利用Python实现可视化交互界面：Dash

Dash是一个低代码数据框架，用Python实现可视化交互界面，不用写Javascript，开源，支持回调、HTML组件等功能。

安装

pip install dash

使用

# Import packages
from dash import Dash, html, dash_table, dcc, callback, Output, Input
import pandas as pd
import plotly.express as px
import dash_mantine_components as dmc# Incorporate data
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder2007.csv')# Initialize the app - incorporate a Dash Mantine theme
external_stylesheets = [dmc.theme.DEFAULT_COLORS]
app = Dash(__name__, external_stylesheets=external_stylesheets)# App layout
app.layout = dmc.Container([dmc.Title('My First App with Data, Graph, and Controls', color="blue", size="h3"),dmc.RadioGroup([dmc.Radio(i, value=i) for i in  ['pop', 'lifeExp', 'gdpPercap']],id='my-dmc-radio-item',value='lifeExp',size="sm"),dmc.Grid([dmc.Col([dash_table.DataTable(data=df.to_dict('records'), page_size=12, style_table={'overflowX': 'auto'})], span=6),dmc.Col([dcc.Graph(figure={}, id='graph-placeholder')], span=6),]),], fluid=True)# Add controls to build the interaction
@callback(Output(component_id='graph-placeholder', component_property='figure'),Input(component_id='my-dmc-radio-item', component_property='value')
)
def update_graph(col_chosen):fig = px.histogram(df, x='continent', y=col_chosen, histfunc='avg')return fig# Run the App
if __name__ == '__main__':app.run(debug=True)