value']].values.tolist()map=(Map().add("", data_list, "world",is_map_symbol_show=False,).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="历届夏奥会获奖国家分布图————{}年".format(years[i])),visualmap_opts=opts.VisualMapOpts(is_show=False,range_color=['#9b95c9','#6f599c'])))timeline.add(map,'{}'.format(years[i]))
timeline.render("./Visual/[时间线地图]历届夏奥会获奖国家分布图.html")

11. 历届冬奥会获奖国家分布时间线地图

#[可视化]历届冬奥会获奖国家分布自动轮播地图
from pyecharts import options as opts
from pyecharts.charts import Map,Timeline# 统计年份数量
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
count_df=athletes_df[athletes_df['Season']=='Winter'].groupby(['Year','Team'])['Medal'].count().reset_index()
count_df['value']=1
count=len(count_df['Year'].unique())
years=count_df['Year'].sort_values().unique().tolist()timeline =(Timeline().add_schema(is_auto_play=True, is_loop_play=True,is_timeline_show=True, play_interval=500,checkpointstyle_opts=opts.TimelineCheckPointerStyle(color='#4e72b8',border_color='#90d7ec'))
)
for i in range(count):year=years[i]data_list=count_df.loc[(count_df['Year']==year)&(count_df['Medal']!=0),['Team','value']].values.tolist()map=(Map().add("", data_list, "world",is_map_symbol_show=False,).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="历届冬奥会获奖国家分布图————{}年".format(years[i])),visualmap_opts=opts.VisualMapOpts(is_show=False)))timeline.add(map,'{}'.format(years[i]))
timeline.render("./Visual/[时间线地图]历届冬奥会获奖国家分布图.html")

12. 历届夏奥会参赛国家分布时间线地图

#[可视化]历届夏奥会参赛国家分布自动轮播地图
from pyecharts import options as opts
from pyecharts.charts import Map,Timeline# 统计年份数量
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
count_df=athletes_df[athletes_df['Season']=='Summer'].groupby(['Year','Team'])['Medal'].count().reset_index()
count_df['value']=1
count=len(count_df['Year'].unique())
years=count_df['Year'].sort_values().unique().tolist()
timeline =(Timeline().add_schema(is_auto_play=True, is_loop_play=True,is_timeline_show=True, play_interval=500,checkpointstyle_opts=opts.TimelineCheckPointerStyle(color='#fcaf17',border_color='#ffce7b'))
)
for i in range(count):year=years[i]data_list=count_df.loc[count_df['Year']==year,['Team','value']].values.tolist()map=(Map().add("", data_list, "world",is_map_symbol_show=False,).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="历届夏奥会参赛国家分布图————{}年".format(years[i])),visualmap_opts=opts.VisualMapOpts(is_show=False,range_color=['#fdb933','#6f599c'])))timeline.add(map,'{}'.format(years[i]))
timeline.render("./Visual/[时间线地图]历届夏奥会参赛国家分布图.html")

13. 历届冬奥会参赛国家分布时间线地图

#[可视化]历届冬奥会参赛国家分布自动轮播地图
from pyecharts import options as opts
from pyecharts.charts import Map,Timeline# 统计年份数量
athlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
count_df=athletes_df[athletes_df['Season']=='Winter'].groupby(['Year','Team'])['Medal'].count().reset_index()
count_df['value']=1
count=len(count_df['Year'].unique())
years=count_df['Year'].sort_values().unique().tolist()timeline =(Timeline().add_schema(is_auto_play=True, is_loop_play=True,is_timeline_show=True, play_interval=500,checkpointstyle_opts=opts.TimelineCheckPointerStyle(color='#00ae9d',border_color='#afdfe4'))
)
for i in range(count):year=years[i]data_list=count_df.loc[count_df['Year']==year,['Team','value']].values.tolist()map=(Map().add("", data_list, "world",is_map_symbol_show=False,).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="历届冬奥会参赛国家分布图————{}年".format(years[i])),visualmap_opts=opts.VisualMapOpts(is_show=False,range_color=['#00ae9d','#6f599c'])))timeline.add(map,'{}'.format(years[i]))
timeline.render("./Visual/[时间线地图]历届冬奥会参赛国家分布图.html")

2.3 趋势可视化

1. 男性｜女性｜全体历届奥运会中国参赛人数折线图

利用Pandas得到中国历届奥运会男性｜女性｜全体参赛人数：

观察发现，在1932年和1952年没有女性运动员，因此对数据进行补充：

#[可视化]男性|女性|全体历届奥运会中国参赛人数折线图
import pandas as pd
from pyecharts.charts import Line,Timeline
import pyecharts.options as opts
from pyecharts.globals import ThemeType,JsCodeathlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
china_df=athlete_df[athlete_df['Team']=='China']
# 全体
all_df=china_df.groupby(by=['Year'])['ID'].count().reset_index()
all=china_df.groupby(by=['Year'])['ID'].count().reset_index().values.tolist()
# 男性
male_df=china_df[china_df['Sex']=='M']
male=male_df.groupby(by=['Year'])['ID'].count().reset_index().values.tolist()
# 女性
female_df=china_df[china_df['Sex']=='F']
female=female_df.groupby(by=['Year'])['ID'].count().reset_index()
# 在1932年和1952年没有女性运动员
lost=pd.DataFrame({'Year':[1932,1952],'ID':[0,0]})
female=female.append(lost,ignore_index=True).sort_values(by=['Year']).values.tolist()# 背景色
background_color_js = ("new echarts.graphic.LinearGradient(0, 0, 0, 1, ""[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)# 线条样式
linestyle_dic = { 'normal': {'width': 4,  'shadowColor': '#696969', 'shadowBlur': 10,  'shadowOffsetY': 10,  'shadowOffsetX': 10,  }}timeline = Timeline(init_opts=opts.InitOpts(width='980px',height='600px'))
timeline.add_schema(is_auto_play=True, is_loop_play=True, is_timeline_show=True, play_interval=500)ALL, MALE, FEMALE = [], [], []
x_data=all_df['Year'].values.tolist()
x=[]
for i in x_data:x.append(str(i))
print(x)for i in range(len(x)):ALL.append(all[i][1])# print(ALL)MALE.append(male[i][1])# print(MALE)FEMALE.append(female[i][1])# print(FEMALE)line = (Line(init_opts=opts.InitOpts(width='980px',height='600px')).add_xaxis(x)# 全体线条.add_yaxis('全体',ALL,symbol_size=10,is_smooth=True,label_opts=opts.LabelOpts(is_show=True),markpoint_opts=opts.MarkPointOpts(data=[ opts.MarkPointItem(name="",type_='max',value_index=0,symbol_size=[40, 25],)],label_opts=opts.LabelOpts(is_show=False),))# 男性线条.add_yaxis('男性',MALE,symbol_size=5,is_smooth=True,label_opts=opts.LabelOpts(is_show=True),markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name="",type_='max',value_index=0,symbol_size=[40, 25],)],label_opts=opts.LabelOpts(is_show=False),))# 女性线条.add_yaxis('女性',FEMALE,symbol_size=5,is_smooth=True,label_opts=opts.LabelOpts(is_show=True),markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name="",type_='max',value_index=0,symbol_size=[40, 25],)],label_opts=opts.LabelOpts(is_show=False),)).set_series_opts(linestyle_opts=linestyle_dic).set_global_opts(title_opts=opts.TitleOpts(title='男性|女性|全体历届奥运会中国参赛人数折线图',pos_left='center',pos_top='2%',title_textstyle_opts=opts.TextStyleOpts(color='#DC143C', font_size=20)),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=14, color='red'),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2, color='#DB7093'))),yaxis_opts=opts.AxisOpts(name='数量/人',            is_scale=True,max_=750,name_textstyle_opts=opts.TextStyleOpts(font_size=16,font_weight='bold',color='#DC143C'),axislabel_opts=opts.LabelOpts(font_size=13),splitline_opts=opts.SplitLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(type_='dashed')),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2, color='#DB7093'))),legend_opts=opts.LegendOpts(is_show=True, pos_right='1%', pos_top='2%',legend_icon='roundRect',orient = 'vertical'),))timeline.add(line, '{}'.format(x[i]))timeline.render("./Visual/[时间线折线图]男性-女性-全体历届奥运会中国参赛人数折线图.html")

2. 男性｜女性｜全体历届奥运会中国参赛人数柱状折线图

#[可视化]男性|女性|全体历届奥运会中国参赛人数柱状折线图
import pandas as pd
from pyecharts.charts import Bar
import pyecharts.options as opts
from pyecharts.globals import ThemeType,JsCodeathlete_df=pd.read_csv("./DataSet/History/athlete_events.csv")
china_df=athlete_df[athlete_df['Team']=='China']
# 全体
all_df=china_df.groupby(by=['Year'])['ID'].count().reset_index()
all=china_df.groupby(by=['Year'])['ID'].count().reset_index()['ID'].values.tolist()
# 男性
male_df=china_df[china_df['Sex']=='M']
male=male_df.groupby(by=['Year'])['ID'].count().reset_index()['ID'].values.tolist()
# 女性
female_df=china_df[china_df['Sex']=='F']
female=female_df.groupby(by=['Year'])['ID'].count().reset_index()
# 在1932年和1952年没有女性运动员
lost=pd.DataFrame({'Year':[1932,1952],'ID':[0,0]})
female=female.append(lost,ignore_index=True).sort_values(by=['Year'])['ID'].values.tolist()
# x轴数据
x_data=all_df['Year'].values.tolist()
x=[]
for i in x_data:x.append(str(i))bar=(Bar().add_xaxis(x).add_yaxis("男性", male, gap="0%").add_yaxis("女性", female, gap="0%").extend_axis(yaxis=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value}"), interval=100, name='总人数/人')).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="男性|女性|全体历届奥运会中国参赛人数柱状折线图"),yaxis_opts=opts.AxisOpts(max_=800,name='人数/人',axislabel_opts=opts.LabelOpts(formatter="{value}")),legend_opts=opts.LegendOpts(pos_left='right'), datazoom_opts=opts.DataZoomOpts())
)line = Line().add_xaxis(x).add_yaxis("全体", all, yaxis_index=1)
bar.overlap(line)
bar.render("./Visual/[柱状折线图]男性-女性-全体历届奥运会中国参赛人数柱状折线图.html")

3. 历届夏奥会参赛国家数量时间线折线图

利用Pandas从CSV中获取各国家在各年份的奖牌数量：

#[可视化]历届夏奥会参赛国家数量趋势
from pyecharts import options as opts
from pyecharts.charts import Line,Timeline
from pyecharts.globals import ThemeType,JsCode# 统计各国家在各年份的奖牌数量
athletes_df=pd.read_csv("./DataSet/History/athlete_events.csv")
summer_count_df=athletes_df[athletes_df['Season']=='Summer'].groupby(['Year','Team'])['Medal'].count().reset_index()
# 获取历届夏奥会参赛国家数量
engage_data=[]
years=summer_count_df['Year'].drop_duplicates()
for year in years:engage_data.append([year,summer_count_df.loc[summer_count_df['Year']==year].shape[0]])# 线条样式
linestyle_dic = {'normal': {'width': 4,'shadowColor': '#696969','shadowBlur': 10,'shadowOffsetY': 10,'shadowOffsetX': 10,
}
}timeline = Timeline().add_schema(is_auto_play=True, is_loop_play=False,is_timeline_show=True, play_interval=500)data = []
x_data = []
for year in years:x_data.append(str(year))for i  in range(len(x_data)):data.append(engage_data[i][-1])line = (Line().add_xaxis(x_data).add_yaxis('',data,symbol_size=10,is_smooth=True,label_opts=opts.LabelOpts(is_show=True),markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name="",type_='max',value_index=0,symbol_size=[40, 25],)],label_opts=opts.LabelOpts(is_show=False),)).set_series_opts(linestyle_opts=linestyle_dic, label_opts=opts.LabelOpts(font_size=12)).set_global_opts(title_opts=opts.TitleOpts(title='历届夏奥会参赛国家数量趋势',title_textstyle_opts=opts.TextStyleOpts(font_size=20)),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=14),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),yaxis_opts=opts.AxisOpts(is_scale=True,name_textstyle_opts=opts.TextStyleOpts(font_size=16, font_weight='bold'),axislabel_opts=opts.LabelOpts(font_size=13),splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dashed')),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),legend_opts=opts.LegendOpts(is_show=True, pos_right='1%', pos_top='2%',legend_icon='roundRect'),))timeline.add(line, '{}'.format(engage_data[i][0]))timeline.render("./Visual/[时间线折线图]历届夏奥会参赛国家数量趋势.html")

4. 历届冬奥会参赛国家数量时间线折线图

#[可视化]历届冬奥会参赛国家数量趋势
from pyecharts import options as opts
from pyecharts.charts import Line,Timeline
from pyecharts.globals import ThemeType,JsCode# 统计各国家在各年份的奖牌数量
athletes_df=pd.read_csv("./DataSet/History/athlete_events.csv")
winter_count_df=athletes_df[athletes_df['Season']=='Winter'].groupby(['Year','Team'])['Medal'].count().reset_index()
# 获取历届冬奥会参赛国家数量
engage_data=[]
years=winter_count_df['Year'].drop_duplicates()
for year in years:engage_data.append([year,winter_count_df.loc[winter_count_df['Year']==year].shape[0]])# 线条样式
linestyle_dic = {'normal': {'width': 4,'shadowColor': '#696969','shadowBlur': 10,'shadowOffsetY': 10,'shadowOffsetX': 10,
}
}timeline = Timeline().add_schema(is_auto_play=True, is_loop_play=False,is_timeline_show=True, play_interval=500)data = []
x_data = []
for year in years:x_data.append(str(year))for i  in range(len(x_data)):data.append(engage_data[i][-1])line = (Line().add_xaxis(x_data).add_yaxis('',data,symbol_size=10,is_smooth=True,label_opts=opts.LabelOpts(is_show=True),markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name="",type_='max',value_index=0,symbol_size=[40, 25],)],label_opts=opts.LabelOpts(is_show=False),)).set_series_opts(linestyle_opts=linestyle_dic, label_opts=opts.LabelOpts(font_size=12)).set_global_opts(title_opts=opts.TitleOpts(title='历届冬奥会参赛国家数量趋势',title_textstyle_opts=opts.TextStyleOpts(font_size=20)),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=14),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),yaxis_opts=opts.AxisOpts(is_scale=True,name_textstyle_opts=opts.TextStyleOpts(font_size=16, font_weight='bold'),axislabel_opts=opts.LabelOpts(font_size=13),splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dashed')),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),legend_opts=opts.LegendOpts(is_show=True, pos_right='1%', pos_top='2%',legend_icon='roundRect'),))timeline.add(line, '{}'.format(engage_data[i][0]))timeline.render("./Visual/[时间线折线图]历届冬奥会参赛国家数量趋势.html")

5. 历届夏奥会获奖国家数量时间线折线图

#[可视化]历届夏奥会获奖国家数量趋势
from pyecharts import options as opts
from pyecharts.charts import Line,Timeline
from pyecharts.globals import ThemeType,JsCode# 统计各国家在各年份的奖牌数量
athletes_df=pd.read_csv("./DataSet/History/athlete_events.csv")
summer_count_df=athletes_df[athletes_df['Season']=='Summer'].groupby(['Year','Team'])['Medal'].count().reset_index()
# 获取历届夏奥会获奖国家数量
medals_data=[]
years=summer_count_df['Year'].drop_duplicates()
for year in years:medals_data.append([year,summer_count_df.loc[(summer_count_df['Year']==year)&(summer_count_df['Medal']!=0)].shape[0]])# 线条样式
linestyle_dic = {'normal': {'width': 4,'color':'#6950a1','shadowColor': '#696969','shadowBlur': 10,'shadowOffsetY': 10,'shadowOffsetX': 10,
}
}timeline = Timeline().add_schema(is_auto_play=True, is_loop_play=False,is_timeline_show=True, play_interval=500)data = []
x_data = []
for year in years:x_data.append(str(year))for i  in range(len(x_data)):data.append(medals_data[i][-1])line = (Line().add_xaxis(x_data).add_yaxis('',data,symbol_size=10,is_smooth=True,label_opts=opts.LabelOpts(is_show=True),markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name="",type_='max',value_index=0,symbol_size=[40, 25],itemstyle_opts=opts.ItemStyleOpts(color='#6950a1'))],label_opts=opts.LabelOpts(is_show=False),),color='#6950a1').set_series_opts(linestyle_opts=linestyle_dic, label_opts=opts.LabelOpts(font_size=12,color='#6950a1')).set_global_opts(title_opts=opts.TitleOpts(title='历届夏奥会获奖国家数量趋势',title_textstyle_opts=opts.TextStyleOpts(font_size=20)),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=14),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),yaxis_opts=opts.AxisOpts(is_scale=True,name_textstyle_opts=opts.TextStyleOpts(font_size=16, font_weight='bold'),axislabel_opts=opts.LabelOpts(font_size=13),splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dashed')),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),legend_opts=opts.LegendOpts(is_show=True, pos_right='1%', pos_top='2%',legend_icon='roundRect'),))timeline.add(line, '{}'.format(engage_data[i][0]))timeline.render("./Visual/[时间线折线图]历届夏奥会获奖国家数量趋势.html")

6. 历届冬奥会获奖国家数量时间线折线图

#[可视化]历届冬奥会获奖国家数量趋势
from pyecharts import options as opts
from pyecharts.charts import Line,Timeline
from pyecharts.globals import ThemeType,JsCode# 统计各国家在各年份的奖牌数量
athletes_df=pd.read_csv("./DataSet/History/athlete_events.csv")
winter_count_df=athletes_df[athletes_df['Season']=='Winter'].groupby(['Year','Team'])['Medal'].count().reset_index()
# 获取历届冬奥会获奖国家数量
medals_data=[]
years=winter_count_df['Year'].drop_duplicates()
for year in years:medals_data.append([year,winter_count_df.loc[(winter_count_df['Year']==year)&(winter_count_df['Medal']!=0)].shape[0]])# 线条样式
linestyle_dic = {'normal': {'width': 4,'color':'#6950a1','shadowColor': '#696969','shadowBlur': 10,'shadowOffsetY': 10,'shadowOffsetX': 10,
}
}timeline = Timeline().add_schema(is_auto_play=True, is_loop_play=False,is_timeline_show=True, play_interval=500)data = []
x_data = []
for year in years:x_data.append(str(year))for i  in range(len(x_data)):data.append(medals_data[i][-1])line = (Line().add_xaxis(x_data).add_yaxis('',data,symbol_size=10,is_smooth=True,label_opts=opts.LabelOpts(is_show=True),markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name="",type_='max',value_index=0,symbol_size=[40, 25],itemstyle_opts=opts.ItemStyleOpts(color='#6950a1'))],label_opts=opts.LabelOpts(is_show=False),),color='#6950a1').set_series_opts(linestyle_opts=linestyle_dic, label_opts=opts.LabelOpts(font_size=12,color='#6950a1')).set_global_opts(title_opts=opts.TitleOpts(title='历届冬奥会获奖国家数量趋势',title_textstyle_opts=opts.TextStyleOpts(font_size=20)),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=14),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),yaxis_opts=opts.AxisOpts(is_scale=True,name_textstyle_opts=opts.TextStyleOpts(font_size=16, font_weight='bold'),axislabel_opts=opts.LabelOpts(font_size=13),splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dashed')),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),legend_opts=opts.LegendOpts(is_show=True, pos_right='1%', pos_top='2%',legend_icon='roundRect'),))timeline.add(line, '{}'.format(engage_data[i][0]))timeline.render("./Visual/[时间线折线图]历届冬奥会获奖国家数量趋势.html")

7. 历届夏奥会或冬奥会参赛｜获奖国家数量时间线折线图

将上述图表组合起来，得到下列图表：

2.4 比例可视化

1. 历届夏奥会参赛国家获奖比例堆叠柱状折线图

#[可视化]历届夏奥会获奖｜未获奖国家比例
from pyecharts import options as opts
from pyecharts.charts import Bar, Line
from pyecharts.globals import ThemeType# 统计各国家在各年份的奖牌数量
athletes_df=pd.read_csv("./DataSet/History/athlete_events.csv")
summer_count_df=athletes_df[athletes_df['Season']=='Summer'].groupby(['Year','Team'])['Medal'].count().reset_index()
# 获取历届夏奥会参赛国家数量
others_data=[]
medals_data=[]
years=summer_count_df['Year'].drop_duplicates()
for year in years:others_data.append(summer_count_df.loc[(summer_count_df['Year']==year)&(summer_count_df['Medal']==0)].shape[0])medals_data.append(summer_count_df.loc[(summer_count_df['Year']==year)&(summer_count_df['Medal']!=0)].shape[0])
# 折线图数据
line_data=[]
for i in range(len(medals_data)):line_data.append(round(medals_data[i]/(medals_data[i]+others_data[i]),2))
x_data = []
for year in years:x_data.append(str(year))
bar = (Bar(init_opts=opts.InitOpts(theme='light')).add_xaxis(x_data).add_yaxis("获奖",medals_data,stack=1,z_level=2).add_yaxis("未获奖",others_data,stack=1).extend_axis(yaxis=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value}"),max_=1)).set_series_opts(label_opts=opts.LabelOpts(font_size=12)).set_global_opts(title_opts=opts.TitleOpts(title='历届夏奥会获奖｜未获奖国家分布',title_textstyle_opts=opts.TextStyleOpts(font_size=20)),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=14),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),yaxis_opts=opts.AxisOpts(is_scale=True,name_textstyle_opts=opts.TextStyleOpts(font_size=16, font_weight='bold'),axislabel_opts=opts.LabelOpts(font_size=13),splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dashed')),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),legend_opts=opts.LegendOpts(is_show=True, pos_right='1%', pos_top='2%'),datazoom_opts=opts.DataZoomOpts())
)
line = Line().add_xaxis(x_data).add_yaxis("占比", line_data, yaxis_index=1,z_level=100)
bar.overlap(line)
bar.render("./Visual/[堆叠柱状折线图]历届夏奥会获奖-未获奖国家比例.html")

2. 历届冬奥会参赛国家获奖比例堆叠柱状折线图

#[可视化]历届夏奥会获奖｜未获奖国家比例
from pyecharts import options as opts
from pyecharts.charts import Bar, Line
from pyecharts.globals import ThemeType# 统计各国家在各年份的奖牌数量
athletes_df=pd.read_csv("./DataSet/History/athlete_events.csv")
count_df=athletes_df[athletes_df['Season']=='Winter'].groupby(['Year','Team'])['Medal'].count().reset_index()
# 获取历届夏奥会参赛国家数量
others_data=[]
medals_data=[]
years=count_df['Year'].drop_duplicates()
for year in years:others_data.append(count_df.loc[(count_df['Year']==year)&(count_df['Medal']==0)].shape[0])medals_data.append(count_df.loc[(count_df['Year']==year)&(count_df['Medal']!=0)].shape[0])
# 折线图数据
line_data=[]
for i in range(len(medals_data)):line_data.append(round(medals_data[i]/(medals_data[i]+others_data[i]),2))
x_data = []
for year in years:x_data.append(str(year))
bar = (Bar(init_opts=opts.InitOpts(theme='light')).add_xaxis(x_data).add_yaxis("获奖",medals_data,stack=1,z_level=2).add_yaxis("未获奖",others_data,stack=1).extend_axis(yaxis=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value}"),max_=1)).set_series_opts(label_opts=opts.LabelOpts(font_size=12)).set_global_opts(title_opts=opts.TitleOpts(title='历届冬奥会获奖｜未获奖国家分布',title_textstyle_opts=opts.TextStyleOpts(font_size=20)),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=14),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),yaxis_opts=opts.AxisOpts(is_scale=True,name_textstyle_opts=opts.TextStyleOpts(font_size=16, font_weight='bold'),axislabel_opts=opts.LabelOpts(font_size=13),splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dashed')),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2))),legend_opts=opts.LegendOpts(is_show=True, pos_right='1%', pos_top='2%'),datazoom_opts=opts.DataZoomOpts())
)
line = Line().add_xaxis(x_data).add_yaxis("占比", line_data, yaxis_index=1,z_level=100)
bar.overlap(line)
bar.render("./Visual/[堆叠柱状折线图]历届冬奥会获奖-未获奖国家比例.html")

至此，利用Kaggle120年奥运会数据集的数据可视化就完成啦，欢迎交流！

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