martinfleis/Donut wards.ipynb

## Donut wards.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import shapely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "wards = gpd.read_file('/Users/martin/Downloads/Wards_December_2018_Generalised_Clipped_Boundaries_UK/Wards_December_2018_Generalised_Clipped_Boundaries_UK.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bac3828>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wards.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ids = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx, row in wards.iterrows():\n",
    "    geom = row.geometry\n",
    "    if geom.type == 'MultiPolygon':\n",
    "        for polygon in geom:\n",
    "            if len(polygon.interiors) > 0:\n",
    "                ids.append(idx)\n",
    "    else:\n",
    "        if len(row.geometry.interiors) > 0:\n",
    "            ids.append(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2003, 2580, 3570, 4428, 4449, 5108, 8285, 8485, 8616]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>objectid</th>\n",
       "      <th>wd18cd</th>\n",
       "      <th>wd18nm</th>\n",
       "      <th>wd18nmw</th>\n",
       "      <th>bng_e</th>\n",
       "      <th>bng_n</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>st_areasha</th>\n",
       "      <th>st_lengths</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>2004</td>\n",
       "      <td>E05003517</td>\n",
       "      <td>Cullompton Outer</td>\n",
       "      <td>None</td>\n",
       "      <td>300921</td>\n",
       "      <td>106074</td>\n",
       "      <td>-3.40869</td>\n",
       "      <td>50.845661</td>\n",
       "      <td>6.097201e+07</td>\n",
       "      <td>66559.367883</td>\n",
       "      <td>POLYGON ((310163.0015000002 108374.0976, 30983...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2580</th>\n",
       "      <td>2581</td>\n",
       "      <td>E05004647</td>\n",
       "      <td>Romsey Extra</td>\n",
       "      <td>None</td>\n",
       "      <td>435703</td>\n",
       "      <td>120086</td>\n",
       "      <td>-1.49279</td>\n",
       "      <td>50.979069</td>\n",
       "      <td>3.248336e+07</td>\n",
       "      <td>46619.638924</td>\n",
       "      <td>POLYGON ((438594.4961999999 122107.7962999996,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3570</th>\n",
       "      <td>3571</td>\n",
       "      <td>E05006762</td>\n",
       "      <td>Beckington and Selwood</td>\n",
       "      <td>None</td>\n",
       "      <td>378777</td>\n",
       "      <td>144028</td>\n",
       "      <td>-2.30509</td>\n",
       "      <td>51.195061</td>\n",
       "      <td>4.284758e+07</td>\n",
       "      <td>54954.464463</td>\n",
       "      <td>POLYGON ((383041.4017000003 151580.1998999994,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4428</th>\n",
       "      <td>4429</td>\n",
       "      <td>E05008394</td>\n",
       "      <td>Sherston</td>\n",
       "      <td>None</td>\n",
       "      <td>387815</td>\n",
       "      <td>186546</td>\n",
       "      <td>-2.17722</td>\n",
       "      <td>51.577641</td>\n",
       "      <td>8.719224e+07</td>\n",
       "      <td>78065.579948</td>\n",
       "      <td>POLYGON ((394137.5966999996 191539.8015999999,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4449</th>\n",
       "      <td>4450</td>\n",
       "      <td>E05008415</td>\n",
       "      <td>West Selkley</td>\n",
       "      <td>None</td>\n",
       "      <td>415647</td>\n",
       "      <td>171748</td>\n",
       "      <td>-1.77624</td>\n",
       "      <td>51.444500</td>\n",
       "      <td>1.727967e+08</td>\n",
       "      <td>85796.940022</td>\n",
       "      <td>POLYGON ((410911.4973999998 179446.0034999996,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>5109</td>\n",
       "      <td>E05009195</td>\n",
       "      <td>Lanivet and Blisland</td>\n",
       "      <td>None</td>\n",
       "      <td>210280</td>\n",
       "      <td>69461</td>\n",
       "      <td>-4.67625</td>\n",
       "      <td>50.494141</td>\n",
       "      <td>1.315793e+08</td>\n",
       "      <td>96207.151539</td>\n",
       "      <td>POLYGON ((215792.7034999998 76228.89900000021,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8285</th>\n",
       "      <td>8286</td>\n",
       "      <td>W05000064</td>\n",
       "      <td>Dolgellau North</td>\n",
       "      <td>Dolgellau - Gogledd</td>\n",
       "      <td>273170</td>\n",
       "      <td>316285</td>\n",
       "      <td>-3.87962</td>\n",
       "      <td>52.729301</td>\n",
       "      <td>3.261040e+07</td>\n",
       "      <td>40793.084857</td>\n",
       "      <td>POLYGON ((271565.9988000002 318811.4005999994,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8485</th>\n",
       "      <td>8486</td>\n",
       "      <td>W05000288</td>\n",
       "      <td>Blaen Hafren</td>\n",
       "      <td>Blaen Hafren</td>\n",
       "      <td>290089</td>\n",
       "      <td>284783</td>\n",
       "      <td>-3.61872</td>\n",
       "      <td>52.449921</td>\n",
       "      <td>2.689864e+08</td>\n",
       "      <td>95660.224479</td>\n",
       "      <td>POLYGON ((300311.8025000002 290970.3000000007,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8616</th>\n",
       "      <td>8617</td>\n",
       "      <td>W05000433</td>\n",
       "      <td>Narberth Rural</td>\n",
       "      <td>Arberth Gwledig</td>\n",
       "      <td>210592</td>\n",
       "      <td>212348</td>\n",
       "      <td>-4.74703</td>\n",
       "      <td>51.777672</td>\n",
       "      <td>2.534989e+07</td>\n",
       "      <td>38109.968684</td>\n",
       "      <td>POLYGON ((212038.8014000002 209993.5993000008,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      objectid     wd18cd                  wd18nm              wd18nmw  \\\n",
       "2003      2004  E05003517        Cullompton Outer                 None   \n",
       "2580      2581  E05004647            Romsey Extra                 None   \n",
       "3570      3571  E05006762  Beckington and Selwood                 None   \n",
       "4428      4429  E05008394                Sherston                 None   \n",
       "4449      4450  E05008415            West Selkley                 None   \n",
       "5108      5109  E05009195    Lanivet and Blisland                 None   \n",
       "8285      8286  W05000064         Dolgellau North  Dolgellau - Gogledd   \n",
       "8485      8486  W05000288            Blaen Hafren         Blaen Hafren   \n",
       "8616      8617  W05000433          Narberth Rural      Arberth Gwledig   \n",
       "\n",
       "       bng_e   bng_n     long        lat    st_areasha    st_lengths  \\\n",
       "2003  300921  106074 -3.40869  50.845661  6.097201e+07  66559.367883   \n",
       "2580  435703  120086 -1.49279  50.979069  3.248336e+07  46619.638924   \n",
       "3570  378777  144028 -2.30509  51.195061  4.284758e+07  54954.464463   \n",
       "4428  387815  186546 -2.17722  51.577641  8.719224e+07  78065.579948   \n",
       "4449  415647  171748 -1.77624  51.444500  1.727967e+08  85796.940022   \n",
       "5108  210280   69461 -4.67625  50.494141  1.315793e+08  96207.151539   \n",
       "8285  273170  316285 -3.87962  52.729301  3.261040e+07  40793.084857   \n",
       "8485  290089  284783 -3.61872  52.449921  2.689864e+08  95660.224479   \n",
       "8616  210592  212348 -4.74703  51.777672  2.534989e+07  38109.968684   \n",
       "\n",
       "                                               geometry  \n",
       "2003  POLYGON ((310163.0015000002 108374.0976, 30983...  \n",
       "2580  POLYGON ((438594.4961999999 122107.7962999996,...  \n",
       "3570  POLYGON ((383041.4017000003 151580.1998999994,...  \n",
       "4428  POLYGON ((394137.5966999996 191539.8015999999,...  \n",
       "4449  POLYGON ((410911.4973999998 179446.0034999996,...  \n",
       "5108  POLYGON ((215792.7034999998 76228.89900000021,...  \n",
       "8285  POLYGON ((271565.9988000002 318811.4005999994,...  \n",
       "8485  POLYGON ((300311.8025000002 290970.3000000007,...  \n",
       "8616  POLYGON ((212038.8014000002 209993.5993000008,...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wards.iloc[ids]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cullompton Outer', 'Romsey Extra', 'Beckington and Selwood',\n",
       "       'Sherston', 'West Selkley', 'Lanivet and Blisland',\n",
       "       'Dolgellau North', 'Blaen Hafren', 'Narberth Rural'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wards.iloc[ids].wd18nm.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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