Dataviz_workshop_Columbia_Science_PO_SIPA.ipynb

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    "<small><i><b>June the 2nd 2017</b> - Alliance Science Po / Columbia University- PhD Summer school</i><br>\n",
    "Thomas Roca, PhD - Agence Française de Développement<br>\n",
    "Twitter: [@Thomas Roca](https://twitter.com/Thomas_Roca) || *[Lecture's Github Folder](https://github.com/ThomasRoca/Lecture-Columbia-Science-Po-2017)*</small><br>\n",
    "\n",
    "\n",
    "## Introduction to Datavisualization\n",
    "    \"A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.\"\n",
    "      \n",
    "    \n",
    "<img src='http://stats4dev.com/prez/cool_data_3.gif'>\n",
    "\n",
    "\n"
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    "# 1. What is datavisualization\n",
    "\n",
    "- It must diasplay data..\n",
    "- interactive inforgraphic\n",
    "- Most of the time HTML and javascript\n",
    "\n",
    "Example: http://data.afd.fr/ "
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       "<iframe src=\"http://www.oecdbetterlifeindex.org/#/11111111111\" scrolling=\"no\" frameborder=\"0\" width=100%\" height=\"675\"></iframe>"
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       "<IPython.core.display.HTML object>"
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    "HTML('''<iframe src=\"http://www.oecdbetterlifeindex.org/#/11111111111\" scrolling=\"no\" frameborder=\"0\" width=100%\" height=\"675\"></iframe>''')"
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    "### Basic instroduction ###\n",
    "-   using packages with Stata and Python (15 min) \n",
    "-\tSIG using [Carto](https://carto.com/) and [leaflet](http://leafletjs.com/)\n",
    "-\tBasic introduction to [HTML](https://www.w3schools.com/html/default.asp), [CSS](https://www.w3schools.com/css/default.asp)\n",
    "-\tJavaScript & dataviz\n",
    "    + quick intro to [JavaScript](https://www.w3schools.com/js/default.asp)\n",
    "    + Intro to [JSON](https://en.wikipedia.org/wiki/JSON)\n",
    "    + dataviz the example of [highchart](https://www.highcharts.com/) and [d3.js](https://d3js.org/)\n",
    "   "
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   "source": [
    "# Dataviz for all: packages and software..\n",
    "- Stata (see my plugins [Himap](http://econpapers.repec.org/software/bocbocode/S457920.htm), [3D plot](http://econpapers.repec.org/software/bocbocode/S457922.htm), etc. :)\n",
    "- R: [ggplot](http://www.statmethods.net/advgraphs/ggplot2.html)\n",
    "- Python: [matplotlib](https://matplotlib.org/gallery.html), [Seaborn](https://seaborn.pydata.org/), [bokeh](), [d3js package](http://nvd3.org/), etc.\n"
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   "source": [
    " ### Funky stuff... like XKCD"
   ]
  },
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UrFgRvr6++N///gc3Nzf4+flh37592LVrV65DhzKJiYkAIFLoBQKBoIAIA8uAvIC5ePHi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\nRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('http://jakevdp.github.com/figures/xkcd_version.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Example of Matplotlib for HDR2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python34\\lib\\site-packages\\matplotlib\\cbook.py:137: MatplotlibDeprecationWarning: The set_color_cycle attribute was deprecated in version 1.5. Use set_prop_cycle instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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yxdib2ujaW0d4ZjL+/v7eLscrFH4iIlNM45vvY+UmkTJFL3mCwk9EZMo5sHID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\ndvEchZ+ITFjtO6rpPtBEeE6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Gxo7ql7a+vj6am5vZV9vMzqomqvd3UNtsp7XHj6YuG0H+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\n0z7KO4++Fvy+zjMuC833AjE/BjyttTaBIaXUi8TPwe0kubm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6619eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+hekMCj8RERdWumQjgcOy2iX4DMNg5rJCFu1t4tlHhzEkJ9bh93SWqwo/k8n0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\n3QQcILDmd77LHnNf6xL4hv4lSqlcIE1rvdme9CTwDTdr9uJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x83e3630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LTdMcASwBHgQwDGM0MAsYBVwOPGcYhv4WRES6SMXmXQT2j7O7jF6vowHnOMqx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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x86bbe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "Year=np.array([1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011])\n",
    "\n",
    "#Productivity\n",
    "US_p=np.array([100,103.3014096,105.2530664,107.1445328,109.2128503,114.1487449,111.8010891,114.9564671,115.308162,120.5297607,121.2367635,124.5858159,130.5790409,139.5242903,146.2320682,151.7957437,154.7141778,154.7677332,160.6847743,162.9637761,163.470189,174.0190279,181.4770087,190.5291264,185.9504221,192.3939511,202.8309182,210.468272,212.3561129,215.2584574,213.6200006,225.2725304,227.2143176,243.3043327,250.1417376,259.0358946,263.2471097,281.4700738,292.3211162,293.8618617,297.3530599,305.628367,317.4000172,325.8365609,339.4042571,350.9844405,362.9905603,377.9786019,397.8735771,422.1302571,454.9260367,464.3022204,512.1115487,555.6012635,604.1336965,632.118673,653.6456222,688.2453704,674.900632,702.9663436,781.915389,797.5705382])\n",
    "France_p=np.array([100,104.4784298,103.4413049,105.7293641,112.1571925,114.4112798,120.9047753,122.3220121,125.2185794,128.6481285,136.7275667,144.2869002,151.2186001,165.460256,170.2880304,185.3685356,198.6106266,209.9374644,233.7349511,248.32795,265.1901913,278.5620005,284.5480029,298.4344243,326.3294799,333.6319977,349.0207734,368.735091,377.9600239,393.4983054,400.5358038,418.0209231,440.4787713,460.8166829,475.0107301,496.1974851,499.3509108,504.7186452,524.4409629,546.3938301,557.3915267,571.9610497,587.2924174,606.1041738,643.6823382,669.9322166,672.9654306,705.7525384,740.6184052,772.8410799,818.002482,828.2792508,859.7924734,888.3666385,921.3607266,963.8959974,1001.528675,1028.635601,992.8604891,991.9123876,1052.331439,1075.737975])\n",
    "Japan_p=np.array([100,125.0000473,131.2090751,149.1566693,158.8382573,165.2431356,182.55445,204.7813993,192.419427,226.739601,266.2138251,302.1652916,319.3841811,345.595205,392.395648,410.3128246,449.1871922,506.635426,566.8278336,649.0976575,729.4150669,773.4027031,848.2401996,924.895592,948.2837293,975.2146565,1034.354328,1075.315047,1118.993196,1210.590004,1229.657024,1269.578434,1314.163207,1317.139213,1344.82546,1472.756996,1477.889611,1540.689499,1613.403222,1699.371447,1820.963729,1915.290741,1935.764438,1973.497258,2007.960256,2140.159086,2239.706111,2318.198994,2342.222519,2403.733552,2531.244246,2477.256385,2543.511083,2678.145429,2837.046105,3022.579207,3092.1164,3278.341613,3415.957599,3201.570906,3674.289123,3570.501147])\n",
    "#hourly wages\n",
    "US_w=np.array([100,101.8964668,106.438877,111.4074358,115.7375678,120.4421562,126.3180785,129.6228298,132.0573978,135.7397278,139.1376355,141.7910027,145.4311113,147.4540833,151.4989856,152.2813247,154.4817083,158.2634315,163.2353971,166.1192039,168.2615767,170.6854444,173.894616,176.2220153,176.2306562,181.1633026,185.370173,189.5957739,190.4078728,190.514987,191.7890817,192.6994021,198.4625267,196.0960563,194.7678607,197.9345175,203.504863,203.1601599,202.9341732,200.4019088,200.114878,204.6360649,209.0086854,208.5604391,209.3170708,207.6312569,205.6737864,206.2405116,216.0269951,220.8894479,230.2643958,232.4569036,241.360629,254.9662399,250.2288741,250.1909213,247.1475573,248.062292,249.0886482,260.2828443,258.9306097,257.6565])\n",
    "France_w=np.array([100,106.7498219,117.0619556,124.8338519,133.4280232,142.5364715,157.9401988,168.8326385,168.9978624,174.627203,181.7555765,193.5915896,202.3254014,214.2376159,224.3637927,238.9766241,248.4418841,265.1189629,290.229952,310.3026399,329.7305689,346.5256082,367.858602,391.8918703,410.3561427,449.1670321,455.3982792,475.138127,491.7556568,502.5969315,509.2552796,520.9726878,551.8304392,562.805952,576.4820996,590.0209514,600.3113075,605.2826294,611.0729842,622.6368105,630.1514295,646.2572864,661.16366,674.5457564,686.3973985,695.2709966,693.0300723,709.2868511,711.9813282,731.9924164,755.9757876,764.3952234,793.772079,796.981536,805.058476,818.2112984,836.6649412,840.2005407,838.2189836,871.9327718,874.5599925,877.5879489])\n",
    "Japan_w=np.array([100,108.8056534,118.4787251,117.54999,120.9823196,127.4365165,132.8319585,134.8380871,138.1057333,152.3146087,162.1500853,179.3409676,192.3972377,199.0515768,215.4523925,228.2139276,239.3977206,258.5363458,284.412512,319.705692,354.0329996,383.7171628,422.0400302,461.4903172,491.4474942,512.1535211,503.9342643,514.6234076,525.6590964,539.7868422,532.5632593,544.5806963,555.3646304,560.308636,563.878743,573.4275468,593.7206044,611.5217506,621.4710816,644.0357936,667.4932795,687.98305,700.8224243,710.7097189,719.8789913,744.6148319,750.9364739,761.8836356,776.2914631,777.4882953,778.1765001,794.3097936,802.1701734,787.4262029,778.1581011,768.5775367,754.2975412,752.3122578,767.7725456,781.1448363,777.6227545,796.3582535])\n",
    "\n",
    "country_list=['Japan','France','US']\n",
    "\n",
    "for country in country_list:\n",
    "    #Set serie 1 and 2\n",
    "    y1=vars()[country+'_w']\n",
    "    y2=vars()[country+'_p']\n",
    "    \n",
    "    # Find the max of the two series to set up y-axis max\n",
    "    maxy=max(np.amax(y1),np.amax(y2))\n",
    "    \n",
    "    #set country and colors for the series\n",
    "    country=country\n",
    "    color_y1='#355CA8'\n",
    "    color_y2='#BC456B'\n",
    "\n",
    "    fig,(ax)= plt.subplots( figsize=(7,7),dpi=400)\n",
    "    plt.gca().set_color_cycle([color_y1,color_y2])\n",
    "    ax.plot(Year, y1, Year, y2)\n",
    "\n",
    "    ax.fill_between(Year, y1, y2, where=y2<=y1, facecolor=color_y1, alpha=0.3, interpolate=True)\n",
    "    ax.fill_between(Year, y1, y2, where=y2>=y1, facecolor=color_y2, alpha=0.3, interpolate=True)\n",
    "    ax.set_ylim((50, maxy))\n",
    "    ax.set_xlim((1945, 2011))\n",
    "    ax.set_xticks([1945,1950,1955,1960,1965,1970,1975,1980,1985,1990,1995,2000,2005,2011])\n",
    "    ax.tick_params(axis='x', colors='#7c7c7c')\n",
    "    ax.tick_params(axis='y', colors='#7c7c7c')\n",
    "    \n",
    "    ax.set_title(country, fontsize=15, color='#535459')\n",
    "  \n",
    "    ax.yaxis.label.set_color('#7c7c7c')\n",
    "    ax.xaxis.label.set_color('#7c7c7c')\n",
    "    \n",
    "    leg = plt.legend(['Wages','Productivity'],loc=2, fontsize=12, frameon=False)\n",
    "    for text in leg.get_texts():\n",
    "        text.set_color(\"#535459\")\n",
    "        \n",
    "    ax.spines['left'].set_bounds(100, maxy)\n",
    "    ax.spines['left'].set_color('#dddddd') \n",
    "    ax.spines['bottom'].set_color('#dddddd') \n",
    "\n",
    "    # Hide the right and top spines\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    \n",
    "    # Only show ticks on the left and bottom spines\n",
    "    ax.yaxis.set_ticks_position('left')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    \n",
    "    ax.annotate('[1950=100]', xy=(1948,maxy), xycoords='data', xytext=(0,0), textcoords='offset points',size=11, color=\"#535459\")\n",
    " \n",
    "    fig.savefig(country+'.svg', bbox_inches='tight')\n",
    "    fig.savefig(country+'.png', bbox_inches='tight')\n",
    "    fig.savefig(country+'.pdf', bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Using javascript library within Python\n",
    "\n",
    "... The example of d3.js"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"http://stats4dev.com/prez/chart_d3.html\" scrolling=\"no\" frameborder=\"0\" width=100%\" height=\"500px\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from string import Template\n",
    "from IPython.display import HTML\n",
    "from nvd3 import pieChart\n",
    "type = 'pieChart'\n",
    "chart = pieChart(name=type, color_category='category20c', height=450, width=450)\n",
    "xdata = [\"Orange\", \"Banana\", \"Pear\", \"Kiwi\", \"Apple\", \"Strawberry\", \"Pineapple\"]\n",
    "ydata = [3, 4, 0, 1, 5, 7, 3]\n",
    "extra_serie = {\"tooltip\": {\"y_start\": \"\", \"y_end\": \" calories\"}}\n",
    "chart.add_serie(y=ydata, x=xdata, extra=extra_serie)\n",
    "chart.buildcontent() \n",
    "d3script='<script src=\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\"></script><script src=\"https://cdnjs.cloudflare.com/ajax/libs/nvd3/1.7.1/nv.d3.min.js\"></script>'\n",
    "f = open(\"chart_d3.html\",'w')\n",
    "f.write(d3script+chart.htmlcontent)\n",
    "f.close()\n",
    "HTML('<iframe src=\"http://stats4dev.com/prez/chart_d3.html\" scrolling=\"no\" frameborder=\"0\" width=100%\" height=\"500px\"></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## GIS with web soft: Carto\n",
    "\n",
    "NB. Carto rocks... but its proprietary..\n",
    "\n",
    "**A little demo...**\n",
    "https://rocathomas.carto.com/dashboard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## GIS with Python: Leaflet Library package - [folium](https://github.com/python-visualization/folium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python34\\lib\\site-packages\\folium\\folium.py:504: UserWarning: This method is deprecated. Please use Map.choropleth instead.\n",
      "  warnings.warn('This method is deprecated. '\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe src=\"http://stats4dev.com/prez/map.html\" \"scrolling=\"no\" frameborder=\"0\" width=100%\" height=\"500px\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "from IPython.display import HTML\n",
    "# fichier au format GeoJSON\n",
    "mongeojson = '../ipyleaflet/sahel.json'\n",
    "carte = folium.Map(location=[12.2383,-1.56159],tiles='OpenStreetMap', zoom_start=5)\n",
    "carte.geo_json(geo_path=mongeojson)\n",
    "folium.Map.save(carte, \"map.html\")\n",
    "HTML('<iframe src=\"http://stats4dev.com/prez/map.html\" \"scrolling=\"no\" frameborder=\"0\" width=100%\" height=\"500px\"></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# II. Enough playing, let's dig'in\n",
    "\n",
    "Let's code a bit now.. HTML, CSS, Javascript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<h2> a. Basic introduction to HTML, CSS</h2>             \n",
       "<br>\n",
       "<h1>Hello world ! </h1>\n",
       "<br>\n",
       "<span style=\"color:red; font-size:36px; font-family:Magneto \">\n",
       "Hello world! with style</span>\n",
       "            \n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML('''\n",
    "\n",
    "<h2> a. Basic introduction to HTML, CSS</h2>             \n",
    "<br>\n",
    "<h1>Hello world ! </h1>\n",
    "<br>\n",
    "<span style=\"color:red; font-size:36px; font-family:Magneto \">\n",
    "Hello world! with style</span>\n",
    "            \n",
    "'''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<h2> B. Introduction to SVG</h2>\n",
       "<br>\n",
       "<h1>Hello SVG</h1>\n",
       "<svg width=\"100\" height=\"100\">\n",
       "   <circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"rgb(0, 119, 181)\" stroke-width=\"4\" fill=\"blue\" />\n",
       "</svg> \n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML('''\n",
    "<h2> B. Introduction to SVG</h2>\n",
    "<br>\n",
    "<h1>Hello SVG</h1>\n",
    "<svg width=\"100\" height=\"100\">\n",
    "   <circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"rgb(0, 119, 181)\" stroke-width=\"4\" fill=\"blue\" />\n",
    "</svg> \n",
    "\n",
    "'''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## SVG : Vector versus pixel\n",
    "\n",
    "<img src=\"http://i.vimeocdn.com/video/131954497_1280x720.jpg\" width=\"75%\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<iframe width=\"100%\" height=\"300\" src=\"//jsfiddle.net/fxcjzufw/1/embedded/html,result/\" allowfullscreen=\"allowfullscreen\" frameborder=\"0\"></iframe> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML('''\n",
    "<iframe width=\"100%\" height=\"300\" src=\"//jsfiddle.net/fxcjzufw/1/embedded/html,result/\" allowfullscreen=\"allowfullscreen\" frameborder=\"0\"></iframe> '''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## C Basic introduction to Javascript\n",
    "\n",
    "- jsfiddle\n",
    "- jquerry\n",
    "- console"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Python \n",
    "vector=[\"H\",\"e\",\"l\",\"l\",\"o\",\" \",\"w\",\"o\",\"r\",\"l\",\"d\",\"!\"]\n",
    "\n",
    "for letter in vector:\n",
    "    print(letter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "%%javascript\n",
    "\n",
    "var vector=[\"H\",\"e\",\"l\",\"l\",\"o\",\" \",\"w\",\"o\",\"r\",\"l\",\"d\",\"!\"]\n",
    "for (letter in vector) {\n",
    "document.write(vector[letter])\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Dataviz: leveraging javascript libraries\n",
    "- Highcharts\n",
    "- d3js\n",
    "- leafleft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# C. When Data comes in : API, JSON, XLM, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What is an API ?\n",
    "![](http://stats4dev.com/prez/api.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Why using an API\n",
    "\n",
    "- Programmatic access = Automate\n",
    "- Always up to date\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Introduction to the code behind an API\n",
    "\n",
    "http://api.dataspace.fr/?dataset=WDI&indicator=NY.GDP.MKTP.KD.ZG&year=2001&country=FRA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "###  The JSON format\n",
    "\n",
    "JSON datasets are text data. It uses keys to organize the data and is readable by JavaScript. \n",
    "Here is an example of the data structure.\n",
    "```javascript\n",
    "{\n",
    "  \"indicator_value\": {\n",
    "    \"103606\": {\n",
    "      \"AFG\": {\n",
    "        \"2009\": \"0.404\",\n",
    "        \"2010\": \"0.426\",\n",
    "        \"2011\": \"0.428\",\n",
    "        \"2012\": \"0.443\",\n",
    "        \"2013\": \"0.445\"\n",
    "      }\n",
    "    }\n",
    "  },\n",
    "  \"country_name\": {\n",
    "    \"AFG\": \"Afghanistan\"\n",
    "  },\n",
    "  \"indicator_name\": {\n",
    "    \"103606\": \"HDI: Income index\"\n",
    "  }\n",
    "}```\n",
    "\n",
    "To read it easily you can use online editor such as [json editor](http://www.jsoneditoronline.org/)\n",
    "The following request:<span style=\"color:#1174d5;\"> json.indicator_value[\"103606\"].AFG[\"2005\"]</span> will return the value: <span style=\"color:#1174d5;\">0.366</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## C. How to use API\n",
    "- Exemple of a request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"100%\" height=\"400\" src=\"//jsfiddle.net/ThomasRoca/zceqa7jf/embedded/js,result/\" allowfullscreen=\"allowfullscreen\" frameborder=\"0\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('''<iframe width=\"100%\" height=\"400\" src=\"//jsfiddle.net/ThomasRoca/zceqa7jf/embedded/js,result/\" allowfullscreen=\"allowfullscreen\" frameborder=\"0\"></iframe>''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Then it is easy to parse the data to create a dataviz:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## A bit more complex.. \n",
    "## <span style=\"color:#336da9;\"> Visualizing all the WDI from the World Bank!</span>\n",
    "## The Big Browser project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"100%\" height=\"485\" src=\"http://jsfiddle.net/ThomasRoca/1vpypyc9/embedded/result,js,html,css\" allowfullscreen=\"allowfullscreen\" scrolling:\"no\" frameborder=\"0\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('''<iframe width=\"100%\" height=\"485\" src=\"http://jsfiddle.net/ThomasRoca/1vpypyc9/embedded/result,js,html,css\" allowfullscreen=\"allowfullscreen\" scrolling:\"no\" frameborder=\"0\"></iframe>''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Use case 2: Scrap the web !\n",
    "Scrapping the web with [YQL](https://developer.yahoo.com/yql/)(and twitter again)\n",
    "example: [wacky twitter race](https://www.highcharts.com/blog/post/query-api-twitter-wacky-race-using-highcharts/) !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Do it yourself:  Building a Vélib App' !\n",
    "\n",
    "Link API: https://developer.jcdecaux.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<iframe width=\"100%\" height=\"300\" src=\"//jsfiddle.net/ThomasRoca/zceqa7jf/embedded/js,html,result/\" allowfullscreen=\"allowfullscreen\" frameborder=\"0\"></iframe>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('''\n",
    "<iframe width=\"100%\" height=\"300\" src=\"//jsfiddle.net/ThomasRoca/zceqa7jf/embedded/js,html,result/\" allowfullscreen=\"allowfullscreen\" frameborder=\"0\"></iframe>\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## To get further\n",
    "\n",
    "Blog highcharts: \n",
    "- [Open Data API for Chart Creation (Part 1)](http://www.highcharts.com/blog/233-world-bank-open-data-api-highcharts-part1)\n",
    "- [Open Data API for Chart Creation (Part 2)](http://www.highcharts.com/blog/235-open-data-api-for-chart-creation-part-2)\n",
    "- [Way to react to API failure](http://www.highcharts.com/blog/238-way-to-react-to-api-failure)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>table,td,tr,th {border:0px solid black!important}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>table,td,tr,th {border:0px solid black!important}\n",
    "</style>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from IPython.display import HTML\n",
    "from IPython.display import IFrame\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}