03_exploratory_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Exploratory Data Analysis<a id='3_Exploratory_Data_Analysis'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Contents<a id='3.1_Contents'></a>\n",
    "* [3 Exploratory Data Analysis](#3_Exploratory_Data_Analysis)\n",
    "  * [3.1 Contents](#3.1_Contents)\n",
    "  * [3.2 Introduction](#3.2_Introduction)\n",
    "  * [3.3 Imports](#3.3_Imports)\n",
    "  * [3.4 Load The Data](#3.4_Load_The_Data)\n",
    "    * [3.4.1 Ski data](#3.4.1_Ski_data)\n",
    "    * [3.4.2 State-wide summary data](#3.4.2_State-wide_summary_data)\n",
    "  * [3.5 Explore The Data](#3.5_Explore_The_Data)\n",
    "    * [3.5.1 Top States By Order Of Each Of The Summary Statistics](#3.5.1_Top_States_By_Order_Of_Each_Of_The_Summary_Statistics)\n",
    "      * [3.5.1.1 Total state area](#3.5.1.1_Total_state_area)\n",
    "      * [3.5.1.2 Total state population](#3.5.1.2_Total_state_population)\n",
    "      * [3.5.1.3 Resorts per state](#3.5.1.3_Resorts_per_state)\n",
    "      * [3.5.1.4 Total skiable area](#3.5.1.4_Total_skiable_area)\n",
    "      * [3.5.1.5 Total night skiing area](#3.5.1.5_Total_night_skiing_area)\n",
    "      * [3.5.1.6 Total days open](#3.5.1.6_Total_days_open)\n",
    "    * [3.5.2 Resort density](#3.5.2_Resort_density)\n",
    "      * [3.5.2.1 Top states by resort density](#3.5.2.1_Top_states_by_resort_density)\n",
    "    * [3.5.3 Visualizing High Dimensional Data](#3.5.3_Visualizing_High_Dimensional_Data)\n",
    "      * [3.5.3.1 Scale the data](#3.5.3.1_Scale_the_data)\n",
    "        * [3.5.3.1.1 Verifying the scaling](#3.5.3.1.1_Verifying_the_scaling)\n",
    "      * [3.5.3.2 Calculate the PCA transformation](#3.5.3.2_Calculate_the_PCA_transformation)\n",
    "      * [3.5.3.3 Average ticket price by state](#3.5.3.3_Average_ticket_price_by_state)\n",
    "      * [3.5.3.4 Adding average ticket price to scatter plot](#3.5.3.4_Adding_average_ticket_price_to_scatter_plot)\n",
    "    * [3.5.4 Conclusion On How To Handle State Label](#3.5.4_Conclusion_On_How_To_Handle_State_Label)\n",
    "    * [3.5.5 Ski Resort Numeric Data](#3.5.5_Ski_Resort_Numeric_Data)\n",
    "      * [3.5.5.1 Feature engineering](#3.5.5.1_Feature_engineering)\n",
    "      * [3.5.5.2 Feature correlation heatmap](#3.5.5.2_Feature_correlation_heatmap)\n",
    "      * [3.5.5.3 Scatterplots of numeric features against ticket price](#3.5.5.3_Scatterplots_of_numeric_features_against_ticket_price)\n",
    "  * [3.6 Summary](#3.6_Summary)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Introduction<a id='3.2_Introduction'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, you should have a firm idea of what your data science problem is and have the data you believe could help solve it. The business problem was a general one of modeling resort revenue. The data you started with contained some ticket price values, but with a number of missing values that led to several rows being dropped completely. You also had two kinds of ticket price. There were also some obvious issues with some of the other features in the data that, for example, led to one column being completely dropped, a data error corrected, and some other rows dropped. You also obtained some additional US state population and size data with which to augment the dataset, which also required some cleaning.\n",
    "\n",
    "The data science problem you subsequently identified is to predict the adult weekend ticket price for ski resorts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Imports<a id='3.3_Imports'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-07T07:04:19.124917Z",
     "iopub.status.busy": "2020-10-07T07:04:19.124711Z",
     "iopub.status.idle": "2020-10-07T07:04:19.128523Z",
     "shell.execute_reply": "2020-10-07T07:04:19.128112Z",
     "shell.execute_reply.started": "2020-10-07T07:04:19.124888Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "from library.sb_utils import save_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Load The Data<a id='3.4_Load_The_Data'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.1 Ski data<a id='3.4.1_Ski_data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ski_data = pd.read_csv('../data/ski_data_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 277 entries, 0 to 276\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: 54.2+ KB\n"
     ]
    }
   ],
   "source": [
    "ski_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Region</th>\n",
       "      <th>state</th>\n",
       "      <th>summit_elev</th>\n",
       "      <th>vertical_drop</th>\n",
       "      <th>base_elev</th>\n",
       "      <th>trams</th>\n",
       "      <th>fastSixes</th>\n",
       "      <th>fastQuads</th>\n",
       "      <th>quad</th>\n",
       "      <th>...</th>\n",
       "      <th>TerrainParks</th>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <th>yearsOpen</th>\n",
       "      <th>averageSnowfall</th>\n",
       "      <th>AdultWeekend</th>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <th>NightSkiing_ac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alyeska Resort</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>3939</td>\n",
       "      <td>2500</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Eaglecrest Ski Area</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2600</td>\n",
       "      <td>1540</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hilltop Ski Area</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2090</td>\n",
       "      <td>294</td>\n",
       "      <td>1796</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona Snowbowl</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>11500</td>\n",
       "      <td>2300</td>\n",
       "      <td>9200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>777.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunrise Park Resort</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>11100</td>\n",
       "      <td>1800</td>\n",
       "      <td>9200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>800.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name   Region    state  summit_elev  vertical_drop  \\\n",
       "0       Alyeska Resort   Alaska   Alaska         3939           2500   \n",
       "1  Eaglecrest Ski Area   Alaska   Alaska         2600           1540   \n",
       "2     Hilltop Ski Area   Alaska   Alaska         2090            294   \n",
       "3     Arizona Snowbowl  Arizona  Arizona        11500           2300   \n",
       "4  Sunrise Park Resort  Arizona  Arizona        11100           1800   \n",
       "\n",
       "   base_elev  trams  fastSixes  fastQuads  quad  ...  TerrainParks  \\\n",
       "0        250      1          0          2     2  ...           2.0   \n",
       "1       1200      0          0          0     0  ...           1.0   \n",
       "2       1796      0          0          0     0  ...           1.0   \n",
       "3       9200      0          1          0     2  ...           4.0   \n",
       "4       9200      0          0          1     2  ...           2.0   \n",
       "\n",
       "   LongestRun_mi  SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  \\\n",
       "0            1.0             1610.0           113.0             150.0   \n",
       "1            2.0              640.0            60.0              45.0   \n",
       "2            1.0               30.0            30.0             150.0   \n",
       "3            2.0              777.0           104.0             122.0   \n",
       "4            1.2              800.0            80.0             115.0   \n",
       "\n",
       "   yearsOpen  averageSnowfall  AdultWeekend  projectedDaysOpen  NightSkiing_ac  \n",
       "0       60.0            669.0          85.0              150.0           550.0  \n",
       "1       44.0            350.0          53.0               90.0             NaN  \n",
       "2       36.0             69.0          34.0              152.0            30.0  \n",
       "3       81.0            260.0          89.0              122.0             NaN  \n",
       "4       49.0            250.0          78.0              104.0            80.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.2 State-wide summary data<a id='3.4.2_State-wide_summary_data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_summary = pd.read_csv('../data/state_summary.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35 entries, 0 to 34\n",
      "Data columns (total 8 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   state                        35 non-null     object \n",
      " 1   resorts_per_state            35 non-null     int64  \n",
      " 2   state_total_skiable_area_ac  35 non-null     float64\n",
      " 3   state_total_days_open        35 non-null     float64\n",
      " 4   state_total_terrain_parks    35 non-null     float64\n",
      " 5   state_total_night_skiing     35 non-null     float64\n",
      " 6   state_population             35 non-null     int64  \n",
      " 7   state_area_sq_miles          35 non-null     int64  \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 2.3+ KB\n"
     ]
    }
   ],
   "source": [
    "state_summary.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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_night_skiing</th>\n",
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       "    </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  state_total_night_skiing  \\\n",
       "0                  345.0                        4.0                     580.0   \n",
       "1                  237.0                        6.0                      80.0   \n",
       "2                 2738.0                       81.0                     587.0   \n",
       "3                 3258.0                       74.0                     428.0   \n",
       "4                  353.0                       10.0                     256.0   \n",
       "\n",
       "   state_population  state_area_sq_miles  \n",
       "0            731545               665384  \n",
       "1           7278717               113990  \n",
       "2          39512223               163695  \n",
       "3           5758736               104094  \n",
       "4           3565278                 5543  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5 Explore The Data<a id='3.5_Explore_The_Data'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5.1 Top States By Order Of Each Of The Summary Statistics<a id='3.5.1_Top_States_By_Order_Of_Each_Of_The_Summary_Statistics'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does the state-wide picture for your market look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_summary_newind = state_summary.set_index('state')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.1.1 Total state area<a id='3.5.1.1_Total_state_area'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Alaska        665384\n",
       "California    163695\n",
       "Montana       147040\n",
       "New Mexico    121590\n",
       "Arizona       113990\n",
       "Name: state_area_sq_miles, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary_newind.state_area_sq_miles.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your home state, Montana, comes in at third largest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.1.2 Total state population<a id='3.5.1.2_Total_state_population'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "California      39512223\n",
       "New York        19453561\n",
       "Pennsylvania    12801989\n",
       "Illinois        12671821\n",
       "Ohio            11689100\n",
       "Name: state_population, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary_newind.state_population.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "California dominates the state population figures despite coming in second behind Alaska in size (by a long way). The resort's state of Montana was in the top five for size, but doesn't figure in the most populous states. Thus your state is less densely populated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.1.3 Resorts per state<a id='3.5.1.3_Resorts_per_state'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "New York        33\n",
       "Michigan        28\n",
       "Colorado        22\n",
       "California      21\n",
       "Pennsylvania    19\n",
       "Name: resorts_per_state, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary_newind.resorts_per_state.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New York comes top in the number of resorts in our market. Is this because of its proximity to wealthy New Yorkers wanting a convenient skiing trip? Or is it simply that its northerly location means there are plenty of good locations for resorts in that state?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.1.4 Total skiable area<a id='3.5.1.4_Total_skiable_area'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Colorado      43682.0\n",
       "Utah          30508.0\n",
       "California    25948.0\n",
       "Montana       21410.0\n",
       "Idaho         16396.0\n",
       "Name: state_total_skiable_area_ac, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary_newind.state_total_skiable_area_ac.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New York state may have the most resorts, but they don't account for the most skiing area. In fact, New York doesn't even make it into the top five of skiable area. Good old Montana makes it into the top five, though. You may start to think that New York has more, smaller resorts, whereas Montana has fewer, larger resorts. Colorado seems to have a name for skiing; it's in the top five for resorts and in top place for total skiable area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.1.5 Total night skiing area<a id='3.5.1.5_Total_night_skiing_area'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "New York        2836.0\n",
       "Washington      1997.0\n",
       "Michigan        1946.0\n",
       "Pennsylvania    1528.0\n",
       "Oregon          1127.0\n",
       "Name: state_total_night_skiing, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary_newind.state_total_night_skiing.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New York dominates the area of skiing available at night. Looking at the top five in general, they are all the more northerly states. Is night skiing in and of itself an appeal to customers, or is a consequence of simply trying to extend the skiing day where days are shorter? Is New York's domination here because it's trying to maximize its appeal to visitors who'd travel a shorter distance for a shorter visit? You'll find the data generates more (good) questions rather than answering them. This is a positive sign! You might ask your executive sponsor or data provider for some additional data about typical length of stays at these resorts, although you might end up with data that is very granular and most likely proprietary to each resort. A useful level of granularity might be \"number of day tickets\" and \"number of weekly passes\" sold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.1.6 Total days open<a id='3.5.1.6_Total_days_open'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Colorado         3258.0\n",
       "California       2738.0\n",
       "Michigan         2389.0\n",
       "New York         2384.0\n",
       "New Hampshire    1847.0\n",
       "Name: state_total_days_open, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary_newind.state_total_days_open.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total days open seem to bear some resemblance to the number of resorts. This is plausible. The season will only be so long, and so the more resorts open through the skiing season, the more total days open we'll see. New Hampshire makes a good effort at making it into the top five, for a small state that didn't make it into the top five of resorts per state. Does its location mean resorts there have a longer season and so stay open longer, despite there being fewer of them?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5.2 Resort density<a id='3.5.2_Resort_density'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are big states which are not necessarily the most populous. There are states that host many resorts, but other states host a larger total skiing area. The states with the most total days skiing per season are not necessarily those with the most resorts. And New York State boasts an especially large night skiing area. New York had the most resorts but wasn't in the top five largest states, so the reason for it having the most resorts can't be simply having lots of space for them. New York has the second largest population behind California. Perhaps many resorts have sprung up in New York because of the population size? Does this mean there is a high competition between resorts in New York State, fighting for customers and thus keeping prices down? You're not concerned, per se, with the absolute size or population of a state, but you could be interested in the ratio of resorts serving a given population or a given area.\n",
    "\n",
    "So, calculate those ratios! Think of them as measures of resort density, and drop the absolute population and state size columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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_night_skiing</th>\n",
       "      <th>resorts_per_100kcapita</th>\n",
       "      <th>resorts_per_100ksq_mile</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>0.410091</td>\n",
       "      <td>0.450867</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>0.027477</td>\n",
       "      <td>1.754540</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>0.053148</td>\n",
       "      <td>12.828736</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>0.382028</td>\n",
       "      <td>21.134744</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>0.140242</td>\n",
       "      <td>90.203861</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  state_total_night_skiing  \\\n",
       "0                  345.0                        4.0                     580.0   \n",
       "1                  237.0                        6.0                      80.0   \n",
       "2                 2738.0                       81.0                     587.0   \n",
       "3                 3258.0                       74.0                     428.0   \n",
       "4                  353.0                       10.0                     256.0   \n",
       "\n",
       "   resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
       "0                0.410091                 0.450867  \n",
       "1                0.027477                 1.754540  \n",
       "2                0.053148                12.828736  \n",
       "3                0.382028                21.134744  \n",
       "4                0.140242                90.203861  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The 100_000 scaling is simply based on eyeballing the magnitudes of the data\n",
    "state_summary['resorts_per_100kcapita'] = 100_000 * state_summary.resorts_per_state / state_summary.state_population\n",
    "state_summary['resorts_per_100ksq_mile'] = 100_000 * state_summary.resorts_per_state / state_summary.state_area_sq_miles\n",
    "state_summary.drop(columns=['state_population', 'state_area_sq_miles'], inplace=True)\n",
    "state_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the removal of the two columns that only spoke to state-specific data, you now have a Dataframe that speaks to the skiing competitive landscape of each state. It has the number of resorts per state, total skiable area, and days of skiing. You've translated the plain state data into something more useful that gives you an idea of the density of resorts relative to the state population and size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do the distributions of these two new features look?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "state_summary.resorts_per_100kcapita.hist(bins=30)\n",
    "plt.xlabel('Number of resorts per 100k population')\n",
    "plt.ylabel('count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "state_summary.resorts_per_100ksq_mile.hist(bins=30)\n",
    "plt.xlabel('Number of resorts per 100k square miles')\n",
    "plt.ylabel('count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So they have quite some long tails on them, but there's definitely some structure there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.2.1 Top states by resort density<a id='3.5.2.1_Top_states_by_resort_density'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Vermont          2.403889\n",
       "Wyoming          1.382268\n",
       "New Hampshire    1.176721\n",
       "Montana          1.122778\n",
       "Idaho            0.671492\n",
       "Name: resorts_per_100kcapita, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary.set_index('state').resorts_per_100kcapita.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "New Hampshire    171.141299\n",
       "Vermont          155.990017\n",
       "Massachusetts    104.225886\n",
       "Connecticut       90.203861\n",
       "Rhode Island      64.724919\n",
       "Name: resorts_per_100ksq_mile, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary.set_index('state').resorts_per_100ksq_mile.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vermont seems particularly high in terms of resorts per capita, and both New Hampshire and Vermont top the chart for resorts per area. New York doesn't appear in either!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5.3 Visualizing High Dimensional Data<a id='3.5.3_Visualizing_High_Dimensional_Data'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may be starting to feel there's a bit of a problem here, or at least a challenge. You've constructed some potentially useful and business relevant features, derived from summary statistics, for each of the states you're concerned with. You've explored many of these features in turn and found various trends. Some states are higher in some but not in others. Some features will also be more correlated with one another than others.\n",
    "\n",
    "One way to disentangle this interconnected web of relationships is via [principle components analysis](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA) (PCA). This technique will find linear combinations of the original features that are uncorrelated with one another and order them by the amount of variance they explain. You can use these derived features to visualize the data in a lower dimension (e.g. 2 down from 7) and know how much variance the representation explains. You can also explore how the original features contribute to these derived features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic steps in this process are:\n",
    "\n",
    "1. scale the data (important here because our features are heterogenous)\n",
    "2. fit the PCA transformation (learn the transformation from the data)\n",
    "3. apply the transformation to the data to create the derived features\n",
    "4. (optionally) use the derived features to look for patterns in the data and explore the coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.3.1 Scale the data<a id='3.5.3.1_Scale_the_data'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You only want numeric data here, although you don't want to lose track of the state labels, so it's convenient to set the state as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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_night_skiing</th>\n",
       "      <th>resorts_per_100kcapita</th>\n",
       "      <th>resorts_per_100ksq_mile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\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>0.410091</td>\n",
       "      <td>0.450867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\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>0.027477</td>\n",
       "      <td>1.754540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\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>0.053148</td>\n",
       "      <td>12.828736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\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>0.382028</td>\n",
       "      <td>21.134744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\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>0.140242</td>\n",
       "      <td>90.203861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             resorts_per_state  state_total_skiable_area_ac  \\\n",
       "state                                                         \n",
       "Alaska                       3                       2280.0   \n",
       "Arizona                      2                       1577.0   \n",
       "California                  21                      25948.0   \n",
       "Colorado                    22                      43682.0   \n",
       "Connecticut                  5                        358.0   \n",
       "\n",
       "             state_total_days_open  state_total_terrain_parks  \\\n",
       "state                                                           \n",
       "Alaska                       345.0                        4.0   \n",
       "Arizona                      237.0                        6.0   \n",
       "California                  2738.0                       81.0   \n",
       "Colorado                    3258.0                       74.0   \n",
       "Connecticut                  353.0                       10.0   \n",
       "\n",
       "             state_total_night_skiing  resorts_per_100kcapita  \\\n",
       "state                                                           \n",
       "Alaska                          580.0                0.410091   \n",
       "Arizona                          80.0                0.027477   \n",
       "California                      587.0                0.053148   \n",
       "Colorado                        428.0                0.382028   \n",
       "Connecticut                     256.0                0.140242   \n",
       "\n",
       "             resorts_per_100ksq_mile  \n",
       "state                                 \n",
       "Alaska                      0.450867  \n",
       "Arizona                     1.754540  \n",
       "California                 12.828736  \n",
       "Colorado                   21.134744  \n",
       "Connecticut                90.203861  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 1#\n",
    "#Create a new dataframe, `state_summary_scale` from `state_summary` whilst setting the index to 'state'\n",
    "state_summary_scale = state_summary.set_index('state')\n",
    "#Save the state labels (using the index attribute of `state_summary_scale`) into the variable 'state_summary_index'\n",
    "state_summary_index = state_summary_scale.index\n",
    "#Save the column names (using the `columns` attribute) of `state_summary_scale` into the variable 'state_summary_columns'\n",
    "state_summary_columns = state_summary_scale.columns\n",
    "state_summary_scale.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above shows what we expect: the columns we want are all numeric and the state has been moved to the index. Although, it's not necessary to step through the sequence so laboriously, it is often good practice even for experienced professionals. It's easy to make a mistake or forget a step, or the data may have been holding out a surprise! Stepping through like this helps  validate both your work and the data!\n",
    "\n",
    "Now use `scale()` to scale the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_summary_scale = scale(state_summary_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, `scale()` returns an ndarray, so you lose the column names. Because you want to visualise scaled data, you already copied the column names. Now you can construct a dataframe from the ndarray here and reintroduce the column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>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_night_skiing</th>\n",
       "      <th>resorts_per_100kcapita</th>\n",
       "      <th>resorts_per_100ksq_mile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.806912</td>\n",
       "      <td>-0.392012</td>\n",
       "      <td>-0.689059</td>\n",
       "      <td>-0.816118</td>\n",
       "      <td>0.069410</td>\n",
       "      <td>0.139593</td>\n",
       "      <td>-0.689999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.933558</td>\n",
       "      <td>-0.462424</td>\n",
       "      <td>-0.819038</td>\n",
       "      <td>-0.726994</td>\n",
       "      <td>-0.701326</td>\n",
       "      <td>-0.644706</td>\n",
       "      <td>-0.658125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.472706</td>\n",
       "      <td>1.978574</td>\n",
       "      <td>2.190933</td>\n",
       "      <td>2.615141</td>\n",
       "      <td>0.080201</td>\n",
       "      <td>-0.592085</td>\n",
       "      <td>-0.387368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.599351</td>\n",
       "      <td>3.754811</td>\n",
       "      <td>2.816757</td>\n",
       "      <td>2.303209</td>\n",
       "      <td>-0.164893</td>\n",
       "      <td>0.082069</td>\n",
       "      <td>-0.184291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.553622</td>\n",
       "      <td>-0.584519</td>\n",
       "      <td>-0.679431</td>\n",
       "      <td>-0.548747</td>\n",
       "      <td>-0.430027</td>\n",
       "      <td>-0.413557</td>\n",
       "      <td>1.504408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   resorts_per_state  state_total_skiable_area_ac  state_total_days_open  \\\n",
       "0          -0.806912                    -0.392012              -0.689059   \n",
       "1          -0.933558                    -0.462424              -0.819038   \n",
       "2           1.472706                     1.978574               2.190933   \n",
       "3           1.599351                     3.754811               2.816757   \n",
       "4          -0.553622                    -0.584519              -0.679431   \n",
       "\n",
       "   state_total_terrain_parks  state_total_night_skiing  \\\n",
       "0                  -0.816118                  0.069410   \n",
       "1                  -0.726994                 -0.701326   \n",
       "2                   2.615141                  0.080201   \n",
       "3                   2.303209                 -0.164893   \n",
       "4                  -0.548747                 -0.430027   \n",
       "\n",
       "   resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
       "0                0.139593                -0.689999  \n",
       "1               -0.644706                -0.658125  \n",
       "2               -0.592085                -0.387368  \n",
       "3                0.082069                -0.184291  \n",
       "4               -0.413557                 1.504408  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 2#\n",
    "#Create a new dataframe from `state_summary_scale` using the column names we saved in `state_summary_columns`\n",
    "state_summary_scaled_df = pd.DataFrame(state_summary_scale, columns=state_summary_columns)\n",
    "state_summary_scaled_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.5.3.1.1 Verifying the scaling<a id='3.5.3.1.1_Verifying_the_scaling'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is definitely going the extra mile for validating your steps, but provides a worthwhile lesson."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, check the mean of the scaled features using panda's `mean()` DataFrame method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resorts_per_state             -6.344132e-17\n",
       "state_total_skiable_area_ac   -5.432163e-17\n",
       "state_total_days_open          9.754102e-17\n",
       "state_total_terrain_parks      4.282289e-17\n",
       "state_total_night_skiing       6.344132e-17\n",
       "resorts_per_100kcapita         5.075305e-17\n",
       "resorts_per_100ksq_mile        5.075305e-17\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 3#\n",
    "#Call `state_summary_scaled_df`'s `mean()` method\n",
    "state_summary_scaled_df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is pretty much zero!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform a similar check for the standard deviation using pandas's `std()` DataFrame method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resorts_per_state              1.014599\n",
       "state_total_skiable_area_ac    1.014599\n",
       "state_total_days_open          1.014599\n",
       "state_total_terrain_parks      1.014599\n",
       "state_total_night_skiing       1.014599\n",
       "resorts_per_100kcapita         1.014599\n",
       "resorts_per_100ksq_mile        1.014599\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 4#\n",
    "#Call `state_summary_scaled_df`'s `std()` method\n",
    "state_summary_scaled_df.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, this is a little embarrassing. The numbers should be closer to 1 than this! Check the documentation for [scale](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html) to see if you used it right. What about [std](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.std.html), did you mess up there? Is one of them not working right?\n",
    "\n",
    "The keen observer, who already has some familiarity with statistical inference and biased estimators, may have noticed what's happened here. `scale()` uses the biased estimator for standard deviation (ddof=0). This doesn't mean it's bad! It simply means it calculates the standard deviation of the sample it was given. The `std()` method, on the other hand, defaults to using ddof=1, that is it's normalized by N-1. In other words, the `std()` method default is to assume you want your best estimate of the population parameter based on the given sample. You can tell it to return the biased estimate instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resorts_per_state              1.0\n",
       "state_total_skiable_area_ac    1.0\n",
       "state_total_days_open          1.0\n",
       "state_total_terrain_parks      1.0\n",
       "state_total_night_skiing       1.0\n",
       "resorts_per_100kcapita         1.0\n",
       "resorts_per_100ksq_mile        1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 5#\n",
    "#Repeat the previous call to `std()` but pass in ddof=0 \n",
    "state_summary_scaled_df.std(ddof=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There! Now it agrees with `scale()` and our expectation. This just goes to show different routines to do ostensibly the same thing can have different behaviours. Good practice is to keep validating your work and checking the documentation!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.3.2 Calculate the PCA transformation<a id='3.5.3.2_Calculate_the_PCA_transformation'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the PCA transformation using the scaled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_pca = PCA().fit(state_summary_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the cumulative variance ratio with number of components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 6#\n",
    "#Call the `cumsum()` method on the 'explained_variance_ratio_' attribute of `state_pca` and\n",
    "#create a line plot to visualize the cumulative explained variance ratio with number of components\n",
    "#Set the xlabel to 'Component #', the ylabel to 'Cumulative ratio variance', and the\n",
    "#title to 'Cumulative variance ratio explained by PCA components for state/resort summary statistics'\n",
    "#Hint: remember the handy ';' at the end of the last plot call to suppress that untidy output\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.plot(state_pca.explained_variance_ratio_.cumsum())\n",
    "plt.xlabel('Component #')\n",
    "plt.ylabel('Cumulative ratio variance')\n",
    "plt.title('Cumulative variance ratio explained by PCA components for state/resort summary statistics');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first two components seem to account for over 75% of the variance, and the first four for over 95%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** It is important to move quickly when performing exploratory data analysis. You should not spend hours trying to create publication-ready figures. However, it is crucially important that you can easily review and summarise the findings from EDA. Descriptive axis labels and titles are _extremely_ useful here. When you come to reread your notebook to summarise your findings, you will be thankful that you created descriptive plots and even made key observations in adjacent markdown cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the transformation to the data to obtain the derived features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code task 7#\n",
    "#Call `state_pca`'s `transform()` method, passing in `state_summary_scale` as its argument\n",
    "state_pca_x = state_pca.transform(state_summary_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 7)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_pca_x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the first two derived features (the first two principle components) and label each point with the name of the state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a moment to familiarize yourself with the code below. It will extract the first and second columns from the transformed data (`state_pca_x`) as x and y coordinates for plotting. Recall the state labels you saved (for this purpose) for subsequent calls to `plt.annotate`. Grab the second (index 1) value of the cumulative variance ratio to include in your descriptive title; this helpfully highlights the percentage variance explained\n",
    "by the two PCA components you're visualizing. Then create an appropriately sized and well-labelled scatterplot\n",
    "to convey all of this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = state_pca_x[:, 0]\n",
    "y = state_pca_x[:, 1]\n",
    "state = state_summary_index\n",
    "pc_var = 100 * state_pca.explained_variance_ratio_.cumsum()[1]\n",
    "plt.subplots(figsize=(10,8))\n",
    "plt.scatter(x=x, y=y)\n",
    "plt.xlabel('First component')\n",
    "plt.ylabel('Second component')\n",
    "plt.title(f'Ski states summary PCA, {pc_var:.1f}% variance explained')\n",
    "for s, x, y in zip(state, x, y):\n",
    "    plt.annotate(s, (x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.3.3 Average ticket price by state<a id='3.5.3.3_Average_ticket_price_by_state'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, all point markers for the states are the same size and colour. You've visualized relationships between the states based on features such as the total skiable terrain area, but your ultimate interest lies in ticket prices. You know ticket prices for resorts in each state, so it might be interesting to see if there's any pattern there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Alaska         57.333333\n",
       "Arizona        83.500000\n",
       "California     81.416667\n",
       "Colorado       90.714286\n",
       "Connecticut    56.800000\n",
       "Name: AdultWeekend, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 8#\n",
    "#Calculate the average 'AdultWeekend' ticket price by state\n",
    "state_avg_price = ski_data.groupby('state')['AdultWeekend'].mean()\n",
    "state_avg_price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "state_avg_price.hist(bins=30)\n",
    "plt.title('Distribution of state averaged prices')\n",
    "plt.xlabel('Mean state adult weekend ticket price')\n",
    "plt.ylabel('count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.3.4 Adding average ticket price to scatter plot<a id='3.5.3.4_Adding_average_ticket_price_to_scatter_plot'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point you have several objects floating around. You have just calculated average ticket price by state from our ski resort data, but you've been looking at principle components generated from other state summary data. We extracted indexes and column names from a dataframe and the first two principle components from an array. It's becoming a bit hard to keep track of them all. You'll create a new DataFrame to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>-1.336533</td>\n",
       "      <td>-0.182208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>-1.839049</td>\n",
       "      <td>-0.387959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>3.537857</td>\n",
       "      <td>-1.282509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4.402210</td>\n",
       "      <td>-0.898855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>-0.988027</td>\n",
       "      <td>1.020218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  PC1       PC2\n",
       "state                          \n",
       "Alaska      -1.336533 -0.182208\n",
       "Arizona     -1.839049 -0.387959\n",
       "California   3.537857 -1.282509\n",
       "Colorado     4.402210 -0.898855\n",
       "Connecticut -0.988027  1.020218"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 9#\n",
    "#Create a dataframe containing the values of the first two PCA components\n",
    "#Remember the first component was given by state_pca_x[:, 0],\n",
    "#and the second by state_pca_x[:, 1]\n",
    "#Call these 'PC1' and 'PC2', respectively and set the dataframe index to `state_summary_index`\n",
    "pca_df = pd.DataFrame({'PC1': state_pca_x[:,0], 'PC2': state_pca_x[:,1]}, index=state_summary_index)\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That worked, and you have state as an index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Alaska         57.333333\n",
       "Arizona        83.500000\n",
       "California     81.416667\n",
       "Colorado       90.714286\n",
       "Connecticut    56.800000\n",
       "Name: AdultWeekend, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# our average state prices also have state as an index\n",
    "state_avg_price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>57.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>83.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>81.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>90.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>56.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             AdultWeekend\n",
       "state                    \n",
       "Alaska          57.333333\n",
       "Arizona         83.500000\n",
       "California      81.416667\n",
       "Colorado        90.714286\n",
       "Connecticut     56.800000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also cast it to a dataframe using Series' to_frame() method:\n",
    "state_avg_price.to_frame().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can concatenate both parts on axis 1 and using the indexes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>AdultWeekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>-1.336533</td>\n",
       "      <td>-0.182208</td>\n",
       "      <td>57.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>-1.839049</td>\n",
       "      <td>-0.387959</td>\n",
       "      <td>83.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>3.537857</td>\n",
       "      <td>-1.282509</td>\n",
       "      <td>81.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4.402210</td>\n",
       "      <td>-0.898855</td>\n",
       "      <td>90.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>-0.988027</td>\n",
       "      <td>1.020218</td>\n",
       "      <td>56.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  PC1       PC2  AdultWeekend\n",
       "state                                        \n",
       "Alaska      -1.336533 -0.182208     57.333333\n",
       "Arizona     -1.839049 -0.387959     83.500000\n",
       "California   3.537857 -1.282509     81.416667\n",
       "Colorado     4.402210 -0.898855     90.714286\n",
       "Connecticut -0.988027  1.020218     56.800000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 10#\n",
    "#Use pd.concat to concatenate `pca_df` and `state_avg_price` along axis 1\n",
    "# remember, pd.concat will align on index\n",
    "pca_df = pd.concat([pca_df, state_avg_price], axis=1)\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You saw some range in average ticket price histogram above, but it may be hard to pick out differences if you're thinking of using the value for point size. You'll add another column where you seperate these prices into quartiles; that might show something."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>AdultWeekend</th>\n",
       "      <th>Quartile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>-1.336533</td>\n",
       "      <td>-0.182208</td>\n",
       "      <td>57.333333</td>\n",
       "      <td>(53.1, 60.4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>-1.839049</td>\n",
       "      <td>-0.387959</td>\n",
       "      <td>83.500000</td>\n",
       "      <td>(78.4, 93.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>3.537857</td>\n",
       "      <td>-1.282509</td>\n",
       "      <td>81.416667</td>\n",
       "      <td>(78.4, 93.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4.402210</td>\n",
       "      <td>-0.898855</td>\n",
       "      <td>90.714286</td>\n",
       "      <td>(78.4, 93.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>-0.988027</td>\n",
       "      <td>1.020218</td>\n",
       "      <td>56.800000</td>\n",
       "      <td>(53.1, 60.4]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  PC1       PC2  AdultWeekend      Quartile\n",
       "state                                                      \n",
       "Alaska      -1.336533 -0.182208     57.333333  (53.1, 60.4]\n",
       "Arizona     -1.839049 -0.387959     83.500000  (78.4, 93.0]\n",
       "California   3.537857 -1.282509     81.416667  (78.4, 93.0]\n",
       "Colorado     4.402210 -0.898855     90.714286  (78.4, 93.0]\n",
       "Connecticut -0.988027  1.020218     56.800000  (53.1, 60.4]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df['Quartile'] = pd.qcut(pca_df.AdultWeekend, q=4, precision=1)\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PC1              float64\n",
       "PC2              float64\n",
       "AdultWeekend     float64\n",
       "Quartile        category\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that Quartile is a new data type: category\n",
    "# This will affect how we handle it later on\n",
    "pca_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks great. But, let's have a healthy paranoia about it. You've just created a whole new DataFrame by combining information. Do we have any missing values? It's a narrow DataFrame, only four columns, so you'll just print out any rows that have any null values, expecting an empty DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>AdultWeekend</th>\n",
       "      <th>Quartile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>-1.843646</td>\n",
       "      <td>0.761339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PC1       PC2  AdultWeekend Quartile\n",
       "state                                                  \n",
       "Rhode Island -1.843646  0.761339           NaN      NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df[pca_df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah, Rhode Island. How has this happened? Recall you created the original ski resort state summary dataset in the previous step before removing resorts with missing prices. This made sense because you wanted to capture all the other available information. However, Rhode Island only had one resort and its price was missing. You have two choices here. If you're interested in looking for any pattern with price, drop this row. But you are also generally interested in any clusters or trends, then you'd like to see Rhode Island even if the ticket price is unknown. So, replace these missing values to make it easier to handle/display them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because `Quartile` is a category type, there's an extra step here. Add the category (the string 'NA') that you're going to use as a replacement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PC1             -1.843646\n",
       "PC2              0.761339\n",
       "AdultWeekend    64.124388\n",
       "Quartile               NA\n",
       "Name: Rhode Island, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df['AdultWeekend'].fillna(pca_df.AdultWeekend.mean(), inplace=True)\n",
    "pca_df['Quartile'] = pca_df['Quartile'].cat.add_categories('NA')\n",
    "pca_df['Quartile'].fillna('NA', inplace=True)\n",
    "pca_df.loc['Rhode Island']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, in the above Quartile has the string value 'NA' that you inserted. This is different to `numpy`'s NaN type.\n",
    "\n",
    "You now have enough information to recreate the scatterplot, now adding marker size for ticket price and colour for the discrete quartile."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice in the code below how you're iterating over each quartile and plotting the points in the same quartile group as one. This gives a list of quartiles for an informative legend with points coloured by quartile and sized by ticket price (higher prices are represented by larger point markers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = pca_df.PC1\n",
    "y = pca_df.PC2\n",
    "price = pca_df.AdultWeekend\n",
    "quartiles = pca_df.Quartile\n",
    "state = pca_df.index\n",
    "pc_var = 100 * state_pca.explained_variance_ratio_.cumsum()[1]\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "for q in quartiles.cat.categories:\n",
    "    im = quartiles == q\n",
    "    ax.scatter(x=x[im], y=y[im], s=price[im], label=q)\n",
    "ax.set_xlabel('First component')\n",
    "ax.set_ylabel('Second component')\n",
    "plt.legend()\n",
    "ax.set_title(f'Ski states summary PCA, {pc_var:.1f}% variance explained')\n",
    "for s, x, y in zip(state, x, y):\n",
    "    plt.annotate(s, (x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you see the same distribution of states as before, but with additional information about the average price. There isn't an obvious pattern. The red points representing the upper quartile of price can be seen to the left, the right, and up top. There's also a spread of the other quartiles as well. In this representation of the ski summaries for each state, which accounts for some 77% of the variance, you simply do not seeing a pattern with price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above scatterplot was created using matplotlib. This is powerful, but took quite a bit of effort to set up. You have to iterate over the categories, plotting each separately, to get a colour legend. You can also tell that the points in the legend have different sizes as well as colours. As it happens, the size and the colour will be a 1:1 mapping here, so it happily works for us here. If we were using size and colour to display fundamentally different aesthetics, you'd have a lot more work to do. So matplotlib is powerful, but not ideally suited to when we want to visually explore multiple features as here (and intelligent use of colour, point size, and even shape can be incredibly useful for EDA).\n",
    "\n",
    "Fortunately, there's another option: seaborn. You saw seaborn in action in the previous notebook, when you wanted to distinguish between weekend and weekday ticket prices in the boxplot. After melting the dataframe to have ticket price as a single column with the ticket type represented in a new column, you asked seaborn to create separate boxes for each type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 11#\n",
    "#Create a seaborn scatterplot by calling `sns.scatterplot`\n",
    "#Specify the dataframe pca_df as the source of the data,\n",
    "#specify 'PC1' for x and 'PC2' for y,\n",
    "#specify 'AdultWeekend' for the pointsize (scatterplot's `size` argument),\n",
    "#specify 'Quartile' for `hue`\n",
    "#specify pca_df.Quartile.cat.categories for `hue_order` - what happens with/without this?\n",
    "x = pca_df.PC1\n",
    "y = pca_df.PC2\n",
    "state = pca_df.index\n",
    "plt.subplots(figsize=(12, 10))\n",
    "# Note the argument below to make sure we get the colours in the ascending\n",
    "# order we intuitively expect!\n",
    "sns.scatterplot(x='PC1', y='PC2', size='AdultWeekend', hue='Quartile', \n",
    "                 hue_order=pca_df.Quartile.cat.categories,data=pca_df)\n",
    "#and we can still annotate with the state labels\n",
    "for s, x, y in zip(state, x, y):\n",
    "    plt.annotate(s, (x, y))   \n",
    "plt.title(f'Ski states summary PCA, {pc_var:.1f}% variance explained');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn does more! You should always care about your output. What if you want the ordering of the colours in the legend to align intuitively with the ordering of the quartiles? Add a `hue_order` argument! Seaborn has thrown in a few nice other things:\n",
    "\n",
    "* the aesthetics are separated in the legend\n",
    "* it defaults to marker sizes that provide more contrast (smaller to larger)\n",
    "* when starting with a DataFrame, you have less work to do to visualize patterns in the data\n",
    "\n",
    "The last point is important. Less work means less chance of mixing up objects and jumping to erroneous conclusions. This also emphasizes the importance of getting data into a suitable DataFrame. In the previous notebook, you `melt`ed the data to make it longer, but with fewer columns, in order to get a single column of price with a new column representing a categorical feature you'd want to use. A **key skill** is being able to wrangle data into a form most suited to the particular use case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having gained a good visualization of the state summary data, you can discuss and follow up on your findings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first two components, there is a spread of states across the first component. It looks like Vermont and New Hampshire might be off on their own a little in the second dimension, although they're really no more extreme than New York and Colorado are in the first dimension. But if you were curious, could you get an idea what it is that pushes Vermont and New Hampshire up?\n",
    "\n",
    "The `components_` attribute of the fitted PCA object tell us how important (and in what direction) each feature contributes to each score (or coordinate on the plot). **NB we were sensible and scaled our original features (to zero mean and unit variance)**. You may not always be interested in interpreting the coefficients of the PCA transformation in this way, although it's more likely you will when using PCA for EDA as opposed to a preprocessing step as part of a machine learning pipeline. The attribute is actually a numpy ndarray, and so has been stripped of helpful index and column names. Fortunately, you thought ahead and saved these. This is how we were able to annotate the scatter plots above. It also means you can construct a DataFrame of `components_` with the feature names for context:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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_night_skiing</th>\n",
       "      <th>resorts_per_100kcapita</th>\n",
       "      <th>resorts_per_100ksq_mile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.486079</td>\n",
       "      <td>0.318224</td>\n",
       "      <td>0.489997</td>\n",
       "      <td>0.488420</td>\n",
       "      <td>0.334398</td>\n",
       "      <td>0.187154</td>\n",
       "      <td>0.192250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.085092</td>\n",
       "      <td>-0.142204</td>\n",
       "      <td>-0.045071</td>\n",
       "      <td>-0.041939</td>\n",
       "      <td>-0.351064</td>\n",
       "      <td>0.662458</td>\n",
       "      <td>0.637691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.177937</td>\n",
       "      <td>0.714835</td>\n",
       "      <td>0.115200</td>\n",
       "      <td>0.005509</td>\n",
       "      <td>-0.511255</td>\n",
       "      <td>0.220359</td>\n",
       "      <td>-0.366207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.056163</td>\n",
       "      <td>-0.118347</td>\n",
       "      <td>-0.162625</td>\n",
       "      <td>-0.177072</td>\n",
       "      <td>0.438912</td>\n",
       "      <td>0.685417</td>\n",
       "      <td>-0.512443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.209186</td>\n",
       "      <td>0.573462</td>\n",
       "      <td>-0.250521</td>\n",
       "      <td>-0.388608</td>\n",
       "      <td>0.499801</td>\n",
       "      <td>-0.065077</td>\n",
       "      <td>0.399461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.818390</td>\n",
       "      <td>-0.092319</td>\n",
       "      <td>0.238198</td>\n",
       "      <td>0.448118</td>\n",
       "      <td>0.246196</td>\n",
       "      <td>0.058911</td>\n",
       "      <td>-0.009146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.090273</td>\n",
       "      <td>-0.127021</td>\n",
       "      <td>0.773728</td>\n",
       "      <td>-0.613576</td>\n",
       "      <td>0.022185</td>\n",
       "      <td>-0.007887</td>\n",
       "      <td>-0.005631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   resorts_per_state  state_total_skiable_area_ac  state_total_days_open  \\\n",
       "0           0.486079                     0.318224               0.489997   \n",
       "1          -0.085092                    -0.142204              -0.045071   \n",
       "2          -0.177937                     0.714835               0.115200   \n",
       "3           0.056163                    -0.118347              -0.162625   \n",
       "4          -0.209186                     0.573462              -0.250521   \n",
       "5          -0.818390                    -0.092319               0.238198   \n",
       "6          -0.090273                    -0.127021               0.773728   \n",
       "\n",
       "   state_total_terrain_parks  state_total_night_skiing  \\\n",
       "0                   0.488420                  0.334398   \n",
       "1                  -0.041939                 -0.351064   \n",
       "2                   0.005509                 -0.511255   \n",
       "3                  -0.177072                  0.438912   \n",
       "4                  -0.388608                  0.499801   \n",
       "5                   0.448118                  0.246196   \n",
       "6                  -0.613576                  0.022185   \n",
       "\n",
       "   resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
       "0                0.187154                 0.192250  \n",
       "1                0.662458                 0.637691  \n",
       "2                0.220359                -0.366207  \n",
       "3                0.685417                -0.512443  \n",
       "4               -0.065077                 0.399461  \n",
       "5                0.058911                -0.009146  \n",
       "6               -0.007887                -0.005631  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(state_pca.components_, columns=state_summary_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the row associated with the second component, are there any large values?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like `resorts_per_100kcapita` and `resorts_per_100ksq_mile` might count for quite a lot, in a positive sense. Be aware that sign matters; a large negative coefficient multiplying a large negative feature will actually produce a large positive PCA score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>17</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>Vermont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resorts_per_state</th>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_skiable_area_ac</th>\n",
       "      <td>3427.0</td>\n",
       "      <td>7239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_days_open</th>\n",
       "      <td>1847.0</td>\n",
       "      <td>1777.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_terrain_parks</th>\n",
       "      <td>43.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_night_skiing</th>\n",
       "      <td>376.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resorts_per_100kcapita</th>\n",
       "      <td>1.176721</td>\n",
       "      <td>2.403889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resorts_per_100ksq_mile</th>\n",
       "      <td>171.141299</td>\n",
       "      <td>155.990017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        17          29\n",
       "state                        New Hampshire     Vermont\n",
       "resorts_per_state                       16          15\n",
       "state_total_skiable_area_ac         3427.0      7239.0\n",
       "state_total_days_open               1847.0      1777.0\n",
       "state_total_terrain_parks             43.0        50.0\n",
       "state_total_night_skiing             376.0        50.0\n",
       "resorts_per_100kcapita            1.176721    2.403889\n",
       "resorts_per_100ksq_mile         171.141299  155.990017"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary[state_summary.state.isin(['New Hampshire', 'Vermont'])].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>17</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>resorts_per_state</th>\n",
       "      <td>0.839478</td>\n",
       "      <td>0.712833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_skiable_area_ac</th>\n",
       "      <td>-0.277128</td>\n",
       "      <td>0.104681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_days_open</th>\n",
       "      <td>1.118608</td>\n",
       "      <td>1.034363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_terrain_parks</th>\n",
       "      <td>0.921793</td>\n",
       "      <td>1.233725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_night_skiing</th>\n",
       "      <td>-0.245050</td>\n",
       "      <td>-0.747570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resorts_per_100kcapita</th>\n",
       "      <td>1.711066</td>\n",
       "      <td>4.226572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resorts_per_100ksq_mile</th>\n",
       "      <td>3.483281</td>\n",
       "      <td>3.112841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   17        29\n",
       "resorts_per_state            0.839478  0.712833\n",
       "state_total_skiable_area_ac -0.277128  0.104681\n",
       "state_total_days_open        1.118608  1.034363\n",
       "state_total_terrain_parks    0.921793  1.233725\n",
       "state_total_night_skiing    -0.245050 -0.747570\n",
       "resorts_per_100kcapita       1.711066  4.226572\n",
       "resorts_per_100ksq_mile      3.483281  3.112841"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary_scaled_df[state_summary.state.isin(['New Hampshire', 'Vermont'])].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, yes, both states have particularly large values of `resorts_per_100ksq_mile` in absolute terms, and these put them more than 3 standard deviations from the mean. Vermont also has a notably large value for `resorts_per_100kcapita`. New York, then, does not seem to be a stand-out for density of ski resorts either in terms of state size or population count."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5.4 Conclusion On How To Handle State Label<a id='3.5.4_Conclusion_On_How_To_Handle_State_Label'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can offer some justification for treating all states equally, and work towards building a pricing model that considers all states together, without treating any one particularly specially. You haven't seen any clear grouping yet, but you have captured potentially relevant state data in features most likely to be relevant to your business use case. This answers a big question!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5.5 Ski Resort Numeric Data<a id='3.5.5_Ski_Resort_Numeric_Data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After what may feel a detour, return to examining the ski resort data. It's worth noting, the previous EDA was valuable because it's given us some potentially useful features, as well as validating an approach for how to subsequently handle the state labels in your modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Alyeska Resort</td>\n",
       "      <td>Eaglecrest Ski Area</td>\n",
       "      <td>Hilltop Ski Area</td>\n",
       "      <td>Arizona Snowbowl</td>\n",
       "      <td>Sunrise Park Resort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>3939</td>\n",
       "      <td>2600</td>\n",
       "      <td>2090</td>\n",
       "      <td>11500</td>\n",
       "      <td>11100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>2500</td>\n",
       "      <td>1540</td>\n",
       "      <td>294</td>\n",
       "      <td>2300</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>250</td>\n",
       "      <td>1200</td>\n",
       "      <td>1796</td>\n",
       "      <td>9200</td>\n",
       "      <td>9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>76.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>1610.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>777.0</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>113.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>150.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>60.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>669.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>85.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>150.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                0                    1                 2  \\\n",
       "Name               Alyeska Resort  Eaglecrest Ski Area  Hilltop Ski Area   \n",
       "Region                     Alaska               Alaska            Alaska   \n",
       "state                      Alaska               Alaska            Alaska   \n",
       "summit_elev                  3939                 2600              2090   \n",
       "vertical_drop                2500                 1540               294   \n",
       "base_elev                     250                 1200              1796   \n",
       "trams                           1                    0                 0   \n",
       "fastSixes                       0                    0                 0   \n",
       "fastQuads                       2                    0                 0   \n",
       "quad                            2                    0                 0   \n",
       "triple                          0                    0                 1   \n",
       "double                          0                    4                 0   \n",
       "surface                         2                    0                 2   \n",
       "total_chairs                    7                    4                 3   \n",
       "Runs                         76.0                 36.0              13.0   \n",
       "TerrainParks                  2.0                  1.0               1.0   \n",
       "LongestRun_mi                 1.0                  2.0               1.0   \n",
       "SkiableTerrain_ac          1610.0                640.0              30.0   \n",
       "Snow Making_ac              113.0                 60.0              30.0   \n",
       "daysOpenLastYear            150.0                 45.0             150.0   \n",
       "yearsOpen                    60.0                 44.0              36.0   \n",
       "averageSnowfall             669.0                350.0              69.0   \n",
       "AdultWeekend                 85.0                 53.0              34.0   \n",
       "projectedDaysOpen           150.0                 90.0             152.0   \n",
       "NightSkiing_ac              550.0                  NaN              30.0   \n",
       "\n",
       "                                  3                    4  \n",
       "Name               Arizona Snowbowl  Sunrise Park Resort  \n",
       "Region                      Arizona              Arizona  \n",
       "state                       Arizona              Arizona  \n",
       "summit_elev                   11500                11100  \n",
       "vertical_drop                  2300                 1800  \n",
       "base_elev                      9200                 9200  \n",
       "trams                             0                    0  \n",
       "fastSixes                         1                    0  \n",
       "fastQuads                         0                    1  \n",
       "quad                              2                    2  \n",
       "triple                            2                    3  \n",
       "double                            1                    1  \n",
       "surface                           2                    0  \n",
       "total_chairs                      8                    7  \n",
       "Runs                           55.0                 65.0  \n",
       "TerrainParks                    4.0                  2.0  \n",
       "LongestRun_mi                   2.0                  1.2  \n",
       "SkiableTerrain_ac             777.0                800.0  \n",
       "Snow Making_ac                104.0                 80.0  \n",
       "daysOpenLastYear              122.0                115.0  \n",
       "yearsOpen                      81.0                 49.0  \n",
       "averageSnowfall               260.0                250.0  \n",
       "AdultWeekend                   89.0                 78.0  \n",
       "projectedDaysOpen             122.0                104.0  \n",
       "NightSkiing_ac                  NaN                 80.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.5.1 Feature engineering<a id='3.5.5.1_Feature_engineering'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having previously spent some time exploring the state summary data you derived, you now start to explore the resort-level data in more detail. This can help guide you on how (or whether) to use the state labels in the data. It's now time to merge the two datasets and engineer some intuitive features. For example, you can engineer a resort's share of the supply for a given state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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_night_skiing</th>\n",
       "      <th>resorts_per_100kcapita</th>\n",
       "      <th>resorts_per_100ksq_mile</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>0.410091</td>\n",
       "      <td>0.450867</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>0.027477</td>\n",
       "      <td>1.754540</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>0.053148</td>\n",
       "      <td>12.828736</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>0.382028</td>\n",
       "      <td>21.134744</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>0.140242</td>\n",
       "      <td>90.203861</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  state_total_night_skiing  \\\n",
       "0                  345.0                        4.0                     580.0   \n",
       "1                  237.0                        6.0                      80.0   \n",
       "2                 2738.0                       81.0                     587.0   \n",
       "3                 3258.0                       74.0                     428.0   \n",
       "4                  353.0                       10.0                     256.0   \n",
       "\n",
       "   resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
       "0                0.410091                 0.450867  \n",
       "1                0.027477                 1.754540  \n",
       "2                0.053148                12.828736  \n",
       "3                0.382028                21.134744  \n",
       "4                0.140242                90.203861  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Alyeska Resort</td>\n",
       "      <td>Eaglecrest Ski Area</td>\n",
       "      <td>Hilltop Ski Area</td>\n",
       "      <td>Arizona Snowbowl</td>\n",
       "      <td>Sunrise Park Resort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>3939</td>\n",
       "      <td>2600</td>\n",
       "      <td>2090</td>\n",
       "      <td>11500</td>\n",
       "      <td>11100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>2500</td>\n",
       "      <td>1540</td>\n",
       "      <td>294</td>\n",
       "      <td>2300</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>250</td>\n",
       "      <td>1200</td>\n",
       "      <td>1796</td>\n",
       "      <td>9200</td>\n",
       "      <td>9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>76.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>1610.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>777.0</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>113.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>150.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>60.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>669.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>85.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>150.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resorts_per_state</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_skiable_area_ac</th>\n",
       "      <td>2280.0</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>1577.0</td>\n",
       "      <td>1577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_days_open</th>\n",
       "      <td>345.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_terrain_parks</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_total_night_skiing</th>\n",
       "      <td>580.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resorts_per_100kcapita</th>\n",
       "      <td>0.410091</td>\n",
       "      <td>0.410091</td>\n",
       "      <td>0.410091</td>\n",
       "      <td>0.027477</td>\n",
       "      <td>0.027477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resorts_per_100ksq_mile</th>\n",
       "      <td>0.450867</td>\n",
       "      <td>0.450867</td>\n",
       "      <td>0.450867</td>\n",
       "      <td>1.75454</td>\n",
       "      <td>1.75454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          0                    1  \\\n",
       "Name                         Alyeska Resort  Eaglecrest Ski Area   \n",
       "Region                               Alaska               Alaska   \n",
       "state                                Alaska               Alaska   \n",
       "summit_elev                            3939                 2600   \n",
       "vertical_drop                          2500                 1540   \n",
       "base_elev                               250                 1200   \n",
       "trams                                     1                    0   \n",
       "fastSixes                                 0                    0   \n",
       "fastQuads                                 2                    0   \n",
       "quad                                      2                    0   \n",
       "triple                                    0                    0   \n",
       "double                                    0                    4   \n",
       "surface                                   2                    0   \n",
       "total_chairs                              7                    4   \n",
       "Runs                                   76.0                 36.0   \n",
       "TerrainParks                            2.0                  1.0   \n",
       "LongestRun_mi                           1.0                  2.0   \n",
       "SkiableTerrain_ac                    1610.0                640.0   \n",
       "Snow Making_ac                        113.0                 60.0   \n",
       "daysOpenLastYear                      150.0                 45.0   \n",
       "yearsOpen                              60.0                 44.0   \n",
       "averageSnowfall                       669.0                350.0   \n",
       "AdultWeekend                           85.0                 53.0   \n",
       "projectedDaysOpen                     150.0                 90.0   \n",
       "NightSkiing_ac                        550.0                  NaN   \n",
       "resorts_per_state                         3                    3   \n",
       "state_total_skiable_area_ac          2280.0               2280.0   \n",
       "state_total_days_open                 345.0                345.0   \n",
       "state_total_terrain_parks               4.0                  4.0   \n",
       "state_total_night_skiing              580.0                580.0   \n",
       "resorts_per_100kcapita             0.410091             0.410091   \n",
       "resorts_per_100ksq_mile            0.450867             0.450867   \n",
       "\n",
       "                                            2                 3  \\\n",
       "Name                         Hilltop Ski Area  Arizona Snowbowl   \n",
       "Region                                 Alaska           Arizona   \n",
       "state                                  Alaska           Arizona   \n",
       "summit_elev                              2090             11500   \n",
       "vertical_drop                             294              2300   \n",
       "base_elev                                1796              9200   \n",
       "trams                                       0                 0   \n",
       "fastSixes                                   0                 1   \n",
       "fastQuads                                   0                 0   \n",
       "quad                                        0                 2   \n",
       "triple                                      1                 2   \n",
       "double                                      0                 1   \n",
       "surface                                     2                 2   \n",
       "total_chairs                                3                 8   \n",
       "Runs                                     13.0              55.0   \n",
       "TerrainParks                              1.0               4.0   \n",
       "LongestRun_mi                             1.0               2.0   \n",
       "SkiableTerrain_ac                        30.0             777.0   \n",
       "Snow Making_ac                           30.0             104.0   \n",
       "daysOpenLastYear                        150.0             122.0   \n",
       "yearsOpen                                36.0              81.0   \n",
       "averageSnowfall                          69.0             260.0   \n",
       "AdultWeekend                             34.0              89.0   \n",
       "projectedDaysOpen                       152.0             122.0   \n",
       "NightSkiing_ac                           30.0               NaN   \n",
       "resorts_per_state                           3                 2   \n",
       "state_total_skiable_area_ac            2280.0            1577.0   \n",
       "state_total_days_open                   345.0             237.0   \n",
       "state_total_terrain_parks                 4.0               6.0   \n",
       "state_total_night_skiing                580.0              80.0   \n",
       "resorts_per_100kcapita               0.410091          0.027477   \n",
       "resorts_per_100ksq_mile              0.450867           1.75454   \n",
       "\n",
       "                                               4  \n",
       "Name                         Sunrise Park Resort  \n",
       "Region                                   Arizona  \n",
       "state                                    Arizona  \n",
       "summit_elev                                11100  \n",
       "vertical_drop                               1800  \n",
       "base_elev                                   9200  \n",
       "trams                                          0  \n",
       "fastSixes                                      0  \n",
       "fastQuads                                      1  \n",
       "quad                                           2  \n",
       "triple                                         3  \n",
       "double                                         1  \n",
       "surface                                        0  \n",
       "total_chairs                                   7  \n",
       "Runs                                        65.0  \n",
       "TerrainParks                                 2.0  \n",
       "LongestRun_mi                                1.2  \n",
       "SkiableTerrain_ac                          800.0  \n",
       "Snow Making_ac                              80.0  \n",
       "daysOpenLastYear                           115.0  \n",
       "yearsOpen                                   49.0  \n",
       "averageSnowfall                            250.0  \n",
       "AdultWeekend                                78.0  \n",
       "projectedDaysOpen                          104.0  \n",
       "NightSkiing_ac                              80.0  \n",
       "resorts_per_state                              2  \n",
       "state_total_skiable_area_ac               1577.0  \n",
       "state_total_days_open                      237.0  \n",
       "state_total_terrain_parks                    6.0  \n",
       "state_total_night_skiing                    80.0  \n",
       "resorts_per_100kcapita                  0.027477  \n",
       "resorts_per_100ksq_mile                  1.75454  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame's merge method provides SQL-like joins\n",
    "# here 'state' is a column (not an index)\n",
    "ski_data = ski_data.merge(state_summary, how='left', on='state')\n",
    "ski_data.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having merged your state summary features into the ski resort data, add \"state resort competition\" features:\n",
    "\n",
    "* ratio of resort skiable area to total state skiable area\n",
    "* ratio of resort days open to total state days open\n",
    "* ratio of resort terrain park count to total state terrain park count\n",
    "* ratio of resort night skiing area to total state night skiing area\n",
    "\n",
    "Once you've derived these features to put each resort within the context of its state,drop those state columns. Their main purpose was to understand what share of states' skiing \"assets\" is accounted for by each resort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ski_data['resort_skiable_area_ac_state_ratio'] = ski_data.SkiableTerrain_ac / ski_data.state_total_skiable_area_ac\n",
    "ski_data['resort_days_open_state_ratio'] = ski_data.daysOpenLastYear / ski_data.state_total_days_open\n",
    "ski_data['resort_terrain_park_state_ratio'] = ski_data.TerrainParks / ski_data.state_total_terrain_parks\n",
    "ski_data['resort_night_skiing_state_ratio'] = ski_data.NightSkiing_ac / ski_data.state_total_night_skiing\n",
    "\n",
    "ski_data.drop(columns=['state_total_skiable_area_ac', 'state_total_days_open', \n",
    "                       'state_total_terrain_parks', 'state_total_night_skiing'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.5.2 Feature correlation heatmap<a id='3.5.5.2_Feature_correlation_heatmap'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A great way to gain a high level view of relationships amongst the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 12#\n",
    "#Show a seaborn heatmap of correlations in ski_data\n",
    "#Hint: call pandas' `corr()` method on `ski_data` and pass that into `sns.heatmap`\n",
    "plt.subplots(figsize=(8,6))\n",
    "sns.heatmap(ski_data.corr());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a lot to take away from this. First, summit and base elevation are quite highly correlated. This isn't a surprise. You can also see that you've introduced a lot of multicollinearity with your new ratio features; they are negatively correlated with the number of resorts in each state. This latter observation makes sense! If you increase the number of resorts in a state, the share of all the other state features will drop for each. An interesting observation in this region of the heatmap is that there is some positive correlation between the ratio of night skiing area with the number of resorts per capita. In other words, it seems that when resorts are more densely located with population, more night skiing is provided.\n",
    "\n",
    "Turning your attention to your target feature, `AdultWeekend` ticket price, you see quite a few reasonable correlations. `fastQuads` stands out, along with `Runs` and `Snow Making_ac`. The last one is interesting. Visitors would seem to value more guaranteed snow, which would cost in terms of snow making equipment, which would drive prices and costs up. Of the new features, `resort_night_skiing_state_ratio` seems the most correlated with ticket price. If this is true, then perhaps seizing a greater share of night skiing capacity is positive for the price a resort can charge.\n",
    "\n",
    "As well as `Runs`, `total_chairs` is quite well correlated with ticket price. This is plausible; the more runs you have, the more chairs you'd need to ferry people to them! Interestingly, they may count for more than the total skiable terrain area. For sure, the total skiable terrain area is not as useful as the area with snow making. People seem to put more value in guaranteed snow cover rather than more variable terrain area.\n",
    "\n",
    "The vertical drop seems to be a selling point that raises ticket prices as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.5.3 Scatterplots of numeric features against ticket price<a id='3.5.5.3_Scatterplots_of_numeric_features_against_ticket_price'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlations, particularly viewing them together as a heatmap, can be a great first pass at identifying patterns. But correlation can mask relationships between two variables. You'll now create a series of scatterplots to really dive into how ticket price varies with other numeric features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define useful function to create scatterplots of ticket prices against desired columns\n",
    "def scatterplots(columns, ncol=None, figsize=(15, 8)):\n",
    "    if ncol is None:\n",
    "        ncol = len(columns)\n",
    "    nrow = int(np.ceil(len(columns) / ncol))\n",
    "    fig, axes = plt.subplots(nrow, ncol, figsize=figsize, squeeze=False)\n",
    "    fig.subplots_adjust(wspace=0.5, hspace=0.6)\n",
    "    for i, col in enumerate(columns):\n",
    "        ax = axes.flatten()[i]\n",
    "        ax.scatter(x = col, y = 'AdultWeekend', data=ski_data, alpha=0.5)\n",
    "        ax.set(xlabel=col, ylabel='Ticket price')\n",
    "    nsubplots = nrow * ncol    \n",
    "    for empty in range(i+1, nsubplots):\n",
    "        axes.flatten()[empty].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code task 13#\n",
    "#Use a list comprehension to build a list of features from the columns of `ski_data` that\n",
    "#are _not_ any of 'Name', 'Region', 'state', or 'AdultWeekend'\n",
    "features = [x for x in ski_data.columns if x not in ['Name', 'Region', 'state', 'AdultWeekend']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 28 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatterplots(features, ncol=4, figsize=(15, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the scatterplots you see what some of the high correlations were clearly picking up on. There's a strong positive correlation with `vertical_drop`. `fastQuads` seems very useful. `Runs` and `total_chairs` appear quite similar and also useful. `resorts_per_100kcapita` shows something interesting that you don't see from just a headline correlation figure. When the value is low, there is quite a variability in ticket price, although it's capable of going quite high. Ticket price may drop a little before then climbing upwards as the number of resorts per capita increases. Ticket price could climb with the number of resorts serving a population because it indicates a popular area for skiing with plenty of demand. The lower ticket price when fewer resorts serve a population may similarly be because it's a less popular state for skiing. The high price for some resorts when resorts are rare (relative to the population size) may indicate areas where a small number of resorts can benefit from a monopoly effect. It's not a clear picture, although we have some interesting signs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, think of some further features that may be useful in that they relate to how easily a resort can transport people around. You have the numbers of various chairs, and the number of runs, but you don't have the ratio of chairs to runs. It seems logical that this ratio would inform you how easily, and so quickly, people could get to their next ski slope! Create these features now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "ski_data['total_chairs_runs_ratio'] = ski_data.total_chairs / ski_data.Runs\n",
    "ski_data['total_chairs_skiable_ratio'] = ski_data.total_chairs / ski_data.SkiableTerrain_ac\n",
    "ski_data['fastQuads_runs_ratio'] = ski_data.fastQuads / ski_data.Runs\n",
    "ski_data['fastQuads_skiable_ratio'] = ski_data.fastQuads / ski_data.SkiableTerrain_ac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatterplots(['total_chairs_runs_ratio', 'total_chairs_skiable_ratio', \n",
    "              'fastQuads_runs_ratio', 'fastQuads_skiable_ratio'], ncol=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first these relationships are quite counterintuitive. It seems that the more chairs a resort has to move people around, relative to the number of runs, ticket price rapidly plummets and stays low. What we may be seeing here is an exclusive vs. mass market resort effect; if you don't have so many chairs, you can charge more for your tickets, although with fewer chairs you're inevitably going to be able to serve fewer visitors. Your price per visitor is high but your number of visitors may be low. Something very useful that's missing from the data is the number of visitors per year.\n",
    "\n",
    "It also appears that having no fast quads may limit the ticket price, but if your resort covers a wide area then getting a small number of fast quads may be beneficial to ticket price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6 Summary<a id='3.6_Summary'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: 1** Write a summary of the exploratory data analysis above. What numerical or categorical features were in the data? Was there any pattern suggested of a relationship between state and ticket price? What did this lead us to decide regarding which features to use in subsequent modeling? What aspects of the data (e.g. relationships between features) should you remain wary of when you come to perform feature selection for modeling? Two key points that must be addressed are the choice of target feature for your modelling and how, if at all, you're going to handle the states labels in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A: 1** We took the datasets developed in the earlier Data Wrangling portion to run some exploratory data analysis. Initially, we found that CA dominates in terms for population size, but NY has the highest number of resorts. CO seems to have the most skiing area. To make sense of how population and resort count are connected, we calculated the resort densities based on 100k capita (population) and 100k square miles (skiiable area). VT has the highest ratio per 100k capita, whereas NH has the highest in terms of 100k sq. miles.\n",
    "\n",
    "To better understand how the features are related to each other, we use PCA technique. First we scaled the data as the features are heterogeneous, then fit the PCA transformation and applied the transformation to create the derived features. We found that the first two components after the PCA transformation accounted over 75% of the variances, and 95% over the first four. After plotting some scatterplots for further insight, we appended the AdultWeekend prices to the dataframe containing the first two PCA components, and categorized them by quartiles. After plotting the first two components on each other and labelling by quartile, we found that not much of a pattern emerged.\n",
    "\n",
    "We then used seaborn to assist with further analysis, including its additional functionalities such as heat maps. We found that summit and base elevation are highly correlated, and that AdultWeekend prices are relatively correlated with fastQuads, Runs, Snow Making and Total Chairs. We then took several ratios that involved some seemingly important features, and ran a few more scatterplots for more insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Alyeska Resort</td>\n",
       "      <td>Eaglecrest Ski Area</td>\n",
       "      <td>Hilltop Ski Area</td>\n",
       "      <td>Arizona Snowbowl</td>\n",
       "      <td>Sunrise Park Resort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>3939</td>\n",
       "      <td>2600</td>\n",
       "      <td>2090</td>\n",
       "      <td>11500</td>\n",
       "      <td>11100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>2500</td>\n",
       "      <td>1540</td>\n",
       "      <td>294</td>\n",
       "      <td>2300</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>250</td>\n",
       "      <td>1200</td>\n",
       "      <td>1796</td>\n",
       "      <td>9200</td>\n",
       "      <td>9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>76.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>1610.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>777.0</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>113.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>150.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>60.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>669.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>85.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>150.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resorts_per_state</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resorts_per_100kcapita</th>\n",
       "      <td>0.410091</td>\n",
       "      <td>0.410091</td>\n",
       "      <td>0.410091</td>\n",
       "      <td>0.027477</td>\n",
       "      <td>0.027477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resorts_per_100ksq_mile</th>\n",
       "      <td>0.450867</td>\n",
       "      <td>0.450867</td>\n",
       "      <td>0.450867</td>\n",
       "      <td>1.75454</td>\n",
       "      <td>1.75454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resort_skiable_area_ac_state_ratio</th>\n",
       "      <td>0.70614</td>\n",
       "      <td>0.280702</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.492708</td>\n",
       "      <td>0.507292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resort_days_open_state_ratio</th>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.514768</td>\n",
       "      <td>0.485232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resort_terrain_park_state_ratio</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resort_night_skiing_state_ratio</th>\n",
       "      <td>0.948276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.051724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs_runs_ratio</th>\n",
       "      <td>0.092105</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.145455</td>\n",
       "      <td>0.107692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs_skiable_ratio</th>\n",
       "      <td>0.004348</td>\n",
       "      <td>0.00625</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.010296</td>\n",
       "      <td>0.00875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads_runs_ratio</th>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads_skiable_ratio</th>\n",
       "      <td>0.001242</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 0                    1  \\\n",
       "Name                                Alyeska Resort  Eaglecrest Ski Area   \n",
       "Region                                      Alaska               Alaska   \n",
       "state                                       Alaska               Alaska   \n",
       "summit_elev                                   3939                 2600   \n",
       "vertical_drop                                 2500                 1540   \n",
       "base_elev                                      250                 1200   \n",
       "trams                                            1                    0   \n",
       "fastSixes                                        0                    0   \n",
       "fastQuads                                        2                    0   \n",
       "quad                                             2                    0   \n",
       "triple                                           0                    0   \n",
       "double                                           0                    4   \n",
       "surface                                          2                    0   \n",
       "total_chairs                                     7                    4   \n",
       "Runs                                          76.0                 36.0   \n",
       "TerrainParks                                   2.0                  1.0   \n",
       "LongestRun_mi                                  1.0                  2.0   \n",
       "SkiableTerrain_ac                           1610.0                640.0   \n",
       "Snow Making_ac                               113.0                 60.0   \n",
       "daysOpenLastYear                             150.0                 45.0   \n",
       "yearsOpen                                     60.0                 44.0   \n",
       "averageSnowfall                              669.0                350.0   \n",
       "AdultWeekend                                  85.0                 53.0   \n",
       "projectedDaysOpen                            150.0                 90.0   \n",
       "NightSkiing_ac                               550.0                  NaN   \n",
       "resorts_per_state                                3                    3   \n",
       "resorts_per_100kcapita                    0.410091             0.410091   \n",
       "resorts_per_100ksq_mile                   0.450867             0.450867   \n",
       "resort_skiable_area_ac_state_ratio         0.70614             0.280702   \n",
       "resort_days_open_state_ratio              0.434783             0.130435   \n",
       "resort_terrain_park_state_ratio                0.5                 0.25   \n",
       "resort_night_skiing_state_ratio           0.948276                  NaN   \n",
       "total_chairs_runs_ratio                   0.092105             0.111111   \n",
       "total_chairs_skiable_ratio                0.004348              0.00625   \n",
       "fastQuads_runs_ratio                      0.026316                  0.0   \n",
       "fastQuads_skiable_ratio                   0.001242                  0.0   \n",
       "\n",
       "                                                   2                 3  \\\n",
       "Name                                Hilltop Ski Area  Arizona Snowbowl   \n",
       "Region                                        Alaska           Arizona   \n",
       "state                                         Alaska           Arizona   \n",
       "summit_elev                                     2090             11500   \n",
       "vertical_drop                                    294              2300   \n",
       "base_elev                                       1796              9200   \n",
       "trams                                              0                 0   \n",
       "fastSixes                                          0                 1   \n",
       "fastQuads                                          0                 0   \n",
       "quad                                               0                 2   \n",
       "triple                                             1                 2   \n",
       "double                                             0                 1   \n",
       "surface                                            2                 2   \n",
       "total_chairs                                       3                 8   \n",
       "Runs                                            13.0              55.0   \n",
       "TerrainParks                                     1.0               4.0   \n",
       "LongestRun_mi                                    1.0               2.0   \n",
       "SkiableTerrain_ac                               30.0             777.0   \n",
       "Snow Making_ac                                  30.0             104.0   \n",
       "daysOpenLastYear                               150.0             122.0   \n",
       "yearsOpen                                       36.0              81.0   \n",
       "averageSnowfall                                 69.0             260.0   \n",
       "AdultWeekend                                    34.0              89.0   \n",
       "projectedDaysOpen                              152.0             122.0   \n",
       "NightSkiing_ac                                  30.0               NaN   \n",
       "resorts_per_state                                  3                 2   \n",
       "resorts_per_100kcapita                      0.410091          0.027477   \n",
       "resorts_per_100ksq_mile                     0.450867           1.75454   \n",
       "resort_skiable_area_ac_state_ratio          0.013158          0.492708   \n",
       "resort_days_open_state_ratio                0.434783          0.514768   \n",
       "resort_terrain_park_state_ratio                 0.25          0.666667   \n",
       "resort_night_skiing_state_ratio             0.051724               NaN   \n",
       "total_chairs_runs_ratio                     0.230769          0.145455   \n",
       "total_chairs_skiable_ratio                       0.1          0.010296   \n",
       "fastQuads_runs_ratio                             0.0               0.0   \n",
       "fastQuads_skiable_ratio                          0.0               0.0   \n",
       "\n",
       "                                                      4  \n",
       "Name                                Sunrise Park Resort  \n",
       "Region                                          Arizona  \n",
       "state                                           Arizona  \n",
       "summit_elev                                       11100  \n",
       "vertical_drop                                      1800  \n",
       "base_elev                                          9200  \n",
       "trams                                                 0  \n",
       "fastSixes                                             0  \n",
       "fastQuads                                             1  \n",
       "quad                                                  2  \n",
       "triple                                                3  \n",
       "double                                                1  \n",
       "surface                                               0  \n",
       "total_chairs                                          7  \n",
       "Runs                                               65.0  \n",
       "TerrainParks                                        2.0  \n",
       "LongestRun_mi                                       1.2  \n",
       "SkiableTerrain_ac                                 800.0  \n",
       "Snow Making_ac                                     80.0  \n",
       "daysOpenLastYear                                  115.0  \n",
       "yearsOpen                                          49.0  \n",
       "averageSnowfall                                   250.0  \n",
       "AdultWeekend                                       78.0  \n",
       "projectedDaysOpen                                 104.0  \n",
       "NightSkiing_ac                                     80.0  \n",
       "resorts_per_state                                     2  \n",
       "resorts_per_100kcapita                         0.027477  \n",
       "resorts_per_100ksq_mile                         1.75454  \n",
       "resort_skiable_area_ac_state_ratio             0.507292  \n",
       "resort_days_open_state_ratio                   0.485232  \n",
       "resort_terrain_park_state_ratio                0.333333  \n",
       "resort_night_skiing_state_ratio                     1.0  \n",
       "total_chairs_runs_ratio                        0.107692  \n",
       "total_chairs_skiable_ratio                      0.00875  \n",
       "fastQuads_runs_ratio                           0.015385  \n",
       "fastQuads_skiable_ratio                         0.00125  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A file already exists with this name.\n",
      "\n",
      "Do you want to overwrite? (Y/N)y\n",
      "Writing file.  \"../data/ski_data_step3_features.csv\"\n"
     ]
    }
   ],
   "source": [
    "# Save the data \n",
    "\n",
    "datapath = '../data'\n",
    "save_file(ski_data, 'ski_data_step3_features.csv', datapath)"
   ]
  }
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