{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from bra_scraper import BRA\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drabbas kommuner som tar emot många flyktingar av ökad brottslighet?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Det påstås ofta att det finns ett samband mellan flyktinginvandring och brottslighet. Framför allt då sexualbrottslighet.\n",
    "\n",
    "Under 2015 och 2016 tog svenska kommuner emot rekordmånga flyktingar från framför allt Syrien och Afghanistan. Det här borde, om teorin om sambandet mellan invandring och brottslighet, synas i statistiken. Vi borde se en uppgång i sexual- och våldsbrott i de kommuner som tagit emot flest flyktingar?\n",
    "\n",
    "Men blev det så?\n",
    "\n",
    "Brottsförebyggande rådet publicerade nyligen detaljerad statistik över antal anmälda brott i alla svenska kommuner. Nedan följer ett försök att se om det finns något samband mellan flyktingmottagande och antal anmälda brott.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steg 1: Hämta data\n",
    "\n",
    "Vi börjar med att hämta data från Brå i för ett antal olika brottstyper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "21 in surfer.py at 2017-03-17 14:47:19,096: Start new session\n",
      "137 in topic.py at 2017-03-17 14:47:19,861: Making query of 1 regions, 10 crimes and 17 periods in Årsvis - Land och län 1975-2014, land och region 2015-.\n",
      "140 in topic.py at 2017-03-17 14:47:19,862: Getting expected 170 datapoints\n",
      "21 in surfer.py at 2017-03-17 14:47:19,863: Start new session\n",
      "169 in topic.py at 2017-03-17 14:47:19,995: Parse result page 1 out of 1\n",
      "185 in topic.py at 2017-03-17 14:47:21,909: Parsed 170 datapoints\n",
      "9 in dataset.py at 2017-03-17 14:47:21,919: Writing to data/hela_landet.csv\n"
     ]
    }
   ],
   "source": [
    "# Det här är de brott vi är intresserade av\n",
    "crimes = [\n",
    "    \"Totalt antal brott\",\n",
    "    \"Sexualbrott\",\n",
    "    u\"Sexualbrott, Våldtäkt inkl. grov (ingår även i våldsbrott)\",\n",
    "    u\"Sexualbrott, Våldtäkt inkl. grov (ingår även i våldsbrott), Mot samtliga personer, Utomhus\",\n",
    "    u\"Våldsbrott\",\n",
    "    u\"Våldsbrott, Misshandel inkl.grov\",\n",
    "    u\"Stöld-, rån- och häleribrott\", \n",
    "    u\"Stöld-, rån- och häleribrott, Stöld och snatteri\",\n",
    "    u\"Stöld-, rån- och häleribrott, Stöld och snatteri, I butik, varuhus, kommersiell utställningslokal\",\n",
    "    u\"Skadegörelse (inkl. mordbrand)\",\n",
    "]\n",
    "\n",
    "bra = BRA()\n",
    "\n",
    "# Hämta data för riket och spara till fil\n",
    "topic = bra.topic(u\"Årsvis - Land och län 1975-2014, land och region 2015-\")\n",
    "topic.query(\n",
    "    regions=[\"Hela landet\"],\n",
    "    crimes=crimes, \n",
    "    period_start=\"2000-01-01\").to_csv(\"data/hela_landet.csv\")\n",
    "\n",
    "\n",
    "# Hämta data för kommuner och spara till fil\n",
    "topic = bra.topic(u\"Årsvis - Kommun och storstädernas stadsdelar 1996-\")\n",
    "topic.query(\n",
    "    crimes=crimes, \n",
    "    period_start=\"2000-01-01\").to_csv(\"data/kommuner.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Läs in fil för att harmonisera regionnamn från olika myndigeheter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>region</th>\n",
       "      <th>kolada_id</th>\n",
       "      <th>migrationsverket_id</th>\n",
       "      <th>brå_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ale</td>\n",
       "      <td>Ale</td>\n",
       "      <td>ALE</td>\n",
       "      <td>Ale kommun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alingsås</td>\n",
       "      <td>Alingsås</td>\n",
       "      <td>ALINGSÅS</td>\n",
       "      <td>Alingsås kommun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alvesta</td>\n",
       "      <td>Alvesta</td>\n",
       "      <td>ALVESTA</td>\n",
       "      <td>Alvesta kommun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aneby</td>\n",
       "      <td>Aneby</td>\n",
       "      <td>ANEBY</td>\n",
       "      <td>Aneby kommun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arboga</td>\n",
       "      <td>Arboga</td>\n",
       "      <td>ARBOGA</td>\n",
       "      <td>Arboga kommun</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     region kolada_id migrationsverket_id           brå_id\n",
       "0       Ale       Ale                 ALE       Ale kommun\n",
       "1  Alingsås  Alingsås            ALINGSÅS  Alingsås kommun\n",
       "2   Alvesta   Alvesta             ALVESTA   Alvesta kommun\n",
       "3     Aneby     Aneby               ANEBY     Aneby kommun\n",
       "4    Arboga    Arboga              ARBOGA    Arboga kommun"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translate_df = pd.read_csv(\"../region_translations.csv\", encoding=\"utf-8\")\n",
    "translate_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Läs in befolkningsdata hämtad från Kolada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">population</th>\n",
       "      <th>mean_pop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ale</th>\n",
       "      <td>27442</td>\n",
       "      <td>27577</td>\n",
       "      <td>27842</td>\n",
       "      <td>28074</td>\n",
       "      <td>28423</td>\n",
       "      <td>28862</td>\n",
       "      <td>29549</td>\n",
       "      <td>29205.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alingsås</th>\n",
       "      <td>37796</td>\n",
       "      <td>38053</td>\n",
       "      <td>38355</td>\n",
       "      <td>38619</td>\n",
       "      <td>39188</td>\n",
       "      <td>39602</td>\n",
       "      <td>40045</td>\n",
       "      <td>39823.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alvesta</th>\n",
       "      <td>18802</td>\n",
       "      <td>18917</td>\n",
       "      <td>19034</td>\n",
       "      <td>19280</td>\n",
       "      <td>19503</td>\n",
       "      <td>19581</td>\n",
       "      <td>19850</td>\n",
       "      <td>19715.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aneby</th>\n",
       "      <td>6393</td>\n",
       "      <td>6415</td>\n",
       "      <td>6407</td>\n",
       "      <td>6375</td>\n",
       "      <td>6426</td>\n",
       "      <td>6537</td>\n",
       "      <td>6603</td>\n",
       "      <td>6570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arboga</th>\n",
       "      <td>13285</td>\n",
       "      <td>13302</td>\n",
       "      <td>13353</td>\n",
       "      <td>13493</td>\n",
       "      <td>13631</td>\n",
       "      <td>13858</td>\n",
       "      <td>13903</td>\n",
       "      <td>13880.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         population                                           mean_pop\n",
       "year           2010   2011   2012   2013   2014   2015   2016         \n",
       "region                                                                \n",
       "Ale           27442  27577  27842  28074  28423  28862  29549  29205.5\n",
       "Alingsås      37796  38053  38355  38619  39188  39602  40045  39823.5\n",
       "Alvesta       18802  18917  19034  19280  19503  19581  19850  19715.5\n",
       "Aneby          6393   6415   6407   6375   6426   6537   6603   6570.0\n",
       "Arboga        13285  13302  13353  13493  13631  13858  13903  13880.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_df = pd.DataFrame().from_csv(\"data/kolada-befolkning.csv\",encoding=\"utf-8\").reset_index()\n",
    "\n",
    "# Harmonisera region namnn\n",
    "population_df = pd.merge(population_df, translate_df[[\"region\",\"kolada_id\"]], left_on=\"Region\", right_on=u\"kolada_id\")\\\n",
    "    .rename(columns={ u\"År\": \"year\", u\"Invånare\": \"population\" })[[\"region\", \"year\", \"population\" ]]\\\n",
    "\n",
    "\n",
    "population_df = population_df.set_index([\"region\",\"year\"]).unstack()\n",
    "\n",
    "# Lägg till Hela landet\n",
    "country_pop = population_df.sum()\n",
    "country_pop.name = \"Hela landet\"\n",
    "population_df = population_df.append(country_pop)\n",
    "\n",
    "# Räkna ut medelbefolkning senaste två åren\n",
    "population_df[\"mean_pop\"] = population_df.population[[2015, 2016]].mean(axis=1)\n",
    "population_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Läs in data för antal mottagna asylsökande från Migrationsverket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>refugees_count</th>\n",
       "      <th>mean_pop</th>\n",
       "      <th>refugees_per_capita</th>\n",
       "      <th>refugees_rank</th>\n",
       "      <th>top_refugee_muni</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Högsby</th>\n",
       "      <td>475.0</td>\n",
       "      <td>5968.5</td>\n",
       "      <td>795.844852</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hylte</th>\n",
       "      <td>678.0</td>\n",
       "      <td>10734.0</td>\n",
       "      <td>631.637786</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hultsfred</th>\n",
       "      <td>886.0</td>\n",
       "      <td>14263.0</td>\n",
       "      <td>621.187688</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lessebo</th>\n",
       "      <td>510.0</td>\n",
       "      <td>8638.0</td>\n",
       "      <td>590.414448</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ljusnarsberg</th>\n",
       "      <td>271.0</td>\n",
       "      <td>4967.0</td>\n",
       "      <td>545.600966</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avesta</th>\n",
       "      <td>1246.0</td>\n",
       "      <td>22971.0</td>\n",
       "      <td>542.423055</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Östra Göinge</th>\n",
       "      <td>756.0</td>\n",
       "      <td>14254.0</td>\n",
       "      <td>530.377438</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mellerud</th>\n",
       "      <td>479.0</td>\n",
       "      <td>9246.0</td>\n",
       "      <td>518.061865</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hällefors</th>\n",
       "      <td>324.0</td>\n",
       "      <td>7085.0</td>\n",
       "      <td>457.304164</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ronneby</th>\n",
       "      <td>1237.0</td>\n",
       "      <td>28952.0</td>\n",
       "      <td>427.258911</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              refugees_count  mean_pop  refugees_per_capita  refugees_rank  \\\n",
       "region                                                                       \n",
       "Högsby                 475.0    5968.5           795.844852            1.0   \n",
       "Hylte                  678.0   10734.0           631.637786            2.0   \n",
       "Hultsfred              886.0   14263.0           621.187688            3.0   \n",
       "Lessebo                510.0    8638.0           590.414448            4.0   \n",
       "Ljusnarsberg           271.0    4967.0           545.600966            5.0   \n",
       "Avesta                1246.0   22971.0           542.423055            6.0   \n",
       "Östra Göinge           756.0   14254.0           530.377438            7.0   \n",
       "Mellerud               479.0    9246.0           518.061865            8.0   \n",
       "Hällefors              324.0    7085.0           457.304164            9.0   \n",
       "Ronneby               1237.0   28952.0           427.258911           10.0   \n",
       "\n",
       "              top_refugee_muni  \n",
       "region                          \n",
       "Högsby                     1.0  \n",
       "Hylte                      1.0  \n",
       "Hultsfred                  1.0  \n",
       "Lessebo                    1.0  \n",
       "Ljusnarsberg               1.0  \n",
       "Avesta                     1.0  \n",
       "Östra Göinge               1.0  \n",
       "Mellerud                   1.0  \n",
       "Hällefors                  1.0  \n",
       "Ronneby                    1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refugees_df = pd.DataFrame().from_csv(\"data/migrationsverket-mottagna-2015-2016.csv\",encoding=\"utf-8\").reset_index()\n",
    "\n",
    "# Harmonisera regionnamnn\n",
    "refugees_df = pd.merge(refugees_df, translate_df[[\"region\",\"migrationsverket_id\"]], left_on=\"Region\", right_on=u\"migrationsverket_id\")\\\n",
    "    [[\"region\", \"2015\", \"2016\"]].set_index(\"region\")\n",
    "\n",
    "refugees_df[\"refugees_count\"] = refugees_df.sum(axis=1)\n",
    "\n",
    "# Lägg till Hela landet\n",
    "country_refugees = refugees_df.sum()\n",
    "country_refugees.name = \"Hela landet\"\n",
    "refugees_df = refugees_df.append(country_refugees)\n",
    "\n",
    "# Hur många toppmottagande kommuner ska vi närstudera?\n",
    "N_TOP_MUNIS = 20\n",
    "\n",
    "refugees_df = refugees_df.join(population_df.mean_pop)[[\"refugees_count\",\"mean_pop\"]]\n",
    "refugees_df[\"refugees_per_capita\"] = refugees_df.refugees_count / refugees_df.mean_pop * 10000\n",
    "refugees_df[\"refugees_rank\"] = refugees_df.refugees_per_capita.rank(ascending=False)\n",
    "is_top_recipient = refugees_df.refugees_rank <= N_TOP_MUNIS\n",
    "refugees_df[\"top_refugee_muni\"] = is_top_recipient\n",
    "refugees_df.sort_values(\"refugees_per_capita\", ascending=False).head(15).round()\n",
    "\n",
    "# Lista på toppmottagare\n",
    "TOP_REFUGEE_MUNIS = refugees_df[is_top_recipient].index.unique()\n",
    "\n",
    "top_refugee_munis = refugees_df[is_top_recipient][[\"refugees_count\",\"mean_pop\"]].sum()\n",
    "top_refugee_munis[\"refugees_per_capita\"] = top_refugee_munis.refugees_count / top_refugee_munis.mean_pop * 10000\n",
    "top_refugee_munis.name = \"Toppmottagare\"\n",
    "\n",
    "refugees_df = refugees_df.append(top_refugee_munis)\n",
    "refugees_df.sort_values(\"refugees_rank\").head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Slå ihop de tre dataseten (befolkning, flyktingmottagande och antal anmälda brott)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "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></th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>...</th>\n",
       "      <th>refugees_per_capita</th>\n",
       "      <th>refugees_count</th>\n",
       "      <th>top_refugee_muni</th>\n",
       "      <th>mean_pop</th>\n",
       "      <th>latest</th>\n",
       "      <th>avg</th>\n",
       "      <th>stdev</th>\n",
       "      <th>latest_vs_mean</th>\n",
       "      <th>latest_vs_2015</th>\n",
       "      <th>latest_vs_mean_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th>crime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Ale</th>\n",
       "      <th>Sexualbrott</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>64.371437</td>\n",
       "      <td>188.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29205.5</td>\n",
       "      <td>28</td>\n",
       "      <td>19.529412</td>\n",
       "      <td>6.539473</td>\n",
       "      <td>1.433735</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>1.295301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexualbrott, Våldtäkt inkl. grov (ingår även i våldsbrott)</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>64.371437</td>\n",
       "      <td>188.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29205.5</td>\n",
       "      <td>11</td>\n",
       "      <td>6.470588</td>\n",
       "      <td>3.393333</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.334797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexualbrott, Våldtäkt inkl. grov (ingår även i våldsbrott), Mot samtliga personer, Utomhus</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>64.371437</td>\n",
       "      <td>188.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29205.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skadegörelse (inkl. mordbrand)</th>\n",
       "      <td>219</td>\n",
       "      <td>191</td>\n",
       "      <td>209</td>\n",
       "      <td>238</td>\n",
       "      <td>321</td>\n",
       "      <td>295</td>\n",
       "      <td>227</td>\n",
       "      <td>334</td>\n",
       "      <td>550</td>\n",
       "      <td>541</td>\n",
       "      <td>...</td>\n",
       "      <td>64.371437</td>\n",
       "      <td>188.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29205.5</td>\n",
       "      <td>330</td>\n",
       "      <td>316.000000</td>\n",
       "      <td>100.657836</td>\n",
       "      <td>1.044304</td>\n",
       "      <td>1.031250</td>\n",
       "      <td>0.139085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stöld-, rån- och häleribrott</th>\n",
       "      <td>1551</td>\n",
       "      <td>1478</td>\n",
       "      <td>1504</td>\n",
       "      <td>1633</td>\n",
       "      <td>1762</td>\n",
       "      <td>1504</td>\n",
       "      <td>1107</td>\n",
       "      <td>1442</td>\n",
       "      <td>1467</td>\n",
       "      <td>1321</td>\n",
       "      <td>...</td>\n",
       "      <td>64.371437</td>\n",
       "      <td>188.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29205.5</td>\n",
       "      <td>922</td>\n",
       "      <td>1318.529412</td>\n",
       "      <td>244.110661</td>\n",
       "      <td>0.699264</td>\n",
       "      <td>0.913776</td>\n",
       "      <td>-1.624384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           2000  2001  2002  \\\n",
       "region crime                                                                  \n",
       "Ale    Sexualbrott                                           10    14     8   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...     3     2     1   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...     0     0     0   \n",
       "       Skadegörelse (inkl. mordbrand)                       219   191   209   \n",
       "       Stöld-, rån- och häleribrott                        1551  1478  1504   \n",
       "\n",
       "                                                           2003  2004  2005  \\\n",
       "region crime                                                                  \n",
       "Ale    Sexualbrott                                           16    22    18   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...     3     2     8   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...     0     2     2   \n",
       "       Skadegörelse (inkl. mordbrand)                       238   321   295   \n",
       "       Stöld-, rån- och häleribrott                        1633  1762  1504   \n",
       "\n",
       "                                                           2006  2007  2008  \\\n",
       "region crime                                                                  \n",
       "Ale    Sexualbrott                                           22    17    12   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...     8     4     6   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...     1     0     2   \n",
       "       Skadegörelse (inkl. mordbrand)                       227   334   550   \n",
       "       Stöld-, rån- och häleribrott                        1107  1442  1467   \n",
       "\n",
       "                                                           2009  \\\n",
       "region crime                                                      \n",
       "Ale    Sexualbrott                                           20   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...     9   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...     5   \n",
       "       Skadegörelse (inkl. mordbrand)                       541   \n",
       "       Stöld-, rån- och häleribrott                        1321   \n",
       "\n",
       "                                                                  ...          \\\n",
       "region crime                                                      ...           \n",
       "Ale    Sexualbrott                                                ...           \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...         ...           \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...         ...           \n",
       "       Skadegörelse (inkl. mordbrand)                             ...           \n",
       "       Stöld-, rån- och häleribrott                               ...           \n",
       "\n",
       "                                                           refugees_per_capita  \\\n",
       "region crime                                                                     \n",
       "Ale    Sexualbrott                                                   64.371437   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...            64.371437   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...            64.371437   \n",
       "       Skadegörelse (inkl. mordbrand)                                64.371437   \n",
       "       Stöld-, rån- och häleribrott                                  64.371437   \n",
       "\n",
       "                                                           refugees_count  \\\n",
       "region crime                                                                \n",
       "Ale    Sexualbrott                                                  188.0   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...           188.0   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...           188.0   \n",
       "       Skadegörelse (inkl. mordbrand)                               188.0   \n",
       "       Stöld-, rån- och häleribrott                                 188.0   \n",
       "\n",
       "                                                           top_refugee_muni  \\\n",
       "region crime                                                                  \n",
       "Ale    Sexualbrott                                                      0.0   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...               0.0   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...               0.0   \n",
       "       Skadegörelse (inkl. mordbrand)                                   0.0   \n",
       "       Stöld-, rån- och häleribrott                                     0.0   \n",
       "\n",
       "                                                           mean_pop  latest  \\\n",
       "region crime                                                                  \n",
       "Ale    Sexualbrott                                          29205.5      28   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...   29205.5      11   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...   29205.5       0   \n",
       "       Skadegörelse (inkl. mordbrand)                       29205.5     330   \n",
       "       Stöld-, rån- och häleribrott                         29205.5     922   \n",
       "\n",
       "                                                                   avg  \\\n",
       "region crime                                                             \n",
       "Ale    Sexualbrott                                           19.529412   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...     6.470588   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...     1.000000   \n",
       "       Skadegörelse (inkl. mordbrand)                       316.000000   \n",
       "       Stöld-, rån- och häleribrott                        1318.529412   \n",
       "\n",
       "                                                                stdev  \\\n",
       "region crime                                                            \n",
       "Ale    Sexualbrott                                           6.539473   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...    3.393333   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...    1.414214   \n",
       "       Skadegörelse (inkl. mordbrand)                      100.657836   \n",
       "       Stöld-, rån- och häleribrott                        244.110661   \n",
       "\n",
       "                                                           latest_vs_mean  \\\n",
       "region crime                                                                \n",
       "Ale    Sexualbrott                                               1.433735   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...        1.700000   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...        0.000000   \n",
       "       Skadegörelse (inkl. mordbrand)                            1.044304   \n",
       "       Stöld-, rån- och häleribrott                              0.699264   \n",
       "\n",
       "                                                           latest_vs_2015  \\\n",
       "region crime                                                                \n",
       "Ale    Sexualbrott                                               1.120000   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...        1.000000   \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...        0.000000   \n",
       "       Skadegörelse (inkl. mordbrand)                            1.031250   \n",
       "       Stöld-, rån- och häleribrott                              0.913776   \n",
       "\n",
       "                                                           latest_vs_mean_std  \n",
       "region crime                                                                   \n",
       "Ale    Sexualbrott                                                   1.295301  \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...            1.334797  \n",
       "       Sexualbrott, Våldtäkt inkl. grov (ingår även i ...           -0.707107  \n",
       "       Skadegörelse (inkl. mordbrand)                                0.139085  \n",
       "       Stöld-, rån- och häleribrott                                 -1.624384  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country = pd.DataFrame().from_csv(u\"data/hela_landet.csv\", encoding=\"utf-8\")\n",
    "df_municipality = pd.DataFrame().from_csv(u\"data/kommuner.csv\", encoding=\"utf-8\")\n",
    "# Remove old Heby\n",
    "df_municipality= df_municipality[df_municipality.region_id!=8608]\n",
    "\n",
    "df_long = pd.concat([df_municipality, df_country])\n",
    "\n",
    "\n",
    "# Harmonisera regionnamnn\n",
    "df_long = pd.merge(df_long, translate_df[[\"region\",u\"brå_id\"]], left_on=\"region\", right_on=u\"brå_id\")\\\n",
    "    .rename(columns={\"region_y\":\"region\"})\n",
    "\n",
    "df_long.loc[:, \"year\"] = df_long.timepoint.str[:4]\n",
    "\n",
    "df_long = df_long.set_index([\"region\",\"crime\", \"year\"])[[\"value\"]]\n",
    "\n",
    "top_refugee_munis = df_long[df_long.index.get_level_values(level=0).isin(TOP_REFUGEE_MUNIS)].groupby(level=[1,2]).sum()\n",
    "top_refugee_munis[\"region\"] = \"Toppmottagare\"\n",
    "top_refugee_munis = top_refugee_munis.reset_index().set_index([\"region\", \"crime\",\"year\"])\n",
    "df_long = pd.concat([df_long, top_refugee_munis])\n",
    "\n",
    "YEARS = df_long.index.get_level_values(2).unique()\n",
    "df = df_long.unstack()\n",
    "df = pd.merge(df.value, refugees_df[[\"refugees_per_capita\", \"refugees_count\",\"top_refugee_muni\",\"mean_pop\"]], left_index=True, right_index=True, how=\"left\")\n",
    "\n",
    "\n",
    "# Computed columns\n",
    "df.loc[:, \"latest\"] = df[\"2016\"]\n",
    "df.loc[:, \"avg\"] = df[YEARS].mean(axis=1)\n",
    "\n",
    "    # Standardavvikelse\n",
    "df.loc[:, \"stdev\"] = df[YEARS].std(axis=1)\n",
    "   \n",
    "    # Hur långt från medel ligger senaste värdet medelvärdet innan 2016?\n",
    "df.loc[:, \"latest_vs_mean\"] = df.latest / df.avg\n",
    "\n",
    "df.loc[:, \"latest_vs_2015\"] = df.latest / df[\"2015\"]\n",
    "\n",
    "\n",
    "    # Hur många standardavvikelser ligger senaste värdet från medel?\n",
    "df.loc[:, \"latest_vs_mean_std\"] = ( df.latest - df.avg ) / df.stdev\n",
    "\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steg 2: Analys\n",
    "\n",
    "Vi börjar med att bara titta på hur antal brottsanmälningar utvecklats i hela landet under de senaste tio åren.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11179c750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d900050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11133d590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111837150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f3f5590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d70fe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "wB38AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb79c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for crime, _df in df.groupby(level=1):\n",
    "    _df.loc[\"Hela landet\"][YEARS].T.plot.bar(title=crime, legend=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Låt oss titta närmare på de kommuner som tagit emot flest flyktingar i förhållande till sin folkmängd. Låg antalet anmälda brott 2016 över eller under medel sedan år 2000?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Toppmottagare: Avesta, Bengtsfors, Filipstad, Flen, Hultsfred, Hylte, Hällefors, Högsby, Lessebo, Ljusnarsberg, Markaryd, Mellerud, Ockelbo, Oxelösund, Ronneby, Sorsele, Storfors, Uppvidinge, Åmål, Östra Göinge\n"
     ]
    }
   ],
   "source": [
    "print u\"Toppmottagare: {}\".format(\", \".join(TOP_REFUGEE_MUNIS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>Toppmottagare</th>\n",
       "      <th>Hela landet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sexualbrott</th>\n",
       "      <td>1.41</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexualbrott, Våldtäkt inkl. grov (ingår även i våldsbrott)</th>\n",
       "      <td>1.41</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexualbrott, Våldtäkt inkl. grov (ingår även i våldsbrott), Mot samtliga personer, Utomhus</th>\n",
       "      <td>0.99</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skadegörelse (inkl. mordbrand)</th>\n",
       "      <td>0.90</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stöld-, rån- och häleribrott</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stöld-, rån- och häleribrott, Stöld och snatteri</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stöld-, rån- och häleribrott, Stöld och snatteri, I butik, varuhus, kommersiell utställningslokal</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Totalt antal brott</th>\n",
       "      <td>0.99</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Våldsbrott</th>\n",
       "      <td>1.33</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Våldsbrott, Misshandel inkl.grov</th>\n",
       "      <td>1.33</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Toppmottagare  Hela landet\n",
       "crime                                                                         \n",
       "Sexualbrott                                                  1.41         1.39\n",
       "Sexualbrott, Våldtäkt inkl. grov (ingår även i ...           1.41         1.40\n",
       "Sexualbrott, Våldtäkt inkl. grov (ingår även i ...           0.99         1.07\n",
       "Skadegörelse (inkl. mordbrand)                               0.90         1.20\n",
       "Stöld-, rån- och häleribrott                                 0.75         0.86\n",
       "Stöld-, rån- och häleribrott, Stöld och snatteri             0.78         0.90\n",
       "Stöld-, rån- och häleribrott, Stöld och snatter...           0.78         0.85\n",
       "Totalt antal brott                                           0.99         1.12\n",
       "Våldsbrott                                                   1.33         1.13\n",
       "Våldsbrott, Misshandel inkl.grov                             1.33         1.14"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "COLS = [\"latest_vs_mean\"]\n",
    "table = pd.concat([df.loc[\"Toppmottagare\"][COLS], df.loc[\"Hela landet\"][COLS]], axis=1).round(2)\n",
    "table.columns = [\"Toppmottagare\", \"Hela landet\"]\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slutsats: Nej, det finns ingen betydande skillnad i antal anmälda brott mellan toppmottagande kommuner och resten av landet.\n",
    "\n",
    "Nedan motsvarande jämförelse som en serie grafer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
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       "JGlgTsYlSZIkSRqYk3FJkiRJkgbmZFySJEmSpIH9H/QczYSFQ5jBAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110ef410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=len(crimes), ncols=2)\n",
    "\n",
    "i = 0\n",
    "for crime, _df in df.loc[\"Toppmottagare\"].groupby(level=0):\n",
    "    _df[YEARS].T.plot.bar(title=u\"Toppmottagare, {}\".format(crime), legend=False, ax = axs[i][0]).set_ylim(0)\n",
    "    df.loc[(\"Hela landet\", crime)][YEARS].T.plot.bar(title=u\"Hela landet\", legend=False, ax = axs[i][1]).set_ylim(0)\n",
    "    i += 1\n",
    "\n",
    "fig.set_size_inches(15, 6 * len(crimes))\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Låt oss även se om det finns nåt samband i form av korrelation. Vi jämför **antalet brott 2016 som avvikelse från medel** (mätt i standardavvikelser) mot **antal flyktingar per 10 000 invånare**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f4ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "rkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b63910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1145a1150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111608a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "4ziTuFJwHMdxJvn/vOKTDVQ2IG8AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dad0550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ae4c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111351110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149d1610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f11f510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_relation(crime, _df):\n",
    "    measure = \"latest_vs_mean_std\" # latest_vs_mean | latest_vs_mean_std\n",
    "    ax = _df.plot.scatter(\"refugees_per_capita\", measure, title=crime)\n",
    "    ax.set_xlabel(u\"Flyktingar per 10 000 inv\")\n",
    "    ax.set_ylabel(u\"Anmälda brott, ökn./minskn. 2016\")\n",
    "    corr = _df[[\"refugees_per_capita\", measure]].corr().iloc[0,1]\n",
    "    ax.text(_df.refugees_per_capita.max(),_df[measure].max(),corr, ha=\"right\")\n",
    "    \n",
    "for crime, _df in df.groupby(level=1):\n",
    "    # Plotta \n",
    "    plot_relation(crime, _df[_df.avg > 20])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#REGION = u\"Toppmottagare\"\n",
    "#for crime, _df in df.groupby(level=1):\n",
    "#    _df.loc[REGION][YEARS].T.plot.bar(title=crime, legend=False)\n"
   ]
  }
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