fun_with_jupyter.ipynb

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     "data": {
      "text/html": [
       "\n",
       "<br><br>\n",
       "<a href=http://wwwgong.pythonanywhere.com/cuspea/default/list_talks target=new>\n",
       "<font size=+3 color=blue>CUSPEA Talks</font>\n",
       "</a>\n",
       "<br><br>\n",
       "<img src=images/jupyter-notebook-wen-gong.jpg><br> \n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML(\"\"\"\n",
    "<br><br>\n",
    "<a href=http://wwwgong.pythonanywhere.com/cuspea/default/list_talks target=new>\n",
    "<font size=+3 color=blue>CUSPEA Talks</font>\n",
    "</a>\n",
    "<br><br>\n",
    "<img src=images/jupyter-notebook-wen-gong.jpg><br> \n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "6ee77bce-39d1-46a1-802d-c7aa0f07f653"
    }
   },
   "source": [
    "# Fun with [Jupyter](http://jupyter.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5676905a-4d3a-478a-bd10-06df67ffce84"
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   },
   "source": [
    "## Table of Contents\n",
    "\n",
    "* [Motivation](#hid_why)\n",
    "* [Introduction](#hid_intro)\n",
    "* [Problem Statement](#hid_problem)\n",
    "* [Import packages](#hid_pkg)\n",
    "* [Estimate x range](#hid_guess)\n",
    "* [Use IPython as a calculator](#hid_calculator)\n",
    "* [Use Python programming to find solution](#hid_program)\n",
    "* [Graph the solution with matplotlib](#hid_graph)\n",
    "* [Solve equation precisely using SymPy](#hid_sympy)\n",
    "* [Pandas for Big Data Analytics](#hid_panda)\n",
    "* [Multimedia with HTML5 -Text, Image, Audio, Video](#hid_html5)\n",
    "* [Interactive widgets](#hid_widget)\n",
    "* [Working with SQLite Databases](#hid_sqlite)\n",
    "* [References](#hid_ref)\n",
    "* [Contributors](#hid_author)\n",
    "* [Appendix](#hid_apend)\n",
    "    - [How to install Jupyter Notebook](#hid_setup)\n",
    "    - [How to share a notebook](#hid_share)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivation  <a class=\"anchor\" id=\"hid_why\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Current Choice\n",
    "\n",
    "<img src=http://www.cctechlimited.com/pics/office1.jpg>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A New Option\n",
    "\n",
    "> The __Jupyter Notebook__ is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.\n",
    "\n",
    "Useful for many tasks\n",
    "\n",
    "* Programming\n",
    "* Blogging\n",
    "* Learning\n",
    "* Research\n",
    "* Documenting work\n",
    "* Collaborating\n",
    "* Communicating\n",
    "* Publishing results\n",
    "\n",
    "or even\n",
    "\n",
    "* Doing homework as a student\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=images/office-suite.jpg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"<img src=images/office-suite.jpg>\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See this [Demo Notebook](https://nbviewer.jupyter.org/github/waltherg/notebooks/blob/master/2013-12-03-Crank_Nicolson.ipynb) \n",
    "\n",
    "<img src=https://nbviewer.jupyter.org/static/img/example-nb/pde_solver_with_numpy.png>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "031da43c-0284-4433-bd3d-c6c596c92b27"
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   },
   "source": [
    "## Introduction  <a class=\"anchor\" id=\"hid_intro\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "dc9f34ec-b74d-4c7b-8fd8-3be534221c35"
    }
   },
   "source": [
    "By solving a simple math problem here, I hope to demonstrate many basic  features of Jupyter Notebook. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "81e52f61-9b24-49b2-9953-191e6fe26656"
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   "source": [
    "## Problem Statement <a class=\"anchor\" id=\"hid_problem\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "fcc24795-9fe9-4a15-ae38-3444d4044697"
    }
   },
   "source": [
    "A hot startup is selling a hot product. \n",
    "\n",
    "In 2014, it had 30 customers; 3 years later, its customer base expands to 250.\n",
    "\n",
    "Question: What is its annual growth rate?\n",
    "\n",
    "_Translate into math:_  Let $x$ be annual growth rate, \n",
    "\n",
    "then\n",
    "$$ 30 \\cdot (1 + x)^3 = 250 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "30861e9b-f7e6-41b2-be2d-d1636961816b"
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   },
   "source": [
    "## Import packages  <a class=\"anchor\" id=\"hid_pkg\"></a> (check out from Library)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "source": [
    "# math function\n",
    "import math\n",
    "\n",
    "# create np array\n",
    "import numpy as np\n",
    "\n",
    "# pandas for data analysis\n",
    "import pandas as pd\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# symbolic math\n",
    "import sympy as sy\n",
    "\n",
    "# html5\n",
    "from IPython.display import HTML, SVG, YouTubeVideo\n",
    "\n",
    "# widgets\n",
    "from collections import OrderedDict\n",
    "from IPython.display import display, clear_output\n",
    "from ipywidgets import Dropdown\n",
    "\n",
    "# csv file\n",
    "import csv\n",
    "\n",
    "# work with Sqlite database\n",
    "import sqlite3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "17336b61-d6bd-496d-863a-1ad759a9e4b7"
    }
   },
   "source": [
    "## Estimate x range  <a class=\"anchor\" id=\"hid_guess\"></a> (in Elementary School)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "If $x$ = 1, then _l.h.s_ = 240, therefore $x$ > 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d63d625e-79d0-4bf8-a405-36c9df127bb5"
    }
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   "source": [
    "If $x$ = 1.2, then l.h.s = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
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     "id": "0ff80a09-136d-4033-ab6d-4169cff99d42"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "319.4400000000001"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "30*(1+1.2)**3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "7e061caa-9505-4452-ae28-62a29553ed8b"
    }
   },
   "source": [
    "therefore  we know $x$ range = (1.0, 1.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "1a60ecbe-653f-43fe-bd30-b52425a51920"
    }
   },
   "source": [
    "## Use IPython as a calculator  <a class=\"anchor\" id=\"hid_calculator\"></a> (in Middle School)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
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     "id": "5ca9a4ea-0a1b-491f-97bb-03275691c867"
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   "outputs": [],
   "source": [
    "# import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "09fdb815-ef9e-4fd7-8dd3-c0f30437990a"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0274006651911334"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.exp(math.log(250/30)/3) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "543b2781-e45b-4dbb-894e-0b6dfac0dfd3"
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    {
     "data": {
      "text/plain": [
       "1.0274006651911334"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10**(math.log10(250/30)/3) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "b21b7c89-1442-4320-ae43-9e150e087ff0"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0274006651911334"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.pow(10, math.log10(250/30)/3) -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f5c44fb8-78ff-46e5-b444-f484e81030ab"
    }
   },
   "source": [
    "** annual customer growth rate = 102% **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8b8d1cf7-3571-43f8-b3a0-66121ce196de"
    }
   },
   "source": [
    "## Use Python programming to find solution  <a class=\"anchor\" id=\"hid_program\"></a> (in High School)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66c4b853-a7be-4619-b348-514df558e698"
    }
   },
   "source": [
    "### use loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "8642a48c-db83-4b9a-81d3-84c6255763f8"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nstep = 100\n",
    "x_min, x_max = 1.0, 1.2\n",
    "dd = (x_max-x_min)/float(nstep)\n",
    "x_l = [(x_min + i*dd) for i in range(nstep)]\n",
    "type(x_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "957a73e1-cb5e-4b00-af89-30cff8918b7b"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 1.002, 1.004, 1.006, 1.008, 1.01, 1.012, 1.014, 1.016, 1.018, 1.02, 1.022, 1.024, 1.026, 1.028, 1.03, 1.032, 1.034, 1.036, 1.038, 1.04, 1.042, 1.044, 1.046, 1.048, 1.05, 1.052, 1.054, 1.056, 1.058, 1.06, 1.062, 1.064, 1.066, 1.068, 1.07, 1.072, 1.074, 1.076, 1.078, 1.08, 1.082, 1.084, 1.086, 1.088, 1.09, 1.092, 1.094, 1.096, 1.0979999999999999, 1.1, 1.1019999999999999, 1.104, 1.1059999999999999, 1.108, 1.1099999999999999, 1.1119999999999999, 1.1139999999999999, 1.1159999999999999, 1.1179999999999999, 1.1199999999999999, 1.1219999999999999, 1.1239999999999999, 1.126, 1.128, 1.13, 1.132, 1.134, 1.136, 1.138, 1.14, 1.142, 1.144, 1.146, 1.148, 1.15, 1.152, 1.154, 1.156, 1.158, 1.16, 1.162, 1.164, 1.166, 1.168, 1.17, 1.172, 1.174, 1.176, 1.178, 1.18, 1.182, 1.184, 1.186, 1.188, 1.19, 1.192, 1.194, 1.196, 1.198]\n"
     ]
    }
   ],
   "source": [
    "print(x_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "79871358-f571-4f61-90fb-ee2e2fe1e792"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t=1.028: error=0.2218\n"
     ]
    }
   ],
   "source": [
    "for t in x_l:\n",
    "    err = abs(30*(1+t)**3 - 250)\n",
    "    if err <= 0.5:\n",
    "        print(\"t={x}: error={e:.4f}\".format(x=t,e=err))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c017894a-42a7-4936-9a3e-b6ad9b916f7e"
    }
   },
   "source": [
    "### create a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "fb3debd8-f726-4a26-9ebf-7c7adfab7996"
    }
   },
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "64c1a6f3-7751-487c-b71b-0d501cce1199"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 1.002, 1.004, 1.006, 1.008, 1.01, 1.012, 1.014, 1.016, 1.018, 1.02, 1.022, 1.024, 1.026, 1.028, 1.03, 1.032, 1.034, 1.036, 1.038, 1.04, 1.042, 1.044, 1.046, 1.048, 1.05, 1.052, 1.054, 1.056, 1.058, 1.06, 1.062, 1.064, 1.066, 1.068, 1.07, 1.072, 1.074, 1.076, 1.078, 1.08, 1.082, 1.084, 1.086, 1.088, 1.09, 1.092, 1.094, 1.096, 1.0979999999999999, 1.1, 1.1019999999999999, 1.104, 1.1059999999999999, 1.108, 1.1099999999999999, 1.1119999999999999, 1.1139999999999999, 1.1159999999999999, 1.1179999999999999, 1.1199999999999999, 1.1219999999999999, 1.1239999999999999, 1.126, 1.128, 1.13, 1.132, 1.134, 1.136, 1.138, 1.14, 1.142, 1.144, 1.146, 1.148, 1.15, 1.152, 1.154, 1.156, 1.158, 1.16, 1.162, 1.164, 1.166, 1.168, 1.17, 1.172, 1.174, 1.176, 1.178, 1.18, 1.182, 1.184, 1.186, 1.188, 1.19, 1.192, 1.194, 1.196, 1.198]\n"
     ]
    }
   ],
   "source": [
    "print(x_l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "dda6cbdf-98e7-4ce7-97c9-ad3715163327"
    }
   },
   "source": [
    "####  using arange()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "46cadb76-3a49-44e6-922b-24f60a9398b5"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.     1.005  1.01   1.015  1.02   1.025  1.03   1.035  1.04   1.045\n",
      "  1.05   1.055  1.06   1.065  1.07   1.075  1.08   1.085  1.09   1.095  1.1\n",
      "  1.105  1.11   1.115  1.12   1.125  1.13   1.135  1.14   1.145  1.15\n",
      "  1.155  1.16   1.165  1.17   1.175  1.18   1.185  1.19   1.195]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(1.0, 1.2, 0.005)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c157bb0f-5d4d-456b-8f87-5c8cde8945ff"
    }
   },
   "source": [
    "check its type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "76c1bb8b-1f47-4325-9e65-3089408d86b8"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "b33222f5-ca6f-4f6b-91f4-fa13ae22864a"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "3a342312-18db-4f98-8621-34b695a12c4d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-10.          -8.19549625  -6.38197     -4.55939875  -2.72776     -0.88703125\n",
      "   0.96281      2.82178625   4.68992      6.56723375   8.45375     10.34949125\n",
      "  12.25448     14.16873875  16.09229     18.02515625  19.96736     21.91892375\n",
      "  23.87987     25.85022125  27.83        29.81922875  31.81793     33.82612625\n",
      "  35.84384     37.87109375  39.90791     41.95431125  44.01032     46.07595875\n",
      "  48.15125     50.23621625  52.33088     54.43526375  56.54939     58.67328125\n",
      "  60.80696     62.95044875  65.10377     67.26694625]\n"
     ]
    }
   ],
   "source": [
    "print(30*(1+x)**3 - 250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "a59b53cf-e6b9-4dae-9f5a-b94aca860131"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_ge_0 = (30*(1+x)**3 - 250) >= 0\n",
    "x_ge_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "b8c7bd40-31ba-4950-93a7-b9793c3b28d7"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_lt_0 = (30*(1+x)**3 - 250) < 0\n",
    "x_lt_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "755745d2-55dc-4e42-85b9-42713e2dae83"
    }
   },
   "source": [
    "x_ge_0 and x_lt_0 are logical array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "ab275db8-3d80-4efb-b221-2db8de59a314"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t=1.0249999999999995: error=0.8870\n",
      "t=1.0299999999999994: error=0.9628\n"
     ]
    }
   ],
   "source": [
    "for t in x:\n",
    "    err = abs(30*(1+t)**3 - 250)\n",
    "    if err <= 1.0:\n",
    "        print(\"t={x}: error={e:.4f}\".format(x=t,e=err))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c9c0cdfb-e6b9-448a-bc7f-9c54f53089a0"
    }
   },
   "source": [
    "#### using [linspace](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "240d239b-5ffb-4fba-bc1f-66593f0d0d5b"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.0020202 ,  1.0040404 ,  1.00606061,  1.00808081,\n",
       "        1.01010101,  1.01212121,  1.01414141,  1.01616162,  1.01818182,\n",
       "        1.02020202,  1.02222222,  1.02424242,  1.02626263,  1.02828283,\n",
       "        1.03030303,  1.03232323,  1.03434343,  1.03636364,  1.03838384,\n",
       "        1.04040404,  1.04242424,  1.04444444,  1.04646465,  1.04848485,\n",
       "        1.05050505,  1.05252525,  1.05454545,  1.05656566,  1.05858586,\n",
       "        1.06060606,  1.06262626,  1.06464646,  1.06666667,  1.06868687,\n",
       "        1.07070707,  1.07272727,  1.07474747,  1.07676768,  1.07878788,\n",
       "        1.08080808,  1.08282828,  1.08484848,  1.08686869,  1.08888889,\n",
       "        1.09090909,  1.09292929,  1.09494949,  1.0969697 ,  1.0989899 ,\n",
       "        1.1010101 ,  1.1030303 ,  1.10505051,  1.10707071,  1.10909091,\n",
       "        1.11111111,  1.11313131,  1.11515152,  1.11717172,  1.11919192,\n",
       "        1.12121212,  1.12323232,  1.12525253,  1.12727273,  1.12929293,\n",
       "        1.13131313,  1.13333333,  1.13535354,  1.13737374,  1.13939394,\n",
       "        1.14141414,  1.14343434,  1.14545455,  1.14747475,  1.14949495,\n",
       "        1.15151515,  1.15353535,  1.15555556,  1.15757576,  1.15959596,\n",
       "        1.16161616,  1.16363636,  1.16565657,  1.16767677,  1.16969697,\n",
       "        1.17171717,  1.17373737,  1.17575758,  1.17777778,  1.17979798,\n",
       "        1.18181818,  1.18383838,  1.18585859,  1.18787879,  1.18989899,\n",
       "        1.19191919,  1.19393939,  1.1959596 ,  1.1979798 ,  1.2       ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = np.linspace(1.0, 1.2, 100)\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "fb165454-8031-48d4-ad53-13a529881c4f"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t=1.0262626262626262: error=0.4208\n",
      "t=1.0282828282828282: error=0.3265\n"
     ]
    }
   ],
   "source": [
    "for t in x1:\n",
    "    err = math.fabs(30*(1+t)**3 - 250)\n",
    "    if err <= 1.0:\n",
    "        print(\"t={x}: error={e:.4f}\".format(x=t,e=err))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5c813ea9-fbf2-46b2-b5e1-fb75d2497744"
    }
   },
   "source": [
    "## Graph the solution using Matplotlib (in High School)\n",
    "<a class=\"anchor\" id=\"hid_graph\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e52f4a27-8dc3-4f11-9d71-1e05704cdedf"
    }
   },
   "source": [
    "![logo](http://matplotlib.org/_static/logo2.svg)\n",
    "\n",
    "[matplotlib](http://matplotlib.org/contents.html?v=20170307111739)   is visualization pkg for python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "31fd2fc9-6d40-42a5-9802-412c8149cb49"
    }
   },
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "95cf6bd7-4a1d-430c-9c92-f3f0ecfcb1e2"
    }
   },
   "outputs": [],
   "source": [
    "x2 = np.linspace(1.0, 1.05, 100)\n",
    "f1 = 30*(1+x2)**3 - 250\n",
    "\n",
    "f2 = np.zeros_like(x2)    # draw a horizontal line at y=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "92028685-116f-4efe-bf56-d5059998a976"
    }
   },
   "source": [
    "$x$ intersection of two lines f1/f2 gives the solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "635887f9-ec1e-4761-87e3-10dd2fb8f229"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SZYMF2Aq8OyIOBY4E3ibp0AblvhMRh6fpg8VW0cy2415K36tssETEnRHx4zT/EHATsH+5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad9f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(r'$x$')\n",
    "plt.ylabel(r'$\\delta$')\n",
    "plt.grid(True)\n",
    "plt.title('IPython is a great analytical tool')\n",
    "plt.axis([0.995,1.055, -11, 9])\n",
    "#plt.axis([1.02, 1.04, -11, 9])\n",
    "plt.plot(x2, f1, 'r+')\n",
    "plt.plot(x2, f2, 'b-', lw=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c4b654eb-9403-4aef-b9b3-e3c53b91a5c3"
    }
   },
   "source": [
    "## Solve equation precisely using SymPy  (in High School) <a class=\"anchor\" id=\"hid_sympy\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "28c3e44a-666b-4f62-9a86-23056fbbcc0b"
    }
   },
   "outputs": [],
   "source": [
    "# from sympy import *\n",
    "# import sympy as sy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "46a79e4d-b1cd-43ee-a68d-9263f5b3ddb5"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sy.var('x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "cad6f08b-37b5-4539-8e94-66f490c45466"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-1 + 15**(2/3)/3,\n",
       " -1 + 15**(2/3)*(-1/2 - sqrt(3)*I/2)/3,\n",
       " -1 + 15**(2/3)*(-1/2 + sqrt(3)*I/2)/3]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sy.solve(30*(1+x)**3 - 250, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4c21da2f-faa6-4ff0-a645-9722cc922919"
    }
   },
   "source": [
    "Ignore other 2 solutions because they are complex numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "b439d6a8-2071-4e06-b827-1dc3ac81b767"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.027400665191133"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grow_rate = -1 + 15**(2/3)/3\n",
    "grow_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "521ce1d7-514f-4d86-bae5-2fcc4bf4ab7f"
    }
   },
   "source": [
    "## Pandas for Big Data Analytics  (in College) <a class=\"anchor\" id=\"hid_panda\"></a>\n",
    "\n",
    "[pandas](http://pandas.pydata.org/pandas-docs/stable/?v=20170307111739) stands for powerful Python data analysis toolkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "95d6b24e-5799-4179-a690-4c1b492405df"
    }
   },
   "outputs": [],
   "source": [
    "# import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "cd8c9d7c-1a28-4894-9382-726f892959ab"
    }
   },
   "outputs": [],
   "source": [
    "year = [2014, 2015, 2016, 2017]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "1b7a87cb-30d0-41d7-ae84-3e44c821b406"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[30.0, 60.822019955733985, 123.31060371652346, 249.9999999999998]\n"
     ]
    }
   ],
   "source": [
    "customer_count = [30*(1+grow_rate)**i  for i in range(4)]\n",
    "print(customer_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "3d742078-bbfa-4a9c-a99d-0b432f40d5a3"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Customers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>60.822020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>123.310604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year   Customers\n",
       "0  2014   30.000000\n",
       "1  2015   60.822020\n",
       "2  2016  123.310604\n",
       "3  2017  250.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(list(zip(year,customer_count)), columns=['Year','Customers'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "340592f6-78d1-48b0-ac3b-bfa9808a4dc1"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Customers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>30.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>60.82202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Customers\n",
       "0  2014   30.00000\n",
       "1  2015   60.82202"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "286db730-6d49-4f87-9e9a-44770f14101a"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Customers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>123.310604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year   Customers\n",
       "2  2016  123.310604\n",
       "3  2017  250.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "941ecb8e-8d62-4cba-8d4d-49b851313ca1"
    }
   },
   "source": [
    "#### Line chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "1c17a7bf-7077-48d0-b8f5-38845853660e"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb63ad30>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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66D/amMM9/1zH3sNftmOuHqJ2jIhIZSGdhPEtGzOoc2tuS4pjRI9YOrVVO0ZE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb453b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Year'], df['Customers'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e669accf-2611-4273-8ba0-01d92517a89b"
    }
   },
   "source": [
    "#### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "8e314949-a08d-475c-8098-43f8cfa78dc6"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb68c7f0>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r4a67GNe6Nce1ahV3MpGsSOecxwMpzzcBs919bpbyiOSeOXPg6qth6FA45hh4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb670518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Year')\n",
    "plt.ylabel('Customer Count')\n",
    "plt.grid(True)\n",
    "plt.title('Customer Growth')\n",
    "\n",
    "plt.bar(df['Year'], df['Customers'])\n",
    "\n",
    "plt.plot(df['Year'], df['Customers'], 'r-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d040f9c4-f02a-46c8-b4c6-ab7cb7c0ab46"
    }
   },
   "source": [
    "## Multimedia with HTML5 -Text, Image, Audio, Video  <a class=\"anchor\" id=\"hid_html5\"></a> (Having graduated from all the schools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "1ec80f66-2106-432d-b0cc-2293f8324bd6"
    }
   },
   "outputs": [],
   "source": [
    "# from IPython.display import HTML, SVG, YouTubeVideo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5d438cf4-af7e-4df7-8942-90761ccdc03d"
    }
   },
   "source": [
    "###   create an HTML table dynamically with Python, and we display it in the (HTML-based) notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "469d012d-4381-4c97-93c2-e5170b43df46"
    }
   },
   "source": [
    "Let me create a multiplication table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "d4eefee9-e016-4e0f-9b67-497f560b01c4"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<table style=\"border: 2px solid black;\">\n",
       "<tr><td>0x0=0</td><td>0x1=0</td><td>0x2=0</td><td>0x3=0</td><td>0x4=0</td><td>0x5=0</td><td>0x6=0</td><td>0x7=0</td><td>0x8=0</td><td>0x9=0</td></tr><tr><td>1x0=0</td><td>1x1=1</td><td>1x2=2</td><td>1x3=3</td><td>1x4=4</td><td>1x5=5</td><td>1x6=6</td><td>1x7=7</td><td>1x8=8</td><td>1x9=9</td></tr><tr><td>2x0=0</td><td>2x1=2</td><td>2x2=4</td><td>2x3=6</td><td>2x4=8</td><td>2x5=10</td><td>2x6=12</td><td>2x7=14</td><td>2x8=16</td><td>2x9=18</td></tr><tr><td>3x0=0</td><td>3x1=3</td><td>3x2=6</td><td>3x3=9</td><td>3x4=12</td><td>3x5=15</td><td>3x6=18</td><td>3x7=21</td><td>3x8=24</td><td>3x9=27</td></tr><tr><td>4x0=0</td><td>4x1=4</td><td>4x2=8</td><td>4x3=12</td><td>4x4=16</td><td>4x5=20</td><td>4x6=24</td><td>4x7=28</td><td>4x8=32</td><td>4x9=36</td></tr><tr><td>5x0=0</td><td>5x1=5</td><td>5x2=10</td><td>5x3=15</td><td>5x4=20</td><td>5x5=25</td><td>5x6=30</td><td>5x7=35</td><td>5x8=40</td><td>5x9=45</td></tr><tr><td>6x0=0</td><td>6x1=6</td><td>6x2=12</td><td>6x3=18</td><td>6x4=24</td><td>6x5=30</td><td>6x6=36</td><td>6x7=42</td><td>6x8=48</td><td>6x9=54</td></tr><tr><td>7x0=0</td><td>7x1=7</td><td>7x2=14</td><td>7x3=21</td><td>7x4=28</td><td>7x5=35</td><td>7x6=42</td><td>7x7=49</td><td>7x8=56</td><td>7x9=63</td></tr><tr><td>8x0=0</td><td>8x1=8</td><td>8x2=16</td><td>8x3=24</td><td>8x4=32</td><td>8x5=40</td><td>8x6=48</td><td>8x7=56</td><td>8x8=64</td><td>8x9=72</td></tr><tr><td>9x0=0</td><td>9x1=9</td><td>9x2=18</td><td>9x3=27</td><td>9x4=36</td><td>9x5=45</td><td>9x6=54</td><td>9x7=63</td><td>9x8=72</td><td>9x9=81</td></tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('''\n",
    "<table style=\"border: 2px solid black;\">\n",
    "''' + \n",
    "''.join(['<tr>' + \n",
    "         ''.join(['<td>{row}x{col}={prod}</td>'.format(\n",
    "            row=row, col=col, prod=row*col\n",
    "            ) for col in range(10)]) +\n",
    "         '</tr>' for row in range(10)]) +\n",
    "'''\n",
    "</table>\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "df9e7c92-a0a3-496c-b213-eafdb304e767"
    }
   },
   "source": [
    "### display image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "6605234f-93e2-41e7-8ce0-7c7aac5e7c4b"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<img src=\"https://s-media-cache-ak0.pinimg.com/564x/59/b6/cc/59b6cc26b9502fa7d8f494050ca80ac4.jpg\" alt=\"sunset\" style=\"width:250px;height:400px;\">\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<img src=\"https://s-media-cache-ak0.pinimg.com/564x/59/b6/cc/59b6cc26b9502fa7d8f494050ca80ac4.jpg\" alt=\"sunset\" style=\"width:250px;height:400px;\">\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "42ac8c27-526c-4f9e-ac2e-a398b3776c0a"
    }
   },
   "source": [
    "### create a SVG graphics dynamically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "eb0843a1-e45a-4ef9-b191-3d12a6dc8c7c"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"80\" width=\"600\"><circle cx=\"300\" cy=\"30\" fill=\"red\" r=\"0.0\" stroke=\"black\" stroke-width=\"2\">\n",
       "        </circle><circle cx=\"297\" cy=\"30\" fill=\"red\" r=\"3.0\" stroke=\"black\" stroke-width=\"2\">\n",
       "        </circle><circle cx=\"288\" cy=\"30\" fill=\"red\" r=\"6.0\" stroke=\"black\" stroke-width=\"2\">\n",
       "        </circle><circle cx=\"273\" cy=\"30\" fill=\"red\" r=\"9.0\" stroke=\"black\" stroke-width=\"2\">\n",
       "        </circle><circle cx=\"252\" cy=\"30\" fill=\"red\" r=\"12.0\" stroke=\"black\" stroke-width=\"2\">\n",
       "        </circle><circle cx=\"225\" cy=\"30\" fill=\"red\" r=\"15.0\" stroke=\"black\" stroke-width=\"2\">\n",
       "        </circle><circle cx=\"192\" cy=\"30\" fill=\"red\" r=\"18.0\" stroke=\"black\" stroke-width=\"2\">\n",
       "        </circle><circle cx=\"153\" cy=\"30\" fill=\"red\" r=\"21.0\" stroke=\"black\" stroke-width=\"2\">\n",
       "        </circle><circle cx=\"108\" cy=\"30\" fill=\"red\" r=\"24.0\" stroke=\"black\" stroke-width=\"2\">\n",
       "        </circle><circle cx=\"57\" cy=\"30\" fill=\"red\" r=\"27.0\" stroke=\"black\" stroke-width=\"2\">\n",
       "        </circle></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVG('''<svg width=\"600\" height=\"80\">''' + \n",
    "''.join(['''<circle cx=\"{x}\" cy=\"{y}\" r=\"{r}\"\n",
    "        fill=\"red\" stroke-width=\"2\" stroke=\"black\">\n",
    "        </circle>'''.format(\n",
    "            x=(30+3*i)*(10-i), y=30, r=3.*float(i)\n",
    "        ) for i in range(10)]) + \n",
    "'''</svg>''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8abf318a-6523-40c3-97d8-aa6e113db74a"
    }
   },
   "source": [
    "### embed an audio clip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "1384a433-443d-4b5c-b6b7-ec7486028927"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<a href=https://talkpython.fm>TalkPython </a>\n",
       "<h2> Guido van Rossum</h2> (Python Creator & Tsar)\n",
       "<h3> Python past, present, and future </h3>\n",
       "<br/>\n",
       "<audio controls>\n",
       "  <source src=\"https://downloads.talkpython.fm/static/episode_cache/100-guido-van-rossum.mp3\" type=\"audio/mpeg\">\n",
       "Your browser does not support the audio element.\n",
       "</audio>\n",
       "</td>\n",
       "<td>\n",
       "<img src=\"https://pbs.twimg.com/profile_images/424495004/GuidoAvatar_400x400.jpg\" alt=\"Guido\" style=\"width:200px;height:200px;\"\n",
       "</td>\n",
       "</tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "\n",
    "<table>\n",
    "<tr>\n",
    "<td>\n",
    "<a href=https://talkpython.fm>TalkPython </a>\n",
    "<h2> Guido van Rossum</h2> (Python Creator & Tsar)\n",
    "<h3> Python past, present, and future </h3>\n",
    "<br/>\n",
    "<audio controls>\n",
    "  <source src=\"https://downloads.talkpython.fm/static/episode_cache/100-guido-van-rossum.mp3\" type=\"audio/mpeg\">\n",
    "Your browser does not support the audio element.\n",
    "</audio>\n",
    "</td>\n",
    "<td>\n",
    "<img src=\"https://pbs.twimg.com/profile_images/424495004/GuidoAvatar_400x400.jpg\" alt=\"Guido\" style=\"width:200px;height:200px;\"\n",
    "</td>\n",
    "</tr>\n",
    "</table>\n",
    "\"\"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "cf191bef-5eb9-4a6d-8628-d3041143a995"
    }
   },
   "source": [
    "### display a Youtube video by giving its identifier to YoutubeVideo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "6d56798b-148a-4838-98fc-b32974781ceb"
    }
   },
   "source": [
    "#### SciPy 2013 Keynote: IPython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "f0a8f2a1-b6c8-4454-932f-c6faf1d4a4b9"
    }
   },
   "outputs": [
    {
     "data": {
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XFGX2DiXFaVH+VkrA8T0dGVnV9jy2GswziV7VWNW10tsZrNdHEfFbVnGnJpeN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\nPPVnlmfdwopZf5ao+mfwNct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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/j9YpkSX7NNM\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0xb67a3c8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('j9YpkSX7NNM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b35dd3ab-1fe9-4328-b416-61ab83ee438a"
    }
   },
   "source": [
    "## Interactive widgets  <a class=\"anchor\" id=\"hid_widget\"></a>\n",
    "\n",
    "we illustrate the latest interactive features in IPython 2.0+. \n",
    "\n",
    "This version brings graphical widgets in the notebook that can interact with Python objects. \n",
    "\n",
    "We will create a drop-down menu allowing us to display one among several videos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "89084255-cc83-4070-b2f5-5434e7f433f8"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' \\nfrom collections import OrderedDict\\nfrom IPython.display import display, clear_output\\nfrom ipywidgets import Dropdown\\n'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How to comment out multiple lines of code in python\n",
    "\"\"\" \n",
    "from collections import OrderedDict\n",
    "from IPython.display import display, clear_output\n",
    "from ipywidgets import Dropdown\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "469b7baf-79e3-46f6-a5a6-d2219c22f23d"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/o1dBg__wsuo\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0xb187fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed properly. Did you enable the widgetsnbextension? If not, then run \"jupyter nbextension enable --py --sys-prefix widgetsnbextension\"\n"
     ]
    }
   ],
   "source": [
    "# We create a Dropdown widget, with a dictionary containing\n",
    "# the keys (video name) and the values (Youtube identifier) of every menu item.\n",
    "\n",
    "dw = Dropdown(options=OrderedDict([\n",
    "                        ('SciPy 2012', 'iwVvqwLDsJo'), \n",
    "                        ('PyCon 2012', '2G5YTlheCbw'),\n",
    "                        ('SciPy 2013', 'j9YpkSX7NNM'),\n",
    "                        ('Guido Van Rossum', 'EBRMq2Ioxsc'),\n",
    "                        ('Mendelssohn Violin', 'o1dBg__wsuo')\n",
    "        ]))\n",
    "\n",
    "# We create a callback function that displays the requested Youtube video.\n",
    "def on_value_change(name, val):\n",
    "    clear_output()\n",
    "    display(YouTubeVideo(val))\n",
    "# Every time the user selects an item, the function\n",
    "# `on_value_change` is called, and the `val` argument\n",
    "# contains the value of the selected item.\n",
    "dw.on_trait_change(on_value_change, 'value')\n",
    "# We choose a default value.\n",
    "dw.value = dw.options['Mendelssohn Violin']\n",
    "\n",
    "# Finally, we display the widget.\n",
    "display(dw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with SQLite Databases  <a class=\"anchor\" id=\"hid_sqlite\"></a>\n",
    "\n",
    "read [blog](https://www.dataquest.io/blog/python-pandas-databases/) at [DataQuest](https://www.dataquest.io)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>subject</th>\n",
       "      <th>subjecturl</th>\n",
       "      <th>person</th>\n",
       "      <th>picture</th>\n",
       "      <th>history</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1983</td>\n",
       "      <td>GNU Project : gcc, Emacs, gdb</td>\n",
       "      <td>https://en.wikipedia.org/wiki/GNU_Project</td>\n",
       "      <td>Richard Stallman</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "      <td>Launch of the free software movement and found...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1984</td>\n",
       "      <td>X Window Systerm</td>\n",
       "      <td>https://en.wikipedia.org/wiki/X_Window_System</td>\n",
       "      <td>X.Org</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "      <td>basic framework for a GUI environment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1983</td>\n",
       "      <td>GNU Project : gcc, Emacs, gdb</td>\n",
       "      <td>https://en.wikipedia.org/wiki/GNU_Project</td>\n",
       "      <td>Richard Stallman</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "      <td>Launch of the free software movement and found...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>X Window Systerm</td>\n",
       "      <td>https://en.wikipedia.org/wiki/X_Window_System</td>\n",
       "      <td>X.Org</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "      <td>basic framework for a GUI environment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>GNU Manifesto: GNU's Not Unix</td>\n",
       "      <td>https://en.wikipedia.org/wiki/GNU_Manifesto</td>\n",
       "      <td>Richard Stallman</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/en/thum...</td>\n",
       "      <td>disagreement between Stallman and Symbolics, I...</td>\n",
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      "text/plain": [
       "   year                        subject  \\\n",
       "0  1983  GNU Project : gcc, Emacs, gdb   \n",
       "1  1984               X Window Systerm   \n",
       "2  1983  GNU Project : gcc, Emacs, gdb   \n",
       "3  1984               X Window Systerm   \n",
       "4  1985  GNU Manifesto: GNU's Not Unix   \n",
       "\n",
       "                                      subjecturl            person  \\\n",
       "0      https://en.wikipedia.org/wiki/GNU_Project  Richard Stallman   \n",
       "1  https://en.wikipedia.org/wiki/X_Window_System             X.Org   \n",
       "2      https://en.wikipedia.org/wiki/GNU_Project  Richard Stallman   \n",
       "3  https://en.wikipedia.org/wiki/X_Window_System             X.Org   \n",
       "4    https://en.wikipedia.org/wiki/GNU_Manifesto  Richard Stallman   \n",
       "\n",
       "                                             picture  \\\n",
       "0  https://upload.wikimedia.org/wikipedia/commons...   \n",
       "1  https://upload.wikimedia.org/wikipedia/commons...   \n",
       "2  https://upload.wikimedia.org/wikipedia/commons...   \n",
       "3  https://upload.wikimedia.org/wikipedia/commons...   \n",
       "4  https://upload.wikimedia.org/wikipedia/en/thum...   \n",
       "\n",
       "                                             history  \n",
       "0  Launch of the free software movement and found...  \n",
       "1              basic framework for a GUI environment  \n",
       "2  Launch of the free software movement and found...  \n",
       "3              basic framework for a GUI environment  \n",
       "4  disagreement between Stallman and Symbolics, I...  "
      ]
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   "source": [
    "# open connection to db\n",
    "conn = sqlite3.connect(\"dataset/open_src.sqlite\")\n",
    "\n",
    "# create a cursor\n",
    "cur = conn.cursor()\n",
    "\n",
    "# select query\n",
    "results = cur.execute(\"select * from os_history limit 100;\").fetchall()\n",
    "#print(results)\n",
    "\n",
    "# count # of rows\n",
    "results = cur.execute(\"select count(*) from os_history;\").fetchall()\n",
    "#print(results)\n",
    "\n",
    "# store data from csv file into db\n",
    "with open('dataset/open_src_move_v2_1.csv') as csvfile:\n",
    "    reader = csv.DictReader(csvfile)\n",
    "    for row in reader:\n",
    "        #print(row['Year'])\n",
    "        insert_str=\"\"\"\n",
    "            insert into os_history(year,subject,subjecturl,person,picture,history)\n",
    "            values ({year}, \"{subject}\", \"{subject_url}\", \"{person}\", \"{picture}\", \"{history}\")\n",
    "        \"\"\"\n",
    "        #print(insert_str.format(year=row['Year'],subject=row['Subject'],subject_url=row['SubjectURL'],person=row['Person'],picture=row['Picture'],history=row['History']))\n",
    "        cur.execute(insert_str.format(year=row['Year'],subject=row['Subject'],subject_url=row['SubjectURL'],person=row['Person'],picture=row['Picture'],history=row['History']))\n",
    "    conn.commit()\n",
    "    \n",
    "# create a dataframe\n",
    "df = pd.read_sql_query(\"select * from os_history limit 5;\",conn)\n",
    "\n",
    "# inspect data\n",
    "df"
   ]
  },
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   },
   "source": [
    "## References <a class=\"anchor\" id=\"hid_ref\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ac06c191-b6a8-48f6-86f6-c78c76462861"
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   },
   "source": [
    "### Websites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "bcf75c29-93f1-453d-81c1-fbd5d2c95c2c"
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   },
   "source": [
    "* [DataCamp - Jupyter Notebook Tutorial](https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook#gs.ClmI4Jc)\n",
    "\n",
    "\n",
    "* http://docs.python.org\n",
    "\n",
    "It goes without saying that Pythonâ€™s own online documentation is an excellent resource if you need to delve into the finer details of the language and modules. Just make sure youâ€™re looking at the documentation for Python 3 and not earlier versions.\n",
    "\n",
    "* http://www.python.org/dev/peps\n",
    "\n",
    "Python Enhancement Proposals (PEPs) are invaluable if you want to understand the motivation for adding new features to the Python language as well as subtle implementation details. This is especially true for some of the more advanced language features. In writing this book, the PEPs were often more useful than the official documentation.\n",
    "\n",
    "* http://pyvideo.org\n",
    "\n",
    "This is a large collection of video presentations and tutorials from past PyCon conferences, user group meetings, and more. It can be an invaluable resource for learning about modern Python development. Many of the videos feature Python core developers talking about the new features being added in Python 3.\n",
    "\n",
    "* http://code.activestate.com/recipes/langs/python\n",
    "\n",
    "The ActiveState Python recipes site has long been a resource for finding the solution to thousands of specific programming problems. As of this writing, it contains approximately 300 recipes specific to Python 3. Youâ€™ll find that many of its recipes either expand upon topics covered in this book or focus on more narrowly defined tasks. As such, itâ€™s a good companion.\n",
    "\n",
    "* http://stackoverflow.com/questions/tagged/python\n",
    "\n",
    "Stack Overflow currently has more than 175,000 questions tagged as Python-related (and almost 5000 questions specific to Python 3). Although the quality of the questions and answers varies, there is a lot of good material to be found."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66662698-f8b7-4482-b9dc-d220a918e51d"
    }
   },
   "source": [
    "### Books"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "28979679-24a9-455b-a06c-f82ace24848b"
    }
   },
   "source": [
    "*  [Learning IPython for Interactive Computing and Data Visualization - Second Edition (By Cyrille Rossant)](https://github.com/ipython-books/minibook-2nd-code)\n",
    "*  [IPython Interactive Computing and Visualization Cookbook  (By Cyrille Rossant)](https://github.com/ipython-books/cookbook-code)\n",
    "*  [Python Cookbook, 3rd Edition by David Beazley; Brian K. Jones](https://github.com/dabeaz/python-cookbook)\n",
    "*  [Python for Data Analysis by Wes McKinney](https://github.com/wesm/pydata-book)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "06ce80e4-29de-4994-9808-c8feffa25d8d"
    }
   },
   "source": [
    "### Other Resources\n",
    "\n",
    "* Idea\n",
    "    - [Google Search](http://www.google.com)\n",
    "* Text\n",
    "    - [Wikipedia](https://www.wikipedia.org/)\n",
    "* Image\n",
    "    - [Google Images](https://www.google.com/imghp)\n",
    "* Video\n",
    "    - [YouTube](https://www.youtube.com/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "095385ad-d26d-4168-9bd6-09029a9fe701"
    }
   },
   "source": [
    "## Contributors <a class=\"anchor\" id=\"hid_author\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "3c647eed-ff6d-4b34-ae0b-08ee99798711"
    }
   },
   "source": [
    "* wen.gong@oracle.com (first created on 2017-03-09)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "08d52625-4758-4290-b292-0f166e9ae95d"
    }
   },
   "source": [
    "## Appendix <a class=\"anchor\" id=\"hid_apend\"></a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "ec7bcfc3-e3fc-413c-ab52-59da41e9d885"
    }
   },
   "source": [
    "### How to install Jupyter Notebook <a class=\"anchor\" id=\"hid_setup\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "6d1f3b83-78be-4853-bc1d-e61c7e1950c6"
    }
   },
   "source": [
    "I use Windows and follow this link to [install Ananconda Python distribution](https://www.tensorflow.org/install/)\n",
    "\n",
    "* Follow the instructions on the [Anaconda download site](https://www.continuum.io/downloads) to download and install Anaconda.\n",
    "\n",
    "* open a DOS command box, launch jupyter by typing\n",
    "    > jupyter notebook\n",
    "    \n",
    "* wait till Jupyter homepage to open in a browser\n",
    "\n",
    "* start to create your own Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "fa5bff1d-0529-4f92-8a18-3acc585bcbd7"
    }
   },
   "source": [
    "### How to share a notebook <a class=\"anchor\" id=\"hid_share\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "02fbc4ce-f18f-4ac0-a8b1-3cca3e3d29e4"
    }
   },
   "source": [
    "share your learning or work via nbviewer at http://nbviewer.jupyter.org/"
   ]
  },
  {
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