• 120年奥运历史数据分析

    目录

    一、数据概览

    1.背景描述

    2.数据说明

    3.属性描述

    4.数据来源

    二、数据集可探索、研究的方向

    三、可视化分析

    1.🏆各国累计奖牌数

    ​编辑

    2.⚽️各项运动产生金牌数

    3.⛳️运动员层面

    ①参赛人数趋势

     ②女性参赛比例趋势

    ③获得金牌最多的运动员

    ④获得奖牌/金牌比例

    ⑤各项目运动员平均体质数据

    4.中国表现

    ①中国历届奥运会参赛人数

    ②中国历届奥运会奖牌数

    ③中国优势项目

    5.💥被单个国家统治的奥运会项目

    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：

    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ID
    2NameString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

    1. import pandas as pd
    2. from pyecharts.charts import *
    3. from pyecharts import options as opts
    4. from pyecharts.commons.utils import JsCode
    5.  
    6. athlete_data = pd.read_csv('./data/athlete_events.csv')
    7. noc_region = pd.read_csv('./data/noc_regions.csv')
    8.  
    9. # 关联代表国家
    10. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    11. print(data.head())
    12.  
    13. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    14. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    15. medal_data = medal_data.sort_values(by="Year", ascending=True)
    16.  
    17.  
    18. def medal_stat(year, season='Summer'):
    19.     t_data = medal_data[(medal_data['Year'] <= year) & (medal_data['Season'] == season)]
    20.     t_data = t_data.groupby(['region', 'Medal'])['Nums'].sum().reset_index()
    21.     t_data = t_data.set_index(['region', 'Medal']).unstack().reset_index().fillna(0, inplace=False)
    22.     t_data = sorted(
    23.         [(row['region'][0], int(row['Nums']['Gold']), int(row['Nums']['Silver']), int(row['Nums']['Bronze']))
    24.          for _, row in t_data.iterrows()], key=lambda x: x[1] + x[2] + x[3], reverse=True)[:20]
    25.     return t_data
    26.  
    27.  
    28. year_list = sorted(list(set(medal_data['Year'].to_list())), reverse=True)
    29.  
    30. tl = Timeline(init_opts=opts.InitOpts(theme='dark', width='1000px', height='1000px'))
    31. tl.add_schema(is_timeline_show=True, is_rewind_play=True, is_inverse=False,
    32.               label_opts=opts.LabelOpts(is_show=False))
    33.  
    34. for year in year_list:
    35.     t_data = medal_stat(year)[::-1]
    36.     bar = (
    37.         Bar(init_opts=opts.InitOpts())
    38.         .add_xaxis([x[0] for x in t_data])
    39.         .add_yaxis("铜牌🥉", [x[3] for x in t_data],
    40.                    stack='stack1',
    41.                    itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)', color='rgb(218,165,32)'))
    42.         .add_yaxis("银牌🥈", [x[2] for x in t_data],
    43.                    stack='stack1',
    44.                    itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)', color='rgb(192,192,192)'))
    45.         .add_yaxis("金牌🏅️", [x[1] for x in t_data],
    46.                    stack='stack1',
    47.                    itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)', color='rgb(255,215,0)'))
    48.         .set_series_opts(label_opts=opts.LabelOpts(is_show=True,
    49.                                                    position='insideRight',
    50.                                                    font_style='italic'), )
    51.         .set_global_opts(
    52.             title_opts=opts.TitleOpts(title="各国累计奖牌数（夏季奥运会）"),
    53.             xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
    54.             legend_opts=opts.LegendOpts(is_show=True),
    55.             graphic_opts=[opts.GraphicGroup(graphic_item=opts.GraphicItem(
    56.                 rotation=JsCode("Math.PI / 4"),
    57.                 bounding="raw",
    58.                 right=110,
    59.                 bottom=110,
    60.                 z=100),
    61.                 children=[
    62.                     opts.GraphicRect(
    63.                         graphic_item=opts.GraphicItem(
    64.                             left="center", top="center", z=100
    65.                         ),
    66.                         graphic_shape_opts=opts.GraphicShapeOpts(
    67.                             width=400, height=50
    68.                         ),
    69.                         graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
    70.                             fill="rgba(0,0,0,0.3)"
    71.                         ),
    72.                     ),
    73.                     opts.GraphicText(
    74.                         graphic_item=opts.GraphicItem(
    75.                             left="center", top="center", z=100
    76.                         ),
    77.                         graphic_textstyle_opts=opts.GraphicTextStyleOpts(
    78.                             text=year,
    79.                             font="bold 26px Microsoft YaHei",
    80.                             graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
    81.                                 fill="#fff"
    82.                             ),
    83.                         ),
    84.                     ),
    85.                 ],
    86.             )
    87.             ], )
    88.         .reversal_axis())
    89.     tl.add(bar, year)
    90.  
    91. tl.render(r".\htmlRender\01_各国累计奖牌数（夏季奥运会）.html")

    1. import pandas as pd
    2. from pyecharts.charts import *
    3. from pyecharts import options as opts
    4. from pyecharts.commons.utils import JsCode
    5.  
    6. athlete_data = pd.read_csv('./data/athlete_events.csv')
    7. noc_region = pd.read_csv('./data/noc_regions.csv')
    8.  
    9. # 关联代表国家
    10. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    11. print(data.head())
    12.  
    13. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    14. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    15. medal_data = medal_data.sort_values(by="Year", ascending=True)
    16.  
    17.  
    18. def medal_stat(year, season='Summer'):
    19.     t_data = medal_data[(medal_data['Year'] <= year) & (medal_data['Season'] == season)]
    20.     t_data = t_data.groupby(['region', 'Medal'])['Nums'].sum().reset_index()
    21.     t_data = t_data.set_index(['region', 'Medal']).unstack().reset_index().fillna(0, inplace=False)
    22.     t_data = sorted(
    23.         [(row['region'][0], int(row['Nums']['Gold']), int(row['Nums']['Silver']), int(row['Nums']['Bronze']))
    24.          for _, row in t_data.iterrows()], key=lambda x: x[1] + x[2] + x[3], reverse=True)[:20]
    25.     return t_data
    26.  
    27.  
    28. year_list = sorted(list(set(medal_data['Year'].to_list())), reverse=True)
    29.  
    30. tl = Timeline(init_opts=opts.InitOpts(theme='dark', width='1000px', height='1000px'))
    31. tl.add_schema(is_timeline_show=True, is_rewind_play=True, is_inverse=False,
    32.               label_opts=opts.LabelOpts(is_show=False))
    33.  
    34. year_list = sorted(list(set(medal_data['Year'][medal_data.Season == 'Winter'].to_list())), reverse=True)
    35.  
    36. tl = Timeline(init_opts=opts.InitOpts(theme='dark', width='1000px', height='1000px'))
    37. tl.add_schema(is_timeline_show=True, is_rewind_play=True, is_inverse=False,
    38.               label_opts=opts.LabelOpts(is_show=False))
    39.  
    40. for year in year_list:
    41.     t_data = medal_stat(year, 'Winter')[::-1]
    42.     bar = (
    43.         Bar(init_opts=opts.InitOpts(theme='dark'))
    44.         .add_xaxis([x[0] for x in t_data])
    45.         .add_yaxis("铜牌🥉", [x[3] for x in t_data],
    46.                    stack='stack1',
    47.                    itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)', color='rgb(218,165,32)'))
    48.         .add_yaxis("银牌🥈", [x[2] for x in t_data],
    49.                    stack='stack1',
    50.                    itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)', color='rgb(192,192,192)'))
    51.         .add_yaxis("金牌🏅️", [x[1] for x in t_data],
    52.                    stack='stack1',
    53.                    itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)', color='rgb(255,215,0)'))
    54.         .set_series_opts(label_opts=opts.LabelOpts(is_show=True,
    55.                                                    position='insideRight',
    56.                                                    font_style='italic'), )
    57.         .set_global_opts(
    58.             title_opts=opts.TitleOpts(title="各国累计奖牌数（冬季奥运会）"),
    59.             xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
    60.             legend_opts=opts.LegendOpts(is_show=True),
    61.             graphic_opts=[opts.GraphicGroup(graphic_item=opts.GraphicItem(
    62.                 rotation=JsCode("Math.PI / 4"),
    63.                 bounding="raw",
    64.                 right=110,
    65.                 bottom=110,
    66.                 z=100),
    67.                 children=[
    68.                     opts.GraphicRect(
    69.                         graphic_item=opts.GraphicItem(
    70.                             left="center", top="center", z=100
    71.                         ),
    72.                         graphic_shape_opts=opts.GraphicShapeOpts(
    73.                             width=400, height=50
    74.                         ),
    75.                         graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
    76.                             fill="rgba(0,0,0,0.3)"
    77.                         ),
    78.                     ),
    79.                     opts.GraphicText(
    80.                         graphic_item=opts.GraphicItem(
    81.                             left="center", top="center", z=100
    82.                         ),
    83.                         graphic_textstyle_opts=opts.GraphicTextStyleOpts(
    84.                             text='截止{}'.format(year),
    85.                             font="bold 26px Microsoft YaHei",
    86.                             graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
    87.                                 fill="#fff"
    88.                             ),
    89.                         ),
    90.                     ),
    91.                 ],
    92.             )
    93.             ], )
    94.         .reversal_axis())
    95.     tl.add(bar, year)
    96.  
    97. 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

    1. import pandas as pd
    2. from pyecharts.charts import *
    3. from pyecharts import options as opts
    4. from pyecharts.commons.utils import JsCode
    5.  
    6. athlete_data = pd.read_csv('./data/athlete_events.csv')
    7. noc_region = pd.read_csv('./data/noc_regions.csv')
    8.  
    9. # 关联代表国家
    10. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    11. print(data.head())
    12.  
    13. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    14. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    15. medal_data = medal_data.sort_values(by="Year", ascending=True)
    16.  
    17. background_color_js = """new echarts.graphic.RadialGradient(0.5, 0.5, 1, [{
    18.                                         offset: 0,
    19.                                         color: '#696969'
    20.                                     }, {
    21.                                         offset: 1,
    22.                                         color: '#000000'
    23.                                     }])"""
    24.  
    25. tab = Tab()
    26. temp = data[(data['Medal'] == 'Gold') & (data['Year'] == 2016) & (data['Season'] == 'Summer')]
    27.  
    28. event_medal = temp.groupby(['Sport'])['Event'].nunique().reset_index()
    29. event_medal.columns = ['Sport', 'Nums']
    30. event_medal = event_medal.sort_values(by="Nums", ascending=False)
    31.  
    32. pie = (Pie(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='800px'))
    33.        .add('金牌🏅️', [(row['Sport'], row['Nums']) for _, row in event_medal.iterrows()],
    34.             radius=["30%", "75%"],
    35.             rosetype="radius")
    36.        .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",
    37.                                                   pos_left="center",
    38.                                                   title_textstyle_opts=opts.TextStyleOpts(color="white",
    39.                                                                                           font_size=20), ),
    40.                         legend_opts=opts.LegendOpts(is_show=False))
    41.        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"),
    42.                         tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} 
      {b}: {c} ({d}%)"      ), )
    43.        )
    44. tab.add(pie, '2016年夏奥会')
    45.  
    46. temp = data[(data['Medal'] == 'Gold') & (data['Year'] == 2014) & (data['Season'] == 'Winter')]
    47.  
    48. event_medal = temp.groupby(['Sport'])['Event'].nunique().reset_index()
    49. event_medal.columns = ['Sport', 'Nums']
    50. event_medal = event_medal.sort_values(by="Nums", ascending=False)
    51.  
    52. pie = (Pie(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='800px'))
    53.        .add('金牌🏅️', [(row['Sport'], row['Nums']) for _, row in event_medal.iterrows()],
    54.             radius=["30%", "75%"],
    55.             rosetype="radius")
    56.        .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",
    57.                                                   pos_left="center",
    58.                                                   title_textstyle_opts=opts.TextStyleOpts(color="white",
    59.                                                                                           font_size=20), ),
    60.                         legend_opts=opts.LegendOpts(is_show=False))
    61.        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"),
    62.                         tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} 
      {b}: {c} ({d}%)"
    63.                                                       ), )
    64.        )
    65. tab.add(pie, '2014年冬奥会')
    66. 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

    1. import pandas as pd
    2. from pyecharts.charts import *
    3. from pyecharts import options as opts
    4. from pyecharts.commons.utils import JsCode
    5.  
    6. athlete_data = pd.read_csv('./data/athlete_events.csv')
    7. noc_region = pd.read_csv('./data/noc_regions.csv')
    8.  
    9. # 关联代表国家
    10. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    11. print(data.head())
    12.  
    13. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    14. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    15. medal_data = medal_data.sort_values(by="Year", ascending=True)
    16. athlete = data.groupby(['Year', 'Season'])['Name'].nunique().reset_index()
    17. athlete.columns = ['Year', 'Season', 'Nums']
    18. athlete = athlete.sort_values(by="Year", ascending=True)
    19.  
    20. x_list, y1_list, y2_list = [], [], []
    21.  
    22. for _, row in athlete.iterrows():
    23.     x_list.append(str(row['Year']))
    24.     if row['Season'] == 'Summer':
    25.         y1_list.append(row['Nums'])
    26.         y2_list.append(None)
    27.     else:
    28.         y2_list.append(row['Nums'])
    29.         y1_list.append(None)
    30.  
    31. background_color_js = (
    32.     "new echarts.graphic.LinearGradient(1, 1, 0, 0, "
    33.     "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
    34. )
    35.  
    36. line = (
    37.     Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px'))
    38.     .add_xaxis(x_list)
    39.     .add_yaxis("夏季奥运会",
    40.                y1_list,
    41.                is_smooth=True,
    42.                is_connect_nones=True,
    43.                symbol="circle",
    44.                symbol_size=6,
    45.                linestyle_opts=opts.LineStyleOpts(color="#fff"),
    46.                label_opts=opts.LabelOpts(is_show=False, position="top", color="white"),
    47.                itemstyle_opts=opts.ItemStyleOpts(
    48.                    color="green", border_color="#fff", border_width=3),
    49.                tooltip_opts=opts.TooltipOpts(is_show=True))
    50.     .add_yaxis("冬季季奥运会",
    51.                y2_list,
    52.                is_smooth=True,
    53.                is_connect_nones=True,
    54.                symbol="circle",
    55.                symbol_size=6,
    56.                linestyle_opts=opts.LineStyleOpts(color="#FF4500"),
    57.                label_opts=opts.LabelOpts(is_show=False, position="top", color="white"),
    58.                itemstyle_opts=opts.ItemStyleOpts(
    59.                    color="red", border_color="#fff", border_width=3),
    60.                tooltip_opts=opts.TooltipOpts(is_show=True))
    61.     .set_series_opts(
    62.         markarea_opts=opts.MarkAreaOpts(
    63.             label_opts=opts.LabelOpts(is_show=True, position="bottom", color="white"),
    64.             data=[
    65.                 opts.MarkAreaItem(name="第一次世界大战", x=(1914, 1918)),
    66.                 opts.MarkAreaItem(name="第二次世界大战", x=(1939, 1945)),
    67.             ]
    68.         )
    69.     )
    70.     .set_global_opts(title_opts=opts.TitleOpts(title="历届奥运会参赛人数",
    71.                                                pos_left="center",
    72.                                                title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20), ),
    73.                      legend_opts=opts.LegendOpts(is_show=True, pos_top='5%',
    74.                                                  textstyle_opts=opts.TextStyleOpts(color="white", font_size=12)),
    75.                      xaxis_opts=opts.AxisOpts(type_="value",
    76.                                               min_=1904,
    77.                                               max_=2016,
    78.                                               boundary_gap=False,
    79.                                               axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63",
    80.                                                                             formatter=JsCode("""function (value) 
    81.                                                                                {return value+'年';}""")),
    82.                                               axisline_opts=opts.AxisLineOpts(is_show=False),
    83.                                               axistick_opts=opts.AxisTickOpts(
    84.                                                   is_show=True,
    85.                                                   length=25,
    86.                                                   linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
    87.                                               ),
    88.                                               splitline_opts=opts.SplitLineOpts(
    89.                                                   is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
    90.                                               ),
    91.                                               ),
    92.                      yaxis_opts=opts.AxisOpts(
    93.                          type_="value",
    94.                          position="right",
    95.                          axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
    96.                          axisline_opts=opts.AxisLineOpts(
    97.                              linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
    98.                          ),
    99.                          axistick_opts=opts.AxisTickOpts(
    100.                              is_show=True,
    101.                              length=15,
    102.                              linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
    103.                          ),
    104.                          splitline_opts=opts.SplitLineOpts(
    105.                              is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
    106.                          ),
    107.                      ), )
    108. )
    109.  
    110. line.render(r".\htmlRender\04_历届奥运会参赛人数.html")

     ②女性参赛比例趋势

    1.  
    2. import pandas as pd
    3. from pyecharts.charts import *
    4. from pyecharts import options as opts
    5. from pyecharts.commons.utils import JsCode
    6.  
    7. athlete_data = pd.read_csv('./data/athlete_events.csv')
    8. noc_region = pd.read_csv('./data/noc_regions.csv')
    9.  
    10. # 关联代表国家
    11. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    12. print(data.head())
    13.  
    14. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    15. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    16. medal_data = medal_data.sort_values(by="Year", ascending=True)
    17.  
    18. # 历年男性运动员人数
    19. m_data = data[data.Sex == 'M'].groupby(['Year', 'Season'])['Name'].nunique().reset_index()
    20. m_data.columns = ['Year', 'Season', 'M-Nums']
    21. m_data = m_data.sort_values(by="Year", ascending=True)
    22.  
    23. # 历年女性运动员人数
    24. f_data = data[data.Sex == 'F'].groupby(['Year', 'Season'])['Name'].nunique().reset_index()
    25. f_data.columns = ['Year', 'Season', 'F-Nums']
    26. f_data = f_data.sort_values(by="Year", ascending=True)
    27.  
    28. t_data = pd.merge(m_data, f_data, on=['Year', 'Season'])
    29. t_data['F-rate'] = round(t_data['F-Nums'] / (t_data['F-Nums'] + t_data['M-Nums']), 4)
    30.  
    31. x_list, y1_list, y2_list = [], [], []
    32.  
    33. for _, row in t_data.iterrows():
    34.     x_list.append(str(row['Year']))
    35.     if row['Season'] == 'Summer':
    36.         y1_list.append(row['F-rate'])
    37.         y2_list.append(None)
    38.     else:
    39.         y2_list.append(row['F-rate'])
    40.         y1_list.append(None)
    41.  
    42. background_color_js = (
    43.     "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
    44.     "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
    45. )
    46.  
    47. line = (
    48.     Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px'))
    49.     .add_xaxis(x_list)
    50.     .add_yaxis("夏季奥运会",
    51.                y1_list,
    52.                is_smooth=True,
    53.                is_connect_nones=True,
    54.                symbol="circle",
    55.                symbol_size=6,
    56.                linestyle_opts=opts.LineStyleOpts(color="#fff"),
    57.                label_opts=opts.LabelOpts(is_show=False, position="top", color="white"),
    58.                itemstyle_opts=opts.ItemStyleOpts(color="green", border_color="#fff", border_width=3),
    59.                tooltip_opts=opts.TooltipOpts(is_show=True), )
    60.     .add_yaxis("冬季季奥运会",
    61.                y2_list,
    62.                is_smooth=True,
    63.                is_connect_nones=True,
    64.                symbol="circle",
    65.                symbol_size=6,
    66.                linestyle_opts=opts.LineStyleOpts(color="#FF4500"),
    67.                label_opts=opts.LabelOpts(is_show=False, position="top", color="white"),
    68.                itemstyle_opts=opts.ItemStyleOpts(color="red", border_color="#fff", border_width=3),
    69.                tooltip_opts=opts.TooltipOpts(is_show=True), )
    70.     .set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter=JsCode("""function (params) 
    71.                                                                            {return params.data[0]+ '年: ' + Number(params.data[1])*100 +'%';}""")), )
    72.     .set_global_opts(title_opts=opts.TitleOpts(title="历届奥运会参赛女性占比趋势",
    73.                                                pos_left="center",
    74.                                                title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20), ),
    75.                      legend_opts=opts.LegendOpts(is_show=True, pos_top='5%',
    76.                                                  textstyle_opts=opts.TextStyleOpts(color="white", font_size=12)),
    77.                      xaxis_opts=opts.AxisOpts(type_="value",
    78.                                               min_=1904,
    79.                                               max_=2016,
    80.                                               boundary_gap=False,
    81.                                               axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63",
    82.                                                                             formatter=JsCode("""function (value) 
    83.                                                                                {return value+'年';}""")),
    84.                                               axisline_opts=opts.AxisLineOpts(is_show=False),
    85.                                               axistick_opts=opts.AxisTickOpts(
    86.                                                   is_show=True,
    87.                                                   length=25,
    88.                                                   linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
    89.                                               ),
    90.                                               splitline_opts=opts.SplitLineOpts(
    91.                                                   is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
    92.                                               ),
    93.                                               ),
    94.                      yaxis_opts=opts.AxisOpts(
    95.                          type_="value",
    96.                          position="right",
    97.                          axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63",
    98.                                                        formatter=JsCode("""function (value) 
    99.                                                                            {return Number(value *100)+'%';}""")),
    100.                          axisline_opts=opts.AxisLineOpts(
    101.                              linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
    102.                          ),
    103.                          axistick_opts=opts.AxisTickOpts(
    104.                              is_show=True,
    105.                              length=15,
    106.                              linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
    107.                          ),
    108.                          splitline_opts=opts.SplitLineOpts(
    109.                              is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
    110.                          ),
    111.                      ), )
    112. )
    113.  
    114. 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

    1.  
    2. import pandas as pd
    3. from pyecharts.charts import *
    4. from pyecharts import options as opts
    5. from pyecharts.commons.utils import JsCode
    6.  
    7. athlete_data = pd.read_csv('./data/athlete_events.csv')
    8. noc_region = pd.read_csv('./data/noc_regions.csv')
    9.  
    10. # 关联代表国家
    11. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    12. print(data.head())
    13.  
    14. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    15. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    16. medal_data = medal_data.sort_values(by="Year", ascending=True)
    17.  
    18. temp = data[(data['Medal'] == 'Gold')]
    19.  
    20. athlete = temp.groupby(['Name'])['Medal'].count().reset_index()
    21. athlete.columns = ['Name', 'Nums']
    22. athlete = athlete.sort_values(by="Nums", ascending=True)
    23.  
    24. background_color_js = (
    25.     "new echarts.graphic.LinearGradient(0, 0, 1, 1, "
    26.     "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
    27. )
    28.  
    29. pb = (
    30.     PictorialBar(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='800px'))
    31.     .add_xaxis([x.replace(' ', '\n') for x in athlete['Name'].tail(10).tolist()])
    32.     .add_yaxis(
    33.         "",
    34.         athlete['Nums'].tail(10).tolist(),
    35.         label_opts=opts.LabelOpts(is_show=False),
    36.         symbol_size=25,
    37.         symbol_repeat='fixed',
    38.         symbol_offset=[0, 0],
    39.         is_symbol_clip=True,
    40.         symbol='image://https://cdn.kesci.com/upload/image/q8f8otrlfc.png')
    41.     .reversal_axis()
    42.     .set_global_opts(
    43.         title_opts=opts.TitleOpts(title="获得金牌数量最多的运动员", pos_left='center',
    44.                                   title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20), ),
    45.         xaxis_opts=opts.AxisOpts(is_show=False, ),
    46.         yaxis_opts=opts.AxisOpts(
    47.             axistick_opts=opts.AxisTickOpts(is_show=False),
    48.             axisline_opts=opts.AxisLineOpts(
    49.                 linestyle_opts=opts.LineStyleOpts(opacity=0)
    50.             ),
    51.         ),
    52.     ))
    53.  
    54. 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

    1.  
    2. import pandas as pd
    3. from pyecharts.charts import *
    4. from pyecharts import options as opts
    5. from pyecharts.commons.utils import JsCode
    6.  
    7. athlete_data = pd.read_csv('./data/athlete_events.csv')
    8. noc_region = pd.read_csv('./data/noc_regions.csv')
    9.  
    10. # 关联代表国家
    11. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    12. print(data.head())
    13.  
    14. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    15. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    16. medal_data = medal_data.sort_values(by="Year", ascending=True)
    17.  
    18. total_athlete = len(set(data['Name']))
    19. medal_athlete = len(set(data['Name'][data['Medal'].isin(['Gold', 'Silver', 'Bronze'])]))
    20. gold_athlete = len(set(data['Name'][data['Medal'] == 'Gold']))
    21.  
    22. l1 = Liquid(init_opts=opts.InitOpts(theme='dark', width='1000px', height='800px'))
    23. l1.add("获得奖牌", [medal_athlete / total_athlete],
    24.        center=["70%", "50%"],
    25.        label_opts=opts.LabelOpts(font_size=50,
    26.                                  formatter=JsCode(
    27.                                      """function (param) {
    28.                                              return (Math.floor(param.value * 10000) / 100) + '%';
    29.                                          }"""),
    30.                                  position="inside",
    31.                                  ))
    32. l1.set_global_opts(title_opts=opts.TitleOpts(title="获得过奖牌比例", pos_left='62%', pos_top='8%'))
    33. l1.set_series_opts(tooltip_opts=opts.TooltipOpts(is_show=False))
    34.  
    35. l2 = Liquid(init_opts=opts.InitOpts(theme='dark', width='1000px', height='800px'))
    36. l2.add("获得金牌",
    37.        [gold_athlete / total_athlete],
    38.        center=["25%", "50%"],
    39.        label_opts=opts.LabelOpts(font_size=50,
    40.                                  formatter=JsCode(
    41.                                      """function (param) {
    42.                                              return (Math.floor(param.value * 10000) / 100) + '%';
    43.                                          }"""),
    44.                                  position="inside",
    45.                                  ), )
    46. l2.set_global_opts(title_opts=opts.TitleOpts(title="获得过金牌比例", pos_left='17%', pos_top='8%'))
    47. l2.set_series_opts(tooltip_opts=opts.TooltipOpts(is_show=False))
    48.  
    49. grid = Grid().add(l1, grid_opts=opts.GridOpts()).add(l2, grid_opts=opts.GridOpts())
    50. 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

    1.  
    2. import pandas as pd
    3. from pyecharts.charts import *
    4. from pyecharts import options as opts
    5. from pyecharts.commons.utils import JsCode
    6.  
    7. athlete_data = pd.read_csv('./data/athlete_events.csv')
    8. noc_region = pd.read_csv('./data/noc_regions.csv')
    9.  
    10. # 关联代表国家
    11. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    12. print(data.head())
    13.  
    14. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    15. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    16. medal_data = medal_data.sort_values(by="Year", ascending=True)
    17.  
    18. tool_js = """function (param) {return param.data[2] +'
      ' 
    19.             +'平均体重： '+Number(param.data[0]).toFixed(2)+' kg
      '
    20.             +'平均身高： '+Number(param.data[1]).toFixed(2)+' cm
      '
    21.             +'平均年龄： '+Number(param.data[3]).toFixed(2);}"""
    22.  
    23. background_color_js = (
    24.     "new echarts.graphic.LinearGradient(1, 0, 0, 1, "
    25.     "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
    26. )
    27.  
    28. temp_data = data[data['Sex'] == 'M'].groupby(['Sport'])[['Age', 'Height', 'Weight']].mean().reset_index().dropna(
    29.     how='any')
    30.  
    31. scatter = (Scatter(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px'))
    32. .add_xaxis(temp_data['Weight'].tolist())
    33. .add_yaxis("男性", [[row['Height'], row['Sport'], row['Age']] for _, row in temp_data.iterrows()],
    34.            # 渐变效果实现部分
    35.            color=JsCode("""new echarts.graphic.RadialGradient(0.4, 0.3, 1, [{
    36.                                         offset: 0,
    37.                                         color: 'rgb(129, 227, 238)'
    38.                                     }, {
    39.                                         offset: 1,
    40.                                         color: 'rgb(25, 183, 207)'
    41.                                     }])"""))
    42. .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    43. .set_global_opts(
    44.     title_opts=opts.TitleOpts(title="各项目运动员平均升高体重年龄", pos_left="center",
    45.                               title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20)),
    46.     legend_opts=opts.LegendOpts(is_show=True, pos_top='5%',
    47.                                 textstyle_opts=opts.TextStyleOpts(color="white", font_size=12)),
    48.     tooltip_opts=opts.TooltipOpts(formatter=JsCode(tool_js)),
    49.     xaxis_opts=opts.AxisOpts(
    50.         name='体重/kg',
    51.         # 设置坐标轴为数值类型
    52.         type_="value",
    53.         is_scale=True,
    54.         # 显示分割线
    55.         axislabel_opts=opts.LabelOpts(margin=30, color="white"),
    56.         axisline_opts=opts.AxisLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
    57.         axistick_opts=opts.AxisTickOpts(is_show=True, length=25,
    58.                                         linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
    59.         splitline_opts=opts.SplitLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
    60.                                           )),
    61.     yaxis_opts=opts.AxisOpts(
    62.         name='身高/cm',
    63.         # 设置坐标轴为数值类型
    64.         type_="value",
    65.         # 默认为False表示起始为0
    66.         is_scale=True,
    67.         axislabel_opts=opts.LabelOpts(margin=30, color="white"),
    68.         axisline_opts=opts.AxisLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
    69.         axistick_opts=opts.AxisTickOpts(is_show=True, length=25,
    70.                                         linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
    71.         splitline_opts=opts.SplitLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
    72.                                           )),
    73.     visualmap_opts=opts.VisualMapOpts(is_show=False, type_='size', range_size=[5, 50], min_=10, max_=40)
    74. ))
    75.  
    76. temp_data = data[data['Sex'] == 'F'].groupby(['Sport'])[['Age', 'Height', 'Weight']].mean().reset_index().dropna(
    77.     how='any')
    78.  
    79. scatter1 = (Scatter()
    80.             .add_xaxis(temp_data['Weight'].tolist())
    81.             .add_yaxis("女性", [[row['Height'], row['Sport'], row['Age']] for _, row in temp_data.iterrows()],
    82.                        itemstyle_opts=opts.ItemStyleOpts(
    83.                            color=JsCode("""new echarts.graphic.RadialGradient(0.4, 0.3, 1, [{
    84.                                         offset: 0,
    85.                                         color: 'rgb(251, 118, 123)'
    86.                                     }, {
    87.                                         offset: 1,
    88.                                         color: 'rgb(204, 46, 72)'
    89.                                     }])""")))
    90.             .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    91.             )
    92. scatter.overlap(scatter1)
    93. 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

    1.  
    2. import pandas as pd
    3. from pyecharts.charts import *
    4. from pyecharts import options as opts
    5. from pyecharts.commons.utils import JsCode
    6.  
    7. athlete_data = pd.read_csv('./data/athlete_events.csv')
    8. noc_region = pd.read_csv('./data/noc_regions.csv')
    9.  
    10. # 关联代表国家
    11. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    12. print(data.head())
    13.  
    14. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    15. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    16. medal_data = medal_data.sort_values(by="Year", ascending=True)
    17.  
    18. CN_data = data[data.region == 'China']
    19. CN_data.head()
    20.  
    21. background_color_js = (
    22.     "new echarts.graphic.LinearGradient(1, 0, 0, 1, "
    23.     "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
    24. )
    25.  
    26. athlete = CN_data.groupby(['Year', 'Season'])['Name'].nunique().reset_index()
    27. athlete.columns = ['Year', 'Season', 'Nums']
    28. athlete = athlete.sort_values(by="Year", ascending=False)
    29.  
    30. s_bar = (
    31.     Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px'))
    32.     .add_xaxis([row['Year'] for _, row in athlete[athlete.Season == 'Summer'].iterrows()])
    33.     .add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season == 'Summer'].iterrows()],
    34.                category_gap='40%',
    35.                itemstyle_opts=opts.ItemStyleOpts(
    36.                    border_color='rgb(220,220,220)',
    37.                    color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, 
    38.                                              [{
    39.                                                  offset: 1,
    40.                                                  color: '#00BFFF'
    41.                                              }, {
    42.                                                  offset: 0,
    43.                                                  color: '#32CD32'
    44.                                              }])""")))
    45.     .set_series_opts(label_opts=opts.LabelOpts(is_show=True,
    46.                                                position='top',
    47.                                                font_style='italic'))
    48.     .set_global_opts(
    49.         title_opts=opts.TitleOpts(title="中国历年奥运会参赛人数-夏奥会", pos_left='center'),
    50.         xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
    51.         legend_opts=opts.LegendOpts(is_show=False),
    52.         yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),
    53.         graphic_opts=[
    54.             opts.GraphicImage(
    55.                 graphic_item=opts.GraphicItem(
    56.                     id_="logo", right=0, top=0, z=-10, bounding="raw", origin=[75, 75]
    57.                 ),
    58.                 graphic_imagestyle_opts=opts.GraphicImageStyleOpts(
    59.                     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",
    60.                     width=1000,
    61.                     height=600,
    62.                     opacity=0.6, ),
    63.             )
    64.         ], )
    65. )
    66.  
    67. w_bar = (
    68.     Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px'))
    69.     .add_xaxis([row['Year'] for _, row in athlete[athlete.Season == 'Winter'].iterrows()])
    70.     .add_yaxis("参赛人数", [row['Nums'] for _, row in athlete[athlete.Season == 'Winter'].iterrows()],
    71.                category_gap='50%',
    72.                itemstyle_opts=opts.ItemStyleOpts(
    73.                    border_color='rgb(220,220,220)',
    74.                    color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, 
    75.                                              [{
    76.                                                  offset: 1,
    77.                                                  color: '#00BFFF'
    78.                                              }, {
    79.                                                  offset: 0.8,
    80.                                                  color: '#FFC0CB'
    81.                                              }, {
    82.                                                  offset: 0,
    83.                                                  color: '#40E0D0'
    84.                                              }])""")))
    85.     .set_series_opts(label_opts=opts.LabelOpts(is_show=True,
    86.                                                position='top',
    87.                                                font_style='italic'))
    88.     .set_global_opts(
    89.         title_opts=opts.TitleOpts(title="中国历年奥运会参赛人数-冬奥会", pos_left='center'),
    90.         xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
    91.         legend_opts=opts.LegendOpts(is_show=False),
    92.         yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),
    93.         graphic_opts=[
    94.             opts.GraphicImage(
    95.                 graphic_item=opts.GraphicItem(
    96.                     id_="logo", right=0, top=-300, z=-10, bounding="raw", origin=[75, 75]
    97.                 ),
    98.                 graphic_imagestyle_opts=opts.GraphicImageStyleOpts(
    99.                     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",
    100.                     width=1000,
    101.                     height=600,
    102.                     opacity=0.6, ),
    103.             )
    104.         ], )
    105. )
    106.  
    107. page = (
    108.     Page()
    109.     .add(s_bar, )
    110.     .add(w_bar, )
    111. )
    112. 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

    1.  
    2. import pandas as pd
    3. from pyecharts.charts import *
    4. from pyecharts import options as opts
    5. from pyecharts.commons.utils import JsCode
    6.  
    7. athlete_data = pd.read_csv('./data/athlete_events.csv')
    8. noc_region = pd.read_csv('./data/noc_regions.csv')
    9.  
    10. # 关联代表国家
    11. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    12. print(data.head())
    13.  
    14. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    15. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    16. medal_data = medal_data.sort_values(by="Year", ascending=True)
    17.  
    18. CN_data = data[data.region == 'China']
    19. CN_data.head()
    20.  
    21. background_color_js = (
    22.     "new echarts.graphic.LinearGradient(1, 0, 0, 1, "
    23.     "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
    24. )
    25.  
    26. CN_medals = CN_data.groupby(['Year', 'Season', 'Medal'])['Event'].nunique().reset_index()
    27. CN_medals.columns = ['Year', 'Season', 'Medal', 'Nums']
    28. CN_medals = CN_medals.sort_values(by="Year", ascending=False)
    29.  
    30. s_bar = (
    31.     Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px'))
    32.     .add_xaxis(
    33.         sorted(list(set([row['Year'] for _, row in CN_medals[CN_medals.Season == 'Summer'].iterrows()])), reverse=True))
    34.     .add_yaxis("金牌", [row['Nums'] for _, row in
    35.                       CN_medals[(CN_medals.Season == 'Summer') & (CN_medals.Medal == 'Gold')].iterrows()],
    36.                category_gap='20%',
    37.                itemstyle_opts=opts.ItemStyleOpts(
    38.                    border_color='rgb(220,220,220)',
    39.                    color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, 
    40.                                              [{
    41.                                                  offset: 0,
    42.                                                  color: '#FFD700'
    43.                                              }, {
    44.                                                  offset: 1,
    45.                                                  color: '#FFFFF0'
    46.                                              }])""")))
    47.     .add_yaxis("银牌", [row['Nums'] for _, row in
    48.                       CN_medals[(CN_medals.Season == 'Summer') & (CN_medals.Medal == 'Silver')].iterrows()],
    49.                category_gap='20%',
    50.                itemstyle_opts=opts.ItemStyleOpts(
    51.                    border_color='rgb(220,220,220)',
    52.                    color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, 
    53.                                              [{
    54.                                                  offset: 0,
    55.                                                  color: '#C0C0C0'
    56.                                              }, {
    57.                                                  offset: 1,
    58.                                                  color: '#FFFFF0'
    59.                                              }])""")))
    60.     .add_yaxis("铜牌", [row['Nums'] for _, row in
    61.                       CN_medals[(CN_medals.Season == 'Summer') & (CN_medals.Medal == 'Bronze')].iterrows()],
    62.                category_gap='20%',
    63.                itemstyle_opts=opts.ItemStyleOpts(
    64.                    border_color='rgb(220,220,220)',
    65.                    color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, 
    66.                                              [{
    67.                                                  offset: 0,
    68.                                                  color: '#DAA520'
    69.                                              }, {
    70.                                                  offset: 1,
    71.                                                  color: '#FFFFF0'
    72.                                              }])""")))
    73.     .set_series_opts(label_opts=opts.LabelOpts(is_show=True,
    74.                                                position='top',
    75.                                                font_style='italic'))
    76.     .set_global_opts(
    77.         title_opts=opts.TitleOpts(title="中国历年奥运会获得奖牌数数-夏奥会", pos_left='center'),
    78.         xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
    79.         legend_opts=opts.LegendOpts(is_show=False),
    80.         yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),
    81.         graphic_opts=[
    82.             opts.GraphicImage(
    83.                 graphic_item=opts.GraphicItem(
    84.                     id_="logo", right=0, top=0, z=-10, bounding="raw", origin=[75, 75]
    85.                 ),
    86.                 graphic_imagestyle_opts=opts.GraphicImageStyleOpts(
    87.                     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",
    88.                     width=1000,
    89.                     height=600,
    90.                     opacity=0.6, ),
    91.             )
    92.         ], )
    93. )
    94.  
    95. w_bar = (
    96.     Bar(init_opts=opts.InitOpts(theme='dark', width='1000px', height='300px'))
    97.     .add_xaxis(
    98.         sorted(list(set([row['Year'] for _, row in CN_medals[CN_medals.Season == 'Winter'].iterrows()])), reverse=True))
    99.     .add_yaxis("金牌", [row['Nums'] for _, row in
    100.                       CN_medals[(CN_medals.Season == 'Winter') & (CN_medals.Medal == 'Gold')].iterrows()],
    101.                category_gap='20%',
    102.                itemstyle_opts=opts.ItemStyleOpts(
    103.                    border_color='rgb(220,220,220)',
    104.                    color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, 
    105.                                              [{
    106.                                                  offset: 0,
    107.                                                  color: '#FFD700'
    108.                                              }, {
    109.                                                  offset: 1,
    110.                                                  color: '#FFFFF0'
    111.                                              }])""")))
    112.     .add_yaxis("银牌", [row['Nums'] for _, row in
    113.                       CN_medals[(CN_medals.Season == 'Winter') & (CN_medals.Medal == 'Silver')].iterrows()],
    114.                category_gap='20%',
    115.                itemstyle_opts=opts.ItemStyleOpts(
    116.                    border_color='rgb(220,220,220)',
    117.                    color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, 
    118.                                              [{
    119.                                                  offset: 0,
    120.                                                  color: '#C0C0C0'
    121.                                              }, {
    122.                                                  offset: 1,
    123.                                                  color: '#FFFFF0'
    124.                                              }])""")))
    125.     .add_yaxis("铜牌", [row['Nums'] for _, row in
    126.                       CN_medals[(CN_medals.Season == 'Winter') & (CN_medals.Medal == 'Bronze')].iterrows()],
    127.                category_gap='20%',
    128.                itemstyle_opts=opts.ItemStyleOpts(
    129.                    border_color='rgb(220,220,220)',
    130.                    color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, 
    131.                                              [{
    132.                                                  offset: 0,
    133.                                                  color: '#DAA520'
    134.                                              }, {
    135.                                                  offset: 1,
    136.                                                  color: '#FFFFF0'
    137.                                              }])""")))
    138.     .set_series_opts(label_opts=opts.LabelOpts(is_show=True,
    139.                                                position='top',
    140.                                                font_style='italic'))
    141.     .set_global_opts(
    142.         title_opts=opts.TitleOpts(title="中国历年奥运会获得奖牌数-冬奥会", pos_left='center'),
    143.         xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
    144.         legend_opts=opts.LegendOpts(is_show=False),
    145.         yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63")),
    146.         graphic_opts=[
    147.             opts.GraphicImage(
    148.                 graphic_item=opts.GraphicItem(
    149.                     id_="logo", right=0, top=-300, z=-10, bounding="raw", origin=[75, 75]
    150.                 ),
    151.                 graphic_imagestyle_opts=opts.GraphicImageStyleOpts(
    152.                     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",
    153.                     width=1000,
    154.                     height=600,
    155.                     opacity=0.6, ),
    156.             )
    157.         ], )
    158. )
    159.  
    160. page = (
    161.     Page()
    162.     .add(s_bar, )
    163.     .add(w_bar, )
    164. )
    165. page.render(r".\htmlRender\10_中国历届奥运会奖牌数.html")

    ③中国优势项目

    1.  
    2. import pandas as pd
    3. from pyecharts.charts import *
    4. from pyecharts import options as opts
    5. from pyecharts.commons.utils import JsCode
    6.  
    7. athlete_data = pd.read_csv('./data/athlete_events.csv')
    8. noc_region = pd.read_csv('./data/noc_regions.csv')
    9.  
    10. # 关联代表国家
    11. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    12. print(data.head())
    13.  
    14. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    15. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    16. medal_data = medal_data.sort_values(by="Year", ascending=True)
    17.  
    18. CN_data = data[data.region == 'China']
    19. CN_data.head()
    20.  
    21. background_color_js = (
    22.     "new echarts.graphic.LinearGradient(1, 0, 0, 1, "
    23.     "[{offset: 0.5, color: '#FFC0CB'}, {offset: 1, color: '#F0FFFF'}, {offset: 0, color: '#EE82EE'}], false)"
    24. )
    25.  
    26. CN_events = CN_data[CN_data.Medal == 'Gold'].groupby(['Year', 'Sport'])['Event'].nunique().reset_index()
    27. CN_events = CN_events.groupby(['Sport'])['Event'].sum().reset_index()
    28. CN_events.columns = ['Sport', 'Nums']
    29.  
    30. data_pair = [(row['Sport'], row['Nums']) for _, row in CN_events.iterrows()]
    31.  
    32. wc = (WordCloud(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js), width='1000px', height='600px'))
    33.       .add("", data_pair, word_size_range=[30, 80])
    34.       .set_global_opts(title_opts=opts.TitleOpts(title="中国获得过金牌运动项目", pos_left="center",
    35.                                                  title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20)))
    36.       )
    37.  
    38. 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

    1.  
    2. import pandas as pd
    3. from pyecharts.charts import *
    4. from pyecharts import options as opts
    5. from pyecharts.commons.utils import JsCode
    6.  
    7. athlete_data = pd.read_csv('./data/athlete_events.csv')
    8. noc_region = pd.read_csv('./data/noc_regions.csv')
    9.  
    10. # 关联代表国家
    11. data = pd.merge(athlete_data, noc_region, on='NOC', how='left')
    12. print(data.head())
    13.  
    14. medal_data = data.groupby(['Year', 'Season', 'region', 'Medal'])['Event'].nunique().reset_index()
    15. medal_data.columns = ['Year', 'Season', 'region', 'Medal', 'Nums']
    16. medal_data = medal_data.sort_values(by="Year", ascending=True)
    17.  
    18. f1 = lambda x: max(x['Event']) / sum(x['Event'])
    19. f2 = lambda x: x.sort_values('Event', ascending=False).head(1)
    20.  
    21. t_data = \
    22.     data[(data.Medal == 'Gold') & (data.Year >= 2000) & (data.Season == 'Summer')].groupby(['Year', 'Sport', 'region'])[
    23.         'Event'].nunique().reset_index()
    24. t_data = t_data.groupby(['Sport', 'region'])['Event'].sum().reset_index()
    25. t1 = t_data.groupby(['Sport']).apply(f2).reset_index(drop=True)
    26. t2 = t_data.groupby(['Sport'])['Event'].sum().reset_index()
    27. t_data = pd.merge(t1, t2, on='Sport', how='inner')
    28. t_data['gold_rate'] = t_data.Event_x / t_data.Event_y
    29. t_data = t_data.sort_values('gold_rate', ascending=False).reset_index(drop=True)
    30.  
    31. t_data = t_data[(t_data.gold_rate >= 0.5) & (t_data.Event_y >= 10)]
    32.  
    33. background_color_js = (
    34.     "new echarts.graphic.LinearGradient(1, 0, 0, 1, "
    35.     "[{offset: 0, color: '#008B8B'}, {offset: 1, color: '#FF6347'}], false)"
    36. )
    37.  
    38. fn = """
    39.     function(params) {
    40.         if(params.name == '其他国家')
    41.             return '\\n\\n\\n' + params.name + ' : ' + params.value ;
    42.         return params.seriesName+ '\\n' + params.name + ' : ' + params.value;
    43.     }
    44.     """
    45.  
    46.  
    47. def new_label_opts():
    48.     return opts.LabelOpts(formatter=JsCode(fn), position="center")
    49.  
    50.  
    51. pie = Pie(init_opts=opts.InitOpts(theme='dark', width='1000px', height='1000px'))
    52. idx = 0
    53.  
    54. for _, row in t_data.iterrows():
    55.  
    56.     if idx % 2 == 0:
    57.         x = 30
    58.         y = int(idx / 2) * 22 + 18
    59.     else:
    60.         x = 70
    61.         y = int(idx / 2) * 22 + 18
    62.     idx += 1
    63.     pos_x = str(x) + '%'
    64.     pos_y = str(y) + '%'
    65.     pie.add(
    66.         row['Sport'],
    67.         [[row['region'], row['Event_x']], ['其他国家', row['Event_y'] - row['Event_x']]],
    68.         center=[pos_x, pos_y],
    69.         radius=[70, 100],
    70.         label_opts=new_label_opts(), )
    71.  
    72. pie.set_global_opts(
    73.     title_opts=opts.TitleOpts(title="被单个国家统治的项目",
    74.                               subtitle='统计周期：2000年悉尼奥运会起',
    75.                               pos_left="center",
    76.                               title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=20)),
    77.     legend_opts=opts.LegendOpts(is_show=False),
    78. )
    79.  
    80. pie.render(r".\htmlRender\12_被单个国家统治的奥运会项目.html")

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  • 原文地址：https://blog.csdn.net/c_lanxiaofang/article/details/126311987