Q048-Combinations.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Q048 - Il risultato della tua attivit\u00e1 \u00e9 un'innovazione? Se s\u00ed, di che tipo?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# -*- coding: UTF-8 -*-\n",
      "\n",
      "# Render our plots inline\n",
      "%matplotlib inline \n",
      "\n",
      "import pandas as pd\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import seaborn\n",
      "import shutil\n",
      "\n",
      "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier, overridden by seaborn\n",
      "pd.set_option('display.max_columns', None) # Display all the columns\n",
      "plt.rcParams['font.family'] = 'sans-serif' # Sans Serif fonts for all the graphs\n",
      "\n",
      "# Reference for color palettes: http://web.stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html\n",
      "\n",
      "# Change the font\n",
      "matplotlib.rcParams.update({'font.family': 'Source Sans Pro'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load csv file first\n",
      "data = pd.read_csv(\"data/results-makers-40.csv\", encoding=\"utf-8\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Check data\n",
      "#data[0:4] # Equals to data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Range: Q048[SQ001] - Q048[SQ005] - Q048[other]\n",
      "\n",
      "innovation_columns = ['Q048[SQ001]','Q048[SQ002]','Q048[SQ003]','Q048[SQ004]','Q048[SQ005]']\n",
      "innovation_options = ['Innovazione di prodotto',\n",
      "                  'Innovazione di processo (progettazione, produzione, distribuzione)',\n",
      "                  'Innovazione di sistema-prodotto (prodotto, servizio e processo)',\n",
      "                  'Innovazione tecnologica',\n",
      "                  'Innovazione sociale']\n",
      "innovation = data[innovation_columns]\n",
      "innovation.replace(u'S\u00ec', 'Si', inplace=True) # Get rid of accented characters \n",
      "innovation_other = data['Q048[other]'].str.lower().value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:10: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#innovation[0:4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Combinations..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create all the possible combinations from the main options\n",
      "# See http://stackoverflow.com/questions/17176887/python-get-all-permutation-of-a-list-w-o-repetitions\n",
      "\n",
      "import itertools \n",
      "\n",
      "all_combinations = {}\n",
      "all_combinations_columns = []\n",
      "\n",
      "for i in range(1, len(innovation_columns)+1):\n",
      "    comb = list(itertools.combinations(innovation_columns, i))\n",
      "    for k in comb:\n",
      "        #\u00a0Each combination\n",
      "        all_combinations[k] = {}\n",
      "        all_combinations[k][\"col_list\"] = list(k)\n",
      "        #\u00a0Build the string and boolean list of each combination\n",
      "        comb_list = []\n",
      "        comb_bool_list = []\n",
      "        # Put default False value\n",
      "        for l in innovation_columns:\n",
      "            comb_bool_list.append(False)\n",
      "        for j in k:\n",
      "            pos = innovation_columns.index(j) # Get position\n",
      "            comb_list.append(innovation_options[pos])\n",
      "            comb_bool_list[pos] = True\n",
      "        all_combinations[k][\"list\"] = comb_list\n",
      "        all_combinations[k][\"bool_list\"] = comb_bool_list\n",
      "        all_combinations[k][\"str\"] = \", \".join(comb_list)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#\u00a0Check which combinations correspond each row\n",
      "str_values = []\n",
      "for i in innovation.index:\n",
      "    current_bool_list = list(innovation.ix[i].isin([\"Si\"]))\n",
      "    for i in all_combinations:\n",
      "        if current_bool_list == all_combinations[i][\"bool_list\"]:\n",
      "            str_values.append(all_combinations[i][\"str\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Add combination column\n",
      "innovation[\"Combination\"] = pd.Series(str_values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:2: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Gather data\n",
      "resulting_combinations = innovation[\"Combination\"].value_counts(dropna=False)\n",
      "resulting_combinations_percentage = innovation[\"Combination\"].value_counts(normalize=True,dropna=False)*100"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#\u00a0Plotting the first 10 values of the most popular combinations\n",
      "resulting_combinations[0:10].plot(kind='bar',figsize=(20,10),rot=90)\n",
      "plt.title(u\"Il risultato della tua attivit\u00e1 \u00e9 un'innovazione? Se s\u00ed, di che tipo? Combinazioni\", fontsize=18, y=1.02)\n",
      "plt.ylabel(\"Persone\", fontsize=16)\n",
      "plt.xlabel(\"Combinazioni\", fontsize=16)\n",
      "plt.savefig(\"svg/Q048-Combinazioni.svg\")\n",
      "plt.savefig(\"png/Q048-Combinazioni.png\")\n",
      "plt.savefig(\"pdf/Q048-Combinazioni.pdf\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bb9XevXvldrt16aWXasCAAaYjwQfv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\nAIBzJigoSHv27NGFF16o6upq2e125ebmSuKWV1gDh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       "text": [
        "<matplotlib.figure.Figure at 0x11264cc10>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%capture output\n",
      "\n",
      "# Save the output as a variable that can be saved to a file\n",
      "# Data of the combinations\n",
      "print \"Data:\"\n",
      "print resulting_combinations\n",
      "print\n",
      "# Data of the combinations: percentage\n",
      "print \"Data %:\"\n",
      "print resulting_combinations_percentage"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Save+show the output to a text file\n",
      "%save Q048-Combinazioni.py str(output)\n",
      "shutil.move(\"Q048-Combinazioni.py\", \"text/Q048-Combinazioni.txt\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The following commands were written to file `Q048-Combinazioni.py`:\n",
        "Data:\n",
        "NaN                                                             27\n",
        "Innovazione di prodotto                                         19\n",
        "Innovazione di processo (progettazione, produzione, distribuzione)    13\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione)     9\n",
        "Innovazione di processo (progettazione, produzione, distribuzione), Innovazione sociale     8\n",
        "Innovazione di sistema-prodotto (prodotto, servizio e processo)     8\n",
        "Innovazione sociale                                              7\n",
        "Innovazione di prodotto, Innovazione tecnologica                 6\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione tecnologica     5\n",
        "Innovazione tecnologica                                          4\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione tecnologica, Innovazione sociale     4\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione sociale     3\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo)     3\n",
        "Innovazione di prodotto, Innovazione sociale                     3\n",
        "Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione sociale     2\n",
        "Innovazione tecnologica, Innovazione sociale                     2\n",
        "Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione sociale     2\n",
        "Innovazione di processo (progettazione, produzione, distribuzione), Innovazione tecnologica     2\n",
        "Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione tecnologica     1\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione tecnologica     1\n",
        "Innovazione di prodotto, Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione sociale     1\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione tecnologica, Innovazione sociale     1\n",
        "Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo)     1\n",
        "Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione tecnologica, Innovazione sociale     1\n",
        "Innovazione di prodotto, Innovazione di sistema-prodotto (prodotto, servizio e processo)     1\n",
        "dtype: int64\n",
        "\n",
        "Data %:\n",
        "NaN                                                             20.149254\n",
        "Innovazione di prodotto                                         14.179104\n",
        "Innovazione di processo (progettazione, produzione, distribuzione)     9.701493\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione)     6.716418\n",
        "Innovazione di processo (progettazione, produzione, distribuzione), Innovazione sociale     5.970149\n",
        "Innovazione di sistema-prodotto (prodotto, servizio e processo)     5.970149\n",
        "Innovazione sociale                                              5.223881\n",
        "Innovazione di prodotto, Innovazione tecnologica                 4.477612\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione tecnologica     3.731343\n",
        "Innovazione tecnologica                                          2.985075\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione tecnologica, Innovazione sociale     2.985075\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione sociale     2.238806\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo)     2.238806\n",
        "Innovazione di prodotto, Innovazione sociale                     2.238806\n",
        "Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione sociale     1.492537\n",
        "Innovazione tecnologica, Innovazione sociale                     1.492537\n",
        "Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione sociale     1.492537\n",
        "Innovazione di processo (progettazione, produzione, distribuzione), Innovazione tecnologica     1.492537\n",
        "Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione tecnologica     0.746269\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione tecnologica     0.746269\n",
        "Innovazione di prodotto, Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione sociale     0.746269\n",
        "Innovazione di prodotto, Innovazione di processo (progettazione, produzione, distribuzione), Innovazione tecnologica, Innovazione sociale     0.746269\n",
        "Innovazione di processo (progettazione, produzione, distribuzione), Innovazione di sistema-prodotto (prodotto, servizio e processo)     0.746269\n",
        "Innovazione di sistema-prodotto (prodotto, servizio e processo), Innovazione tecnologica, Innovazione sociale     0.746269\n",
        "Innovazione di prodotto, Innovazione di sistema-prodotto (prodotto, servizio e processo)     0.746269\n",
        "dtype: float64\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}