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项目全部代码 & 数据集都可以访问我的KLab --【Pyecharts】奥运会数据集可视化分析～获取，点击Fork即可～
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受疫情影响，2020东京奥运会将延期至2021年举行；
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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；
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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🎉🎉～
本项目将会从以下角度来呈现奥运会历史：
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🏆各国累计奖牌数；
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⚽️各项运动产生金牌数
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⛳️运动员层面
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参赛人数趋势
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女性参赛比例趋势
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获得金牌最多的运动员
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获得奖牌/金牌比例
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各项目运动员平均体质数据
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主要国家表现
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🇨🇳中国表现
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🇺🇸美国表现
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💥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()
数据示例
累计奖牌数
夏季奥运会 & 冬季奥运会分别统计
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🏖️夏季奥运会开始于1896年雅典奥运会；
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❄️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
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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；
- 🏃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
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从人数来看，每届夏奥会参赛人数都是冬奥会的4-5倍；
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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_notebook()
🇨🇳中国奥运会表现
CN_data = data[data.region=='China']
CN_data.head()
历届奥运会参赛人数
background_color_js = (
"new echarts.graphic.LinearGradient(1, 0, 0, 1, "
"[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)
athlete = CN_data.groupby(['Year', 'Season'])['Name'].nunique().reset_index()
athlete.columns = ['Year', 'Season', 'Nums']
athlete = athlete.sort_values(by="Year" , ascending=False)
s_bar = (
Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px'))
.add_xaxis([row['Year'] for _, row in athlete[athlete.Season=='Summer'].iterrows()])
.add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season=='Summer'].iterrows()],
category_gap='40%',
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 1,
color: '#00BFFF'
}, {
offset: 0,
color: '#32CD32'
}])""")))
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='top',
font_style='italic'))
.set_global_opts(
title_opts=opts.TitleOpts(title="中国历年奥运会参赛人数-夏奥会", pos_left='center'),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),
graphic_opts=[
opts.GraphicImage(
graphic_item=opts.GraphicItem(
id_="logo", right=0, top=0, z=-10, bounding="raw", origin=[75, 75]
),
graphic_imagestyle_opts=opts.GraphicImageStyleOpts(
image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",
width=1000,
height=600,
opacity=0.6,),
)
],)
)
w_bar = (
Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='300px'))
.add_xaxis([row['Year'] for _, row in athlete[athlete.Season=='Winter'].iterrows()])
.add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season=='Winter'].iterrows()],
category_gap='50%',
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 1,
color: '#00BFFF'
}, {
offset: 0.8,
color: '#FFC0CB'
}, {
offset: 0,
color: '#40E0D0'
}])""")))
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='top',
font_style='italic'))
.set_global_opts(
title_opts=opts.TitleOpts(title="中国历年奥运会参赛人数-冬奥会", pos_left='center'),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),
graphic_opts=[
opts.GraphicImage(
graphic_item=opts.GraphicItem(
id_="logo", right=0, top=-300, z=-10, bounding="raw", origin=[75, 75]
),
graphic_imagestyle_opts=opts.GraphicImageStyleOpts(
image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",
width=1000,
height=600,
opacity=0.6,),
)
],)
)
page = (
Page()
.add(s_bar,)
.add(w_bar,)
)
page.render_notebook()
历届奥运会奖牌数
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()
优势项目
跳水，体操，射击，举重，乒乓球，羽毛球
background_color_js = (
"new echarts.graphic.LinearGradient(1, 0, 0, 1, "
"[{offset: 0.5, color: '#FFC0CB'}, {offset: 1, color: '#F0FFFF'}, {offset: 0, color: '#EE82EE'}], false)"
)
CN_events = CN_data[CN_data.Medal=='Gold'].groupby(['Year', 'Sport'])['Event'].nunique().reset_index()
CN_events = CN_events.groupby(['Sport'])['Event'].sum().reset_index()
CN_events.columns = ['Sport', 'Nums']
data_pair = [(row['Sport'], row['Nums']) for _, row in CN_events.iterrows()]
wc = (WordCloud(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px'))
.add("", data_pair,word_size_range=[30, 80])
.set_global_opts(title_opts=opts.TitleOpts(title="中国获得过金牌运动项目",pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20)))
)
wc.render_notebook()
🇺🇸美国奥运会表现
USA_data = data[data.region=='USA']
USA_data.head()
历届奥运会参加人数
background_color_js = (
"new echarts.graphic.LinearGradient(1, 0, 0, 1, "
"[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)
athlete = USA_data.groupby(['Year', 'Season'])['Name'].nunique().reset_index()
athlete.columns = ['Year', 'Season', 'Nums']
athlete = athlete.sort_values(by="Year" , ascending=False)
s_bar = (
Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px'))
.add_xaxis([row['Year'] for _, row in athlete[athlete.Season=='Summer'].iterrows()])
.add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season=='Summer'].iterrows()],
category_gap='40%',
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 1,
color: '#00BFFF'
}, {
offset: 0,
color: '#32CD32'
}])""")))
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='top',
font_style='italic'))
.set_global_opts(
title_opts=opts.TitleOpts(title="美国历年奥运会参赛人数-夏奥会", pos_left='center'),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),
graphic_opts=[
opts.GraphicImage(
graphic_item=opts.GraphicItem(
id_="logo", right=0, top=0, z=-10, bounding="raw", origin=[75, 75]
),
graphic_imagestyle_opts=opts.GraphicImageStyleOpts(
image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",
width=1000,
height=600,
opacity=0.6,),
)
],)
)
w_bar = (
Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='300px'))
.add_xaxis([row['Year'] for _, row in athlete[athlete.Season=='Winter'].iterrows()])
.add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season=='Winter'].iterrows()],
category_gap='50%',
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 1,
color: '#00BFFF'
}, {
offset: 0.8,
color: '#FFC0CB'
}, {
offset: 0,
color: '#40E0D0'
}])""")))
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='top',
font_style='italic'))
.set_global_opts(
title_opts=opts.TitleOpts(title="美国历年奥运会参赛人数-冬奥会", pos_left='center'),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),
graphic_opts=[
opts.GraphicImage(
graphic_item=opts.GraphicItem(
id_="logo", right=0, top=-300, z=-10, bounding="raw", origin=[75, 75]
),
graphic_imagestyle_opts=opts.GraphicImageStyleOpts(
image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",
width=1000,
height=600,
opacity=0.6,),
)
],)
)
page = (
Page()
.add(s_bar,)
.add(w_bar,)
)
page.render_notebook()
历届奥运会获得奖牌数
background_color_js = (
"new echarts.graphic.LinearGradient(1, 0, 0, 1, "
"[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)
medals = USA_data.groupby(['Year', 'Season', 'Medal'])['Event'].nunique().reset_index()
medals.columns = ['Year', 'Season', 'Medal', 'Nums']
medals = medals.sort_values(by="Year" , ascending=False)
s_bar = (
Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px'))
.add_xaxis(sorted(list(set([row['Year'] for _, row in medals[medals.Season=='Summer'].iterrows()])), reverse=True))
.add_yaxis("金牌", [row['Nums'] for _, row in medals[(medals.Season=='Summer') & (medals.Medal=='Gold')].iterrows()],
category_gap='20%',
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: '#FFD700'
}, {
offset: 1,
color: '#FFFFF0'
}])""")))
.add_yaxis("银牌", [row['Nums'] for _, row in medals[(medals.Season=='Summer') & (medals.Medal=='Silver')].iterrows()],
category_gap='20%',
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: '#C0C0C0'
}, {
offset: 1,
color: '#FFFFF0'
}])""")))
.add_yaxis("铜牌", [row['Nums'] for _, row in medals[(medals.Season=='Summer') & (medals.Medal=='Bronze')].iterrows()],
category_gap='20%',
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: '#DAA520'
}, {
offset: 1,
color: '#FFFFF0'
}])""")))
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='top',
font_style='italic'))
.set_global_opts(
title_opts=opts.TitleOpts(title="美国历年奥运会获得奖牌数数-夏奥会", pos_left='center'),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),
graphic_opts=[
opts.GraphicImage(
graphic_item=opts.GraphicItem(
id_="logo", right=0, top=0, z=-10, bounding="raw", origin=[75, 75]
),
graphic_imagestyle_opts=opts.GraphicImageStyleOpts(
image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",
width=1000,
height=600,
opacity=0.6,),
)
],)
)
w_bar = (
Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px'))
.add_xaxis(sorted(list(set([row['Year'] for _, row in medals[medals.Season=='Winter'].iterrows()])), reverse=True))
.add_yaxis("金牌", [row['Nums'] for _, row in medals[(medals.Season=='Winter') & (medals.Medal=='Gold')].iterrows()],
category_gap='20%',
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: '#FFD700'
}, {
offset: 1,
color: '#FFFFF0'
}])""")))
.add_yaxis("银牌", [row['Nums'] for _, row in medals[(medals.Season=='Winter') & (medals.Medal=='Silver')].iterrows()],
category_gap='20%',
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: '#C0C0C0'
}, {
offset: 1,
color: '#FFFFF0'
}])""")))
.add_yaxis("铜牌", [row['Nums'] for _, row in medals[(medals.Season=='Winter') & (medals.Medal=='Bronze')].iterrows()],
category_gap='20%',
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: '#DAA520'
}, {
offset: 1,
color: '#FFFFF0'
}])""")))
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='top',
font_style='italic'))
.set_global_opts(
title_opts=opts.TitleOpts(title="美国历年奥运会获得奖牌数-冬奥会", pos_left='center'),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),
graphic_opts=[
opts.GraphicImage(
graphic_item=opts.GraphicItem(
id_="logo", right=0, top=-300, z=-10, bounding="raw", origin=[75, 75]
),
graphic_imagestyle_opts=opts.GraphicImageStyleOpts(
image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg",
width=1000,
height=600,
opacity=0.6,),
)
],)
)
page = (
Page()
.add(s_bar,)
.add(w_bar,)
)
page.render_notebook()
优势项目
田径，游泳
background_color_js = (
"new echarts.graphic.LinearGradient(1, 0, 0, 1, "
"[{offset: 0.5, color: '#FFC0CB'}, {offset: 1, color: '#F0FFFF'}, {offset: 0, color: '#EE82EE'}], false)"
)
events = USA_data[USA_data.Medal=='Gold'].groupby(['Year', 'Sport'])['Event'].nunique().reset_index()
events = events.groupby(['Sport'])['Event'].sum().reset_index()
events.columns = ['Sport', 'Nums']
data_pair = [(row['Sport'], row['Nums']) for _, row in events.iterrows()]
wc = (WordCloud(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px'))
.add("", data_pair,word_size_range=[30, 80])
.set_global_opts(title_opts=opts.TitleOpts(title="美国获得过金牌运动项目",pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20)))
)
wc.render_notebook()
被单个国家统治的奥运会项目
很多运动长期以来一直是被某个国家统治，譬如我们熟知的中国🇨🇳的乒乓球，美国🇺🇸的篮球；
此次筛选了近5届奥运会（2000年悉尼奥运会之后）上累计产生10枚金牌以上且存在单个国家「夺金率」超过50%的项目；
-
俄罗斯🇷🇺包揽了2000年以后花样游泳 & 艺术体操两个项目上所有的20枚金牌；
-
中国🇨🇳在乒乓球项目上获得了2000年之后10枚金牌中的9枚，丢失金牌的一次是在04年雅典奥运会男单项目上；
-
美国🇺🇸在篮球项目上同样获得了过去10枚金牌中的9枚，丢失金牌的一次同样在04年，男篮半决赛中输给了阿根廷，最终获得铜牌；
-
跳水项目上，中国🇨🇳获得了过去40枚金牌中的31枚，梦之队名不虚传；
-
射箭项目上，韩国🇰🇷获得了过去20枚金牌中的15枚；
-
羽毛球项目上，中国🇨🇳获得了过去25枚金牌中的17枚；
-
沙滩排球项目上，美国🇺🇸获得了过去10枚金牌中的5枚；
f1 = lambda x:max(x['Event']) / sum(x['Event'])
f2 = lambda x: x.sort_values('Event', ascending=False).head(1)
t_data = data[(data.Medal=='Gold') & (data.Year>=2000) &(data.Season=='Summer')].groupby(['Year', 'Sport', 'region'])['Event'].nunique().reset_index()
t_data = t_data.groupby(['Sport', 'region'])['Event'].sum().reset_index()
t1 = t_data.groupby(['Sport']).apply(f2).reset_index(drop=True)
t2 = t_data.groupby(['Sport'])['Event'].sum().reset_index()
t_data = pd.merge(t1, t2, on='Sport', how='inner')
t_data['gold_rate'] = t_data.Event_x/ t_data.Event_y
t_data = t_data.sort_values('gold_rate', ascending=False).reset_index(drop=True)
t_data = t_data[(t_data.gold_rate>=0.5) & (t_data.Event_y>=10)]
background_color_js = (
"new echarts.graphic.LinearGradient(1, 0, 0, 1, "
"[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
)
fn = """
function(params) {
if(params.name == '其他国家')
return '\\n\\n\\n' + params.name + ' : ' + params.value ;
return params.seriesName+ '\\n' + params.name + ' : ' + params.value;
}
"""
def new_label_opts():
return opts.LabelOpts(formatter=JsCode(fn), position="center")
pie = Pie(init_opts=opts.InitOpts(theme='dark', width='1000px', height='1000px'))
idx = 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_notebook()
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