Python交互式数据分析报告框架——Dash
Dash是一个基于Web应用程序、用于分析的Python库，开发时间已达两年之久。最初，Plotly在GitHub上公布了Dash的概念验证，经过长时间不间断的开发，Plotly发布了作为企业级解决方案和开源工具的Dash。Dash项目可用于数据分析、数据挖掘、可视化、建模、仪器控制和报告。这是一个轻量级的项目，只有40行Python，并且提供了一个用于键入UI控件的界面，包含滑块，下拉列表和带代码的图形等等。最重要的是Dash是完全可定制化的。
有企业曾表示，我们与Dash Enterprise的目标是在内部共享Dash应用程序，无需开发人员，尽可能简单和安全。Dash Enterprise负责URL路由、监控、故障处理、部署、版本控制和程序包管理。用户可以在本地无限制的使用开源版本，也可以通过Heroku或Digital Ocean等平台管理Dash应用程序的部署。
Plotly公司在Dash项目网站上写道：Explore data, tweak your models, monitor your experiments, or roll your own business intelligence platform. Dash is the front-end to your analytical Python backend。（来自搜狐科技）
关于具体介绍可参见作者Plotly,Chris Pamer写的内容，原文链接：
https://link.jianshu.com/?t=https%3A%2F%2Fmedium.com%2F%40plotlygraphs%2Fintroducing-dash-5ecf7191b503
下面是一个将下拉菜单与支持D3.js的Plotly图形结合的Dash应用。用户通过筛选下拉菜单来选择不同的值，程序代码就能动态地从谷歌金融导入数据到Pandas的DataFrame。然而，这个应用仅用了43行代码！
Dash应用：Hello World，更多请查阅用户指南
下面是官网上的一个超级精彩的交互式界面的例子：
import dash
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html
from plotly import graph_objs as go
from datetime import datetime as dt
import json
import pandas as pd
df_fund_data = pd.read_csv('https://plot.ly/~jackp/17534.csv')
df_fund_data.head()
df_perf_summary = pd.read_csv('https://plot.ly/~jackp/17530.csv')
df_perf_summary.head()
df_cal_year = pd.read_csv('https://plot.ly/~jackp/17528.csv')
df_cal_year.head()
df_perf_pc = pd.read_csv('https://plot.ly/~jackp/17532.csv')
def make_dash_table( df ):
''' Return a dash definitio of an HTML table for a Pandas dataframe '''
table = []
for index, row in df.iterrows():
html_row = []
for i in range(len(row)):
html_row.append( html.Td([ row[i] ]) )
table.append( html.Tr( html_row ) )
return table
modifed_perf_table = make_dash_table( df_perf_summary )
modifed_perf_table.insert(
0, html.Tr( [
html.Td([]),
html.Td(['Cumulative'], colSpan=4, style=dict(textAlign="center")),
html.Td(['Annualised'], colSpan=4, style=dict(textAlign="center"))
], style = dict( background='white', fontWeight=600 )
)
)
df_fund_info = pd.read_csv('https://plot.ly/~jackp/17544.csv')
df_fund_characteristics = pd.read_csv('https://plot.ly/~jackp/17542.csv')
df_fund_facts = pd.read_csv('https://plot.ly/~jackp/17540.csv')
df_bond_allocation = pd.read_csv('https://plot.ly/~jackp/17538.csv')
dash.__version__
app = dash.Dash('GS Bond II Portfolio')
# Describe the layout, or the UI, of the app
app.layout = html.Div([
html.Div([ # page 1
html.A([ 'Print PDF' ],
className="button no-print",
style=dict(position="absolute", top=-40, right=0)),
html.Div([ # subpage 1
# Row 1 (Header)
html.Div([
html.Div([
html.H5('Goldman Sachs Strategic Absolute Return Bond II Portfolio'),
html.H6('A sub-fund of Goldman Sachs Funds, SICAV', style=dict(color='#7F90AC')),
], className = "nine columns padded" ),
html.Div([
html.H1([html.Span('03', style=dict(opacity=0.5)), html.Span('17')]),
html.H6('Monthly Fund Update')
], className = "three columns gs-header gs-accent-header padded", style=dict(float='right') ),
], className = "row gs-header gs-text-header"),
html.Br([]),
# Row 2
html.Div([
html.Div([
html.H6('Investor Profile', className = "gs-header gs-text-header padded"),
html.Strong('Investor objective'),
html.P('Capital appreciation and income.', className = 'blue-text'),
html.Strong('Position in your overall investment portfolio*'),
html.P('The fund can complement your portfolio.', className = 'blue-text'),
html.Strong('The fund is designed for:'),
html.P('The fund is designed for investors who are looking for a flexible \
global investment and sub-investment grade fixed income portfolio \
that has the ability to alter its exposure with an emphasis on interest \
rates, currencies and credit markets and that seeks to generate returns \
through different market conditions with a riskier investment strategy \
than GS Strategic Absolute Return Bond I Portfolio.', className = 'blue-text' ),
], className = "four columns" ),
html.Div([
html.H6(["Performance (Indexed)"],
className = "gs-header gs-table-header padded"),
html.Iframe(src="https://plot.ly/~jackp/17553.embed?modebar=false&link=false&autosize=true", \
seamless="seamless", style=dict(border=0), width="100%", height="250")
], className = "eight columns" ),
], className = "row "),
# Row 2.5
html.Div([
html.Div([
html.H6('Performance (%)', className = "gs-header gs-text-header padded"),
html.Table( make_dash_table( df_perf_pc ), className = 'tiny-header' )
], className = "four columns" ),
html.Div([
html.P("This is an actively managed fund that is not designed to track its reference benchmark. \
Therefore the performance of the fund and the performance of its reference benchmark \
may diverge. In addition stated reference benchmark returns do not reflect any management \
or other charges to the fund, whereas stated returns of the fund do."),
html.Strong("Past performance does not guarantee future results, which may vary. \
The value of investments and the income derived from investments will fluctuate and \
can go down as well as up. A loss of capital may occur.")
], className = "eight columns" ),
], className = "row "),
# Row 3
html.Div([
html.Div([
html.H6('Fund Data', className = "gs-header gs-text-header padded"),
html.Table( make_dash_table( df_fund_data ) )
], className = "four columns" ),
html.Div([
html.H6("Performance Summary (%)", className = "gs-header gs-table-header padded"),
html.Table( modifed_perf_table, className = "reversed" ),
html.H6("Calendar Year Performance (%)", className = "gs-header gs-table-header padded"),
html.Table( make_dash_table( df_cal_year ) )
], className = "eight columns" ),
], className = "row "),
], className = "subpage" ),
], className = "page" ),
html.Div([ # page 2
html.A([ 'Print PDF' ],
className="button no-print",
style=dict(position="absolute", top=-40, right=0)),
html.Div([ # subpage 2
# Row 1 (Header)
html.Div([
html.Div([
html.H5('Goldman Sachs Strategic Absolute Return Bond II Portfolio'),
html.H6('A sub-fund of Goldman Sachs Funds, SICAV', style=dict(color='#7F90AC')),
], className = "nine columns padded" ),
html.Div([
html.H1([html.Span('03', style=dict(opacity=0.5)), html.Span('17')]),
html.H6('Monthly Fund Update')
], className = "three columns gs-header gs-accent-header padded", style=dict(float='right') ),
], className = "row gs-header gs-text-header"),
# Row 2
html.Div([
# Data tables on this page:
# ---
# df_fund_info = pd.read_csv('https://plot.ly/~jackp/17544/.csv')
# df_fund_characteristics = pd.read_csv('https://plot.ly/~jackp/17542/.csv')
# df_fund_facts = pd.read_csv('https://plot.ly/~jackp/17540/.csv')
# df_bond_allocation = pd.read_csv('https://plot.ly/~jackp/17538/')
# Column 1
html.Div([
html.H6('Financial Information', className = "gs-header gs-text-header padded"),
html.Table( make_dash_table( df_fund_info ) ),
html.H6('Fund Characteristics', className = "gs-header gs-text-header padded"),
html.Table( make_dash_table( df_fund_characteristics ) ),
html.H6('Fund Facts', className = "gs-header gs-text-header padded"),
html.Table( make_dash_table( df_fund_facts ) ),
], className = "four columns" ),
# Column 2
html.Div([
html.H6('Sector Allocation (%)', className = "gs-header gs-table-header padded"),
html.Iframe(src="https://plot.ly/~jackp/17560.embed?modebar=false&link=false&autosize=true", \
seamless="seamless", style=dict(border=0), width="100%", height="300"),
html.H6('Country Bond Allocation (%)', className = "gs-header gs-table-header padded"),
html.Table( make_dash_table( df_bond_allocation ) ),
], className = "four columns" ),
# Column 3
html.Div([
html.H6('Top 10 Currency Weights (%)', className = "gs-header gs-table-header padded"),
html.Iframe(src="https://plot.ly/~jackp/17555.embed?modebar=false&link=false&autosize=true", \
seamless="seamless", style=dict(border=0), width="100%", height="300"),
html.H6('Credit Allocation (%)', className = "gs-header gs-table-header padded"),
html.Iframe(src="https://plot.ly/~jackp/17557.embed?modebar=false&link=false&autosize=true", \
seamless="seamless", style=dict(border=0), width="100%", height="300"),
], className = "four columns" ),
], className = "row"),
], className = "subpage" ),
], className = "page" ),
])
external_css = [ "https://cdnjs.cloudflare.com/ajax/libs/normalize/7.0.0/normalize.min.css",
"https://cdnjs.cloudflare.com/ajax/libs/skeleton/2.0.4/skeleton.min.css",
"//fonts.googleapis.com/css?family=Raleway:400,300,600",
"https://codepen.io/plotly/pen/KmyPZr.css",
"https://maxcdn.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css"]
for css in external_css:
app.css.append_css({ "external_url": css })
external_js = [ "https://code.jquery.com/jquery-3.2.1.min.js",
"https://codepen.io/plotly/pen/KmyPZr.js" ]
for js in external_js:
app.scripts.append_script({ "external_url": js })
app.server.run()
#* Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
运行：
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