diff --git a/DataScienceGuidedCapstone b/DataScienceGuidedCapstone
new file mode 160000
index 000000000..752307a89
--- /dev/null
+++ b/DataScienceGuidedCapstone
@@ -0,0 +1 @@
+Subproject commit 752307a894bc05dc095427868c7c103ea4ce6153
diff --git a/Notebooks/02_data_wrangling.ipynb b/Notebooks/02_data_wrangling.ipynb
index 115e4463e..5807c8872 100644
--- a/Notebooks/02_data_wrangling.ipynb
+++ b/Notebooks/02_data_wrangling.ipynb
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -261,7 +261,7 @@
        "[330 rows x 27 columns]>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -288,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 23,
    "metadata": {
     "scrolled": true
    },
@@ -502,7 +502,7 @@
        "[5 rows x 27 columns]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -543,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -714,7 +714,7 @@
        "NightSkiing_ac                   600.0"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -750,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -831,7 +831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -949,7 +949,7 @@
        "[330 rows x 3 columns]"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -979,7 +979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1018,7 +1018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1032,7 +1032,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1045,7 +1045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1059,7 +1059,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1079,7 +1079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1136,7 +1136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -1145,7 +1145,7 @@
        "33"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1172,7 +1172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1220,7 +1220,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1238,7 +1238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -1253,7 +1253,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1283,7 +1283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -1294,7 +1294,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1329,7 +1329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -1397,7 +1397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -1470,7 +1470,7 @@
        "Connecticut     47.800000     56.800000"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1485,7 +1485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -1536,7 +1536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1555,7 +1555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -1628,7 +1628,7 @@
        "4  Arizona  AdultWeekday   74.0"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1646,7 +1646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -1718,7 +1718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -2075,7 +2075,7 @@
        "NightSkiing_ac      114.00    650.0  "
       ]
      },
-     "execution_count": 124,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2097,7 +2097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -2109,7 +2109,7 @@
        "Name: count, dtype: float64"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2142,7 +2142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 88,
    "metadata": {
     "scrolled": true
    },
@@ -2153,7 +2153,7 @@
        "<function matplotlib.pyplot.show(close=None, block=None)>"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2210,7 +2210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -2596,7 +2596,7 @@
        "[66 rows x 27 columns]"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2616,7 +2616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -3041,7 +3041,7 @@
        "NightSkiing_ac                NaN            300.0  "
       ]
      },
-     "execution_count": 143,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3092,7 +3092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
@@ -3101,7 +3101,7 @@
        "26819.0"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3114,7 +3114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3125,7 +3125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
@@ -3134,7 +3134,7 @@
        "1819.0"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3161,7 +3161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
@@ -3198,7 +3198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -3209,7 +3209,7 @@
        "Name: Snow Making_ac, dtype: float64"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3220,7 +3220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
@@ -3391,7 +3391,7 @@
        "NightSkiing_ac                          NaN"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3448,7 +3448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -3460,7 +3460,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3478,7 +3478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3496,7 +3496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
@@ -3621,7 +3621,7 @@
        "[2 rows x 26 columns]"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3648,7 +3648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
@@ -3688,7 +3688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
@@ -3705,7 +3705,7 @@
        "Name: yearsOpen, dtype: float64"
       ]
      },
-     "execution_count": 211,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3723,7 +3723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
@@ -3823,8 +3823,117 @@
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 135,
    "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>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  \n",
+       "0                       580.0  \n",
+       "1                        80.0  \n",
+       "2                       587.0  \n",
+       "3                       428.0  \n",
+       "4                       256.0  "
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 27#\n",
     "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n",
@@ -3835,212 +3944,844 @@
     "state_summary = ski_data.groupby('state').agg(\n",
     "    resorts_per_state=pd.NamedAgg(column='Name', aggfunc='size'), #could pick any column here\n",
     "    state_total_skiable_area_ac=pd.NamedAgg(column='SkiableTerrain_ac', aggfunc='sum'),\n",
-    "    state_total_days_open=pd.NamedAgg(column=__, aggfunc='sum'),\n",
-    "    ___=pd.NamedAgg(column=___, aggfunc=___),\n",
-    "    ___=pd.NamedAgg(column=___, aggfunc=___)\n",
-    ").___\n",
+    "    state_total_days_open=pd.NamedAgg(column='daysOpenLastYear', aggfunc='sum'),\n",
+    "    state_total_terrain_parks=pd.NamedAgg(column='TerrainParks', aggfunc='sum'),\n",
+    "    state_total_nightskiing_ac=pd.NamedAgg(column= 'NightSkiing_ac', aggfunc='sum'),\n",
+    ").reset_index()\n",
     "state_summary.head()"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## 2.8 Drop Rows With No Price Data<a id='2.8_Drop_Rows_With_No_Price_Data'></a>"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "You know there are two columns that refer to price: 'AdultWeekend' and 'AdultWeekday'. You can calculate the number of price values missing per row. This will obviously have to be either 0, 1, or 2, where 0 denotes no price values are missing and 2 denotes that both are missing."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 138,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    82.317073\n",
+       "2    14.329268\n",
+       "1     3.353659\n",
+       "Name: count, dtype: float64"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
     "missing_price.value_counts()/len(missing_price) * 100"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "About 14% of the rows have no price data. As the price is your target, these rows are of no use. Time to lose them."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 140,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 281 entries, 0 to 329\n",
+      "Data columns (total 26 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Name               281 non-null    object \n",
+      " 1   Region             281 non-null    object \n",
+      " 2   state              281 non-null    object \n",
+      " 3   summit_elev        281 non-null    int64  \n",
+      " 4   vertical_drop      281 non-null    int64  \n",
+      " 5   base_elev          281 non-null    int64  \n",
+      " 6   trams              281 non-null    int64  \n",
+      " 7   fastSixes          281 non-null    int64  \n",
+      " 8   fastQuads          281 non-null    int64  \n",
+      " 9   quad               281 non-null    int64  \n",
+      " 10  triple             281 non-null    int64  \n",
+      " 11  double             281 non-null    int64  \n",
+      " 12  surface            281 non-null    int64  \n",
+      " 13  total_chairs       281 non-null    int64  \n",
+      " 14  Runs               278 non-null    float64\n",
+      " 15  TerrainParks       237 non-null    float64\n",
+      " 16  LongestRun_mi      276 non-null    float64\n",
+      " 17  SkiableTerrain_ac  279 non-null    float64\n",
+      " 18  Snow Making_ac     244 non-null    float64\n",
+      " 19  daysOpenLastYear   237 non-null    float64\n",
+      " 20  yearsOpen          281 non-null    float64\n",
+      " 21  averageSnowfall    272 non-null    float64\n",
+      " 22  AdultWeekday       274 non-null    float64\n",
+      " 23  AdultWeekend       277 non-null    float64\n",
+      " 24  projectedDaysOpen  240 non-null    float64\n",
+      " 25  NightSkiing_ac     166 non-null    float64\n",
+      "dtypes: float64(12), int64(11), object(3)\n",
+      "memory usage: 59.3+ KB\n"
+     ]
+    }
+   ],
    "source": [
     "#Code task 28#\n",
     "#Use `missing_price` to remove rows from ski_data where both price values are missing\n",
-    "ski_data = ski_data[___ != 2]"
+    "ski_data = ski_data[missing_price != 2]\n",
+    "ski_data.info()"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## 2.9 Review distributions<a id='2.9_Review_distributions'></a>"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 142,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ski_data.hist(figsize=(15, 10))\n",
     "plt.subplots_adjust(hspace=0.5);"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "These distributions are much better. There are clearly some skewed distributions, so keep an eye on `fastQuads`, `fastSixes`, and perhaps `trams`. These lack much variance away from 0 and may have a small number of relatively extreme values.  Models failing to rate a feature as important when domain knowledge tells you it should be is an issue to look out for, as is a model being overly influenced by some extreme values. If you build a good machine learning pipeline, hopefully it will be robust to such issues, but you may also wish to consider nonlinear transformations of features."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## 2.10 Population data<a id='2.10_Population_data'></a>"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Population and area data for the US states can be obtained from [wikipedia](https://simple.wikipedia.org/wiki/List_of_U.S._states). Listen, you should have a healthy concern about using data you \"found on the Internet\". Make sure it comes from a reputable source. This table of data is useful because it allows you to easily pull and incorporate an external data set. It also allows you to proceed with an analysis that includes state sizes and populations for your 'first cut' model. Be explicit about your source (we documented it here in this workflow) and ensure it is open to inspection. All steps are subject to review, and it may be that a client has a specific source of data they trust that you should use to rerun the analysis."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 146,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[   Name & postal abbs. [1]                                    Cities  \\\n",
+      "   Name & postal abbs. [1] Name & postal abbs. [1].1         Capital   \n",
+      "0                  Alabama                        AL      Montgomery   \n",
+      "1                   Alaska                        AK          Juneau   \n",
+      "2                  Arizona                        AZ         Phoenix   \n",
+      "3                 Arkansas                        AR     Little Rock   \n",
+      "4               California                        CA      Sacramento   \n",
+      "5                 Colorado                        CO          Denver   \n",
+      "6              Connecticut                        CT        Hartford   \n",
+      "7                 Delaware                        DE           Dover   \n",
+      "8                  Florida                        FL     Tallahassee   \n",
+      "9                  Georgia                        GA         Atlanta   \n",
+      "10                 Hawaiʻi                        HI        Honolulu   \n",
+      "11                   Idaho                        ID           Boise   \n",
+      "12                Illinois                        IL     Springfield   \n",
+      "13                 Indiana                        IN    Indianapolis   \n",
+      "14                    Iowa                        IA      Des Moines   \n",
+      "15                  Kansas                        KS          Topeka   \n",
+      "16             Kentucky[C]                        KY       Frankfort   \n",
+      "17               Louisiana                        LA     Baton Rouge   \n",
+      "18                   Maine                        ME         Augusta   \n",
+      "19                Maryland                        MD       Annapolis   \n",
+      "20        Massachusetts[C]                        MA          Boston   \n",
+      "21                Michigan                        MI         Lansing   \n",
+      "22               Minnesota                        MN        St. Paul   \n",
+      "23             Mississippi                        MS         Jackson   \n",
+      "24                Missouri                        MO  Jefferson City   \n",
+      "25                 Montana                        MT          Helena   \n",
+      "26                Nebraska                        NE         Lincoln   \n",
+      "27                  Nevada                        NV     Carson City   \n",
+      "28           New Hampshire                        NH         Concord   \n",
+      "29              New Jersey                        NJ         Trenton   \n",
+      "30              New Mexico                        NM        Santa Fe   \n",
+      "31                New York                        NY          Albany   \n",
+      "32          North Carolina                        NC         Raleigh   \n",
+      "33            North Dakota                        ND        Bismarck   \n",
+      "34                    Ohio                        OH        Columbus   \n",
+      "35                Oklahoma                        OK   Oklahoma City   \n",
+      "36                  Oregon                        OR           Salem   \n",
+      "37         Pennsylvania[C]                        PA      Harrisburg   \n",
+      "38         Rhode Island[D]                        RI      Providence   \n",
+      "39          South Carolina                        SC        Columbia   \n",
+      "40            South Dakota                        SD          Pierre   \n",
+      "41               Tennessee                        TN       Nashville   \n",
+      "42                   Texas                        TX          Austin   \n",
+      "43                    Utah                        UT  Salt Lake City   \n",
+      "44                 Vermont                        VT      Montpelier   \n",
+      "45             Virginia[C]                        VA        Richmond   \n",
+      "46              Washington                        WA         Olympia   \n",
+      "47           West Virginia                        WV      Charleston   \n",
+      "48               Wisconsin                        WI         Madison   \n",
+      "49                 Wyoming                        WY        Cheyenne   \n",
+      "\n",
+      "                   Established[A] Population [B][3] Total area[4]           \\\n",
+      "        Largest[5] Established[A] Population [B][3]           mi2      km2   \n",
+      "0       Birmingham   Dec 14, 1819           4903185         52420   135767   \n",
+      "1        Anchorage    Jan 3, 1959            731545        665384  1723337   \n",
+      "2          Phoenix   Feb 14, 1912           7278717        113990   295234   \n",
+      "3      Little Rock   Jun 15, 1836           3017804         53179   137732   \n",
+      "4      Los Angeles    Sep 9, 1850          39512223        163695   423967   \n",
+      "5           Denver    Aug 1, 1876           5758736        104094   269601   \n",
+      "6       Bridgeport    Jan 9, 1788           3565278          5543    14357   \n",
+      "7       Wilmington    Dec 7, 1787            973764          2489     6446   \n",
+      "8     Jacksonville    Mar 3, 1845          21477737         65758   170312   \n",
+      "9          Atlanta    Jan 2, 1788          10617423         59425   153910   \n",
+      "10        Honolulu   Aug 21, 1959           1415872         10932    28313   \n",
+      "11           Boise    Jul 3, 1890           1787065         83569   216443   \n",
+      "12         Chicago    Dec 3, 1818          12671821         57914   149995   \n",
+      "13    Indianapolis   Dec 11, 1816           6732219         36420    94326   \n",
+      "14      Des Moines   Dec 28, 1846           3155070         56273   145746   \n",
+      "15         Wichita   Jan 29, 1861           2913314         82278   213100   \n",
+      "16      Louisville    Jun 1, 1792           4467673         40408   104656   \n",
+      "17     New Orleans   Apr 30, 1812           4648794         52378   135659   \n",
+      "18        Portland   Mar 15, 1820           1344212         35380    91633   \n",
+      "19       Baltimore   Apr 28, 1788           6045680         12406    32131   \n",
+      "20          Boston    Feb 6, 1788           6892503         10554    27336   \n",
+      "21         Detroit   Jan 26, 1837           9986857         96714   250487   \n",
+      "22     Minneapolis   May 11, 1858           5639632         86936   225163   \n",
+      "23         Jackson   Dec 10, 1817           2976149         48432   125438   \n",
+      "24     Kansas City   Aug 10, 1821           6137428         69707   180540   \n",
+      "25        Billings    Nov 8, 1889           1068778        147040   380831   \n",
+      "26           Omaha    Mar 1, 1867           1934408         77348   200330   \n",
+      "27       Las Vegas   Oct 31, 1864           3080156        110572   286380   \n",
+      "28      Manchester   Jun 21, 1788           1359711          9349    24214   \n",
+      "29          Newark   Dec 18, 1787           8882190          8723    22591   \n",
+      "30     Albuquerque    Jan 6, 1912           2096829        121590   314917   \n",
+      "31        New York   Jul 26, 1788          19453561         54555   141297   \n",
+      "32       Charlotte   Nov 21, 1789          10488084         53819   139391   \n",
+      "33           Fargo    Nov 2, 1889            762062         70698   183108   \n",
+      "34        Columbus    Mar 1, 1803          11689100         44826   116098   \n",
+      "35   Oklahoma City   Nov 16, 1907           3956971         69899   181037   \n",
+      "36        Portland   Feb 14, 1859           4217737         98379   254799   \n",
+      "37    Philadelphia   Dec 12, 1787          12801989         46054   119280   \n",
+      "38      Providence   May 29, 1790           1059361          1545     4001   \n",
+      "39      Charleston   May 23, 1788           5148714         32020    82933   \n",
+      "40     Sioux Falls    Nov 2, 1889            884659         77116   199729   \n",
+      "41       Nashville    Jun 1, 1796           6829174         42144   109153   \n",
+      "42         Houston   Dec 29, 1845          28995881        268596   695662   \n",
+      "43  Salt Lake City    Jan 4, 1896           3205958         84897   219882   \n",
+      "44      Burlington    Mar 4, 1791            623989          9616    24906   \n",
+      "45  Virginia Beach   Jun 25, 1788           8535519         42775   110787   \n",
+      "46         Seattle   Nov 11, 1889           7614893         71298   184661   \n",
+      "47      Charleston   Jun 20, 1863           1792147         24230    62756   \n",
+      "48       Milwaukee   May 29, 1848           5822434         65496   169635   \n",
+      "49        Cheyenne   Jul 10, 1890            578759         97813   253335   \n",
+      "\n",
+      "   Land area[4]          Water area[4]         Number of Reps.  \n",
+      "            mi2      km2           mi2     km2 Number of Reps.  \n",
+      "0         50645   131171          1775    4597               7  \n",
+      "1        570641  1477953         94743  245384               1  \n",
+      "2        113594   294207           396    1026               9  \n",
+      "3         52035   134771          1143    2961               4  \n",
+      "4        155779   403466          7916   20501              53  \n",
+      "5        103642   268431           452    1170               7  \n",
+      "6          4842    12542           701    1816               5  \n",
+      "7          1949     5047           540    1399               1  \n",
+      "8         53625   138887         12133   31424              27  \n",
+      "9         57513   148959          1912    4951              14  \n",
+      "10         6423    16635          4509   11678               2  \n",
+      "11        82643   214045           926    2398               2  \n",
+      "12        55519   143793          2395    6202              18  \n",
+      "13        35826    92789           593    1537               9  \n",
+      "14        55857   144669           416    1077               4  \n",
+      "15        81759   211754           520    1346               4  \n",
+      "16        39486   102269           921    2387               6  \n",
+      "17        43204   111898          9174   23761               6  \n",
+      "18        30843    79883          4537   11750               2  \n",
+      "19         9707    25142          2699    6990               8  \n",
+      "20         7800    20202          2754    7134               9  \n",
+      "21        56539   146435         40175  104052              14  \n",
+      "22        79627   206232          7309   18930               8  \n",
+      "23        46923   121531          1508    3907               4  \n",
+      "24        68742   178040           965    2501               8  \n",
+      "25       145546   376962          1494    3869               1  \n",
+      "26        76824   198974           524    1356               3  \n",
+      "27       109781   284332           791    2048               4  \n",
+      "28         8953    23187           397    1027               2  \n",
+      "29         7354    19047          1368    3544              12  \n",
+      "30       121298   314161           292     757               3  \n",
+      "31        47126   122057          7429   19240              27  \n",
+      "32        48618   125920          5201   13471              13  \n",
+      "33        69001   178711          1698    4397               1  \n",
+      "34        40861   105829          3965   10269              16  \n",
+      "35        68595   177660          1304    3377               5  \n",
+      "36        95988   248608          2391    6191               5  \n",
+      "37        44743   115883          1312    3397              18  \n",
+      "38         1034     2678           511    1324               2  \n",
+      "39        30061    77857          1960    5076               7  \n",
+      "40        75811   196350          1305    3379               1  \n",
+      "41        41235   106798           909    2355               9  \n",
+      "42       261232   676587          7365   19075              36  \n",
+      "43        82170   212818          2727    7064               4  \n",
+      "44         9217    23871           400    1035               1  \n",
+      "45        39490   102279          3285    8508              11  \n",
+      "46        66456   172119          4842   12542              10  \n",
+      "47        24038    62259           192     497               3  \n",
+      "48        54158   140268         11339   29367               8  \n",
+      "49        97093   251470           720    1864               1  ]\n"
+     ]
+    }
+   ],
    "source": [
     "#Code task 29#\n",
     "#Use pandas' `read_html` method to read the table from the URL below\n",
     "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n",
-    "usa_states = pd.___(___)"
+    "usa_states = pd.read_html(states_url)\n",
+    "print(usa_states)"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 147,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "type(usa_states)"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 148,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "len(usa_states)"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 149,
    "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Name &amp; postal abbs. [1]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Cities</th>\n",
+       "      <th>Established[A]</th>\n",
+       "      <th>Population [B][3]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Total area[4]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Land area[4]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Water area[4]</th>\n",
+       "      <th>Number of Reps.</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Name &amp; postal abbs. [1]</th>\n",
+       "      <th>Name &amp; postal abbs. [1].1</th>\n",
+       "      <th>Capital</th>\n",
+       "      <th>Largest[5]</th>\n",
+       "      <th>Established[A]</th>\n",
+       "      <th>Population [B][3]</th>\n",
+       "      <th>mi2</th>\n",
+       "      <th>km2</th>\n",
+       "      <th>mi2</th>\n",
+       "      <th>km2</th>\n",
+       "      <th>mi2</th>\n",
+       "      <th>km2</th>\n",
+       "      <th>Number of Reps.</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>Montgomery</td>\n",
+       "      <td>Birmingham</td>\n",
+       "      <td>Dec 14, 1819</td>\n",
+       "      <td>4903185</td>\n",
+       "      <td>52420</td>\n",
+       "      <td>135767</td>\n",
+       "      <td>50645</td>\n",
+       "      <td>131171</td>\n",
+       "      <td>1775</td>\n",
+       "      <td>4597</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AK</td>\n",
+       "      <td>Juneau</td>\n",
+       "      <td>Anchorage</td>\n",
+       "      <td>Jan 3, 1959</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "      <td>1723337</td>\n",
+       "      <td>570641</td>\n",
+       "      <td>1477953</td>\n",
+       "      <td>94743</td>\n",
+       "      <td>245384</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>Phoenix</td>\n",
+       "      <td>Phoenix</td>\n",
+       "      <td>Feb 14, 1912</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "      <td>295234</td>\n",
+       "      <td>113594</td>\n",
+       "      <td>294207</td>\n",
+       "      <td>396</td>\n",
+       "      <td>1026</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>AR</td>\n",
+       "      <td>Little Rock</td>\n",
+       "      <td>Little Rock</td>\n",
+       "      <td>Jun 15, 1836</td>\n",
+       "      <td>3017804</td>\n",
+       "      <td>53179</td>\n",
+       "      <td>137732</td>\n",
+       "      <td>52035</td>\n",
+       "      <td>134771</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>2961</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>Sacramento</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>Sep 9, 1850</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "      <td>423967</td>\n",
+       "      <td>155779</td>\n",
+       "      <td>403466</td>\n",
+       "      <td>7916</td>\n",
+       "      <td>20501</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Name & postal abbs. [1]                                 Cities               \\\n",
+       "  Name & postal abbs. [1] Name & postal abbs. [1].1      Capital   Largest[5]   \n",
+       "0                 Alabama                        AL   Montgomery   Birmingham   \n",
+       "1                  Alaska                        AK       Juneau    Anchorage   \n",
+       "2                 Arizona                        AZ      Phoenix      Phoenix   \n",
+       "3                Arkansas                        AR  Little Rock  Little Rock   \n",
+       "4              California                        CA   Sacramento  Los Angeles   \n",
+       "\n",
+       "  Established[A] Population [B][3] Total area[4]          Land area[4]  \\\n",
+       "  Established[A] Population [B][3]           mi2      km2          mi2   \n",
+       "0   Dec 14, 1819           4903185         52420   135767        50645   \n",
+       "1    Jan 3, 1959            731545        665384  1723337       570641   \n",
+       "2   Feb 14, 1912           7278717        113990   295234       113594   \n",
+       "3   Jun 15, 1836           3017804         53179   137732        52035   \n",
+       "4    Sep 9, 1850          39512223        163695   423967       155779   \n",
+       "\n",
+       "           Water area[4]         Number of Reps.  \n",
+       "       km2           mi2     km2 Number of Reps.  \n",
+       "0   131171          1775    4597               7  \n",
+       "1  1477953         94743  245384               1  \n",
+       "2   294207           396    1026               9  \n",
+       "3   134771          1143    2961               4  \n",
+       "4   403466          7916   20501              53  "
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "usa_states = usa_states[0]\n",
     "usa_states.head()"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Note, in even the last year, the capability of `pd.read_html()` has improved. The merged cells you see in the web table are now handled much more conveniently, with 'Phoenix' now being duplicated so the subsequent columns remain aligned. But check this anyway. If you extract the established date column, you should just get dates. Recall previously you used the `.loc` accessor, because you were using labels. Now you want to refer to a column by its index position and so use `.iloc`. For a discussion on the difference use cases of `.loc` and `.iloc` refer to the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html)."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 151,
    "metadata": {},
+   "outputs": [],
    "source": [
     "#Code task 30#\n",
     "#Use the iloc accessor to get the pandas Series for column number 4 from `usa_states`\n",
     "#It should be a column of dates\n",
-    "established = usa_sates.___[:, 4]"
+    "established = usa_states.iloc[:, 4]"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 152,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     Dec 14, 1819\n",
+       "1      Jan 3, 1959\n",
+       "2     Feb 14, 1912\n",
+       "3     Jun 15, 1836\n",
+       "4      Sep 9, 1850\n",
+       "5      Aug 1, 1876\n",
+       "6      Jan 9, 1788\n",
+       "7      Dec 7, 1787\n",
+       "8      Mar 3, 1845\n",
+       "9      Jan 2, 1788\n",
+       "10    Aug 21, 1959\n",
+       "11     Jul 3, 1890\n",
+       "12     Dec 3, 1818\n",
+       "13    Dec 11, 1816\n",
+       "14    Dec 28, 1846\n",
+       "15    Jan 29, 1861\n",
+       "16     Jun 1, 1792\n",
+       "17    Apr 30, 1812\n",
+       "18    Mar 15, 1820\n",
+       "19    Apr 28, 1788\n",
+       "20     Feb 6, 1788\n",
+       "21    Jan 26, 1837\n",
+       "22    May 11, 1858\n",
+       "23    Dec 10, 1817\n",
+       "24    Aug 10, 1821\n",
+       "25     Nov 8, 1889\n",
+       "26     Mar 1, 1867\n",
+       "27    Oct 31, 1864\n",
+       "28    Jun 21, 1788\n",
+       "29    Dec 18, 1787\n",
+       "30     Jan 6, 1912\n",
+       "31    Jul 26, 1788\n",
+       "32    Nov 21, 1789\n",
+       "33     Nov 2, 1889\n",
+       "34     Mar 1, 1803\n",
+       "35    Nov 16, 1907\n",
+       "36    Feb 14, 1859\n",
+       "37    Dec 12, 1787\n",
+       "38    May 29, 1790\n",
+       "39    May 23, 1788\n",
+       "40     Nov 2, 1889\n",
+       "41     Jun 1, 1796\n",
+       "42    Dec 29, 1845\n",
+       "43     Jan 4, 1896\n",
+       "44     Mar 4, 1791\n",
+       "45    Jun 25, 1788\n",
+       "46    Nov 11, 1889\n",
+       "47    Jun 20, 1863\n",
+       "48    May 29, 1848\n",
+       "49    Jul 10, 1890\n",
+       "Name: (Established[A], Established[A]), dtype: object"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "established"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Extract the state name, population, and total area (square miles) columns."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 154,
    "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>state</th>\n",
+       "      <th>state_population</th>\n",
+       "      <th>state_area_sq_miles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>4903185</td>\n",
+       "      <td>52420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>3017804</td>\n",
+       "      <td>53179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state  state_population  state_area_sq_miles\n",
+       "0     Alabama           4903185                52420\n",
+       "1      Alaska            731545               665384\n",
+       "2     Arizona           7278717               113990\n",
+       "3    Arkansas           3017804                53179\n",
+       "4  California          39512223               163695"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 31#\n",
     "#Now use the iloc accessor again to extract columns 0, 5, and 6 and the dataframe's `copy()` method\n",
     "#Set the names of these extracted columns to 'state', 'state_population', and 'state_area_sq_miles',\n",
     "#respectively.\n",
-    "usa_states_sub = usa_states.___[:, [___]].copy()\n",
-    "usa_states_sub.columns = [___]\n",
+    "usa_states_sub = usa_states.iloc[:, [0, 5, 6]].copy()\n",
+    "usa_states_sub.columns = ['state', 'state_population',  'state_area_sq_miles']\n",
     "usa_states_sub.head()"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Do you have all the ski data states accounted for?"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 156,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 32#\n",
     "#Find the states in `state_summary` that are not in `usa_states_sub`\n",
     "#Hint: set(list1) - set(list2) is an easy way to get items in list1 that are not in list2\n",
-    "missing_states = ___(state_summary.state) - ___(usa_states_sub.state)\n",
+    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
     "missing_states"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "No?? "
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "If you look at the table on the web, you can perhaps start to guess what the problem is. You can confirm your suspicion by pulling out state names that _contain_ 'Massachusetts', 'Pennsylvania', or 'Virginia' from usa_states_sub:"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 159,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20    Massachusetts[C]\n",
+       "37     Pennsylvania[C]\n",
+       "38     Rhode Island[D]\n",
+       "45         Virginia[C]\n",
+       "47       West Virginia\n",
+       "Name: state, dtype: object"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Delete square brackets and their contents and try again:"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 161,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:8: SyntaxWarning: invalid escape sequence '\\['\n",
+      "<>:8: SyntaxWarning: invalid escape sequence '\\['\n",
+      "C:\\Users\\wdwbe\\AppData\\Local\\Temp\\ipykernel_21908\\95665047.py:8: SyntaxWarning: invalid escape sequence '\\['\n",
+      "  usa_states_sub.state.replace(to_replace='\\[.*\\]', value='', regex=True, inplace=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "20    Massachusetts\n",
+       "37     Pennsylvania\n",
+       "38     Rhode Island\n",
+       "45         Virginia\n",
+       "47    West Virginia\n",
+       "Name: state, dtype: object"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 33#\n",
     "#Use pandas' Series' `replace()` method to replace anything within square brackets (including the brackets)\n",
@@ -4049,146 +4790,531 @@
     "#value='' #empty string as replacement\n",
     "#regex=True #we used a regex in our `to_replace` argument\n",
     "#inplace=True #Do this \"in place\"\n",
-    "usa_states_sub.state.___(to_replace=___, value=__, regex=___, inplace=___)\n",
+    "usa_states_sub.state.replace(to_replace='\\[.*\\]', value='', regex=True, inplace=True)\n",
     "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 162,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 34#\n",
     "#And now verify none of our states are missing by checking that there are no states in\n",
     "#state_summary that are not in usa_states_sub (as earlier using `set()`)\n",
-    "missing_states = ___(state_summary.state) - ___(usa_states_sub.state)\n",
+    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
     "missing_states"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Better! You have an empty set for missing states now. You can confidently add the population and state area columns to the ski resort data."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 164,
    "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>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>state_population</th>\n",
+       "      <th>state_area_sq_miles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>5758736</td>\n",
+       "      <td>104094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>3565278</td>\n",
+       "      <td>5543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  state_population  state_area_sq_miles  \n",
+       "0                       580.0            731545               665384  \n",
+       "1                        80.0           7278717               113990  \n",
+       "2                       587.0          39512223               163695  \n",
+       "3                       428.0           5758736               104094  \n",
+       "4                       256.0           3565278                 5543  "
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 35#\n",
     "#Use 'state_summary's `merge()` method to combine our new data in 'usa_states_sub'\n",
     "#specify the arguments how='left' and on='state'\n",
-    "state_summary = state_summary.___(usa_states_sub, ___=___, ___=___)\n",
+    "state_summary = state_summary.merge(usa_states_sub, on='state', how='left')\n",
     "state_summary.head()"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Having created this data frame of summary statistics for various states, it would seem obvious to join this with the ski resort data to augment it with this additional data. You will do this, but not now. In the next notebook you will be exploring the data, including the relationships between the states. For that you want a separate row for each state, as you have here, and joining the data this soon means you'd need to separate and eliminate redundances in the state data when you wanted it."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## 2.11 Target Feature<a id='2.11_Target_Feature'></a>"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Finally, what will your target be when modelling ticket price? What relationship is there between weekday and weekend prices?"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 168,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Code task 36#\n",
     "#Use ski_data's `plot()` method to create a scatterplot (kind='scatter') with 'AdultWeekday' on the x-axis and\n",
     "#'AdultWeekend' on the y-axis\n",
-    "ski_data.___(x=___, y=___, kind=___);"
+    "ski_data.plot(x='AdultWeekday', y='AdultWeekend', kind='scatter');"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "A couple of observations can be made. Firstly, there is a clear line where weekend and weekday prices are equal. Weekend prices being higher than weekday prices seem restricted to sub $100 resorts. Recall from the boxplot earlier that the distribution for weekday and weekend prices in Montana seemed equal. Is this confirmed in the actual data for each resort? Big Mountain resort is in Montana, so the relationship between these quantities in this state are particularly relevant."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 170,
    "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>AdultWeekend</th>\n",
+       "      <th>AdultWeekday</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>42.0</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>49.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>48.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>46.0</td>\n",
+       "      <td>46.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>67.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>81.0</td>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     AdultWeekend  AdultWeekday\n",
+       "141          42.0          42.0\n",
+       "142          63.0          63.0\n",
+       "143          49.0          49.0\n",
+       "144          48.0          48.0\n",
+       "145          46.0          46.0\n",
+       "146          39.0          39.0\n",
+       "147          50.0          50.0\n",
+       "148          67.0          67.0\n",
+       "149          47.0          47.0\n",
+       "150          39.0          39.0\n",
+       "151          81.0          81.0"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 37#\n",
     "#Use the loc accessor on ski_data to print the 'AdultWeekend' and 'AdultWeekday' columns for Montana only\n",
-    "ski_data.___[ski_data.state == ___, [___, ___]]"
+    "ski_data.loc[ski_data.state == 'Montana', ['AdultWeekend', 'AdultWeekday']]"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Is there any reason to prefer weekend or weekday prices? Which is missing the least?"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 172,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AdultWeekend    4\n",
+       "AdultWeekday    7\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum()"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Weekend prices have the least missing values of the two, so drop the weekday prices and then keep just the rows that have weekend price."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 174,
    "metadata": {},
+   "outputs": [],
    "source": [
     "ski_data.drop(columns='AdultWeekday', inplace=True)\n",
     "ski_data.dropna(subset=['AdultWeekend'], inplace=True)"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 175,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277, 25)"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ski_data.shape"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Perform a final quick check on the data."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### 2.11.1 Number Of Missing Values By Row - Resort<a id='2.11.1_Number_Of_Missing_Values_By_Row_-_Resort'></a>"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Having dropped rows missing the desired target ticket price, what degree of missingness do you have for the remaining rows?"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 179,
    "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>count</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     count     %\n",
+       "329      5  20.0\n",
+       "62       5  20.0\n",
+       "141      5  20.0\n",
+       "86       5  20.0\n",
+       "74       5  20.0\n",
+       "146      5  20.0\n",
+       "184      4  16.0\n",
+       "108      4  16.0\n",
+       "198      4  16.0\n",
+       "39       4  16.0"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "missing = pd.concat([ski_data.isnull().sum(axis=1), 100 * ski_data.isnull().mean(axis=1)], axis=1)\n",
     "missing.columns=['count', '%']\n",
@@ -4196,78 +5322,192 @@
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "These seem possibly curiously quantized..."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 181,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  4.,  8., 12., 16., 20.])"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "missing['%'].unique()"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Yes, the percentage of missing values per row appear in multiples of 4."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 183,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "%\n",
+       "0.0     107\n",
+       "4.0      94\n",
+       "8.0      45\n",
+       "12.0     15\n",
+       "16.0     10\n",
+       "20.0      6\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "missing['%'].value_counts()"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "This is almost as if values have been removed artificially... Nevertheless, what you don't know is how useful the missing features are in predicting ticket price. You shouldn't just drop rows that are missing several useless features."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 185,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 277 entries, 0 to 329\n",
+      "Data columns (total 25 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Name               277 non-null    object \n",
+      " 1   Region             277 non-null    object \n",
+      " 2   state              277 non-null    object \n",
+      " 3   summit_elev        277 non-null    int64  \n",
+      " 4   vertical_drop      277 non-null    int64  \n",
+      " 5   base_elev          277 non-null    int64  \n",
+      " 6   trams              277 non-null    int64  \n",
+      " 7   fastSixes          277 non-null    int64  \n",
+      " 8   fastQuads          277 non-null    int64  \n",
+      " 9   quad               277 non-null    int64  \n",
+      " 10  triple             277 non-null    int64  \n",
+      " 11  double             277 non-null    int64  \n",
+      " 12  surface            277 non-null    int64  \n",
+      " 13  total_chairs       277 non-null    int64  \n",
+      " 14  Runs               274 non-null    float64\n",
+      " 15  TerrainParks       233 non-null    float64\n",
+      " 16  LongestRun_mi      272 non-null    float64\n",
+      " 17  SkiableTerrain_ac  275 non-null    float64\n",
+      " 18  Snow Making_ac     240 non-null    float64\n",
+      " 19  daysOpenLastYear   233 non-null    float64\n",
+      " 20  yearsOpen          277 non-null    float64\n",
+      " 21  averageSnowfall    268 non-null    float64\n",
+      " 22  AdultWeekend       277 non-null    float64\n",
+      " 23  projectedDaysOpen  236 non-null    float64\n",
+      " 24  NightSkiing_ac     163 non-null    float64\n",
+      "dtypes: float64(11), int64(11), object(3)\n",
+      "memory usage: 56.3+ KB\n"
+     ]
+    }
+   ],
    "source": [
     "ski_data.info()"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "There are still some missing values, and it's good to be aware of this, but leave them as is for now."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 187,
    "metadata": {},
+   "outputs": [],
    "source": [
     "## 2.12 Save data<a id='2.12_Save_data'></a>"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 188,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277, 25)"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ski_data.shape"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Save this to your data directory, separately. Note that you were provided with the data in `raw_data` and you should saving derived data in a separate location. This guards against overwriting our original data."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 190,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A file already exists with this name.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Do you want to overwrite? (Y/N) Y\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing file.  \"../data\\ski_data_cleaned.csv\"\n"
+     ]
+    }
+   ],
    "source": [
     "# save the data to a new csv file\n",
     "datapath = '../data'\n",
@@ -4275,8 +5515,33 @@
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 191,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A file already exists with this name.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Do you want to overwrite? (Y/N) Y\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing file.  \"../data\\state_summary.csv\"\n"
+     ]
+    }
+   ],
    "source": [
     "# save the state_summary separately.\n",
     "datapath = '../data'\n",
@@ -4284,25 +5549,129 @@
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## 2.13 Summary<a id='2.13_Summary'></a>"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "**Q: 3** Write a summary statement that highlights the key processes and findings from this notebook. This should include information such as the original number of rows in the data, whether our own resort was actually present etc. What columns, if any, have been removed? Any rows? Summarise the reasons why. Were any other issues found? What remedial actions did you take? State where you are in the project. Can you confirm what the target feature is for your desire to predict ticket price? How many rows were left in the data? Hint: this is a great opportunity to reread your notebook, check all cells have been executed in order and from a \"blank slate\" (restarting the kernel will do this), and that your workflow makes sense and follows a logical pattern. As you do this you can pull out salient information for inclusion in this summary. Thus, this section will provide an important overview of \"what\" and \"why\" without having to dive into the \"how\" or any unproductive or inconclusive steps along the way."
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 3** Your answer here"
+    "**A: 3 Becky's Answer Below**\n",
+    "\n",
+    "***Data Wrangling Summary - Big Mountain Resort Ticket Pricing Model Project***\n",
+    "\n",
+    "**Initial Data**\n",
+    "* ski_resort_data.csv\n",
+    "* 330 Rows\n",
+    "* 27 Columns\n",
+    "    - Including details such as (but not limited to) State/Region, Altitude, Trams & Lifts, Days Open, Resort Features, and Ticket Prices\n",
+    "* Big Mountain Resort is in this dataset\n",
+    "    - Index = 151\n",
+    "\n",
+    "**Data Review**\n",
+    "* Missing Data\n",
+    "    - Approximately half of the resorts did not have any data for:\n",
+    "        - Fast Eight Lifts (50.3%)\n",
+    "        - Night Skiing (43.3%)\n",
+    "    - Approximately 15% of the resorts did not have any data regarding the following\n",
+    "        - Snow Making \n",
+    "        - Projected Days Open\n",
+    "        - Days Open Last Year\n",
+    "        - Ticket Prices (Both Weekend and Weekday)\n",
+    "\n",
+    "* Duplicates\n",
+    "    - Two Resorts are both named \"Crystal Mountain\"\n",
+    "    - But they are in different states, so this is not considered a duplicate.\n",
+    "\n",
+    "* Data Distributions\n",
+    "    - State\n",
+    "        - Montana ranks 13th for number of resorts\n",
+    "        - New York has the most resorts\n",
+    "        - Maryland has the least resorts\n",
+    "    - Ticket Prices\n",
+    "        - Vary between states\n",
+    "        - Most ticket prices are between $25 and $100. \n",
+    "        - Some have significantly higher weekend prices.\n",
+    "        - Many resorts' Weekday and Weekend prices are the same.\n",
+    "        - In General, Ticket prices are less than $100 where there are different Weekend and Weekday prices.\n",
+    "        - There is no difference in Weekday vs Weekend ticket prices for Resorts in Montana.\n",
+    "    - Calculated the average values for each feature and reviewed the distributions\n",
+    "        - Unusal Data:\n",
+    "            - Outliers\n",
+    "                - Silverton Mountain: Skiable Area =  26819 acres.\n",
+    "                - Heavenly Mountain Resort: Snowmaking = 3379 acres\n",
+    "                    - Note: there ticket data for this resort\n",
+    "            - Fast Eight\n",
+    "                - Only one resort has this type of lift.\n",
+    "                - Half the resorts have no data\n",
+    "            - Years Open\n",
+    "                - Pine Knob Ski Resort recorded as being open for 2019 years\n",
+    "             \n",
+    "* Caclulated the following totals across each state. This could be useful in comparing a single resort's features to the total available in that resorts market/state:\n",
+    "    -  Skiable Area\n",
+    "    -  Total Days Open\n",
+    "    -  Terrain Parks\n",
+    "    -  Night Skiing\n",
+    "\n",
+    "\n",
+    "* Collected State Population Data\n",
+    "    - Source: https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473\n",
+    "\n",
+    "             \n",
+    "      \n",
+    "\n",
+    "**Data Cleansing**\n",
+    "* Removed the Fast Eight column because there was virtually no data for this feature.\n",
+    "* Researched Silverton Mountain Skiable Area\n",
+    "    - Updated to  1819 acres\n",
+    "* Removed Pine Knob Ski Resort because we do not know how many years it has been opened.\n",
+    "* Removed the resorts that had no ticket price information as we can not use them to in a ticket price calculation model.\n",
+    "* Cleaned State Populattion data by removing the brackets attached to some states.\n",
+    "* Removed Weekday Ticket prices because:\n",
+    "    - Our resort is in Montana and other resorts in Montana do not have different weekday and weekend ticket prices.\n",
+    "    - Less Weekend Ticket price data is missing than Weekday. Therefore, we can use just the weekend price data for our calculation.\n",
+    "* Cleaned Data File\n",
+    "    - File Name: ski_data_cleaned.csv\n",
+    "    - 277 Rows\n",
+    "    - 25 Columns \n",
+    "\n",
+    "\n",
+    "\n",
+    "**Project Progress**\n",
+    "* All available data has been reviewed\n",
+    "* Data has been cleaned\n",
+    "* Next Step: Exploratory Data Analysis\n",
+    "    - Determining which features could be used in a model to calculate ticket prices.\n",
+    "\n",
+    "\n",
+    "\n",
+    "**Questions for SMEs**\n",
+    "* Should we include data from all the States or just Montana?\n",
+    "    - Do you want to compare data from different states?\n",
+    "    - For Example: Are there resorts in other states that compete with Big Mountain Resort?\n",
+    "* Is state population data relevent?\n",
+    "* Are the number of chair lifts relevent?\n",
+    "  \n",
+    "\n",
+    "\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Notebooks/Untitled.ipynb b/Notebooks/Untitled.ipynb
new file mode 100644
index 000000000..363fcab7e
--- /dev/null
+++ b/Notebooks/Untitled.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Notebooks/sb_utils.py b/Notebooks/sb_utils.py
new file mode 100644
index 000000000..94372eb88
--- /dev/null
+++ b/Notebooks/sb_utils.py
@@ -0,0 +1,59 @@
+import pandas as pd
+import os
+import pickle
+    
+    
+# This utility was created because students were getting confused when they ran 
+# their notebooks twice, the previous write-to-file code would do nothing and say  
+# nothing. The students thought the file was over-written when in fact, it was not -
+# generating hidden bugs in subsequent notebooks.
+
+def save_file(data, fname, dname):
+    """Save a datafile (data) to a specific location (dname) and filename (fname)
+    
+    Currently valid formats are limited to CSV or PKL."""
+    
+    if not os.path.exists(dname):
+        os.mkdir(dname)
+        print(f'Directory {dname} was created.')
+        
+    fpath = os.path.join(dname, fname)
+    
+    
+    if os.path.exists(fpath):
+        print("A file already exists with this name.\n")
+
+        yesno = None
+        while yesno != "Y" and yesno != "N":
+            yesno = input('Do you want to overwrite? (Y/N)').strip()[0].capitalize()
+            if yesno == "Y":
+                print(f'Writing file.  "{fpath}"')
+                _save_file(data, fpath)
+                break  # Not required
+            elif yesno == "N":
+                print('\nPlease re-run this cell with a new filename.')
+                break  # Not required
+            else:
+                print('\nUnknown input, please enter "Y" or "N".')
+
+    else:  # path does not exist, ok to save the file
+        print(f'Writing file.  "{fpath}"')
+        _save_file(data, fpath)
+        
+        
+        
+        
+        
+        
+def _save_file(data, fpath):
+    valid_ftypes = ['.csv', '.pkl']
+    
+    assert (fpath[-4:] in valid_ftypes), "Invalid file type.  Use '.csv' or '.pkl'"
+
+    # Figure out what kind of file we're dealing with by name
+    if fpath[-3:] == 'csv':
+        data.to_csv(fpath, index=False)
+    elif fpath[-3:] == 'pkl':
+        with open(fpath, 'wb') as f:
+            pickle.dump(data, f)
+            
\ No newline at end of file
diff --git a/data/ski_data_cleaned.csv b/data/ski_data_cleaned.csv
new file mode 100644
index 000000000..4259e45d8
--- /dev/null
+++ b/data/ski_data_cleaned.csv
@@ -0,0 +1,278 @@
+Name,Region,state,summit_elev,vertical_drop,base_elev,trams,fastSixes,fastQuads,quad,triple,double,surface,total_chairs,Runs,TerrainParks,LongestRun_mi,SkiableTerrain_ac,Snow Making_ac,daysOpenLastYear,yearsOpen,averageSnowfall,AdultWeekend,projectedDaysOpen,NightSkiing_ac
+Alyeska Resort,Alaska,Alaska,3939,2500,250,1,0,2,2,0,0,2,7,76.0,2.0,1.0,1610.0,113.0,150.0,60.0,669.0,85.0,150.0,550.0
+Eaglecrest Ski Area,Alaska,Alaska,2600,1540,1200,0,0,0,0,0,4,0,4,36.0,1.0,2.0,640.0,60.0,45.0,44.0,350.0,53.0,90.0,
+Hilltop Ski Area,Alaska,Alaska,2090,294,1796,0,0,0,0,1,0,2,3,13.0,1.0,1.0,30.0,30.0,150.0,36.0,69.0,34.0,152.0,30.0
+Arizona Snowbowl,Arizona,Arizona,11500,2300,9200,0,1,0,2,2,1,2,8,55.0,4.0,2.0,777.0,104.0,122.0,81.0,260.0,89.0,122.0,
+Sunrise Park Resort,Arizona,Arizona,11100,1800,9200,0,0,1,2,3,1,0,7,65.0,2.0,1.2,800.0,80.0,115.0,49.0,250.0,78.0,104.0,80.0
+Yosemite Ski & Snowboard Area,Northern California,California,7800,600,7200,0,0,0,0,1,3,1,5,10.0,2.0,0.4,88.0,,110.0,84.0,300.0,47.0,107.0,
+Dodge Ridge,Sierra Nevada,California,8200,1600,6600,0,0,0,1,2,5,4,12,67.0,5.0,2.0,862.0,,,69.0,350.0,78.0,140.0,
+Donner Ski Ranch,Sierra Nevada,California,8012,750,7031,0,0,0,0,1,5,2,8,52.0,2.0,1.5,505.0,60.0,163.0,82.0,400.0,75.0,170.0,
+Mammoth Mountain Ski Area,Sierra Nevada,California,11053,3100,7953,3,2,9,1,6,4,0,25,154.0,7.0,3.0,3500.0,700.0,243.0,66.0,400.0,159.0,,
+Mt. Shasta Ski Park,Sierra Nevada,California,6890,1435,5500,0,0,0,0,3,0,1,4,32.0,2.0,1.1,425.0,225.0,140.0,34.0,300.0,59.0,130.0,
+Mountain High,Sierra Nevada,California,8200,1600,6600,0,0,2,2,2,5,3,14,59.0,1.0,1.6,290.0,275.0,118.0,95.0,108.0,84.0,150.0,73.0
+Mt. Baldy,Sierra Nevada,California,8600,2100,6500,0,0,0,0,0,4,0,4,26.0,,2.5,400.0,80.0,175.0,67.0,178.0,69.0,200.0,
+Ski China Peak,Sierra Nevada,California,8709,1679,7030,0,0,0,1,4,2,4,11,45.0,1.0,2.2,1400.0,150.0,140.0,62.0,300.0,83.0,144.0,
+Snow Valley,Sierra Nevada,California,7841,1041,6800,0,0,0,0,5,6,1,12,28.0,6.0,1.2,240.0,188.0,111.0,82.0,160.0,79.0,143.0,164.0
+Soda Springs,Sierra Nevada,California,7352,652,6700,0,0,0,0,1,1,2,4,18.0,,0.4,200.0,20.0,150.0,83.0,400.0,50.0,144.0,
+Sugar Bowl Resort,Sierra Nevada,California,8383,1500,6883,1,0,5,3,1,0,2,12,105.0,3.0,3.0,1650.0,375.0,151.0,80.0,500.0,125.0,150.0,
+Tahoe Donner,Sierra Nevada,California,7350,600,6750,0,0,0,1,1,0,3,5,14.0,2.0,1.0,120.0,,150.0,48.0,400.0,69.0,144.0,
+Arapahoe Basin Ski Area,Colorado,Colorado,13050,2530,10780,0,0,1,2,1,2,3,9,145.0,3.0,1.5,1428.0,125.0,230.0,73.0,350.0,85.0,233.0,
+Aspen / Snowmass,Colorado,Colorado,12510,4406,8104,3,1,15,4,3,5,9,40,336.0,10.0,5.3,5517.0,658.0,138.0,72.0,300.0,179.0,138.0,
+Copper Mountain Resort,Colorado,Colorado,12313,2738,9712,1,2,4,0,4,4,9,24,150.0,6.0,1.7,2527.0,364.0,164.0,47.0,300.0,158.0,164.0,
+Purgatory,Colorado,Colorado,10822,2029,8793,0,1,2,0,3,3,3,12,101.0,9.0,1.3,1605.0,250.0,130.0,54.0,260.0,89.0,130.0,
+Howelsen Hill,Colorado,Colorado,7136,440,6696,0,0,0,0,0,1,3,4,17.0,1.0,6.0,50.0,25.0,100.0,104.0,150.0,25.0,100.0,10.0
+Loveland,Colorado,Colorado,13010,2210,10800,0,0,1,3,3,2,1,10,94.0,1.0,2.0,1800.0,240.0,205.0,82.0,422.0,79.0,184.0,
+Monarch Mountain,Colorado,Colorado,11952,1162,10790,0,0,0,1,0,4,2,7,64.0,2.0,1.0,800.0,,143.0,80.0,350.0,89.0,136.0,
+Powderhorn,Colorado,Colorado,9850,1650,8200,0,0,1,0,0,2,2,5,42.0,2.0,1.5,1600.0,42.0,111.0,53.0,250.0,71.0,110.0,
+Silverton Mountain,Colorado,Colorado,13487,3087,10400,0,0,0,0,0,1,0,1,,,1.5,1819.0,,175.0,17.0,400.0,79.0,181.0,
+Cooper,Colorado,Colorado,11700,1200,10500,0,0,0,0,1,1,2,4,41.0,1.0,1.0,400.0,,130.0,74.0,260.0,56.0,130.0,
+Ski Granby Ranch,Colorado,Colorado,9202,1000,8202,0,0,2,0,1,1,1,5,40.0,1.0,0.6,406.0,170.0,116.0,36.0,220.0,84.0,92.0,100.0
+Sunlight Mountain Resort,Colorado,Colorado,9895,2010,7885,0,0,0,0,1,2,0,3,67.0,1.0,2.5,680.0,30.0,100.0,53.0,250.0,65.0,135.0,
+Telluride,Colorado,Colorado,13150,4425,8725,2,0,6,1,2,2,4,17,148.0,3.0,4.6,2000.0,220.0,131.0,47.0,280.0,139.0,137.0,
+Wolf Creek Ski Area,Colorado,Colorado,11904,1604,10300,0,0,3,1,2,1,3,10,120.0,,2.0,1600.0,5.0,130.0,80.0,430.0,72.0,150.0,
+Mohawk Mountain,Connecticut,Connecticut,1600,650,950,0,0,0,0,5,0,3,8,25.0,,1.5,107.0,100.0,,72.0,92.0,65.0,110.0,64.0
+Mount Southington Ski Area,Connecticut,Connecticut,525,425,100,0,0,0,0,2,2,3,7,14.0,2.0,0.3,51.0,51.0,63.0,55.0,80.0,60.0,95.0,51.0
+Powder Ridge Park,Connecticut,Connecticut,720,550,170,0,0,0,0,1,2,2,5,19.0,4.0,0.5,80.0,68.0,80.0,60.0,80.0,55.0,100.0,40.0
+Ski Sundown,Connecticut,Connecticut,1075,625,450,0,0,0,0,3,0,2,5,16.0,2.0,1.0,70.0,70.0,84.0,50.0,45.0,62.0,95.0,66.0
+Woodbury Ski Area,Connecticut,Connecticut,730,300,430,0,0,0,0,0,1,4,5,12.0,2.0,0.2,50.0,50.0,126.0,57.0,70.0,42.0,180.0,35.0
+Bogus Basin,Idaho,Idaho,7582,1800,5800,0,0,3,0,1,3,4,11,91.0,3.0,1.5,2600.0,,134.0,77.0,250.0,64.0,130.0,165.0
+Brundage Mountain Resort,Idaho,Idaho,7640,1800,5840,0,0,1,0,4,0,1,6,51.0,2.0,3.2,1920.0,2.0,126.0,58.0,320.0,70.0,,
+Kelly Canyon Ski Area,Idaho,Idaho,6600,1000,5600,0,0,0,0,0,4,2,6,51.0,1.0,1.3,740.0,,,62.0,200.0,42.0,,
+Lookout Pass Ski Area,Idaho,Idaho,5650,1150,4500,0,0,0,0,1,3,0,4,35.0,2.0,1.5,540.0,,113.0,84.0,400.0,47.0,140.0,
+Magic Mountain Ski Area,Idaho,Idaho,7200,700,6500,0,0,0,0,0,1,2,3,11.0,,1.5,280.0,,65.0,81.0,180.0,32.0,70.0,
+Pebble Creek Ski Area,Idaho,Idaho,8560,2200,6360,0,0,0,0,3,0,0,3,54.0,2.0,1.3,1100.0,30.0,85.0,70.0,250.0,47.0,91.0,30.0
+Schweitzer,Idaho,Idaho,6400,2400,4000,0,1,2,0,1,3,2,9,92.0,3.0,2.1,2900.0,47.0,136.0,56.0,300.0,81.0,,100.0
+Silver Mountain,Idaho,Idaho,6300,2200,4100,1,0,0,1,2,2,1,7,80.0,2.0,2.5,1600.0,225.0,130.0,29.0,300.0,62.0,193.0,20.0
+Soldier Mountain Ski Area,Idaho,Idaho,7200,1400,5800,0,0,0,0,0,2,1,3,36.0,,0.4,1142.0,,60.0,71.0,,43.0,,
+Tamarack Resort,Idaho,Idaho,7700,2800,4900,0,0,2,2,0,0,2,6,48.0,3.0,1.5,1020.0,200.0,,15.0,300.0,71.0,150.0,
+Chestnut Mountain Resort,Illinois,Illinois,1040,475,565,0,0,0,2,4,0,3,9,22.0,3.0,0.2,139.0,139.0,87.0,60.0,50.0,55.0,112.0,139.0
+Ski Snowstar Winter Sports Park,Illinois,Illinois,790,262,528,0,0,0,2,0,2,2,6,15.0,1.0,0.8,28.0,28.0,56.0,38.0,38.0,35.0,86.0,28.0
+Villa Olivia,Illinois,Illinois,500,180,320,0,0,0,1,0,0,6,7,7.0,1.0,0.1,15.0,15.0,,53.0,25.0,40.0,70.0,15.0
+Paoli Peaks,Indiana,Indiana,900,300,600,0,0,1,1,3,1,2,8,15.0,2.0,0.4,65.0,65.0,75.0,41.0,18.0,45.0,80.0,65.0
+Perfect North Slopes,Indiana,Indiana,800,400,400,0,0,0,2,3,0,6,11,23.0,2.0,1.0,100.0,100.0,82.0,39.0,24.0,52.0,90.0,100.0
+Mt. Crescent Ski Area,Iowa,Iowa,1500,300,1200,0,0,0,1,0,1,0,2,11.0,1.0,0.2,50.0,50.0,,58.0,30.0,39.0,,50.0
+Seven Oaks,Iowa,Iowa,975,275,800,0,0,0,0,2,0,2,4,11.0,2.0,1.0,35.0,35.0,100.0,22.0,40.0,40.0,100.0,35.0
+Sundown Mountain,Iowa,Iowa,1059,475,584,0,0,0,1,1,2,2,6,21.0,2.0,0.6,55.0,55.0,,46.0,45.0,46.0,,55.0
+Big Squaw Mountain Ski Resort,Maine,Maine,3200,660,1750,0,0,0,0,1,0,0,1,29.0,,0.8,,,67.0,6.0,,30.0,58.0,
+Camden Snow Bowl,Maine,Maine,1080,850,150,0,0,0,0,1,1,1,3,26.0,2.0,1.0,100.0,48.0,68.0,83.0,69.0,43.0,70.0,48.0
+Lost Valley,Maine,Maine,495,240,255,0,0,0,0,0,2,2,4,22.0,2.0,0.3,45.0,45.0,87.0,58.0,50.0,55.0,104.0,45.0
+Mt. Abram Ski Resort,Maine,Maine,2250,1150,1050,0,0,0,0,0,2,3,5,54.0,1.0,0.5,640.0,175.0,120.0,59.0,125.0,49.0,120.0,
+New Hermon Mountain,Maine,Maine,450,350,100,0,0,0,0,0,1,2,3,20.0,,1.9,70.0,70.0,102.0,55.0,90.0,32.0,117.0,45.0
+Shawnee Peak,Maine,Maine,1900,1350,600,0,0,0,1,2,1,2,6,43.0,3.0,0.8,239.0,234.0,97.0,81.0,110.0,75.0,103.0,110.0
+Sugarloaf,Maine,Maine,4237,2820,1417,0,0,2,3,1,5,2,13,162.0,4.0,3.5,1240.0,618.0,159.0,68.0,200.0,99.0,155.0,
+Sunday River,Maine,Maine,3140,2340,800,1,0,4,5,3,1,1,15,135.0,5.0,3.0,870.0,552.0,165.0,60.0,167.0,105.0,169.0,140.0
+Wisp,Maryland,Maryland,3115,700,2415,0,0,0,2,5,0,5,12,34.0,3.0,1.5,172.0,118.0,121.0,64.0,100.0,79.0,120.0,118.0
+Berkshire East,Massachusetts,Massachusetts,1720,1180,540,0,0,0,2,1,1,2,6,47.0,2.0,2.0,180.0,165.0,120.0,68.0,120.0,68.0,120.0,80.0
+Blandford Ski Area,Massachusetts,Massachusetts,1685,465,1035,0,0,0,0,0,3,2,5,29.0,2.0,0.5,132.0,70.0,,83.0,50.0,45.0,,70.0
+Blue Hills Ski Area,Massachusetts,Massachusetts,635,309,326,0,0,0,0,0,1,3,4,16.0,1.0,,60.0,60.0,,19.0,,45.0,,
+Bousquet Ski Area,Massachusetts,Massachusetts,1875,750,1125,0,0,0,0,0,3,2,5,23.0,1.0,1.0,200.0,98.0,,19.0,83.0,49.0,,100.0
+Bradford Ski Area,Massachusetts,Massachusetts,1548,248,1300,0,0,0,0,2,0,8,10,15.0,1.0,0.3,48.0,48.0,,71.0,,55.0,,
+Jiminy Peak,Massachusetts,Massachusetts,2380,1150,1230,0,1,0,2,3,1,2,9,45.0,3.0,2.0,167.0,163.0,121.0,71.0,90.0,81.0,120.0,104.0
+Nashoba Valley,Massachusetts,Massachusetts,440,240,200,0,0,0,0,3,1,7,11,17.0,2.0,0.5,52.0,52.0,112.0,55.0,55.0,58.0,126.0,52.0
+Otis Ridge Ski Area,Massachusetts,Massachusetts,1700,400,1300,0,0,0,0,0,1,3,4,11.0,1.0,1.0,60.0,55.0,106.0,73.0,70.0,40.0,106.0,35.0
+Ski Butternut,Massachusetts,Massachusetts,1800,1000,800,0,0,0,3,1,1,6,11,22.0,2.0,1.5,110.0,110.0,107.0,56.0,115.0,60.0,110.0,
+Wachusett Mountain Ski Area,Massachusetts,Massachusetts,2006,1000,1006,0,0,3,0,1,0,4,8,27.0,2.0,1.5,112.0,112.0,,57.0,100.0,71.0,120.0,104.0
+Alpine Valley Ski Area,Michigan,Michigan,500,240,126,0,0,0,1,2,5,6,14,25.0,3.0,0.2,100.0,100.0,,57.0,20.0,47.0,,100.0
+Apple Mountain,Michigan,Michigan,820,220,600,0,0,0,1,0,0,5,6,12.0,,,80.0,42.0,,58.0,52.0,35.0,,80.0
+Big Powderhorn Mountain,Michigan,Michigan,1800,600,1200,0,0,0,0,0,9,1,10,45.0,2.0,1.0,253.0,228.0,100.0,55.0,214.0,69.0,108.0,
+Bittersweet Ski Area,Michigan,Michigan,850,350,450,0,0,0,1,7,0,4,12,20.0,2.0,0.2,100.0,100.0,80.0,36.0,90.0,48.0,,100.0
+Big Snow Resort - Blackjack,Michigan,Michigan,850,465,385,0,0,0,0,0,4,2,6,26.0,2.0,1.0,170.0,86.0,95.0,42.0,210.0,65.0,115.0,
+Boyne Highlands,Michigan,Michigan,1290,552,745,0,0,1,3,4,0,2,10,55.0,4.0,1.2,435.0,400.0,97.0,56.0,140.0,98.0,120.0,150.0
+Caberfae Peaks,Michigan,Michigan,1569,485,1060,0,0,0,1,2,1,1,5,34.0,2.0,1.2,200.0,200.0,118.0,82.0,140.0,49.0,130.0,150.0
+Cannonsburg,Michigan,Michigan,1100,250,850,0,0,0,1,1,1,7,10,21.0,5.0,0.1,100.0,,100.0,54.0,100.0,37.0,100.0,
+Crystal Mountain,Michigan,Michigan,1132,375,757,0,0,1,3,2,0,2,8,58.0,3.0,0.3,102.0,96.0,120.0,63.0,132.0,64.0,135.0,56.0
+Big Snow Resort - Indianhead Mountain,Michigan,Michigan,1935,638,1297,0,0,0,1,1,5,2,9,32.0,2.0,1.0,240.0,150.0,120.0,60.0,204.0,49.0,120.0,
+Mont Ripley,Michigan,Michigan,1140,440,700,0,0,0,0,0,2,2,4,25.0,2.0,0.8,112.0,112.0,114.0,83.0,275.0,49.0,100.0,100.0
+Mount Bohemia,Michigan,Michigan,1500,900,600,0,0,0,0,1,1,0,2,,,2.3,585.0,,83.0,19.0,273.0,68.0,100.0,
+Mt. Brighton,Michigan,Michigan,1330,230,1100,0,0,0,2,3,0,8,13,25.0,5.0,0.1,130.0,130.0,111.0,59.0,60.0,59.0,100.0,130.0
+Mt. Holiday Ski Area,Michigan,Michigan,440,200,240,0,0,0,0,0,2,2,4,12.0,,0.1,45.0,45.0,100.0,70.0,120.0,34.0,90.0,45.0
+Mount Holly,Michigan,Michigan,1105,350,755,0,0,1,2,3,1,6,13,19.0,,0.1,100.0,100.0,,63.0,42.0,45.0,102.0,100.0
+Mulligan's Hollow Ski Bowl,Michigan,Michigan,700,130,570,0,0,0,0,0,0,5,5,6.0,,0.2,10.0,10.0,,19.0,60.0,20.0,,10.0
+Norway Mountain,Michigan,Michigan,1335,500,835,0,0,0,0,1,2,3,6,17.0,1.0,1.4,186.0,186.0,110.0,45.0,100.0,45.0,110.0,40.0
+Nubs Nob Ski Area,Michigan,Michigan,1338,427,911,0,0,0,3,4,2,1,10,53.0,3.0,0.9,248.0,248.0,133.0,61.0,135.0,85.0,130.0,160.0
+Pine Mountain,Michigan,Michigan,1650,500,1150,0,0,0,0,1,2,1,4,28.0,1.0,0.5,160.0,160.0,110.0,80.0,60.0,45.0,126.0,80.0
+Schuss Mountain at Shanty Creek,Michigan,Michigan,1125,450,675,0,0,0,5,0,0,3,8,42.0,3.0,1.0,70.0,70.0,94.0,57.0,160.0,78.0,111.0,70.0
+Ski Brule,Michigan,Michigan,1860,500,1360,0,0,0,0,0,5,7,12,17.0,3.0,1.0,150.0,150.0,164.0,62.0,150.0,49.0,165.0,40.0
+Snow Snake Mountain Ski Area,Michigan,Michigan,1230,210,1020,0,0,0,0,1,0,5,6,12.0,2.0,0.0,40.0,40.0,,72.0,,35.0,,40.0
+Swiss Valley,Michigan,Michigan,1200,225,975,0,0,0,2,1,0,4,7,11.0,2.0,0.1,60.0,60.0,89.0,51.0,60.0,42.0,80.0,60.0
+The Homestead,Michigan,Michigan,900,320,580,0,0,0,0,2,1,2,5,15.0,1.0,0.2,16.0,16.0,47.0,34.0,150.0,50.0,42.0,16.0
+Timber Ridge,Michigan,Michigan,850,250,600,0,0,0,1,1,2,4,8,16.0,2.0,0.3,50.0,50.0,80.0,58.0,,45.0,,50.0
+Afton Alps,Minnesota,Minnesota,1530,350,1180,0,0,0,1,3,14,4,22,48.0,5.0,0.5,250.0,250.0,135.0,56.0,60.0,60.0,135.0,250.0
+Andes Tower Hills Ski Area,Minnesota,Minnesota,1620,290,1330,0,0,0,1,2,0,3,6,15.0,2.0,0.2,35.0,35.0,100.0,38.0,55.0,45.0,110.0,35.0
+Buck Hill,Minnesota,Minnesota,1225,309,919,0,0,0,2,1,0,5,8,16.0,,0.2,45.0,45.0,115.0,65.0,60.0,47.0,112.0,45.0
+Buena Vista Ski Area,Minnesota,Minnesota,1510,230,1280,0,0,0,0,2,2,2,6,17.0,,0.3,30.0,30.0,57.0,70.0,78.0,44.0,60.0,30.0
+Coffee Mill Ski & Snowboard Resort,Minnesota,Minnesota,1150,425,725,0,0,0,0,0,2,1,3,14.0,1.0,1.0,40.0,35.0,57.0,39.0,48.0,37.0,56.0,35.0
+Elm Creek Winter Recreation Area,Minnesota,Minnesota,928,60,868,0,0,0,0,0,0,3,3,3.0,,1.0,15.0,20.0,105.0,13.0,45.0,17.0,102.0,15.0
+Giants Ridge Resort,Minnesota,Minnesota,1972,500,1472,0,0,1,1,1,2,2,7,35.0,2.0,0.8,202.0,202.0,120.0,35.0,85.0,58.0,125.0,121.0
+Hyland Ski & Snowboard Area,Minnesota,Minnesota,1075,175,900,0,0,0,2,1,0,5,8,14.0,1.0,1.0,35.0,35.0,110.0,61.0,55.0,35.34,115.0,35.0
+Lutsen Mountains,Minnesota,Minnesota,1688,825,800,1,1,0,0,1,4,1,8,62.0,2.0,2.0,393.0,231.0,135.0,71.0,120.0,84.0,127.0,
+Mount Kato Ski Area,Minnesota,Minnesota,540,240,300,0,0,0,5,0,3,2,10,19.0,4.0,1.0,55.0,55.0,115.0,43.0,50.0,46.0,120.0,50.0
+Powder Ridge Ski Area,Minnesota,Minnesota,790,300,500,0,0,0,1,0,2,3,6,15.0,4.0,,60.0,60.0,97.0,58.0,45.0,48.0,113.0,60.0
+Spirit Mountain,Minnesota,Minnesota,1320,700,620,0,0,1,1,2,1,2,7,22.0,3.0,1.0,175.0,175.0,100.0,45.0,100.0,59.0,125.0,144.0
+Welch Village,Minnesota,Minnesota,1060,360,700,0,0,0,3,1,4,2,10,50.0,1.0,0.8,125.0,125.0,114.0,54.0,45.0,60.0,122.0,100.0
+Wild Mountain Ski & Snowboard Area,Minnesota,Minnesota,1113,300,813,0,0,0,4,0,0,4,8,26.0,4.0,0.9,100.0,100.0,130.0,47.0,50.0,55.0,140.0,100.0
+Hidden Valley Ski Area,Missouri,Missouri,2566,310,2316,0,0,0,2,2,0,3,7,17.0,,0.1,30.0,30.0,,37.0,26.0,49.0,,17.0
+Snow Creek,Missouri,Missouri,1100,300,800,0,0,0,0,2,1,2,5,14.0,2.0,0.3,30.0,30.0,69.0,33.0,20.0,47.0,85.0,30.0
+Blacktail Mountain Ski Area,Montana,Montana,6676,1440,5236,0,0,0,0,1,2,1,4,27.0,,0.7,1000.0,,,21.0,250.0,42.0,,
+Bridger Bowl,Montana,Montana,8700,2600,6100,0,0,0,1,6,1,3,11,105.0,2.0,1.5,2000.0,100.0,122.0,64.0,350.0,63.0,133.0,
+Discovery Ski Area,Montana,Montana,8150,2380,5770,0,0,0,0,5,2,1,8,74.0,1.0,1.5,2400.0,25.0,116.0,46.0,225.0,49.0,116.0,
+Great Divide,Montana,Montana,7330,1580,5750,0,0,0,0,0,5,1,6,110.0,6.0,3.0,1600.0,150.0,94.0,78.0,180.0,48.0,100.0,100.0
+Lost Trail - Powder Mtn,Montana,Montana,8200,1800,6400,0,0,0,0,0,5,3,8,69.0,2.0,2.5,1800.0,,84.0,81.0,325.0,46.0,80.0,
+Maverick Mountain,Montana,Montana,8520,2020,6500,0,0,0,0,0,1,1,2,22.0,,1.3,255.0,,,83.0,160.0,39.0,,
+Montana Snowbowl,Montana,Montana,7600,2600,5000,0,0,0,0,0,2,2,4,37.0,,1.2,950.0,20.0,,58.0,300.0,50.0,,10.0
+Red Lodge Mountain,Montana,Montana,9416,2400,7016,0,0,2,0,1,3,1,7,70.0,2.0,2.5,1635.0,496.0,142.0,59.0,250.0,67.0,136.0,
+Showdown Montana,Montana,Montana,8200,1400,6800,0,0,0,0,1,2,1,4,36.0,1.0,1.8,640.0,,86.0,83.0,250.0,47.0,85.0,
+Teton Pass Ski Resort,Montana,Montana,7200,1010,6190,0,0,0,0,0,1,2,3,43.0,1.0,3.0,330.0,,40.0,54.0,250.0,39.0,150.0,
+Big Mountain Resort,Montana,Montana,6817,2353,4464,0,0,3,2,6,0,3,14,105.0,4.0,3.3,3000.0,600.0,123.0,72.0,333.0,81.0,123.0,600.0
+Diamond Peak,Sierra Nevada,Nevada,8540,1840,6700,0,0,1,2,0,3,1,7,30.0,3.0,2.5,655.0,492.0,100.0,53.0,300.0,99.0,122.0,
+Elko SnoBowl,Nevada,Nevada,7000,700,6300,0,0,0,0,0,1,1,2,10.0,,1.0,60.0,2.0,19.0,23.0,24.0,20.0,30.0,
+Lee Canyon,Nevada,Nevada,11289,860,8510,0,0,0,2,1,0,0,3,24.0,1.0,0.3,195.0,50.0,144.0,56.0,161.0,70.0,150.0,
+Mt. Rose - Ski Tahoe,Sierra Nevada,Nevada,9700,1800,8260,0,2,0,2,2,0,2,8,65.0,5.0,2.5,1200.0,330.0,152.0,55.0,350.0,135.0,150.0,
+Attitash,New Hampshire,New Hampshire,2350,1750,600,0,0,2,1,3,2,1,9,68.0,3.0,3.0,311.0,240.0,115.0,54.0,120.0,89.0,130.0,
+Black Mountain,New Hampshire,New Hampshire,2350,1100,1250,0,0,0,0,1,1,3,5,45.0,,1.6,143.0,120.0,110.0,84.0,125.0,59.0,107.0,
+Bretton Woods,New Hampshire,New Hampshire,3100,1500,1600,0,0,4,1,1,0,3,9,63.0,2.0,2.0,464.0,427.0,180.0,46.0,200.0,99.0,180.0,45.0
+Cannon Mountain,New Hampshire,New Hampshire,4080,2180,1900,1,0,1,2,3,1,3,11,97.0,3.0,2.3,285.0,192.0,124.0,81.0,160.0,79.0,143.0,
+Crotched Mountain,New Hampshire,New Hampshire,2066,1016,1050,0,0,1,1,1,1,1,5,25.0,3.0,1.2,100.0,100.0,105.0,16.0,105.0,69.0,100.0,100.0
+Dartmouth Skiway,New Hampshire,New Hampshire,1943,969,974,0,0,0,1,0,1,2,4,28.0,1.0,1.1,107.0,54.0,104.0,63.0,100.0,50.0,105.0,
+Gunstock,New Hampshire,New Hampshire,2300,1400,900,0,0,1,2,2,0,1,6,55.0,4.0,1.5,227.0,176.0,106.0,82.0,120.0,92.0,,60.0
+King Pine,New Hampshire,New Hampshire,850,350,500,0,0,0,0,3,0,3,6,17.0,2.0,0.3,48.0,45.0,105.0,57.0,120.0,58.0,107.0,23.0
+Mount Sunapee,New Hampshire,New Hampshire,2743,1510,1233,0,0,2,1,2,1,4,10,66.0,4.0,0.8,232.0,215.0,130.0,71.0,100.0,93.0,136.0,
+Pats Peak,New Hampshire,New Hampshire,1460,770,690,0,0,0,0,4,2,5,11,28.0,3.0,1.5,115.0,115.0,109.0,56.0,100.0,72.0,112.0,93.0
+Ragged Mountain Resort,New Hampshire,New Hampshire,2250,1250,1000,0,1,1,0,1,0,3,6,57.0,3.0,0.7,250.0,200.0,,54.0,100.0,84.0,140.0,
+Waterville Valley,New Hampshire,New Hampshire,4004,2020,1984,0,0,2,0,2,3,4,11,62.0,4.0,1.9,265.0,220.0,142.0,54.0,148.0,93.0,142.0,
+Whaleback Mountain,New Hampshire,New Hampshire,1800,700,1100,0,0,0,0,0,1,3,4,30.0,1.0,1.0,85.0,60.0,105.0,64.0,110.0,45.0,105.0,55.0
+Wildcat Mountain,New Hampshire,New Hampshire,4062,2112,1950,0,0,1,0,3,0,1,5,48.0,,2.8,225.0,200.0,156.0,61.0,200.0,89.0,150.0,
+Mountain Creek Resort,New Jersey,New Jersey,1480,1040,440,1,0,2,2,1,1,3,10,46.0,3.0,2.0,167.0,167.0,90.0,54.0,65.0,79.99,100.0,167.0
+Angel Fire Resort,New Mexico,New Mexico,10677,2077,8600,0,0,2,0,0,3,2,7,81.0,3.0,3.0,560.0,230.0,101.0,53.0,210.0,77.0,101.0,50.0
+Enchanted Forest Ski Area,New Mexico,New Mexico,10078,400,9820,0,0,0,0,0,0,0,0,33.0,,2.5,600.0,,130.0,34.0,240.0,20.0,140.0,
+Pajarito Mountain Ski Area,New Mexico,New Mexico,10441,1410,9031,0,0,0,1,1,3,1,6,45.0,2.0,0.6,750.0,35.0,89.0,62.0,163.0,49.0,117.0,
+Red River,New Mexico,New Mexico,10350,1600,8750,0,0,0,1,3,1,2,7,63.0,3.0,2.5,209.0,,110.0,60.0,214.0,79.0,,
+Sandia Peak,New Mexico,New Mexico,10378,1700,8678,0,0,0,0,0,4,1,5,39.0,1.0,2.0,200.0,30.0,32.0,82.0,100.0,55.0,38.0,
+Sipapu Ski Resort,New Mexico,New Mexico,9255,1055,8200,0,0,0,1,2,0,3,6,42.0,4.0,0.5,200.0,140.0,127.0,67.0,190.0,47.0,143.0,
+Ski Apache,New Mexico,New Mexico,11500,1900,9600,1,0,0,2,6,0,2,11,55.0,3.0,2.0,750.0,270.0,133.0,58.0,185.0,74.0,124.0,
+Ski Santa Fe,New Mexico,New Mexico,12075,1725,10350,0,0,0,1,2,2,2,7,83.0,1.0,3.0,660.0,275.0,107.0,73.0,225.0,80.0,130.0,
+Taos Ski Valley,New Mexico,New Mexico,12481,3281,9200,1,0,1,3,4,1,4,14,111.0,1.0,5.0,1294.0,647.0,137.0,64.0,300.0,110.0,136.0,
+Belleayre,New York,New York,3429,1404,2025,1,0,1,1,1,2,2,8,50.0,2.0,2.2,175.0,168.0,154.0,70.0,130.0,72.0,150.0,
+Brantling Ski Slopes,New York,New York,850,250,600,0,0,0,0,0,0,5,5,10.0,,0.1,20.0,16.0,,19.0,110.0,32.0,,
+Bristol Mountain,New York,New York,2200,1200,1000,0,0,2,1,1,1,1,6,34.0,3.0,2.0,160.0,148.0,129.0,55.0,60.0,76.0,129.0,154.0
+Buffalo Ski Club Ski Area,New York,New York,3429,500,2025,0,0,0,0,0,2,4,6,43.0,1.0,,225.0,150.0,,12.0,,50.0,,100.0
+Catamount,New York,New York,2000,1000,1000,0,0,0,1,1,2,3,7,36.0,5.0,2.0,133.0,130.0,100.0,80.0,108.0,69.0,90.0,55.0
+Dry Hill Ski Area,New York,New York,950,300,650,0,0,0,0,0,1,2,3,7.0,1.0,0.2,35.0,26.0,,55.0,125.0,35.0,,26.0
+Gore Mountain,New York,New York,3600,2537,998,1,0,2,2,3,2,4,14,110.0,7.0,4.5,439.0,338.0,142.0,55.0,150.0,88.0,,15.0
+Greek Peak,New York,New York,2100,952,1148,0,0,0,1,1,4,2,8,56.0,4.0,1.5,220.0,184.0,110.0,62.0,122.0,63.2,113.0,175.0
+Holiday Mountain,New York,New York,1550,400,1150,0,0,0,1,0,1,2,4,9.0,,0.4,37.0,37.0,75.0,60.0,50.0,42.0,85.0,37.0
+Holiday Valley,New York,New York,2250,750,1500,0,0,3,8,0,0,2,13,60.0,5.0,1.0,290.0,266.0,116.0,62.0,180.0,78.0,129.0,189.0
+Holimont Ski Area,New York,New York,2260,700,1560,0,0,1,1,2,3,1,8,53.0,3.0,1.5,135.0,135.0,110.0,57.0,180.0,75.0,119.0,
+Hunt Hollow Ski Club,New York,New York,2030,825,1000,0,0,0,0,1,1,1,3,19.0,1.0,1.0,400.0,400.0,,52.0,130.0,58.0,75.0,400.0
+Hunter Mountain,New York,New York,3200,1600,1600,0,2,1,2,2,2,4,13,67.0,4.0,2.0,320.0,320.0,148.0,59.0,120.0,89.0,155.0,
+Kissing Bridge,New York,New York,1700,550,1150,0,0,0,2,1,4,3,10,39.0,5.0,0.5,700.0,550.0,103.0,59.0,120.0,60.0,100.0,650.0
+Labrador Mt.,New York,New York,1825,700,1125,0,0,0,0,1,2,1,4,23.0,1.0,1.0,250.0,237.0,,62.0,125.0,59.0,100.0,180.0
+Maple Ski Ridge,New York,New York,1200,450,750,0,0,0,0,1,1,1,3,10.0,,0.3,25.0,25.0,,57.0,,38.0,,20.0
+McCauley Mountain Ski Center,New York,New York,2250,633,1563,0,0,0,0,0,1,4,5,23.0,1.0,0.3,70.0,55.0,105.0,61.0,200.0,30.0,105.0,
+Mount Peter Ski Area,New York,New York,1250,450,750,0,0,0,1,0,2,2,5,14.0,1.0,1.0,69.0,69.0,100.0,83.0,50.0,54.0,100.0,69.0
+Oak Mountain,New York,New York,2400,650,1750,0,0,0,1,0,0,3,4,22.0,1.0,1.2,46.0,18.0,,71.0,120.0,40.0,,12.0
+Peek'n Peak,New York,New York,1800,400,1400,0,0,0,0,8,0,2,10,27.0,4.0,2.4,110.0,110.0,110.0,55.0,225.0,63.0,,110.0
+Plattekill Mountain,New York,New York,3500,1100,2400,0,0,0,0,1,1,2,4,38.0,1.0,2.0,110.0,75.0,65.0,26.0,175.0,67.0,65.0,
+Royal Mountain Ski Area,New York,New York,1800,550,1250,0,0,0,0,0,3,0,3,14.0,,0.3,35.0,28.0,,63.0,90.0,45.0,,
+Snow Ridge,New York,New York,2000,650,1350,0,0,0,0,0,4,2,6,21.0,2.0,0.8,130.0,65.0,73.0,74.0,230.0,48.0,100.0,40.0
+Song Mountain,New York,New York,1940,700,1240,0,0,0,0,1,1,3,5,24.0,,0.4,93.0,70.0,90.0,55.0,125.0,59.0,122.0,70.0
+Swain,New York,New York,1970,650,1320,0,0,0,3,0,1,1,5,35.0,3.0,1.0,130.0,90.0,102.0,72.0,120.0,59.0,100.0,80.0
+Thunder Ridge,New York,New York,1270,500,770,0,0,0,0,1,2,3,6,30.0,,0.4,100.0,100.0,121.0,60.0,,57.0,121.0,100.0
+Titus Mountain,New York,New York,2025,1200,825,0,0,0,0,2,6,2,10,50.0,3.0,2.0,200.0,150.0,101.0,59.0,150.0,49.0,100.0,70.0
+Toggenburg Mountain,New York,New York,2000,700,1300,0,0,0,0,1,1,3,5,22.0,2.0,0.4,85.0,,,66.0,130.0,55.0,122.0,73.0
+West Mountain,New York,New York,1470,1010,460,0,0,0,0,1,2,2,5,29.0,1.0,0.6,124.0,105.0,,58.0,80.0,59.0,120.0,105.0
+Whiteface Mountain Resort,New York,New York,4650,3430,1220,1,0,1,1,2,5,2,12,86.0,5.0,2.1,288.0,220.0,122.0,61.0,168.0,96.0,141.0,
+Willard Mountain,New York,New York,1415,505,910,0,0,0,0,0,2,3,5,16.0,,0.4,50.0,35.0,85.0,19.0,80.0,46.0,120.0,35.0
+Windham Mountain,New York,New York,3100,1600,1500,0,1,2,0,3,1,5,12,54.0,6.0,2.0,285.0,280.0,123.0,59.0,105.0,95.0,130.0,56.0
+Woods Valley Ski Area,New York,New York,1400,500,900,0,0,0,0,0,2,4,6,21.0,,0.3,25.0,16.0,,55.0,180.0,39.0,,15.0
+Appalachian Ski Mountain,North Carolina,North Carolina,4000,365,3635,0,0,0,2,0,1,2,5,12.0,3.0,0.5,27.0,27.0,100.0,57.0,50.0,64.0,100.0,27.0
+Cataloochee Ski Area,North Carolina,North Carolina,5400,740,4660,0,0,0,1,1,1,2,5,18.0,2.0,1.0,50.0,50.0,141.0,58.0,50.0,70.0,108.0,50.0
+Sapphire Valley,North Carolina,North Carolina,3450,200,3200,0,0,0,1,0,0,2,3,,1.0,1.0,8.0,8.0,53.0,55.0,24.0,43.0,60.0,8.0
+Beech Mountain Resort,North Carolina,North Carolina,5506,830,4675,0,0,0,3,0,3,2,8,17.0,1.0,1.0,95.0,95.0,98.0,52.0,31.0,68.0,,95.0
+Sugar Mountain Resort,North Carolina,North Carolina,5300,1200,4100,0,1,0,0,1,4,2,8,21.0,1.0,1.5,125.0,125.0,114.0,50.0,77.0,75.0,120.0,95.0
+Wolf Ridge Ski Resort,North Carolina,North Carolina,4700,720,4000,0,0,0,1,0,1,2,4,15.0,1.0,0.6,65.0,65.0,,49.0,65.0,65.0,100.0,60.0
+Alpine Valley,Ohio,Ohio,1500,230,1260,0,0,0,1,2,1,1,5,11.0,1.0,0.2,72.0,72.0,105.0,53.0,120.0,43.0,,72.0
+Boston Mills,Ohio,Ohio,871,264,631,0,0,0,0,4,2,2,8,7.0,2.0,0.3,40.0,40.0,92.0,56.0,51.0,44.0,110.0,40.0
+Brandywine,Ohio,Ohio,871,240,631,0,0,0,2,7,2,5,16,11.0,2.0,0.3,85.0,85.0,92.0,56.0,51.0,44.0,110.0,85.0
+Mad River Mountain,Ohio,Ohio,1460,300,1160,0,0,0,1,2,3,6,12,20.0,4.0,0.5,144.0,144.0,99.0,57.0,36.0,44.0,90.0,144.0
+Snow Trails,Ohio,Ohio,1475,301,1174,0,0,0,0,4,2,3,9,17.0,3.0,0.2,80.0,80.0,101.0,58.0,50.0,52.0,70.0,80.0
+Anthony Lakes Mountain Resort,Oregon,Oregon,8000,900,7100,0,0,0,0,1,0,2,3,21.0,2.0,1.5,1100.0,,75.0,56.0,300.0,40.0,80.0,
+Cooper Spur,Mt. Hood,Oregon,4000,350,3500,0,0,0,0,0,1,1,2,10.0,,0.1,50.0,,78.0,66.0,100.0,39.0,90.0,
+Hoodoo Ski Area,Oregon,Oregon,5703,1035,4668,0,0,0,3,1,1,0,5,34.0,,0.4,806.0,,80.0,81.0,350.0,59.0,108.0,200.0
+Mt. Ashland,Oregon,Oregon,7533,1150,6383,0,0,0,0,2,2,1,5,23.0,2.0,1.0,220.0,,94.0,55.0,300.0,52.0,92.0,40.0
+Mt. Bachelor,Oregon,Oregon,9065,3365,5700,0,0,8,0,3,0,0,11,101.0,5.0,4.0,4318.0,20.0,185.0,61.0,462.0,99.0,185.0,
+Mt. Hood Skibowl,Mt. Hood,Oregon,5100,1500,3600,0,0,0,0,0,4,5,9,65.0,2.0,3.0,960.0,29.0,125.0,82.0,300.0,70.0,144.0,317.0
+Willamette Pass,Oregon,Oregon,6683,1563,5120,0,1,0,0,3,0,1,5,29.0,,2.1,555.0,60.0,3.0,78.0,430.0,60.0,100.0,
+Bear Creek Mountain Resort,Pennsylvania,Pennsylvania,1100,510,600,0,0,0,3,1,0,2,6,23.0,3.0,1.0,86.0,86.0,91.0,52.0,30.0,60.0,90.0,86.0
+Ski Big Bear,Pennsylvania,Pennsylvania,1250,650,600,0,0,0,0,0,4,2,6,18.0,1.0,1.5,26.0,26.0,75.0,43.0,69.0,62.0,75.0,26.0
+Big Boulder,Pennsylvania,Pennsylvania,2175,600,1700,0,0,0,0,2,5,1,8,16.0,8.0,,55.0,55.0,76.0,72.0,50.0,65.0,95.0,55.0
+Blue Knob,Pennsylvania,Pennsylvania,3146,1072,2074,0,0,0,0,2,2,2,6,34.0,1.0,2.0,100.0,84.0,87.0,56.0,120.0,68.0,105.0,42.0
+Blue Mountain Resort,Pennsylvania,Pennsylvania,1600,1082,460,0,1,1,1,1,3,9,16,39.0,5.0,1.2,164.0,164.0,122.0,42.0,33.0,65.0,112.0,164.0
+Camelback Mountain Resort,Pennsylvania,Pennsylvania,2100,800,1250,0,0,2,0,3,5,6,16,37.0,5.0,1.0,166.0,166.0,100.0,56.0,50.0,70.0,100.0,160.0
+Elk Mountain Ski Resort,Pennsylvania,Pennsylvania,2693,1000,1693,0,0,0,1,0,5,1,7,27.0,2.0,0.7,180.0,146.0,,60.0,60.0,69.0,100.0,90.0
+Jack Frost,Pennsylvania,Pennsylvania,2000,600,1400,0,0,0,1,2,5,1,9,20.0,1.0,1.0,100.0,100.0,96.0,47.0,50.0,65.0,105.0,
+Liberty,Pennsylvania,Pennsylvania,1190,620,570,0,0,0,5,0,0,3,8,16.0,3.0,1.0,100.0,100.0,107.0,54.0,31.0,77.0,97.0,100.0
+Mount Pleasant of Edinboro,Pennsylvania,Pennsylvania,1540,340,1200,0,0,0,0,1,0,1,2,10.0,,0.5,40.0,35.0,75.0,48.0,100.0,33.0,90.0,35.0
+Roundtop Mountain Resort,Pennsylvania,Pennsylvania,1400,600,800,0,0,0,3,2,0,3,8,20.0,2.0,0.4,103.0,103.0,,55.0,30.0,73.0,,100.0
+Seven Springs,Pennsylvania,Pennsylvania,2994,750,2240,0,2,0,3,5,0,4,14,33.0,7.0,1.2,285.0,285.0,99.0,87.0,135.0,87.0,115.0,200.0
+Shawnee Mountain Ski Area,Pennsylvania,Pennsylvania,1350,700,650,0,0,1,1,0,4,4,10,23.0,2.0,1.6,125.0,125.0,100.0,44.0,50.0,65.0,122.0,120.0
+Ski Sawmill,Pennsylvania,Pennsylvania,2215,515,1700,0,0,0,0,1,1,3,5,14.0,1.0,0.1,15.0,13.0,,50.0,24.0,44.0,90.0,15.0
+Tussey Mountain,Pennsylvania,Pennsylvania,1750,520,1230,0,0,0,1,0,1,3,5,8.0,1.0,0.3,38.0,30.0,100.0,39.0,41.0,45.0,100.0,30.0
+Whitetail Resort,Pennsylvania,Pennsylvania,1800,935,865,0,0,1,3,0,2,2,8,23.0,2.0,1.0,120.0,120.0,116.0,28.0,40.0,71.0,100.0,120.0
+Deer Mountain Ski Resort,South Dakota,South Dakota,6850,940,6040,0,0,0,0,1,1,2,4,63.0,2.0,1.6,500.0,50.0,69.0,51.0,200.0,45.0,81.0,
+Terry Peak Ski Area,South Dakota,South Dakota,7100,1100,5900,0,0,3,0,1,0,1,5,30.0,1.0,1.2,450.0,225.0,114.0,65.0,150.0,58.0,120.0,
+Ober Gatlinburg Ski Resort,Tennessee,Tennessee,3300,600,2700,0,0,0,2,0,1,1,4,10.0,1.0,1.0,,,83.0,44.0,35.0,65.0,94.0,
+Alta Ski Area,Salt Lake City,Utah,11068,2538,8530,0,0,3,0,1,2,0,6,116.0,,1.3,2614.0,140.0,150.0,81.0,545.0,116.0,140.0,
+Beaver Mountain,Utah,Utah,8600,1600,7232,0,0,0,0,1,3,1,5,48.0,2.0,0.8,464.0,,120.0,81.0,400.0,50.0,120.0,
+Brian Head Resort,Utah,Utah,10970,1548,9600,0,0,1,0,6,1,0,8,71.0,2.0,0.6,650.0,216.0,149.0,54.0,360.0,59.0,148.0,
+Brighton Resort,Salt Lake City,Utah,10500,1745,8755,0,0,3,1,1,0,2,7,66.0,4.0,1.2,1050.0,200.0,138.0,83.0,500.0,85.0,138.0,200.0
+Deer Valley Resort,Salt Lake City,Utah,9570,3000,6570,1,0,13,0,5,2,0,21,103.0,,2.8,2026.0,660.0,,39.0,300.0,169.0,,
+Eagle Point,Utah,Utah,10600,1500,9100,0,0,0,1,1,2,1,5,40.0,1.0,0.9,650.0,,,9.0,400.0,60.0,109.0,
+Powder Mountain,Utah,Utah,9422,2522,6900,0,0,1,4,1,0,3,9,167.0,2.0,3.5,8464.0,,120.0,47.0,500.0,88.0,146.0,300.0
+Snowbasin,Utah,Utah,9350,2900,6450,3,1,2,0,3,0,2,11,107.0,4.0,3.5,3000.0,625.0,143.0,79.0,300.0,115.0,138.0,
+Snowbird,Salt Lake City,Utah,11000,3240,7760,1,0,6,0,0,4,3,14,170.0,1.0,2.5,2500.0,,188.0,48.0,500.0,125.0,180.0,2.0
+Solitude Mountain Resort,Salt Lake City,Utah,10488,2494,7994,0,0,4,2,1,1,1,9,80.0,,3.0,1200.0,150.0,161.0,62.0,500.0,119.0,148.0,
+Sundance,Utah,Utah,8250,2150,6100,0,0,0,2,2,0,1,5,45.0,1.0,0.6,450.0,112.0,128.0,50.0,320.0,80.0,129.0,
+Nordic Valley Resort,Utah,Utah,6400,960,5440,0,0,0,0,0,2,2,4,23.0,1.0,0.4,140.0,84.0,105.0,51.0,300.0,50.0,105.0,140.0
+Bolton Valley,Vermont,Vermont,3150,1704,1446,0,0,0,2,0,3,1,6,71.0,3.0,0.6,300.0,90.0,133.0,53.0,300.0,79.0,132.0,50.0
+Bromley Mountain,Vermont,Vermont,3284,1334,1950,0,0,1,1,0,4,3,9,47.0,1.0,2.5,178.0,153.0,,83.0,168.0,91.0,152.0,
+Burke Mountain,Vermont,Vermont,3267,2011,1210,0,0,2,1,0,0,3,6,50.0,3.0,2.2,178.0,125.0,110.0,62.0,217.0,73.0,125.0,
+Jay Peak,Vermont,Vermont,3968,2153,1815,1,0,1,3,1,1,2,9,79.0,2.0,3.0,385.0,300.0,155.0,64.0,349.0,89.0,160.0,
+Killington Resort,Vermont,Vermont,4241,3050,1165,3,1,5,4,3,1,5,22,155.0,6.0,6.0,1515.0,600.0,192.0,61.0,250.0,119.0,,
+Magic Mountain,Vermont,Vermont,2850,1500,1350,0,0,0,0,0,3,3,6,50.0,1.0,1.6,205.0,95.0,76.0,59.0,150.0,74.0,80.0,
+Pico Mountain,Vermont,Vermont,3967,1967,2000,0,0,2,0,2,2,1,7,59.0,1.0,4.0,260.0,156.0,,82.0,250.0,81.0,,
+Smugglers' Notch Resort,Vermont,Vermont,3640,2610,1030,0,0,0,0,0,6,2,8,78.0,6.0,3.0,1000.0,192.0,136.0,63.0,280.0,79.0,135.0,
+Sugarbush,Vermont,Vermont,4083,2600,1483,0,0,5,5,2,1,3,16,111.0,4.0,3.0,581.0,406.0,150.0,61.0,250.0,119.0,156.0,
+Suicide Six,Vermont,Vermont,1200,650,550,0,0,0,0,0,2,1,3,24.0,,0.4,100.0,50.0,100.0,85.0,90.0,75.0,106.0,
+Bryce Resort,Virginia,Virginia,1750,500,1250,0,0,0,1,0,1,5,7,8.0,,0.4,25.0,25.0,100.0,54.0,30.0,68.0,95.0,20.0
+49 Degrees North,Washington,Washington,5774,1851,3932,0,0,0,1,0,5,1,7,89.0,1.0,2.0,2325.0,,101.0,48.0,301.0,62.0,135.0,250.0
+Bluewood,Washington,Washington,5670,1125,4545,0,0,0,0,2,0,1,3,24.0,2.0,2.0,355.0,,70.0,40.0,300.0,47.0,110.0,
+Crystal Mountain,Washington,Washington,7012,3100,4400,1,2,2,1,2,2,0,10,57.0,1.0,2.5,2600.0,10.0,,57.0,486.0,99.0,,
+Mt. Baker,Washington,Washington,5000,1500,3500,0,0,0,8,0,0,2,10,38.0,,0.7,1000.0,,143.0,66.0,663.0,60.01,165.0,
+Mt. Spokane Ski and Snowboard Park,Washington,Washington,5889,2000,4200,0,0,0,0,1,5,1,7,52.0,3.0,0.6,1704.0,,100.0,81.0,300.0,59.0,103.0,45.0
+The Summit at Snoqualmie,Washington,Washington,3865,1025,2840,0,0,3,3,4,10,7,27,112.0,5.0,0.8,1994.0,5.0,120.0,82.0,428.0,95.0,140.0,541.0
+White Pass,Washington,Washington,6550,2050,4500,0,0,2,1,1,2,2,8,45.0,2.0,2.5,1402.0,30.0,148.0,67.0,400.0,69.0,144.0,90.0
+Canaan Valley Resort,West Virginia,West Virginia,4280,850,3430,0,0,0,1,2,0,1,4,47.0,1.0,1.2,95.0,75.0,,48.0,160.0,68.0,93.0,
+Snowshoe Mountain Resort,West Virginia,West Virginia,4848,1500,3348,0,0,3,2,6,0,3,14,60.0,5.0,1.5,257.0,257.0,125.0,46.0,180.0,87.0,138.0,86.0
+Timberline Four Seasons,West Virginia,West Virginia,4265,1000,3268,0,0,0,0,2,1,1,4,40.0,1.0,2.0,100.0,100.0,97.0,37.0,150.0,92.0,115.0,27.0
+Winterplace Ski Resort,West Virginia,West Virginia,3600,603,2997,0,0,0,2,3,2,2,9,27.0,2.0,1.2,90.0,90.0,120.0,36.0,100.0,72.0,120.0,74.0
+Alpine Valley Resort,Wisconsin,Wisconsin,1040,388,820,0,0,3,0,3,1,5,12,21.0,3.0,0.2,90.0,90.0,100.0,55.0,80.0,65.0,120.0,90.0
+Bruce Mound,Wisconsin,Wisconsin,1375,375,1000,0,0,0,0,1,0,4,5,12.0,2.0,0.5,40.0,30.0,42.0,71.0,42.0,25.0,42.0,30.0
+Cascade Mountain,Wisconsin,Wisconsin,1280,460,820,0,0,2,2,3,2,3,12,47.0,4.0,1.1,175.0,175.0,120.0,57.0,56.0,64.0,120.0,
+Christie Mountain,Wisconsin,Wisconsin,1650,350,1300,0,0,0,0,0,1,5,6,30.0,4.0,0.8,45.0,41.0,92.0,43.0,48.0,38.0,120.0,35.0
+Devils Head,Wisconsin,Wisconsin,995,500,495,0,0,0,3,1,6,2,12,27.0,1.0,1.0,260.0,260.0,110.0,48.0,45.0,65.0,135.0,200.0
+Grand Geneva,Wisconsin,Wisconsin,1086,211,875,0,0,0,0,0,3,3,6,20.0,1.0,0.2,30.0,30.0,90.0,25.0,25.0,49.0,93.0,30.0
+Granite Peak Ski Area,Wisconsin,Wisconsin,1942,700,1242,0,1,2,0,2,0,2,7,75.0,4.0,0.6,220.0,160.0,136.0,82.0,75.0,92.0,135.0,200.0
+Mount La Crosse,Wisconsin,Wisconsin,1110,516,594,0,0,0,0,0,3,1,4,19.0,1.0,0.4,100.0,100.0,115.0,60.0,40.0,56.0,100.0,90.0
+Nordic Mountain,Wisconsin,Wisconsin,1137,265,872,0,0,0,0,1,1,5,7,18.0,4.0,1.0,60.0,60.0,68.0,43.0,80.0,47.0,90.0,60.0
+Sunburst,Wisconsin,Wisconsin,1100,214,866,0,0,0,0,0,3,6,9,13.0,4.0,0.5,37.0,37.0,99.0,59.0,50.0,44.0,115.0,37.0
+Trollhaugen,Wisconsin,Wisconsin,1200,260,920,0,0,0,2,0,1,5,8,24.0,4.0,0.5,86.0,86.0,130.0,69.0,50.0,54.0,120.0,86.0
+Tyrol Basin,Wisconsin,Wisconsin,1160,300,860,0,0,0,0,3,0,2,5,18.0,5.0,0.5,32.0,32.0,112.0,61.0,41.0,48.0,103.0,32.0
+Whitecap Mountain,Wisconsin,Wisconsin,1750,400,1295,0,0,0,1,0,4,0,5,43.0,1.0,1.0,400.0,300.0,105.0,57.0,200.0,60.0,118.0,
+Wilmot Mountain,Wisconsin,Wisconsin,1030,230,800,0,0,0,3,2,2,3,10,23.0,2.0,0.5,135.0,135.0,125.0,81.0,70.0,66.0,139.0,135.0
+Grand Targhee Resort,Wyoming,Wyoming,9920,2270,7851,0,0,2,2,0,0,1,5,95.0,1.0,2.7,2602.0,,152.0,50.0,500.0,90.0,152.0,
+Hogadon Basin,Wyoming,Wyoming,8000,640,7400,0,0,0,0,0,1,1,2,28.0,1.0,0.6,92.0,32.0,121.0,61.0,80.0,48.0,95.0,
+Sleeping Giant Ski Resort,Wyoming,Wyoming,7428,810,6619,0,0,0,0,1,1,1,3,48.0,1.0,1.0,184.0,18.0,61.0,81.0,310.0,42.0,77.0,
+Snow King Resort,Wyoming,Wyoming,7808,1571,6237,0,0,0,1,1,1,0,3,32.0,2.0,1.0,400.0,250.0,121.0,80.0,300.0,59.0,123.0,110.0
+Snowy Range Ski & Recreation Area,Wyoming,Wyoming,9663,990,8798,0,0,0,0,1,3,1,5,33.0,2.0,0.7,75.0,30.0,131.0,59.0,250.0,49.0,,
+White Pine Ski Area,Wyoming,Wyoming,9500,1100,8400,0,0,0,0,2,0,0,2,25.0,,0.4,370.0,,,81.0,150.0,49.0,,
diff --git a/data/state_summary.csv b/data/state_summary.csv
new file mode 100644
index 000000000..53979a710
--- /dev/null
+++ b/data/state_summary.csv
@@ -0,0 +1,36 @@
+state,resorts_per_state,state_total_skiable_area_ac,state_total_days_open,state_total_terrain_parks,state_total_nightskiing_ac,state_population,state_area_sq_miles
+Alaska,3,2280.0,345.0,4.0,580.0,731545,665384
+Arizona,2,1577.0,237.0,6.0,80.0,7278717,113990
+California,21,25948.0,2738.0,81.0,587.0,39512223,163695
+Colorado,22,43682.0,3258.0,74.0,428.0,5758736,104094
+Connecticut,5,358.0,353.0,10.0,256.0,3565278,5543
+Idaho,12,16396.0,1136.0,27.0,415.0,1787065,83569
+Illinois,4,191.0,221.0,6.0,191.0,12671821,57914
+Indiana,2,165.0,157.0,4.0,165.0,6732219,36420
+Iowa,3,140.0,100.0,5.0,140.0,3155070,56273
+Maine,9,3216.0,865.0,17.0,388.0,1344212,35380
+Maryland,1,172.0,121.0,3.0,118.0,6045680,12406
+Massachusetts,11,1166.0,671.0,18.0,583.0,6892503,10554
+Michigan,28,4406.0,2389.0,63.0,1946.0,9986857,96714
+Minnesota,14,1560.0,1490.0,29.0,1020.0,5639632,86936
+Missouri,2,60.0,69.0,2.0,47.0,6137428,69707
+Montana,12,21410.0,951.0,27.0,710.0,1068778,147040
+Nevada,4,2110.0,415.0,9.0,0.0,3080156,110572
+New Hampshire,16,3427.0,1847.0,43.0,376.0,1359711,9349
+New Jersey,2,190.0,170.0,4.0,181.0,8882190,8723
+New Mexico,9,5223.0,966.0,18.0,50.0,2096829,121590
+New York,33,5514.0,2384.0,72.0,2836.0,19453561,54555
+North Carolina,6,370.0,506.0,9.0,335.0,10488084,53819
+Ohio,5,421.0,489.0,12.0,421.0,11689100,44826
+Oregon,10,11774.0,1180.0,22.0,1127.0,4217737,98379
+Pennsylvania,19,1888.0,1404.0,47.0,1528.0,12801989,46054
+Rhode Island,1,30.0,100.0,1.0,30.0,1059361,1545
+South Dakota,2,950.0,183.0,3.0,0.0,884659,77116
+Tennessee,1,0.0,83.0,1.0,0.0,6829174,42144
+Utah,13,30508.0,1544.0,26.0,642.0,3205958,84897
+Vermont,15,7239.0,1777.0,50.0,50.0,623989,9616
+Virginia,4,269.0,366.0,4.0,135.0,8535519,42775
+Washington,10,15330.0,1022.0,21.0,1997.0,7614893,71298
+West Virginia,4,542.0,342.0,9.0,187.0,1792147,24230
+Wisconsin,15,1750.0,1519.0,40.0,1065.0,5822434,65496
+Wyoming,8,6523.0,716.0,14.0,110.0,578759,97813