数据交互式可视化《Dash》框架学习(一)

一. Dash的介绍

Dash是一个用于构建Web应用程序的高效Python框架。

Dash基于Flask,Plotly.js和React.js,非常适合在纯Python中使用高度自定义的用户界面构建数据可视化应用程序。它特别适合在Python中使用数据的人。

通过几个简单的模式,Dash抽象出构建基于Web的交互式应用程序所需的所有技术和协议。 Dash非常简单,您可以在一个下午之内绑定Python代码周围的用户界面。

Dash应用程序在Web浏览器中呈现。您可以将应用程序部署到服务器,然后通过URL共享它们。由于Dash应用程序是在Web浏览器中查看的,因此Dash本质上是跨平台和移动就绪的。

二. Dash的安装

数据交互式可视化《Dash》框架学习(一)_第1张图片

 

 三. Dash layout

1.

# -*- coding: utf-8 -*-
import dash
import dash_core_components as dcc
import dash_html_components as html

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div(children=[
    html.H1(children='Hello Dash'),

    html.Div(children='''
        Dash: A web application framework for Python.
    '''),

    dcc.Graph(
        id='example-graph',
        figure={
            'data': [
                {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},
                {'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},
            ],
            'layout': {
                'title': 'Dash Data Visualization'
            }
        }
    )
])

if __name__ == '__main__':
    app.run_server(debug=True)

数据交互式可视化《Dash》框架学习(一)_第2张图片

 

2. 

# -*- coding: utf-8 -*-
import dash
import dash_core_components as dcc
import dash_html_components as html

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

colors = {
    'background': '#111111',
    'text': '#7FDBFF'
}

app.layout = html.Div(style={'backgroundColor': colors['background']}, children=[
    html.H1(
        children='Hello Dash',
        style={
            'textAlign': 'center',
            'color': colors['text']
        }
    ),

    # html.Div(children='Dash: A web application framework for Python.', style={
        'textAlign': 'center',
        'color': colors['text']
    }),

    dcc.Graph(
        id='example-graph-2',
        figure={
            'data': [
                {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},
                {'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},
            ],
            'layout': {
                'plot_bgcolor': colors['background'],
                'paper_bgcolor': colors['background'],
                'font': {
                    'color': colors['text']
                }
            }
        }
    )
])

if __name__ == '__main__':
    app.run_server(debug=True)

数据交互式可视化《Dash》框架学习(一)_第3张图片

 

 3.

import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd

df = pd.read_csv(
    'https://gist.githubusercontent.com/chriddyp/'
    'c78bf172206ce24f77d6363a2d754b59/raw/'
    'c353e8ef842413cae56ae3920b8fd78468aa4cb2/'
    'usa-agricultural-exports-2011.csv')


def generate_table(dataframe, max_rows=10):
    return html.Table(
        # Header
        [html.Tr([html.Th(col) for col in dataframe.columns])] +

        # Body
        [html.Tr([
            html.Td(dataframe.iloc[i][col]) for col in dataframe.columns
        ]) for i in range(min(len(dataframe), max_rows))]
    )


external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div(children=[
    html.H4(children='US Agriculture Exports (2011)'),
    generate_table(df)
])

if __name__ == '__main__':
    app.run_server(debug=True)

 4.

import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.graph_objs as go

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

df = pd.read_csv(
    'https://gist.githubusercontent.com/chriddyp/' +
    '5d1ea79569ed194d432e56108a04d188/raw/' +
    'a9f9e8076b837d541398e999dcbac2b2826a81f8/'+
    'gdp-life-exp-2007.csv')


app.layout = html.Div([
    dcc.Graph(
        id='life-exp-vs-gdp',
        figure={
            'data': [
                go.Scatter(
                    x=df[df['continent'] == i]['gdp per capita'],
                    y=df[df['continent'] == i]['life expectancy'],
                    text=df[df['continent'] == i]['country'],
                    mode='markers',
                    opacity=0.7,
                    marker={
                        'size': 15,
                        'line': {'width': 0.5, 'color': 'white'}
                    },
                    name=i
                ) for i in df.continent.unique()
            ],
            'layout': go.Layout(
                xaxis={'type': 'log', 'title': 'GDP Per Capita'},
                yaxis={'title': 'Life Expectancy'},
                margin={'l': 40, 'b': 40, 't': 10, 'r': 10},
                legend={'x': 0, 'y': 1},
                hovermode='closest'
            )
        }
    )
])

if __name__ == '__main__':
    app.run_server()

 数据交互式可视化《Dash》框架学习(一)_第4张图片

 5. 基础demo

# -*- coding: utf-8 -*-
import dash
import dash_core_components as dcc
import dash_html_components as html

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div([
    html.Label('Dropdown'),
    dcc.Dropdown(
        options=[
            {'label': 'New York City', 'value': 'NYC'},
            {'label': u'Montréal', 'value': 'MTL'},
            {'label': 'San Francisco', 'value': 'SF'}
        ],
        value='MTL'
    ),

    html.Label('Multi-Select Dropdown'),
    dcc.Dropdown(
        options=[
            {'label': 'New York City', 'value': 'NYC'},
            {'label': u'Montréal', 'value': 'MTL'},
            {'label': 'San Francisco', 'value': 'SF'}
        ],
        value=['MTL', 'SF'],
        multi=True
    ),

    html.Label('Radio Items'),
    dcc.RadioItems(
        options=[
            {'label': 'New York City', 'value': 'NYC'},
            {'label': u'Montréal', 'value': 'MTL'},
            {'label': 'San Francisco', 'value': 'SF'}
        ],
        value='MTL'
    ),

    html.Label('Checkboxes'),
    dcc.Checklist(
        options=[
            {'label': 'New York City', 'value': 'NYC'},
            {'label': u'Montréal', 'value': 'MTL'},
            {'label': 'San Francisco', 'value': 'SF'}
        ],
        values=['MTL', 'SF']
    ),

    html.Label('Text Input'),
    dcc.Input(value='MTL', type='text'),

    html.Label('Slider'),
    dcc.Slider(
        min=0,
        max=9,
        marks={i: 'Label {}'.format(i) if i == 1 else str(i) for i in range(1, 6)},
        value=5,
    ),
], style={'columnCount': 2})

if __name__ == '__main__':
    app.run_server(debug=True)

数据交互式可视化《Dash》框架学习(一)_第5张图片

 

你可能感兴趣的:(数据可视化)