{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Data wrangling<a id='2_Data_wrangling'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Contents<a id='2.1_Contents'></a>\n",
    "* [2 Data wrangling](#2_Data_wrangling)\n",
    "  * [2.1 Contents](#2.1_Contents)\n",
    "  * [2.2 Introduction](#2.2_Introduction)\n",
    "    * [2.2.1 Recap Of Data Science Problem](#2.2.1_Recap_Of_Data_Science_Problem)\n",
    "    * [2.2.2 Introduction To Notebook](#2.2.2_Introduction_To_Notebook)\n",
    "  * [2.3 Imports](#2.3_Imports)\n",
    "  * [2.4 Objectives](#2.4_Objectives)\n",
    "  * [2.5 Load The Ski Resort Data](#2.5_Load_The_Ski_Resort_Data)\n",
    "  * [2.6 Explore The Data](#2.6_Explore_The_Data)\n",
    "    * [2.6.1 Find Your Resort Of Interest](#2.6.1_Find_Your_Resort_Of_Interest)\n",
    "    * [2.6.2 Number Of Missing Values By Column](#2.6.2_Number_Of_Missing_Values_By_Column)\n",
    "    * [2.6.3 Categorical Features](#2.6.3_Categorical_Features)\n",
    "      * [2.6.3.1 Unique Resort Names](#2.6.3.1_Unique_Resort_Names)\n",
    "      * [2.6.3.2 Region And State](#2.6.3.2_Region_And_State)\n",
    "      * [2.6.3.3 Number of distinct regions and states](#2.6.3.3_Number_of_distinct_regions_and_states)\n",
    "      * [2.6.3.4 Distribution Of Resorts By Region And State](#2.6.3.4_Distribution_Of_Resorts_By_Region_And_State)\n",
    "      * [2.6.3.5 Distribution Of Ticket Price By State](#2.6.3.5_Distribution_Of_Ticket_Price_By_State)\n",
    "        * [2.6.3.5.1 Average weekend and weekday price by state](#2.6.3.5.1_Average_weekend_and_weekday_price_by_state)\n",
    "        * [2.6.3.5.2 Distribution of weekday and weekend price by state](#2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state)\n",
    "    * [2.6.4 Numeric Features](#2.6.4_Numeric_Features)\n",
    "      * [2.6.4.1 Numeric data summary](#2.6.4.1_Numeric_data_summary)\n",
    "      * [2.6.4.2 Distributions Of Feature Values](#2.6.4.2_Distributions_Of_Feature_Values)\n",
    "        * [2.6.4.2.1 SkiableTerrain_ac](#2.6.4.2.1_SkiableTerrain_ac)\n",
    "        * [2.6.4.2.2 Snow Making_ac](#2.6.4.2.2_Snow_Making_ac)\n",
    "        * [2.6.4.2.3 fastEight](#2.6.4.2.3_fastEight)\n",
    "        * [2.6.4.2.4 fastSixes and Trams](#2.6.4.2.4_fastSixes_and_Trams)\n",
    "  * [2.7 Derive State-wide Summary Statistics For Our Market Segment](#2.7_Derive_State-wide_Summary_Statistics_For_Our_Market_Segment)\n",
    "  * [2.8 Drop Rows With No Price Data](#2.8_Drop_Rows_With_No_Price_Data)\n",
    "  * [2.9 Review distributions](#2.9_Review_distributions)\n",
    "  * [2.10 Population data](#2.10_Population_data)\n",
    "  * [2.11 Target Feature](#2.11_Target_Feature)\n",
    "    * [2.11.1 Number Of Missing Values By Row - Resort](#2.11.1_Number_Of_Missing_Values_By_Row_-_Resort)\n",
    "  * [2.12 Save data](#2.12_Save_data)\n",
    "  * [2.13 Summary](#2.13_Summary)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Introduction<a id='2.2_Introduction'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This step focuses on collecting our data, organizing it, and ensuring it is well defined. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 Recap Of Data Science Problem<a id='2.2.1_Recap_Of_Data_Science_Problem'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our client is Big Mountain (a/k/a \"the Client\", \"the Company\"), a Montana-based ski resort. Management at Big Mountain suspects it may not be maximizing its return on investment (ROI) relative to its peers in the market. Furthermore, Company insiders also lack a strong sense of which facilities matter most to visitors, particularly ones for which skiers are likely to pay more. This project aims to build a predictive model for ticket price based on resort assets so as to assist the company in maximizing its ROI, while at the same time providing intel on which assets are most valued by customers. The latter will guide Big Mountain's capital expenditure decisions going forward."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 Introduction To Notebook<a id='2.2.2_Introduction_To_Notebook'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we try to use well structured, helpful headings that are frequently self-explanatory. We do our best to include brief notes after any results and highlight key takeaways."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Imports<a id='2.3_Imports'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per usual, we being with our imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "from library.sb_utils import save_file\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Objectives<a id='2.4_Objectives'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following are fundamental questions to resolve in this notebook before we move on.\n",
    "\n",
    "* Do we have the data we need to tackle the desired question?\n",
    "\n",
    "    * Have we identified the required target value?\n",
    "    * Do we have potentially useful features?\n",
    " \n",
    "* Do we have any fundamental issues with the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5 Load The Ski Resort Data<a id='2.5_Load_The_Ski_Resort_Data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ski_data = pd.read_csv('../raw_data/ski_resort_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we do an initial audit of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of                                   Name   Region    state  summit_elev  \\\n",
       "0                       Alyeska Resort   Alaska   Alaska         3939   \n",
       "1                  Eaglecrest Ski Area   Alaska   Alaska         2600   \n",
       "2                     Hilltop Ski Area   Alaska   Alaska         2090   \n",
       "3                     Arizona Snowbowl  Arizona  Arizona        11500   \n",
       "4                  Sunrise Park Resort  Arizona  Arizona        11100   \n",
       "..                                 ...      ...      ...          ...   \n",
       "325               Meadowlark Ski Lodge  Wyoming  Wyoming         9500   \n",
       "326          Sleeping Giant Ski Resort  Wyoming  Wyoming         7428   \n",
       "327                   Snow King Resort  Wyoming  Wyoming         7808   \n",
       "328  Snowy Range Ski & Recreation Area  Wyoming  Wyoming         9663   \n",
       "329                White Pine Ski Area  Wyoming  Wyoming         9500   \n",
       "\n",
       "     vertical_drop  base_elev  trams  fastEight  fastSixes  fastQuads  quad  \\\n",
       "0             2500        250      1        0.0          0          2     2   \n",
       "1             1540       1200      0        0.0          0          0     0   \n",
       "2              294       1796      0        0.0          0          0     0   \n",
       "3             2300       9200      0        0.0          1          0     2   \n",
       "4             1800       9200      0        NaN          0          1     2   \n",
       "..             ...        ...    ...        ...        ...        ...   ...   \n",
       "325           1000       8500      0        NaN          0          0     0   \n",
       "326            810       6619      0        0.0          0          0     0   \n",
       "327           1571       6237      0        NaN          0          0     1   \n",
       "328            990       8798      0        0.0          0          0     0   \n",
       "329           1100       8400      0        NaN          0          0     0   \n",
       "\n",
       "     triple  double  surface  total_chairs  Runs  TerrainParks  LongestRun_mi  \\\n",
       "0         0       0        2             7  76.0           2.0            1.0   \n",
       "1         0       4        0             4  36.0           1.0            2.0   \n",
       "2         1       0        2             3  13.0           1.0            1.0   \n",
       "3         2       1        2             8  55.0           4.0            2.0   \n",
       "4         3       1        0             7  65.0           2.0            1.2   \n",
       "..      ...     ...      ...           ...   ...           ...            ...   \n",
       "325       1       1        1             3  14.0           1.0            1.5   \n",
       "326       1       1        1             3  48.0           1.0            1.0   \n",
       "327       1       1        0             3  32.0           2.0            1.0   \n",
       "328       1       3        1             5  33.0           2.0            0.7   \n",
       "329       2       0        0             2  25.0           NaN            0.4   \n",
       "\n",
       "     SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
       "0               1610.0           113.0             150.0       60.0   \n",
       "1                640.0            60.0              45.0       44.0   \n",
       "2                 30.0            30.0             150.0       36.0   \n",
       "3                777.0           104.0             122.0       81.0   \n",
       "4                800.0            80.0             115.0       49.0   \n",
       "..                 ...             ...               ...        ...   \n",
       "325              300.0             NaN               NaN        9.0   \n",
       "326              184.0            18.0              61.0       81.0   \n",
       "327              400.0           250.0             121.0       80.0   \n",
       "328               75.0            30.0             131.0       59.0   \n",
       "329              370.0             NaN               NaN       81.0   \n",
       "\n",
       "     averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
       "0              669.0          65.0          85.0              150.0   \n",
       "1              350.0          47.0          53.0               90.0   \n",
       "2               69.0          30.0          34.0              152.0   \n",
       "3              260.0          89.0          89.0              122.0   \n",
       "4              250.0          74.0          78.0              104.0   \n",
       "..               ...           ...           ...                ...   \n",
       "325              NaN           NaN           NaN                NaN   \n",
       "326            310.0          42.0          42.0               77.0   \n",
       "327            300.0          59.0          59.0              123.0   \n",
       "328            250.0          49.0          49.0                NaN   \n",
       "329            150.0           NaN          49.0                NaN   \n",
       "\n",
       "     NightSkiing_ac  \n",
       "0             550.0  \n",
       "1               NaN  \n",
       "2              30.0  \n",
       "3               NaN  \n",
       "4              80.0  \n",
       "..              ...  \n",
       "325             NaN  \n",
       "326             NaN  \n",
       "327           110.0  \n",
       "328             NaN  \n",
       "329             NaN  \n",
       "\n",
       "[330 rows x 27 columns]>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output above tells us we have a multi-state data set with various data types, in addition to a number of missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are working with data from multiple states, let's determine which states dominate the market for snow skiing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New York          33\n",
       "Michigan          29\n",
       "Colorado          22\n",
       "California        21\n",
       "Pennsylvania      19\n",
       "New Hampshire     16\n",
       "Wisconsin         16\n",
       "Vermont           15\n",
       "Minnesota         14\n",
       "Utah              13\n",
       "Idaho             12\n",
       "Montana           12\n",
       "Massachusetts     11\n",
       "Washington        10\n",
       "Oregon            10\n",
       "New Mexico         9\n",
       "Maine              9\n",
       "Wyoming            8\n",
       "North Carolina     6\n",
       "Ohio               5\n",
       "Connecticut        5\n",
       "Nevada             4\n",
       "West Virginia      4\n",
       "Illinois           4\n",
       "Virginia           4\n",
       "Alaska             3\n",
       "Iowa               3\n",
       "Indiana            2\n",
       "New Jersey         2\n",
       "Arizona            2\n",
       "Missouri           2\n",
       "South Dakota       2\n",
       "Tennessee          1\n",
       "Rhode Island       1\n",
       "Maryland           1\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data['state'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New York, Michigan, and Colorado are our top three markets for ski resorts. Montana, the state in which our target resort is located, ranks 12th."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set contains two ticket prices: `AdultWeekday` and `AdultWeekend`. Returning to our fundamental question of whether or not we have identified our target variable, we can only say, no, not quite yet. We have, however, narrowed it down to two candidates. We will revisit -- and definitively answer -- this question after reviewing more of our data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output below indicates our ski-resort data are well organized. We have plausible column headings and can see one of the many missing values noted earlier. \n",
    "\n",
    "Aside from the `AdultWeekday` and `AdultWeekend` data, the remaining columns are potential \"features\" (i.e., independent variables) we might include in a statistical/machine learning model. But are all of the features useful? It's difficult to tell how many features will be useful to us at this stage, however, the data look promising. For example, the `vertical_drop` at a resort would be of interest to any prospective skier. Intuitively, we know that high drops should be more popular among seasoned skiers than lower drops. `SkiableTerrain_ac` is manifestly important because it measures the acreage available for skiing. `NightSkiing_ac` measures the amount of acreage available for skiing during a particular part of the day, which implies some resorts may offer guests the opportunity to extend their ski day into the night. Resorts with more skiing acreage and skiing opportunities should be preferable to low acreage resorts that offer only daytime skiing. So, looking at these three variables alone, high numbers are preferred to lower ones. Hence, resorts offering large vertical drops, ample skiing terrain, and the opportunity to ski at night should command a price premium relative to resorts that do not.\n",
    "\n",
    "We examined all features in the manner described above, asking ourselves if it was reasonable to assume any given variable could affect ticket price(s). We ultimately concluded all features could be potentially useful and should be retained for further analysis.  \n",
    "\n",
    "While the number of features used in any model will likely be lower than the total number of features available to us, we have answered one of our aforementioned fundamental questions: We have the data we need to answer our research question. Furthermore, should we find ourselves wanting for a piece of information, we can always supplement what we have with external data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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       "    .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>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>fastEight</th>\n",
       "      <th>fastSixes</th>\n",
       "      <th>fastQuads</th>\n",
       "      <th>quad</th>\n",
       "      <th>triple</th>\n",
       "      <th>double</th>\n",
       "      <th>surface</th>\n",
       "      <th>total_chairs</th>\n",
       "      <th>Runs</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>AdultWeekday</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>76.0</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>65.0</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>36.0</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>47.0</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>13.0</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>30.0</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>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>55.0</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>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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>65.0</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>74.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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  fastEight  fastSixes  fastQuads  quad  triple  double  \\\n",
       "0        250      1        0.0          0          2     2       0       0   \n",
       "1       1200      0        0.0          0          0     0       0       4   \n",
       "2       1796      0        0.0          0          0     0       1       0   \n",
       "3       9200      0        0.0          1          0     2       2       1   \n",
       "4       9200      0        NaN          0          1     2       3       1   \n",
       "\n",
       "   surface  total_chairs  Runs  TerrainParks  LongestRun_mi  \\\n",
       "0        2             7  76.0           2.0            1.0   \n",
       "1        0             4  36.0           1.0            2.0   \n",
       "2        2             3  13.0           1.0            1.0   \n",
       "3        2             8  55.0           4.0            2.0   \n",
       "4        0             7  65.0           2.0            1.2   \n",
       "\n",
       "   SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
       "0             1610.0           113.0             150.0       60.0   \n",
       "1              640.0            60.0              45.0       44.0   \n",
       "2               30.0            30.0             150.0       36.0   \n",
       "3              777.0           104.0             122.0       81.0   \n",
       "4              800.0            80.0             115.0       49.0   \n",
       "\n",
       "   averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
       "0            669.0          65.0          85.0              150.0   \n",
       "1            350.0          47.0          53.0               90.0   \n",
       "2             69.0          30.0          34.0              152.0   \n",
       "3            260.0          89.0          89.0              122.0   \n",
       "4            250.0          74.0          78.0              104.0   \n",
       "\n",
       "   NightSkiing_ac  \n",
       "0           550.0  \n",
       "1             NaN  \n",
       "2            30.0  \n",
       "3             NaN  \n",
       "4            80.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns = None\n",
    "ski_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.6 Explore The Data<a id='2.6_Explore_The_Data'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.1 Finding Our Resort Of Interest<a id='2.6.1_Find_Our_Resort_Of_Interest'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our resort of interest is Big Mountain Resort. Let's explore the data we have on the property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\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>151</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Big Mountain Resort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>Montana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Montana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>6817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>2353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>4464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastEight</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   151\n",
       "Name               Big Mountain Resort\n",
       "Region                         Montana\n",
       "state                          Montana\n",
       "summit_elev                       6817\n",
       "vertical_drop                     2353\n",
       "base_elev                         4464\n",
       "trams                                0\n",
       "fastEight                          0.0\n",
       "fastSixes                            0\n",
       "fastQuads                            3\n",
       "quad                                 2\n",
       "triple                               6\n",
       "double                               0\n",
       "surface                              3\n",
       "total_chairs                        14\n",
       "Runs                             105.0\n",
       "TerrainParks                       4.0\n",
       "LongestRun_mi                      3.3\n",
       "SkiableTerrain_ac               3000.0\n",
       "Snow Making_ac                   600.0\n",
       "daysOpenLastYear                 123.0\n",
       "yearsOpen                         72.0\n",
       "averageSnowfall                  333.0\n",
       "AdultWeekday                      81.0\n",
       "AdultWeekend                      81.0\n",
       "projectedDaysOpen                123.0\n",
       "NightSkiing_ac                   600.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[ski_data.Name == 'Big Mountain Resort'].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our resort doesn't appear to have any missing values, which is terrific! Missing values do, however, exist in our data frame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.2 Number Of Missing Values By Column<a id='2.6.2_Number_Of_Missing_Values_By_Column'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counting the number of missing values in each column and sorting them could help us determine if a feature should be kept or eliminated from our data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>166</td>\n",
       "      <td>50.303030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>143</td>\n",
       "      <td>43.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>54</td>\n",
       "      <td>16.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>51</td>\n",
       "      <td>15.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
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       "      <th>TerrainParks</th>\n",
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       "      <td>14.242424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>46</td>\n",
       "      <td>13.939394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>14</td>\n",
       "      <td>4.242424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>5</td>\n",
       "      <td>1.515152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>4</td>\n",
       "      <td>1.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>3</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>quad</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count          %\n",
       "fastEight            166  50.303030\n",
       "NightSkiing_ac       143  43.333333\n",
       "AdultWeekday          54  16.363636\n",
       "AdultWeekend          51  15.454545\n",
       "daysOpenLastYear      51  15.454545\n",
       "TerrainParks          51  15.454545\n",
       "projectedDaysOpen     47  14.242424\n",
       "Snow Making_ac        46  13.939394\n",
       "averageSnowfall       14   4.242424\n",
       "LongestRun_mi          5   1.515152\n",
       "Runs                   4   1.212121\n",
       "SkiableTerrain_ac      3   0.909091\n",
       "yearsOpen              1   0.303030\n",
       "total_chairs           0   0.000000\n",
       "Name                   0   0.000000\n",
       "Region                 0   0.000000\n",
       "double                 0   0.000000\n",
       "triple                 0   0.000000\n",
       "quad                   0   0.000000\n",
       "fastQuads              0   0.000000\n",
       "fastSixes              0   0.000000\n",
       "trams                  0   0.000000\n",
       "base_elev              0   0.000000\n",
       "vertical_drop          0   0.000000\n",
       "summit_elev            0   0.000000\n",
       "state                  0   0.000000\n",
       "surface                0   0.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = pd.concat([ski_data.isnull().sum(), 100*ski_data.isnull().mean()], axis = 1)\n",
    "missing.columns=['count', '%']\n",
    "missing.sort_values(by='%', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`fastEight` is missing the most values, at just over 50%. Our potential target variables, `AdultWeekend` and `AdultWeekday`, are not immune to the problem of missing values. 15% to 16% of ticket prices are missing values. `AdultWeekday` is missing in a few more data points than `AdultWeekend`. \n",
    "\n",
    "A natural question to ask is whether there is any overlap between the sets of missing `AdultWeekend` and `AdultWeekday` tickets. Restated, are any resorts missing prices for both types of tickets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47 resorts are missing both AdultWeekend and AdultWeekDay prices. Only one such resort is in Montana.\n"
     ]
    }
   ],
   "source": [
    "print(str(len(ski_data[(ski_data.AdultWeekday.isnull()) & (ski_data.AdultWeekend.isnull())])) + \" resorts are missing both AdultWeekend and AdultWeekDay prices. Only one such resort is in Montana.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "47 resorts are missing entries for both types of ticket prices. Is there any other missing data, which is often coded with unusual or extreme values like -1 or 999.  As shown below, none of our data are coded as such. We are now confident we have a grasp on how much data is missing.  Note that, in this data frame, a feature coded as 0 could make sense. Hence, we've opted not to assume that zeros are a proxy for missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [Name, Region, state, summit_elev, vertical_drop, base_elev, trams, fastEight, fastSixes, fastQuads, quad, triple, double, surface, total_chairs, Runs, TerrainParks, LongestRun_mi, SkiableTerrain_ac, Snow Making_ac, daysOpenLastYear, yearsOpen, averageSnowfall, AdultWeekday, AdultWeekend, projectedDaysOpen, NightSkiing_ac]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " ski_data[np.isin(ski_data, [-1, 999]).any(axis=1)].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.3 Categorical Features<a id='2.6.3_Categorical_Features'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus far, we've examined only the numeric features. We'll now turn our attention to categorical data such as resort `Name` and `state`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical data is often coded as an `object`. Hence, gathering together the object columns in our feature space is a reasonable place to begin our investigation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Name</th>\n",
       "      <th>Region</th>\n",
       "      <th>state</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Eaglecrest Ski Area</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hilltop Ski Area</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona Snowbowl</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunrise Park Resort</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>Meadowlark Ski Lodge</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Sleeping Giant Ski Resort</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Snow King Resort</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Snowy Range Ski &amp; Recreation Area</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>White Pine Ski Area</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Name   Region    state\n",
       "0                       Alyeska Resort   Alaska   Alaska\n",
       "1                  Eaglecrest Ski Area   Alaska   Alaska\n",
       "2                     Hilltop Ski Area   Alaska   Alaska\n",
       "3                     Arizona Snowbowl  Arizona  Arizona\n",
       "4                  Sunrise Park Resort  Arizona  Arizona\n",
       "..                                 ...      ...      ...\n",
       "325               Meadowlark Ski Lodge  Wyoming  Wyoming\n",
       "326          Sleeping Giant Ski Resort  Wyoming  Wyoming\n",
       "327                   Snow King Resort  Wyoming  Wyoming\n",
       "328  Snowy Range Ski & Recreation Area  Wyoming  Wyoming\n",
       "329                White Pine Ski Area  Wyoming  Wyoming\n",
       "\n",
       "[330 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find columns of type 'object'\n",
    "ski_data.select_dtypes('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We saw earlier that these three columns had no missing values. Other issues, however, could be present.\n",
    "\n",
    "* Is `Name` always (or at least a combination of Name/Region/State) unique?\n",
    "* Is `Region` always the same as `state`?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.3.1 Unique Resort Names<a id='2.6.3.1_Unique_Resort_Names'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to know if all resort names are unique. Duplicate names could result in double counting of values, which could lead us to draw erroneous conclusions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Crystal Mountain    2\n",
       "Alyeska Resort      1\n",
       "Brandywine          1\n",
       "Boston Mills        1\n",
       "Alpine Valley       1\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find duplicate resort names\n",
    "ski_data['Name'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All resort names are not unique. We see there are two resorts named Crystal Mountain. This warrants further investigation. We'll use our `Region` and `state` data to broaden our understanding of what we are witnessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alyeska Resort, Alaska    1\n",
       "Snow Trails, Ohio         1\n",
       "Brandywine, Ohio          1\n",
       "Boston Mills, Ohio        1\n",
       "Alpine Valley, Ohio       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n",
    "(ski_data['Name'] + ', ' + ski_data['Region']).value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `value_counts()` sorts in descending order, our application of the `head()` method, which returns the first five rows of a data frame, implies the rest of the counts must also be 1. Consequently, we know there is only one Crystal Lake in any given region. But what about a view of the data from the state level?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two Crystal Mountain resorts are in different states. We see this is the case in the output below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>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>fastEight</th>\n",
       "      <th>fastSixes</th>\n",
       "      <th>fastQuads</th>\n",
       "      <th>quad</th>\n",
       "      <th>triple</th>\n",
       "      <th>double</th>\n",
       "      <th>surface</th>\n",
       "      <th>total_chairs</th>\n",
       "      <th>Runs</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>AdultWeekday</th>\n",
       "      <th>AdultWeekend</th>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <th>NightSkiing_ac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Crystal Mountain</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>1132</td>\n",
       "      <td>375</td>\n",
       "      <td>757</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>102.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Crystal Mountain</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Washington</td>\n",
       "      <td>7012</td>\n",
       "      <td>3100</td>\n",
       "      <td>4400</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name      Region       state  summit_elev  vertical_drop  \\\n",
       "104  Crystal Mountain    Michigan    Michigan         1132            375   \n",
       "295  Crystal Mountain  Washington  Washington         7012           3100   \n",
       "\n",
       "     base_elev  trams  fastEight  fastSixes  fastQuads  quad  triple  double  \\\n",
       "104        757      0        0.0          0          1     3       2       0   \n",
       "295       4400      1        NaN          2          2     1       2       2   \n",
       "\n",
       "     surface  total_chairs  Runs  TerrainParks  LongestRun_mi  \\\n",
       "104        2             8  58.0           3.0            0.3   \n",
       "295        0            10  57.0           1.0            2.5   \n",
       "\n",
       "     SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
       "104              102.0            96.0             120.0       63.0   \n",
       "295             2600.0            10.0               NaN       57.0   \n",
       "\n",
       "     averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
       "104            132.0          54.0          64.0              135.0   \n",
       "295            486.0          99.0          99.0                NaN   \n",
       "\n",
       "     NightSkiing_ac  \n",
       "104            56.0  \n",
       "295             NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[ski_data['Name'] == 'Crystal Mountain']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The duplicated resort name was not an error. There are two distinct resorts with the same name that reside in different states and regions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.3.2 Region And State<a id='2.6.3.2_Region_And_State'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The meaning of `Region` is not immediately obvious. Are regions the same as states? What's the relationship between a `Region` and a `state`? In how many cases do the two variables differ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A resort's region and state are different 33 times.\n",
      "Hence, the answer to the question posed above is no: Regions and states are not always the same.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the number of times Region does not equal state\n",
    "print(\"A resort's region and state are different \" + str((ski_data.Region != ski_data.state).sum()) + ' times.')\n",
    "print('Hence, the answer to the question posed above is no: Regions and states are not always the same.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we tabulate the number of distinct regions, in search of insight into how 1) the term region is defined and 2) where the regions can be found. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New York               33\n",
       "Michigan               29\n",
       "Sierra Nevada          22\n",
       "Colorado               22\n",
       "Pennsylvania           19\n",
       "Wisconsin              16\n",
       "New Hampshire          16\n",
       "Vermont                15\n",
       "Minnesota              14\n",
       "Idaho                  12\n",
       "Montana                12\n",
       "Massachusetts          11\n",
       "Washington             10\n",
       "New Mexico              9\n",
       "Maine                   9\n",
       "Wyoming                 8\n",
       "Utah                    7\n",
       "Salt Lake City          6\n",
       "North Carolina          6\n",
       "Oregon                  6\n",
       "Connecticut             5\n",
       "Ohio                    5\n",
       "Virginia                4\n",
       "West Virginia           4\n",
       "Illinois                4\n",
       "Mt. Hood                4\n",
       "Alaska                  3\n",
       "Iowa                    3\n",
       "South Dakota            2\n",
       "Arizona                 2\n",
       "Nevada                  2\n",
       "Missouri                2\n",
       "Indiana                 2\n",
       "New Jersey              2\n",
       "Rhode Island            1\n",
       "Tennessee               1\n",
       "Maryland                1\n",
       "Northern California     1\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data['Region'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A casual inspection of the data reveals some non-state names such as Sierra Nevada, Salt Lake City, and Northern California. From this, we can infer that states can contain one or more regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state       Region             \n",
       "California  Sierra Nevada          20\n",
       "            Northern California     1\n",
       "Nevada      Sierra Nevada           2\n",
       "Oregon      Mt. Hood                4\n",
       "Utah        Salt Lake City          6\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ski_data[ski_data.Region != ski_data.state].groupby('state')['Region'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further digging reveals that California has the most regions: 20 are called Sierra Nevada and just 1 is referred to as Northern California."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.3.3 Number of distinct regions and states<a id='2.6.3.3_Number_of_distinct_regions_and_states'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because a few states are partitioned into multiple regions, there are slightly more unique regions than states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region    38\n",
       "state     35\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[['Region', 'state']].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.3.4 Distribution Of Resorts By Region And State<a id='2.6.3.4_Distribution_Of_Resorts_By_Region_And_State'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we examine where our resorts are located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create two subplots on 1 row and 2 columns with a figsize of (12, 8)\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12,8))\n",
    "\n",
    "# Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
    "ski_data.Region.value_counts().plot(kind='barh', ax=ax[0])\n",
    "\n",
    "# Give the plot a helpful title of 'Region'\n",
    "ax[0].set_title('Region', fontsize=15)\n",
    "\n",
    "# Label the xaxis 'Count'\n",
    "ax[0].set_xlabel('Count', fontsize=15)\n",
    "\n",
    "# Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
    "ski_data.state.value_counts().plot(kind='barh', ax=ax[1])\n",
    "\n",
    "# Give the plot a helpful title of 'state'\n",
    "ax[1].set_title('State', fontsize=15)\n",
    "\n",
    "# Label the xaxis 'Count'\n",
    "ax[1].set_xlabel('Count', fontsize=15)\n",
    "\n",
    "# Change label size inspired by https://stackoverflow.com/questions/6390393\n",
    "ax[0].tick_params(axis='y', which='major', labelsize=13)\n",
    "ax[1].tick_params(axis='y', which='major', labelsize=13)\n",
    "\n",
    "# Give the subplots a little \"breathing room\" with a wspace of 0.5\n",
    "plt.subplots_adjust(wspace=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, New York accounts for the majority of resorts. Our client's resort is in Montana, which ranks 11th by region and 12th by state. We should think carefully about how, or whether, we use this information. Does New York command a premium because of its proximity to a large population center? Even if a resort's `State` were a useful predictor of ticket price, our main interest lies in Montana. Would we want a model that is skewed for accuracy by New York? Should we just filter for Montana and create a Montana-specific model? This would slash our available data volume. It's impossible to resolve this issue at present. We will circle back to it later in our study and stick to data wrangling (a/k/a \"cleaning\") for the moment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.3.5 Distribution Of Ticket Price By State<a id='2.6.3.5_Distribution_Of_Ticket_Price_By_State'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our primary focus is Big Mountain resort in Montana. A salient question to ask is whether or not the state in which a resort is located gives us any clue as to what our primary target variable should be (weekend or weekday ticket prices)? Restated, what do `AdultWeekday` and `AdultWeekend` prices look like across state lines?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.3.5.1 Average weekend and weekday price by state<a id='2.6.3.5.1_Average_weekend_and_weekday_price_by_state'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick glance at average ticket prices across states for both `AdultWeekday` and `AdultWeekend` reveals weekday prices are consistently lower than weekend prices, with few exceptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>AdultWeekday</th>\n",
       "      <th>AdultWeekend</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>47.333333</td>\n",
       "      <td>57.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>81.500000</td>\n",
       "      <td>83.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>78.214286</td>\n",
       "      <td>81.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>90.714286</td>\n",
       "      <td>90.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>47.800000</td>\n",
       "      <td>56.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>56.555556</td>\n",
       "      <td>55.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>43.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>48.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>35.666667</td>\n",
       "      <td>41.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>51.500000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>40.900000</td>\n",
       "      <td>57.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>45.458333</td>\n",
       "      <td>52.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>44.595714</td>\n",
       "      <td>49.667143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>51.909091</td>\n",
       "      <td>51.909091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AdultWeekday  AdultWeekend\n",
       "state                                    \n",
       "Alaska            47.333333     57.333333\n",
       "Arizona           81.500000     83.500000\n",
       "California        78.214286     81.416667\n",
       "Colorado          90.714286     90.714286\n",
       "Connecticut       47.800000     56.800000\n",
       "Idaho             56.555556     55.900000\n",
       "Illinois          35.000000     43.333333\n",
       "Indiana           45.000000     48.500000\n",
       "Iowa              35.666667     41.666667\n",
       "Maine             51.500000     61.000000\n",
       "Maryland          59.000000     79.000000\n",
       "Massachusetts     40.900000     57.200000\n",
       "Michigan          45.458333     52.576923\n",
       "Minnesota         44.595714     49.667143\n",
       "Missouri          43.000000     48.000000\n",
       "Montana           51.909091     51.909091"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate average weekday and weekend price by state and sort by the average of the two\n",
    "state_price_means = ski_data.groupby('state')[['AdultWeekday', 'AdultWeekend']].mean()\n",
    "state_price_means.head(16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we present a visualization of the tabular data above. This will further illuminate the relationship between `AdultWeekday` and `AdultWeekend` prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n",
    "    .sort_values(ascending=False)\n",
    "    .index)\n",
    "    .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n",
    "plt.xlabel('Price ($)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above represents a data frame with two columns: one for the average price of each kind of ticket. This tells us how average ticket prices varies from state to state. We can, however, get more insight into the difference in the distributions between states."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.3.5.2 Distribution of weekday and weekend price by state<a id='2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we transform (i.e., melt) our data frame into one in which there is a single column for price with a new categorical column that represents the ticket type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "ticket_prices = pd.melt(ski_data[['state', 'AdultWeekday', 'AdultWeekend']], id_vars='state', var_name='Ticket', value_vars=['AdultWeekday','AdultWeekend'], value_name='Price')"
   ]
  },
  {
   "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>state</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state        Ticket  Price\n",
       "0   Alaska  AdultWeekday   65.0\n",
       "1   Alaska  AdultWeekday   47.0\n",
       "2   Alaska  AdultWeekday   30.0\n",
       "3  Arizona  AdultWeekday   89.0\n",
       "4  Arizona  AdultWeekday   74.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket_prices.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our ticket price and state data are now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type by state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(x='state', y='Price', hue='Ticket', data=ticket_prices)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('Price ($)')\n",
    "plt.xlabel('State')\n",
    "plt.title('Distribution of Adult Weekend and Adult Weekday Ticket Prices by State');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around $25 to $125. Some states show more variability than others. Montana and South Dakota, for example, both show fairly little price variance. Nevada and Utah, on the other hand, exhibit significant price variability.\n",
    "\n",
    "This exploration returns us to one of our fundamental questions: Do we have a target variable? We could model one ticket price, both prices, or use the difference between the two as a feature. Furthermore, how should we use our `State` data? There are several options including the following:\n",
    "\n",
    "* disregard `State` completely\n",
    "* retain all `State` information\n",
    "* retain `State` in the form of Montana vs not Montana, as our target resort is in Montana\n",
    "* examine states that abut Montana only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.4 Numeric Features<a id='2.6.4_Numeric_Features'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we turn our attention to cleaning numeric features not discussed earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.4.1 Numeric data summary<a id='2.6.4.1_Numeric_data_summary'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we present a statistical summary of numeric features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>330.0</td>\n",
       "      <td>4591.818182</td>\n",
       "      <td>3735.535934</td>\n",
       "      <td>315.0</td>\n",
       "      <td>1403.75</td>\n",
       "      <td>3127.5</td>\n",
       "      <td>7806.00</td>\n",
       "      <td>13487.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>330.0</td>\n",
       "      <td>1215.427273</td>\n",
       "      <td>947.864557</td>\n",
       "      <td>60.0</td>\n",
       "      <td>461.25</td>\n",
       "      <td>964.5</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>4425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>330.0</td>\n",
       "      <td>3374.000000</td>\n",
       "      <td>3117.121621</td>\n",
       "      <td>70.0</td>\n",
       "      <td>869.00</td>\n",
       "      <td>1561.5</td>\n",
       "      <td>6325.25</td>\n",
       "      <td>10800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>330.0</td>\n",
       "      <td>0.172727</td>\n",
       "      <td>0.559946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastEight</th>\n",
       "      <td>164.0</td>\n",
       "      <td>0.006098</td>\n",
       "      <td>0.078087</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>330.0</td>\n",
       "      <td>0.184848</td>\n",
       "      <td>0.651685</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>330.0</td>\n",
       "      <td>1.018182</td>\n",
       "      <td>2.198294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>330.0</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.312245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>330.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.619130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>330.0</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.815028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>330.0</td>\n",
       "      <td>2.621212</td>\n",
       "      <td>2.059636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>330.0</td>\n",
       "      <td>8.266667</td>\n",
       "      <td>5.798683</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>326.0</td>\n",
       "      <td>48.214724</td>\n",
       "      <td>46.364077</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>33.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>279.0</td>\n",
       "      <td>2.820789</td>\n",
       "      <td>2.008113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>325.0</td>\n",
       "      <td>1.433231</td>\n",
       "      <td>1.156171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>327.0</td>\n",
       "      <td>739.801223</td>\n",
       "      <td>1816.167441</td>\n",
       "      <td>8.0</td>\n",
       "      <td>85.00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>690.00</td>\n",
       "      <td>26819.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>284.0</td>\n",
       "      <td>174.873239</td>\n",
       "      <td>261.336125</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.50</td>\n",
       "      <td>3379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>279.0</td>\n",
       "      <td>115.103943</td>\n",
       "      <td>35.063251</td>\n",
       "      <td>3.0</td>\n",
       "      <td>97.00</td>\n",
       "      <td>114.0</td>\n",
       "      <td>135.00</td>\n",
       "      <td>305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>329.0</td>\n",
       "      <td>63.656535</td>\n",
       "      <td>109.429928</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>316.0</td>\n",
       "      <td>185.316456</td>\n",
       "      <td>136.356842</td>\n",
       "      <td>18.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>150.0</td>\n",
       "      <td>300.00</td>\n",
       "      <td>669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>276.0</td>\n",
       "      <td>57.916957</td>\n",
       "      <td>26.140126</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>71.00</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>279.0</td>\n",
       "      <td>64.166810</td>\n",
       "      <td>24.554584</td>\n",
       "      <td>17.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>77.50</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>283.0</td>\n",
       "      <td>120.053004</td>\n",
       "      <td>31.045963</td>\n",
       "      <td>30.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>120.0</td>\n",
       "      <td>139.50</td>\n",
       "      <td>305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>187.0</td>\n",
       "      <td>100.395722</td>\n",
       "      <td>105.169620</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>72.0</td>\n",
       "      <td>114.00</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean          std    min      25%     50%  \\\n",
       "summit_elev        330.0  4591.818182  3735.535934  315.0  1403.75  3127.5   \n",
       "vertical_drop      330.0  1215.427273   947.864557   60.0   461.25   964.5   \n",
       "base_elev          330.0  3374.000000  3117.121621   70.0   869.00  1561.5   \n",
       "trams              330.0     0.172727     0.559946    0.0     0.00     0.0   \n",
       "fastEight          164.0     0.006098     0.078087    0.0     0.00     0.0   \n",
       "fastSixes          330.0     0.184848     0.651685    0.0     0.00     0.0   \n",
       "fastQuads          330.0     1.018182     2.198294    0.0     0.00     0.0   \n",
       "quad               330.0     0.933333     1.312245    0.0     0.00     0.0   \n",
       "triple             330.0     1.500000     1.619130    0.0     0.00     1.0   \n",
       "double             330.0     1.833333     1.815028    0.0     1.00     1.0   \n",
       "surface            330.0     2.621212     2.059636    0.0     1.00     2.0   \n",
       "total_chairs       330.0     8.266667     5.798683    0.0     5.00     7.0   \n",
       "Runs               326.0    48.214724    46.364077    3.0    19.00    33.0   \n",
       "TerrainParks       279.0     2.820789     2.008113    1.0     1.00     2.0   \n",
       "LongestRun_mi      325.0     1.433231     1.156171    0.0     0.50     1.0   \n",
       "SkiableTerrain_ac  327.0   739.801223  1816.167441    8.0    85.00   200.0   \n",
       "Snow Making_ac     284.0   174.873239   261.336125    2.0    50.00   100.0   \n",
       "daysOpenLastYear   279.0   115.103943    35.063251    3.0    97.00   114.0   \n",
       "yearsOpen          329.0    63.656535   109.429928    6.0    50.00    58.0   \n",
       "averageSnowfall    316.0   185.316456   136.356842   18.0    69.00   150.0   \n",
       "AdultWeekday       276.0    57.916957    26.140126   15.0    40.00    50.0   \n",
       "AdultWeekend       279.0    64.166810    24.554584   17.0    47.00    60.0   \n",
       "projectedDaysOpen  283.0   120.053004    31.045963   30.0   100.00   120.0   \n",
       "NightSkiing_ac     187.0   100.395722   105.169620    2.0    40.00    72.0   \n",
       "\n",
       "                       75%      max  \n",
       "summit_elev        7806.00  13487.0  \n",
       "vertical_drop      1800.00   4425.0  \n",
       "base_elev          6325.25  10800.0  \n",
       "trams                 0.00      4.0  \n",
       "fastEight             0.00      1.0  \n",
       "fastSixes             0.00      6.0  \n",
       "fastQuads             1.00     15.0  \n",
       "quad                  1.00      8.0  \n",
       "triple                2.00      8.0  \n",
       "double                3.00     14.0  \n",
       "surface               3.00     15.0  \n",
       "total_chairs         10.00     41.0  \n",
       "Runs                 60.00    341.0  \n",
       "TerrainParks          4.00     14.0  \n",
       "LongestRun_mi         2.00      6.0  \n",
       "SkiableTerrain_ac   690.00  26819.0  \n",
       "Snow Making_ac      200.50   3379.0  \n",
       "daysOpenLastYear    135.00    305.0  \n",
       "yearsOpen            69.00   2019.0  \n",
       "averageSnowfall     300.00    669.0  \n",
       "AdultWeekday         71.00    179.0  \n",
       "AdultWeekend         77.50    179.0  \n",
       "projectedDaysOpen   139.50    305.0  \n",
       "NightSkiing_ac      114.00    650.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall, we're missing the ticket prices for ~16% of resorts. This will require us to drop those records. Still, we may have a weekend price and not a weekday price or vice versa.  For now, we want to keep any price data we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    82.424242\n",
       "2    14.242424\n",
       "1     3.333333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
    "missing_price.value_counts()/len(missing_price) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just over 82% of resorts have no missing ticket prices, 3% are missing one price, and 14% are missing both. Ultimately, we will drop the records for which we have no price information, however, we will not do so until we discern whether, or not, useful information about the distributions of other features lies in that 14% of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.4.2 Distributions Of Feature Values<a id='2.6.4.2_Distributions_Of_Feature_Values'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we are still in the 'data wrangling' phase of our study, rather than exploratory data analysis, looking at feature distributions is warranted. Here, we examine whether distributions look plausible or wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# `hist` method used to plot histograms of each of the numeric features w/ figsize and space between plots specified\n",
    "ski_data.hist(figsize=(15,10))\n",
    "plt.subplots_adjust(hspace=.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What features do we have possible cause for concern about and why?\n",
    "\n",
    "* `SkiableTerrain_ac` because values are clustered down the low end,\n",
    "* `Snow Making_ac` for the same reason,\n",
    "* `fastEight` because all but one value is 0 so it has very little variance, and half the values are missing,\n",
    "* `fastSixes` raises a red flag; it has more variability, but still mostly 0,\n",
    "* `trams` also may get a red flag for the same reason,\n",
    "* `yearsOpen` because most values are low but it has a maximum of 2019, which strongly suggests someone recorded a calendar year rather than number of years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.4.2.1 SkiableTerrain_ac<a id='2.6.4.2.1_SkiableTerrain_ac'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of skiable terrain looks odd. As it so happens, one resort has a massive amount of skiable terrain: Silverton Mountain, Colorado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>39</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Silverton Mountain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>Colorado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Colorado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>13487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>3087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>10400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastEight</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>26819.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   39\n",
       "Name               Silverton Mountain\n",
       "Region                       Colorado\n",
       "state                        Colorado\n",
       "summit_elev                     13487\n",
       "vertical_drop                    3087\n",
       "base_elev                       10400\n",
       "trams                               0\n",
       "fastEight                         0.0\n",
       "fastSixes                           0\n",
       "fastQuads                           0\n",
       "quad                                0\n",
       "triple                              0\n",
       "double                              1\n",
       "surface                             0\n",
       "total_chairs                        1\n",
       "Runs                              NaN\n",
       "TerrainParks                      NaN\n",
       "LongestRun_mi                     1.5\n",
       "SkiableTerrain_ac             26819.0\n",
       "Snow Making_ac                    NaN\n",
       "daysOpenLastYear                175.0\n",
       "yearsOpen                        17.0\n",
       "averageSnowfall                 400.0\n",
       "AdultWeekday                     79.0\n",
       "AdultWeekend                     79.0\n",
       "projectedDaysOpen               181.0\n",
       "NightSkiing_ac                    NaN"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[ski_data.SkiableTerrain_ac > 10000].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll Google this resort to see if there is anything unusual to be found at [Silverton](https://www.google.com/search?q=silverton+mountain+skiable+area)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spot checking the data, we see our top and base elevation values agree, but the skiable area is blatantly wrong. Our data set says this value is 26,819, but the value we've just looked up is 1,819. The last three digits agree. This looks like human error. We can confidently replace the incorrect value with the one we've just found. Had we access to the client, we would report this back and ask for the correction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26819.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the 'SkiableTerrain_ac' value only for this resort w/ .loc\n",
    "ski_data.loc[39, 'SkiableTerrain_ac']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify this value with the correct value\n",
    "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1819.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify that the value has been modified\n",
    "ski_data.loc[39, 'SkiableTerrain_ac']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does the distribution of skiable area look like now?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ski_data.SkiableTerrain_ac.hist(bins=30)\n",
    "plt.xlabel('SkiableTerrain_ac')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of skiable area (acres) after replacing erroneous value');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now see a rather long tailed distribution. To be sure, there are extreme values. The above distribution, however, is plausible, so we leave it unchanged."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.4.2.2 Snow Making_ac<a id='2.6.4.2.2_Snow_Making_ac'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our snow making data exhibits a heavy, positive skew. Is this the influence of an outlier?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    3379.0\n",
       "18    1500.0\n",
       "Name: Snow Making_ac, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation 11 appears to be an outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Heavenly Mountain Resort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>Sierra Nevada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>10067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>7170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastEight</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>4800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>3379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         11\n",
       "Name               Heavenly Mountain Resort\n",
       "Region                        Sierra Nevada\n",
       "state                            California\n",
       "summit_elev                           10067\n",
       "vertical_drop                          3500\n",
       "base_elev                              7170\n",
       "trams                                     2\n",
       "fastEight                               0.0\n",
       "fastSixes                                 2\n",
       "fastQuads                                 7\n",
       "quad                                      1\n",
       "triple                                    5\n",
       "double                                    3\n",
       "surface                                   8\n",
       "total_chairs                             28\n",
       "Runs                                   97.0\n",
       "TerrainParks                            3.0\n",
       "LongestRun_mi                           5.5\n",
       "SkiableTerrain_ac                    4800.0\n",
       "Snow Making_ac                       3379.0\n",
       "daysOpenLastYear                      155.0\n",
       "yearsOpen                              64.0\n",
       "averageSnowfall                       360.0\n",
       "AdultWeekday                            NaN\n",
       "AdultWeekend                            NaN\n",
       "projectedDaysOpen                     157.0\n",
       "NightSkiing_ac                          NaN"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[ski_data['Snow Making_ac'] > 3000].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While this is vaguely interesting, we have no ticket pricing information at all for this resort, so we will drop this observation from our data frame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "ski_data.drop([11], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.4.2.3 fastEight & yearsOpen<a id='2.6.4.2.3_fastEight'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we explore the `fastEight` values more closely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    162\n",
       "1.0      1\n",
       "Name: fastEight, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.fastEight.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since most values are either missing or zero, we can safely drop the `fastEight` feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "ski_data.drop(columns='fastEight', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of `yearsOpen` has caught our eye. How many resorts have purportedly been open for more than 100 years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>34</th>\n",
       "      <th>115</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Howelsen Hill</td>\n",
       "      <td>Pine Knob Ski Resort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Michigan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Michigan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>7136</td>\n",
       "      <td>1308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>440</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>6696</td>\n",
       "      <td>1009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>50.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>25.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>104.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>25.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>25.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             34                    115\n",
       "Name               Howelsen Hill  Pine Knob Ski Resort\n",
       "Region                  Colorado              Michigan\n",
       "state                   Colorado              Michigan\n",
       "summit_elev                 7136                  1308\n",
       "vertical_drop                440                   300\n",
       "base_elev                   6696                  1009\n",
       "trams                          0                     0\n",
       "fastSixes                      0                     0\n",
       "fastQuads                      0                     0\n",
       "quad                           0                     0\n",
       "triple                         0                     0\n",
       "double                         1                     0\n",
       "surface                        3                     6\n",
       "total_chairs                   4                     6\n",
       "Runs                        17.0                  14.0\n",
       "TerrainParks                 1.0                   3.0\n",
       "LongestRun_mi                6.0                   1.0\n",
       "SkiableTerrain_ac           50.0                  80.0\n",
       "Snow Making_ac              25.0                  80.0\n",
       "daysOpenLastYear           100.0                   NaN\n",
       "yearsOpen                  104.0                2019.0\n",
       "averageSnowfall            150.0                   NaN\n",
       "AdultWeekday                25.0                  49.0\n",
       "AdultWeekend                25.0                  57.0\n",
       "projectedDaysOpen          100.0                   NaN\n",
       "NightSkiing_ac              10.0                   NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the 'yearsOpen' column for values greater than 100\n",
    "ski_data[ski_data.yearsOpen > 100].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One resort has been open for 104 years. The other has allegedly been open for 2019 years. This is a blatant error, so we drop the observation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "ski_data.drop([115], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does the distribution of `yearsOpen` look like if you exclude just the obviously wrong one?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ski_data[ski_data.yearsOpen < 1000]['yearsOpen'], bins=30)\n",
    "plt.xlabel('Years open')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of years open excluding 2019');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above distribution of years seems entirely plausible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's review the summary statistics for the years under 1000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    327.000000\n",
       "mean      57.675841\n",
       "std       16.863366\n",
       "min        6.000000\n",
       "25%       50.000000\n",
       "50%       58.000000\n",
       "75%       68.500000\n",
       "max      104.000000\n",
       "Name: yearsOpen, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.yearsOpen[ski_data.yearsOpen < 1000].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The newest resort has been open for 6 years, while the oldest has been open for 104. We see no reason to doubt that our distribution of `yearsOpen` data is now accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.4.2.4 fastSixes and Trams<a id='2.6.4.2.4_fastSixes_and_Trams'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These features do not raise major concerns, but we will take care in using them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.7 Derive State-wide Summary Statistics For Our Market Segment<a id='2.7_Derive_State-wide_Summary_Statistics_For_Our_Market_Segment'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many features in our data pertain to chairlifts, that is for getting people around each resort.  Features that we may be interested in are:\n",
    "\n",
    "* `TerrainParks`\n",
    "* `SkiableTerrain_ac`\n",
    "* `daysOpenLastYear`\n",
    "* `NightSkiing_ac`\n",
    "\n",
    "When we think about it, these are features it makes sense to sum: the total number of terrain parks, the total skiable area, the total number of days open, and the total area available for night skiing. We might consider the total number of ski runs, but the skiable area seems more informative than just the number of runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>resorts_per_state</th>\n",
       "      <th>state_total_skiable_area_ac</th>\n",
       "      <th>state_total_days_open</th>\n",
       "      <th>state_total_terrain_parks</th>\n",
       "      <th>state_total_nightskiing_ac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>3</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2</td>\n",
       "      <td>1577.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>California</td>\n",
       "      <td>20</td>\n",
       "      <td>21148.0</td>\n",
       "      <td>2583.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>587.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>22</td>\n",
       "      <td>43682.0</td>\n",
       "      <td>3258.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>5</td>\n",
       "      <td>358.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
       "0       Alaska                  3                       2280.0   \n",
       "1      Arizona                  2                       1577.0   \n",
       "2   California                 20                      21148.0   \n",
       "3     Colorado                 22                      43682.0   \n",
       "4  Connecticut                  5                        358.0   \n",
       "\n",
       "   state_total_days_open  state_total_terrain_parks  \\\n",
       "0                  345.0                        4.0   \n",
       "1                  237.0                        6.0   \n",
       "2                 2583.0                       78.0   \n",
       "3                 3258.0                       74.0   \n",
       "4                  353.0                       10.0   \n",
       "\n",
       "   state_total_nightskiing_ac  \n",
       "0                       580.0  \n",
       "1                        80.0  \n",
       "2                       587.0  \n",
       "3                       428.0  \n",
       "4                       256.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n",
    "state_summary = ski_data.groupby('state').agg(\n",
    "    resorts_per_state=pd.NamedAgg(column='Name', aggfunc='size'), #could pick any column here\n",
    "    state_total_skiable_area_ac=pd.NamedAgg(column='SkiableTerrain_ac', aggfunc='sum'),\n",
    "    state_total_days_open=pd.NamedAgg(column='daysOpenLastYear', aggfunc='sum'),\n",
    "    state_total_terrain_parks=pd.NamedAgg(column='TerrainParks', aggfunc='sum'),\n",
    "    state_total_nightskiing_ac=pd.NamedAgg(column='NightSkiing_ac', aggfunc='sum')\n",
    ").reset_index()\n",
    "state_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.8 Drop Rows With No Price Data<a id='2.8_Drop_Rows_With_No_Price_Data'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now return to the features that speak directly to price: `AdultWeekend` and `AdultWeekday`. As before, we calculate the number of price values missing per row. This will obviously have to be either 0, 1, or 2, where 0 denotes no price values are missing and 2 denotes that both are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    82.621951\n",
       "2    14.024390\n",
       "1     3.353659\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
    "missing_price.value_counts()/len(missing_price) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 14% of the rows have no price data. Given these rows are missing data on our target variable, they can be safely removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# `missing_price` used to remove rows from ski_data where both price values are missing\n",
    "ski_data = ski_data[missing_price != 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.9 Review Distributions<a id='2.9_Review_distributions'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's review the distributions of numerical features now that we have made some changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ski_data.hist(figsize=(15, 10))\n",
    "plt.subplots_adjust(hspace=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distributions above seem much more reasonable. There are clearly some skewed distributions. We will keep an eye out for 1) the failure of our model to rate a feature as important when domain knowledge tells us it should be is an issue and 2) extreme values that may influence our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.10 Population data<a id='2.10_Population_data'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Population and area data for the US states can be obtained from [wikipedia](https://simple.wikipedia.org/wiki/List_of_U.S._states). This table is useful because it allows us to proceed with an analysis that includes state sizes and populations. Intuitively, these are variables that could influence resort prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# `read_html` method to read the table from the URL below\n",
    "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n",
    "usa_states = pd.read_html(states_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(usa_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(usa_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Name &amp; postal abbs. [1]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cities</th>\n",
       "      <th>Established[A]</th>\n",
       "      <th>Population [B][3]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Total area[4]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Land area[4]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Water area[4]</th>\n",
       "      <th>Number of Reps.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Name &amp; postal abbs. [1]</th>\n",
       "      <th>Name &amp; postal abbs. [1].1</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Largest[5]</th>\n",
       "      <th>Established[A]</th>\n",
       "      <th>Population [B][3]</th>\n",
       "      <th>mi2</th>\n",
       "      <th>km2</th>\n",
       "      <th>mi2</th>\n",
       "      <th>km2</th>\n",
       "      <th>mi2</th>\n",
       "      <th>km2</th>\n",
       "      <th>Number of Reps.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>Dec 14, 1819</td>\n",
       "      <td>4903185</td>\n",
       "      <td>52420</td>\n",
       "      <td>135767</td>\n",
       "      <td>50645</td>\n",
       "      <td>131171</td>\n",
       "      <td>1775</td>\n",
       "      <td>4597</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>Juneau</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>Jan 3, 1959</td>\n",
       "      <td>731545</td>\n",
       "      <td>665384</td>\n",
       "      <td>1723337</td>\n",
       "      <td>570641</td>\n",
       "      <td>1477953</td>\n",
       "      <td>94743</td>\n",
       "      <td>245384</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Feb 14, 1912</td>\n",
       "      <td>7278717</td>\n",
       "      <td>113990</td>\n",
       "      <td>295234</td>\n",
       "      <td>113594</td>\n",
       "      <td>294207</td>\n",
       "      <td>396</td>\n",
       "      <td>1026</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>Jun 15, 1836</td>\n",
       "      <td>3017804</td>\n",
       "      <td>53179</td>\n",
       "      <td>137732</td>\n",
       "      <td>52035</td>\n",
       "      <td>134771</td>\n",
       "      <td>1143</td>\n",
       "      <td>2961</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Sep 9, 1850</td>\n",
       "      <td>39512223</td>\n",
       "      <td>163695</td>\n",
       "      <td>423967</td>\n",
       "      <td>155779</td>\n",
       "      <td>403466</td>\n",
       "      <td>7916</td>\n",
       "      <td>20501</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name & postal abbs. [1]                                 Cities               \\\n",
       "  Name & postal abbs. [1] Name & postal abbs. [1].1      Capital   Largest[5]   \n",
       "0                 Alabama                        AL   Montgomery   Birmingham   \n",
       "1                  Alaska                        AK       Juneau    Anchorage   \n",
       "2                 Arizona                        AZ      Phoenix      Phoenix   \n",
       "3                Arkansas                        AR  Little Rock  Little Rock   \n",
       "4              California                        CA   Sacramento  Los Angeles   \n",
       "\n",
       "  Established[A] Population [B][3] Total area[4]          Land area[4]  \\\n",
       "  Established[A] Population [B][3]           mi2      km2          mi2   \n",
       "0   Dec 14, 1819           4903185         52420   135767        50645   \n",
       "1    Jan 3, 1959            731545        665384  1723337       570641   \n",
       "2   Feb 14, 1912           7278717        113990   295234       113594   \n",
       "3   Jun 15, 1836           3017804         53179   137732        52035   \n",
       "4    Sep 9, 1850          39512223        163695   423967       155779   \n",
       "\n",
       "           Water area[4]         Number of Reps.  \n",
       "       km2           mi2     km2 Number of Reps.  \n",
       "0   131171          1775    4597               7  \n",
       "1  1477953         94743  245384               1  \n",
       "2   294207           396    1026               9  \n",
       "3   134771          1143    2961               4  \n",
       "4   403466          7916   20501              53  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_states = usa_states[0]\n",
    "usa_states.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that two layers of column headings are at work in the above table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the pandas Series for column number 4 from `usa_states`\n",
    "established = usa_states.iloc[:, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Dec 14, 1819\n",
       "1      Jan 3, 1959\n",
       "2     Feb 14, 1912\n",
       "3     Jun 15, 1836\n",
       "4      Sep 9, 1850\n",
       "5      Aug 1, 1876\n",
       "6      Jan 9, 1788\n",
       "7      Dec 7, 1787\n",
       "8      Mar 3, 1845\n",
       "9      Jan 2, 1788\n",
       "10    Aug 21, 1959\n",
       "11     Jul 3, 1890\n",
       "12     Dec 3, 1818\n",
       "13    Dec 11, 1816\n",
       "14    Dec 28, 1846\n",
       "15    Jan 29, 1861\n",
       "16     Jun 1, 1792\n",
       "17    Apr 30, 1812\n",
       "18    Mar 15, 1820\n",
       "19    Apr 28, 1788\n",
       "20     Feb 6, 1788\n",
       "21    Jan 26, 1837\n",
       "22    May 11, 1858\n",
       "23    Dec 10, 1817\n",
       "24    Aug 10, 1821\n",
       "25     Nov 8, 1889\n",
       "26     Mar 1, 1867\n",
       "27    Oct 31, 1864\n",
       "28    Jun 21, 1788\n",
       "29    Dec 18, 1787\n",
       "30     Jan 6, 1912\n",
       "31    Jul 26, 1788\n",
       "32    Nov 21, 1789\n",
       "33     Nov 2, 1889\n",
       "34     Mar 1, 1803\n",
       "35    Nov 16, 1907\n",
       "36    Feb 14, 1859\n",
       "37    Dec 12, 1787\n",
       "38    May 29, 1790\n",
       "39    May 23, 1788\n",
       "40     Nov 2, 1889\n",
       "41     Jun 1, 1796\n",
       "42    Dec 29, 1845\n",
       "43     Jan 4, 1896\n",
       "44     Mar 4, 1791\n",
       "45    Jun 25, 1788\n",
       "46    Nov 11, 1889\n",
       "47    Jun 20, 1863\n",
       "48    May 29, 1848\n",
       "49    Jul 10, 1890\n",
       "Name: (Established[A], Established[A]), dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "established"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want each state's name, population, and total area (square miles)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>state_population</th>\n",
       "      <th>state_area_sq_miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>4903185</td>\n",
       "      <td>52420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>731545</td>\n",
       "      <td>665384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>7278717</td>\n",
       "      <td>113990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>3017804</td>\n",
       "      <td>53179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>39512223</td>\n",
       "      <td>163695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state  state_population  state_area_sq_miles\n",
       "0     Alabama           4903185                52420\n",
       "1      Alaska            731545               665384\n",
       "2     Arizona           7278717               113990\n",
       "3    Arkansas           3017804                53179\n",
       "4  California          39512223               163695"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract columns 0, 5, and 6 and the dataframe's `copy()` method\n",
    "usa_states_sub = usa_states.iloc[:, [0,5,6]].copy()\n",
    "usa_states_sub.columns = ['state', 'state_population', 'state_area_sq_miles']\n",
    "usa_states_sub.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we check whether all states for which we have ski data are accounted for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the states in `state_summary` that are not in `usa_states_sub`\n",
    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
    "missing_states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears as though we are missing data on certain states. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The website data contains a reasonable explanation for why 'Massachusetts', 'Pennsylvania', 'Rhode Island', and 'Virginia' are missing from usa_states_sub: There are brackets and abbreviations after these names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20    Massachusetts[C]\n",
       "37     Pennsylvania[C]\n",
       "38     Rhode Island[D]\n",
       "45         Virginia[C]\n",
       "47       West Virginia\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should delete the square brackets seen above and their contents then try again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20    Massachusetts\n",
       "37     Pennsylvania\n",
       "38     Rhode Island\n",
       "45         Virginia\n",
       "47    West Virginia\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_states_sub.state.replace(to_replace='\\[.*\\]', value='', regex=True, inplace=True)\n",
    "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having edited the data, let's verify that all states are now present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
    "missing_states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have an empty set for missing states. Thus, we confidently add the population and state area columns to our ski resort data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>resorts_per_state</th>\n",
       "      <th>state_total_skiable_area_ac</th>\n",
       "      <th>state_total_days_open</th>\n",
       "      <th>state_total_terrain_parks</th>\n",
       "      <th>state_total_nightskiing_ac</th>\n",
       "      <th>state_population</th>\n",
       "      <th>state_area_sq_miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>3</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>731545</td>\n",
       "      <td>665384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2</td>\n",
       "      <td>1577.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7278717</td>\n",
       "      <td>113990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>California</td>\n",
       "      <td>20</td>\n",
       "      <td>21148.0</td>\n",
       "      <td>2583.0</td>\n",
       "      <td>78.0</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                 20                      21148.0   \n",
       "3     Colorado                 22                      43682.0   \n",
       "4  Connecticut                  5                        358.0   \n",
       "\n",
       "   state_total_days_open  state_total_terrain_parks  \\\n",
       "0                  345.0                        4.0   \n",
       "1                  237.0                        6.0   \n",
       "2                 2583.0                       78.0   \n",
       "3                 3258.0                       74.0   \n",
       "4                  353.0                       10.0   \n",
       "\n",
       "   state_total_nightskiing_ac  state_population  state_area_sq_miles  \n",
       "0                       580.0            731545               665384  \n",
       "1                        80.0           7278717               113990  \n",
       "2                       587.0          39512223               163695  \n",
       "3                       428.0           5758736               104094  \n",
       "4                       256.0           3565278                 5543  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine our new data with 'usa_states_sub'\n",
    "state_summary = state_summary.merge(usa_states_sub, how='left', on='state')\n",
    "state_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will merge our data sets in our next notebook, in which we explore our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.11 Target Feature<a id='2.11_Target_Feature'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we return to our most important question: Which ticket price(s) will we model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ski_data.plot(x='AdultWeekday', y='AdultWeekend', kind='scatter');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a clear line where `AdultWeekend` and `AdultWeekday` prices are equal. Weekend prices being higher than weekday prices seem restricted to sub $100 resorts. Recall from the boxplot earlier that the distributions for weekday and weekend prices in Montana seemed equal. This is confirmed by the table below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AdultWeekend</th>\n",
       "      <th>AdultWeekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>49.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>81.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     AdultWeekend  AdultWeekday\n",
       "141          42.0          42.0\n",
       "142          63.0          63.0\n",
       "143          49.0          49.0\n",
       "144          48.0          48.0\n",
       "145          46.0          46.0\n",
       "146          39.0          39.0\n",
       "147          50.0          50.0\n",
       "148          67.0          67.0\n",
       "149          47.0          47.0\n",
       "150          39.0          39.0\n",
       "151          81.0          81.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print 'AdultWeekend' and 'AdultWeekday' columns for Montana only\n",
    "ski_data.loc[ski_data.state == 'Montana', ['AdultWeekend','AdultWeekday']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, we see that weekend prices have the fewest missing values of the two ticket types, so we drop the weekday prices and keep only those rows that have weekend price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdultWeekend    4\n",
       "AdultWeekday    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "ski_data = ski_data.copy(deep=True)\n",
    "ski_data.drop(columns='AdultWeekday', inplace=True)\n",
    "ski_data.dropna(subset=['AdultWeekend'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having made these adjustments, we should check the overall shape of our data frame to ascertain if it is reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(278, 25)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.11.1 Number Of Missing Values By Row - Resort<a id='2.11.1_Number_Of_Missing_Values_By_Row_-_Resort'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having dropped rows missing the desired target ticket price, what degree of missingness exists in the remaining rows?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count     %\n",
       "329      5  20.0\n",
       "62       5  20.0\n",
       "74       5  20.0\n",
       "141      5  20.0\n",
       "146      5  20.0\n",
       "86       5  20.0\n",
       "184      4  16.0\n",
       "264      4  16.0\n",
       "88       4  16.0\n",
       "96       4  16.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = pd.concat([ski_data.isnull().sum(axis=1), 100 * ski_data.isnull().mean(axis=1)], axis=1)\n",
    "missing.columns=['count', '%']\n",
    "missing.sort_values(by='count', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These seem possibly curiously quantized..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  4.,  8., 12., 16., 20.])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing['%'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, the percentage of missing values per row appear in multiples of 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     107\n",
       "4.0      94\n",
       "8.0      45\n",
       "12.0     15\n",
       "16.0     11\n",
       "20.0      6\n",
       "Name: %, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing['%'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's almost as if values have been removed artificially. Nevertheless, we don't know how useful the missing features are in predicting ticket price. Thus, we shouldn't just drop rows that are missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 278 entries, 0 to 329\n",
      "Data columns (total 25 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Name               278 non-null    object \n",
      " 1   Region             278 non-null    object \n",
      " 2   state              278 non-null    object \n",
      " 3   summit_elev        278 non-null    int64  \n",
      " 4   vertical_drop      278 non-null    int64  \n",
      " 5   base_elev          278 non-null    int64  \n",
      " 6   trams              278 non-null    int64  \n",
      " 7   fastSixes          278 non-null    int64  \n",
      " 8   fastQuads          278 non-null    int64  \n",
      " 9   quad               278 non-null    int64  \n",
      " 10  triple             278 non-null    int64  \n",
      " 11  double             278 non-null    int64  \n",
      " 12  surface            278 non-null    int64  \n",
      " 13  total_chairs       278 non-null    int64  \n",
      " 14  Runs               275 non-null    float64\n",
      " 15  TerrainParks       234 non-null    float64\n",
      " 16  LongestRun_mi      273 non-null    float64\n",
      " 17  SkiableTerrain_ac  276 non-null    float64\n",
      " 18  Snow Making_ac     241 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       278 non-null    float64\n",
      " 23  projectedDaysOpen  236 non-null    float64\n",
      " 24  NightSkiing_ac     164 non-null    float64\n",
      "dtypes: float64(11), int64(11), object(3)\n",
      "memory usage: 56.5+ KB\n"
     ]
    }
   ],
   "source": [
    "ski_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.12 Save data<a id='2.12_Save_data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(278, 25)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We save our work to our data directory, separately. Note that we were provided with the data in `raw_data`, and we should save derived data in a separate location. This guards against overwriting our original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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_cleaned.csv\"\n"
     ]
    }
   ],
   "source": [
    "# save the data to a new csv file\n",
    "datapath = '../data'\n",
    "save_file(ski_data, 'ski_data_cleaned.csv', datapath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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/state_summary.csv\"\n"
     ]
    }
   ],
   "source": [
    "# save the state_summary separately.\n",
    "datapath = '../data'\n",
    "save_file(state_summary, 'state_summary.csv', datapath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.13 Summary<a id='2.13_Summary'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The above analysis is the first step in answering the question of what Big Mountain's optimal price might be. \n",
    "* The data set contained 330 rows, each representing a resort, and 27 features.\n",
    "* 34 states are included in the data set.\n",
    "* Data integrity checks were run using `.isnull`, as were checks for missing values coded as \"extreme\" integers (e.g., -1, 999).\n",
    "* An initial review of the data revealed the feature `fastEight` was missing ~50% of observations and, thus, was dropped.\n",
    "* `AdultWeekday` and `AdultWeekend` data were missing ~16% and ~15% of values, respectively, both prices were present for our target resort.\n",
    "* 47 resorts were missing both ticket prices, though only one was domiciled in Montana. These observations were also dropped.\n",
    "* No duplicate resorts were inadvertently included in the data.\n",
    "* Features such as `SkiState` and `Region` were disambiguated. Our 38 regions were spread across 25 states. The variables failed to coincide 33 times owing to multiple divisions in CA, OR, and UT.\n",
    "* Variance among ticket prices by was considerable in many, though not all, ski destinations. Considerable interquartile spread was witnessed in UT, NV, NH, ME, and CA. Montana exhibited very little.\n",
    "* Most prices were range bound between 25.00 and 100.00.\n",
    "* 82% of resorts contain all data, while ~14% are missing at least one data point.\n",
    "* Features of interest were identified based on a review of their respective distributions combined with knowledge of missing values and domain knowledge.\n",
    "* Adjustments for missing values normalized some of their distributions, however, many have a distinctive positive skew due to the data's inability to go below zero for most predictors and potential outliers.\n",
    "* Resort data were supplemented by state-level data fetched from the web. These data provided insight into state populations and total area.\n",
    "* Per Montana specifically, more weekday prices were missing than weekend. The `AdultWeekday` feature was, therefore, dropped, and we will focus on modeling weekend pricing, going forward.\n",
    "* Missing values across all resorts revealed a quantile pattern. This indicates systematic removal of information from this data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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