如何优雅得搭建可视化大屏呢？
郑重阐明: 本文不间接提供最终代码
白嫖怪福音, 收费开源 —pyechart 官网
通过多年钻研, 终于被我这只菜鸡整进去了！手动滑稽 …
咱们先上一张效果图瞧瞧:
注: 数据纯属虚构
看起来如同是那么回事？！？！
上面咱们步入正题↓
实现过程:
- 爬取业务零碎数据, 存储至 mysql(数据体量大举荐)或者 excel 内都可
- 利用 pyecharts 可视化
- 嵌入到前端框架内
- 局域网共享
须要用到的知识点:
- 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 装置命令: pip install pyecharts=1.9.0
- 如果已装置 pyecharts 然而不晓得版本怎么办?
- 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- 程度）"),
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…
布局款式设置:
如何把这些图表组合起来呢？
集体抉择比拟无脑的一种,
利用 BeautifulSoup 获取标签设置 style
想让它去哪儿就去哪儿
css 款式如果遗记 - 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')
# 款式
lables = html_bf.select('tbody')
lables[0]["style"] = "text-align:center;"提醒: 个别一个图表在一个 div 内, 获取 div 标签循环遍历设置即可
待续 …