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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本文转载自: https://blog.csdn.net/c_lanxiaofang/article/details/126311987
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版权归原作者 懒笑翻 所有， 如有侵权，请联系我们删除。