pyecharts可视化大屏

如何优雅得搭建可视化大屏呢?

郑重说明:本文不直接提供最终代码

白嫖怪福音,免费开源---pyechart官网

经过多年钻研,终于被我这只菜鸡整出来了!手动滑稽...
我们先上一张效果图瞧瞧:

pyecharts可视化大屏

 

注:数据纯属虚构
看起来好像是那么回事?!?!
下面我们步入正题↓

 

实现过程:

 

  1. 爬取业务系统数据,存储至mysql(数据体量大推荐)或者excel内都可
  2. 利用pyecharts可视化
  3. 嵌入到前端框架内
  4. 局域网共享

需要用到的知识点:

 

  • python、javascript、html、css、mysql、pandas、pyecharts

版本不兼容解决方案:

  • python 版本 :3.6
  • pyecharts 版本:1.9.0
  • CMD安装命令: pip install pyecharts=1.9.0
  • 如果已安装 pyecharts 但是不知道版本怎么办?
  • CMD命令: pip list内查看即可

 

Page示例(多图组合):

 

from pyecharts import options as opts
from pyecharts.charts import Bar, Grid, Line, Liquid, Page, Pie
from pyecharts.commons.utils import JsCode
from pyecharts.components import Table
from pyecharts.faker import Faker


def bar_datazoom_slider() -> Bar:
    c = (
        Bar()
        .add_xaxis(Faker.days_attrs)
        .add_yaxis("商家A", Faker.days_values)
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Bar-DataZoom(slider-水平)"),
            datazoom_opts=[opts.DataZoomOpts()],
        )
    )
    return c


def line_markpoint() -> Line:
    c = (
        Line()
        .add_xaxis(Faker.choose())
        .add_yaxis(
            "商家A",
            Faker.values(),
            markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="min")]),
        )
        .add_yaxis(
            "商家B",
            Faker.values(),
            markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max")]),
        )
        .set_global_opts(title_opts=opts.TitleOpts(title="Line-MarkPoint"))
    )
    return c


def pie_rosetype() -> Pie:
    v = Faker.choose()
    c = (
        Pie()
        .add(
            "",
            [list(z) for z in zip(v, Faker.values())],
            radius=["30%", "75%"],
            center=["25%", "50%"],
            rosetype="radius",
            label_opts=opts.LabelOpts(is_show=False),
        )
        .add(
            "",
            [list(z) for z in zip(v, Faker.values())],
            radius=["30%", "75%"],
            center=["75%", "50%"],
            rosetype="area",
        )
        .set_global_opts(title_opts=opts.TitleOpts(title="Pie-玫瑰图示例"))
    )
    return c


def grid_mutil_yaxis() -> Grid:
    x_data = ["{}月".format(i) for i in range(1, 13)]
    bar = (
        Bar()
        .add_xaxis(x_data)
        .add_yaxis(
            "蒸发量",
            [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3],
            yaxis_index=0,
            color="#d14a61",
        )
        .add_yaxis(
            "降水量",
            [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],
            yaxis_index=1,
            color="#5793f3",
        )
        .extend_axis(
            yaxis=opts.AxisOpts(
                name="蒸发量",
                type_="value",
                min_=0,
                max_=250,
                position="right",
                axisline_opts=opts.AxisLineOpts(
                    linestyle_opts=opts.LineStyleOpts(color="#d14a61")
                ),
                axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
            )
        )
        .extend_axis(
            yaxis=opts.AxisOpts(
                type_="value",
                name="温度",
                min_=0,
                max_=25,
                position="left",
                axisline_opts=opts.AxisLineOpts(
                    linestyle_opts=opts.LineStyleOpts(color="#675bba")
                ),
                axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
                splitline_opts=opts.SplitLineOpts(
                    is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)
                ),
            )
        )
        .set_global_opts(
            yaxis_opts=opts.AxisOpts(
                name="降水量",
                min_=0,
                max_=250,
                position="right",
                offset=80,
                axisline_opts=opts.AxisLineOpts(
                    linestyle_opts=opts.LineStyleOpts(color="#5793f3")
                ),
                axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
            ),
            title_opts=opts.TitleOpts(title="Grid-多 Y 轴示例"),
            tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
        )
    )

    line = (
        Line()
        .add_xaxis(x_data)
        .add_yaxis(
            "平均温度",
            [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
            yaxis_index=2,
            color="#675bba",
            label_opts=opts.LabelOpts(is_show=False),
        )
    )

    bar.overlap(line)
    return Grid().add(
        bar, opts.GridOpts(pos_left="5%", pos_right="20%"), is_control_axis_index=True
    )


def liquid_data_precision() -> Liquid:
    c = (
        Liquid()
        .add(
            "lq",
            [0.3254],
            label_opts=opts.LabelOpts(
                font_size=50,
                formatter=JsCode(
                    """function (param) {
                        return (Math.floor(param.value * 10000) / 100) + '%';
                    }"""
                ),
                position="inside",
            ),
        )
        .set_global_opts(title_opts=opts.TitleOpts(title="Liquid-数据精度"))
    )
    return c


def table_base() -> Table:
    table = Table()

    headers = ["City name", "Area", "Population", "Annual Rainfall"]
    rows = [
        ["Brisbane", 5905, 1857594, 1146.4],
        ["Adelaide", 1295, 1158259, 600.5],
        ["Darwin", 112, 120900, 1714.7],
        ["Hobart", 1357, 205556, 619.5],
        ["Sydney", 2058, 4336374, 1214.8],
        ["Melbourne", 1566, 3806092, 646.9],
        ["Perth", 5386, 1554769, 869.4],
    ]
    table.add(headers, rows).set_global_opts(
        title_opts=opts.ComponentTitleOpts(title="Table")
    )
    return table


def page_draggable_layout():
    page = Page(layout=Page.DraggablePageLayout)
    page.add(
        bar_datazoom_slider(),
        line_markpoint(),
        pie_rosetype(),
        grid_mutil_yaxis(),
        liquid_data_precision(),
        table_base(),
    )
    page.render("page_draggable_layout.html")


if __name__ == "__main__":
    page_draggable_layout()

  

快捷入口:https://gallery.pyecharts.org...

 

布局样式设置:

 

如何把这些图表组合起来呢?
个人选择比较无脑的一种,
利用BeautifulSoup获取标签设置style
想让它去哪儿就去哪儿
css样式如果忘记-请点击它 菜鸟CSS官网教学示例

示例:

with open("YG_view.html", "r+", encoding='utf-8') as html:
        html_bf = BeautifulSoup(html, 'lxml')
        # 样式
        lables = html_bf.select('tbody')
        lables[0]["style"] = "text-align:center;"

  

提示:一般一个图表在一个div内,获取div标签循环遍历设置即可

 

 

 

 

上一篇:pyecharts可视化之前戏


下一篇:pyechart与jupyter交互式画图