Orphaned wells in NYS.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Assessing orphaned wells in NY\n",
    "\n",
    "**Data source:**\n",
    "[Orphaned Wells](https://data.ny.gov/Energy-Environment/Orphaned-Wells/vgue-bamz): \"List of wells that are regulated under the Oil, Gas and Solution Mining Law (ECL Article 23) in New York State that are orphaned and not plugged,\" Last Updated: 12/16/21 | Downloaded: 3/25/22\n",
    "\n",
    "In this notebook, we are breaking down orphaned wells in New York by region, county and town. \n",
    "\n",
    "**Key terminology:**\n",
    "\n",
    "'Orphaned wells' and 'abandoned wells' are **NOT** the same thing. It's also important to note that the definitions of these types of wells can vary state by state. Here's a brief explanation of how the NY DEC defines the two. For more info, [click here](https://www.dec.ny.gov/energy/111211.html#:~:text=Orphaned%20and%20Abandoned%20Wells&text=Orphaned%20wells%20are%20a%20subset,regulatory%20framework%20in%20New%20York)\n",
    "* Abandoned wells: \"Abandoned wells are unplugged wells (primarily oil or gas wells) that have not been operated and maintained in accordance with prevailing statute and regulation.\"\n",
    "* Orphaned wells: \"Orphaned wells are a subset of abandoned wells for which no owner can be determined.\"\n",
    "\n",
    "In other words, all orphaned wells are abandoned wells, but not all abandoned wells are orphaned wells.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessieblaeser/.pyenv/versions/3.10.0/lib/python3.10/site-packages/pandas/compat/__init__.py:124: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1.3.4'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "#Import pandas\n",
    "from __future__ import print_function\n",
    "\n",
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "hKzkdUUH_gk_"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "QwFFvwwa2fSH"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data_32522/data_raw_32522/orphaned_wells_32522.csv\")\n",
    "#Replace spaces in column names and change to lowercase\n",
    "df.columns = df.columns.str.replace(' ', '_').str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['company_name', 'county', 'town', 'api_well_number', 'well_name',\n",
       "       'well_status', 'well_type_code', 'surface_longitude',\n",
       "       'surface_latitude', 'verified_location', 'region', 'georeference'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reference note:** It seems the DEC has updated their dataset since we last downloaded in Oct. 2021. Previously, the below column called for 'WELLSTATUSCODE', but the values referenced in this column are now reflected in the 'well_status' column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Looking at wells by status:\n",
    "\n",
    "[Data dictionary here](https://data.ny.gov/Energy-Environment/Orphaned-Wells/vgue-bamz)\n",
    "\n",
    "* UN: well status unknown, but detail on location, etc, provided to DEC through misc. means\n",
    "* UL: well status unknown, but well located and inspection performed\n",
    "* UM: well status unknown, DEC was unable to locate the well\n",
    "\n",
    "For now, we want to filter out wells the DEC has NOT been able to locate. \n",
    "\n",
    "**Conclusions as of 3/25/22:**\n",
    "* There are 6,808 known orphaned wells in New York state. \n",
    "* There are 4,649 wells that have been successfully located by the DEC, but have an unknown status. These wells remain unplugged. \n",
    "* There are 2,159 wells the DEC knows to exist, but has yet to locate. These wells are also unplugged. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-jxx63Mw6P7u",
    "outputId": "c917ab63-b7c2-4ccd-81ce-2c3009b2c869"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "UN    2337\n",
       "UL    2312\n",
       "UM    2159\n",
       "Name: well_status, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.well_status.value_counts()\n",
    "#UN: well status unknown, but detail on location, etc, provided to DEC through misc. means\n",
    "#UL: well status unknown, but well located and inspection performed\n",
    "#UM: well status unknown, DEC was unable to locate the well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "ILW2F4aA60G5"
   },
   "outputs": [],
   "source": [
    "# Filtering out wells the DEC was unable to locate\n",
    "df_located = df[df['well_status']!='UM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2CrFOE2DqsTD",
    "outputId": "376e710b-980e-42e5-df36-8456479d26e4"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "4649"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_located)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6808"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of total orphaned wells in the state\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reference note:** Since our last download in Oct. 2021, the DEC has located over 60 wells. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Counting wells by NY DEC Region \n",
    "\n",
    "Here, we look at the distribution of orphaned wells by the regions in the state, determined by the DEC (more info [here](https://www.dec.ny.gov/about/558.html) and [here](https://www.dec.ny.gov/about/76070.html))\n",
    "\n",
    "**Conclusions as of 3/25/22:**\n",
    "* Region 9, or Western NY has the most orphaned wells at 3,815 **(82 percent of the orphaned wells in the state)**\n",
    "* Region 8, or the Finger Lakes, is a distant second with 558 orphaned wells "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
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    "id": "uLTJewf_4UN8",
    "outputId": "3daab050-938c-407b-d519-b6f6872c6abf"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>3815</td>\n",
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       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>558</td>\n",
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       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>217</td>\n",
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       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>48</td>\n",
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      "text/plain": [
       "   region  count\n",
       "0       9   3815\n",
       "1       8    558\n",
       "2       7    217\n",
       "3       6     48\n",
       "4       4      7\n",
       "5       5      3\n",
       "6       3      1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting number of wells in each region\n",
    "wells_region = df_located.region.value_counts().rename_axis('region').reset_index(name='count')\n",
    "wells_region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
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    "id": "TiWTIDD8mYDZ",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Count of orphaned wells by NYDEC Region'}, xlabel='region'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of well count by region in New York\n",
    "wells_region.plot.bar(x='region', y='count', rot=0, title='Count of orphaned wells by NYDEC Region')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-LbgqoSS4irJ",
    "outputId": "39a756d9-f8f5-4ac8-ef9b-a61b2864c0b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9    0.820607\n",
       "8    0.120026\n",
       "7    0.046677\n",
       "6    0.010325\n",
       "4    0.001506\n",
       "5    0.000645\n",
       "3    0.000215\n",
       "Name: region, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating percentage of wells in each NYSDEC region\n",
    "df_located.region.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Counting wells by county\n",
    "\n",
    "Now, we're taking a look at the distribution of orphaned wells by county. \n",
    "\n",
    "**Conclusions as of 3/25/22:**\n",
    "* Allegany County has the majority of orphaned wells in the state at 2,324 **(49.9 percent of the orphaned wells in the state)**\n",
    "* Cattaraugus County is in second at 1,083 orphaned wells (23.23 percent of the orphaned wells in the state) \n",
    "* Steuben County is in third at 421 orphaned wells (9 percent of the orphaned wells in the state)\n",
    "* These three counties account for over 80 percent (82.3 percent) of the orphaned wells in the state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "id": "ywxaiFJs20xk",
    "outputId": "9b07fc7b-69ba-480f-e16b-bbad12266430"
   },
   "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>county</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allegany</td>\n",
       "      <td>2324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cattaraugus</td>\n",
       "      <td>1083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Steuben</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Erie</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chautauqua</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Oswego</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ontario</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tompkins</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Oneida</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        county  count\n",
       "0     Allegany   2324\n",
       "1  Cattaraugus   1083\n",
       "2      Steuben    421\n",
       "3         Erie    248\n",
       "4   Chautauqua    127\n",
       "5       Oswego    110\n",
       "6      Ontario     43\n",
       "7     Tompkins     36\n",
       "8       Oneida     33\n",
       "9      Wyoming     30"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count number of wells in each county\n",
    "wells_county = df_located.county.value_counts().rename_axis('county').reset_index(name='count')\n",
    "wells_county.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Allegany       0.499892\n",
       "Cattaraugus    0.232953\n",
       "Steuben        0.090557\n",
       "Erie           0.053345\n",
       "Chautauqua     0.027318\n",
       "Oswego         0.023661\n",
       "Ontario        0.009249\n",
       "Tompkins       0.007744\n",
       "Oneida         0.007098\n",
       "Wyoming        0.006453\n",
       "Name: county, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage split of orphaned wells by county \n",
    "df_located.county.value_counts(normalize=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8234028823402882"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The top 3 counties with orphaned wells account for 82% of the orphaned wells in the state\n",
    "df_located.county.value_counts(normalize=True).head(3).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "egCRGQiHwAiq"
   },
   "outputs": [],
   "source": [
    "#Download CSV of number of unplugged and abandoned wells in each county – remove # in below script to run the download\n",
    "#wells_county.to_csv('unplug_orphaned_32522.csv') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 324
    },
    "id": "Mtq1Sk2lozJI",
    "outputId": "047b1dbc-ce7f-40e7-924b-e290de9cd33b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
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       "  Text(0, 0, ''),\n",
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       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
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     },
     "execution_count": 59,
     "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": [
    "# Visualization of orphaned wells by county \n",
    "\n",
    "county = wells_county['county'].head(10)\n",
    "county_ct = wells_county['count'].head(10)\n",
    "\n",
    "plt.bar(county, county_ct, width = 0.4)\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xTVm7tHQ4zAf",
    "outputId": "ed6c139e-ff8e-460c-f18b-53e3a5c6398d"
   },
   "outputs": [],
   "source": [
    "# Calculating percent of wells in each county\n",
    "# df_located.COUNTY.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Counting wells by town\n",
    "\n",
    "And finally, we're looking at the distribution of orphaned wells by town. \n",
    "\n",
    "**Conclusions as of 3/25/22:**\n",
    "* Bolivar has the most orphaned wells at 1,079 (24.3 percent of orphaned wells in the state)\n",
    "* Allegany is second with 886 orphaned wells (19.9 percent of orphaned wells in the state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 824
    },
    "id": "pAGCvQTR3Kzk",
    "outputId": "a4116ddd-7a6b-4bdf-d9a3-ec03cdea9a35"
   },
   "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>city</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bolivar</td>\n",
       "      <td>1079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allegany</td>\n",
       "      <td>886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wirt</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Union</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alma</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Genesee</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Scio</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Carrollton</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Collins</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sandy Creek</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Brant</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Richland</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Busti</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Olean</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Clarksville</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Perrysburg</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Red House</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Sheridan</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Alden</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>UNK</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Hanover</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Tonawanda</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Willing</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lancaster</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Camden</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           city  count\n",
       "0       Bolivar   1079\n",
       "1      Allegany    886\n",
       "2          Wirt    356\n",
       "3    West Union    340\n",
       "4          Alma    339\n",
       "5       Genesee    160\n",
       "6          Scio     86\n",
       "7    Carrollton     77\n",
       "8       Collins     56\n",
       "9   Sandy Creek     52\n",
       "10        Brant     48\n",
       "11     Richland     36\n",
       "12        Busti     36\n",
       "13        Olean     32\n",
       "14  Clarksville     30\n",
       "15   Perrysburg     29\n",
       "16    Red House     28\n",
       "17     Sheridan     26\n",
       "18        Alden     26\n",
       "19          UNK     19\n",
       "20      Hanover     19\n",
       "21    Tonawanda     17\n",
       "22      Willing     17\n",
       "23    Lancaster     17\n",
       "24       Camden     17"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating number of wells in each town\n",
    "wells_city = df_located.town.value_counts().rename_axis('city').reset_index(name='count')\n",
    "wells_city.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 325
    },
    "id": "YxjWmronsTfE",
    "outputId": "8c6bb5cb-283c-4481-8acc-96d17f6a76b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 63,
     "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": [
    "# Visualization of orphaned wells by town \n",
    "\n",
    "city = wells_city['city'].head(10)\n",
    "city_ct = wells_city['count'].head(10)\n",
    "\n",
    "plt.bar(city, city_ct, width = 0.4)\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DFMFe9fB45Ya",
    "outputId": "4eafb0c9-75dc-4006-acc6-c8f10a478a15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bolivar        0.243457\n",
       "Allegany       0.199910\n",
       "Wirt           0.080325\n",
       "West Union     0.076715\n",
       "Alma           0.076489\n",
       "Genesee        0.036101\n",
       "Scio           0.019404\n",
       "Carrollton     0.017374\n",
       "Collins        0.012635\n",
       "Sandy Creek    0.011733\n",
       "Brant          0.010830\n",
       "Richland       0.008123\n",
       "Busti          0.008123\n",
       "Olean          0.007220\n",
       "Clarksville    0.006769\n",
       "Perrysburg     0.006543\n",
       "Red House      0.006318\n",
       "Sheridan       0.005866\n",
       "Alden          0.005866\n",
       "UNK            0.004287\n",
       "Hanover        0.004287\n",
       "Tonawanda      0.003836\n",
       "Willing        0.003836\n",
       "Lancaster      0.003836\n",
       "Camden         0.003836\n",
       "Name: town, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating percent of wells in each town\n",
    "\n",
    "df_located.town.value_counts(normalize=True).head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "wells_abandoned_github.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}