pyecharts绘制可视化图表案例（持续完善）

1. 绘制柱状图

from pyecharts.charts import Bar
from pyecharts import options as opts

index = ['商品948', '商品856', '商品621', '商品272', '商品563', '商品347', '商品597', '商品468', '商品986', '商品1000']
values = [29, 28, 27, 26, 24, 21, 19 ,18 ,16 ,13]
(Bar()
    .add_xaxis(xaxis_data=index)
    .add_yaxis(y_axis=values,
               itemstyle_opts = opts.ItemStyleOpts(color='cornflowerblue'),
               series_name='商品销量后十' ) 
    .set_global_opts(title_opts = opts.TitleOpts(title='商品销量后十排名情况',subtitle='横轴表示商品ID'))
).render_notebook()

2. 绘制折线图

from pyecharts.charts import Line
from pyecharts import options as opts

(
    Line()
    .add_xaxis(xaxis_data=is_workday.index)
    .add_yaxis(series_name='工作日',y_axis=is_workday,symbol="arrow",is_symbol_show=True)
    .add_yaxis(series_name='非工作日',y_axis=not_workday)
    .set_global_opts(title_opts=opts.TitleOpts(title='学生消费次数与工作日/非工作日的关系'))
).render_notebook()

3. 绘制柱形折线组合图

4. 绘制散点图

from pyecharts.charts import Scatter
from pyecharts import options as opts

scatter = Scatter()

scatter.add_xaxis(xaxis_data=labels)
scatter.add_yaxis(series_name='', y_axis=values, symbol_size=30) # 点的大小

scatter.render_notebook()

5. 绘制饼图

from pyecharts.charts import Pie
from pyecharts import options as opts

season_labels = ['春季','夏季','秋季','冬季']
season_values = [471348,918589,1061129,841613]
(
    Pie()
    .add(
        '季节',
        [list(z) for z in zip(season_labels,season_values)],
        radius=['30%', '75%'],
        center=['50%', '50%'],
        rosetype='radius',
        label_opts=opts.LabelOpts(is_show=False),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="不同季节中共享单车的使用情况"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{c} {d}%"))
).render('时间维度分析_季节.html')

weather_labels = ['晴天 多云','雾天 阴天','小雪 小雨','大雨 大雪 大雾']
weather_values = [2338173,795952,158331,223]
(
    Pie()
    .add('',
         [list(z) for z in zip(weather_labels, weather_values)],
         radius=['30%', '75%'],
         center=['50%', '50%'],
         rosetype='radius',
         label_opts=opts.LabelOpts(is_show=False),)

    .set_global_opts(title_opts=opts.TitleOpts(title='不同天气中，共享单车使用情况'))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{c} {d}%"))
).render('天气维度分析.html')

6.圆环图

from pyecharts.charts import Pie
from pyecharts import options as opts

edu_labels = ['本科', '大专', '硕士', '中技/中专', '高中', '博士']
edu_counts = [10503,5158,195,29,12,4]
(
    Pie()   # 图形的大小设置
    .add(
        '学历',
        data_pair=[list(z) for z in zip(edu_labels, edu_counts)],
        radius=["45%", "70%"],   # 饼图内圈和外圈的大小比例
        center=["50%", "50%"],   # 饼图的位置：左边距和上边距
        label_opts=opts.LabelOpts(is_show=True),   # 显示数据和百分比  
    )
    .set_global_opts(title_opts=opts.TitleOpts(title='学历占比圆环图',pos_left='40%'),
     legend_opts=opts.LegendOpts(type_="scroll", pos_top="20%", pos_left="85%", orient="vertical")) # 图例配置项
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{c} {d}%")) # 显示百分比
).render_notebook()

7. 绘制玫瑰饼图

from pyecharts.charts import Pie
from pyecharts import options as opts

people_labels = ['50-150人','150-500人','少于50人','1000-5000人','500-1000人','10000人以上','5000-10000人']
people_values = [4764, 3628, 3263, 1813, 1609,  505,  319]
(
    Pie()
    .add(
        '人数',
        [list(z) for z in zip(people_labels,people_values)],
        radius=['30%', '75%'],
        center=['50%', '50%'],
        rosetype='radius',
        label_opts=opts.LabelOpts(is_show=False),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title='人数占比玫瑰图',pos_left='40%'),
     legend_opts=opts.LegendOpts(type_="scroll", pos_top="20%", pos_left="85%", orient="vertical")) # 图例配置项
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{c} {d}%")) # 显示百分比
).render_notebook()

8. 绘制嵌套子母饼图

## 子母饼图
# 数据一
inner_x_data = ['直达','营销广告','搜索引擎']
inner_y_data = [335, 679, 1548]
inner_data_pair = [list(z) for z in zip(inner_x_data, inner_y_data)]

# 数据二
outer_x_data = ['直达','营销广告','搜索引擎','邮件营销','联盟广告','视频广告','百度','谷歌','必应','其他']
outer_y_data = [335, 310, 234, 135, 1048, 251, 147, 102]
outer_data_pair = [list(z) for z in zip(outer_x_data, outer_y_data)]

# 选择图表
pie_2 = Pie(init_opts=opts.InitOpts(width='800px', height='600px'))
# 图一
pie_2.add(
    series_name='访问来源',
    data_pair= inner_data_pair,
    radius=[0, "30%"],  # 半径
    label_opts=opts.LabelOpts(position='inner')
)
# 图二
pie_2.add(
    series_name='访问',
    data_pair= outer_data_pair,
    radius=['40%', "60%"],  # 半径
    label_opts=opts.LabelOpts(position='outside',     # 位置
                             background_color='#eee', # 背景颜色
                             border_color='#aaa', # 边界颜色
                              border_width=1,     # 边界宽度
                              border_radius=40    # 边界弧度
                             )
)
# 图例配置项
pie_2.set_global_opts(
    legend_opts=opts.LegendOpts(pos_left='left', # 此处接受百分比等
                                orient='vertical')
)
# 显示
pie_2.render_notebook()

9. 绘制雷达图

from pyecharts import options as opts
from pyecharts.charts import Radar

exp = [[468,322,265,164,31,21,4]]
(
    Radar()
    .add_schema(
        schema=[
            opts.RadarIndicatorItem(name='3-4年经验',max_=500),
            opts.RadarIndicatorItem(name='2年经验', max_=400),
            opts.RadarIndicatorItem(name='1年经验', max_=300),
            opts.RadarIndicatorItem(name='5-7年经验', max_=200),
            opts.RadarIndicatorItem(name='无需经验', max_=100),
            opts.RadarIndicatorItem(name='8-9年经验', max_=50),
            opts.RadarIndicatorItem(name='10年以上经验', max_=50),
        ]
    )
    .add(
        '广州',exp,
        color='red',
        areastyle_opts=opts.AreaStyleOpts(
            opacity=0.5,
            color='red'
        ),
    )
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(title_opts=opts.TitleOpts(title='广州地区经验要求雷达图'))
).render_notebook()

10. 绘制词云图

11. 绘制中国地图

(
    Map()
    .add(series_name='', data_pair=[(i,int(j)) for i,j in zip(local_labels, local_values)],
        )
    # 全局配置项
    .set_global_opts(
        # 设置标题
        title_opts=opts.TitleOpts(title='职位地理位置分布图'),
        visualmap_opts=opts.VisualMapOpts(max_=2000, is_piecewise=False),
    )
    .set_series_opts(label_opts=opts.LabelOpts(is_show=True),showLegendSymbol=False) #是否显示地理信息标记
).render_notebook()

11. 绘制世界地图

# 导入模块
from pyecharts import options as opts
from pyecharts.charts import Pie
#准备数据
label=['Mac口红','Tom Ford口红','圣罗兰','纪梵希','花西子','迪奥','阿玛尼','香奈儿']  
values = [300,300,300,300,44,300,300,300]
# 自定义函数
def pie_base():
    c = (
        Pie()
        .add("",[list(z) for z in zip(label,values)])
        .set_global_opts(title_opts = opts.TitleOpts(title="口红品牌分析"))
        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{c} {d}%"))   # 值得一提的是，{d}%为百分比
    )
    return c
# 调用自定义函数生成render.html
pie_base().render()

12.漏斗图

exp = [6054,3911,2862,2161, 633,193,84,3]
phase = ['3-4年经验 ', '2年经验 ', '1年经验 ', '5-7年经验 ', '无需经验 ', '8-9年经验 ','10年以上经验 ', '在校生/应届生 ']
(
    Funnel()
    .add("阶段", [list(z) for z in zip(phase, exp)])
    .set_global_opts(title_opts=opts.TitleOpts(title="经验漏斗图", pos_left='45%'),
                    legend_opts=opts.LegendOpts(type_="scroll", pos_top="30%", pos_left="80%", orient="vertical")) # 图例配置项
).render_notebook()