Spider_Diagramm_Fragebogen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook zur Auswertung des Data-Literacy-Fragebogens mit Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das Data Literacy - Modell hilft Einzelpersonen und Organisation dabei, ihre Fähigkeiten beim Umgang mit Daten einzuschätzen. Feedback zum Modell ist jederzeit willkommen! Wir nutzen das Modell, um unsere Workshops besser auf die Bedürfnisse unserer Partner auszurichten und die Wirkung unserer Aktivitäten zu messen. Den <a  href=\"https://docs.google.com/a/okfn.de/forms/d/15SU7FVi8VU7OyVE2CSz9aLxUSWkN9ob9vz60vOxueVo/edit\" />Data-Literacy Fragebogen </a> könnt ihr hier finden. In diesem Notebook informieren wir über die Auswertungsschritten und stellen ein Script bereit, mit dem sich ein Bild über den Data-Literacy Stand einer Organisation erstellen lässt. Das Script basiert auf der Programmiersprache Python (3.6)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zunächst müssen wir die Libraries <a  href=\"http://pandas.pydata.org/\">Pandas</a>, <a  href=\"http://www.numpy.org/\"> Numpy</a> und <a  href=\"http://matplotlib.org/\">Matplotlib</a> importieren, die wir für die Auswertung und die Visualisierung benötigen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_answer = pd.read_excel(io=\"../Data_Literacy_Matrix/Answers/Fragebogen Data Literacy-Modell (Antworten).xlsx\")\n",
    "df_matrix = pd.read_excel(io=\"../Data_Literacy_Matrix/Matrix/D_Literacy_matrix.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Über die \"read_excel\"-Funktion von Pandas können wir die Antworten auf den Fragebogen und das Modell des Fragebogens als DataFrame importieren. <br> In dem DataFrame df_answer sind die Antworten enthalten, in df_matrix die Punkte-Skala für die jeweiligen Antworten.\n",
    "<br>\n",
    "<br>\n",
    "Die Excel-Datei mit den Antworten muss dafür in den Ordner \"Answers\" abgelegt werden."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1)   Datentransformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1)   Antworten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_answer = df_answer.ix[:,16:36]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_answer.rename(columns={'Welche Aussage beschreibt am besten die Kultur in deiner Organisation hinsichtlich der Arbeit mit Daten?':'culture',\n",
    "       'Welche Aussage beschreibt am besten den Umgang deiner Organisation mit Datenethik und Datenschutz?':'protection',\n",
    "       'Welche Aussage beschreibt am besten deine Fähigkeiten, Fragen zu formulieren, um sinnvolle Antworten in Daten zu finden?':'questions',\n",
    "       'Welche Aussage beschreibt am besten deine Ansätze, Daten zu finden?':'finding',\n",
    "       'Welche Aussage beschreibt am besten deine Ansätze, Daten zu bekommen?':'getting',\n",
    "       'Welche Aussage beschreibt am besten deine Ansätze, die Qualität einer Datenquelle zu prüfen?':'verify',\n",
    "       'Welche Aussage beschreibt am besten deine Ansätze, Daten zu „bereinigen“?':'clean',\n",
    "       'Welche Aussage beschreibt am besten deine Ansätze bei der Datenanalyse? ':'analyse',\n",
    "       'Welche Aussage beschreibt am besten deine Ansätze der Datenvisualisierung?':'visualize',\n",
    "       'Welche Aussage beschreibt am besten deine Ansätze, die Ergebnisse deiner datenbezogenen Arbeit zu kommunizieren?':'communicate',\n",
    "       'Welche Aussage beschreibt am besten deine Ansätze, Ergebnissen aus vorliegenden Daten zu interpretieren?':'interpretation'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zunächst nehmen wir uns nur die Antworten vor, die sich auf die Data-Literacy Skala beziehen (Daten finden, bekommen, verifizieren, säubern, analysieren, visualisieren, kommunizieren und interpretieren). Ein kurzer Blick in die Antworten zeigt: die Spaltenüberschriften sind sehr lang, da sie die gesamten Fragen beinhalten (Das wird automatisch bei einem Google-Form  generiert). Mit der \"Rename\"-Funktionen benennen wir diese in kürzere Überschriften um."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>culture</th>\n",
       "      <th>protection</th>\n",
       "      <th>questions</th>\n",
       "      <th>finding</th>\n",
       "      <th>getting</th>\n",
       "      <th>verify</th>\n",
       "      <th>clean</th>\n",
       "      <th>analyse</th>\n",
       "      <th>visualize</th>\n",
       "      <th>communicate</th>\n",
       "      <th>interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Daten werden nicht als Quelle von Unsicherheit...</td>\n",
       "      <td>Konsequente, organisationsweite Richtlinien fü...</td>\n",
       "      <td>Möglichkeit, Fragen zu formulieren, die durch ...</td>\n",
       "      <td>Die fortgeschrittene Nutzung von Suchmaschinen...</td>\n",
       "      <td>Daten werden als Datei heruntergeladen. Datenf...</td>\n",
       "      <td>Best Practices bestehen, Mitarbeiter/innen wer...</td>\n",
       "      <td>Bewusstsein besteht, dass Daten nicht nach Spe...</td>\n",
       "      <td>Einfache Nutzung von Tabellen, um Informatione...</td>\n",
       "      <td>Balken- und Kuchendiagramme. Grundlegende Bere...</td>\n",
       "      <td>Statische Visualisierungen unterstützen einfac...</td>\n",
       "      <td>Ergebnisse werden stetig hinterfragt und kriti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Daten werden nicht als Quelle von Unsicherheit...</td>\n",
       "      <td>Konsequente, organisationsweite Richtlinien fü...</td>\n",
       "      <td>Ganze Projekte basieren auf vielschichtigen Fr...</td>\n",
       "      <td>Die fortgeschrittene Nutzung von Suchmaschinen...</td>\n",
       "      <td>Daten werden als Datei heruntergeladen. Datenf...</td>\n",
       "      <td>Best Practices bestehen, Mitarbeiter/innen wer...</td>\n",
       "      <td>Bewusstsein besteht, dass Daten nicht nach Spe...</td>\n",
       "      <td>Einfache Nutzung von Tabellen, um Informatione...</td>\n",
       "      <td>Pivot-Tabellen, Zusammenfassungen, Histogramme...</td>\n",
       "      <td>Statische Visualisierungen unterstützen einfac...</td>\n",
       "      <td>Ergebnisse werden stetig hinterfragt und kriti...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             culture  \\\n",
       "0  Daten werden nicht als Quelle von Unsicherheit...   \n",
       "1  Daten werden nicht als Quelle von Unsicherheit...   \n",
       "\n",
       "                                          protection  \\\n",
       "0  Konsequente, organisationsweite Richtlinien fü...   \n",
       "1  Konsequente, organisationsweite Richtlinien fü...   \n",
       "\n",
       "                                           questions  \\\n",
       "0  Möglichkeit, Fragen zu formulieren, die durch ...   \n",
       "1  Ganze Projekte basieren auf vielschichtigen Fr...   \n",
       "\n",
       "                                             finding  \\\n",
       "0  Die fortgeschrittene Nutzung von Suchmaschinen...   \n",
       "1  Die fortgeschrittene Nutzung von Suchmaschinen...   \n",
       "\n",
       "                                             getting  \\\n",
       "0  Daten werden als Datei heruntergeladen. Datenf...   \n",
       "1  Daten werden als Datei heruntergeladen. Datenf...   \n",
       "\n",
       "                                              verify  \\\n",
       "0  Best Practices bestehen, Mitarbeiter/innen wer...   \n",
       "1  Best Practices bestehen, Mitarbeiter/innen wer...   \n",
       "\n",
       "                                               clean  \\\n",
       "0  Bewusstsein besteht, dass Daten nicht nach Spe...   \n",
       "1  Bewusstsein besteht, dass Daten nicht nach Spe...   \n",
       "\n",
       "                                             analyse  \\\n",
       "0  Einfache Nutzung von Tabellen, um Informatione...   \n",
       "1  Einfache Nutzung von Tabellen, um Informatione...   \n",
       "\n",
       "                                           visualize  \\\n",
       "0  Balken- und Kuchendiagramme. Grundlegende Bere...   \n",
       "1  Pivot-Tabellen, Zusammenfassungen, Histogramme...   \n",
       "\n",
       "                                         communicate  \\\n",
       "0  Statische Visualisierungen unterstützen einfac...   \n",
       "1  Statische Visualisierungen unterstützen einfac...   \n",
       "\n",
       "                                      interpretation  \n",
       "0  Ergebnisse werden stetig hinterfragt und kriti...  \n",
       "1  Ergebnisse werden stetig hinterfragt und kriti...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_answer.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2)   Data-Literacy Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_matrix\n",
    "df_matrix.index = np.arange(1, len(df_matrix) + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dict_matrix = df_matrix.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Daten werden als unklarer Begriff wahrgenommen, der Unsicherheiten verursacht. Es gibt keine Mitarbeiter/innen mit datenbezogenem Hintergrund.',\n",
       " 2: 'Daten werden als ein interessantes Konzept wahrgenommen und Vorteile werden geschätzt. Unsicherheiten bestehen bei der Anwendung der Fähigkeiten und darin, was konkret beim Arbeiten mit Daten zu erwarten ist. Mitarbeiter/innen mit einigen Datenanalytik-Kenntnissen sind in bedeutenden Positionen vertreten.',\n",
       " 3: 'Daten werden nicht als Quelle von Unsicherheit verstanden, sondern als „Ermöglicher“ für die Planung und Weiterentwicklung von bestehenden und zukünftigen Aktivitäten. Höheres Management und Projektmanager/innen sind sich der Notwendigkeit bewusst, Daten zu sammeln, zu speichern und zielgerichtet einzusetzen. Datenanalyse ist Bestandteil der täglichen Arbeit.',\n",
       " 4: 'Psychologische Barrieren bezüglich der Arbeit mit Daten wurden abgebaut und es herrscht eine Atmosphäre, die das Arbeiten mit Daten aktiv fördert. Datenanalyse / Erkenntnisse werden auf allen Ebenen des Workflows implementiert und optimiert. Datenkompetenz ist eine wichtige Qualifikation für die meisten Mitarbeiter/innen - auch auf höheren Management-Ebenen. Ausreichend Ressourcen (Zeit, Budget, Personal) für das Arbeiten mit Daten existieren.'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_matrix['culture']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um die Antworten mit der Matrix abzugleichen und die korrekte Punktzahl für ein Themenfeld zu erhalten, ist es sinnvoll sich zunächst ein dictionary in python zu erstellen. Dieses ordnet die jeweilige Antwort einem Wert zu. Die Funktion *to_dict()* ist hierbei hilfreich. Da in python der Index eines Arrays immer mit Null beginnt, müssen wir diesen um eins erhöhen um eine korrekte Bewertung zu erhalten, denn die Data-Literacy Skala bewegt sich zwischen 1 und 4. Auf diese Weise erhalten wir die korrekten Werte für die jeweiligen Antworten."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2)  Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def match_points(col):\n",
    "    for key, value in dict_matrix.items():\n",
    "        for k,v in value.items():\n",
    "            if str(v) in col:\n",
    "                return(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nachdem wir die Data-Literacy Matrix transformiert haben, schreiben wir uns eine Funktion um die Matrix mit den Antworten abzugleichen. Dazu vergleichen wir jeweils ob die Antwort in der Matrix vorkommt und geben den entsprechenden Wert zurück. Die Funktion nennen wir *match_points*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>culture</th>\n",
       "      <th>protection</th>\n",
       "      <th>questions</th>\n",
       "      <th>finding</th>\n",
       "      <th>getting</th>\n",
       "      <th>verify</th>\n",
       "      <th>clean</th>\n",
       "      <th>analyse</th>\n",
       "      <th>visualize</th>\n",
       "      <th>communicate</th>\n",
       "      <th>interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   culture  protection  questions  finding  getting  verify  clean  analyse  \\\n",
       "0        3           4        NaN        2        2       4      2      NaN   \n",
       "1        3           4        NaN        2        2       4      2      NaN   \n",
       "2        2           3        4.0        4        3       3      2      1.0   \n",
       "3        2           2        1.0        1        2       3      1      1.0   \n",
       "4        3           4        3.0        1        2       3      2      1.0   \n",
       "5        3           3        NaN        3        2       4      2      1.0   \n",
       "\n",
       "   visualize  communicate  interpretation  \n",
       "0          1            2               4  \n",
       "1          2            2               4  \n",
       "2          4            4               2  \n",
       "3          1            1               2  \n",
       "4          1            2               2  \n",
       "5          1            2               4  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = df_answer.applymap(match_points)\n",
    "df_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mit Hilfe der *applymap-Funktion* können wir die *match_points-Funktion* auf das gesamte Dataframe anwenden. Wir erhalten ein Dataframe mit den jeweiligen Werten für die einzelnen Antworten und nennen es *df_results*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medianvalues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>culture</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protection</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>questions</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>finding</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>getting</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verify</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clean</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analyse</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visualize</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>communicate</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interpretation</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                medianvalues\n",
       "culture                  3.0\n",
       "protection               3.5\n",
       "questions                3.0\n",
       "finding                  2.0\n",
       "getting                  2.0\n",
       "verify                   3.5\n",
       "clean                    2.0\n",
       "analyse                  1.0\n",
       "visualize                1.0\n",
       "communicate              2.0\n",
       "interpretation           3.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results_median = pd.DataFrame(df_results.median())\n",
    "df_results_median = df_results_median.rename(columns={0:'medianvalues'})\n",
    "df_results_median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Im nächsten Schritt erstellen wir uns einen Datensatz, der für jede Antwortktegorie den Medianwert aus den Antworten aller Teilnehmenden enthält. Der Median ist dabei robuster gegenüber Außreißern als der Durchschnitt. Das neue Dataframe nennen wir *df_results_median*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_results.to_csv('Results/Results.csv',index=False)\n",
    "df_results_median.to_csv('Results/Median.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Die Ergebnisse speichern wir in zwei. Eine für die Gesamtresultate *(Results)* und einen für die Medianwerte *(Median)*. Die Dateien werden in den Ordner Results abgelegt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Visualisierung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "class Radar(object):\n",
    "    def __init__(self, fig, titles, labels, rotation=0, rect=None):\n",
    "        if rect is None:\n",
    "            rect = [0.05,0.05,0.88,0.88]\n",
    "        self.n = len(titles)\n",
    "        self.angles = np.arange(0, 360, 360.0/self.n)\n",
    "\n",
    "        self.axes = [fig.add_axes(rect, projection=\"polar\", label=\"axes%d\" % i) for i in range(self.n)]\n",
    " \n",
    "        self.ax = self.axes[0]\n",
    "        self.ax.set_thetagrids(self.angles, labels=titles, fontsize=10)\n",
    " \n",
    " \n",
    "        for ax in self.axes[1:]:\n",
    "            ax.patch.set_visible(False)\n",
    "            ax.grid(\"off\")\n",
    "            ax.xaxis.set_visible(False)\n",
    " \n",
    "        for ax, angle, label in zip(self.axes, self.angles, labels):\n",
    "            ax.set_rgrids(range(1, 6), angle=angle, labels=label)\n",
    "            ax.spines[\"polar\"].set_visible(False)\n",
    "            ax.set_ylim(0, 5)\n",
    "            ax.set_theta_offset(np.deg2rad(90))\n",
    " \n",
    "    def plot(self, values, *args, **kw):\n",
    "        angle = np.deg2rad(np.r_[self.angles, self.angles[0]])\n",
    "        values = np.r_[values, values[0]]\n",
    "        self.ax.plot(angle, values, *args, **kw)\n",
    " \n",
    "    def fill(self, values, colores):\n",
    "        angle = np.deg2rad(np.r_[self.angles, self.angles[0]])\n",
    "        values = np.r_[values, values[0]]\n",
    "        self.ax.fill_between(angle, values, alpha=\"0.5\", color=colores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um eine übersichtliche Darstellung der Ergebnisse zu erhalten, erstellen wir ein Spider-Diagramm. Das Diagramm ist gut geeignet, um die unterschiedlichen Stärken in zuvor festgelegten Kategorien darzustellen, die alle eine gleiche Skala verwenden. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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6aIFYXO/evQkODrY6hipbMDAfeNDqIKr2055E5SnNjx07tiomJqaTn5+f1Vkq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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112766f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pl.figure(figsize=(6,6))\n",
    "titles = ['culture',\n",
    "          'data protection',\n",
    "          'ask questions',\n",
    "          'find',\n",
    "          'get',\n",
    "          'verify',\n",
    "          'clean', \n",
    "          'analyse', \n",
    "          'visualize', \n",
    "          'communicate',\n",
    "          'interpret'\n",
    "         ]\n",
    " \n",
    "labels =  [list(\"1234\")]*11\n",
    "\n",
    "radar_stat = Radar(fig, titles, labels)\n",
    "\n",
    "radar_stat.plot(df_results_median['medianvalues'],  \"-\", lw=2, color=\"black\", alpha=0.15)\n",
    "radar_stat.fill(df_results_median['medianvalues'], colores=\"orange\")\n",
    "\n",
    "radar_stat.ax.legend()\n",
    " \n",
    "pl.show()\n",
    "fig.savefig('../Data_Literacy_Matrix/Results/Data_Literacy_Matrix.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unter *radar_stat.plot()* und *radar_stat.fill()* fügen wir das Datenset *df_results_median* ein. Daraufhin erhalten wir ein Spider-Diagramm mit der Gesamtauswertung der Data-Literacy Matrix als Png. Die Datei legen wir ebenfalls im Ordner *Results* ab. "
   ]
  }
 ],
 "metadata": {
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}