08_uebung_wirtschaftswissenschaften_loesung.ipynb

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    "_Lizenz: Das folgende Lehrmaterial kann unter einer [CC-BY-SA 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) Lizenz frei verwendet, verbreitet und modifiziert werden._   \n",
    " _Authoren: Jana Lasser (jana.lasser@ds.mpg.de)_  \n",
    " _Das Lehrmaterial wurde im Zuge des Projektes \"Daten Lesen Lernen\", gefördert vom Stifterverband und der Heinz Nixdorf Stiftung erstellt._"
   ]
  },
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   "source": [
    "# Lösung zu Übung 08 - Wirtschaftswissenschaften: Bruttoinlandsprodukt und Inferenz\n",
    "### Praktische Hinweise\n",
    "$\\rightarrow$ Übungen sind dafür gedacht, dass ihr sie euch daheim anseht und versucht, ein paar Aufgaben selbst zu lösen. In den Tutorien könnt ihr euch weiter mit dem Lösungen der Aufgaben beschäftigen und dabei Hilfe von den Tutor*innen bekommen.  \n",
    "\n",
    "$\\rightarrow$ Wenn ein Fehler auftritt:\n",
    "1. Fehlermeldung _lesen_ und _verstehen_\n",
    "2. Versuchen, selbst eine Lösung zu finden ($\\rightarrow$ hier findet das Lernen statt!)\n",
    "3. Das Problem googlen (Stichwort: Stackoverflow) oder den/die Nachbar*in fragen\n",
    "4. Tutor*in fragen\n",
    "\n",
    "$\\rightarrow$ Unter <font color='green'>**HINWEIS**</font> werden Hinweise gegeben, die für die Lösung der Aufgabe hilfreich sind und oftmals auch weiterführende Informationen zur Aufgabe liefern.\n",
    "\n",
    "$\\rightarrow$ Mit **(Optional)** gekennzeichnete Übungsteile sind für besonders schnelle Menschen :-)."
   ]
  },
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   "source": [
    "<a name=\"top\"></a>Inhalt\n",
    "--- \n",
    "\n",
    "* [Datenquellen aggregieren](#datenquellen)\n",
    "* [Pro-Kopf Streiktage](#pro_kopf_streiktage)\n",
    "* [(optional) Sinkt das BIP durch Streiks?](#inferenz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"datenquellen\"></a>1. Datenquellen aggregieren\n",
    "---\n",
    "Im ersten Schritt laden wir alle benötigten Daten in das Notebook. Wir benutzten für die folgende Analyse das \"broad\" Format der Tabellen, da dieses für unsere Zwecke einfacher zu handhaben ist. Die Tabelle mit den Streiktagen haben wir schon gespeichert und können sie einfach laden:"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ccode</th>\n",
       "      <th>country name</th>\n",
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       "      <th>1501</th>\n",
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       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "   ccode country name  1500  1501  1502  1503  1504  1505  1506  1507  ...   \\\n",
       "0    4.0  Afghanistan   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "1    8.0      Albania   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "2   12.0      Algeria   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "\n",
       "   2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  \n",
       "0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "1   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
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   "source": [
    "# importieren der benötigten Module\n",
    "from os.path import join\n",
    "import pandas as pd\n",
    "\n",
    "# laden der Streiktage-Daten \n",
    "fname_tage = 'NumberofDaysLostinLabourDisputes_Broad.xlsx'\n",
    "tage = pd.read_excel(join(\"../daten/\", fname_tage))\n",
    "tage.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Tabelle mit dem Zeitverlauf des Bruttolinlandsproduktes musst du erst [hier](https://clio-infra.eu/data/GDPperCapita_Broad.xlsx) herunterladen, in den JupyterHub hochladen und dann mit pandas als DataFrame laden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "      <th>2011</th>\n",
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       "      <th>2015</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>790.0000</td>\n",
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       "      <td>869.0000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Albania</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4367.4382</td>\n",
       "      <td>4647.3138</td>\n",
       "      <td>5010.0318</td>\n",
       "      <td>5178.8669</td>\n",
       "      <td>5374.9908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>3385.7719</td>\n",
       "      <td>3427.7116</td>\n",
       "      <td>3447.4191</td>\n",
       "      <td>3466.5426</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>3 rows × 518 columns</p>\n",
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       "   ccode country name  1500  1501  1502  1503  1504  1505  1506  1507  ...   \\\n",
       "0    4.0  Afghanistan   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "1    8.0      Albania   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "2   12.0      Algeria   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "\n",
       "        2006       2007       2008       2009       2010  2011  2012  2013  \\\n",
       "0   790.0000   863.0000   869.0000        NaN        NaN   NaN   NaN   NaN   \n",
       "1  4367.4382  4647.3138  5010.0318  5178.8669  5374.9908   NaN   NaN   NaN   \n",
       "2  3385.7719  3427.7116  3447.4191  3466.5426  3512.5807   NaN   NaN   NaN   \n",
       "\n",
       "   2014  2015  \n",
       "0   NaN   NaN  \n",
       "1   NaN   NaN  \n",
       "2   NaN   NaN  \n",
       "\n",
       "[3 rows x 518 columns]"
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   "source": [
    "# laden der GDP-Daten\n",
    "fname_GDP = 'GDPperCapita_Broad.xlsx'\n",
    "GDP = pd.read_excel(join(\"../daten/\", fname_GDP))\n",
    "GDP.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Tabelle ```Data_Extract_From_World_Development_Indicators.xlsx'``` mit den Daten zur Landesbevölkerung kannst du aus dem StudIP herunterladen. Die Daten stammen aus [dieser](https://databank.worldbank.org/data/reports.aspx?source=2&series=SP.POP.TOTL&country=) Quelle."
   ]
  },
  {
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       "      <th>country name</th>\n",
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       "      <th>1961_pop</th>\n",
       "      <th>1962_pop</th>\n",
       "      <th>1963_pop</th>\n",
       "      <th>1964_pop</th>\n",
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       "      <th>1966_pop</th>\n",
       "      <th>1967_pop</th>\n",
       "      <th>1968_pop</th>\n",
       "      <th>...</th>\n",
       "      <th>2006_pop</th>\n",
       "      <th>2007_pop</th>\n",
       "      <th>2008_pop</th>\n",
       "      <th>2009_pop</th>\n",
       "      <th>2010_pop</th>\n",
       "      <th>2011_pop</th>\n",
       "      <th>2012_pop</th>\n",
       "      <th>2013_pop</th>\n",
       "      <th>2014_pop</th>\n",
       "      <th>2015_pop</th>\n",
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       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>8996351.0</td>\n",
       "      <td>9166764.0</td>\n",
       "      <td>9345868.0</td>\n",
       "      <td>9533954.0</td>\n",
       "      <td>9731361.0</td>\n",
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       "      <td>32758020.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1608800.0</td>\n",
       "      <td>1659800.0</td>\n",
       "      <td>1711319.0</td>\n",
       "      <td>1762621.0</td>\n",
       "      <td>1814135.0</td>\n",
       "      <td>1864791.0</td>\n",
       "      <td>1914573.0</td>\n",
       "      <td>1965598.0</td>\n",
       "      <td>2022272.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2992547.0</td>\n",
       "      <td>2970017.0</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>2927519.0</td>\n",
       "      <td>2913021.0</td>\n",
       "      <td>2905195.0</td>\n",
       "      <td>2900401.0</td>\n",
       "      <td>2895092.0</td>\n",
       "      <td>2889104.0</td>\n",
       "      <td>2880703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>11124888.0</td>\n",
       "      <td>11404859.0</td>\n",
       "      <td>11690153.0</td>\n",
       "      <td>11985136.0</td>\n",
       "      <td>12295970.0</td>\n",
       "      <td>12626952.0</td>\n",
       "      <td>12980267.0</td>\n",
       "      <td>13354197.0</td>\n",
       "      <td>13744387.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33777915.0</td>\n",
       "      <td>34300076.0</td>\n",
       "      <td>34860715.0</td>\n",
       "      <td>35465760.0</td>\n",
       "      <td>36117637.0</td>\n",
       "      <td>36819558.0</td>\n",
       "      <td>37565847.0</td>\n",
       "      <td>38338562.0</td>\n",
       "      <td>39113313.0</td>\n",
       "      <td>39871528.0</td>\n",
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       "<p>3 rows × 57 columns</p>\n",
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       "  country name    1960_pop    1961_pop    1962_pop    1963_pop    1964_pop  \\\n",
       "0  Afghanistan   8996351.0   9166764.0   9345868.0   9533954.0   9731361.0   \n",
       "1      Albania   1608800.0   1659800.0   1711319.0   1762621.0   1814135.0   \n",
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       "\n",
       "     1965_pop    1966_pop    1967_pop    1968_pop     ...        2006_pop  \\\n",
       "0   9938414.0  10152331.0  10372630.0  10604346.0     ...      25893450.0   \n",
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       "\n",
       "     2007_pop    2008_pop    2009_pop    2010_pop    2011_pop    2012_pop  \\\n",
       "0  26616792.0  27294031.0  28004331.0  28803167.0  29708599.0  30696958.0   \n",
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       "2  34300076.0  34860715.0  35465760.0  36117637.0  36819558.0  37565847.0   \n",
       "\n",
       "     2013_pop    2014_pop    2015_pop  \n",
       "0  31731688.0  32758020.0  33736494.0  \n",
       "1   2895092.0   2889104.0   2880703.0  \n",
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       "\n",
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     },
     "execution_count": 3,
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   ],
   "source": [
    "# laden der Populationsdaten \n",
    "fname_pop = 'Data_Extract_From_World_Development_Indicators.xlsx'\n",
    "pop = pd.read_excel(join(\"../daten/\", fname_pop))\n",
    "pop.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unser Ziel ist es, die drei Tabellen zu einer einzigen Tabelle zusammenzuführen. Da die Populationsdaten nur ab 1960 vorliegen, können wir die Einträge vor 1960 in den anderen beiden Datensätzen getrost entfernen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "obsolete_spalten= ['{}'.format(i) for i in range(1500, 1960)]\n",
    "GDP.drop(columns = obsolete_spalten, inplace=True)\n",
    "tage.drop(columns = obsolete_spalten, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A.** Entferne auch die Spalten ```ccode``` aus den beiden DataFrames. Entferne mit der Funktion ```drop_duplicates()``` alle Duplikate ([Dokumentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html)) aus allen drei DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tage.drop(columns=['ccode'], inplace=True)\n",
    "GDP.drop(columns=['ccode'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tage.drop_duplicates(subset = 'country name', inplace = True)\n",
    "GDP.drop_duplicates(subset = 'country name', inplace = True)\n",
    "pop.drop_duplicates(subset = 'country name', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nun wollen wir noch die Spalten so umbenennen, dass selbst nachdem wir die DataFrames zusammengeführt haben klar ist, was die Zahlen bedeuten. Dafür hängen wir an die Jahreszahlen den Suffix ```_GDP``` an."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country name</th>\n",
       "      <th>1960_GDP</th>\n",
       "      <th>1961_GDP</th>\n",
       "      <th>1962_GDP</th>\n",
       "      <th>1963_GDP</th>\n",
       "      <th>1964_GDP</th>\n",
       "      <th>1965_GDP</th>\n",
       "      <th>1966_GDP</th>\n",
       "      <th>1967_GDP</th>\n",
       "      <th>1968_GDP</th>\n",
       "      <th>...</th>\n",
       "      <th>2006_GDP</th>\n",
       "      <th>2007_GDP</th>\n",
       "      <th>2008_GDP</th>\n",
       "      <th>2009_GDP</th>\n",
       "      <th>2010_GDP</th>\n",
       "      <th>2011_GDP</th>\n",
       "      <th>2012_GDP</th>\n",
       "      <th>2013_GDP</th>\n",
       "      <th>2014_GDP</th>\n",
       "      <th>2015_GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>739.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>...</td>\n",
       "      <td>790.0000</td>\n",
       "      <td>863.0000</td>\n",
       "      <td>869.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1451.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>1511.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1616.0</td>\n",
       "      <td>1675.0</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>1804.0</td>\n",
       "      <td>1869.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4367.4382</td>\n",
       "      <td>4647.3138</td>\n",
       "      <td>5010.0318</td>\n",
       "      <td>5178.8669</td>\n",
       "      <td>5374.9908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>1768.0</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>1870.0</td>\n",
       "      <td>1725.0</td>\n",
       "      <td>1824.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3385.7719</td>\n",
       "      <td>3427.7116</td>\n",
       "      <td>3447.4191</td>\n",
       "      <td>3466.5426</td>\n",
       "      <td>3512.5807</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  country name  1960_GDP  1961_GDP  1962_GDP  1963_GDP  1964_GDP  1965_GDP  \\\n",
       "0  Afghanistan     739.0     730.0     726.0     723.0     720.0     720.0   \n",
       "1      Albania    1451.0    1463.0    1511.0    1563.0    1616.0    1675.0   \n",
       "2      Algeria    2088.0    1799.0    1433.0    1768.0    1806.0    1870.0   \n",
       "\n",
       "   1966_GDP  1967_GDP  1968_GDP    ...      2006_GDP   2007_GDP   2008_GDP  \\\n",
       "0     710.0     712.0     719.0    ...      790.0000   863.0000   869.0000   \n",
       "1    1738.0    1804.0    1869.0    ...     4367.4382  4647.3138  5010.0318   \n",
       "2    1725.0    1824.0    1977.0    ...     3385.7719  3427.7116  3447.4191   \n",
       "\n",
       "    2009_GDP   2010_GDP  2011_GDP  2012_GDP  2013_GDP  2014_GDP  2015_GDP  \n",
       "0        NaN        NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "1  5178.8669  5374.9908       NaN       NaN       NaN       NaN       NaN  \n",
       "2  3466.5426  3512.5807       NaN       NaN       NaN       NaN       NaN  \n",
       "\n",
       "[3 rows x 57 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GDP.rename(columns={'{}'.format(i):'{}_GDP'.format(i) for i in range(1960, 2016)}, inplace=True)\n",
    "GDP.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**B.** Benenne auch die Spalten im ```tage```-DataFrame entsprechend um, indem du den suffix ```_days``` an die Spaltennamen hängst."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anzahl Zeilen (Länder): 195\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>country name</th>\n",
       "      <th>1960_days</th>\n",
       "      <th>1961_days</th>\n",
       "      <th>1962_days</th>\n",
       "      <th>1963_days</th>\n",
       "      <th>1964_days</th>\n",
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       "      <th>1966_days</th>\n",
       "      <th>1967_days</th>\n",
       "      <th>1968_days</th>\n",
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       "      <th>2012_days</th>\n",
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       "      <th>2014_days</th>\n",
       "      <th>2015_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  country name  1960_days  1961_days  1962_days  1963_days  1964_days  \\\n",
       "0  Afghanistan        NaN        NaN        NaN        NaN        NaN   \n",
       "1      Albania        NaN        NaN        NaN        NaN        NaN   \n",
       "2      Algeria        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   1965_days  1966_days  1967_days  1968_days    ...      2006_days  \\\n",
       "0        NaN        NaN        NaN        NaN    ...            NaN   \n",
       "1        NaN        NaN        NaN        NaN    ...            NaN   \n",
       "2        NaN        NaN        NaN        NaN    ...            NaN   \n",
       "\n",
       "   2007_days  2008_days  2009_days  2010_days  2011_days  2012_days  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   2013_days  2014_days  2015_days  \n",
       "0        NaN        NaN        NaN  \n",
       "1        NaN        NaN        NaN  \n",
       "2        NaN        NaN        NaN  \n",
       "\n",
       "[3 rows x 57 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tage.rename(columns={'{}'.format(i):'{}_days'.format(i) for i in range(1960, 2016)}, inplace=True)\n",
    "print('Anzahl Zeilen (Länder): {}'.format(len(tage)))\n",
    "tage.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zwei DataFrames lassen sich mit Hilfe der Funktion ```DataFrame.merge()``` zusammenführen (siehe [Dokumentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html)). Dabei bezeichnet das Argument ```on``` die Spalte, anhand derer die DataFrames zusammengeführt werden. Bei uns wird das die Spalte ```country name``` sein, da diese als einzige in allen drei DataFrames gleich ist. Das Argument ```how``` gibt an, wie die DataFrames zusammengeführt werden. Währen wir ```how=inner``` dann wird die Schnittmenge der Einträge in der Spalte, anhand derer wir die DataFrames zusammenführen, genommen. Das heißt, Zeilen, die nur in einem DataFrame vorhanden sind aber nicht in dem zweiten, fallen weg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anzahl Zeilen (Länder): 171\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>country name</th>\n",
       "      <th>1960_days</th>\n",
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       "      <th>1962_days</th>\n",
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       "      <th>2014_pop</th>\n",
       "      <th>2015_pop</th>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25893450.0</td>\n",
       "      <td>26616792.0</td>\n",
       "      <td>27294031.0</td>\n",
       "      <td>28004331.0</td>\n",
       "      <td>28803167.0</td>\n",
       "      <td>29708599.0</td>\n",
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       "      <td>31731688.0</td>\n",
       "      <td>32758020.0</td>\n",
       "      <td>33736494.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2992547.0</td>\n",
       "      <td>2970017.0</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>2927519.0</td>\n",
       "      <td>2913021.0</td>\n",
       "      <td>2905195.0</td>\n",
       "      <td>2900401.0</td>\n",
       "      <td>2895092.0</td>\n",
       "      <td>2889104.0</td>\n",
       "      <td>2880703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>33777915.0</td>\n",
       "      <td>34300076.0</td>\n",
       "      <td>34860715.0</td>\n",
       "      <td>35465760.0</td>\n",
       "      <td>36117637.0</td>\n",
       "      <td>36819558.0</td>\n",
       "      <td>37565847.0</td>\n",
       "      <td>38338562.0</td>\n",
       "      <td>39113313.0</td>\n",
       "      <td>39871528.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 113 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  country name  1960_days  1961_days  1962_days  1963_days  1964_days  \\\n",
       "0  Afghanistan        NaN        NaN        NaN        NaN        NaN   \n",
       "1      Albania        NaN        NaN        NaN        NaN        NaN   \n",
       "2      Algeria        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   1965_days  1966_days  1967_days  1968_days     ...        2006_pop  \\\n",
       "0        NaN        NaN        NaN        NaN     ...      25893450.0   \n",
       "1        NaN        NaN        NaN        NaN     ...       2992547.0   \n",
       "2        NaN        NaN        NaN        NaN     ...      33777915.0   \n",
       "\n",
       "     2007_pop    2008_pop    2009_pop    2010_pop    2011_pop    2012_pop  \\\n",
       "0  26616792.0  27294031.0  28004331.0  28803167.0  29708599.0  30696958.0   \n",
       "1   2970017.0   2947314.0   2927519.0   2913021.0   2905195.0   2900401.0   \n",
       "2  34300076.0  34860715.0  35465760.0  36117637.0  36819558.0  37565847.0   \n",
       "\n",
       "     2013_pop    2014_pop    2015_pop  \n",
       "0  31731688.0  32758020.0  33736494.0  \n",
       "1   2895092.0   2889104.0   2880703.0  \n",
       "2  38338562.0  39113313.0  39871528.0  \n",
       "\n",
       "[3 rows x 113 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# zusammenfügen der DataFrames mit den Daten zu den Streiktagen und der Bevölkerung\n",
    "data = tage.merge(pop, on = 'country name', how = 'inner')\n",
    "# wir sehen, dass 24 Länder weggefallen sind, da sie nur im \"tage\", nicht\n",
    "# aber im \"pop\"-DataFrame vorhanden waren\n",
    "print('Anzahl Zeilen (Länder): {}'.format(len(data)))\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**C.** Füge das neu geschaffene DataFrame ```data``` mit dem verbleibenden DataFrame ```GDP``` zusammen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country name</th>\n",
       "      <th>1960_days</th>\n",
       "      <th>1961_days</th>\n",
       "      <th>1962_days</th>\n",
       "      <th>1963_days</th>\n",
       "      <th>1964_days</th>\n",
       "      <th>1965_days</th>\n",
       "      <th>1966_days</th>\n",
       "      <th>1967_days</th>\n",
       "      <th>1968_days</th>\n",
       "      <th>...</th>\n",
       "      <th>2006_GDP</th>\n",
       "      <th>2007_GDP</th>\n",
       "      <th>2008_GDP</th>\n",
       "      <th>2009_GDP</th>\n",
       "      <th>2010_GDP</th>\n",
       "      <th>2011_GDP</th>\n",
       "      <th>2012_GDP</th>\n",
       "      <th>2013_GDP</th>\n",
       "      <th>2014_GDP</th>\n",
       "      <th>2015_GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>790.0000</td>\n",
       "      <td>863.0000</td>\n",
       "      <td>869.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4367.4382</td>\n",
       "      <td>4647.3138</td>\n",
       "      <td>5010.0318</td>\n",
       "      <td>5178.8669</td>\n",
       "      <td>5374.9908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3385.7719</td>\n",
       "      <td>3427.7116</td>\n",
       "      <td>3447.4191</td>\n",
       "      <td>3466.5426</td>\n",
       "      <td>3512.5807</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  country name  1960_days  1961_days  1962_days  1963_days  1964_days  \\\n",
       "0  Afghanistan        NaN        NaN        NaN        NaN        NaN   \n",
       "1      Albania        NaN        NaN        NaN        NaN        NaN   \n",
       "2      Algeria        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   1965_days  1966_days  1967_days  1968_days    ...      2006_GDP   2007_GDP  \\\n",
       "0        NaN        NaN        NaN        NaN    ...      790.0000   863.0000   \n",
       "1        NaN        NaN        NaN        NaN    ...     4367.4382  4647.3138   \n",
       "2        NaN        NaN        NaN        NaN    ...     3385.7719  3427.7116   \n",
       "\n",
       "    2008_GDP   2009_GDP   2010_GDP  2011_GDP  2012_GDP  2013_GDP  2014_GDP  \\\n",
       "0   869.0000        NaN        NaN       NaN       NaN       NaN       NaN   \n",
       "1  5010.0318  5178.8669  5374.9908       NaN       NaN       NaN       NaN   \n",
       "2  3447.4191  3466.5426  3512.5807       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   2015_GDP  \n",
       "0       NaN  \n",
       "1       NaN  \n",
       "2       NaN  \n",
       "\n",
       "[3 rows x 169 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.merge(GDP, on = 'country name', how = 'inner')\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Anfang](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"pro_kopf_streiktage\"></a>2. Pro-Kopf Streiktage\n",
    "---\n",
    "Aktuell liegt die Anzahl der Streiktage für ein Jahr in einem Land als absoluter Wert vor. Natürlich haben größere Ländern tendentiell mehr Streiktage als kleinere."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A. (optional)** Überprüfe diese Aussage, indem du analysierst, ob die mittlere Anzahl der Streiktage zwischen 1980 und 2000 tatsälich mit der mittleren Bevölkerungsanzahl in diesen Jahren korreliert. Benutze dafür die in Teil **3** vorgestellte lineare Regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Verbleibende Länder im Zeitraum 1980 bis 2000: 96\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "# start-Jahr ab dem gemittelt wird\n",
    "start = 1980\n",
    "# Zeitraum (in Jahren) über den gemittelt wird\n",
    "window = 20\n",
    "\n",
    "# mittlere Streikanzahl im Zeitraum\n",
    "strike_columns = ['{}_days'.format(i) for i in range(start, start + window)]\n",
    "average_strikes = data[strike_columns].mean(axis=1)\n",
    "\n",
    "# mittlere Bevölkerungszahl im Zeitraum\n",
    "pop_columns = ['{}_pop'.format(i) for i in range(start, start + window)]\n",
    "average_pop = data[pop_columns].mean(axis=1)\n",
    "\n",
    "# temporäres DataFrame zum speichern der Werte\n",
    "temp_df = pd.DataFrame({'country name':data['country name'],\\\n",
    "                        'strikes':average_strikes,\\\n",
    "                        'population':average_pop})\n",
    "\n",
    "# entferne alle Einträge, die fehlende Werte aufweisen\n",
    "temp_df.dropna(inplace=True)\n",
    "print('Verbleibende Länder im Zeitraum {} bis {}: {}'\\\n",
    "      .format(start, start + window, len(temp_df)))\n",
    "\n",
    "# lineare Regression der Streiktage über der Bevölkerungszahl\n",
    "slope, intercept, rvalue, pvalue, stderr = \\\n",
    "    scipy.stats.linregress(temp_df['population'], temp_df['strikes'].astype(float))\n",
    "\n",
    "# Scatterplot der mittleren Streiktage über der mittleren\n",
    "# Bevölkerungsanzahl\n",
    "plt.scatter(temp_df['population'], temp_df['strikes'], label='Daten')\n",
    "x = np.arange(temp_df['population'].min(), temp_df['population'].max(), 1e3)\n",
    "y = intercept + slope * x\n",
    "plt.plot(x, y, '--', color='red', label='Regression')\n",
    "\n",
    "# Beschriftung der Datenpunkte mit Ländernamen\n",
    "for name, y, x in zip(temp_df['country name'], temp_df['strikes'], temp_df['population']):\n",
    "    plt.text(x, y + 1e6, name, fontsize=8)\n",
    "    \n",
    "# Beschriftung mit Werten aus der linearen Regression\n",
    "plt.text(1e5, 2*1e7, 'Korrelationskoeffizient: {:1.2f}'.format(rvalue))\n",
    "plt.text(1e5, 1.75*1e7, 'p-Wert: {:1.2f}'.format(pvalue))\n",
    "    \n",
    "# Beschriftung des Plots\n",
    "plt.ylabel('Mittlere Anzahl Streiktage')\n",
    "plt.xlabel('Mittlere Bevölkerungsanzahl')\n",
    "plt.title('Streiktage über Bevölkerung,\\n gemittelt von {} bis {}'\\\n",
    "          .format(start, start + window))\n",
    "plt.legend()\n",
    "\n",
    "# logarithmische x-achse, damit überhaupt irgendetwas zu erkennen ist\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**B.** Teile für jedes Jahr die Anzahl der Streiktage durch die Bevölkerungszahl und füge die resultierende genormte Anzahl an Streiktagen dem DataFrame als neue Spalten hinzu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country name</th>\n",
       "      <th>1960_days</th>\n",
       "      <th>1961_days</th>\n",
       "      <th>1962_days</th>\n",
       "      <th>1963_days</th>\n",
       "      <th>1964_days</th>\n",
       "      <th>1965_days</th>\n",
       "      <th>1966_days</th>\n",
       "      <th>1967_days</th>\n",
       "      <th>1968_days</th>\n",
       "      <th>...</th>\n",
       "      <th>2006_norm</th>\n",
       "      <th>2007_norm</th>\n",
       "      <th>2008_norm</th>\n",
       "      <th>2009_norm</th>\n",
       "      <th>2010_norm</th>\n",
       "      <th>2011_norm</th>\n",
       "      <th>2012_norm</th>\n",
       "      <th>2013_norm</th>\n",
       "      <th>2014_norm</th>\n",
       "      <th>2015_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 225 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  country name  1960_days  1961_days  1962_days  1963_days  1964_days  \\\n",
       "0  Afghanistan        NaN        NaN        NaN        NaN        NaN   \n",
       "1      Albania        NaN        NaN        NaN        NaN        NaN   \n",
       "2      Algeria        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   1965_days  1966_days  1967_days  1968_days    ...      2006_norm  \\\n",
       "0        NaN        NaN        NaN        NaN    ...            NaN   \n",
       "1        NaN        NaN        NaN        NaN    ...            NaN   \n",
       "2        NaN        NaN        NaN        NaN    ...            NaN   \n",
       "\n",
       "   2007_norm  2008_norm  2009_norm  2010_norm  2011_norm  2012_norm  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   2013_norm  2014_norm  2015_norm  \n",
       "0        NaN        NaN        NaN  \n",
       "1        NaN        NaN        NaN  \n",
       "2        NaN        NaN        NaN  \n",
       "\n",
       "[3 rows x 225 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalisierte Streiktage\n",
    "for i in range(1960, 2016):\n",
    "    data['{}_norm'.format(i)] = data['{}_days'.format(i)] / data['{}_pop'.format(i)]\n",
    "    \n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wie gewohnt, können wir das DataFrame nach einzelnen Ländern filtern. Die der Verlauf der absoluten Anzahl der Streiktage lässt sich dann einfach als Zeitreihe visualisieren:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country name</th>\n",
       "      <th>1960_days</th>\n",
       "      <th>1961_days</th>\n",
       "      <th>1962_days</th>\n",
       "      <th>1963_days</th>\n",
       "      <th>1964_days</th>\n",
       "      <th>1965_days</th>\n",
       "      <th>1966_days</th>\n",
       "      <th>1967_days</th>\n",
       "      <th>1968_days</th>\n",
       "      <th>...</th>\n",
       "      <th>2006_norm</th>\n",
       "      <th>2007_norm</th>\n",
       "      <th>2008_norm</th>\n",
       "      <th>2009_norm</th>\n",
       "      <th>2010_norm</th>\n",
       "      <th>2011_norm</th>\n",
       "      <th>2012_norm</th>\n",
       "      <th>2013_norm</th>\n",
       "      <th>2014_norm</th>\n",
       "      <th>2015_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3024000.0</td>\n",
       "      <td>3046000.0</td>\n",
       "      <td>5798000.0</td>\n",
       "      <td>1755000.0</td>\n",
       "      <td>2277000.0</td>\n",
       "      <td>2925000.0</td>\n",
       "      <td>2398000.0</td>\n",
       "      <td>2787000.0</td>\n",
       "      <td>4690000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.016977</td>\n",
       "      <td>0.012278</td>\n",
       "      <td>0.007309</td>\n",
       "      <td>0.00582</td>\n",
       "      <td>0.021973</td>\n",
       "      <td>0.003909</td>\n",
       "      <td>0.006917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 225 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country name  1960_days  1961_days  1962_days  1963_days  1964_days  \\\n",
       "163  United Kingdom  3024000.0  3046000.0  5798000.0  1755000.0  2277000.0   \n",
       "\n",
       "     1965_days  1966_days  1967_days  1968_days    ...      2006_norm  \\\n",
       "163  2925000.0  2398000.0  2787000.0  4690000.0    ...         0.0124   \n",
       "\n",
       "     2007_norm  2008_norm  2009_norm  2010_norm  2011_norm  2012_norm  \\\n",
       "163   0.016977   0.012278   0.007309    0.00582   0.021973   0.003909   \n",
       "\n",
       "     2013_norm  2014_norm  2015_norm  \n",
       "163   0.006917        NaN        NaN  \n",
       "\n",
       "[1 rows x 225 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country = 'United Kingdom'\n",
    "filter_country = data['country name'] == country\n",
    "data_country = data[filter_country]\n",
    "data_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'United Kingdom')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "years = list(range(1960, 2015))\n",
    "y = data_country[['{}_days'.format(i) for i in years]].values[0]\n",
    "plt.plot(years, y)\n",
    "plt.xlabel('Jahr')\n",
    "plt.ylabel('absolute Anzahl Streiktage')\n",
    "plt.title('{}'.format(country))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**C.** Nachdem wir die Streiktage normalisiert haben, sind auch Länder mit drastisch unterschiedlicher Bevölkerungszahl verlgeichbar. Stelle für drei Länder deiner Wahl, deren Bevölkerungszahl sich stark unterscheidet, den Zeitverlauf der mit der Bevölkerungszahl normalisierten Streiktage dar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drei Länder\n",
    "countries = ['India', 'Spain', 'Austria']\n",
    "# filter\n",
    "country_filters = [data['country name'] == c for c in countries]\n",
    "# nach Ländern gefilterte DataFrames\n",
    "country_data = [data[f] for f in country_filters]\n",
    "\n",
    "# Zeitraum\n",
    "years = list(range(1960, 2015))\n",
    "\n",
    "# Liniendiagramme der Zeitreihen für die drei Länder\n",
    "for c, d in zip(countries, country_data):\n",
    "    y = d[['{}_norm'.format(i) for i in years]].values[0]\n",
    "    plt.plot(years, y, label = c)\n",
    "\n",
    "# Beschriftungen\n",
    "plt.xlabel('Jahr')\n",
    "plt.ylabel('Streiktage / Einwohner')\n",
    "plt.legend()\n",
    "plt.savefig('streiktage_einwohner.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**D. (optional)** Aktuell normalisieren wir die Anzahl der Streiktage in einem Jahr mit der Bevölkerungszahl im selben Jahr. Untersuche anhand des Beispiels von einem Land, wie groß der Unterschied zwischen dieser Vorgehensweise und einer Normalisierung aller Jahre mit der Bevölkerungszahl im Jahr 1960 bzw. 2015 is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd60ff76fd0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# wähle ein Land aus\n",
    "country = 'India'\n",
    "country_filter = data['country name'] == country\n",
    "country_data = data[country_filter]\n",
    "\n",
    "# Zeitraum\n",
    "years = list(range(1960, 2015))\n",
    "\n",
    "# Zeitverlauf der Bevölkerungszahl\n",
    "country_pop = country_data[['{}_pop'.format(i) for i in years]].values[0]\n",
    "# Zeitverlauf der (nicht normalisierten) Streiktage\n",
    "country_days = country_data[['{}_days'.format(i) for i in years]].values[0]\n",
    "\n",
    "# mit dem Verlauf der Bevölkerungsanzahl normalisierter Verlauf der Streiktage\n",
    "y1 = country_data[['{}_norm'.format(i) for i in years]].values[0]\n",
    "# mit der Bevölkerungszahl aus 1960 normalisierter Verlauf der Streiktage\n",
    "y2 = country_days / country_pop[0]\n",
    "# mit der Bevölkerungszahl aus 2015 normalisierter Verlauf der Streiktage\n",
    "y3 = country_days / country_pop[-1]\n",
    "\n",
    "# plotte drei Liniendiagramme für die drei Zeitreihen\n",
    "plt.plot(years, y1, label='mit Bevölkerungsverlauf normalisiert')\n",
    "plt.plot(years, y2, label='mit Bevölkerungswert von 1960 normalisiert')\n",
    "plt.plot(years, y3, label='mit Bevölkerungswert von 2015 normalisiert')\n",
    "\n",
    "# Beschriftungen\n",
    "plt.xlabel('Jahr')\n",
    "plt.ylabel('Streiktage / Bewohner')\n",
    "plt.legend(fontsize=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E.** Stelle für die selben drei Länder wie in Aufgabe **2.C** den Verlauf der Bevölkerungszahl über die Zeit dar. <font color='green'>**HINWEIS:**  mit ```plt.yscale('log')``` stellst du die y-Achse auf logarithmische Darstellung um.</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd60ffce588>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drei Länder\n",
    "countries = ['India', 'Spain', 'Austria']\n",
    "# filter\n",
    "country_filters = [data['country name'] == c for c in countries]\n",
    "# nach Ländern gefilterte DataFrames\n",
    "country_data = [data[f] for f in country_filters]\n",
    "\n",
    "# Liniendiagramme der Zeitreihen für die drei Länder\n",
    "for c, d in zip(countries, country_data):\n",
    "    y = d[['{}_pop'.format(i) for i in years]].values[0]\n",
    "    plt.plot(years, y, label = c)\n",
    "\n",
    "# y-Achse auf logarithmische Darstellung umstellen\n",
    "plt.yscale('log')\n",
    "    \n",
    "# Beschriftungen\n",
    "plt.xlabel('Jahr')\n",
    "plt.ylabel('Einwohner')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Anfang](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"inferenz\"></a>3. (optional) Sinkt das BIP durch Streiks?\n",
    "---\n",
    "Schlussendlich wollen wir uns ansehen, ob wir einen statistisch signifikanten Zusammenhang zwischen BIP und (normalisierter) Anzahl der Streiktage in einem Land feststellen können. Um solche Zusammenhänge zwischen Beobachtungen darzustellen, eignet sich der Scatterplot. Die Funktion ```plt.scatter(x,y)``` ([Dokumentation](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.scatter.html)) erlaubt es uns, einen einfachen Scatterplot zu erstellen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd60f926b38>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [3,5,7,4]\n",
    "y = [1,3,2,4]\n",
    "\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A. (optional)** Berechne die mittlere Anzahl an Streiktagen pro Person zwischen 1960 und 1970. Speichere berechneten genormten Streiktage zusammen mit dem BIP und dem Ländernamen in einem neuen DataFrame. Entferne mit der Funktion ```df.dropna()``` alle fehlenden Einträge aus dem DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Verbleibende Länder im Zeitraum 1960 bis 1970: 64\n"
     ]
    }
   ],
   "source": [
    "# start-Jahr ab dem gemittelt wird\n",
    "start = 1960\n",
    "# Zeitraum (in Jahren) über den gemittelt wird\n",
    "window = 10\n",
    "\n",
    "# mittlere Streiktage / Bewohner im Zeitraum\n",
    "strike_columns = ['{}_norm'.format(i) for i in range(start, start + window)]\n",
    "average_strikes = data[strike_columns].mean(axis=1)\n",
    "\n",
    "# temporäres DataFrame zum speichern der Werte\n",
    "temp_df = pd.DataFrame({'country name':data['country name'],\\\n",
    "                        'strikes':average_strikes,\\\n",
    "                        '{}_GDP'.format(start + window):data['{}_GDP'.format(start + window)]})\n",
    "\n",
    "# entferne alle Einträge, die fehlende Werte aufweisen\n",
    "temp_df.dropna(inplace=True)\n",
    "print('Verbleibende Länder im Zeitraum {} bis {}: {}'\\\n",
    "      .format(start, start + window, len(temp_df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**B. (optional)** Stelle das BIP im Jahr 1990 über der mittleren Anzahl an Streiktagen in diesem Zeitraum dar. Benutze dafür das neu erstellte DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd61406eba8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplot der mittleren Streiktage über der mittleren\n",
    "# Bevölkerungsanzahl\n",
    "plt.scatter(temp_df['strikes'], temp_df['{}_GDP'.format(start + window)], label='Daten')\n",
    "\n",
    "# Beschriftung der Datenpunkte mit Ländernamen\n",
    "for name, x, y in zip(temp_df['country name'], temp_df['strikes'],\\\n",
    "                      temp_df['{}_GDP'.format(start + window)]):\n",
    "    plt.text(x, y, name, fontsize=8)\n",
    "    \n",
    "# Beschriftung des Plots\n",
    "plt.ylabel('Mittlere Anzahl Streiktage')\n",
    "plt.xlabel('Mittlere Bevölkerungsanzahl')\n",
    "plt.title('Streiktage über Bevölkerung,\\n gemittelt von {} bis {}'\\\n",
    "          .format(start, start + window))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**C. (optional)** Mache dich mit der Funktion ```linregress()``` der Bibliothek SciPy vertraut ([Dokumentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html)). Was für Eingabewerte verlangt die Funktion? Was für Werte gibt sie aus?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Eingabewerte:**\n",
    "* _die unabhängige Variable ```x``` (in unserem Fall die Tweet-Länge in Zeichen)_\n",
    "* _die abhängige Variable ```y``` (in unserem Fall die Anzahl der Worte je Tweet)_\n",
    "\n",
    "**Ausgabewerte:**\n",
    "Die Ausgabewerte der Funktion beschreiben eine Gerade, sowie Maße für die Unsicherheit der Regression\n",
    "* _```slope``` ist die Steigung der Geraden._\n",
    "* _```intercept``` ist der Achsenabschnitt der Geraden._\n",
    "* _der ```rvalue``` ist der \"Korrelationskoeffizient\" und gibt an, wie stark die abhängige Größe mit der unabhängigen korreliert. Ist ```rvalue=1```, dann steigt die abhängige Größe immer dann, wenn die unabhängige auch steigt. Ist ```rvalue=0```, dann sind die beiden Größen unabhängig voneinander. Ist ```rvalue=-1```, dann sinkt die abhängige größe, wenn die unabhängige Größe steigt (anti-korreliert)._\n",
    "* _der ```pvalue``` ([p-Wert](https://de.wikipedia.org/wiki/P-Wert)) gibt an wie wahrscheinlich es ist eine Testgröße zu erhalten die noch unwahrscheinlicher ist als die beobachtete, unter der Annahme, dass die Nullhypothese (keine Korrelation) wahr ist.._\n",
    "* _der ```stderr``` ist der [Standardfehler](https://de.wikipedia.org/wiki/Standardfehler) der Steigung._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**D. (optional)** führe eine lineare Regression des BIP über den mittleren Streiktagen pro Person im vorangegangenen Jahrzehnt durch. Gib die resultierenden Werte aus. Was bedeuten sie? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultierende Gerade: y(x) = 3624.75 + (30590.71 +- 9918.52) * x\n",
      "Korrelationskoeffizient: 0.36, p-Wert: 0.003\n"
     ]
    }
   ],
   "source": [
    "# importieren des Untermoduls \"stats\" von SciPy\n",
    "from scipy import stats\n",
    "\n",
    "# Lineare Regression des BIP über den Streiktagen.\n",
    "# Die resultierenden fünf Werte werden direkt fünf Variablen zugeordnet\n",
    "slope, intercept, rvalue, pvalue, stderr = \\\n",
    "    scipy.stats.linregress(temp_df['strikes'], temp_df['{}_GDP'.format(start + window)])\n",
    "\n",
    "# Ausgabe der Ergebnisse\n",
    "print('Resultierende Gerade: y(x) = {:1.2f} + ({:1.2f} +- {:1.2f}) * x'.format(intercept, slope, stderr))\n",
    "print('Korrelationskoeffizient: {:1.2f}, p-Wert: {:1.3f}'.format(rvalue, pvalue))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* _Der Korrelationskoeffizient sagt uns, dass das BIP leicht **positiv** mit den Streiktagen pro Person korreliert. Länder mit höherem BIP haben also auch tendentiell mehr Streiktage._\n",
    "* _Der p-Wert sagt uns, dass dieses Ergebnis signifikant ist, wenn wir $p=0.05$ als Signifikanzlevel annehmen. Ein p-Wert von 0.05 bedeutet, dass jeder zwanzigste Versuch, die Datenpunkte zufällig zu verteilen einen p-Wert kleiner als 0.05 produzieren wird. Bei einem niedrigeren p-Wert ist die Chance, das Ergebniss zufällig zu bekommen entsprechend geringer._\n",
    "* _Das Ergebnis (positive Korrelation des BIP mit Streiktagen) ist nicht intuitiv. In erster Näherung würde ich davon ausgehen, dass Streiks das BIP negativ beeinflussen. Eventuell lässt sich die Korrelation damit erklären, dass Länder mit höheren BIP eine stärker organisierte Industrie (Gewerkschaften) aufweisen, und damit auch anfälliger für Streiks sind._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E. (optional)** Stelle die aus der statistischen Regression resultierende Linie über dem Scatterplot aus Aufgabe **2.B** dar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd60f713e80>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplot der mittleren Streiktage über der mittleren\n",
    "# Bevölkerungsanzahl\n",
    "plt.scatter( temp_df['strikes'], temp_df['{}_GDP'.format(start + window)], label='Daten')\n",
    "x = np.arange(temp_df['strikes'].min(), temp_df['strikes'].max(), 0.01)\n",
    "y = intercept + slope * x\n",
    "plt.plot(x, y, '--', color='red', label='Regression')\n",
    "    \n",
    "# Beschriftung mit Werten aus der linearen Regression\n",
    "plt.text(0.16, 7*1e3, 'Korrelationskoeffizient: {:1.2f}'.format(rvalue))\n",
    "plt.text(0.16, 5.5*1e3, 'p-Wert: {:1.3f}'.format(pvalue))\n",
    "    \n",
    "# Beschriftung des Plots\n",
    "plt.ylabel('BIP im Jahr {}'.format(start + window))\n",
    "plt.xlabel('Mittlere Anzahl Streiktage / Person')\n",
    "plt.title('BIP über Streiktage,\\n gemittelt von {} bis {}'\\\n",
    "          .format(start, start + window))\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Anfang](#top)"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}