{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "62d5a33e",
   "metadata": {},
   "source": [
    "# 💻 Reklassifisering av data\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GMGI221-2024/forelesninger/blob/main/06_reklassifisering.ipynb)\n",
    "\n",
    "Reklassifisering av data basert på spesifikke kriterier er en vanlig oppgave når man utfører GIS\n",
    "analyse. Formålet med denne notebooken er å se hvordan vi kan reklassifisere verdier\n",
    "basert på noen kriterier. Man kunne for eksempel klassifisere informasjon basert på\n",
    "reisetider og boligpriser ved hjelp av disse kriteriene:\n",
    "\n",
    "1. Hvis reisetiden til jobben min er mindre enn 30 minutter, **OG**\n",
    "2. husleien for leiligheten er mindre enn 10000 kr per måned\n",
    "\n",
    "Hvis begge kriteriene er oppfylt: Jeg går for å se leiligheten og prøver å leie den\n",
    "Hvis ikke: Jeg fortsetter å lete etter noe annet\n",
    "\n",
    "\n",
    "I denne opplæringen vil vi:\n",
    "\n",
    "1. Bruk klassifiseringsskjemaer fra PySAL [mapclassify\n",
    "   bibliotek](https://pysal.org/mapclassify/) for å klassifisere befolkningstall til\n",
    "   flere klasser.\n",
    "\n",
    "2. Opprett en egendefinert klassifisering for å klassifisere og benytte den\n",
    "\n",
    "\n",
    "## Inputdata\n",
    "\n",
    "Vi vil bruke befolkningsdata fra [SSB](https://kart.ssb.no/) for Oslo, som inneholder 2380 celler for Oslo kommune."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "db569794",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib \n",
    "NOTEBOOK_PATH = pathlib.Path().resolve()\n",
    "DATA_MAPPE = NOTEBOOK_PATH / \"data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc34b2cc-932b-46a9-9f9e-646610fb7643",
   "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>fid</th>\n",
       "      <th>ru250m</th>\n",
       "      <th>pop_tot</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.263751e+13</td>\n",
       "      <td>14</td>\n",
       "      <td>POLYGON ((264000.000 6643000.000, 263750.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.264001e+13</td>\n",
       "      <td>177</td>\n",
       "      <td>POLYGON ((264250.000 6643000.000, 264000.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.264251e+13</td>\n",
       "      <td>169</td>\n",
       "      <td>POLYGON ((264500.000 6643000.000, 264250.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.264501e+13</td>\n",
       "      <td>261</td>\n",
       "      <td>POLYGON ((264750.000 6643000.000, 264500.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.264751e+13</td>\n",
       "      <td>106</td>\n",
       "      <td>POLYGON ((265000.000 6643000.000, 264750.000 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fid        ru250m  pop_tot  \\\n",
       "0  1.0  2.263751e+13       14   \n",
       "1  2.0  2.264001e+13      177   \n",
       "2  3.0  2.264251e+13      169   \n",
       "3  4.0  2.264501e+13      261   \n",
       "4  5.0  2.264751e+13      106   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((264000.000 6643000.000, 263750.000 6...  \n",
       "1  POLYGON ((264250.000 6643000.000, 264000.000 6...  \n",
       "2  POLYGON ((264500.000 6643000.000, 264250.000 6...  \n",
       "3  POLYGON ((264750.000 6643000.000, 264500.000 6...  \n",
       "4  POLYGON ((265000.000 6643000.000, 264750.000 6...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas\n",
    "\n",
    "rutenett = geopandas.read_file(DATA_MAPPE / \"ssb_rutenett\" / \"befolkning_250m_2023_oslo.shp\")\n",
    "\n",
    "rutenett.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e83115b",
   "metadata": {},
   "source": [
    "## Vanlige klassifiseringer\n",
    "\n",
    "### Klassifiseringsskjemaer for tematiske kart\n",
    "\n",
    "[PySAL](https://pysal.org/) -modulen er et omfattende Python-bibliotek for romlig\n",
    "analyse. Det inkluderer også alle de vanligste dataklassifiseringene som er\n",
    "brukt vanlig f.eks. når man visualiserer data. Tilgjengelige kartklassifiseringer i [pysal's\n",
    "mapclassify -modul](https://github.com/pysal/mapclassify):\n",
    "\n",
    "- Box Plot\n",
    "- Equal Interval\n",
    "- Fisher Jenks\n",
    "- Fisher Jenks Sampled\n",
    "- HeadTail Breaks\n",
    "- Jenks Caspall\n",
    "- Jenks Caspall Forced\n",
    "- Jenks Caspall Sampled\n",
    "- Max P Classifier\n",
    "- Maximum Breaks\n",
    "- Natural Breaks\n",
    "- Quantiles\n",
    "- Percentiles\n",
    "- Std Mean\n",
    "- User Defined\n",
    "\n",
    "\n",
    "\n",
    "**NoData-verdiene presenteres med verdi -1**. \n",
    "Derfor må vi først fjerne No Data-verdiene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b2dbb165",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inkluder bare data som er over eller lik 0\n",
    "rutenett = rutenett.loc[rutenett[\"pop_tot\"] >=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e56ed55",
   "metadata": {},
   "source": [
    "La oss plotte dataene og se hvordan det ser ut\n",
    "- `cmap` parameter definerer fargekartet. Les mer om [valg av fargekart i matplotlib](https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html)\n",
    "- `scheme` alternativ skalerer fargene i henhold til et klassifiseringsskjema (krever at `mapclassify` modulen er installert):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fb540ac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plott ved hjelp av 9 klasser og klassifiser verdiene ved hjelp av \"Natural Breaks\" klassifisering\n",
    "rutenett.plot(column=\"pop_tot\", scheme=\"Natural_Breaks\", k=9, cmap=\"RdYlBu\", linewidth=0, legend=True, aspect=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f6591cc",
   "metadata": {},
   "source": [
    "Som vi kan se fra dette kartet, er befolkningen større i sentrum, men det er også noen områder med få beboere i noen andre områder (hvor fargen er rød)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c710848",
   "metadata": {},
   "source": [
    "### Bruk av klassifiseringer på data\n",
    "\n",
    "Som nevnt, definerer `scheme` alternativet klassifiseringsskjemaet ved hjelp av\n",
    "`pysal/mapclassify`. La oss se nærmere på hvordan disse klassifiseringene fungerer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0b50da15",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mapclassify"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b889e486",
   "metadata": {},
   "source": [
    "#### Natural Breaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a92c8e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaturalBreaks\n",
       "\n",
       "     Interval        Count\n",
       "--------------------------\n",
       "[   1.00,   82.00] |   675\n",
       "(  82.00,  198.00] |   461\n",
       "( 198.00,  313.00] |   515\n",
       "( 313.00,  461.00] |   283\n",
       "( 461.00,  654.00] |   182\n",
       "( 654.00,  896.00] |   100\n",
       "( 896.00, 1212.00] |    81\n",
       "(1212.00, 1618.00] |    58\n",
       "(1618.00, 2351.00] |    25"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapclassify.NaturalBreaks(y=rutenett[\"pop_tot\"], k=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ca8389",
   "metadata": {},
   "source": [
    "#### Quantiles (standard er 5 klasser):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b397080f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quantiles\n",
       "\n",
       "     Interval        Count\n",
       "--------------------------\n",
       "[   1.00,   31.00] |   480\n",
       "(  31.00,  160.00] |   476\n",
       "( 160.00,  262.00] |   476\n",
       "( 262.00,  433.20] |   472\n",
       "( 433.20, 2351.00] |   476"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapclassify.Quantiles(y=rutenett[\"pop_tot\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2659d8",
   "metadata": {},
   "source": [
    "#### Trekk ut terskelverdiene\n",
    "\n",
    "Det er mulig å trekke ut terskelverdiene i en matrise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b37a2b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  83.,  198.,  315.,  462.,  658.,  896., 1212., 1618., 2351.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klassifiserer = mapclassify.NaturalBreaks(y=rutenett[\"pop_tot\"], k=9)\n",
    "klassifiserer.bins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ddfcc8e",
   "metadata": {},
   "source": [
    "La oss bruke en av `Pysal` klassifiseringene på dataene våre og klassifisere\n",
    "befolkningen til 9 klasser\n",
    "Klassifiseringen må først initialiseres med `make()` funksjonen som tar\n",
    "antall ønskede klasser som inngangsparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a147e645",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lag en Natural Breaks klassifisering\n",
    "klassifiserer = mapclassify.NaturalBreaks.make(k=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc94ef05",
   "metadata": {},
   "source": [
    "- Nå kan vi bruke klassifiseringen på dataene våre ved å bruke `apply` -funksjonen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3ce4f0c",
   "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>pop_tot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pop_tot\n",
       "0        0\n",
       "1        1\n",
       "2        1\n",
       "3        2\n",
       "4        1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Klassifiser dataene\n",
    "klassifiseringer = rutenett[[\"pop_tot\"]].apply(klassifiserer)\n",
    "\n",
    "# La oss se hva vi har\n",
    "klassifiseringer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0bce101c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(klassifiseringer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c37942d",
   "metadata": {},
   "source": [
    "Ok, så nå har vi en DataFrame der inngangskolonnen vår ble klassifisert til 9\n",
    "forskjellige klasser (tallene 1-9) basert på [Natural Breaks\n",
    "klassifisering](http://wiki-1-1930356585.us-east-1.elb.amazonaws.com/wiki/index.php/Jenks_Natural_Breaks_Classification).\n",
    "\n",
    "Vi kan også legge til klassifiseringsverdiene direkte i en ny kolonne i vår dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1cfa98aa",
   "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>pop_tot</th>\n",
       "      <th>nb_pop_tot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>177</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>261</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pop_tot  nb_pop_tot\n",
       "0       14           0\n",
       "1      177           1\n",
       "2      169           1\n",
       "3      261           2\n",
       "4      106           1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gi nytt navn til kolonnen så vi vet at den ble klassifisert med naturlige brudd\n",
    "rutenett[\"nb_pop_tot\"] = rutenett[[\"pop_tot\"]].apply(klassifiserer)\n",
    "\n",
    "# Sjekk de opprinnelige verdiene og klassifiseringen\n",
    "rutenett[[\"pop_tot\", \"nb_pop_tot\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca2305e5",
   "metadata": {},
   "source": [
    "Flott, nå har vi disse verdiene i GeoDataFramen. La oss\n",
    "visualisere resultatene og se hvordan de ser ut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32c53729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plott\n",
    "rutenett.plot(column=\"nb_pop_tot\", linewidth=0, legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d12dc8a1",
   "metadata": {},
   "source": [
    "Og her går vi, nå har vi et kart der vi har brukt en av de vanlige\n",
    "klassifiseringene for å klassifisere dataene våre i 9 klasser.\n",
    "\n",
    "\n",
    "## Plott et histogram\n",
    "\n",
    "Et histogram er en grafisk representasjon av datafordelingen. Når\n",
    "man klassifiserer data, er det alltid bra å vurdere hvordan dataene er fordelt,\n",
    "og hvordan klassifiseringsskjemaet deler verdier i forskjellige områder.\n",
    "\n",
    "- plott histogrammet ved hjelp av [pandas.DataFrame.plot.hist](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.hist.html)\n",
    "- Antall histogrambinner (grupper av data) kan kontrolleres ved hjelp av parameteren `bins`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70708402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mR5LOnz+v3bt364UXXlBbW5ukr2dV2tvbr+lxDh8+rD/7sz9TQkKC5s+fr+3bt+t73/uevF6vJCkzMzOoPjMzM3DM6/UqPj5eQ4cOvWzNpVRWVsrpdAa2rKysa+oZAAAMHiHN7Jw8eVL33nuvTp06Jb/fr6KiIiUnJ2v16tX66quv9Pzzz1/1Y33nO9/RoUOH9MUXX+jf//3fNWfOHNXV1QWOX/wdPsaYK36vz5Vqli9friVLlgRut7a2EngAALBUSDM7ixcv1tixY9XS0qLExMTA/oceeki//e1vr+mxLlzGPnbsWFVWVmrMmDH6+c9/LpfLJUm9Zmiam5sDsz0ul0udnZ1qaWm5bM2lJCQkBK4Au7ABAAA7hRR29u3bp3/6p39SfHx80P6RI0fq008/7VNDxhj5/X5lZ2fL5XKptrY2cKyzs1N1dXUqLCyUJOXn5ysuLi6oxuPx6MiRI4EaAABwfQvpY6yenh51d3f32t/U1BS4JPxq/OM//qMmT56srKwstbW1qaamRm+//bZ27twph8Oh0tJSVVRUKCcnRzk5OaqoqNCQIUM0c+ZMSZLT6dTcuXNVVlamtLQ0paamqry8XHl5eZo0aVIopwYAACwTUtgpKirSunXr9OKLL0r6el1Ne3u7nnzyyWv6CYnPPvtMs2fPlsfjkdPp1C233KKdO3eqqKhIkrR06VJ1dHRowYIFamlpUUFBgXbt2hUUqNauXavY2FhNnz5dHR0dmjhxoqqrq/mOHQAAICnE79k5c+aM7rrrLsXExOj48eMaO3asjh8/rvT0dL3zzjvKyMjoj177Dd+zAwDA4NOv37Pjdrt16NAhvfLKK3r//ffV09OjuXPnatasWUELlgEAACItpLAjSYmJiXr88cf1+OOPh7MfAACAsAop7GzevPkbjz/yyCMhNQMAABBuIYWdxYsXB93u6urSl19+qfj4eA0ZMoSwAwAAokZI37PT0tIStLW3t+vYsWO6/fbb9corr4S7RwAAgJCF/NtYF8vJydGqVat6zfoAAABEUtjCjiTFxMTozJkz4XxIAACAPglpzc7rr78edNsYI4/Ho/Xr1+tHP/pRWBoDAAAIh5DCzoMPPhh02+Fw6MYbb9Tdd9+tZ555Jhx9AQAAhEXIv40FAAAwGIR1zQ4AAEC0CWlmZ8mSJVddW1VVFcpTAAAAhEVIYefgwYN6//33df78eX3nO9+RJP3hD39QTEyMbr311kCdw+EIT5cAAAAhCins3H///UpOTtamTZs0dOhQSV9/0eBjjz2mO+64Q2VlZWFtEgAAIFQhrdl55plnVFlZGQg6kjR06FA9/fTTXI0FAACiSkhhp7W1VZ999lmv/c3NzWpra+tzUwAAAOESUth56KGH9Nhjj+nXv/61mpqa1NTUpF//+teaO3eupk2bFu4eAQAAQhbSmp3nn39e5eXl+slPfqKurq6vHyg2VnPnztWaNWvC2iAAAEBfhBR2hgwZomeffVZr1qzRxx9/LGOMbr75ZiUlJYW7PwAAgD7p05cKejweeTwejRo1SklJSTLGhKsvAACAsAgp7Jw9e1YTJ07UqFGjdN9998nj8UiS/uZv/obLzgEAQFQJKez89Kc/VVxcnE6dOqUhQ4YE9s+YMUM7d+4MW3MAAAB9FdKanV27dumtt97S8OHDg/bn5OTo5MmTYWkMAAAgHEKa2Tl37lzQjM4Fn3/+uRISEvrcFAAAQLiEFHbuvPNObd68OXDb4XCop6dHa9as0V133RW25gAAAPoqpI+x1qxZowkTJui9995TZ2enli5dqqNHj+pPf/qT/vu//zvcPQIAAIQspJmd733ve/rwww/1wx/+UEVFRTp37pymTZumgwcP6tvf/na4ewQAAAjZNc/sdHV1qbi4WC+88IKeeuqp/ugJAAAgbK55ZicuLk5HjhyRw+Hoj34AAADCKqSPsR555BFt2LAh3L0AAACEXUgLlDs7O/Wv//qvqq2t1dixY3v9JlZVVVVYmgMAAOirawo7n3zyiW666SYdOXJEt956qyTpD3/4Q1ANH28BAIBock1hJycnRx6PR3v37pX09c9D/OIXv1BmZma/NAcAANBX17Rm5+JfNX/zzTd17ty5sDYEAAAQTiEtUL7g4vADAAAQba4p7Dgcjl5rclijAwAAotk1rdkxxujRRx8N/NjnV199pfnz5/e6Gmvbtm3h6xARcdOyHVesObFqygB0AgBA31xT2JkzZ07Q7Z/85CdhbQYAACDcrinsbNy4sb/6AAAA6Bd9WqAMAAAQ7Qg7AADAaoQdAABgtZB+GwuD29VcaQUAgC2Y2QEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAq0U07FRWVuoHP/iBkpOTlZGRoQcffFDHjh0LqjHGaOXKlXK73UpMTNSECRN09OjRoBq/369FixYpPT1dSUlJmjp1qpqamgbyVAAAQJSKaNipq6vTE088of3796u2tlbnz59XcXGxzp07F6hZvXq1qqqqtH79eh04cEAul0tFRUVqa2sL1JSWlmr79u2qqanRvn371N7erpKSEnV3d0fitAAAQBSJ6A+B7ty5M+j2xo0blZGRoYaGBt15550yxmjdunVasWKFpk2bJknatGmTMjMztXXrVs2bN08+n08bNmzQyy+/rEmTJkmStmzZoqysLO3evVv33HPPgJ8XAACIHlG1Zsfn80mSUlNTJUmNjY3yer0qLi4O1CQkJGj8+PGqr6+XJDU0NKirqyuoxu12Kzc3N1BzMb/fr9bW1qANAADYKWrCjjFGS5Ys0e23367c3FxJktfrlSRlZmYG1WZmZgaOeb1excfHa+jQoZetuVhlZaWcTmdgy8rKCvfpAACAKBE1YWfhwoX68MMP9corr/Q65nA4gm4bY3rtu9g31Sxfvlw+ny+wnT59OvTGAQBAVIuKsLNo0SK9/vrr2rt3r4YPHx7Y73K5JKnXDE1zc3Ngtsflcqmzs1MtLS2XrblYQkKCUlJSgjYAAGCniIYdY4wWLlyobdu2ac+ePcrOzg46np2dLZfLpdra2sC+zs5O1dXVqbCwUJKUn5+vuLi4oBqPx6MjR44EagAAwPUroldjPfHEE9q6dav+4z/+Q8nJyYEZHKfTqcTERDkcDpWWlqqiokI5OTnKyclRRUWFhgwZopkzZwZq586dq7KyMqWlpSk1NVXl5eXKy8sLXJ11Pblp2Y5ItwAAQFSJaNh57rnnJEkTJkwI2r9x40Y9+uijkqSlS5eqo6NDCxYsUEtLiwoKCrRr1y4lJycH6teuXavY2FhNnz5dHR0dmjhxoqqrqxUTEzNQpwIAAKKUwxhjIt1EpLW2tsrpdMrn84V9/c7VzLScWDVlQJ8vXMLZNwAA1+pq37+jYoEyAABAfyHsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrRfS3sXD1+IFPAABCw8wOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqfM9OFOA7dAAA6D/M7AAAAKsRdgAAgNUIOwAAwGqs2UHIrmat0YlVUwagEwAALo+ZHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFaLjXQDsNtNy3ZcsebEqikD0AkA4HrFzA4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYLWIhp133nlH999/v9xutxwOh37zm98EHTfGaOXKlXK73UpMTNSECRN09OjRoBq/369FixYpPT1dSUlJmjp1qpqamgbwLAAAQDSLaNg5d+6cxowZo/Xr11/y+OrVq1VVVaX169frwIEDcrlcKioqUltbW6CmtLRU27dvV01Njfbt26f29naVlJSou7t7oE4DAABEsYj+XMTkyZM1efLkSx4zxmjdunVasWKFpk2bJknatGmTMjMztXXrVs2bN08+n08bNmzQyy+/rEmTJkmStmzZoqysLO3evVv33HPPgJ0LAACITlG7ZqexsVFer1fFxcWBfQkJCRo/frzq6+slSQ0NDerq6gqqcbvdys3NDdRcit/vV2tra9AGAADsFLVhx+v1SpIyMzOD9mdmZgaOeb1excfHa+jQoZetuZTKyko5nc7AlpWVFebuAQBAtIjasHOBw+EIum2M6bXvYleqWb58uXw+X2A7ffp0WHoFAADRJ2rDjsvlkqReMzTNzc2B2R6Xy6XOzk61tLRctuZSEhISlJKSErQBAAA7RW3Yyc7OlsvlUm1tbWBfZ2en6urqVFhYKEnKz89XXFxcUI3H49GRI0cCNQAA4PoW0aux2tvb9X//93+B242NjTp06JBSU1M1YsQIlZaWqqKiQjk5OcrJyVFFRYWGDBmimTNnSpKcTqfmzp2rsrIypaWlKTU1VeXl5crLywtcnQUAAK5vEQ077733nu66667A7SVLlkiS5syZo+rqai1dulQdHR1asGCBWlpaVFBQoF27dik5OTlwn7Vr1yo2NlbTp09XR0eHJk6cqOrqasXExAz4+QAAgOjjMMaYSDcRaa2trXI6nfL5fGFfv3PTsh1hfTwbnVg1JdItAAAGoat9/47aNTsAAADhQNgBAABWI+wAAACrEXYAAIDVIno1FiBd3SJuFjEDAELFzA4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYLTbSDQBX46ZlO65Yc2LVlAHoBAAw2DCzAwAArMbMDqzB7A8A4FKY2QEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDWuxgIucjVXdV0NrvwCgOhA2MF1JVxBBgAwePAxFgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1bj0HOgn/Ao7AEQHZnYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNq7GACOKKLQDof4QdwAID+WvuAxm+CIMAwoGPsQAAgNWY2QGi3EDO2uCbMdMEDE6EHQDXhDd8AIMNH2MBAACrMbMDIOyu54/emPkCog9hB8CgRrgAcCXWhJ1nn31Wa9askcfj0ejRo7Vu3TrdcccdkW4LQBQYjIEoXLNj0XZeA2kw/t3RP6wIO6+++qpKS0v17LPP6kc/+pFeeOEFTZ48WR999JFGjBgR6fYAIGJ4wwckhzHGRLqJviooKNCtt96q5557LrDvr/7qr/Tggw+qsrLyivdvbW2V0+mUz+dTSkpKWHu7ntcuALBHuAJRtP03cSDPi1AZflf7/j3oZ3Y6OzvV0NCgZcuWBe0vLi5WfX39Je/j9/vl9/sDt30+n6SvBy3cevxfhv0xAWCgjfjpa5FuoV+E67/7V/Pf+v54j7neXRjTK83bDPqw8/nnn6u7u1uZmZlB+zMzM+X1ei95n8rKSj311FO99mdlZfVLjwCA6ORcZ+dzXW/a2trkdDove3zQh50LHA5H0G1jTK99FyxfvlxLliwJ3O7p6dGf/vQnpaWlXfY+oWhtbVVWVpZOnz4d9o/HcGWMf2Qx/pHF+EcW4z8wjDFqa2uT2+3+xrpBH3bS09MVExPTaxanubm512zPBQkJCUpISAja9+d//uf91aJSUlJ4sUcQ4x9ZjH9kMf6Rxfj3v2+a0blg0H+Dcnx8vPLz81VbWxu0v7a2VoWFhRHqCgAARItBP7MjSUuWLNHs2bM1duxYjRs3Ti+++KJOnTql+fPnR7o1AAAQYVaEnRkzZujs2bP653/+Z3k8HuXm5uqNN97QyJEjI9pXQkKCnnzyyV4fmWFgMP6RxfhHFuMfWYx/dLHie3YAAAAuZ9Cv2QEAAPgmhB0AAGA1wg4AALAaYQcAAFiNsNOPnn32WWVnZ+tb3/qW8vPz9V//9V+RbmnQW7lypRwOR9DmcrkCx40xWrlypdxutxITEzVhwgQdPXo06DH8fr8WLVqk9PR0JSUlaerUqWpqahroUxkU3nnnHd1///1yu91yOBz6zW9+E3Q8XOPd0tKi2bNny+l0yul0avbs2friiy/6+eyi35XG/9FHH+3178Ntt90WVMP4h6ayslI/+MEPlJycrIyMDD344IM6duxYUA2v/8GDsNNPXn31VZWWlmrFihU6ePCg7rjjDk2ePFmnTp2KdGuD3ujRo+XxeALb4cOHA8dWr16tqqoqrV+/XgcOHJDL5VJRUZHa2toCNaWlpdq+fbtqamq0b98+tbe3q6SkRN3d3ZE4nah27tw5jRkzRuvXr7/k8XCN98yZM3Xo0CHt3LlTO3fu1KFDhzR79ux+P79od6Xxl6R777036N+HN954I+g44x+auro6PfHEE9q/f79qa2t1/vx5FRcX69y5c4EaXv+DiEG/+OEPf2jmz58ftO+73/2uWbZsWYQ6ssOTTz5pxowZc8ljPT09xuVymVWrVgX2ffXVV8bpdJrnn3/eGGPMF198YeLi4kxNTU2g5tNPPzU33HCD2blzZ7/2PthJMtu3bw/cDtd4f/TRR0aS2b9/f6Dm3XffNZLM//7v//bzWQ0eF4+/McbMmTPHPPDAA5e9D+MfPs3NzUaSqaurM8bw+h9smNnpB52dnWpoaFBxcXHQ/uLiYtXX10eoK3scP35cbrdb2dnZevjhh/XJJ59IkhobG+X1eoPGPSEhQePHjw+Me0NDg7q6uoJq3G63cnNz+dtco3CN97vvviun06mCgoJAzW233San08nf5Cq8/fbbysjI0KhRo/S3f/u3am5uDhxj/MPH5/NJklJTUyXx+h9sCDv94PPPP1d3d3evHyLNzMzs9YOluDYFBQXavHmz3nrrLb300kvyer0qLCzU2bNnA2P7TePu9XoVHx+voUOHXrYGVydc4+31epWRkdHr8TMyMvibXMHkyZP1b//2b9qzZ4+eeeYZHThwQHfffbf8fr8kxj9cjDFasmSJbr/9duXm5kri9T/YWPFzEdHK4XAE3TbG9NqHazN58uTAP+fl5WncuHH69re/rU2bNgUWZoYy7vxtQheO8b5UPX+TK5sxY0bgn3NzczV27FiNHDlSO3bs0LRp0y57P8b/2ixcuFAffvih9u3b1+sYr//BgZmdfpCenq6YmJheqby5ubnX/wWgb5KSkpSXl6fjx48Hrsr6pnF3uVzq7OxUS0vLZWtwdcI13i6XS5999lmvx//jH//I3+QaDRs2TCNHjtTx48clMf7hsGjRIr3++uvau3evhg8fHtjP639wIez0g/j4eOXn56u2tjZof21trQoLCyPUlZ38fr9+//vfa9iwYcrOzpbL5Qoa987OTtXV1QXGPT8/X3FxcUE1Ho9HR44c4W9zjcI13uPGjZPP59Pvfve7QM3//M//yOfz8Te5RmfPntXp06c1bNgwSYx/XxhjtHDhQm3btk179uxRdnZ20HFe/4NMRJZFXwdqampMXFyc2bBhg/noo49MaWmpSUpKMidOnIh0a4NaWVmZefvtt80nn3xi9u/fb0pKSkxycnJgXFetWmWcTqfZtm2bOXz4sPnxj39shg0bZlpbWwOPMX/+fDN8+HCze/du8/7775u7777bjBkzxpw/fz5SpxW12trazMGDB83BgweNJFNVVWUOHjxoTp48aYwJ33jfe++95pZbbjHvvvuueffdd01eXp4pKSkZ8PONNt80/m1tbaasrMzU19ebxsZGs3fvXjNu3DjzF3/xF4x/GPzd3/2dcTqd5u233zYejyewffnll4EaXv+DB2GnH/3yl780I0eONPHx8ebWW28NXLKI0M2YMcMMGzbMxMXFGbfbbaZNm2aOHj0aON7T02OefPJJ43K5TEJCgrnzzjvN4cOHgx6jo6PDLFy40KSmpprExERTUlJiTp06NdCnMijs3bvXSOq1zZkzxxgTvvE+e/asmTVrlklOTjbJyclm1qxZpqWlZYDOMnp90/h/+eWXpri42Nx4440mLi7OjBgxwsyZM6fX2DL+obnUuEsyGzduDNTw+h88HMYYM9CzSQAAAAOFNTsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWO3/AYTYq2000RyJAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for befolkningsrutenettet\n",
    "rutenett[\"pop_tot\"].plot.hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d00cd27f",
   "metadata": {},
   "source": [
    "La oss også legge til terskelverdier på toppen av histogrammet som vertikale linjer.\n",
    "\n",
    "- Natural Breaks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "004f88fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Definer klassifiserer\n",
    "klassifiserer = mapclassify.NaturalBreaks(y=rutenett[\"pop_tot\"], k=9)\n",
    "\n",
    "# Plott histogram for befolkningsrutenettet\n",
    "rutenett[\"pop_tot\"].plot.hist(bins=50)\n",
    "\n",
    "# Legg til vertikale linjer for klassebrudd\n",
    "for break_point in klassifiserer.bins:\n",
    "    plt.axvline(break_point, color=\"k\", linestyle=\"dashed\", linewidth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd440e4",
   "metadata": {},
   "source": [
    "- Kvartiler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8fcd562c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Definer klassifiserer\n",
    "klassifiserer = mapclassify.Quantiles(y=rutenett['pop_tot'])\n",
    "\n",
    "# Plott histogram for befolkningsrutenettet\n",
    "rutenett[\"pop_tot\"].plot.hist(bins=50)\n",
    "\n",
    "for break_point in klassifiserer.bins:\n",
    "    plt.axvline(break_point, color=\"k\", linestyle=\"dashed\", linewidth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55516b7f",
   "metadata": {},
   "source": [
    "## Bruke en egendefinert klassifisering\n",
    "\n",
    "### Multikriterium dataklassifisering\n",
    "\n",
    "La oss klassifisere geometriene i to klasser basert\n",
    "på en gitt `terskel` -parameter. Hvis området til en polygon er lavere enn terskelverdien, vil utgangskolonnen få en verdi\n",
    "0, hvis den er større, vil den få en verdi 1. Denne typen klassifisering kalles ofte en [binær\n",
    "klassifisering](https://en.wikipedia.org/wiki/Binary_classification).\n",
    "\n",
    "For å klassifisere hver rad i vår GeoDataFrame kan vi iterere over hver rad, eller vi kan bruke\n",
    "en funksjon for hver rad. I vårt tilfelle vil vi bruke en lambda funksjon for hver rad i\n",
    "vår GeoDataFrame, som returnerer en verdi basert på de vilkårene vi gir.\n",
    "\n",
    "La oss klassifisere basert på to kriterier: og finne ut rutenettceller\n",
    "\n",
    "1. Rutenettceller der befolkningen er **mindre eller lik 50** \n",
    "\n",
    "2. *og* Rutenettceller der befolkningen er **større eller lik 15** \n",
    "\n",
    "La oss først se hvordan vi klassifiserer en enkelt rad:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "264281fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rutenett.iloc[0][\"pop_tot\"] <= 50 and rutenett.iloc[0][\"pop_tot\"] > 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef703b73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(rutenett.iloc[2000][\"pop_tot\"] < 50 and rutenett.iloc[2000][\"pop_tot\"] > 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fa0c8ae",
   "metadata": {},
   "source": [
    "La oss nå bruke dette på vår GeoDataFrame og lagre det i en kolonne kalt `\"egnet\"`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d9109649",
   "metadata": {},
   "outputs": [],
   "source": [
    "rutenett[\"egnet\"] = rutenett.apply(lambda row: int(row[\"pop_tot\"] < 50 and row[\"pop_tot\"] > 15), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "51f1ea50",
   "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>fid</th>\n",
       "      <th>ru250m</th>\n",
       "      <th>pop_tot</th>\n",
       "      <th>geometry</th>\n",
       "      <th>nb_pop_tot</th>\n",
       "      <th>egnet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.263751e+13</td>\n",
       "      <td>14</td>\n",
       "      <td>POLYGON ((264000.000 6643000.000, 263750.000 6...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.264001e+13</td>\n",
       "      <td>177</td>\n",
       "      <td>POLYGON ((264250.000 6643000.000, 264000.000 6...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.264251e+13</td>\n",
       "      <td>169</td>\n",
       "      <td>POLYGON ((264500.000 6643000.000, 264250.000 6...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.264501e+13</td>\n",
       "      <td>261</td>\n",
       "      <td>POLYGON ((264750.000 6643000.000, 264500.000 6...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.264751e+13</td>\n",
       "      <td>106</td>\n",
       "      <td>POLYGON ((265000.000 6643000.000, 264750.000 6...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fid        ru250m  pop_tot  \\\n",
       "0  1.0  2.263751e+13       14   \n",
       "1  2.0  2.264001e+13      177   \n",
       "2  3.0  2.264251e+13      169   \n",
       "3  4.0  2.264501e+13      261   \n",
       "4  5.0  2.264751e+13      106   \n",
       "\n",
       "                                            geometry  nb_pop_tot  egnet  \n",
       "0  POLYGON ((264000.000 6643000.000, 263750.000 6...           0      0  \n",
       "1  POLYGON ((264250.000 6643000.000, 264000.000 6...           1      0  \n",
       "2  POLYGON ((264500.000 6643000.000, 264250.000 6...           1      0  \n",
       "3  POLYGON ((264750.000 6643000.000, 264500.000 6...           2      0  \n",
       "4  POLYGON ((265000.000 6643000.000, 264750.000 6...           1      0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rutenett.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "213f26e3",
   "metadata": {},
   "source": [
    "Ok, vi har nye verdier i `egnet` -kolonnen.\n",
    "\n",
    "- Hvor mange polygoner passer for oss? La oss finne ut ved hjelp av en Pandas\n",
    "  funksjon kalt `value_counts()` som returnerer antall forskjellige verdier i\n",
    "  vår kolonne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e2495b61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "egnet\n",
       "0    2216\n",
       "1     164\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Få antall verdier\n",
    "rutenett[\"egnet\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bf222dc",
   "metadata": {},
   "source": [
    "Ok, så det ser ut til å være 164 passende steder.\n",
    "\n",
    "- La oss se hvor de er lokalisert:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "73a84681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plott\n",
    "rutenett.plot(column=\"egnet\", linewidth=0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gmgi221",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}