pyecharts的基本使用

左手的ㄟ右手 2022-10-04 15:00 182阅读 0赞

## [官网链接][Link 1] ##

## 示例 ##

from pyecharts.charts import Bar
    from pyecharts import options as opts
    # 内置主题类型可查看 pyecharts.globals.ThemeType
    from pyecharts.globals import ThemeType
    
    bar = (
        Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK))
        .add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
        .add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
        .add_yaxis("商家B", [15, 6, 45, 20, 35, 66])
        .set_global_opts(title_opts=opts.TitleOpts(title="主标题", subtitle="副标题"))
        #设置全局配置
        .set_global_opts(
            title_opts=opts.TitleOpts(title='商家AB销售额对比'),
            legend_opts =opts.LegendOpts(is_show=True),
            toolbox_opts =opts.ToolboxOpts(is_show=True),
            visualmap_opts=opts.VisualMapOpts(is_show=True),
            tooltip_opts =opts.DataZoomOpts(is_show=True)
        )
    )
    bar.render('haha.html')

疫情数据显示  
Bar - Finance\_indices\_2002  
https://gallery.pyecharts.org/\#/Bar/finance\_indices\_2002

*  全局配置项标注的需要掌握
 *  系列配置项
 *  ctrl+f快速搜索
 *

## 深圳各区均价 ##

import pandas as pd
    from pyecharts.globals import  ThemeType
    # 1.读取数据
    df = pd.read_csv(r'C:\Users\JSJSYS\PycharmProjects\untitled\python实战\lianjia_data.csv')
    
    # 2根据地区分组，求unit_price均值
    temp = df.groupby('area')['unit_price'].mean().reset_index()
    # dataframe迭代遍历*****
    result = []
    for index,value in temp.iterrows():
        # index每行数据索引
        # value每行数据对应Series，其索引为列名
        #print(value['area'],value['unit_price'])
        result.append([value['area'],round(value['unit_price']/10000,1)])
    print(result)
    result2 = [[value['area'],
                round(value['unit_price']/10000,1)
                ]
               for index,value in temp.iterrows()
    ]
    
    from pyecharts import options as opts
    from pyecharts.charts import Map
    c = (
        Map(init_opts=opts.InitOpts(theme=ThemeType.DARK))
        # 地图类型，具体参考 pyecharts.datasets.map_filenames.json 文件
        .add("深圳各区均价", result2, "深圳")
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Map-深圳地图"),
            visualmap_opts=opts.VisualMapOpts(max_=10)
        )
        .render("11-深圳各区均价.html")

## 房价面积散点图 ##

import  pandas as pd
    
    #1. 读取数据
    df = pd.read_csv(r'C:\Users\JSJSYS\PycharmProjects\untitled\python实战\lianjia_data.csv')
    
    #2 散点图
    from pyecharts import options as opts
    from pyecharts.charts import Scatter
    from pyecharts.globals import ThemeType
    c = (
        Scatter(init_opts=opts.InitOpts(theme=ThemeType.DARK))
        .add_xaxis(df['houseSize']) #面积
        .add_yaxis("房价-面积散点图", df['total_price']) #房价（人们的侧重）
        .set_global_opts(
            title_opts=opts.TitleOpts(title="房价-面积散点图"),
            visualmap_opts=opts.VisualMapOpts(max_=150),
        )
        .set_series_opts(
            label_opts=opts.LabelOpts(is_show=False),
            # 标记点
            markpoint_opts=opts.MarkPointOpts(
                data=[
                    opts.MarkPointItem(name="最低廉的房子", type_="min"),
                    opts.MarkPointItem(name="最奢华的房子", type_="max")
                ]
            )
        )
        .render("scatter_visualmap_color.html")
    )

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MDYwNjc0_size_16_color_FFFFFF_t_70_pic_center]

[Link 1]: https://pyecharts.org/#/zh-cn/global_options?id=axispointeropts-%E5%9D%90%E6%A0%87%E8%BD%B4%E6%8C%87%E7%A4%BA%E5%99%A8%E9%85%8D%E7%BD%AE%E9%A1%B9
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MDYwNjc0_size_16_color_FFFFFF_t_70_pic_center]: /images/20221004/c565b54f43254f64a203d9d01da473d9.png