diff --git a/FP_Notebook.ipynb b/FP_Notebook.ipynb
index eb4238a..579a3a4 100644
--- a/FP_Notebook.ipynb
+++ b/FP_Notebook.ipynb
@@ -9,33 +9,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'parks.csv'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-42-4c590e5d08ad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mparks_pandas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"parks.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"id\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mgreenspaces_pandas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"green_spaces.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplaygrounds_pandas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"playgrounds.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"id\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/tljh/user/lib/python3.8/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/tljh/user/lib/python3.8/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/tljh/user/lib/python3.8/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/tljh/user/lib/python3.8/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    931\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/tljh/user/lib/python3.8/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0;31m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1216\u001b[0m             \u001b[0;31m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m             self.handles = get_handle(  # type: ignore[call-overload]\n\u001b[0m\u001b[1;32m   1218\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/tljh/user/lib/python3.8/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    787\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    790\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'parks.csv'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "parks_pandas = pd.read_csv(\"parks.csv\", index_col=\"id\")\n",
+    "parks_pandas = pd.read_csv(\"courts.csv\", index_col=\"id\")\n",
     "greenspaces_pandas = pd.read_csv(\"green_spaces.csv\")\n",
     "playgrounds_pandas = pd.read_csv(\"playgrounds.csv\", index_col=\"id\")"
    ]
@@ -746,13 +728,6 @@
    "source": [
     "I'm not too familiar with the neighborhoods of Pittsburgh, so if I had to pick my favorite neighborhood, it would be Central Oakland. Based on our metric of choosing the best neighborhood to have fun outside in, I think Central Oakland could still work. This is mainly because of Schenley Park, however I think some of the park bleeds into Squirrel Hill South, which is our winning \"best neighborhood.\""
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/GreenSpaces_Notebook.ipynb b/GreenSpaces_Notebook.ipynb
index 34e27df..302d233 100644
--- a/GreenSpaces_Notebook.ipynb
+++ b/GreenSpaces_Notebook.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<h1>Introduction<h1>"
+    "<h1>Introduction"
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<h3>Importing Pandas and MatPlotLib<h3>"
+    "<h3>Importing Pandas and MatPlotLib"
    ]
   },
   {
@@ -38,7 +38,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<h3>Establishing Data<h3>"
+    "<h3>Establishing Data"
    ]
   },
   {
@@ -284,7 +284,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<h3>Cleaning up the data<h3>"
+    "<h3>Cleaning up the data"
    ]
   },
   {
@@ -377,7 +377,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<h3>Found Neighborhood with the most Green Spaces<h3>"
+    "<h3>Found Neighborhood with the most Green Spaces"
    ]
   },
   {
@@ -507,7 +507,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<h3>Plotting the data<h3>"
+    "<h3>Plotting the data"
    ]
   },
   {
@@ -553,248 +553,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<h3>Mapping out the data<h4>\n",
-    "Here I created a map to visualize the neighborhoods with the most green spaces and their locations within the city of Pittsburgh."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>objectid</th>\n",
-       "      <th>fid_blockg</th>\n",
-       "      <th>statefp10</th>\n",
-       "      <th>countyfp10</th>\n",
-       "      <th>tractce10</th>\n",
-       "      <th>blkgrpce10</th>\n",
-       "      <th>geoid10</th>\n",
-       "      <th>namelsad10</th>\n",
-       "      <th>mtfcc10</th>\n",
-       "      <th>funcstat10</th>\n",
-       "      <th>...</th>\n",
-       "      <th>page_number</th>\n",
-       "      <th>plannerassign</th>\n",
-       "      <th>globalid</th>\n",
-       "      <th>created_user</th>\n",
-       "      <th>created_date</th>\n",
-       "      <th>last_edited_user</th>\n",
-       "      <th>last_edited_date</th>\n",
-       "      <th>temp</th>\n",
-       "      <th>shape_area</th>\n",
-       "      <th>shape_length</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "      <td>003</td>\n",
-       "      <td>140500</td>\n",
-       "      <td>3</td>\n",
-       "      <td>420031405003</td>\n",
-       "      <td>Block Group 3</td>\n",
-       "      <td>G5030</td>\n",
-       "      <td>S</td>\n",
-       "      <td>...</td>\n",
-       "      <td>15</td>\n",
-       "      <td>Ose Akinlotan</td>\n",
-       "      <td>a642a6b6-a2a9-48d5-acf2-3e2ce87d4bc0</td>\n",
-       "      <td>SDE</td>\n",
-       "      <td>2020-08-14T12:57:28</td>\n",
-       "      <td>SDE</td>\n",
-       "      <td>2020-08-14T13:02:13</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.153647e+06</td>\n",
-       "      <td>14047.065024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>203</td>\n",
-       "      <td>42</td>\n",
-       "      <td>003</td>\n",
-       "      <td>140100</td>\n",
-       "      <td>3</td>\n",
-       "      <td>420031401003</td>\n",
-       "      <td>Block Group 3</td>\n",
-       "      <td>G5030</td>\n",
-       "      <td>S</td>\n",
-       "      <td>...</td>\n",
-       "      <td>15</td>\n",
-       "      <td>Ose Akinlotan</td>\n",
-       "      <td>20d4605b-258e-441a-a57e-a28fdabb6d30</td>\n",
-       "      <td>SDE</td>\n",
-       "      <td>2020-08-14T12:57:28</td>\n",
-       "      <td>SDE</td>\n",
-       "      <td>2020-08-14T13:02:41</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.407739e+07</td>\n",
-       "      <td>30347.886823</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>86</td>\n",
-       "      <td>42</td>\n",
-       "      <td>003</td>\n",
-       "      <td>101700</td>\n",
-       "      <td>2</td>\n",
-       "      <td>420031017002</td>\n",
-       "      <td>Block Group 2</td>\n",
-       "      <td>G5030</td>\n",
-       "      <td>S</td>\n",
-       "      <td>...</td>\n",
-       "      <td>15</td>\n",
-       "      <td>Ose Akinlotan</td>\n",
-       "      <td>bf4065e7-c29c-48bd-b529-e7fd99077238</td>\n",
-       "      <td>SDE</td>\n",
-       "      <td>2020-08-14T12:57:28</td>\n",
-       "      <td>SDE</td>\n",
-       "      <td>2020-08-14T12:59:55</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.275116e+07</td>\n",
-       "      <td>14577.441571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>214</td>\n",
-       "      <td>42</td>\n",
-       "      <td>003</td>\n",
-       "      <td>050900</td>\n",
-       "      <td>1</td>\n",
-       "      <td>420030509001</td>\n",
-       "      <td>Block Group 1</td>\n",
-       "      <td>G5030</td>\n",
-       "      <td>S</td>\n",
-       "      <td>...</td>\n",
-       "      <td>15</td>\n",
-       "      <td>Ose Akinlotan</td>\n",
-       "      <td>fed3fb32-0cd3-4556-aa41-17ee03b4fe41</td>\n",
-       "      <td>SDE</td>\n",
-       "      <td>2020-08-14T12:57:28</td>\n",
-       "      <td>SDE</td>\n",
-       "      <td>2020-08-14T12:58:41</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.997845e+06</td>\n",
-       "      <td>14216.959213</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>173</td>\n",
-       "      <td>42</td>\n",
-       "      <td>003</td>\n",
-       "      <td>300100</td>\n",
-       "      <td>5</td>\n",
-       "      <td>420033001005</td>\n",
-       "      <td>Block Group 5</td>\n",
-       "      <td>G5030</td>\n",
-       "      <td>S</td>\n",
-       "      <td>...</td>\n",
-       "      <td>15</td>\n",
-       "      <td>Thomas Scharff</td>\n",
-       "      <td>8669d2f0-b886-4519-99d0-bb1015b592b0</td>\n",
-       "      <td>SDE</td>\n",
-       "      <td>2020-08-14T12:57:28</td>\n",
-       "      <td>SDE</td>\n",
-       "      <td>2020-08-14T13:00:19</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.410563e+06</td>\n",
-       "      <td>12003.269761</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 39 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   objectid  fid_blockg statefp10 countyfp10 tractce10 blkgrpce10  \\\n",
-       "0         1         233        42        003    140500          3   \n",
-       "1         2         203        42        003    140100          3   \n",
-       "2         3          86        42        003    101700          2   \n",
-       "3         4         214        42        003    050900          1   \n",
-       "4         5         173        42        003    300100          5   \n",
-       "\n",
-       "        geoid10     namelsad10 mtfcc10 funcstat10  ...  page_number  \\\n",
-       "0  420031405003  Block Group 3   G5030          S  ...           15   \n",
-       "1  420031401003  Block Group 3   G5030          S  ...           15   \n",
-       "2  420031017002  Block Group 2   G5030          S  ...           15   \n",
-       "3  420030509001  Block Group 1   G5030          S  ...           15   \n",
-       "4  420033001005  Block Group 5   G5030          S  ...           15   \n",
-       "\n",
-       "    plannerassign                              globalid created_user  \\\n",
-       "0   Ose Akinlotan  a642a6b6-a2a9-48d5-acf2-3e2ce87d4bc0          SDE   \n",
-       "1   Ose Akinlotan  20d4605b-258e-441a-a57e-a28fdabb6d30          SDE   \n",
-       "2   Ose Akinlotan  bf4065e7-c29c-48bd-b529-e7fd99077238          SDE   \n",
-       "3   Ose Akinlotan  fed3fb32-0cd3-4556-aa41-17ee03b4fe41          SDE   \n",
-       "4  Thomas Scharff  8669d2f0-b886-4519-99d0-bb1015b592b0          SDE   \n",
-       "\n",
-       "          created_date  last_edited_user     last_edited_date  temp  \\\n",
-       "0  2020-08-14T12:57:28               SDE  2020-08-14T13:02:13   NaN   \n",
-       "1  2020-08-14T12:57:28               SDE  2020-08-14T13:02:41   NaN   \n",
-       "2  2020-08-14T12:57:28               SDE  2020-08-14T12:59:55   NaN   \n",
-       "3  2020-08-14T12:57:28               SDE  2020-08-14T12:58:41   NaN   \n",
-       "4  2020-08-14T12:57:28               SDE  2020-08-14T13:00:19   NaN   \n",
-       "\n",
-       "     shape_area  shape_length  \n",
-       "0  8.153647e+06  14047.065024  \n",
-       "1  3.407739e+07  30347.886823  \n",
-       "2  1.275116e+07  14577.441571  \n",
-       "3  4.997845e+06  14216.959213  \n",
-       "4  8.410563e+06  12003.269761  \n",
-       "\n",
-       "[5 rows x 39 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "neighborhoods = pd.read_csv(\"neighborhoods.csv\")\n",
-    "neighborhoods.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here I am taking that dataset, and turning it into a map of Pittsburgh to later on visualize all of the green spaces in the city of Pittsburgh."
+    "<h1>Results and Conclusion"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<h1>Results and Conclusion<h1>\n",
-    "    \n",
-    "<h5>In the bar graph, I found East Liberty to be the best neighborhood for the amount of green spaces. However, in order to check with the other metrics to find the top neighborhood, I have nominated the top 8 neighborhoods in the graph as well to account for the number of neighborhoods that have green spaces.<h5>\n",
+    "In the bar graph, I found **East Liberty** to be the best neighborhood for the amount of green spaces. However, in order to check with the other metrics to find the top neighborhood, I have nominated the top 8 neighborhoods in the graph as well to account for the number of neighborhoods that have green spaces.\n",
     "    \n",
     "    1. East Liberty\n",
     "    2. Point Breeze\n",
@@ -805,13 +571,6 @@
     "    7. Squirrel Hill South\n",
     "    8. Swisshelm Park"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {