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2020东京奥运会奖牌数据可视化

作者：互联网

目录

文章目录

前言

一、导入模块

二、Pandas数据处理

2.1 读取数据

2.2 是否有缺失值

 2.3 查看中国每日数据

 2.4 统计中国、美国、日本、澳大利亚4个国家数据

三、Pyecharts绘图

3.1 绘制基础折线图

 3.2 加载样式

3.3 动态展示中国每日金牌数据 

3.4 增加其他国家每日金牌数据 

3.5 2020东京奥运会奖牌数世界分布 

3.6 2020东京奥运会金牌世界分布

 3.7 2020东京奥运会奖牌世界分布(动态)

总结


前言

刚刚过去的冬季奥运会，中国队取得了不错的成绩。本文将分别基于Pandas和Pyecharts进行数据处理和数据可视化，并利用可视化图表对奥运会相关信息进行展示。


一、导入模块

import pandas as pd
from pyecharts.charts import Timeline, Line, Tree
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.globals import ThemeType


二、Pandas数据处理


2.1 读取数据

df = pd.read_csv('D:/天池数据/2020东京奥运会奖牌数据可视化/2020东京奥运会奖牌数据.csv', index_col=0, encoding = 'gb18030')
df.head(10)


2.2 是否有缺失值

df.isnull().any()

各列数据均不存在确实情况。

 2.3 查看中国每日数据

# 方法一
df1 = df[df['国家']=='中国']
df1

# 方法二
df1 = df[df.国家 == '中国']
df1

 

 2.4 统计中国、美国、日本、澳大利亚4个国家数据

all_country_data = []
flg = {}
cols = ['国家']
countrys = ['中国','美国','日本','澳大利亚']
for country in countrys:
    df1 = df[df['国家']==country]
    df_t = df1.copy()
    df2 = df.loc[df['国家']==country,['金牌','银牌','铜牌','总计']]
    if len(df2.index.tolist()) >= len(cols):
        cols += df2.index.tolist()
    flg[country] = df1.iloc[:1, -1].values[0]
    
    one_country_data = [country]
    datasss = []
    for i in range(df2.shape[0]):    
        datasss.append(df2[:i+1].apply(lambda x:x.sum()).values.tolist())
    d1 = pd.DataFrame(data=datasss, columns=['金牌','银牌','铜牌','总计'])
    for col in d1.columns:
        df_t[col] = d1[col].values
    df_t1 = df_t.loc[:,['金牌']]
    one_country_data += df_t['金牌'].values.tolist()
    all_country_data.append(one_country_data)
all_country_data

 生成新的Dataframe：

d2 = pd.DataFrame(data=all_country_data,columns=cols)
d2 = d2.fillna(method = 'ffill',axis=1)
d2

method='ffill':用前一个非缺失值去填充缺失值。

method='bfill':用下一个非缺失值去填充缺失值。

这里采用前一个非缺失值对缺失值进行填充。同时，可根据需要获取多个国家数据，改变countrys列表即可。 

三、Pyecharts绘图

3.1 绘制基础折线图

CHN = []
x_data=cols[1:]
for d_time in cols[1:]:
    CHN.append(d2[d_time][d2['国家']=='中国'].values.tolist()[0])
l1 = (
    Line()
    .add_xaxis(x_data)
    # 中国线条
    .add_yaxis(
        '中国',
        CHN,
        label_opts=opts.LabelOpts(is_show=True))
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title='中国金牌',
            pos_left='center',
        ),
        yaxis_opts=opts.AxisOpts(
            name='金牌/枚',            
            is_scale=True,
            max_=40),
        legend_opts=opts.LegendOpts(is_show=False),
    )
)
l1.render_notebook()

 3.2 加载样式

# 背景色
background_color_js = (
    "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
    "[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)

# 线条样式
linestyle_dic = { 'normal': {
                    'width': 4,  
                    'shadowColor': '#696969', 
                    'shadowBlur': 10,  
                    'shadowOffsetY': 10,  
                    'shadowOffsetX': 10,  
                    }
                }

timeline = Timeline(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js),
                                            width='980px',height='600px'))
timeline.add_schema(is_auto_play=True, is_loop_play=True, 
                    is_timeline_show=True, play_interval=500)

CHN = []
x_data=cols[1:]
for d_time in cols[1:]:
    CHN.append(d2[d_time][d2['国家']=='中国'].values.tolist()[0])
    
line = (
    Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js),
                                 width='980px',height='600px'))
    .add_xaxis(x_data)
    # 中国线条
    .add_yaxis(
        '中国',
        CHN,
        symbol_size=10,
        is_smooth=True,
        label_opts=opts.LabelOpts(is_show=True),
        markpoint_opts=opts.MarkPointOpts(
                data=[opts.MarkPointItem(
                      name="",
                      type_='max',
                      value_index=0,
                      symbol='image://'+ flg['中国'],
                      symbol_size=[40, 25],
                    )],
                label_opts=opts.LabelOpts(is_show=False),
            )
    )
    .set_series_opts(linestyle_opts=linestyle_dic,label_opts=opts.LabelOpts(font_size=12, color='red' ))
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title='中国金牌',
            pos_left='center',
            pos_top='2%',
            title_textstyle_opts=opts.TextStyleOpts(
                    color='#DC143C', font_size=20)
        ),
        xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=14, color='red'),
                                 axisline_opts=opts.AxisLineOpts(is_show=True,
                                    linestyle_opts=opts.LineStyleOpts(width=2, color='#DB7093'))),
        yaxis_opts=opts.AxisOpts(
            name='金牌/枚',            
            is_scale=True,
            max_=40,
            name_textstyle_opts=opts.TextStyleOpts(font_size=16,font_weight='bold',color='#FFD700'),
            axislabel_opts=opts.LabelOpts(font_size=13,color='red'),
            splitline_opts=opts.SplitLineOpts(is_show=True, 
                                              linestyle_opts=opts.LineStyleOpts(type_='dashed')),
            axisline_opts=opts.AxisLineOpts(is_show=True,
                                    linestyle_opts=opts.LineStyleOpts(width=2, color='#DB7093'))
        ),
        legend_opts=opts.LegendOpts(is_show=False, pos_right='1.5%', pos_top='2%',
                                    legend_icon='roundRect',orient = 'horizontal'),
    )
)
line.render_notebook()

3.3 动态展示中国每日金牌数据 

# 背景色
background_color_js = (
    "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
    "[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)

# 线条样式
linestyle_dic = {'normal': {
    'width': 4,
    'shadowColor': '#696969',
    'shadowBlur': 10,
    'shadowOffsetY': 10,
    'shadowOffsetX': 10,
}
}

timeline = Timeline(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js),
                                            width='980px', height='600px'))
timeline.add_schema(is_auto_play=True, is_loop_play=True,
                    is_timeline_show=True, play_interval=500)

CHN = []
x_data = cols[1:]

for d_time in cols[1:]:
    CHN.append(d2[d_time][d2['国家'] == '中国'].values.tolist()[0]) 
    line = (
        Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js),
                                     width='980px', height='600px'))
        .add_xaxis(x_data)
        # 中国线条
        .add_yaxis(
            '中国',
            CHN,
            symbol_size=10,
            is_smooth=True,
            label_opts=opts.LabelOpts(is_show=True),
            markpoint_opts=opts.MarkPointOpts(
                data=[opts.MarkPointItem(
                    name="",
                    type_='max',
                    value_index=0,
                    symbol='image://' + flg['中国'],
                    symbol_size=[40, 25],
                )],
                label_opts=opts.LabelOpts(is_show=False),
            )
        )
        .set_series_opts(linestyle_opts=linestyle_dic, label_opts=opts.LabelOpts(font_size=12, color='red'))
        .set_global_opts(
            title_opts=opts.TitleOpts(
                title='中国金牌',
                pos_left='center',
                pos_top='2%',
                title_textstyle_opts=opts.TextStyleOpts(color='#DC143C', font_size=20)),
            xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=14, color='red'),
                                     axisline_opts=opts.AxisLineOpts(is_show=True,
                                                                     linestyle_opts=opts.LineStyleOpts(width=2, color='#DB7093'))),
            yaxis_opts=opts.AxisOpts(
                name='金牌/枚',
                is_scale=True,
                max_=40,
                name_textstyle_opts=opts.TextStyleOpts(
                    font_size=16, font_weight='bold', color='#FFD700'),
                axislabel_opts=opts.LabelOpts(
                    font_size=13, color='red', rotate=15),
                splitline_opts=opts.SplitLineOpts(is_show=True,
                                                  linestyle_opts=opts.LineStyleOpts(type_='dashed')),
                axisline_opts=opts.AxisLineOpts(is_show=True,
                                                linestyle_opts=opts.LineStyleOpts(width=2, color='#DB7093'))
            ),
            legend_opts=opts.LegendOpts(is_show=True, pos_right='1%', pos_top='2%',
                                        legend_icon='roundRect', orient='vertical'),
        )
    )
    timeline.add(line, '{}'.format(d_time))
    
timeline.render_notebook()

3.4 增加其他国家每日金牌数据 

# 背景色
background_color_js = (
    "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
    "[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)

# 线条样式
linestyle_dic = { 'normal': {
                    'width': 4,  
                    'shadowColor': '#696969', 
                    'shadowBlur': 10,  
                    'shadowOffsetY': 10,  
                    'shadowOffsetX': 10,  
                    }
                }
    
timeline = Timeline(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js),
                                            width='980px',height='600px'))
timeline.add_schema(is_auto_play=True, is_loop_play=True, 
                    is_timeline_show=True, play_interval=500)

CHN, USA, JPN, AUS = [], [], [], []
x_data=cols[1:]
for d_time in cols[1:]:
    CHN.append(d2[d_time][d2['国家']=='中国'].values.tolist()[0])
    USA.append(d2[d_time][d2['国家']=='美国'].values.tolist()[0])
    JPN.append(d2[d_time][d2['国家']=='日本'].values.tolist()[0])
    AUS.append(d2[d_time][d2['国家']=='澳大利亚'].values.tolist()[0])
    line = (
        Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js),
                                     width='980px',height='600px'))
        .add_xaxis(x_data)
        # 中国线条
        .add_yaxis(
            '中国',
            CHN,
            symbol_size=10,
            is_smooth=True,
            label_opts=opts.LabelOpts(is_show=True),
            markpoint_opts=opts.MarkPointOpts(
                    data=[  opts.MarkPointItem(
                            name="",
                            type_='max',
                            value_index=0,
                            symbol='image://'+ flg['中国'],
                            symbol_size=[40, 25],
                        )],
                    label_opts=opts.LabelOpts(is_show=False),
                )
        )
        # 美国线条
        .add_yaxis(
            '美国',
            USA,
            symbol_size=5,
            is_smooth=True,
            label_opts=opts.LabelOpts(is_show=True),
            markpoint_opts=opts.MarkPointOpts(
                    data=[
                        opts.MarkPointItem(
                            name="",
                            type_='max',
                            value_index=0,
                            symbol='image://'+ flg['美国'],
                            symbol_size=[40, 25],
                        )
                    ],
                    label_opts=opts.LabelOpts(is_show=False),
                )
        )
        # 日本线条
        .add_yaxis(
            '日本',
            JPN,
            symbol_size=5,
            is_smooth=True,
            label_opts=opts.LabelOpts(is_show=True),
            markpoint_opts=opts.MarkPointOpts(
                    data=[  opts.MarkPointItem(
                            name="",
                            type_='max',
                            value_index=0,
                            symbol='image://'+ flg['日本'],
                            symbol_size=[40, 25],
                        )],
                    label_opts=opts.LabelOpts(is_show=False),
                )
        )
        # 澳大利亚线条
        .add_yaxis(
            '澳大利亚',
            AUS,
            symbol_size=5,
            is_smooth=True,
            label_opts=opts.LabelOpts(is_show=True),
            markpoint_opts=opts.MarkPointOpts(
                    data=[  opts.MarkPointItem(
                            name="",
                            type_='max',
                            value_index=0,
                            symbol='image://'+ flg['澳大利亚'],
                            symbol_size=[40, 25],
                        )],
                    label_opts=opts.LabelOpts(is_show=False),
                )
        )
        .set_series_opts(linestyle_opts=linestyle_dic)
        .set_global_opts(
            title_opts=opts.TitleOpts(
                title='中国 VS 美国 VS 日本 VS 澳大利亚',
                pos_left='center',
                pos_top='2%',
                title_textstyle_opts=opts.TextStyleOpts(
                        color='#DC143C', font_size=20)
            ),
            xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=14, color='red'),
                         axisline_opts=opts.AxisLineOpts(is_show=True,
                            linestyle_opts=opts.LineStyleOpts(width=2, color='#DB7093'))),
            yaxis_opts=opts.AxisOpts(
                name='金牌/枚',            
                is_scale=True,
                max_=40,
                name_textstyle_opts=opts.TextStyleOpts(font_size=16,font_weight='bold',color='#FFD700'),
                axislabel_opts=opts.LabelOpts(font_size=13,color='red',rotate=15),
                splitline_opts=opts.SplitLineOpts(is_show=True, 
                                                  linestyle_opts=opts.LineStyleOpts(type_='dashed')),
                axisline_opts=opts.AxisLineOpts(is_show=True,
                                        linestyle_opts=opts.LineStyleOpts(width=2, color='#DB7093'))
            ),
            legend_opts=opts.LegendOpts(is_show=True, pos_right='1%', pos_top='2%',
                                        legend_icon='roundRect',orient = 'vertical'),
        ))
    timeline.add(line, '{}'.format(d_time))

timeline.render_notebook()

3.5 2020东京奥运会奖牌数世界分布 

import requests
from pyecharts.charts import Map

# 获取数据：
url = 'https://app-sc.miguvideo.com/vms-livedata/olympic-medal/total-table/15/110000004609'
r = requests.get(url, timeout=30)
r.raise_for_status()
r.encoding = r.apparent_encoding
datas = r.json()['body']['allMedalData']
df = pd.DataFrame()
for data in datas:
    df = df.append([[
        data['countryName'],
        data['goldMedalNum'],
        data['silverMedalNum'],
        data['bronzeMedalNum'],
        data['totalMedalNum']]])
df.columns = ['国家', '金牌', '银牌', '铜牌', '奖牌']
df = df.reset_index(drop=True)
df['国家'].replace('俄奥委会','俄罗斯',inplace=True)
name_map = {
    'Singapore Rep.': '新加坡',
    ...
    'Comoros': '科摩罗'
}
m0 = (
    Map()
    .add("奖牌数", [list(z) for z in zip(df['国家'].values, df['奖牌'].values)], "world", is_map_symbol_show=False,
         is_roam=False, name_map=name_map)
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="2020东京奥运会奖牌数世界分布"),
        legend_opts=opts.LegendOpts(is_show=False),
        visualmap_opts=opts.VisualMapOpts(
            is_show=True, 
            max_=120,
            is_piecewise=True,
            split_number = 8,
        ),        
    )
)
m0.render_notebook()

 首先，利用requests库爬取相关数据，并对数据进行相应的处理；之后，利用pyecharts绘制map地图对奖牌数的世界发布进行可视化。

3.6 2020东京奥运会金牌世界分布

m1 = (
    Map()
    .add("金牌", [list(z) for z in zip(df['国家'].values, df['金牌'].values)], "world", is_map_symbol_show=False,
         is_roam=False, name_map=name_map)
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="2020东京奥运会金牌世界分布"),
        legend_opts=opts.LegendOpts(is_show=False),
        visualmap_opts=opts.VisualMapOpts(is_show=True, max_=40,
                                          is_piecewise=True,
                                          split_number = 8,
                                          range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000']
                                         ),        
    )
)
m1.render_notebook()

 3.7 2020东京奥运会奖牌世界分布(动态)

timeline = Timeline(init_opts=opts.InitOpts(theme=ThemeType.DARK))
timeline.add_schema(is_auto_play=True, is_loop_play=True, 
                    is_timeline_show=False, play_interval=800)
colls=['奖牌','金牌','银牌','铜牌']
maxx = [120,40,40,40]
for index, col in enumerate(colls): 
    m = (
        Map()        
        .add(col, [list(z) for z in zip(df['国家'].values, df[col].values)], "world", is_map_symbol_show=False,
             is_roam=False, name_map=name_map)
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            legend_opts=opts.LegendOpts(is_show=False),
            visualmap_opts=opts.VisualMapOpts(is_show=False, 
                                              max_=maxx[index],
                                              is_piecewise=True,
                                              split_number = 20,
                                             ),   
            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="2020奥运会{}分布".format(col),
                    font="bold 26px Microsoft YaHei",
                    graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
                        fill="#fff"
                    ),
                ),
            ),
        ],
        )
        ],
        )
    )
    timeline.add(m, "{}分布".format(col))

timeline.render_notebook()


总结

本文利用Pandas对数据进行处理，并利用Pyecharts绘制折线图（Line）和地图（Map），并通过添加时间轴组件（Timeline）对奥运会数据进行动态的可视化展示。Pyecharts相关的参数说明以及其他类型的图表制作可以参阅Pyecharts的官方文档简介 - pyecharts - A Python Echarts Plotting Library built with love.

标签：show,color,data,奖牌,df,可视化,2020,True,opts
来源： https://blog.csdn.net/JUV_7/article/details/120938481