Today I will share with you a tool for making a large data screen. It is very easy to use. PythonA complete large data screen can be made with about 100 lines of code, and the logic of the code is very easy to understand.
PywebIOintroduce
PythonThe PywebIOmodules in it can help developers to quickly build applications or browser-based applications even if they do not have HTMLand can also be combined with some commonly used visualization modules to make a large visual screen.JavaScriptWebGUIPywebIO
Let's first install the modules we need to use
pip install pywebio
pip install cutecharts
cutechartsThe module mentioned above is Pythona hand-painted style visual artifact. I believe you are not unfamiliar with it. Let's take a look at PywebIOthe effect of drawing a chart in combination with the module. The code is as follows
from cutecharts.charts import Bar
from cutecharts.faker import Faker
from pywebio import start_server
from pywebio.output import put_html
def bar_base():
chart = Bar("Bar-基本示例", width="100%")
chart.set_options(labels=Faker.choose(), x_label="I'm xlabel", y_label="I'm ylabel")
chart.add_series("series-A", Faker.values())
put_html(chart.render_notebook())
if __name__ == '__main__':
start_server(bar_base, debug=True, port=8080)
output
The logic of the above code is not difficult to understand. First instantiate a histogram Bar()object, then fill Xin the label corresponding to the Yaxis and the value of the corresponding axis, and finally call PywebIOthe method in the module put_html(), we will see aURL
Enter this in the browser URLto see the chart we drew. Of course cutecharts, there is a method in the module Page()to connect all the charts to make a large visual screen. The code is as follows
def bar_base():
chart = Bar("Bar-基本示例", width="100%")
chart.set_options(labels=Faker.choose(), x_label="I'm xlabel", y_label="I'm ylabel")
chart.add_series("series-A", Faker.values())
return chart
def pie_base() -> Pie:
chart = Pie("标题", width="100%")
........
return chart
def radar_base() -> Radar:
chart = Radar("标题", width="100%")
......
return chart
def line_base() -> Line:
chart = Line("标题", width="100%")
......
return chart
def main():
page = Page()
page.add(pie_base(), pie_base(), radar_base(), line_base(), bar_base())
put_html(page.render_notebook())
if __name__ == '__main__':
start_server(main, debug=True, port=8080)
output
PywebIOPyechartscombination of and
When a PywebIOmodule encounters Pyechartsa module, the logic of the code is basically cutechartsthe same, first instantiate a chart object, then after adding data and setting the style of the chart, and finally calling put_html()the method to render the final result in the browser
# `chart` 是你的图表的实例
pywebio.output.put_html(chart.render_notebook())
In this case, we call Pyechartsthe combined components to present the drawn chart respectively. The code is as follows
def bar_plots():
bar = (
Bar()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(title_opts=opts.TitleOpts(title="Grid-Bar"))
)
return bar
def line_plots():
line = (
Line()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
title_opts=opts.TitleOpts(title="Grid-Line", pos_top="48%"),
legend_opts=opts.LegendOpts(pos_top="48%"),
)
)
return line
def main():
c = (
Grid()
.add(bar_plots(), grid_opts=opts.GridOpts(pos_bottom="60%"))
.add(line_plots(), grid_opts=opts.GridOpts(pos_top="60%"))
)
c.width = "100%"
put_html(c.render_notebook())
if __name__ == '__main__':
start_server(main, debug=True, port=8080)
output
PywebIOBokehcombination of and
PywebIOThe combination of and Bokehwill be slightly different from the above in terms of code syntax, and the specific differences are as follows
from bokeh.io import output_notebook
from bokeh.io import show
output_notebook(notebook_type='pywebio')
fig = figure(...)
...
show(fig)
For example, let's draw a simple histogram, the code is as follows
def bar_plots():
output_notebook(notebook_type='pywebio')
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
counts = [5, 3, 4, 2, 4, 6]
p = figure(x_range=fruits, plot_height=350, title="Fruit Counts",
toolbar_location=None, tools="")
p.vbar(x=fruits, top=counts, width=0.9)
p.xgrid.grid_line_color = None
p.y_range.start = 0
show(p)
if __name__ == "__main__":
start_server(bar_plots, debug=True, port=8080)
output
browser-based GUIapplication
In addition to combining Pywebiomodules with common visualization modules for drawing various charts, we can also use it to build a graphical interface based on browsing. Let's make the simplest application first. The code is as follows
from pywebio.input import *
from pywebio.output import *
data = input_group(
"用户数据",
[
input("请问您的名字是: ", name="name", type=TEXT),
input("输入您的年龄", name="age", type=NUMBER),
radio(
"哪个洲的",
name="continent",
options=[
"非洲",
"亚洲",
"澳大利亚",
"欧洲",
"北美洲",
"南美洲",
],
),
checkbox(
"用户隐私条例", name="agreement", options=["同意"]
),
],
)
put_text("表格输出:")
put_table(
[
["名字", data["name"]],
["年龄", data["age"]],
["位置", data["continent"]],
["条例", data["agreement"]],
]
)
output
Some of the function methods are explained as follows:
-
input(): Input of text content -
radio(): represents a radio button -
checkbox(): Represents a multi-select box -
input_group(): represents the input group -
put_table(): represents the output group -
put_text(): represents the output text
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