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使用Pyecharts进行奥运会可视化分析！

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使用Pyecharts进行奥运会可视化分析！

2024-06-03 08:36| 来源: 网络整理| 查看: 265

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

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

本项目将会从以下角度来呈现奥运会历史：

:trophy:各国累计奖牌数；:soccer:️各项运动产生金牌数:golf:️运动员层面参赛人数趋势女性参赛比例趋势获得金牌最多的运动员获得奖牌/金牌比例各项目运动员平均体质数据主要国家表现:cn:中国表现:us:美国表现:boom: 被单个国家统治的奥运会项目 导入库 & 数据 import pandas as pd import numpy as np import pyecharts from pyecharts.charts import * from pyecharts import options as opts from pyecharts.commons.utils import JsCode athlete_data = pd.read_csv('/home/kesci/input/olympic/athlete_events.csv') noc_region = pd.read_csv('/home/kesci/input/olympic/noc_regions.csv') # 关联代表国家 data = pd.merge(athlete_data, noc_region, on='NOC', how='left') data.head()

累计奖牌数

夏季奥运会 & 冬季奥运会分别统计

️夏季奥运会开始于1896年雅典奥运会；:snowflake:冬季奥运会开始于1924年慕尼黑冬奥会； medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index() medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums'] medal_data = medal_data.sort_values(by="Year" , ascending=True) medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index() medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums'] medal_data = medal_data.sort_values(by="Year" , ascending=True)

各国夏奥会累计奖牌数

截止2016年夏季奥运会， 美俄分别获得了2544和1577枚奖牌 ，位列一二位；中国由于参加奥运会时间较晚，截止2016年累计获得了545枚奖牌，位列第七位； year_list = sorted(list(set(medal_data['Year'].to_list())), reverse=True) tl = Timeline(init_opts=opts.InitOpts(theme='dark', width='1000px', height='1000px')) tl.add_schema(is_timeline_show=True,is_rewind_play=True, is_inverse=False, label_opts=opts.LabelOpts(is_show=False)) for year in year_list: t_data = medal_stat(year)[::-1] bar = ( Bar(init_opts=opts.InitOpts()) .add_xaxis([x[0] for x in t_data]) .add_yaxis("铜牌 ", [x[3] for x in t_data], stack='stack1', itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(218,165,32)')) .add_yaxis("银牌 ", [x[2] for x in t_data], stack='stack1', itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(192,192,192)')) .add_yaxis("金牌 ️", [x[1] for x in t_data], stack='stack1', itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(255,215,0)')) .set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='insideRight', font_style='italic'),) .set_global_opts( title_opts=opts.TitleOpts(title="各国累计奖牌数（夏季奥运会）"), xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)), legend_opts=opts.LegendOpts(is_show=True), graphic_opts=[opts.GraphicGroup(graphic_item=opts.GraphicItem( rotation=JsCode("Math.PI / 4"), bounding="raw", right=110, bottom=110, z=100), children=[ opts.GraphicRect( graphic_item=opts.GraphicItem( left="center", top="center", z=100 ), graphic_shape_opts=opts.GraphicShapeOpts( width=400, height=50 ), graphic_basicstyle_opts=opts.GraphicBasicStyleOpts( fill="rgba(0,0,0,0.3)" ), ), opts.GraphicText( graphic_item=opts.GraphicItem( left="center", top="center", z=100 ), graphic_textstyle_opts=opts.GraphicTextStyleOpts( text=year, font="bold 26px Microsoft YaHei", graphic_basicstyle_opts=opts.GraphicBasicStyleOpts( fill="#fff" ), ), ), ], ) ],) .reversal_axis()) tl.add(bar, year) tl.render_notebook()

各国冬奥会累计奖牌数

year_list = sorted(list(set(medal_data['Year'][medal_data.Season=='Winter'].to_list())), reverse=True) tl = Timeline(init_opts=opts.InitOpts(theme='dark', width='1000px', height='1000px')) tl.add_schema(is_timeline_show=True,is_rewind_play=True, is_inverse=False, label_opts=opts.LabelOpts(is_show=False)) for year in year_list: t_data = medal_stat(year, 'Winter')[::-1] bar = ( Bar(init_opts=opts.InitOpts(theme='dark')) .add_xaxis([x[0] for x in t_data]) .add_yaxis("铜牌 ", [x[3] for x in t_data], stack='stack1', itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(218,165,32)')) .add_yaxis("银牌 ", [x[2] for x in t_data], stack='stack1', itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(192,192,192)')) .add_yaxis("金牌 ️", [x[1] for x in t_data], stack='stack1', itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',color='rgb(255,215,0)')) .set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='insideRight', font_style='italic'),) .set_global_opts( title_opts=opts.TitleOpts(title="各国累计奖牌数（冬季奥运会）"), xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)), legend_opts=opts.LegendOpts(is_show=True), graphic_opts=[opts.GraphicGroup(graphic_item=opts.GraphicItem( rotation=JsCode("Math.PI / 4"), bounding="raw", right=110, bottom=110, z=100), children=[ opts.GraphicRect( graphic_item=opts.GraphicItem( left="center", top="center", z=100 ), graphic_shape_opts=opts.GraphicShapeOpts( width=400, height=50 ), graphic_basicstyle_opts=opts.GraphicBasicStyleOpts( fill="rgba(0,0,0,0.3)" ), ), opts.GraphicText( graphic_item=opts.GraphicItem( left="center", top="center", z=100 ), graphic_textstyle_opts=opts.GraphicTextStyleOpts( text='截止{}'.format(year), font="bold 26px Microsoft YaHei", graphic_basicstyle_opts=opts.GraphicBasicStyleOpts( fill="#fff" ), ), ), ], ) ],) .reversal_axis()) tl.add(bar, year) tl.render_notebook()

各项运动产生金牌数

基于2016年夏奥会和2014年冬奥会统计；

:runner: 田径 & 游泳是大项，在2016年夏奥会上分别产生了47和34枚金牌； background_color_js = """new echarts.graphic.RadialGradient(0.5, 0.5, 1, [{ offset: 0, color: '#696969' }, { offset: 1, color: '#000000' }])""" tab = Tab() temp = data[(data['Medal']=='Gold') & (data['Year']==2016) & (data['Season']=='Summer')] event_medal = temp.groupby(['Sport'])['Event'].nunique().reset_index() event_medal.columns = ['Sport', 'Nums'] event_medal = event_medal.sort_values(by="Nums" , ascending=False) pie = (Pie(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='800px')) .add('金牌 ️', [(row['Sport'], row['Nums']) for _, row in event_medal.iterrows()], radius=["30%", "75%"], rosetype="radius") .set_global_opts(title_opts=opts.TitleOpts(title="2016年夏季奥运会各项运动产生金牌占比", pos_left="center", title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20), ), legend_opts=opts.LegendOpts(is_show=False)) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"), tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} {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} {b}: {c} ({d}%)" ),) ) tab.add(pie, '2014年冬奥会') tab.render_notebook()

运动员层面

历年参赛人数趋势

从人数来看，每届夏奥会参赛人数都是冬奥会的4-5倍；整体参赛人数是上涨趋势，但由于历史原因也出现过波动，如 1980年莫斯科奥运会层遭遇65个国家抵制 ； athlete = data.groupby(['Year', 'Season'])['Name'].nunique().reset_index() athlete.columns = ['Year', 'Season', 'Nums'] athlete = athlete.sort_values(by="Year" , ascending=True) x_list, y1_list, y2_list = [], [], [] for _, row in athlete.iterrows(): x_list.append(str(row['Year'])) if row['Season'] == 'Summer': y1_list.append(row['Nums']) y2_list.append(None) else: y2_list.append(row['Nums']) y1_list.append(None) background_color_js = ( "new echarts.graphic.LinearGradient(1, 1, 0, 0, " "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)" ) line = ( Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px')) .add_xaxis(x_list) .add_yaxis("夏季奥运会", y1_list, is_smooth=True, is_connect_nones=True, symbol="circle", symbol_size=6, linestyle_opts=opts.LineStyleOpts(color="#fff"), label_opts=opts.LabelOpts(is_show=False, position="top", color="white"), itemstyle_opts=opts.ItemStyleOpts( color="green", border_color="#fff", border_width=3), tooltip_opts=opts.TooltipOpts(is_show=True)) .add_yaxis("冬季季奥运会", y2_list, is_smooth=True, is_connect_nones=True, symbol="circle", symbol_size=6, linestyle_opts=opts.LineStyleOpts(color="#FF4500"), label_opts=opts.LabelOpts(is_show=False, position="top", color="white"), itemstyle_opts=opts.ItemStyleOpts( color="red", border_color="#fff", border_width=3), tooltip_opts=opts.TooltipOpts(is_show=True)) .set_series_opts( markarea_opts=opts.MarkAreaOpts( label_opts=opts.LabelOpts(is_show=True, position="bottom", color="white"), data=[ opts.MarkAreaItem(name="第一次世界大战", x=(1914, 1918)), opts.MarkAreaItem(name="第二次世界大战", x=(1939, 1945)), ] ) ) .set_global_opts(title_opts=opts.TitleOpts(title="历届奥运会参赛人数", pos_left="center", title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20),), legend_opts=opts.LegendOpts(is_show=True, pos_top='5%', textstyle_opts=opts.TextStyleOpts(color="white", font_size=12)), xaxis_opts=opts.AxisOpts(type_="value", min_=1904, max_=2016, boundary_gap=False, axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63", formatter=JsCode("""function (value) {return value+'年';}""")), axisline_opts=opts.AxisLineOpts(is_show=False), axistick_opts=opts.AxisTickOpts( is_show=True, length=25, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"), ), splitline_opts=opts.SplitLineOpts( is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f") ), ), yaxis_opts=opts.AxisOpts( type_="value", position="right", axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"), axisline_opts=opts.AxisLineOpts( linestyle_opts=opts.LineStyleOpts(width=2, color="#fff") ), axistick_opts=opts.AxisTickOpts( is_show=True, length=15, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"), ), splitline_opts=opts.SplitLineOpts( is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f") ), ),) ) line.render_notebook()

历年女性运动员占比趋势

一开始奥运会基本是「男人的运动」，女性运动员仅为个位数，到近几届奥运会男女参赛人数基本趋于相等；

# 历年男性运动员人数 m_data = data[data.Sex=='M'].groupby(['Year', 'Season'])['Name'].nunique().reset_index() m_data.columns = ['Year', 'Season', 'M-Nums'] m_data = m_data.sort_values(by="Year" , ascending=True) # 历年女性运动员人数 f_data = data[data.Sex=='F'].groupby(['Year', 'Season'])['Name'].nunique().reset_index() f_data.columns = ['Year', 'Season', 'F-Nums'] f_data = f_data.sort_values(by="Year" , ascending=True) t_data = pd.merge(m_data, f_data, on=['Year', 'Season']) t_data['F-rate'] = round(t_data['F-Nums'] / (t_data['F-Nums'] + t_data['M-Nums'] ), 4) x_list, y1_list, y2_list = [], [], [] for _, row in t_data.iterrows(): x_list.append(str(row['Year'])) if row['Season'] == 'Summer': y1_list.append(row['F-rate']) y2_list.append(None) else: y2_list.append(row['F-rate']) y1_list.append(None) background_color_js = ( "new echarts.graphic.LinearGradient(0, 0, 0, 1, " "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)" ) line = ( Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px')) .add_xaxis(x_list) .add_yaxis("夏季奥运会", y1_list, is_smooth=True, is_connect_nones=True, symbol="circle", symbol_size=6, linestyle_opts=opts.LineStyleOpts(color="#fff"), label_opts=opts.LabelOpts(is_show=False, position="top", color="white"), itemstyle_opts=opts.ItemStyleOpts(color="green", border_color="#fff", border_width=3), tooltip_opts=opts.TooltipOpts(is_show=True),) .add_yaxis("冬季季奥运会", y2_list, is_smooth=True, is_connect_nones=True, symbol="circle", symbol_size=6, linestyle_opts=opts.LineStyleOpts(color="#FF4500"), label_opts=opts.LabelOpts(is_show=False, position="top", color="white"), itemstyle_opts=opts.ItemStyleOpts(color="red", border_color="#fff", border_width=3), tooltip_opts=opts.TooltipOpts(is_show=True),) .set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter=JsCode("""function (params) {return params.data[0]+ '年: ' + Number(params.data[1])*100 +'%';}""")),) .set_global_opts(title_opts=opts.TitleOpts(title="历届奥运会参赛女性占比趋势", pos_left="center", title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20),), legend_opts=opts.LegendOpts(is_show=True, pos_top='5%', textstyle_opts=opts.TextStyleOpts(color="white", font_size=12)), xaxis_opts=opts.AxisOpts(type_="value", min_=1904, max_=2016, boundary_gap=False, axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63", formatter=JsCode("""function (value) {return value+'年';}""")), axisline_opts=opts.AxisLineOpts(is_show=False), axistick_opts=opts.AxisTickOpts( is_show=True, length=25, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"), ), splitline_opts=opts.SplitLineOpts( is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f") ), ), yaxis_opts=opts.AxisOpts( type_="value", position="right", axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63", formatter=JsCode("""function (value) {return Number(value *100)+'%';}""")), axisline_opts=opts.AxisLineOpts( linestyle_opts=opts.LineStyleOpts(width=2, color="#fff") ), axistick_opts=opts.AxisTickOpts( is_show=True, length=15, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"), ), splitline_opts=opts.SplitLineOpts( is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f") ), ),) ) line.render_notebook()

获得金牌最多的运动员

排在第一的是美国泳坛名将「 菲尔普斯 」，截止2016年奥运会总共获得了 23 枚金牌；博尔特累计获得8枚奥运会金牌； temp = data[(data['Medal']=='Gold')] athlete = temp.groupby(['Name'])['Medal'].count().reset_index() athlete.columns = ['Name', 'Nums'] athlete = athlete.sort_values(by="Nums" , ascending=True) background_color_js = ( "new echarts.graphic.LinearGradient(0, 0, 1, 1, " "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)" ) pb = ( PictorialBar(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='800px')) .add_xaxis([x.replace(' ','\n') for x in athlete['Name'].tail(10).tolist()]) .add_yaxis( "", athlete['Nums'].tail(10).tolist(), label_opts=opts.LabelOpts(is_show=False), symbol_size=25, symbol_repeat='fixed', symbol_offset=[0, 0], is_symbol_clip=True, symbol='image://https://cdn.kesci.com/upload/image/q8f8otrlfc.png') .reversal_axis() .set_global_opts( title_opts=opts.TitleOpts(title="获得金牌数量最多的运动员", pos_left='center', title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20),), xaxis_opts=opts.AxisOpts(is_show=False,), yaxis_opts=opts.AxisOpts( axistick_opts=opts.AxisTickOpts(is_show=False), axisline_opts=opts.AxisLineOpts( linestyle_opts=opts.LineStyleOpts(opacity=0) ), ), )) pb.render_notebook()

获得金牌/奖牌比例

看菲尔普斯拿金牌拿到手软，但实际上想获得一块金牌的难度高吗？

整个奥运会（包括夏季，冬季奥运会）历史上参赛人数为134732，获得过金牌的运动员只有10413，占比7.7%;获得过奖牌（包括金银铜）的运动员有28202人，占比20.93%； total_athlete = len(set(data['Name'])) medal_athlete = len(set(data['Name'][data['Medal'].isin(['Gold', 'Silver', 'Bronze'])])) gold_athlete = len(set(data['Name'][data['Medal']=='Gold'])) l1 = Liquid(init_opts=opts.InitOpts(theme='dark', width='1000px', height='800px')) l1.add("获得奖牌", [medal_athlete/total_athlete], center=["70%", "50%"], label_opts=opts.LabelOpts(font_size=50, formatter=JsCode( """function (param) { return (Math.floor(param.value * 10000) / 100) + '%'; }"""), position="inside", )) l1.set_global_opts(title_opts=opts.TitleOpts(title="获得过奖牌比例", pos_left='62%', pos_top='8%')) l1.set_series_opts(tooltip_opts=opts.TooltipOpts(is_show=False)) l2 = Liquid(init_opts=opts.InitOpts(theme='dark', width='1000px', height='800px')) l2.add("获得金牌", [gold_athlete/total_athlete], center=["25%", "50%"], label_opts=opts.LabelOpts(font_size=50, formatter=JsCode( """function (param) { return (Math.floor(param.value * 10000) / 100) + '%'; }"""), position="inside", ),) l2.set_global_opts(title_opts=opts.TitleOpts(title="获得过金牌比例", pos_left='17%', pos_top='8%')) l2.set_series_opts(tooltip_opts=opts.TooltipOpts(is_show=False)) grid = Grid().add(l1, grid_opts=opts.GridOpts()).add(l2, grid_opts=opts.GridOpts()) grid.render_notebook()

运动员平均体质数据

根据不同的运动项目进行统计

运动员平均身高最高的项目是 篮球 ，女子平均身高达182cm，男子平均身高达到194cm；在男子项目中，运动员平均体重最大的项目是 拔河 ，平均体重达到96kg（拔河自第七届奥运会后已取消）；运动员平均年龄最大的项目是 Art competition （自行百度这奇怪的项目），平均年龄46岁，除此之外便是 马术和射击 ，男子平均年龄分别为34.4岁和34.2岁，女子平均年龄34.22岁和29.12s岁； tool_js = """function (param) {return param.data[2] +'' +'平均体重： '+Number(param.data[0]).toFixed(2)+' kg' +'平均身高： '+Number(param.data[1]).toFixed(2)+' cm' +'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()

:cn:中国奥运会表现 CN_data = data[data.region=='China'] CN_data.head()

历届奥运会参赛人数

background_color_js = ( "new echarts.graphic.LinearGradient(1, 0, 0, 1, " "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)" ) athlete = CN_data.groupby(['Year', 'Season'])['Name'].nunique().reset_index() athlete.columns = ['Year', 'Season', 'Nums'] athlete = athlete.sort_values(by="Year" , ascending=False) s_bar = ( Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px')) .add_xaxis([row['Year'] for _, row in athlete[athlete.Season=='Summer'].iterrows()]) .add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season=='Summer'].iterrows()], category_gap='40%', itemstyle_opts=opts.ItemStyleOpts( border_color='rgb(220,220,220)', color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 1, color: '#00BFFF' }, { offset: 0, color: '#32CD32' }])"""))) .set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top', font_style='italic')) .set_global_opts( title_opts=opts.TitleOpts(title="中国历年奥运会参赛人数-夏奥会", pos_left='center'), xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)), legend_opts=opts.LegendOpts(is_show=False), yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")), graphic_opts=[ opts.GraphicImage( graphic_item=opts.GraphicItem( id_="logo", right=0, top=0, z=-10, bounding="raw", origin=[75, 75] ), graphic_imagestyle_opts=opts.GraphicImageStyleOpts( image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg", width=1000, height=600, opacity=0.6,), ) ],) ) w_bar = ( Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='300px')) .add_xaxis([row['Year'] for _, row in athlete[athlete.Season=='Winter'].iterrows()]) .add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season=='Winter'].iterrows()], category_gap='50%', itemstyle_opts=opts.ItemStyleOpts( border_color='rgb(220,220,220)', color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 1, color: '#00BFFF' }, { offset: 0.8, color: '#FFC0CB' }, { offset: 0, color: '#40E0D0' }])"""))) .set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top', font_style='italic')) .set_global_opts( title_opts=opts.TitleOpts(title="中国历年奥运会参赛人数-冬奥会", pos_left='center'), xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)), legend_opts=opts.LegendOpts(is_show=False), yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")), graphic_opts=[ opts.GraphicImage( graphic_item=opts.GraphicItem( id_="logo", right=0, top=-300, z=-10, bounding="raw", origin=[75, 75] ), graphic_imagestyle_opts=opts.GraphicImageStyleOpts( image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg", width=1000, height=600, opacity=0.6,), ) ],) ) page = ( Page() .add(s_bar,) .add(w_bar,) ) page.render_notebook()

历届奥运会奖牌数

background_color_js = ( "new echarts.graphic.LinearGradient(1, 0, 0, 1, " "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)" ) CN_medals = CN_data.groupby(['Year', 'Season', 'Medal'])['Event'].nunique().reset_index() CN_medals.columns = ['Year', 'Season', 'Medal', 'Nums'] CN_medals = CN_medals.sort_values(by="Year" , ascending=False) s_bar = ( Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px')) .add_xaxis(sorted(list(set([row['Year'] for _, row in CN_medals[CN_medals.Season=='Summer'].iterrows()])), reverse=True)) .add_yaxis("金牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Summer') & (CN_medals.Medal=='Gold')].iterrows()], category_gap='20%', itemstyle_opts=opts.ItemStyleOpts( border_color='rgb(220,220,220)', color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: '#FFD700' }, { offset: 1, color: '#FFFFF0' }])"""))) .add_yaxis("银牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Summer') & (CN_medals.Medal=='Silver')].iterrows()], category_gap='20%', itemstyle_opts=opts.ItemStyleOpts( border_color='rgb(220,220,220)', color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: '#C0C0C0' }, { offset: 1, color: '#FFFFF0' }])"""))) .add_yaxis("铜牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Summer') & (CN_medals.Medal=='Bronze')].iterrows()], category_gap='20%', itemstyle_opts=opts.ItemStyleOpts( border_color='rgb(220,220,220)', color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: '#DAA520' }, { offset: 1, color: '#FFFFF0' }])"""))) .set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top', font_style='italic')) .set_global_opts( title_opts=opts.TitleOpts(title="中国历年奥运会获得奖牌数数-夏奥会", pos_left='center'), xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)), legend_opts=opts.LegendOpts(is_show=False), yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")), graphic_opts=[ opts.GraphicImage( graphic_item=opts.GraphicItem( id_="logo", right=0, top=0, z=-10, bounding="raw", origin=[75, 75] ), graphic_imagestyle_opts=opts.GraphicImageStyleOpts( image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg", width=1000, height=600, opacity=0.6,), ) ],) ) w_bar = ( Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px')) .add_xaxis(sorted(list(set([row['Year'] for _, row in CN_medals[CN_medals.Season=='Winter'].iterrows()])), reverse=True)) .add_yaxis("金牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Winter') & (CN_medals.Medal=='Gold')].iterrows()], category_gap='20%', itemstyle_opts=opts.ItemStyleOpts( border_color='rgb(220,220,220)', color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: '#FFD700' }, { offset: 1, color: '#FFFFF0' }])"""))) .add_yaxis("银牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Winter') & (CN_medals.Medal=='Silver')].iterrows()], category_gap='20%', itemstyle_opts=opts.ItemStyleOpts( border_color='rgb(220,220,220)', color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: '#C0C0C0' }, { offset: 1, color: '#FFFFF0' }])"""))) .add_yaxis("铜牌", [row['Nums'] for _, row in CN_medals[(CN_medals.Season=='Winter') & (CN_medals.Medal=='Bronze')].iterrows()], category_gap='20%', itemstyle_opts=opts.ItemStyleOpts( border_color='rgb(220,220,220)', color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: '#DAA520' }, { offset: 1, color: '#FFFFF0' }])"""))) .set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='top', font_style='italic')) .set_global_opts( title_opts=opts.TitleOpts(title="中国历年奥运会获得奖牌数-冬奥会", pos_left='center'), xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)), legend_opts=opts.LegendOpts(is_show=False), yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")), graphic_opts=[ opts.GraphicImage( graphic_item=opts.GraphicItem( id_="logo", right=0, top=-300, z=-10, bounding="raw", origin=[75, 75] ), graphic_imagestyle_opts=opts.GraphicImageStyleOpts( image="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1586619952245&di=981a36305048f93eec791980acc99cf7&imgtype=0&src=http%3A%2F%2Fimg5.mtime.cn%2Fmg%2F2017%2F01%2F06%2F172210.42721559.jpg", width=1000, height=600, opacity=0.6,), ) ],) ) page = ( Page() .add(s_bar,) .add(w_bar,) ) page.render_notebook()

源码获取加群：850591259

 

 



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