makoponi/gist:8864295

## gistfile1.json
{
 "metadata": {
  "name": "Turismo Italia"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "Un'analisi dell'attrattivit\u00e0 turistica dell'Italia"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "- Autore: [Marco Bonifacio](https://plus.google.com/u/0/+MarcoBonifacio/about)\n- Data: 07/02/2014"
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Attivit\u00e0 preliminari"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "L'analisi \u00e8 condotta su dati [UNWTO](http://www2.unwto.org/) distribuiti da [The World Bank](http://www.worldbank.org/) e ottenuti attraverso [Quandl](http://www.quandl.com/).\nIniziamo importando [Pandas](http://pandas.pydata.org) e [Matplotlib](http://matplotlib.org) e settando i grafici all'interno del Notebook."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n%pylab inline",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Populating the interactive namespace from numpy and matplotlib\n"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Importiamo il pacchetto [Quandl](https://pypi.python.org/pypi/Quandl), che ci permette di accedere nativamente alle API del servizio. L'accesso anonimo \u00e8 limitato, la registrazione al sito permette di ottenere un TOKEN da utilizzare per ampliare il numero di richieste quotidiane."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import Quandl as Q\nTOKEN = '*****' # Da sostituire con il TOKEN ottenuto alla registrazione su Quandl",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Gli arrivi turistici internazionali"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Volendo importare le serie storiche di pi\u00f9 paesi, utilizziamo la funzione descritta in questo utile [Notebook](http://nbviewer.ipython.org/url/www.logilab.org/file/187482/raw/quandl-data-with-pandas.ipynb), che consente di estrarre i codici paesi dalla pagina web di Quandl. Dalla stessa pagina sono tratte le altre funzioni di dowload dei dati utilizzate, eventualmente riadattate modificando opportunamente le stringhe di richiesta."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import re\nimport urllib2\n\ndef get_quandl_country_code(url, pattern):\n    \"\"\" Get the country code from an URL and a pattern.\n    \n    Suppose your pattern have the 'country' group name.\n    \"\"\"\n    rawtext = urllib2.urlopen(url).read()\n    regexp = re.compile(pattern)\n    return [m.group('country') for m in regexp.finditer(rawtext)]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Limitiamo la nostra analisi ai paesi del G20: i dati sono disposibili per 382 nazioni, fino a Tuvalu, che \u00e8 lo stato meno frequentato dai turisti (10.000 - 20.000 presenze all'anno)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "URL = 'http://www.quandl.com/society/international-tourism-arrivals-all-countries'\nPATTERN = r'WORLDBANK/(?P<country>[A-Z]+)_ST_INT_ARVL'\ncountry_code = get_quandl_country_code(URL, PATTERN)[:20] # [:20] --> Paesi del G20\nprint 'Nb countries', len(country_code), ': ', country_code",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Nb countries 20 :  ['USA', 'CHN', 'JPN', 'DEU', 'FRA', 'BRA', 'GBR', 'ITA', 'RUS', 'IND', 'CAN', 'AUS', 'ESP', 'MEX', 'KOR', 'IDN', 'TUR', 'SAU', 'ARG', 'ZAF']\n"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Nuova formula per scaricare le serie storiche inserendo via via il codice del paese del G20."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def get_quandl_dataset(code, name=None):\n    \"\"\" Get a Pandas DataFrame from the Quandl website according to the code.\n    \n    name: str or list\n        The name(s) of the col label.\n    \"\"\"\n    df = Q.get(code, authtoken=TOKEN)\n    if name:\n        name = [name] if isinstance(name, str) else name\n        df.columns = pd.Index(name)\n    return df",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "A questo punto, scarichiamo le 20 serie storiche degli arrivi turistici internazionali concatenandole in un unico dataframe."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "code = PATTERN.replace(r\"(?P<country>[A-Z]+)\", \"%s\")\ninbound = pd.concat([get_quandl_dataset(code % name, name) for name in country_code], axis=1)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Token ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/USA_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/CHN_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/JPN_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/DEU_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/FRA_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/BRA_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/GBR_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/ITA_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/RUS_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/IND_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/CAN_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/AUS_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/ESP_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/MEX_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/KOR_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/IDN_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/TUR_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/SAU_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/ARG_ST_INT_ARVL\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/ZAF_ST_INT_ARVL\n"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Diamo un'occhiata alla classifica degli arrivi in base agli ultimi dati disponibili, del 2011. Al primo posto la Francia, con oltre 81 milioni di presenze annue, seguita da Stati Uniti (63 milioni) e Cina (58 milioni). L'Italia \u00e8 quinta (46 milioni), preceduta dalla Spagna (57 milioni); seguono Turchia e gli altri paesi fino a chiudere la classifica con il Brasile (5,4 milioni)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "inbound.loc['2011-12-31', :].order(ascending=False)\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": "FRA    81411000\nUSA    62711000\nCHN    57581000\nESP    56694000\nITA    46119000\nTUR    34038000\nGBR    29306000\nDEU    28374000\nRUS    24932000\nMEX    23403000\nSAU    17498000\nCAN    16014000\nKOR     9795000\nZAF     8339000\nIDN     7650000\nIND     6309000\nJPN     6219000\nAUS     5875000\nARG     5705000\nBRA     5433000\nName: 2011-12-31 00:00:00, dtype: float64"
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Creiamo una funzione per disegnare un grafico a barre della serie degli arrivi internazionali 2011."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def bar_chart(series, title='', xlabel= '', ylabel=''):\n    \"\"\" Plot a bar chart from a Pandas series. \n    \n    title: str\n        The title of the chart.\n    xlabel: str\n        The label of x-axis.\n    ylabel: str\n        The label of y-axis.\n    \"\"\"\n    fig = plt.figure(figsize=(10,10))\n    ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n    ax.set_title(title)\n    x = np.arange(len(series.values))\n    ax.bar(x, series.values)\n    ax.set_xticks(x + 0.4)\n    ax.set_xticklabels(series.index)\n    ax.set_xlabel(xlabel)\n    ax.set_ylabel(ylabel)\n    plt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Disegniamo ora il grafico."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bar_chart(inbound.loc['2011-12-31', :].order(ascending=False), title='Arrivi turistici internazionali 2011', ylabel='Decine milioni')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TrKYNa6Tp3Z5nhjIAAACTsacMAAAgiNhTBgAA4GKeGcpsuFZtS9OGNdI0p0fTrKYNa6Tp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\nlaBKDqgAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x90c44b0>"
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Variazione dei flussi turistici"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Ora proviamo ad analizzare la variazione dei flussi turistici dal 1995 (primo dato disponibile sul DB The World Bank) al 2011. Spiccano i paesi emergenti: Arabia Saudita (+426%), Turchia (+381%), India (+197%), Cina (+187%). Tra le economie sviluppate, notevole il dato tedesco (+91%) e Giapponese (+86%). L'Europa, con gli Stati Uniti, segue: Spagna (+62%), Italia (+49%), USA (+44%), Francia (+36%), Gran Bretagna (+35%). Chiude  la classifica il Canada, unico paese del G20 con una riduzione del flusso turistico (-5%)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "(inbound.loc['2011-12-31', :] / inbound.loc['1995-12-31', :] - 1).order(ascending=False)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": "SAU    4.262556\nTUR    3.805591\nIND    1.970339\nCHN    1.874164\nBRA    1.728780\nKOR    1.609912\nARG    1.492355\nRUS    1.422935\nDEU    0.911093\nJPN    0.859193\nZAF    0.858066\nIDN    0.769195\nESP    0.623540\nAUS    0.576758\nITA    0.485218\nUSA    0.441964\nFRA    0.356104\nGBR    0.349325\nMEX    0.156218\nCAN   -0.054217\ndtype: float64"
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Disegniamo il grafico anche delle variazioni del flusso turistico."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bar_chart((inbound.loc['2011-12-31', :] / inbound.loc['1995-12-31', :] - 1).order(ascending=False), title='Arrivi turistici internazionali: variazione 1995 - 2011', ylabel='Variazione assoluta')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ItHcmJ84FAACwOP6W6EnMsAEAgGCy3HnYAAAAcPZo2GzCxP14K2Q6oUYyzckj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\n",
       "text": "<matplotlib.figure.Figure at 0x8eb78d0>"
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Il peso del turismo sulle esportazioni"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Analizziamo ora il peso del turismo sul totale dell'export dei paesi del G20. I dati sono sempre UNWTO via The World bank, via Quandl."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "URL2 = 'http://www.quandl.com/society/international-tourism-receipts-all-countries'\nPATTERN2 = r'WORLDBANK/(?P<country>[A-Z]+)_ST_INT_RCPT_XP_ZS'\ncountry_code = get_quandl_country_code(URL2, PATTERN2)[:20] # [:20] --> Paesi del G20\nprint 'Nb countries', len(country_code), ': ', country_code",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Nb countries 20 :  ['USA', 'CHN', 'JPN', 'DEU', 'FRA', 'BRA', 'GBR', 'ITA', 'RUS', 'IND', 'CAN', 'AUS', 'ESP', 'MEX', 'KOR', 'IDN', 'TUR', 'SAU', 'ARG', 'ZAF']\n"
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Scarichiamo i relativi dati da Quandl."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "code = PATTERN2.replace(r\"(?P<country>[A-Z]+)\", \"%s\")\ninrecpt = pd.concat([get_quandl_dataset(code % name, name) for name in country_code], axis=1)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Token ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/USA_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/CHN_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/JPN_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/DEU_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/FRA_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/BRA_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/GBR_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/ITA_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/RUS_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/IND_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/CAN_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/AUS_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/ESP_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/MEX_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/KOR_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/IDN_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/TUR_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/SAU_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/ARG_ST_INT_RCPT_XP_ZS\nToken ***** activated and saved for later use.\nReturning Dataframe for "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " WORLDBANK/ZAF_ST_INT_RCPT_XP_ZS\n"
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Verifichiamo i dati 2011. I paesi per i quali il turismo pesa di pi\u00f9 sono Turchia e Spagna (15%), Australia (11%), Sudafrica, USA e Francia (8%-9%). Segue l'Italia (7%) e via via gli altri paesi fino a Brasile (2%) e Giappone (1%)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "inrecpt.loc['2011-12-31', :].order(ascending=False)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": "TUR    15.210521\nESP    15.115124\nAUS    10.567434\nZAF     9.098325\nUSA     8.831108\nFRA     8.103430\nITA     7.449589\nARG     6.098316\nGBR     5.939283\nIDN     4.223467\nIND     4.011339\nCAN     3.651600\nMEX     3.359407\nDEU     2.983771\nRUS     2.969929\nCHN     2.678157\nKOR     2.665438\nSAU     2.481500\nBRA     2.321163\nJPN     1.351666\nName: 2011-12-31 00:00:00, dtype: float64"
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "E creiamo un grafico."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bar_chart(inrecpt.loc['2011-12-31', :].order(ascending=False), title='Peso entrate turistiche internazionali su esportazioni', ylabel='Percentuale')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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p6YU10jSnR9OspolrZFADAABwKPaosUcNAACEEXvUAAAAXIhBzQU4x0/TTU0v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\nUwzUiIiIiHzqC1EnEas76DV5AAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x8ef1530>"
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "I siti World Heritage dell'Unesco"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Proviamo ora ad analizzare il peso del turismo correlandolo al numero di siti nazionali classificati come World Heritage dall'UNESCO. I dati sono reperibili nella pagina web della [World Heritage Convention](http://whc.unesco.org/en/statesparties/stat/) e limitiamo come prima il campo d'azione ai paesi del G20."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "whc_dict = {'USA': 21, 'CHN': 45, 'JPN': 17, 'DEU': 38, 'FRA': 38, 'BRA': 19, 'GBR': 28, 'ITA': 49, 'RUS': 25, 'IND': 30, 'CAN': 17, 'AUS': 19, 'ESP': 44, 'MEX': 32, 'KOR': 10, 'IDN': 8, 'TUR': 11, 'SAU': 2, 'ARG': 8, 'ZAF': 8}\nwhc = pd.DataFrame.from_dict(whc_dict, orient='index')\nwhc.columns = ['WHC']\nwhc['WHC'].order(ascending=False)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": "ITA    49\nCHN    45\nESP    44\nDEU    38\nFRA    38\nMEX    32\nIND    30\nGBR    28\nRUS    25\nUSA    21\nBRA    19\nAUS    19\nCAN    17\nJPN    17\nTUR    11\nKOR    10\nARG     8\nIDN     8\nZAF     8\nSAU     2\nName: WHC, dtype: int64"
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Anche in questo caso, possiamo fare un grafico. L'Italia \u00e8 la nazione con il maggior numero di siti tutelati dall'Unesco (49), seguita da Cina, Spagna, Germania e Francia. Il paese del G20 che chiude questa classifica \u00e8 l'Arabia Saudita, con 2 siti."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bar_chart(whc['WHC'].order(ascending=False), title='Siti World Heritage per nazione', ylabel='# siti')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mDWukaU6PpvubK1asqHUGiuiZtcLCQr3yyivyer2Kj4/Xf/3Xf2n//v3Kyclx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\nULjyu57lAwAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x8ebfcd0>"
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Un'analisi complessiva"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Ora aggiungiamo qualche dato turistico al dataframe dei siti Unesco."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "whc['Inbound'] = inbound.loc['2011-12-31', :]\nwhc['Ingrowth'] = (inbound.loc['2011-12-31', :] / inbound.loc['1995-12-31', :] - 1)\nwhc['Inreceipts'] = inrecpt.loc['2011-12-31', :]\nwhc",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>WHC</th>\n      <th>Inbound</th>\n      <th>Ingrowth</th>\n      <th>Inreceipts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>SAU</th>\n      <td>  2</td>\n      <td> 17498000</td>\n      <td> 4.262556</td>\n      <td>  2.481500</td>\n    </tr>\n    <tr>\n      <th>FRA</th>\n      <td> 38</td>\n      <td> 81411000</td>\n      <td> 0.356104</td>\n      <td>  8.103430</td>\n    </tr>\n    <tr>\n      <th>USA</th>\n      <td> 21</td>\n      <td> 62711000</td>\n      <td> 0.441964</td>\n      <td>  8.831108</td>\n    </tr>\n    <tr>\n      <th>JPN</th>\n      <td> 17</td>\n      <td>  6219000</td>\n      <td> 0.859193</td>\n      <td>  1.351666</td>\n    </tr>\n    <tr>\n      <th>ZAF</th>\n      <td>  8</td>\n      <td>  8339000</td>\n      <td> 0.858066</td>\n      <td>  9.098325</td>\n    </tr>\n    <tr>\n      <th>TUR</th>\n      <td> 11</td>\n      <td> 34038000</td>\n      <td> 3.805591</td>\n      <td> 15.210521</td>\n    </tr>\n    <tr>\n      <th>ESP</th>\n      <td> 44</td>\n      <td> 56694000</td>\n      <td> 0.623540</td>\n      <td> 15.115124</td>\n    </tr>\n    <tr>\n      <th>DEU</th>\n      <td> 38</td>\n      <td> 28374000</td>\n      <td> 0.911093</td>\n      <td>  2.983771</td>\n    </tr>\n    <tr>\n      <th>CHN</th>\n      <td> 45</td>\n      <td> 57581000</td>\n      <td> 1.874164</td>\n      <td>  2.678157</td>\n    </tr>\n    <tr>\n      <th>ITA</th>\n      <td> 49</td>\n      <td> 46119000</td>\n      <td> 0.485218</td>\n      <td>  7.449589</td>\n    </tr>\n    <tr>\n      <th>IDN</th>\n      <td>  8</td>\n      <td>  7650000</td>\n      <td> 0.769195</td>\n      <td>  4.223467</td>\n    </tr>\n    <tr>\n      <th>AUS</th>\n      <td> 19</td>\n      <td>  5875000</td>\n      <td> 0.576758</td>\n      <td> 10.567434</td>\n    </tr>\n    <tr>\n      <th>GBR</th>\n      <td> 28</td>\n      <td> 29306000</td>\n      <td> 0.349325</td>\n      <td>  5.939283</td>\n    </tr>\n    <tr>\n      <th>CAN</th>\n      <td> 17</td>\n      <td> 16014000</td>\n      <td>-0.054217</td>\n      <td>  3.651600</td>\n    </tr>\n    <tr>\n      <th>ARG</th>\n      <td>  8</td>\n      <td>  5705000</td>\n      <td> 1.492355</td>\n      <td>  6.098316</td>\n    </tr>\n    <tr>\n      <th>IND</th>\n      <td> 30</td>\n      <td>  6309000</td>\n      <td> 1.970339</td>\n      <td>  4.011339</td>\n    </tr>\n    <tr>\n      <th>RUS</th>\n      <td> 25</td>\n      <td> 24932000</td>\n      <td> 1.422935</td>\n      <td>  2.969929</td>\n    </tr>\n    <tr>\n      <th>KOR</th>\n      <td> 10</td>\n      <td>  9795000</td>\n      <td> 1.609912</td>\n      <td>  2.665438</td>\n    </tr>\n    <tr>\n      <th>MEX</th>\n      <td> 32</td>\n      <td> 23403000</td>\n      <td> 0.156218</td>\n      <td>  3.359407</td>\n    </tr>\n    <tr>\n      <th>BRA</th>\n      <td> 19</td>\n      <td>  5433000</td>\n      <td> 1.728780</td>\n      <td>  2.321163</td>\n    </tr>\n  </tbody>\n</table>\n<p>20 rows \u00d7 4 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": "     WHC   Inbound  Ingrowth  Inreceipts\nSAU    2  17498000  4.262556    2.481500\nFRA   38  81411000  0.356104    8.103430\nUSA   21  62711000  0.441964    8.831108\nJPN   17   6219000  0.859193    1.351666\nZAF    8   8339000  0.858066    9.098325\nTUR   11  34038000  3.805591   15.210521\nESP   44  56694000  0.623540   15.115124\nDEU   38  28374000  0.911093    2.983771\nCHN   45  57581000  1.874164    2.678157\nITA   49  46119000  0.485218    7.449589\nIDN    8   7650000  0.769195    4.223467\nAUS   19   5875000  0.576758   10.567434\nGBR   28  29306000  0.349325    5.939283\nCAN   17  16014000 -0.054217    3.651600\nARG    8   5705000  1.492355    6.098316\nIND   30   6309000  1.970339    4.011339\nRUS   25  24932000  1.422935    2.969929\nKOR   10   9795000  1.609912    2.665438\nMEX   32  23403000  0.156218    3.359407\nBRA   19   5433000  1.728780    2.321163\n\n[20 rows x 4 columns]"
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Creiamo un grafico a bolle per analizzare le relazioni tra questi dati. Riadattiamo a tale scopo le funzioni utilizzate in questo interessante [post](http://glowingpython.blogspot.it/2011/11/how-to-make-bubble-charts-with.html)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def bubble_chart(df, x=0, y=1, r=2, c=3, title='', xlabel='', ylabel=''):\n    \"\"\" Plot a bubble chart.\n    \n    x: int\n        Column of df for x data.\n    y: int\n        Column of df for y data.\n    r: int\n        Column of df for area data.\n    c: int\n        Column of df for color data.\n    title: str\n        The title of the chart.\n    xlabel: str\n        The label of x-axis.\n    ylabel: str\n        The label of y-axis.\n    \"\"\"\n    fig = plt.figure(figsize=(10,10))\n    ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n    ax.set_title(title)\n    dx = df.iloc[:, x]\n    dy = df.iloc[:, y]\n    dr = df.iloc[:, r] * 2000 # La costante aggiusta l'area delle bolle in modo che sia chiaramente visibile\n    dc = np.log(df.iloc[:, c]) # Il logaritmo serve per modificare la scala di colori riducendo le tonalit\u00e0 scure\n    sct = ax.scatter(dx, dy, c=dc, s=dr)\n    sct.set_alpha(0.3)\n    for t in df.index:\n        ax.text(df.loc[t, :].iloc[x], df.loc[t, :].iloc[y], t, ha='center', va='center')\n    ax.set_xlabel(xlabel)\n    ax.set_ylabel(ylabel)\n    plt.show()    \n    ",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Infine disegniamo il grafico. Sull'asse delle ascisse abbiamo il numero si siti classificati dall'Unesco come World Heritage, sull'asse delle ordinate gli arrivi internazionali del 2011, mentre l'area delle bolle \u00e8 proporzionale alla variazione dei flussi turistici 1995 - 2011 e il colore rappresenta il peso sulle esportazioni (ordinato in senso crescente dai colori freddi ai colori caldi). In questo contesto l'Italia appare \"sottovalutata\" come meta turistica rispetto al patrimonio Unesco e caratterizzata da una crescita dei flussi turistici inferiore alla media, mentre il peso del turismo sulle esportazioni \u00e8 superiore alla media. Altri paesi appaiono \"sottovalutati\", ma - ad eccezione del Regno Unito e soprattutto del Canada - presentano tassi di sviluppo ben superiori a quello italiano."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bubble_chart(whc, title='Analisi flussi turistici internazionali', xlabel='# Siti World Heritage Unesco', ylabel='Arrivi internazionali 2011 (decine milioni)')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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N7kJCYI4ymKMcZimDOcpgjsaImy2kiIiIiKg9QwdqbUV11DujR482ugsJgTnKYI5ymKUM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gXM9RtIjSfp/N8n2PSO3VS21AAw1jVMYiQ+e6JmBHDCNVgMENQLvyS0keDmGvMyU7OzMk1tMLef\nbIvpUZusUQOoGO2CxMwi9KbqHVzqe47ycBNdAz8DFKFAJbGJa1REVgPgujavX/yfhAKVaK3R2qEi\nshrHTaO1xjIC/ODH/0B76zaa6trmb5DroFJJdGhxs0U1EE0oastynzX4Mei65xfv40d/8wTNm9th\nWqKubioiHLhyld3b12etHapIkjQhCp3UqBWBvP2Gs8iZYxE3SJkO4oZqcMtu7dFKJV2uXk7x4Dtr\neeP4rcNmylC0rA1QNt7HzqrLDLxykcbbWqaSNIDKtlrqNjfdenIjgBOpxVAGrWvXLfIXu5GlKaA9\n4nB3XSorSdrNH843m7CXNmRs6FurrkLBKsLBKsYmugFFXeUGtrT9EsFAJT3/f3v3HhxleT58/Psc\n9ryb3ZwIJAECBKIGgaKItP6seCgV9Z1Oq7/+RqvvVNpO0dYRtYK+WrVSkQpSqdqZWg+tgj362mr9\nYctbFalSHWfwEJDAL8ZAOCQcAuS4u89zv3+sWRNy2l2S7IHrM+OM2ew+++zF5tlr7/u67+vQe/H7\nOU0/U8u+ytSyr2KrKB1dhzANF4Wh8fz9X+vRNI2GvTsozB839Imo5Irgj3el51KlpSFVC40tJDSu\nmIYPawmNK47f3j26qpSi5p/v0HmsjfIzpozYeehpSVNHRsZeI7OMxDE9ToHJBJE+iZdih6JeDLRY\nsuYrBu3zt+bOmg4mVbnJyzfx+gz27+ldYB+NKHbXdVE01kGhqxXnsSYmT7RxG0Psum86sTwFoOl4\n7QRq0+IUuqYodVvMzQ9TdRKb2Q7F4xk4EYvaGpbq/5yVstD0z0YHB0j2et5aHKomYnUAiqBvfOy5\nXQXYdqRPsqJpGqbuImrFpoCrK/+DotA4SoomYFlRtmzdkNiLS0JHJD1Jg6FG+Hl7Hr7Hv9Osr36J\n9iO9SwfefeH/xbbnuPeXHGzYx6VLrkUfoflgjdxK1ITIZlKjlgMyt24g8REmHY0Cy8dBoxXbmYcy\nPejtzWiRDrZvbWfOf8SmLKtmeNi+tZ3ZX/LTcijKM2v2c/RwlImVbqac7vnsWIpC53GmFuzkcNjP\nnx7bTVeXIlhRSMVFp4OmoVx52Gbs/i5l4FYODtU3UlhRNuA5AngNi3JvK2V6B84UFkAkKxBwcuRI\n/62XBotutL0Zw12A6QoxZsaNfR7jdPiZWn5p/HaPM8TpE77G9ob/yyf734jf29TdGIYTS32e9Nq2\nRdjqIOSMbSJsOmxajh/kki/9F3n+Ampqtwz9wpLcC8RKU+tJlxrZf+Pr1twOQKAwxNfv+l789oLy\nEq5//P/Ee32ef93/GtHzOJFTGTmVqGXuNTK7SBzTQ2rUxAhK7tu+y3ZQgI/DRhtKd2D5S+k6fJjd\n/7OHg/sjaICtYp/xs7/oJ1QYq1HraLNY/8sm9u0OM268k8KxDg40hpla7aHA2cr3luTz0dYudmw7\nTom/k2PmGNotDyhwKAO/7RnwnNy6RcARIegIE3SEKXDFRvP0rtHZ2ysQGHhDYHuA0Z62A+/Stn8L\nwYqFfR8zyAbDx9p2AxpnTvomAO2dB6nb/xqWFSZqdWLZXexs3EA40opCUZA3FUWU+j0fUJQ/Dl03\ncLtiW60opQaZttVQZnItrxJYBDkiPPapeYn0JFCrKYQYHdLrMwdk7DecAablBuOxnRQAR4w2bGDH\nh52cPm8sX/lmKVrXMbRoJ8//soljLZ/Xv3l8Bud/Ncib/93Cf35vDLO/6OfZXxxg0jQ3ZRUu0AyU\n5sTtgSmlGtCMrcAKe3F2hbDsMDYaldMDGNpRdE3hNiwCZgSX0d9QzuhVDASDyfdb9JXMwVcyp9/f\nRR0DJ6UtbQ1oPaacve4iNDQOH/+EcQWz2XMwNlLmNP1YKkxX5DjBvBA767YTDnfSeKAOTdPQdJ3m\nw42MKRxgUYHDlTVLOJ0YmGhE07T8MV29Pt05lqBm7DUyy0gc0yO3/hpFhklu1KSbx3ZiKoPDZhs7\n3m1izqUTsJ0BcAbQrC6mnqXY8kbLZ93XNVCKqdO9/Ovvx9jX0MW4Ci9X/O9S3nj5MK1Hj+LNc+EJ\nODj3igogVn+Tb3sI6E7wDL15bR8qtdeVCr/ficdj9tvvU09hWV7EdA/4u8njLqSm/k/xnzvDx9A0\ng/Lic4hE2+KLCSC219qBw+8zruQiAC6/cBG6Hku+GvbuoPHA/wyYqCn3YJ0f+qencRbObZu06kPU\nO+YYb44lakJkM6lRywEZWzdgD9wzcygOZVASCbBoybkcNzrj4xnKcDFrQSWzFsR+jmKDstGU4lv3\nTQBNJ6oZlAThP6f3XRHnxCA/6sPRT+3RrtpmKqcV97n9RJpK/XWlorzcz86dfXuMppK72KYL23Cg\nW/0nHray2Nn4+WKA8cVz41OYPZ8v6CunqeUjmo/soCi/NJ6kAThMFwcONmDbdq/b48/hTT5+6RyA\n8yqTVtKTqO2rrR/1UTUNcI9wbd5oy9hrZJaROKaHfG0SI0f5iE0TploJrpFne/AoJ216F+16Vz9H\n0kHTGWpxngsDn+XGazs46d2xbP/JPT5JZWV+6uqOYlm9R9BMXWFoCivJlYmdnny8rf33Su2uTzvR\niYsPAGaffgXFxX2nUh2mk6+ef23/T647sP3J93d1m+nb1MtjO8BIYeQ1S7mUkcQKaCHESJN91HJA\npn7D0dDQVPJ9PE/kUAYhy8vYSIiQ5cGtzCFXpGmAAwO/7aIkmkdxJA+v7WSwJC2R0TQA1Ogmak6n\nQUlJ/3H0GMk3De/0hFDDMJeYl9f/FPBgvT7tvIKU5jHz3Gla9gkEbAdmmi6V6ahRC1kDT49nq0y9\nRmYbiWN6yIiaGFl2AIyhW0klQkfDb7vx27EPEgubsB7FRqE+mxzV0TCVgUMZI7dZ6SiPqAFUVgZp\nbm4nEumdsHhMi9boEE3kT6B0k05PAZ62/rtAJCKQ58DlSjJ50U2sUILJcA8akOdO34iahkaB5aLp\nFBhV09HIt0avBlMIMbS0jqhJjdrw2LlzZ7pPYWB2aOj7pMhAx2M78dmueALntV04lZlSkrartnnI\n+2gqgEZyidFwcLtNqqr6Thl6UxhRA2j3FmOZya8oBTAdOkWFA68ePXh4b7+320Wl4Eg+dj6nSvsi\n0fyoOy27inXvozZagpYzbaOHIymjr5FZROKYHrn3Fykyi11Eqqs/M5KVQHukEVJa6u9TE+Y1k2vF\nFKfrtAZKUyrXKy5y92wckRDlzcMKJtLwvq90Tnt2c2IQsFNLbLNJQQ5OewqR7aRGLQdkct2AhoYW\nHZvu00jI0DVqJlj99AsdRdXVhfh8n49KuQwbU0stkYk6vbQFkvu3yc934fEOXjHRp0bN6SFaMjHZ\n04sLpnHas6d0JDGjWaPmVSZeNfqjxaMhk6+R2UTimB4yoiZGnjWOk15pmQE0aywa6d22wOk0mD17\nDB7P58lSvis8yCMG1+kpoC1QktB9g0En+QVJjo46PURKp4CZWjmsoUFJIMVRw2EWsJ34cnjH/uLo\nwNPZQoj0kRq1HJDpdQMaLjQ7tWmv0TR4jZoG1sCrGUeTx2Ny9tkl8ZG1IlcXyfRVPVGnt5DWYBlq\nkN6l+SEXhUWJjSh116gpX5BIWWVKdWndSgIWzgzKjcoi/lHtgTlaNWoh20WenUMlCifI9GtktpA4\npoeMqInREZ1ANr/dNGsMmsqcEQePx+Scc8ZSWurDZdgEHKktKujW5Q7SUjCZiKv3ilbThDElXvIL\nk/gQ1w2skglESyenPJLWrTyUGaNp3VzKZEw0+c4KmcxEZ1zk5LfREUKMDOn1mQOyoW5AUwGwylFG\nQ7pPZUAD16g5Idq3y0G6ORw606cXUVLipXPrMT4+dHL1Rbbh4FhoAo6u43g6DhNyhCkqdGOYCY4g\nGQ7svEKCk6Zjmydf6xR0K0KezKhP66nY8nDc6KJNO7nkOBGjUaNWFvHl5ErPnrLhGpkNJI7pkUGT\nCiLnRSei6YdQWlu6zyQpWmRqWrbkSFRxsZdLL3DTscVib1NXn73WkuUsLKCwuIyQF7TjR1Bd7Wid\n7XBi2ylNB6cb2+VBefOwfcFhbcpZHsys0bSeyiJ+djmPYqepWftwCVm5PeUpRC6QXp85IFv6r2no\nqEgVOLeSelupkdNfr0/NHoNmF6XpjBLncOjMn2WytdFPS0sXBw920NYWQSWYR5gOnVDQRVGRG/dn\nCxUUYBX2WBUajaBZduw3uo4yHP0mZocaayksm3ZSryfPpSjN4ETNpUzGRX00mq0j+jwj2evTpQxK\no6fGlGe2XCMzncQxPWRETYwqTQUgOgFl1qf7VIamXBCpTPdZJGxMwGZcng2ai1C+C2VDR0eEjo4o\n7e1RLEth2591cNA1XC4Dj9fE63HgTKTLgOlgNBY96hpUj42gZfhC4QLLjYXNfrM93aeSNKfSqQjn\nSU9PIbKA1KjlgGz7hqNZE0HrQhn70n0qvfQeTXOiRWZk9JRnf6rGRDjc7qLLis1Men0OvD4Ho7nm\n9mRH0yYXRglkyN5pQym2vNiaGrH2UiMxmuZApyISxJnmrWZGU7ZdIzOVxDE95OuUSAstOg3NTu/m\nsQMz0cLT0VT2re5zmnB6SWToO2aooFsxqSBzpzz7UxL1UZIlK0GdSmdyOIhLnTpJmhDZTvZRywHZ\nureNFjkdLY0tmU4U20fNiRaeGZuizVJjAjaleelLdg411qb0OCNLpjz7M8byUhr1DfsFdTj3UXMr\ng8nhIM5TMEnL1mtkppE4pofUqIm00qLTQLlQ5qeczKatw3IuyoMWnpVR+6Wl6oySKJ1RjcPt2TFo\nrmswszSC3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       "text": "<matplotlib.figure.Figure at 0x8f68110>"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Crediti"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Parte delle funzioni e del codice utilizzato per l'interazione con le API di Quandl \u00e8 stato preso o riadattato dal lavoro di [Damien Garaud](http://nbviewer.ipython.org/url/www.logilab.org/file/187482/raw/quandl-data-with-pandas.ipynb). Qui il [blog post](http://www.logilab.org/186716) cui fa riferimento il suo Notebook.\n\nIl grafico a bolle \u00e8 stato ispirato dal codice di [The Glowing Python](http://glowingpython.blogspot.it/2011/11/how-to-make-bubble-charts-with.html)."
    }
   ],
   "metadata": {}
  }
 ]
}
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