I'm a stand alone, light-weight web server for building, sharing graphs created in ipython. Build for data science, data analysis guys. Building an interactive visualization, collaborated dashboard, and real-time streaming graph.
If you are do data exploring in IPython notebook, and draw some graph or select some metrics from database, which will be used to present to others, but you don't like to share the code or the complicated logic [ for private or page cleaning ], I think I can help you to do the present part job.
In this tutorial, I'll show you how to do data exploring in ipython notebook and how to share it to others without ipython.
# built-in package
import os
import sys
import json
import time
import datetime as dt
# third-parth package
import dashboard as dash
import pandas as pd
import matplotlib as plt
import seaborn
import mpld3
# package configre
pd.options.display.max_columns = 100
pd.options.display.max_rows = 500
url = """https://github.com/litaotao/IPython-Dashboard/raw/v-0.1.2-visualiza-table/docs/people_number_by_province_lateset_10_years.csv"""
data = pd.read_csv(url)
data.head(3)
地区 | 2014年 | 2013年 | 2012年 | 2011年 | 2010年 | 2009年 | 2008年 | 2007年 | 2006年 | 2005年 | |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | 北京市 | 2152 | 2115 | 2069 | 2019 | 1962 | 1860 | 1771 | 1676 | 1601 | 1538 |
1 | 天津市 | 1517 | 1472 | 1413 | 1355 | 1299 | 1228 | 1176 | 1115 | 1075 | 1043 |
2 | 河北省 | 7384 | 7333 | 7288 | 7241 | 7194 | 7034 | 6989 | 6943 | 6898 | 6851 |
data.plot(x="地区", y=["2014年", "2013年"], kind="bar", figsize=(12, 5))
mpld3.display()
dash.client.sender(data, key='chinese_population', force=True)
True