WI 2022 WARD BAF.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "5109b5c9",
   "metadata": {},
   "source": [
    "#### Sources\n",
    "\n",
    "- [Wisconsin 2022 Wards](https://data-ltsb.opendata.arcgis.com/datasets/LTSB::wisconsin-redistricting-wards-2022/explore) \n",
    "- [RDH Wisconsin 2020 Census Block Boundaries](https://redistrictingdatahub.org/dataset/wisconsin-block-boundaries-2020/)\n",
    "- [RDH 2022 Wisconsin PBER](https://redistrictingdatahub.org/wp-content/uploads/2023/12/readme_wi_gen_22_ru.txt)\n",
    "- [RDH Github 2022 Wisc Processing Code](https://github.com/nonpartisan-redistricting-datahub/pber_collection/blob/main/WI/2022/wi_pber_v0.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8880a67",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gp\n",
    "import maup\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce7c39a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read in 2020 WI census blocks\n",
    "wi_blocks_raw = gp.read_file(\"./raw-from-source/wi_b_2020_bound/wi_b_2020_bound.shp\").to_crs(4326)\n",
    "wi_blocks = wi_blocks_raw[['GEOID20', 'geometry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f2fd924",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read in 2022 WI Wards\n",
    "wiwards22_raw = gp.read_file(\"./raw-from-source/Wisconsin_Redistricting_Wards.geojson\").to_crs(4326)\n",
    "wi_wards = wiwards22_raw[['OBJECTID', 'WARDID','geometry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aeaa5f97",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read in RDH 2022 WI Wards\n",
    "rdh_wiwards_raw = gp.read_file(\"./raw-from-source/LF_rfs/WI_Statewide_WardsOct14_2022/Statewide_WardsOct14_2022.shp\").to_crs(4326)\n",
    "rdh_wards = rdh_wiwards_raw[['WARD_FIPS', 'WARDID','geometry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d6b0e455",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7032 6971\n"
     ]
    }
   ],
   "source": [
    "print(len(wi_wards), len(rdh_wards))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ce80d89",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WARD_FIPS</th>\n",
       "      <th>WARDID</th>\n",
       "      <th>geometry</th>\n",
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      "text/plain": [
       "        WARD_FIPS WARDID                                           geometry\n",
       "0  55001002750001   0001  POLYGON ((-89.82790 43.96682, -89.82784 43.964...\n",
       "1  55001002750002   0002  POLYGON ((-89.80944 43.95336, -89.81006 43.953...\n",
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       "4  55001002750005   0005  POLYGON ((-89.80944 43.95336, -89.80942 43.952..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdh_wards.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10356766",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aditi\\anaconda3\\lib\\site-packages\\maup\\intersections.py:51: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  df = df[df.area > area_cutoff].reset_index(drop=True)\n",
      "C:\\Users\\aditi\\anaconda3\\lib\\site-packages\\maup\\intersections.py:52: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  geometries = geometries[geometries.area > area_cutoff]\n",
      "C:\\Users\\aditi\\anaconda3\\lib\\site-packages\\maup\\assign.py:38: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  return assign_to_max(intersections(sources, targets, area_cutoff=0).area)\n",
      "C:\\Users\\aditi\\anaconda3\\lib\\site-packages\\geopandas\\geodataframe.py:1443: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n"
     ]
    }
   ],
   "source": [
    "wi_blocks['RDH_WARDS'] = maup.assign(wi_blocks, rdh_wards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1eac8e95",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>geometry</th>\n",
       "      <th>RDH_WARDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>4649.0</td>\n",
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       "      <th>1</th>\n",
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      "text/plain": [
       "           GEOID20                                           geometry  \\\n",
       "0  550999704001040  POLYGON ((-90.61666 45.70397, -90.61659 45.704...   \n",
       "1  550999706002032  POLYGON ((-90.52452 45.63381, -90.52446 45.634...   \n",
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       "4  550999707001024  POLYGON ((-90.29082 45.54624, -90.29082 45.546...   \n",
       "\n",
       "   RDH_WARDS  \n",
       "0     4649.0  \n",
       "1     4657.0  \n",
       "2     4651.0  \n",
       "3     4682.0  \n",
       "4     4677.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wi_blocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69026fbb",
   "metadata": {},
   "outputs": [
    {
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       "6891"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wi_blocks['RDH_WARDS'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ec7d5a3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "164"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wi_blocks['RDH_WARDS'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2b8595a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#check these in q gis to make sure they are water\n",
    "null_wards = wi_blocks[wi_blocks['RDH_WARDS'].isnull()]\n",
    "null_wards.to_file(\"./null_wards.geojson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49747107",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "<AxesSubplot:>"
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     "execution_count": 12,
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     "data": {
      "image/png": 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4eVcdHpfw2u4Gzp8ZqkZrX+VZUcLuFYcWawDEBff81WKy0qAa3WfEEKvqOmAdQOhM/A1VvUlEpqjq8dBmfw3sSFYhjXlm67Ehy7weF7esnh1xyF+vPxDxevX4qQ6+8eRW3tx3gpWVE9h4cMgV4Fndevlczp1aFNNnki2eXyc/EJHtIrINuBS4LUFlMmaIG1YOvdb96iVzmBVhojtVpb418kwb+xvbeHPfCWaW5LDpUGwBnl9ewFcvnRPTZ8ZCTBcQqroeWB96/dkklMeYIZo7evg/r+4dsGx2aR5fvmTg7Z1AQHG5BBFhanHukP20d/Xyzv4TAOT5sghoR0zl+LuLKvHG0IFkrFi3S5PWev0BfvrqXo6eGhi47123GJ9n4DQ4rkGDDnr8gf5r12e2HOE3bx/meHMnH5pdwqGmDiom5ABnnhUV3uqqAy6Ig63gVyyclJiDSjALsUlr3312Jw+/fXjAso8vncrFc0fuJdUXYH9AeWNfI7uOtdDdGyCgyrFTsU1st6qyhLKCyE+ISDULsUlbze09PF51ZMCyHK+bb6+JrUvlf2+u4dCJDhZPK8LrdvV38ojFRxeVx/yZsWIhNmnr+e3H6R40vvea86YwaZhnJg3nrf1NtHX1kut1c6qjJzjDR4xlufJcC7ExMdl4sIkfvrR7yPLhHno2nMMn2nlq81E8LpiY76OupSvmsiyaWpiWz2Dqk35NbcYAdz+3i6bQhO7xePtAsDV6+YwJowowwNWLUzfMMBoWYpN2unv8vFfbGvd+AgHloTcO4BbimvzuoykcKxwNq06btLN+TwOPfvGD1Ld2MaMkeL+32x+gpzfAnND0OdF4pbqO7CwX500rRlHOKcvndFdv8HpYwkcnSdj/D1Sa7+2fsiddWYhN2plRkss5pXm8f6KNBZNHP1Lo8aqaAf2s55Tlsa8htsEOX1o9a8ye5DBaFmKTds6ZlE+W2xXXo0Mfr6rh2KlOVoaNEd5TF/scWulelQYLsUlDze09TMz30tYd+XGjI9lb18q3ntrGysoSNhwY/aNHJ+Z5+cCMoRMFpBtr2DJpRVWZmB8cUjjaqZuf3HwkrNvk6F2xsHxM548erbQKsar292M141e3P4CIcP7Mkpg/q6o8u2XosMXRSOdeWuHSqjqd7g0IJvlEhCzX6M8tu463cKw5/gd+52S5Uz6LZbTS6kxsDAwdjRSLrTXNI28UhXmTC8b8YeGjZSE2GaXq0OgbssItrUiv2TvOxkJsMsrb759IyH5S+USHWFmITcZo7eyhtiX+6+HCbA+rZsXeqJYqFmKTMV6priOO/iH9LlswKa1msxxJWrVOGxOPmqYOPjCjuP+9f5SJviKNxw5HYiE2GeP5bcfZXRff6Cev28Ul89NzLq3hWIhNRjjQ2EZxbtaAvtIQmuYu7IQ8uCuCIKGp4oOjmOaWF5Dvc1YsnFVaY4bR0e3nnTj6Sfe5bvm0BJRmbDnn6t2Ys5g/uaB/7HE8rlzorOthsBCbDOF2CZ88vyKufSybXhzzJHzpwKrTJmOsWTKFpzYfQUQQAVfoAtglwWvfvmXh6/qukUWEv3ZgVRosxGYMHDnZTsWE+Ku6I8nOcnPwxOjn0vrJp5YmsDRjx6rTJqm2HTnFK7vqxuS7Yn1AWrg5k/KZXZbec2kNx87EJqmWVBSzpKI46d/TeLpzyEPXYuHUqjTYmdg4nKryhy1H+asH/oJLoCQvugeND/bxJVMTXLKxY2di41htnT2se3obz2yp7V9WXuCjYkIOR05G/9jS4twsppfkJKOIY8JCbBxr+9Fm9te3s3haIa7+1udgS3Oe1xN1F8wFkwscPatM1CEWETdQBRxV1bVhy78B3AuUqWrsj5tLQ6rq6H/U8aLbr+w41hJxXbbHxYLJBVE9SSKeua3TQSzXxLcC1eELRGQ6cCVwOOInHMoC7AyPbTg07LrO3gCHTrQxr3zkFuf5kwsSWawxF1WIRaQCWAP8YtCqnwB3MPAh68Yk3eGmNl6prkck2Fsryy34PC7yfR4Ksz0U5WThy3JzNIprY6eHONrq9H0Ew9p/tCJyLcGq9daznblE5BbgFoAZM2aMuqDGhHtxRy0Tcr0owQenKcHxw4GA0htQ/IEAAQ2+PpvsLBdLx+AWWDKNGGIRWQvUq+omEbkktCwXuBP46EifV9UHgQcBVqxYYWdskxDbj7ZQ3zryo0pnl+axv3H45y/NLy9wxATxZxPNmfgi4FoRuQbIBgqB3wCzgL6zcAWwWURWqWrtsHsyJgGOnGznhR3Ho9p2pNk9nN6oBVGEWFXXAesAQmfib6jq9eHbiMhBYEWmtE6b9LbzWAs9/ugqdf4Rniji9OthsPvExoEONLSxKjSDR3hEFVCl/1FAfevKCnxnNpCwFQKLpo6DM3E4VV0PrI+wvDIxxTFmZO83nGbDwdEPdgjn1EEP4azvtHGcyUWJGbhfkuelNH90fa3TiYXYJE0gEKCloyfhT7rM9SbmGUlzJ+VnRMceC7FJGhGhs8dPIBBI6H4vnlPW/3ppRVF/Zw+vx4XX4yLLfeZ932ufx0WO102u102e102+z8Piac553tLZWMOWSRoRScqcVedVFLF6bilv7A3eDPEHFD8wtOPg4GavgWaX5SW8bKlgZ2LjSG1dvXHvY365828vgYXYOFRzR0/c+6gstTOxMSlTHmc1PTvLxcRRzgKSbizExnF6/AEOxTGrJcDkwuyMaJkGC7FxoN+8dYijp6KffieSguysBJUm9ax12jjK0ZMdvPpeHasqgw8Bd7sIvpYzPSojnV/P9LhUUKGyNPnzYI8VC7FxlJ+/sZ83952Iez9FuZlzJrbqtHGMls4enqiqSci+CrIz5/yVOUdiMt7+hjYqS3Pp6o3QjVN12DmiAhqc/SOAEvofZfm+ZBZ1TFmIjWMsm17MVy+dy1ce2ZzqoqQVq06bcakwx66JjXG0IguxMc6WSQ1bFmIzLuX7LMTGOJqF2BiH82UlZnaQdGAhNuPSidMjTzzvFBZiMy7VtViIjXG02pbOVBchYSzEZlyqz6AQZ04TnclYO442978+3BTfZAB9MulMbCE2ae8TP/sLnT2Jnfa2tjlzQmzVaTMuHc+gENuZ2KS1prZuphTlxPw5VcXtEtwuCQ5R1L6Zp5W+B1K0dfWQ53N+H2oLsUlrpzt7OXCWh4TH43hzF3MmOT/EVp02ae2lndE9THw0jjfHN9leurAQm7S2srKE7Kzk/JhmSocPC7FJa0tnTODDc8tG3nAUGjOk62XUIRYRt4i8KyLPhd7fLSLbRGSLiLwkIlOTV0wznrmSNMl7fYaciWNp2LoVqAYKQ+/vVdVvA4jIPwPfAb6c2OKZ8W5rzUnqWjtZNr14wPK+WMfz5ONEPzc5VaIKsYhUAGuAe4DbAVS1JWyTPOL7+zQmokBAeffwqaTs2+fJjKvJaI/iPuAOYEC3GRG5R0RqgM8QPBMPISK3iEiViFQ1NDTEU1YzDi2fWcJtV8xLyr4bxss1sYisBepVddPgdap6p6pOBx4Bvhbp86r6oKquUNUVZWXJaaAwme0fPjKbPF/iB/E3ZMg1cTRn4ouAa0XkIPBb4DIReXjQNo8C1ye4bMYAkJ3lpiAJPatau3oT8rDyVBsxxKq6TlUrVLUSuAH4k6reJCJzwza7FngvSWU0hpIkPUu4LgNGM8XT7fL7IjKf4HXyIaxl2iSJqsb9KNPh1LZ0MrssPyn7HisxhVhV1wPrQ6+t+mzGhIgwrzyfjQdPJnzfmXCv2AZAGEe49fK53P+nfcM+fzhaIhB+ezgTroktxMYRLp5bxgs7annkncNx7WdKUfaAscRLKoriLVrKZcbdbjMufHvtuVy+YFJc+8jxDrxVVd/q/Oq0hdg4RndvIPiM4TjkDJo0PhNapy3ExjHqWzvZW3c6rn14B3W1zIThiBZi4xhucRHvgCaPa+AO6lo6HT8QwkJsHGNfQys1TfHdLx48rLG9289ph7dQW+u0cYwXdhxn6fSiAePl+m45hU+GF1DFHwhuJAL+gBLQ4HKfx8X0kpz+9RCcvrYg27lzbVmIjSN09vh5flstXb3xzT+9v2HopHsNrV3MLS+Ia7+pZNVp4wi1LR1JGckEUNfq7BZqC7FxhBxPckYygfO7XlqIjSOUF+WQ7U3Oj2uDwzt8WIiNIwQCiluS8+Pq9F5bFmLjCCLgS9L80/UOvya21mnjCCLCzJJc6po7cYU6bPTd8nWJ4BJBwpZJ6D2cuSOlqsig+8SqSnZWchrMxoqF2DjGfTcsT3UR0pJVp41xOAuxMQ5nITbG4SzExjichdgYh7MQG+NwFmJjHM5CbIzDWYiNcTgZy/mFRKSB4CNfEq0UaEzCftONHWfmKQXyVHXUjwwd0xAni4hUqeqKVJcj2ew4M08ijtWq08Y4nIXYGIfLlBA/mOoCjBE7zswT97FmxDWxMeNZppyJjRm3LMTGOJyjQywiS0XkLRHZLiLPikhhaPmVIrIptHyTiFyW6rLGY7jjDK1bJyL7RGS3iHwsleWMl4gsE5G3RWSLiFSJyKrQ8iwR+XXo+KtFZF2qyxqP4Y4ztG5J6N96Z+h4s0fcoao69g+wEfhI6PXngbtDr5cDU0OvFwNHU13WJB3nucBWwAfMAt4H3KkubxzH+RJwdej1NcD60Osbgd+GXucCB4HKVJc3CcfpAbYBS0PvJ0bz7+noMzEwH3g99Ppl4HoAVX1XVY+Flu8EskXEl4LyJUrE4wSuI/jD3aWqB4B9wKoIn3cKBfpqGUXAsbDleSLiAXKAbqBl7IuXMMMd50eBbaq6FUBVT6iqf6SdOT3EO4BrQ68/CUyPsM31wLuq6uTJhYc7zmlATdh2R0LLnOrrwL0iUgP8EOirNj8JtAHHgcPAD1W1KSUlTIyvE/k45wEqIi+KyGYRuSOanaX9bJci8gowOcKqOwlWLX8qIt8BniH4Gzr8s4uA/yD4Gy6tjfI4Iz2tN63vGY5wnJcDt6nqUyLyKeAh4AqCtQs/MBWYALwhIq+o6v4xKnbMRnmcHuBiYCXQDrwqIptU9dWzflmqrw8SeJ0xD9gQ9r4C2ANclOqyJes4Cf4GXxe27kXgwlSXMY5ja+ZM3wUBWkKvHwA+G7bdL4FPpbq8STjOG4BfhW33beCbI+3P0dVpEZkU+q8L+FfgZ6H3xcDzBH/A30xZARNkuOMkeFa+QUR8IjILmAtsSE0pE+IY8JHQ68uAvaHXh4HLJCgPuAB4LwXlS5ThjvNFYImI5Iau/z8C7Bpxb6n+rRTnb7RbCZ5t9wDf58xvt38leA21JezPpFSXN9HHGVp3J8FW6d2EWjyd+odgVXITwRb3d4DzQ8vzgScINlLuIoqzUzr/Ge44Q+tuCh3nDuAH0ezPul0a43COrk4bYyzExjiehdgYh7MQG+NwFmJjHM5CbIzDWYiNcbj/D/HCHOFNVjISAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "null_wards.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b4d8689",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create BAF\n",
    "baf = wi_blocks[['GEOID20', 'RDH_WARDS']]\n",
    "baf.columns = ['GEOID20', 'WARD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e78b5e1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-0056a972c527>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  baf['WARD'] = baf['WARD'].fillna('No Assignment')\n"
     ]
    }
   ],
   "source": [
    "baf['WARD'] = baf['WARD'].fillna('No Assignment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d920782",
   "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>GEOID20</th>\n",
       "      <th>WARD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>550999704001040</td>\n",
       "      <td>4649.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>550999706002032</td>\n",
       "      <td>4657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>550999701003089</td>\n",
       "      <td>4651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>550999701003105</td>\n",
       "      <td>4682.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>550999707001024</td>\n",
       "      <td>4677.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           GEOID20    WARD\n",
       "0  550999704001040  4649.0\n",
       "1  550999706002032  4657.0\n",
       "2  550999701003089  4651.0\n",
       "3  550999701003105  4682.0\n",
       "4  550999707001024  4677.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48100da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"./WI_2022_BAF\"):\n",
    "    os.mkdir(\"./WI_2022_BAF/\")\n",
    "baf.to_csv('./WI_2022_BAF/WI22_WARDS_BAF.csv', index=False)"
   ]
  }
 ],
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