02_data_wrangling.ipynb

{
 "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 your data, organizing it, and making sure it's well defined. Paying attention to these tasks will pay off greatly later on. Some data cleaning can be done at this stage, but it's important not to be overzealous in your cleaning before you've explored the data to better understand it."
   ]
  },
  {
   "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": [
    "The purpose of this data science project is to come up with a pricing model for ski resort tickets in our market segment. Big Mountain suspects it may not be maximizing its returns, relative to its position in the market. It also does not have a strong sense of what facilities matter most to visitors, particularly which ones they're most likely to pay more for. This project aims to build a predictive model for ticket price based on a number of facilities, or properties, boasted by resorts (*at the resorts).* \n",
    "This model will be used to provide guidance for Big Mountain's pricing and future facility investment plans."
   ]
  },
  {
   "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": [
    "Notebooks grow organically as we explore our data. If you used paper notebooks, you could discover a mistake and cross out or revise some earlier work. Later work may give you a reason to revisit earlier work and explore it further. The great thing about Jupyter notebooks is that you can edit, add, and move cells around without needing to cross out figures or scrawl in the margin. However, this means you can lose track of your changes easily. If you worked in a regulated environment, the company may have a a policy of always dating entries and clearly crossing out any mistakes, with your initials and the date.\n",
    "\n",
    "**Best practice here is to commit your changes using a version control system such as Git.** Try to get into the habit of adding and committing your files to the Git repository you're working in after you save them. You're are working in a Git repository, right? If you make a significant change, save the notebook and commit it to Git. In fact, if you're about to make a significant change, it's a good idea to commit before as well. Then if the change is a mess, you've got the previous version to go back to.\n",
    "\n",
    "**Another best practice with notebooks is to try to keep them organized with helpful headings and comments.** Not only can a good structure, but associated headings help you keep track of what you've done and your current focus. Anyone reading your notebook will have a much easier time following the flow of work. Remember, that 'anyone' will most likely be you. Be kind to future you!\n",
    "\n",
    "In this notebook, note how we try to use well structured, helpful headings that frequently are self-explanatory, and we make a brief note after any results to highlight key takeaways. This is an immense help to anyone reading your notebook and it will greatly help you when you come to summarise your findings. **Top tip: jot down key findings in a final summary at the end of the notebook as they arise. You can tidy this up later.** This is a great way to ensure important results don't get lost in the middle of your notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this, and subsequent notebooks, there are coding tasks marked with `#Code task n#` with code to complete. The `___` will guide you to where you need to insert code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Imports<a id='2.3_Imports'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Placing your imports all together at the start of your notebook means you only need to consult one place to check your notebook's dependencies. By all means import something 'in situ' later on when you're experimenting, but if the imported dependency ends up being kept, you should subsequently move the import statement here with the rest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "#Code task 1#\n",
    "#Import pandas, matplotlib.pyplot, and seaborn in the correct lines below\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import os\n",
    "os.getcwd()\n",
    "import seaborn as sns\n",
    "import html5lib\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Objectives<a id='2.4_Objectives'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some fundamental questions to resolve in this notebook before you move on.\n",
    "\n",
    "* Do you think you may have the data you need to tackle the desired question?\n",
    "    * Have you identified the required target value?\n",
    "    * Do you have potentially useful features?\n",
    "* Do you 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": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# the supplied CSV data file is the raw_data directory\n",
    "os.listdir(os.getcwd())\n",
    "ski_data = pd.read_csv('ski_resort_data.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good first steps in auditing the data are the info method and displaying the first few records with head."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 330 entries, 0 to 329\n",
      "Data columns (total 27 columns):\n",
      "Name                 330 non-null object\n",
      "Region               330 non-null object\n",
      "state                330 non-null object\n",
      "summit_elev          330 non-null int64\n",
      "vertical_drop        330 non-null int64\n",
      "base_elev            330 non-null int64\n",
      "trams                330 non-null int64\n",
      "fastEight            164 non-null float64\n",
      "fastSixes            330 non-null int64\n",
      "fastQuads            330 non-null int64\n",
      "quad                 330 non-null int64\n",
      "triple               330 non-null int64\n",
      "double               330 non-null int64\n",
      "surface              330 non-null int64\n",
      "total_chairs         330 non-null int64\n",
      "Runs                 326 non-null float64\n",
      "TerrainParks         279 non-null float64\n",
      "LongestRun_mi        325 non-null float64\n",
      "SkiableTerrain_ac    327 non-null float64\n",
      "Snow Making_ac       284 non-null float64\n",
      "daysOpenLastYear     279 non-null float64\n",
      "yearsOpen            329 non-null float64\n",
      "averageSnowfall      316 non-null float64\n",
      "AdultWeekday         276 non-null float64\n",
      "AdultWeekend         279 non-null float64\n",
      "projectedDaysOpen    283 non-null float64\n",
      "NightSkiing_ac       187 non-null float64\n",
      "dtypes: float64(13), int64(11), object(3)\n",
      "memory usage: 69.7+ KB\n"
     ]
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       "  <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>...</th>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <th>SkiableTerrain_ac</th>\n",
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       "      <th>daysOpenLastYear</th>\n",
       "      <th>yearsOpen</th>\n",
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       "      <th>AdultWeekday</th>\n",
       "      <th>AdultWeekend</th>\n",
       "      <th>projectedDaysOpen</th>\n",
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       "  </thead>\n",
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       "    <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",
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       "      <td>2</td>\n",
       "      <td>...</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",
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       "      <td>0</td>\n",
       "      <td>...</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>...</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>...</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>...</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",
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       "<p>5 rows × 27 columns</p>\n",
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      "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       ...        \\\n",
       "0        250      1        0.0          0          2       ...         \n",
       "1       1200      0        0.0          0          0       ...         \n",
       "2       1796      0        0.0          0          0       ...         \n",
       "3       9200      0        0.0          1          0       ...         \n",
       "4       9200      0        NaN          0          1       ...         \n",
       "\n",
       "   LongestRun_mi  SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  \\\n",
       "0            1.0             1610.0           113.0             150.0   \n",
       "1            2.0              640.0            60.0              45.0   \n",
       "2            1.0               30.0            30.0             150.0   \n",
       "3            2.0              777.0           104.0             122.0   \n",
       "4            1.2              800.0            80.0             115.0   \n",
       "\n",
       "   yearsOpen  averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
       "0       60.0            669.0          65.0          85.0              150.0   \n",
       "1       44.0            350.0          47.0          53.0               90.0   \n",
       "2       36.0             69.0          30.0          34.0              152.0   \n",
       "3       81.0            260.0          89.0          89.0              122.0   \n",
       "4       49.0            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  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 2#\n",
    "#Call the info method on ski_data to see a summary of the data\n",
    "ski_data.info()\n",
    "\n",
    "ski_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`AdultWeekday` is the price of an adult weekday ticket. `AdultWeekend` is the price of an adult weekend ticket. The other columns are potential features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This immediately raises the question of what quantity will you want to model? You know you want to model the ticket price, but you realise there are two kinds of ticket price!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
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       "      <td>Alyeska Resort</td>\n",
       "      <td>Alaska</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</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>...</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>...</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>...</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>...</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",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name   Region    state  summit_elev  vertical_drop  \\\n",
       "0       Alyeska Resort   Alaska   Alaska         3939           2500   \n",
       "1  Eaglecrest Ski Area   Alaska   Alaska         2600           1540   \n",
       "2     Hilltop Ski Area   Alaska   Alaska         2090            294   \n",
       "3     Arizona Snowbowl  Arizona  Arizona        11500           2300   \n",
       "4  Sunrise Park Resort  Arizona  Arizona        11100           1800   \n",
       "\n",
       "   base_elev  trams  fastEight  fastSixes  fastQuads       ...        \\\n",
       "0        250      1        0.0          0          2       ...         \n",
       "1       1200      0        0.0          0          0       ...         \n",
       "2       1796      0        0.0          0          0       ...         \n",
       "3       9200      0        0.0          1          0       ...         \n",
       "4       9200      0        NaN          0          1       ...         \n",
       "\n",
       "   LongestRun_mi  SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  \\\n",
       "0            1.0             1610.0           113.0             150.0   \n",
       "1            2.0              640.0            60.0              45.0   \n",
       "2            1.0               30.0            30.0             150.0   \n",
       "3            2.0              777.0           104.0             122.0   \n",
       "4            1.2              800.0            80.0             115.0   \n",
       "\n",
       "   yearsOpen  averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
       "0       60.0            669.0          65.0          85.0              150.0   \n",
       "1       44.0            350.0          47.0          53.0               90.0   \n",
       "2       36.0             69.0          30.0          34.0              152.0   \n",
       "3       81.0            260.0          89.0          89.0              122.0   \n",
       "4       49.0            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  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 3#\n",
    "#Call the head method on ski_data to print the first several rows of the data\n",
    "ski_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column"
   ]
  },
  {
   "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 Find Your Resort Of Interest<a id='2.6.1_Find_Your_Resort_Of_Interest'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your resort of interest is called Big Mountain Resort. Check it's in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "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>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</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>4</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>600</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\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\n",
       "TerrainParks                         4\n",
       "LongestRun_mi                      3.3\n",
       "SkiableTerrain_ac                 3000\n",
       "Snow Making_ac                     600\n",
       "daysOpenLastYear                   123\n",
       "yearsOpen                           72\n",
       "averageSnowfall                    333\n",
       "AdultWeekday                        81\n",
       "AdultWeekend                        81\n",
       "projectedDaysOpen                  123\n",
       "NightSkiing_ac                     600"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 4#\n",
    "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n",
    "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n",
    "#transpose method, but you can access this conveniently with the `T` property.\n",
    "ski_data[ski_data.Name == 'Big Mountain Resort'].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's good that your resort doesn't appear to have any missing values."
   ]
  },
  {
   "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": [
    "Count the number of missing values in each column and sort them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>surface</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303030</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>Runs</th>\n",
       "      <td>4</td>\n",
       "      <td>1.212121</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>averageSnowfall</th>\n",
       "      <td>14</td>\n",
       "      <td>4.242424</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>projectedDaysOpen</th>\n",
       "      <td>47</td>\n",
       "      <td>14.242424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>51</td>\n",
       "      <td>15.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>51</td>\n",
       "      <td>15.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>51</td>\n",
       "      <td>15.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>54</td>\n",
       "      <td>16.363636</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>fastEight</th>\n",
       "      <td>166</td>\n",
       "      <td>50.303030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count          %\n",
       "Name                   0   0.000000\n",
       "total_chairs           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",
       "surface                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",
       "Region                 0   0.000000\n",
       "yearsOpen              1   0.303030\n",
       "SkiableTerrain_ac      3   0.909091\n",
       "Runs                   4   1.212121\n",
       "LongestRun_mi          5   1.515152\n",
       "averageSnowfall       14   4.242424\n",
       "Snow Making_ac        46  13.939394\n",
       "projectedDaysOpen     47  14.242424\n",
       "TerrainParks          51  15.454545\n",
       "daysOpenLastYear      51  15.454545\n",
       "AdultWeekend          51  15.454545\n",
       "AdultWeekday          54  16.363636\n",
       "NightSkiing_ac       143  43.333333\n",
       "fastEight            166  50.303030"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 5#\n",
    "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n",
    "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n",
    "#Order them (increasing or decreasing) using sort_values\n",
    "ski_data.isnull()\n",
    "ski_data.isnull().sum()\n",
    "missing = pd.concat([ski_data.isnull().sum(),100*ski_data.isnull().mean()], axis=1)\n",
    "\n",
    "missing.columns=['count', '%']\n",
    "# sort Brand - ascending order\n",
    "missing.sort_values(by=['%'], inplace=True)\n",
    "missing.head(30)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.3 Categorical Features<a id='2.6.3_Categorical_Features'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  object\n",
       "Region                object\n",
       "state                 object\n",
       "summit_elev            int64\n",
       "vertical_drop          int64\n",
       "base_elev              int64\n",
       "trams                  int64\n",
       "fastEight            float64\n",
       "fastSixes              int64\n",
       "fastQuads              int64\n",
       "quad                   int64\n",
       "triple                 int64\n",
       "double                 int64\n",
       "surface                int64\n",
       "total_chairs           int64\n",
       "Runs                 float64\n",
       "TerrainParks         float64\n",
       "LongestRun_mi        float64\n",
       "SkiableTerrain_ac    float64\n",
       "Snow Making_ac       float64\n",
       "daysOpenLastYear     float64\n",
       "yearsOpen            float64\n",
       "averageSnowfall      float64\n",
       "AdultWeekday         float64\n",
       "AdultWeekend         float64\n",
       "projectedDaysOpen    float64\n",
       "NightSkiing_ac       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 6#\n",
    "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n",
    "ski_data.dtypes\n",
    "\n",
    "#ski_data.select_dtypes('object')\n",
    "#ski_data.select_dtypes(exclude = 'int64')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n",
    "\n",
    "* Is `Name` (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": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Crystal Mountain            2\n",
       "Plattekill Mountain         1\n",
       "Winter Park Resort          1\n",
       "Bear Valley                 1\n",
       "Spring Mountain Ski Area    1\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 7#\n",
    "#Use pandas' Series method `value_counts` to find any duplicated resort names\n",
    "\n",
    "ski_data['Name'].value_counts().head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have a duplicated resort name: Crystal Mountain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Camelback Mountain Resort, Pennsylvania    1\n",
       "Shawnee Peak, Maine                        1\n",
       "Big Snow Resort - Blackjack, Michigan      1\n",
       "Taos Ski Valley, New Mexico                1\n",
       "Dartmouth Skiway, New Hampshire            1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 8#\n",
    "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n",
    "\n",
    "(ski_data['Name'] + ', ' + ski_data['Region']).value_counts().head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Camelback Mountain Resort, Pennsylvania      1\n",
       "Cooper Spur, Oregon                          1\n",
       "Dartmouth Skiway, New Hampshire              1\n",
       "Bear Valley, California                      1\n",
       "Ski Snowstar Winter Sports Park, Illinois    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 9#\n",
    "#Concatenate 'Name' and 'state' and count the values again (as above)\n",
    "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A: 1** Your answer here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "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>...</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>...</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>...</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",
       "<p>2 rows × 27 columns</p>\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       ...        \\\n",
       "104        757      0        0.0          0          1       ...         \n",
       "295       4400      1        NaN          2          2       ...         \n",
       "\n",
       "     LongestRun_mi  SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  \\\n",
       "104            0.3              102.0            96.0             120.0   \n",
       "295            2.5             2600.0            10.0               NaN   \n",
       "\n",
       "     yearsOpen  averageSnowfall  AdultWeekday  AdultWeekend  \\\n",
       "104       63.0            132.0          54.0          64.0   \n",
       "295       57.0            486.0          99.0          99.0   \n",
       "\n",
       "     projectedDaysOpen  NightSkiing_ac  \n",
       "104              135.0            56.0  \n",
       "295                NaN             NaN  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[ski_data['Name'] == 'Crystal Mountain']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there are two Crystal Mountain resorts, but they are clearly two different resorts in two different states. This is a powerful signal that you have unique records on each row."
   ]
  },
  {
   "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": [
    "What's the relationship between region and state?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You know they are the same in many cases (e.g. both the Region and the state are given as 'Michigan'). In how many cases do they differ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    297\n",
       "True      33\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 10#\n",
    "#Calculate the number of times Region does not equal state\n",
    "(ski_data.Region != ski_data.state).value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You know what a state is. What is a region? You can tabulate the distinct values along with their respective frequencies using `value_counts()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New York               33\n",
       "Michigan               29\n",
       "Colorado               22\n",
       "Sierra Nevada          22\n",
       "Pennsylvania           19\n",
       "Wisconsin              16\n",
       "New Hampshire          16\n",
       "Vermont                15\n",
       "Minnesota              14\n",
       "Montana                12\n",
       "Idaho                  12\n",
       "Massachusetts          11\n",
       "Washington             10\n",
       "Maine                   9\n",
       "New Mexico              9\n",
       "Wyoming                 8\n",
       "Utah                    7\n",
       "North Carolina          6\n",
       "Salt Lake City          6\n",
       "Oregon                  6\n",
       "Ohio                    5\n",
       "Connecticut             5\n",
       "West Virginia           4\n",
       "Mt. Hood                4\n",
       "Illinois                4\n",
       "Virginia                4\n",
       "Alaska                  3\n",
       "Iowa                    3\n",
       "New Jersey              2\n",
       "Arizona                 2\n",
       "South Dakota            2\n",
       "Nevada                  2\n",
       "Missouri                2\n",
       "Indiana                 2\n",
       "Maryland                1\n",
       "Northern California     1\n",
       "Rhode Island            1\n",
       "Tennessee               1\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data['Region'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A casual inspection by eye reveals some non-state names such as Sierra Nevada, Salt Lake City, and Northern California. Tabulate the differences between Region and state. On a note regarding scaling to larger data sets, you might wonder how you could spot such cases when presented with millions of rows. This is an interesting point. Imagine you have access to a database with a Region and state column in a table and there are millions of rows. You wouldn't eyeball all the rows looking for differences! Bear in mind that our first interest lies in establishing the answer to the question \"Are they always the same?\" One approach might be to ask the database to return records where they differ, but limit the output to 10 rows. If there were differences, you'd only get up to 10 results, and so you wouldn't know whether you'd located all differences, but you'd know that there were 'a nonzero number' of differences. If you got an empty result set back, then you would know that the two columns always had the same value. At the risk of digressing, some values in one column only might be NULL (missing) and different databases treat NULL differently, so be aware that on many an occasion a seamingly 'simple' question gets very interesting to answer very quickly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 11#\n",
    "#Filter the ski_data dataframe for rows where 'Region' and 'state' are different,\n",
    "#group that by 'state' and perform `value_counts` on the 'Region'\n",
    "(ski_data[ski_data.Region != ski_data.state]\n",
    " .groupby('state')['Region']\n",
    " .value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vast majority of the differences are in California, with most Regions being called Sierra Nevada and just one 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": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39\n"
     ]
    }
   ],
   "source": [
    "#Code task 12#\n",
    "#Select the 'Region' and 'state' columns from ski_data and use the `nunique` method to calculate the number of \n",
    "#unique values in each\n",
    "\n",
    "#ski_data[['state','Region']].nunique()\n",
    "#ski_data['state','Region'].unique()\n",
    "\n",
    "print(len(ski_data.groupby(['state','Region'])))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because a few states are split across multiple named regions, there are slightly more unique regions than states."
   ]
  },
  {
   "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": [
    "If this is your first time using [matplotlib](https://matplotlib.org/3.2.2/index.html)'s [subplots](https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.subplots.html), you may find the online documentation useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vXPNAzcys6zQ1NdHU1LTQtgkTJjBw4MAuGpEBSPok8HngmQq720ydrTEt94/A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TAVaCCl93wUuLI/7t/Y2MiYMWNqdzQzM7P+NIUUEvhxPt6StPp0yfzzNEkNwDjgIlKx+amSVoqI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\nmZmZmRkA/w9xjABj8KsP9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44e0254828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 13#\n",
    "#Create two subplots on 1 row and 2 columns with a figsize of (20, 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')\n",
    "\n",
    "#Label the xaxis 'Count'\n",
    "ax[0].set_xlabel('Count')\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')\n",
    "\n",
    "#Label the xaxis 'Count'\n",
    "ax[1].set_xlabel('Count')\n",
    "\n",
    "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n",
    "plt.subplots_adjust(wspace=0.5);\n",
    "\n",
    "#You're encouraged to explore a few different figure sizes, orientations, and spacing here\n",
    "# as the importance of easy-to-read and informative figures is frequently understated\n",
    "# and you will find the ability to tweak figures invaluable later on"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How's your geography? Looking at the distribution of States, you see New York accounting for the majority of resorts. Our target resort is in Montana, which comes in at 13th place. You should think carefully about how, or whether, you use this information. Does New York command a premium because of its proximity to population? Even if a resort's State were a useful predictor of ticket price, your main interest lies in Montana. Would you want a model that is skewed for accuracy by New York? Should you just filter for Montana and create a Montana-specific model? This would slash your available data volume. Your problem task includes the contextual insight that the data are for resorts all belonging to the same market share. This suggests one might expect prices to be similar amongst them. You can look into this. A boxplot grouped by State is an ideal way to quickly compare prices. Another side note worth bringing up here is that, in reality, the best approach here definitely would include consulting with the client or other domain expert. They might know of good reasons for treating states equivalently or differently. The data scientist is rarely the final arbiter of such a decision. But here, you'll see if we can find any supporting evidence for treating states the same or differently."
   ]
  },
  {
   "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 our Big Mountain resort, in Montana. Does the state give you any clues to help decide what your primary target response feature should be (weekend or weekday ticket prices)?"
   ]
  },
  {
   "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": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "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>Illinois</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>43.333333</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>Tennessee</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>65.000000</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>North Carolina</th>\n",
       "      <td>41.833333</td>\n",
       "      <td>64.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>42.200000</td>\n",
       "      <td>45.400000</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>Minnesota</th>\n",
       "      <td>44.595714</td>\n",
       "      <td>49.667143</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>Michigan</th>\n",
       "      <td>45.458333</td>\n",
       "      <td>52.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>46.428571</td>\n",
       "      <td>54.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>47.333333</td>\n",
       "      <td>57.333333</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>New York</th>\n",
       "      <td>50.032258</td>\n",
       "      <td>58.945455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>51.500000</td>\n",
       "      <td>51.500000</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>Montana</th>\n",
       "      <td>51.909091</td>\n",
       "      <td>51.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>52.705882</td>\n",
       "      <td>63.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>56.555556</td>\n",
       "      <td>55.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AdultWeekday  AdultWeekend\n",
       "state                                     \n",
       "Illinois           35.000000     43.333333\n",
       "Iowa               35.666667     41.666667\n",
       "Tennessee          36.000000     65.000000\n",
       "Massachusetts      40.900000     57.200000\n",
       "North Carolina     41.833333     64.166667\n",
       "Ohio               42.200000     45.400000\n",
       "Missouri           43.000000     48.000000\n",
       "Minnesota          44.595714     49.667143\n",
       "Indiana            45.000000     48.500000\n",
       "Michigan           45.458333     52.576923\n",
       "Wisconsin          46.428571     54.266667\n",
       "Alaska             47.333333     57.333333\n",
       "Connecticut        47.800000     56.800000\n",
       "New York           50.032258     58.945455\n",
       "Virginia           51.000000     68.000000\n",
       "South Dakota       51.500000     51.500000\n",
       "Maine              51.500000     61.000000\n",
       "Montana            51.909091     51.909091\n",
       "Pennsylvania       52.705882     63.687500\n",
       "Idaho              56.555556     55.900000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 14#\n",
    "# Calculate average weekday and weekend price by state and sort by the average of the two\n",
    "# Hint: use the pattern dataframe.groupby(<grouping variable>)[<list of columns>].mean()\n",
    "state_price_means = ski_data.groupby('state')[['AdultWeekday', 'AdultWeekend']].mean()\n",
    "state_price_means.sort_values(by=['AdultWeekday', 'AdultWeekend'], inplace=True)\n",
    "state_price_means.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f44dba48f28>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xtz85OZm5c+fSvHnzAvNfGzVqRK1atcjJySl0heRIzZs3Z/369fTo0aPYeAFa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\nhP8PysPWmGZFXV4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44e01cc5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n",
    "# Compare the index order you get from\n",
    "# state_price_means.index\n",
    "# with\n",
    "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n",
    "# See how this expression simply sits within the reindex()\n",
    "(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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket.\n",
    "This tells you how the average ticket price varies from state to state.\n",
    "But can you 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": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/verma_neetin/anaconda3/lib/python3.5/site-packages/pandas/core/indexing.py:1472: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  return self._getitem_tuple(key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f44db877d30>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44db772630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 15#\n",
    "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n",
    "#specify 'state' for `id_vars`\n",
    "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n",
    "#call the resultant price column 'Price' via the `value_name` argument,\n",
    "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n",
    "\n",
    "\n",
    "ticket_prices = pd.melt(state_price_means, id_vars=['state'], value_vars=['AdultWeekday', 'Adultweekend'],\n",
    "        var_name='Ticket', value_name='Price')\n",
    "\n",
    "#print(ticket_prices)\n",
    "\n",
    "#plt.xlabel('Price ($)');\n",
    "ticket_prices.plot.hist(bins=12, alpha=0.5)\n",
    "#ski_data.plot.hist(bins=12, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "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>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>35.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>40.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>41.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>42.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>44.595714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>45.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>46.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>47.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>47.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>50.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>51.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>51.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>51.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>52.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>56.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>57.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>58.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>62.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>65.107143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>65.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state        Ticket      Price\n",
       "0     NaN  AdultWeekday  35.000000\n",
       "1     NaN  AdultWeekday  35.666667\n",
       "2     NaN  AdultWeekday  36.000000\n",
       "3     NaN  AdultWeekday  40.900000\n",
       "4     NaN  AdultWeekday  41.833333\n",
       "5     NaN  AdultWeekday  42.200000\n",
       "6     NaN  AdultWeekday  43.000000\n",
       "7     NaN  AdultWeekday  44.595714\n",
       "8     NaN  AdultWeekday  45.000000\n",
       "9     NaN  AdultWeekday  45.458333\n",
       "10    NaN  AdultWeekday  46.428571\n",
       "11    NaN  AdultWeekday  47.333333\n",
       "12    NaN  AdultWeekday  47.800000\n",
       "13    NaN  AdultWeekday  50.032258\n",
       "14    NaN  AdultWeekday  51.000000\n",
       "15    NaN  AdultWeekday  51.500000\n",
       "16    NaN  AdultWeekday  51.500000\n",
       "17    NaN  AdultWeekday  51.909091\n",
       "18    NaN  AdultWeekday  52.705882\n",
       "19    NaN  AdultWeekday  56.555556\n",
       "20    NaN  AdultWeekday  57.600000\n",
       "21    NaN  AdultWeekday  58.857143\n",
       "22    NaN  AdultWeekday  59.000000\n",
       "23    NaN  AdultWeekday  62.500000\n",
       "24    NaN  AdultWeekday  65.107143\n",
       "25    NaN  AdultWeekday  65.571429"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket_prices.head(26)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is 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 for each state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/verma_neetin/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_base.py:2782: UserWarning: Attempting to set identical left==right results\n",
      "in singular transformations; automatically expanding.\n",
      "left=-0.5, right=-0.5\n",
      "  'left=%s, right=%s') % (left, right))\n",
      "/home/verma_neetin/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'set_title'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-7c8d896dc343>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#This will use boxplot's x, y, hue, and data arguments.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'state'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Price'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Ticket'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mticket_prices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'vertical'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Price ($)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/verma_neetin/anaconda3/lib/python3.5/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, notch, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2235\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwhis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnotch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnotch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2237\u001b[0;31m     \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2238\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/verma_neetin/anaconda3/lib/python3.5/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, ax, boxplot_kws)\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;34m\"\"\"Make the plot.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_boxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mboxplot_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotate_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morient\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"h\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvert_yaxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/verma_neetin/anaconda3/lib/python3.5/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mannotate_axes\u001b[0;34m(self, ax)\u001b[0m\n\u001b[1;32m    416\u001b[0m             \u001b[0mleg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"best\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhue_title\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m                 \u001b[0mleg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhue_title\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m                 \u001b[0;31m# Set the title size a roundabout way to maintain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'set_title'"
     ]
    },
    {
     "data": {
      "image/png": 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ej8lD396S5L8e8lQAAACwQaz2VvZ/mWRHVX0yyQlJbkryQ0m+kmR2jWYDAACAo96qwnyM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\nAKDR/wegzUnNl1q1LQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44d935f860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 16#\n",
    "#Create a seaborn boxplot of the ticket price dataframe we created above,\n",
    "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n",
    "#This will use boxplot's x, y, hue, and data arguments.\n",
    "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');"
   ]
  },
  {
   "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 over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you  could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\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",
    "\n",
    "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus we currently have two main questions you want to resolve:\n",
    "\n",
    "* What do you do about the two types of ticket price?\n",
    "* What do you do about the state information?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.4 Numeric Features<a id='2.6.4_Numeric_Features'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.4.1 Numeric data summary<a id='2.6.4.1_Numeric_data_summary'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 17#\n",
    "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n",
    "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n",
    "#will be useful again\n",
    "ski_data.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    82.424242\n",
       "2    14.242424\n",
       "1     3.333333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "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 price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features 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": [
    "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LyDBc2PktgXuAg1R1dQz1qcpDDz3UwKfG4MYIq7FskOP101g9k7WZhzrmyWaG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\nDMMwDMMwDMMwDKMKbEDNMAzDMAzDMAzDMAzDMKrABtQMwzAMw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iTcA7fk8Kbgu5OA/45+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QnV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\nBcfuq+OkRrDxGdgz+5FSvIgMw76cl9K9jk1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E0v4cOBz4hqpeEIlvYkVI4zTjgqhEJVdE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RG0INfHeVFVOyLn/+P/FpaQVVInVpwWcBlwkapOwjXmlwW+nTKtRyrI1zjcjLxvU0dqXD9n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\nbVmyLeuDpztc5rAw5dMDSduHMm0/7XB+pm5N0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/Llyzn77LO31ff393PqqaeOsez444/n\nhhtG78ftt99OX9/Y/Zg7dy6XX345wDbtyn4MDw8Xuh8f+9jHtu1HmiL248gjj9y2HxWK3I/0cU/3\nRxH7kdZO90fe/ejv7+ewww5j2rRpHHLIIfT19TFv3rwx9nSJmcDzgEEz22xmm4G3AWea2dOE1SuN\n/L0O7frzR6vUDQIHA8OZ+vnAwkzdUKKbvdG7mLHZ+zYkbZcDZ6fq+4Gx4wfgzjvvHPVZ/tzefsTu\nz/39/fT19Y3y54h8GQB33+Luf3T3X7j7PxOSjJxJc9fuuixZsoQtWx6kneszpI/7RQR/7qM9f4bw\nXCDtt2l/TlPLny+uUnc74VyTJbsfsH0/Hh1VW80PzjjjjJbuM0btRcYPKuO71fuMbXuR8oO0zxR1\nv/QP//APdfejQrP7cfbZZ/eyP4/LBGCtkh5vsRCjTRCnXTHaJIQQhdFOADbyJSV4X+pzdEkJ1qxZ\nM6auKHpVu2x92V6dWAKlEm7oX5Ep9wDfBPb1Jvy9hm7JSQnWlBx0vJF+6L/Fixf7wMBArnLzzTeP\nqStyrPWqT5StX5Z2LL5cqxBipn3DW/RlT/lz8UkJbi5AK+3XrZwXGp0f8mo2TkpQxlicqJpF63XL\nn3PeaxeQACy+pARlX19aIUab3OO0K0abYr8+q6io9E7JHUPNzCYB0wlPswH2NrPXAo+4+/2EJ2Kf\nNLPfA38CPgv8GfgegLs/bmaXAxea2aOEOC4XASvc/Z689hRFmSmde1W7bH3ZHjfu/iTw23SdmT0J\nPOzuK5Oquv7eHcrum0b6DwA7MHv27EK+bdddd+O++1YWMuZ62Sd62fYYMLPPAbcSlnL9DfABworT\nw5Mmkfny8wvWK6OPW9NcuXJl3e3ZlVPVmDJlStPjtozx3Qua49WvayURMbNKEpFvUyMBmJlVEoB1\n7X67FWLsyxhtgjjtitEmIYQoilaSEuwH3Mm2lSF8Man/JjDH3S8ws92ArwK7A3cBR7r70ymNecBW\nYCmwC3Ab4T0JIUTc+KgPzfn7BOOvhEUAi4F929RaycaNsxkeHtYNqWiXPQjX6ecDjwG/Ag73JHuv\nfLkTFDfZXuREu+g5lABMCCGEiITcE2ru/mMaxF5z9wXAgjrbNxECIFULgiSEiBR3P6RK3QLq+PvE\nZV/CGzRCdB93/1ATbRYgXy6RoibbNdEuhBBCCBEDyvKZkA2oK+3y9WW7KIey+6ZMfflEN/TlzzFy\nZcF6ZfRxq5qVyfZq5ft1tlVKvsm4MsZ3L2iOY78uOQHYTbSTMKidRE1QPaHFwoUL20rMUaHIxBxp\nu4tMZNbufmTtKKM/8u7HiSeeWHp/1NuPyBOACSF6nFZe+RyXbNiwQdod1pftohzK7psy9eUT3dCf\nCP5sZucCRwMvJwQv/wlwjrv/LtPuPOBDhNc+VwAfcfffd9hcYGPBemX0cW9oljG+e0FzvPq1u682\ns3XAoYRXt0kmzt4IXJI0GwC2JG2uT9rsQwj899P63zALWNKEJYezPQTjdi655JIxdTNmzODGG28c\nU/+Zz3xmTN2ee+45pu2GDRs455xzxrTdbbfdquqeeOKJnHjiiWPqlywZu1+HH344hx+efz/S46vZ\n/QD46EfHvpxT5H7Mnz8/136kKWs/Xvayl1XVLrI/0mS/q9pxHBwcZObMmWP+Vggh8mLu3rhVlzGz\nGcBAuD9o7xWqHXd8K7NnT+fKK68swjQhCid1kZ/p7oPdtqdoivPnLcBOyf/b1foWMLsAnaK1BoGZ\nDAwMMGOGXh/tNWLyZTO7BegHfk54mPZ54FWEjL1PJW3OAc4BTiIkJvgX4NVJm6qx1Cr+PGfOHK6+\n+ods3vynNi29IDEhJl+MTUfnhW7QSX/OJAAbBD5GiF/8iLvfb2ZnExzlFLYnEXkl8MqKr5rZpcCR\nwKlsTwA24u4H1vjO5Np8HM1NqGX//kiOOWYSS5cuzf23QnSamK7PQojeRivUhBBCiHGOux+V/mxm\npwAPAjOByvs5ZwKfdfebkzYnEYKYv5eQOVBERKNsoc2QJ1uo6ChKACaEEEL0AJpQE0IIISYeuxN+\nqD8CYGZ7EbL//bDSwN0fN7O7CQGUNKEWDcoWOt5RAjAhhBCiN1BSgoRsQEtpl68v20U5lN03ZerL\nJ7qhP9H82cwMWAQsd/ffJtXTCBNs6zPN1yfbOsyjBeuV0cfd0kxnCx1oovygRv1iNm7c0NL4L8Nn\nitacaH49nomxL2O0CeK0K0abhBCiKAqfUDOz+WY2kim/zbQ5z8zWmtkGM/u+mU0v2o68zJkzR9od\n1pft8WNm55rZPWb2uJmtN7PrzexlVdpF5NNl902Z+vKJbuhPFH9OcSnwCuCEogSXLFnCli0P0mpm\nwPAmWjqD23mE0FF9jJ1kms/YDJtDSdtVjGURo31rQ9J2eaZdPyHcVJaLq9TdTghBlyW7H7B9P57I\n1FfbjxOovh8XA2dl6v6esEBpA6OzgN6XtK98/nLy70LCcRqdLbRatrtGGfXSPlNUZsD3vOc9o+rb\nzdQ4Z86cljMD9vf309fXNyozoLICdo8Yz9Ex2gRx2hWjTUIIURjuXmgh3CH+CngesEdSnpvafg7h\nFZN3E+5GbwD+AOxcR3MG4DDg4G2VHXd8i5988smeZWBgYExdUfSqdtn6sr22dhjvzPCC/TNvAW4B\nPkj45fVq4GZCAORnptrk8uni/Hlz5ThltFrRXVxFp1Zp1CaPViPtMBaKGm+96hNl65elHZMvVwrw\nFWANsGemfi/CsqfXZOp/BHypjt4MwOfMmeM77fTitq/RsDA5ZosL0Er7Yjvnm1o+nVezmXNDM5p5\nzzG12rV+finDZ4rWLEMvNn8usmy/Nh/Xkp+YHeHHHntsoce8QtnXl1aI0Sb3OO2K1abx7M8qKiqd\nK2XFUNvi7g/V2BZl0OMyM2X1qnbZ+rI9frwnA5mX3Tdl6ssnuqE/UfzZzL4CvAd4m7sPpbe5+2oz\nWwccSngohplNBt4IXNJpWyurp4qjjD6euJpl+EzRmhPFrycCMfZljDZBnHbFaJMQQhRFWTHUXmpm\nfzGzP5jZYjN7EdQOegxUgh4LIeKmqUDmyKeFiAozuxT4APB+4Ekzm5qUXVPNFgGfNLNZZvZq4Crg\nz8D3Om+xEKIWvRpeRQghhBhvlDGh9jPgFOCdwGmE10j+w8wmEV3QYyFEs/RGIHMhRA1OAyYTXuFc\nmyrHVRq4+wWE4FtfJUyKPxM40t2f7rSxQoiG/BqYSrjWTgPeWtlgZucAZwAfBvYHngSWmdnOXbBT\nCCGEGLcUPqHm7svc/Tp3/7W7fx84CngOqZv2GMkGrZV2+fqyvecoPJB5OZTdN2Xqyye6oT8R/Nnd\nd3D3HauUqzLtFrj7C9x9N3d/p7v/vjsWZ5MZtEsZfTxxNcvwmaI1J4Bfb3H3h9z9waQ8ktq2LRSD\nu/8aOAl4ASEUQ88RY1/GaBPEaVeMNgkhRFGU9crnNtz9MeB3wHRgHWCEJ2pppibbGnAUozOI5c8i\nNjIyeiFNJdvTihUrRtUXlbVq+fLlDA4ObqtvN2sVjM6+VdFuJmtVK/tx9913b9uPNEXsx6JFi1rK\nvtXsfqSPe7o/itiPtHarWcQq35/OItbX1xdlJrEk9tJRwMHu/kBqUxs+3a4/f7RK3SDwL7SXFbBa\nNr10VsDBVH2trIAAd2Y+N5PdcDD1bx/w6KiW8udAjP7cC1kBzexAM7sxCckwYmZjOjKe18SqZets\nh8HGTaTZvOJg/Jpl2BgZEya8Sox9GaNNEKddMdokhBCFUXbWA+BZhHhLc5PPa4F5qe2TgaeA99XR\nKD3LpxCxEFvmIUJWwPuBvWtsz+XTxflzrSyfrZR2MnOWqVVslk/RWSL05SOA8wiJCbYCfZntLWfh\nLj7LZ0y+OF51dH7JQ0z+TAircmzip4cBK4DVwCTCpNlWYGrmb5YA/XU0o83yKUTRxOTPKioqvV0K\nX6FmZl8ws4PM7MVm9mbgemAzcG3SREGPhegRFMhciPGDu9/m7p929+8RVpZmGVeviYnmWLlyJYOD\ng22VoaGhxl8kCsNLDa9yE+2sHm9n1T60/jZImqLfBtF+9PZ+9MrbIEKIHqXoGTrCO1B/JqxQGQKu\nAfbKtFlAWNWyAVgGTG+gqRVqYsIQ01MzYITwpDtbTsq0a9qni/NnrVATcROTL2dL4tt9qc97JXWv\nybT7EfClOjpaodazOjc77ODbz6Otl1133c3XrFlTghfFQ8z+7O4A9wD/2q4va4WamAjE7s8qKiq9\nU57RcMYtJ+5+YhNtFhB+gAshIsbdm1rFKp/uDYaGhsY8yW2FKVOmsOeeexZgkYgIZeydcPyVMO+y\nGNi3DZ2VbNw4m7vuuot9921HJ6DzS37M7FmEWMXfdPfVZrYOOBT4VbJ9MvBG4JLuWSmEEEKMPwqf\nUOtV+vr6uPHGG6XdQX3ZLsqhDyizb8rUL097aGiIvfbam5GRrW1r7brrbtx338pRP3rlz6JY5gE/\nLlCvDN8aL5r7EhYntar5ALADs2fPbsG2sVQ7v7TCePZrM/sC4d3MNcDfAZ+heniV3wN/Aj5LD4di\niLFoW4AOAAAVUElEQVQvY7QJ4rQrRpuEEKIoSs/y2SucccYZ0u6wvmwX5VB235SpX5728PBwMpm2\nGBhooyxm48YNY1a6yZ97nraycC9ZsoQtWx6knSzc27PdQggFVcl2207WXghzC+k+TmftTVMra+/F\nVepuBx6qUp/dD9i+H09k6qvtx3E0n334KZrbj8q+H8/Y/oAweZml3n4MM/p4Xs72lW6V88TNwEHA\nUkafP84CPpipW560nUf6/NJu3KUzzjhjPGftfSEhpMoqwiTaQ8Cb3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81mtpnwtOxMM3s6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\nDegAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44d931bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 18#\n",
    "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n",
    "#Try passing it an argument figsize=(15,10)\n",
    "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n",
    "#It's important you create legible and easy-to-read plots\n",
    "ski_data.hist(figsize = (15,10))\n",
    "plt.subplots_adjust(hspace=0.6);\n",
    "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook"
   ]
  },
  {
   "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 an amber flag; it has more variability, but still mostly 0,\n",
    "* trams also may get an amber flag for the same reason,\n",
    "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded 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": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "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>...</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>39</th>\n",
       "      <td>Silverton Mountain</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>13487</td>\n",
       "      <td>3087</td>\n",
       "      <td>10400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>26819.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>79.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>Powder Mountain</td>\n",
       "      <td>Utah</td>\n",
       "      <td>Utah</td>\n",
       "      <td>9422</td>\n",
       "      <td>2522</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8464.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>88.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>146.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>Park City</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>Utah</td>\n",
       "      <td>10000</td>\n",
       "      <td>3200</td>\n",
       "      <td>6800</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Big Sky Resort</td>\n",
       "      <td>Montana</td>\n",
       "      <td>Montana</td>\n",
       "      <td>11166</td>\n",
       "      <td>4350</td>\n",
       "      <td>7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Aspen / Snowmass</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>12510</td>\n",
       "      <td>4406</td>\n",
       "      <td>8104</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5517.0</td>\n",
       "      <td>658.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>179.00</td>\n",
       "      <td>179.00</td>\n",
       "      <td>138.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Vail</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>11570</td>\n",
       "      <td>3450</td>\n",
       "      <td>8120</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5289.0</td>\n",
       "      <td>461.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Heavenly Mountain Resort</td>\n",
       "      <td>Sierra Nevada</td>\n",
       "      <td>California</td>\n",
       "      <td>10067</td>\n",
       "      <td>3500</td>\n",
       "      <td>7170</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>3379.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>Mt. Bachelor</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>9065</td>\n",
       "      <td>3365</td>\n",
       "      <td>5700</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4318.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>185.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mammoth Mountain Ski Area</td>\n",
       "      <td>Sierra Nevada</td>\n",
       "      <td>California</td>\n",
       "      <td>11053</td>\n",
       "      <td>3100</td>\n",
       "      <td>7953</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>149.00</td>\n",
       "      <td>159.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Northstar California</td>\n",
       "      <td>Sierra Nevada</td>\n",
       "      <td>California</td>\n",
       "      <td>8610</td>\n",
       "      <td>2280</td>\n",
       "      <td>6330</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3170.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Keystone</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>12408</td>\n",
       "      <td>3128</td>\n",
       "      <td>9280</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3148.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Winter Park Resort</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>12060</td>\n",
       "      <td>3060</td>\n",
       "      <td>9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3081.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>Snowbasin</td>\n",
       "      <td>Utah</td>\n",
       "      <td>Utah</td>\n",
       "      <td>9350</td>\n",
       "      <td>2900</td>\n",
       "      <td>6450</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>115.00</td>\n",
       "      <td>115.00</td>\n",
       "      <td>138.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Big Mountain Resort</td>\n",
       "      <td>Montana</td>\n",
       "      <td>Montana</td>\n",
       "      <td>6817</td>\n",
       "      <td>2353</td>\n",
       "      <td>4464</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>81.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>123.0</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Steamboat</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>10568</td>\n",
       "      <td>3668</td>\n",
       "      <td>6900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2965.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>145.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Breckenridge</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>12998</td>\n",
       "      <td>3398</td>\n",
       "      <td>9600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2908.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Schweitzer</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>6400</td>\n",
       "      <td>2400</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>81.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Alta Ski Area</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>Utah</td>\n",
       "      <td>11068</td>\n",
       "      <td>2538</td>\n",
       "      <td>8530</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2614.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>116.00</td>\n",
       "      <td>116.00</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>Grand Targhee Resort</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>9920</td>\n",
       "      <td>2270</td>\n",
       "      <td>7851</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2602.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>152.0</td>\n",
       "      <td>NaN</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>...</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>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Bogus Basin</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>7582</td>\n",
       "      <td>1800</td>\n",
       "      <td>5800</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>134.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>130.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Copper Mountain Resort</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>12313</td>\n",
       "      <td>2738</td>\n",
       "      <td>9712</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2527.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>158.00</td>\n",
       "      <td>158.00</td>\n",
       "      <td>164.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>Jackson Hole</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>10450</td>\n",
       "      <td>4139</td>\n",
       "      <td>6311</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>Snowbird</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>Utah</td>\n",
       "      <td>11000</td>\n",
       "      <td>3240</td>\n",
       "      <td>7760</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>125.00</td>\n",
       "      <td>180.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Discovery Ski Area</td>\n",
       "      <td>Montana</td>\n",
       "      <td>Montana</td>\n",
       "      <td>8150</td>\n",
       "      <td>2380</td>\n",
       "      <td>5770</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>116.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>49 Degrees North</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Washington</td>\n",
       "      <td>5774</td>\n",
       "      <td>1851</td>\n",
       "      <td>3932</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2325.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>62.00</td>\n",
       "      <td>135.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kirkwood</td>\n",
       "      <td>Sierra Nevada</td>\n",
       "      <td>California</td>\n",
       "      <td>9800</td>\n",
       "      <td>2000</td>\n",
       "      <td>7800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Mt. Hood Meadows</td>\n",
       "      <td>Mt. Hood</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>7300</td>\n",
       "      <td>2777</td>\n",
       "      <td>4523</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160.0</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Sun Valley</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>9150</td>\n",
       "      <td>3400</td>\n",
       "      <td>5750</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2054.0</td>\n",
       "      <td>645.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Deer Valley Resort</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>Utah</td>\n",
       "      <td>9570</td>\n",
       "      <td>3000</td>\n",
       "      <td>6570</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2026.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>169.00</td>\n",
       "      <td>169.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Seven Oaks</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>975</td>\n",
       "      <td>275</td>\n",
       "      <td>800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Andes Tower Hills Ski Area</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>1620</td>\n",
       "      <td>290</td>\n",
       "      <td>1330</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>45.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>110.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Dry Hill Ski Area</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>950</td>\n",
       "      <td>300</td>\n",
       "      <td>650</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>35.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Hyland Ski &amp; Snowboard Area</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>1075</td>\n",
       "      <td>175</td>\n",
       "      <td>900</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>35.34</td>\n",
       "      <td>35.34</td>\n",
       "      <td>115.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Tyrol Basin</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>1160</td>\n",
       "      <td>300</td>\n",
       "      <td>860</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>103.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Buena Vista Ski Area</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>1510</td>\n",
       "      <td>230</td>\n",
       "      <td>1280</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>44.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Grand Geneva</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>1086</td>\n",
       "      <td>211</td>\n",
       "      <td>875</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>93.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Snow Creek</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>1100</td>\n",
       "      <td>300</td>\n",
       "      <td>800</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>37.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>30.0</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>...</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>30.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>152.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Hidden Valley Ski Area</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>2566</td>\n",
       "      <td>310</td>\n",
       "      <td>2316</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>Yawgoo Valley</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>315</td>\n",
       "      <td>245</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Ski Snowstar Winter Sports Park</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>790</td>\n",
       "      <td>262</td>\n",
       "      <td>528</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>86.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Appalachian Ski Mountain</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>4000</td>\n",
       "      <td>365</td>\n",
       "      <td>3635</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Ski Big Bear</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>1250</td>\n",
       "      <td>650</td>\n",
       "      <td>600</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>62.00</td>\n",
       "      <td>75.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Maple Ski Ridge</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>1200</td>\n",
       "      <td>450</td>\n",
       "      <td>750</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Bryce Resort</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>1750</td>\n",
       "      <td>500</td>\n",
       "      <td>1250</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>95.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Woods Valley Ski Area</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>1400</td>\n",
       "      <td>500</td>\n",
       "      <td>900</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Campgaw Mountain</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>726</td>\n",
       "      <td>274</td>\n",
       "      <td>450</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Brantling Ski Slopes</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>850</td>\n",
       "      <td>250</td>\n",
       "      <td>600</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>32.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>The Homestead</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>900</td>\n",
       "      <td>320</td>\n",
       "      <td>580</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>42.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>Ski Sawmill</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>2215</td>\n",
       "      <td>515</td>\n",
       "      <td>1700</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>90.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Elm Creek Winter Recreation Area</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>928</td>\n",
       "      <td>60</td>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>102.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Villa Olivia</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>500</td>\n",
       "      <td>180</td>\n",
       "      <td>320</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>70.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Mt. Jefferson</td>\n",
       "      <td>Maine</td>\n",
       "      <td>Maine</td>\n",
       "      <td>753</td>\n",
       "      <td>432</td>\n",
       "      <td>351</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Mulligan's Hollow Ski Bowl</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>700</td>\n",
       "      <td>130</td>\n",
       "      <td>570</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Four Lakes</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>500</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Sapphire Valley</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>3450</td>\n",
       "      <td>200</td>\n",
       "      <td>3200</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Big Squaw Mountain Ski Resort</td>\n",
       "      <td>Maine</td>\n",
       "      <td>Maine</td>\n",
       "      <td>3200</td>\n",
       "      <td>660</td>\n",
       "      <td>1750</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Eagle Rock</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>1810</td>\n",
       "      <td>550</td>\n",
       "      <td>1260</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Ober Gatlinburg Ski Resort</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>3300</td>\n",
       "      <td>600</td>\n",
       "      <td>2700</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>36.00</td>\n",
       "      <td>65.00</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Name          Region           state  \\\n",
       "39                 Silverton Mountain        Colorado        Colorado   \n",
       "267                   Powder Mountain            Utah            Utah   \n",
       "266                         Park City  Salt Lake City            Utah   \n",
       "140                    Big Sky Resort         Montana         Montana   \n",
       "27                   Aspen / Snowmass        Colorado        Colorado   \n",
       "45                               Vail        Colorado        Colorado   \n",
       "11           Heavenly Mountain Resort   Sierra Nevada      California   \n",
       "231                      Mt. Bachelor          Oregon          Oregon   \n",
       "14          Mammoth Mountain Ski Area   Sierra Nevada      California   \n",
       "18               Northstar California   Sierra Nevada      California   \n",
       "35                           Keystone        Colorado        Colorado   \n",
       "46                 Winter Park Resort        Colorado        Colorado   \n",
       "268                         Snowbasin            Utah            Utah   \n",
       "151               Big Mountain Resort         Montana         Montana   \n",
       "42                          Steamboat        Colorado        Colorado   \n",
       "29                       Breckenridge        Colorado        Colorado   \n",
       "60                         Schweitzer           Idaho           Idaho   \n",
       "260                     Alta Ski Area  Salt Lake City            Utah   \n",
       "322              Grand Targhee Resort         Wyoming         Wyoming   \n",
       "295                  Crystal Mountain      Washington      Washington   \n",
       "53                        Bogus Basin           Idaho           Idaho   \n",
       "30             Copper Mountain Resort        Colorado        Colorado   \n",
       "324                      Jackson Hole         Wyoming         Wyoming   \n",
       "269                          Snowbird  Salt Lake City            Utah   \n",
       "143                Discovery Ski Area         Montana         Montana   \n",
       "292                  49 Degrees North      Washington      Washington   \n",
       "13                           Kirkwood   Sierra Nevada      California   \n",
       "232                  Mt. Hood Meadows        Mt. Hood          Oregon   \n",
       "63                         Sun Valley           Idaho           Idaho   \n",
       "264                Deer Valley Resort  Salt Lake City            Utah   \n",
       "..                                ...             ...             ...   \n",
       "72                         Seven Oaks            Iowa            Iowa   \n",
       "125        Andes Tower Hills Ski Area       Minnesota       Minnesota   \n",
       "188                 Dry Hill Ski Area        New York        New York   \n",
       "131       Hyland Ski & Snowboard Area       Minnesota       Minnesota   \n",
       "319                       Tyrol Basin       Wisconsin       Wisconsin   \n",
       "127              Buena Vista Ski Area       Minnesota       Minnesota   \n",
       "312                      Grand Geneva       Wisconsin       Wisconsin   \n",
       "139                        Snow Creek        Missouri        Missouri   \n",
       "2                    Hilltop Ski Area          Alaska          Alaska   \n",
       "138            Hidden Valley Ski Area        Missouri        Missouri   \n",
       "256                     Yawgoo Valley    Rhode Island    Rhode Island   \n",
       "67    Ski Snowstar Winter Sports Park        Illinois        Illinois   \n",
       "216          Appalachian Ski Mountain  North Carolina  North Carolina   \n",
       "238                      Ski Big Bear    Pennsylvania    Pennsylvania   \n",
       "198                   Maple Ski Ridge        New York        New York   \n",
       "288                      Bryce Resort        Virginia        Virginia   \n",
       "215             Woods Valley Ski Area        New York        New York   \n",
       "172                  Campgaw Mountain      New Jersey      New Jersey   \n",
       "184              Brantling Ski Slopes        New York        New York   \n",
       "121                     The Homestead        Michigan        Michigan   \n",
       "251                       Ski Sawmill    Pennsylvania    Pennsylvania   \n",
       "129  Elm Creek Winter Recreation Area       Minnesota       Minnesota   \n",
       "68                       Villa Olivia        Illinois        Illinois   \n",
       "78                      Mt. Jefferson           Maine           Maine   \n",
       "112        Mulligan's Hollow Ski Bowl        Michigan        Michigan   \n",
       "66                         Four Lakes        Illinois        Illinois   \n",
       "218                   Sapphire Valley  North Carolina  North Carolina   \n",
       "74      Big Squaw Mountain Ski Resort           Maine           Maine   \n",
       "243                        Eagle Rock    Pennsylvania    Pennsylvania   \n",
       "259        Ober Gatlinburg Ski Resort       Tennessee       Tennessee   \n",
       "\n",
       "     summit_elev  vertical_drop  base_elev  trams  fastEight  fastSixes  \\\n",
       "39         13487           3087      10400      0        0.0          0   \n",
       "267         9422           2522       6900      0        0.0          0   \n",
       "266        10000           3200       6800      4        0.0          6   \n",
       "140        11166           4350       7500      1        1.0          2   \n",
       "27         12510           4406       8104      3        0.0          1   \n",
       "45         11570           3450       8120      2        0.0          3   \n",
       "11         10067           3500       7170      2        0.0          2   \n",
       "231         9065           3365       5700      0        0.0          0   \n",
       "14         11053           3100       7953      3        0.0          2   \n",
       "18          8610           2280       6330      2        0.0          1   \n",
       "35         12408           3128       9280      2        0.0          2   \n",
       "46         12060           3060       9000      1        0.0          2   \n",
       "268         9350           2900       6450      3        0.0          1   \n",
       "151         6817           2353       4464      0        0.0          0   \n",
       "42         10568           3668       6900      1        0.0          1   \n",
       "29         12998           3398       9600      1        0.0          5   \n",
       "60          6400           2400       4000      0        0.0          1   \n",
       "260        11068           2538       8530      0        0.0          0   \n",
       "322         9920           2270       7851      0        0.0          0   \n",
       "295         7012           3100       4400      1        NaN          2   \n",
       "53          7582           1800       5800      0        NaN          0   \n",
       "30         12313           2738       9712      1        0.0          2   \n",
       "324        10450           4139       6311      3        0.0          0   \n",
       "269        11000           3240       7760      1        0.0          0   \n",
       "143         8150           2380       5770      0        0.0          0   \n",
       "292         5774           1851       3932      0        0.0          0   \n",
       "13          9800           2000       7800      0        0.0          0   \n",
       "232         7300           2777       4523      0        NaN          0   \n",
       "63          9150           3400       5750      1        0.0          0   \n",
       "264         9570           3000       6570      1        0.0          0   \n",
       "..           ...            ...        ...    ...        ...        ...   \n",
       "72           975            275        800      0        0.0          0   \n",
       "125         1620            290       1330      0        0.0          0   \n",
       "188          950            300        650      0        NaN          0   \n",
       "131         1075            175        900      0        0.0          0   \n",
       "319         1160            300        860      0        0.0          0   \n",
       "127         1510            230       1280      0        NaN          0   \n",
       "312         1086            211        875      0        NaN          0   \n",
       "139         1100            300        800      0        NaN          0   \n",
       "2           2090            294       1796      0        0.0          0   \n",
       "138         2566            310       2316      0        0.0          0   \n",
       "256          315            245         70      0        NaN          0   \n",
       "67           790            262        528      0        NaN          0   \n",
       "216         4000            365       3635      0        0.0          0   \n",
       "238         1250            650        600      0        0.0          0   \n",
       "198         1200            450        750      0        0.0          0   \n",
       "288         1750            500       1250      0        NaN          0   \n",
       "215         1400            500        900      0        NaN          0   \n",
       "172          726            274        450      0        NaN          0   \n",
       "184          850            250        600      0        NaN          0   \n",
       "121          900            320        580      0        NaN          0   \n",
       "251         2215            515       1700      0        0.0          0   \n",
       "129          928             60        868      0        NaN          0   \n",
       "68           500            180        320      0        NaN          0   \n",
       "78           753            432        351      0        NaN          0   \n",
       "112          700            130        570      0        NaN          0   \n",
       "66           500            100        400      0        NaN          0   \n",
       "218         3450            200       3200      0        NaN          0   \n",
       "74          3200            660       1750      0        NaN          0   \n",
       "243         1810            550       1260      0        NaN          0   \n",
       "259         3300            600       2700      0        NaN          0   \n",
       "\n",
       "     fastQuads       ...        LongestRun_mi  SkiableTerrain_ac  \\\n",
       "39           0       ...                  1.5            26819.0   \n",
       "267          1       ...                  3.5             8464.0   \n",
       "266         10       ...                  3.5             7300.0   \n",
       "140          5       ...                  6.0             5800.0   \n",
       "27          15       ...                  5.3             5517.0   \n",
       "45          15       ...                  4.0             5289.0   \n",
       "11           7       ...                  5.5             4800.0   \n",
       "231          8       ...                  4.0             4318.0   \n",
       "14           9       ...                  3.0             3500.0   \n",
       "18           7       ...                  1.4             3170.0   \n",
       "35           4       ...                  3.5             3148.0   \n",
       "46           7       ...                  4.9             3081.0   \n",
       "268          2       ...                  3.5             3000.0   \n",
       "151          3       ...                  3.3             3000.0   \n",
       "42           6       ...                  3.0             2965.0   \n",
       "29           6       ...                  3.5             2908.0   \n",
       "60           2       ...                  2.1             2900.0   \n",
       "260          3       ...                  1.3             2614.0   \n",
       "322          2       ...                  2.7             2602.0   \n",
       "295          2       ...                  2.5             2600.0   \n",
       "53           3       ...                  1.5             2600.0   \n",
       "30           4       ...                  1.7             2527.0   \n",
       "324          4       ...                  4.5             2500.0   \n",
       "269          6       ...                  2.5             2500.0   \n",
       "143          0       ...                  1.5             2400.0   \n",
       "292          0       ...                  2.0             2325.0   \n",
       "13           2       ...                  2.5             2300.0   \n",
       "232          6       ...                  3.0             2150.0   \n",
       "63           9       ...                  3.0             2054.0   \n",
       "264         13       ...                  2.8             2026.0   \n",
       "..         ...       ...                  ...                ...   \n",
       "72           0       ...                  1.0               35.0   \n",
       "125          0       ...                  0.2               35.0   \n",
       "188          0       ...                  0.2               35.0   \n",
       "131          0       ...                  1.0               35.0   \n",
       "319          0       ...                  0.5               32.0   \n",
       "127          0       ...                  0.3               30.0   \n",
       "312          0       ...                  0.2               30.0   \n",
       "139          0       ...                  0.3               30.0   \n",
       "2            0       ...                  1.0               30.0   \n",
       "138          0       ...                  0.1               30.0   \n",
       "256          0       ...                  0.2               30.0   \n",
       "67           0       ...                  0.8               28.0   \n",
       "216          0       ...                  0.5               27.0   \n",
       "238          0       ...                  1.5               26.0   \n",
       "198          0       ...                  0.3               25.0   \n",
       "288          0       ...                  0.4               25.0   \n",
       "215          0       ...                  0.3               25.0   \n",
       "172          0       ...                  0.3               23.0   \n",
       "184          0       ...                  0.1               20.0   \n",
       "121          0       ...                  0.2               16.0   \n",
       "251          0       ...                  0.1               15.0   \n",
       "129          0       ...                  1.0               15.0   \n",
       "68           0       ...                  0.1               15.0   \n",
       "78           0       ...                  0.3               12.0   \n",
       "112          0       ...                  0.2               10.0   \n",
       "66           0       ...                  0.1                9.0   \n",
       "218          0       ...                  1.0                8.0   \n",
       "74           0       ...                  0.8                NaN   \n",
       "243          0       ...                  1.0                NaN   \n",
       "259          0       ...                  1.0                NaN   \n",
       "\n",
       "     Snow Making_ac  daysOpenLastYear  yearsOpen  averageSnowfall  \\\n",
       "39              NaN             175.0       17.0            400.0   \n",
       "267             NaN             120.0       47.0            500.0   \n",
       "266           750.0             142.0       56.0            355.0   \n",
       "140           400.0             144.0       46.0            400.0   \n",
       "27            658.0             138.0       72.0            300.0   \n",
       "45            461.0             149.0       57.0            354.0   \n",
       "11           3379.0             155.0       64.0            360.0   \n",
       "231            20.0             185.0       61.0            462.0   \n",
       "14            700.0             243.0       66.0            400.0   \n",
       "18           1500.0             135.0       47.0            350.0   \n",
       "35            662.0             184.0       49.0            235.0   \n",
       "46            313.0             158.0       79.0            360.0   \n",
       "268           625.0             143.0       79.0            300.0   \n",
       "151           600.0             123.0       72.0            333.0   \n",
       "42            375.0             145.0       56.0            336.0   \n",
       "29            600.0             182.0       58.0            300.0   \n",
       "60             47.0             136.0       56.0            300.0   \n",
       "260           140.0             150.0       81.0            545.0   \n",
       "322             NaN             152.0       50.0            500.0   \n",
       "295            10.0               NaN       57.0            486.0   \n",
       "53              NaN             134.0       77.0            250.0   \n",
       "30            364.0             164.0       47.0            300.0   \n",
       "324           195.0             130.0       54.0            459.0   \n",
       "269             NaN             188.0       48.0            500.0   \n",
       "143            25.0             116.0       46.0            225.0   \n",
       "292             NaN             101.0       48.0            301.0   \n",
       "13            200.0             200.0       47.0            354.0   \n",
       "232             NaN             118.0       51.0            430.0   \n",
       "63            645.0             149.0       83.0            220.0   \n",
       "264           660.0               NaN       39.0            300.0   \n",
       "..              ...               ...        ...              ...   \n",
       "72             35.0             100.0       22.0             40.0   \n",
       "125            35.0             100.0       38.0             55.0   \n",
       "188            26.0               NaN       55.0            125.0   \n",
       "131            35.0             110.0       61.0             55.0   \n",
       "319            32.0             112.0       61.0             41.0   \n",
       "127            30.0              57.0       70.0             78.0   \n",
       "312            30.0              90.0       25.0             25.0   \n",
       "139            30.0              69.0       33.0             20.0   \n",
       "2              30.0             150.0       36.0             69.0   \n",
       "138            30.0               NaN       37.0             26.0   \n",
       "256            30.0             100.0       54.0             40.0   \n",
       "67             28.0              56.0       38.0             38.0   \n",
       "216            27.0             100.0       57.0             50.0   \n",
       "238            26.0              75.0       43.0             69.0   \n",
       "198            25.0               NaN       57.0              NaN   \n",
       "288            25.0             100.0       54.0             30.0   \n",
       "215            16.0               NaN       55.0            180.0   \n",
       "172            23.0              80.0       58.0             45.0   \n",
       "184            16.0               NaN       19.0            110.0   \n",
       "121            16.0              47.0       34.0            150.0   \n",
       "251            13.0               NaN       50.0             24.0   \n",
       "129            20.0             105.0       13.0             45.0   \n",
       "68             15.0               NaN       53.0             25.0   \n",
       "78              NaN               NaN       19.0              NaN   \n",
       "112            10.0               NaN       19.0             60.0   \n",
       "66              9.0              78.0       58.0             24.0   \n",
       "218             8.0              53.0       55.0             24.0   \n",
       "74              NaN              67.0        6.0              NaN   \n",
       "243             NaN               NaN       47.0              NaN   \n",
       "259             NaN              83.0       44.0             35.0   \n",
       "\n",
       "     AdultWeekday  AdultWeekend  projectedDaysOpen  NightSkiing_ac  \n",
       "39          79.00         79.00              181.0             NaN  \n",
       "267         88.00         88.00              146.0           300.0  \n",
       "266           NaN           NaN              143.0             NaN  \n",
       "140           NaN           NaN              144.0             NaN  \n",
       "27         179.00        179.00              138.0             NaN  \n",
       "45            NaN           NaN              142.0             NaN  \n",
       "11            NaN           NaN              157.0             NaN  \n",
       "231         99.00         99.00              185.0             NaN  \n",
       "14         149.00        159.00                NaN             NaN  \n",
       "18            NaN           NaN              150.0             NaN  \n",
       "35            NaN           NaN                NaN           288.0  \n",
       "46            NaN           NaN              151.0             NaN  \n",
       "268        115.00        115.00              138.0             NaN  \n",
       "151         81.00         81.00              123.0           600.0  \n",
       "42            NaN           NaN              145.0            30.0  \n",
       "29            NaN           NaN              163.0             NaN  \n",
       "60          81.00         81.00                NaN           100.0  \n",
       "260        116.00        116.00              140.0             NaN  \n",
       "322         90.00         90.00              152.0             NaN  \n",
       "295         99.00         99.00                NaN             NaN  \n",
       "53          64.00         64.00              130.0           165.0  \n",
       "30         158.00        158.00              164.0             NaN  \n",
       "324           NaN           NaN              133.0             NaN  \n",
       "269        125.00        125.00              180.0             2.0  \n",
       "143         49.00         49.00              116.0             NaN  \n",
       "292         62.00         62.00              135.0           250.0  \n",
       "13            NaN           NaN              167.0             NaN  \n",
       "232           NaN           NaN              160.0           170.0  \n",
       "63            NaN           NaN              137.0             NaN  \n",
       "264        169.00        169.00                NaN             NaN  \n",
       "..            ...           ...                ...             ...  \n",
       "72          40.00         40.00              100.0            35.0  \n",
       "125         45.00         45.00              110.0            35.0  \n",
       "188           NaN         35.00                NaN            26.0  \n",
       "131         35.34         35.34              115.0            35.0  \n",
       "319         17.00         48.00              103.0            32.0  \n",
       "127         44.00         44.00               60.0            30.0  \n",
       "312         28.00         49.00               93.0            30.0  \n",
       "139         37.00         47.00               85.0            30.0  \n",
       "2           30.00         34.00              152.0            30.0  \n",
       "138         49.00         49.00                NaN            17.0  \n",
       "256           NaN           NaN              100.0            30.0  \n",
       "67          25.00         35.00               86.0            28.0  \n",
       "216         40.00         64.00              100.0            27.0  \n",
       "238         50.00         62.00               75.0            26.0  \n",
       "198         26.00         38.00                NaN            20.0  \n",
       "288         43.00         68.00               95.0            20.0  \n",
       "215         31.00         39.00                NaN            15.0  \n",
       "172           NaN           NaN                NaN            14.0  \n",
       "184         32.00         32.00                NaN             NaN  \n",
       "121         50.00         50.00               42.0            16.0  \n",
       "251         30.00         44.00               90.0            15.0  \n",
       "129         17.00         17.00              102.0            15.0  \n",
       "68          40.00         40.00               70.0            15.0  \n",
       "78            NaN           NaN                NaN             NaN  \n",
       "112         15.00         20.00                NaN            10.0  \n",
       "66            NaN           NaN               70.0             9.0  \n",
       "218         40.00         43.00               60.0             8.0  \n",
       "74          30.00         30.00               58.0             NaN  \n",
       "243           NaN           NaN               40.0             NaN  \n",
       "259         36.00         65.00               94.0             NaN  \n",
       "\n",
       "[330 rows x 27 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 19#\n",
    "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n",
    "#ski_data[ski_data['SkiableTerrain_ac'] > 1000].sort\n",
    "\n",
    "ski_data.sort_values(by='SkiableTerrain_ac', ascending=False)\n",
    "\n",
    "#ski_data[ski_data['Name'] == 'Crystal Mountain']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "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</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</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>181</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\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\n",
       "Snow Making_ac                    NaN\n",
       "daysOpenLastYear                  175\n",
       "yearsOpen                          17\n",
       "averageSnowfall                   400\n",
       "AdultWeekday                       79\n",
       "AdultWeekend                       79\n",
       "projectedDaysOpen                 181\n",
       "NightSkiing_ac                    NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 20#\n",
    "#Now you know there's only one, print the whole row to investigate all values, including seeing the resort name\n",
    "#Hint: don't forget the transpose will be helpful here\n",
    "ski_data[ski_data['SkiableTerrain_ac'] > 15000].T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A: 2** Your answer here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But what can you do when you have one record that seems highly suspicious?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see if your data are correct. Search for \"silverton mountain skiable area\". If you do this, you get some [useful information](https://www.google.com/search?q=silverton+mountain+skiable+area)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Silverton Mountain information](images/silverton_mountain_info.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can spot check data. You see your top and base elevation values agree, but the skiable area is very different. Your suspect value is 26819, but the value you've just looked up is 1819. The last three digits agree. This sort of error could have occured in transmission or some editing or transcription stage. You could plausibly replace the suspect value with the one you've just obtained. Another cautionary note to make here is that although you're doing this in order to progress with your analysis, this is most definitely an issue that should have been raised and fed back to the client or data originator as a query. You should view this \"data correction\" step as a means to continue (documenting it carefully as you do in this notebook) rather than an ultimate decision as to what is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26819.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 21#\n",
    "#Use the .loc accessor to print the 'SkiableTerrain_ac' value only for this resort\n",
    "ski_data.loc[39, 'SkiableTerrain_ac']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Code task 22#\n",
    "#Use the .loc accessor again to modify this value with the correct value of 1819\n",
    "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1819.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 23#\n",
    "#Use the .loc accessor a final time to verify that the value has been modified\n",
    "ski_data.loc[39, 'SkiableTerrain_ac']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NB whilst you may become suspicious about your data quality, and you know you have missing values, you will not here dive down the rabbit hole of checking all values or web scraping to replace missing values.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does the distribution of skiable area look like now?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VoZ8ws0+Rvt3vBfyS8i8Q9aIm07Zn8E7vXtK3tqLnM/ibTSdtuxnYu/ihReoA17i/DDeT\nqumiEa9nmNepYTHpsP93zewBdz98mMXuSfrwe5u7X96YaGYvbDH/Nmb2tMLRox1Ir8XKFo9pHDV4\nwt1/2WKeoexL2mG+Mf9t08ze28EybiB11P6ND905dk86ez6KGkfrtiP182hoN8MNpJ3OS+juw/cG\n0o5qZf6bZCvu/hfgL8CXzGxXUgfoD5I6Rzdsl/073FGL/YAb3P0d+Ylm1u6poFbv58Z7/sEut59u\n3E3qY7Z+B+tsZ99WNFbbxXBuJm0325M6ZDfasT7pyNdQGRrta3u767J965Ge46dOmWed5jenu/3q\nDcDTs/3qcOtu57OjcTSu+NnV9MiSu99J6sj8v2Y2iXSK/9O0/gLZcAbp831vUkdryJ1Sy07Nvg74\nrLsfk5u+/TDLbVjn8zcbwFE8Bdju8zesMetzlI3saHzrPX2I+ZqdIrqatJE3Du82NoZmxUo3DrHc\n1bvN7J2kJz0/CuYGYFcz2yA331uB5xWW1UnbzicVqLML048gfdMpjnjq1vnAZDM7oDEh28F8mLSz\n/VWrB7bS5uv0FHc/jTRK5ENmNtxFP1dny3lq+7R0NeTDWsy/AenDszHvhqTTDneTRjWtw93vJn0z\n/oCZTS7en+0Yhmujk/uCYWbbko54tOvM7PGfK95hZutnR4wa6+rk+Sj6bfb4XQrT282wOJvvc132\nAzybtD0f1exOM9si+/cZ2XaZ95fsscVtapds+m+HWfc639bN7F9pf3RMq/fzUtI+4RNmtkmTdQy3\n/XQs63fzI2A/SyMt21lnO/u2orHaLoZzFemo538UjnQczPBdCaDN7W4EzmftpTjyPk56Xn7axTLP\nBKaZ2fTiHWa2We55aOuzw90fJB1dLfa1OZzBo8TXM7P86fpGn6jbaLI/b+IXpALmQNIptSvdPV8c\nNt6HxZrjCNo7oHAD62b4AOseORru+SvO39JoHDky4C1mNjVb/lakivENpPO2fT70heg+Z2a7kzas\nm7PHf4h0vnZJNs8NpFMMHzSzh0g7sN8VXoxO3AMsMbNvk0bBfJR0+O2U3DynkDp7XWhmZ5JGahzM\nuqdeOmnbT0hHXY4xs+1YOxxzBnBcG+d62/V/pA3pO2a2C2uH8k8jjfIqnrduRzuv0yDuflL2Bjwm\nO4LUqkj6DemNdqqZNYYmH0zrN9HtwNxsB/5X0ht0R+A/Ch0+iw4ndd67xsxOJhXuW5Gel+cArxzi\nsT8l9WO50MxOzx53GKlvV1s/NeHuvzazb5JOG72CdHTtCVLH9XeQismz6fz5KK7nJjP7M/B6Bnc4\nbSuDu99gZseQvtxcZmZnkzqv/gupn8anh1n/jWb2GdKRoO1IH6oPkk5r7UPqqHwsaT9xopk1hrxv\nQPo2+iSpKMh7PXC5D3EJi8x5wL7ZYf+fZuv8AKnoavZzRkXN3s9XZP2j3kf6kPpLtu+4lbTd7EXq\nXN5JoVzUqtj4JOlI4hXZNnst6VIWO5Oev2KB1M6+rWhMtovhuPsTZjaPdGr1kmy/uy3pcgnXM8z2\n38F21237/mRm3wXen31Z/BVpKP8hwNnu3vGXTtJlDfqA87LTy0tJI313JB3R25b0mnby2XEKaR9z\nMqng3J10tCu/jT0D+LuZncXaU15vIH0JGXb4u7s/mb3+B5IGiXy8cP+D2Sm7udkXu1tJp+62pfW2\nnncK6WjWWaTTZi/PHl/sW9Tu8ze8doe1tXNj7RC8xu2R7Em4gPRBtEmTxxxFGknT+HtP0gdC48Jg\nt5Cuj/TCwuPeSroY4mMMvtDiJcDVLdp3CXBx7u89ssfuTzod1PJCadn8HyN9+A+Q3givLC5zmLZ9\nm3SIPz/vRFK/l1tYeyGvI5qsezXw9SbTbwQWtvHaTCJtYHey9iKQzS5AdiPw4zaWN+zrlHt+9y08\n9svZ9A8Vtpv80M1dSZcaeChb9pdIH4irgd0Lr+nV2WtxOenD60bgg4V1Pj//WuSmb5u9Lo2LK67K\nXv992ngODs1erwHSh+0hFC5NMdRrl7v/vaRO4w+RPoj/mOXdqtPnY4h1fIz0gV0cot1Whtzr1LiY\nXj/p1Onr2t12SB9IvyJ13nwgW9/Xge1zr8XJpA/vh0k7vl+Qu2BjNt+m2Wt1aJv7pSOztjUuZvhm\nmr8Xm7afFu/n7L4dSX0p7mLtBUgX5dvceD4pXOxuiPY23g+tLqY7iVQwrMyeh1tJhfWsJu+9Yfdt\nLZ6LMreLVvvd4n6h1Xv08NzrdwXpujW/p3AZhW63u/x+pMXjW27XpCMhn2HtRSBXkgZ5bNjOMorP\nTTZtYvaaXUfat95J+hL3MQZf+qDdz46NSV+Q7yHtX04njQJcTTrNBWmgwpdJl0C5L3uelgHvb+c5\nzpaxd7bMJ4Btmty/NWsvuHsP6X2yVb4dhe0//3lgpH3enaQC96ekU+vrfP61+/wNd7NsYSISWHbE\n7gZgrrt/u+r2jISZfYzUWfSFPnRfrXHLzPYgffC+w93Prro9ZcpO4d0N/Mjdm43aExmxyn8E1cw+\nZWZXmtkDZnanmZ1jZv/UZL6jzew2Mxsws58XO3JZum7CSWbWb2YPmtlZWcc4kXHP01D4rwBzqm7L\nSGR9/j4GfEGFUXzW/DcI30M6lTjiTrcirVReHJF+EuME0rna15MO711kuYvAmdmRpI5n7wdeRTrk\nfmF27rLha6Qh5fuRzqluw7r9FETGLXdf4O4vqbodI+HpR0y3dfdOLqgovWtXM1uWfYl+f9ZH72TS\n6LizKm6bBFa702rZiIu7SP0olmTTbgO+4u7HZX9vSjqP+B53PzP7+27Szy2ck82zA2mY767u3uwi\ngCIiIWWn1X4JvLOXT6uZ2fNJ/YNeRTpadA+pv8mnPI2mEhkVVVwhezibk0Yh3AOQ9cSfTLr8N5BO\nEZjZFaRRRWeSetRvUJjnOjNblc2j4khExg1PI6XaHrZcV55G+e5TdTtk/KlVcZR1tPsasMTX/pLv\nZFKxVPzJkTuz+yD1eH/c1/2Jgfw8xXU9izT0cSWpp7+IiIi0Z2PSCNML3f0fFbeldLUqjoBvkK62\n+poxWNcbSb8mLCIiIt15N0Nc2LlX1aY4MrMTSb80vJu753/s8w7SNQ62YvDRo61IlzZvzLORmW1a\nOHq0VXZfMysBTjvtNKZOndpilt5yxBFHcNxxxR+I7k2RsoDy1FmkLKA8dRYpy/Llyzn44IOh9U80\n9bRaFEdZYfQ2YA93H/TLxZ6u7nsH6QJTf8rm35Q0uu2kbLalpKvo7g3kO2RPofXPCzwKMHXqVHba\naadS81Rls802U5aaUp76ipQFlKfOImXJCdktpfLiyMy+Qfql5z7gYTPbKrvrfk+//A6pH9JnzOx6\n1l6B9O+kq702OmgvBI41s3tJV9A8nvTzAuOmM/Ydd7Q6SNZ7ImUB5amzSFlAeeosUpboKi+OSD8Y\n6qQfAc2bCZwK6fosZjaR9Fs4m5MuBf5mH/wbbUeQLjl+FumH8ho/WTJu3HrrrVU3oTSRsoDy1Fmk\nLKA8dRYpS3SVF0fu3taFKN19HjBviPsfI/3K/Ic7Wf8tt9zCZpttNvyMBc973vPYaKONhp9xDO28\n885VN6E0kbKA8tRZpCygPHUWKUt0lRdHVdtnn+4uobH//u/ijDPqNdjtoIMOqroJpYmUBZSnziJl\nAeWps0hZoqvdFbLHipntBCxNv1/5lg4ffSw77ng7V1991Si0TEREpN6WLVvWOBK2s7svq7o9ZRv3\nR47gxcBeHT7mDOD2YecSERGR3lOHH56VksycObPqJpQmUhZQnjqLlAWUp84iZYlOxVEg06dPr7oJ\npYmUBZSnziJlAeWps0hZolOfI04B3tvhoz/IjjtepT5HIiIyLkXvc6QjRyIiIiI5Ko5EREREclQc\nBbJkyZKqm1CaSFlAeeosUhZQnjqLlCU6FUeBLFiwoOomlCZSFlCeOouUBZSnziJliU4dsgN1yB4Y\nGGDixIlVN6MUkbKA8tRZpCygPHUWKYs6ZEvPiPKmg1hZQHnqLFIWUJ46i5QlOhVHIiIiIjkqjkRE\nRERyVBwFMmfOnKqbUJpIWUB56ixSFlCeOouUJToVR4FMmTKl6iaUJlIWUJ46i5QFlKfOImWJTqPV\nAo1WExERGQsarSYiIiIyjqg4EhEREclRcRTIihUrqm5CaSJlAeWps0hZQHnqLFKW6FQcBTJ37tyq\nm1CaSFlAeeosUhZQnjqLlCU6dcgO1CF71apVYUZDRMoCylNnkbKA8tRZpCzqkC09I8qbDmJlAeWp\ns0hZQHnqLFKW6FQciYiIiOSoOBIRERHJUXEUyPz586tuQmkiZQHlqbNIWUB56ixSluhUHAUyMDBQ\ndRNKEykLKE+dRcoCylNnkbJEp9FqgUariYiIjAWNVhMREREZR1QciYiIiOSoOAqkv7+/6iaUJlIW\nUJ46i5QFlKfOImWJTsVRILNmzaq6CaWJlAWUp84iZQHlqbNIWaJTcRTIvHnzqm5CaSJlAeWps0hZ\nQHnqLFKW6DRaTaPVREREOqLRaiIiIiLjiIojERERkRwVR4EsXLiw6iaUJlIWUJ46i5QFlKfOImWJ\nTsVRIMuWxTntGykLKE+dRcoCylNnkbJEpw7Z6pAtIiLSEXXIFhERERlHVByJiIiI5Kg4EhEREclR\ncRRIX19f1U0oTaQsoDx1FikLKE+dRcoSnYqjQGbPnl11E0oTKQsoT51FygLKU2eRskSn0WoarSYi\nItIRjVYTERERGUdUHImIiIjkqDgKZPHixVU3oTSRsoDy1FmkLKA8dRYpS3QqjgJZtGhR1U0oTaQs\noDx1FikLKE+dRcoSnTpkq0O2iIhIR9QhW0RERGQcUXEkIiIikqPiSERERCRHxVEgM2fOrLoJpYmU\nBZSnziJlAeWps0hZolNxFMj06dOrbkJpImUB5amzSFlAeeosUpboNFpNo9VEREQ6otFqIiIiIuOI\niiMRERGRHBVHgSxZsqTqJpQmUhZQnjqLlAWUp84iZYlOxVEgCxYsqLoJpYmUBZSnziJlAeWps0hZ\nolOH7EAdsgcGBpg4cWLVzShFpCygPHUWKQsoT51FyqIO2dIzorzpIFYWUJ46i5QFlKfOImWJTsWR\niIiISI6KIxEREZEcFUeBzJkzp+omlCZSFlCeOouUBZSnziJliU7FUSBTpkypugmliZQFlKfOImUB\n5amzSFmiq8VoNTPbDZgD7AxsDezj7ufm7v828J7Cwy5w97fk5pkAHAscAEwALgQOc/e7Wqwz3Gg1\nERGRsaDRamNjE+CPwGFAq2rtZ8BWwOTsdlDh/q8B/wbsB+wObAP8aDQaKyIiInFtUHUDANz9AuAC\nADOzFrM95u53N7vDzDYFZgEHuvuvsmkzgeVm9ip3v3IUmi0iIiIB1eXIUTv2NLM7zWyFmX3DzLbI\n3bczqdC7uDHB3a8DVgHTxridlVmxYkXVTShNpCygPHUWKQsoT51FyhJdrxRHPwMOAV4HzAX2AM7P\nHWWaDDzu7g8UHndndt+4MHfu3KqbUJpIWUB56ixSFlCeOouUJbpanFYbjrufmfvzL2Z2DXADsCdw\nSSWNqqETTzyx6iaUJlIWUJ46i5QFlKfOImWJrleOHA3i7jcB/cD22aQ7gI2yvkd5W2X3DWEO0Fe4\nTQMWF+a7KLtvsMMPP5yFCxcOmrZs2TL6+vro7+8fNP2oo45i/vz5g6atWrWKvr6+dQ63nnDCCetc\nE2NgYIC+vr51ftl50aJFzJw5c51hogcccACLFw/OcdFFF9HXV+8cMHjIay/naJgyZUqIHJBej2XL\nBg9O6dUcixcvHrSt9XKOhilTpoTIAen1mD17dogcfX196+yjeyXHokWL6OvrY9q0aUyePJm+vj6O\nOOKIdR4TSS2G8ueZ2RoKQ/mbzPNc4Gbgbe5+XlYU3U3qkH1ONs8OwHJg12YdsjWUX0REpDvRh/LX\n4rSamW1COgrU6EP0AjN7OXBPdjuKNCz/jmy++cBfSdcywt0fMLOFwLFmdi/wIHA8cLlGqomIiEgn\n6nJabRfgD8BS0nWOvgosAz4PrAZ2BH4MXAecDPwe2N3dn8gt4wjgPOAs4FLgNtI1j8aN4iHXXhYp\nCyhPnUVZr2dOAAAgAElEQVTKAspTZ5GyRFeLI0fZtYmGKtTe1MYyHgM+nN3GpYGBgaqbUJpIWUB5\n6ixSFlCeOouUJbra9TkaK+pzJCIi0p3ofY7qclpNREREpBZUHImIiIjkqDgKpHhdjF4WKQsoT51F\nygLKU2eRskSn4iiQWbNmVd2E0kTKAspTZ5GygPLUWaQs0ak4CmTevHlVN6E0kbKA8tRZpCygPHUW\nKUt0Gq2m0WoiIiId0Wg1ERERkXFExZGIiIhIjoqjQIq/7tzLImUB5amzSFlAeeosUpboVBwFsmxZ\nnNO+kbKA8tRZpCygPHUWKUt06pCtDtkiIiIdUYdsERERkXFExZGIiIhIjoojERERkRwVR4H09fVV\n3YTSRMoCylNnkbKA8tRZpCzRqTgKZPbs2VU3oTSRsoDy1FmkLKA8dRYpS3QarabRaiIiIh3RaDUR\nERGRcUTFkYiIiEiOiqNAFi9eXHUTShMpCyhPnUXKAspTZ5GyRKfiKJBFixZV3YTSRMoCylNnkbKA\n8tRZpCzRqUO2OmSLiIh0RB2yRURERMYRFUciIiIiOSqORERERHJUHAUyc+bMqptQmkhZQHnqLFIW\nUJ46i5QlOhVHgUyfPr3qJpQmUhZQnjqLlAWUp84iZYlOo9U0Wk1ERKQjGq0mIiIiMo6oOBIRERHJ\nUXEUyJIlS6puQmkiZQHlqbNIWUB56ixSluhUHAWyYMGCqptQmkhZQHnqLFIWUJ46i5QlOnXIDtQh\ne2BggIkTJ1bdjFJEygLKU2eRsoDy1FmkLOqQLT0jypsOYmUB5amzSFlAeeosUpboVByJiIiI5Kg4\nEhEREclRcRTInDlzqm5CaSJlAeWps0hZQHnqLFKW6FQcBTJlypSqm1CaSFlAeeosUhZQnjqLlCU6\njVYLNFpNRERkLGi0moiIiMg4ouJIREREJEfFUSArVqyougmliZQFlKfOImUB5amzSFmiU3EUyNy5\nc6tuQmkiZQHlqbNIWUB56ixSlujUITtQh+xVq1aFGQ0RKQsoT51FygLKU2eRsqhDtvSMKG86iJUF\nlKfOImUB5amzSFmiU3EkIiIikqPiSERERCRHxVEg8+fPr7oJpYmUBZSnziJlAeWps0hZolNxFMjA\nwEDVTShNpCygPHUWKQsoT51FyhKdRqsFGq0mIiIyFjRaTURERGQcUXEkIiIikqPiKJD+/v6qm1Ca\nSFlAeeosUhZQnjqLlCU6FUeBzJo1q+omlCZSFlCeOouUBZSnziJlia6r4sjMbjSzZzWZvrmZ3Tjy\nZkk35s2bV3UTShMpCyhPnUXKAspTZ5GyRNfVaDUzWwNMdve7CtO3Ala5+4SS2jdqNFpNRESkO9FH\nq23Qycxm1pf7841mdn/u7/WBvYGVJbRLREREpBIdFUfA4uxfB75buO8JUmH08RG2SURERKQyHfU5\ncvf13H09YBWwZePv7DbB3Xdw9/NGp6kynIULF1bdhNJEygLKU2eRsoDy1FmkLNF11SHb3bdzd41J\nrJlly+Kc9o2UBZSnziJlAeWps0hZouv650PMbG9SH6MtKRRZ7l778YrqkC0iItIddchuwsyOAj4H\nXAXcTuqDJCIiItLzuiqOgA8Ch7r798psjIiIiEjVur1C9kbAb8psiIiIiEgddFscnQK8q8yGyMj1\n9fUNP1OPiJQFlKfOImUB5amzSFmi6/a02sbA+83s9cCfSNc4eoq7/+dIGyadmz17dtVNKE2kLKA8\ndRYpCyhPnUXKEl23Px9yyRB3u7u/rsPl7QbMAXYGtgb2cfdzC/McDbwP2By4HPiQu1+fu38CcCxw\nADABuBA4rPgTJ7n5NVpNRESkCxqt1oS771VyOzYB/ggsBM4u3mlmRwKzgUNIV+H+InChmU1198ez\n2b4GvBnYD3gAOAn4EbBbyW0VERGRwLo9rVYqd78AuADAzKzJLB8FvtC4+raZHQLcCewDnGlmmwKz\ngAPd/VfZPDOB5Wb2Kne/cgxiiIiISABddcg2s0vM7JetbmU20My2AyYDFzemufsDwBXAtGzSLqRC\nLz/PdaSfOZnGOLF48eLhZ+oRkbKA8tRZpCygPHUWKUt03Y5W+yNwde52LWl4/07ANeU07SmTSReZ\nvLMw/c7sPoCtgMezoqnVPOEtWrSo6iaUJlIWUJ46i5QFlKfOImWJrtvfVjuicJvt7q8l9ft5YrjH\n18scoK9wmwYUK/yLsvsGO/zww9f5McFly5bR19dHf//gn5876qijmD9//qBpq1atoq+vjxUrVgya\nfsIJJzBnzpxB0wYGBujr62PJkiWDpi9atIiZM2dyxhlnDJp+wAEHrPNN5aKLLmo6nLROOYBBWXo5\nR8MZZ5wRIgek1+Oggw4KkWPx4sWDtrVeztFwxhlnhMgB6fV45JFHQuTo6+tbZx/dKzkWLVpEX18f\n06ZNY/LkyfT19XHEEUes85hIuv5ttaYLM9seuNLdtxjBMtaQG62WnVa7AXiFu/8pN9+lwB/c/Qgz\n2wv4BfDM/NEjM1sJHOfuX2+yHo1WExER6UL00WrdnlZrZRrwaJkLdPebgDtIP3ILQNYB+19Ze5Xu\npcCThXl2AKYAvy2zPSIiIhJbtz88Wxxub6TrE+0CfKGL5W0CbJ8tB+AFZvZy4B53v4V0uu4zZnY9\naSj/F4C/Az+G1EHbzBYCx5rZvcCDwPHA5RqpJiIiIp3o9sjR/YXbPcClwFvc/fNdLG8X4A+kI0AO\nfBVYBnwewN0XACcA3ySNUnsa8ObcNY4AjgDOA87K2nIb6ZpH40az88a9KlIWUJ46i5QFlKfOImWJ\nrtuLQJb6CmfXJhqyUHP3ecC8Ie5/DPhwdhuXpk+fXnUTShMpCyhPnUXKAspTZ5GyRDeiDtlmtjMw\nNfvzL+7+h1JaNQbUIVtERKQ70Ttkd9vnaEvgB8CewH3Z5M2z31w70N3vLqd5IiIiImOr2z5HJwDP\nAP7Z3bfIhu6/FNiU1BFaREREpCd1Wxy9ifSL98sbE9z9WuBw0o+/SgWKF/fqZZGygPLUWaQsoDx1\nFilLdN0WR+vR/ErYT4xgmTJCCxYsqLoJpYmUBZSnziJlAeWps0hZouuqQ7aZ/RjYHDjI3W/Lpj0H\n+D5wr7u/vdRWjoKIHbIHBgaYOHFi1c0oRaQsoDx1FikLKE+dRcoSvUN2t0d5ZpP6F600sxvM7Abg\npmzauB1KX7UobzqIlQWUp84iZQHlqbNIWaLr9jpHt2RHXl4PvDibvNzdf1Fay0REREQq0NGRIzN7\nnZlda2abevJzdz/B3U8Afm9mfzGzN45SW0VERERGXaen1T4GnOy+9pfvG9z9ftLPe+i0WkXmzJlT\ndRNKEykLKE+dRcoCylNnkbJE12lx9HLggiHuvwjYsfvmyEhMmTKl6iaUJlIWUJ46i5QFlKfOImWJ\nrqPRamb2KPBSd7++xf3bA9e4+9NKat+oiThaTUREZCxotNpgt5KuhN3KjsDt3TdHREREpFqdFkfn\nA18ws42Ld5jZ04DPA+eV0TARERGRKnRaHH0R2AL4q5nNNbO3Zbcjgeuy+44pu5HSnhUrVlTdhNJE\nygLKU2eRsoDy1FmkLNF1VBy5+53Aq4E/A/8NnJPdvpRNe202j1Rg7ty5VTehNJGygPLUWaQsoDx1\nFilLdF39fAiAmT0T2B4w4G/ufm+ZDRttETtkr1q1KsxoiEhZQHnqLFIWUJ46i5Qleofsrq6QDZAV\nQ78vsS0yQlHedBArCyhPnUXKAspTZ5GyRNftb6uJiIiIhKTiSERERCRHxVEg8+fPr7oJpYmUBZSn\nziJlAeWps0hZolNxFMjAwEDVTShNpCygPHUWKQsoT51FyhJd16PVel3E0WoiIiJjIfpoNR05EhER\nEclRcSQiIiKSo+IokP7+/qqbUJpIWUB56ixSFlCeOouUJToVR4HMmjWr6iaUJlIWUJ46i5QFlKfO\nImWJTsVRIPPmzau6CaWJlAWUp84iZQHlqbNIWaLTaDWNVhMREemIRquJiIiIjCMqjkRERERyVBwF\nsnDhwqqbUJpIWUB56ixSFlCeOouUJToVR4EsWxbntG+kLKA8dRYpCyhPnUXKEp06ZKtDtoiISEfU\nIVtERERkHFFxJCIiIpKj4khEREQkR8VRIH19fVU3oTSRsoDy1FmkLKA8dRYpS3QqjgKZPXt21U0o\nTaQsoDx1FikLKE+dRcoSnUarabSaiIhIRzRaTURERGQcUXEkIiIikqPiKJDFixdX3YTSRMoCylNn\nkbKA8tRZpCzRqTgKZNGiRVU3oTSRsoDy1FmkLKA8dRYpS3TqkK0O2SIiIh1Rh2wRERGRcUTFkYiI\niEiOiiMRERGRHBVHgcycObPqJpQmUhZQnjqLlAWUp84iZYlOxVEg06dPr7oJpYmUBZSnziJlAeWp\ns0hZotNoNY1WExER6YhGq4mIiIiMIyqORERERHJUHAWyZMmSqptQmkhZQHnqLFIWUJ46i5QlOhVH\ngSxYsKDqJpQmUhZQnjqLlAWUp84iZYlOHbIDdcgeGBhg4sSJVTejFJGygPLUWaQsoDx1FimLOmRL\nz4jypoNYWUB56ixSFlCeOouUJToVRyIiIiI5Ko5EREREclQcBTJnzpyqm1CaSFlAeeosUhZQnjqL\nlCU6FUeBTJkypeomlCZSFlCeOouUBZSnziJliU6j1QKNVhMRERkLGq0mIiIiMo6oOBIRERHJUXEU\nyIoVK6puQmkiZQHlqbNIWUB56ixSluhUHAUyd+7cqptQmkhZQHnqLFIWUJ46i5Qlup4ojszsKDNb\nU7hdW5jnaDO7zcwGzOznZrZ9Ve2tyoknnlh1E0oTKQsoT51FygLKU2eRskTXE8VR5s/AVsDk7Pba\nxh1mdiQwG3g/8CrgYeBCM9uognZWJtIw0UhZQHnqLFIWUJ46i5Qlug2qbkAHnnT3u1vc91HgC+5+\nHoCZHQLcCewDnDlG7RMREZEAeunI0YvM7FYzu8HMTjOz5wGY2XakI0kXN2Z09weAK4Bp1TRVRERE\nelWvFEe/Aw4F3gh8ENgO+LWZbUIqjJx0pCjvzuy+cWP+/PlVN6E0kbKA8tRZpCygPHUWKUt0PXFa\nzd0vzP35ZzO7ErgZ2B/Q2MjMwMBA1U0oTaQsoDx1FikLKE+dRcoSnrv35A24EjiGdBRpDbBj4f5L\ngeOGePxOgMMzHWYUbrs6nOPguduF2X3u8AHfcced3d39sMMO81NOOcXzli5d6jNmzPC777570PTP\nfe5z/uUvf3nQtJtvvtlnzJjhy5cvHzT9+OOP90984hODpj388MM+Y8YMv+yyywZNP/300/3QQw/1\nov3339/POeecQdMuvPBCnzFjxjrzKodyKIdyKIdyNMtx+umn+4wZM3zXXXf1rbbaymfMmOG77767\np89QdvIa1ARl33ryt9XM7OnAKuCz7n6Smd0GfMXdj8vu35R0Wu0Qd/9hi2Xot9VERES6EP231Xri\ntJqZfQX4CelU2nOAzwNPAD/IZvka8Bkzux5YCXwB+Dvw4zFvrIiIiPS0XumQ/VzgdFL/oh8AdwO7\nuvs/ANx9AXAC8E3SKLWnAW9298eraW41+vv7q25CaSJlAeWps0hZQHnqLFKW6HqiOHL3g9z9ue7+\nNHef4u7vcvebCvPMc/dt3H2iu7/R3a+vqr1VmTVrVtVNKE2kLKA8dRYpCyhPnUXKEl1PFEfSnnnz\n5lXdhNJEygLKU2eRsoDy1FmkLNH1ZIfsMqhDtoiISHeid8jWkSMRERGRHBVHIiIiIjkqjgJZuHBh\n1U0oTaQsoDx1FikLKE+dRcoSnYqjQJYti3PaN1IWUJ46i5QFlKfOImWJTh2y1SFbRESkI+qQLSIi\nIjKOqDgSERERyVFxJCIiIpKj4iiQvr6+qptQmkhZQHnqLFIWUJ46i5QlOhVHgcyePbvqJpQmUhZQ\nnjqLlAWUp84iZYlOo9U0Wk1ERKQjGq0mIiIiMo6oOBIRERHJUXEUyOLFi6tuQmkiZQHlqbNIWUB5\n6ixSluhUHAWyaNGiqptQmkhZQHnqLFIWUJ46i5QlOnXIVodsERGRjqhDtoiIiMg4skHVDehVjz/+\neNe/sDxp0iSmTJlScotERESkDCqOuvIg1123vHFIsWMbbzyR665brgJJRESkhnRarSuP4v4kcBqw\ntMPbaTz66AD9/f2lt2rmzJmlL7MqkbKA8tRZpCygPHUWKUt0OnI0IlOBnapuxFOmT59edRNKEykL\nKE+dRcoCylNnkbJEp9FqXY1W2w84Oz284+JoGbAzS5cuZaed6lNYiYiItEuj1URERETGERVHIiIi\nIjkqjgJZsmRJ1U0oTaQsoDx1FikLKE+dRcoSnYqjQBYsWFB1E0oTKQsoT51FygLKU2eRskSnDtmB\nOmQPDAwwceLEUpdZlUhZQHnqLFIWUJ46i5RFHbKlZ0R500GsLKA8dRYpCyhPnUXKEp2KIxEREZEc\nFUciIiIiOSqOApkzZ07VTShNpCygPHUWKQsoT51FyhKdiqNAIv2QbaQsoDx1FikLKE+dRcoSnUar\nVTRa7bTTTmPq1KkdPXLSpEl6c4mISOWij1bTD8+OuduB9Tj44IM7fuTGG0/kuuuWq0ASEREZRSqO\nxtx9wBrgNKCTI0fLefTRg+nv71dxJCIiMorU56gyU0mn5Nq9DV9IrVixYpTaOvYiZQHlqbNIWUB5\n6ixSluhUHAUyd+7cqptQmkhZQHnqLFIWUJ46i5QlOhVHgZx44olVN6E0kbKA8tRZpCygPHUWKUt0\nKo4CidQXKVIWUJ46i5QFlKfOImWJTsWRiIiISI6KIxEREZEcFUeBzJ8/v+omlCZSFlCeOouUBZSn\nziJliU7FUSADAwNVN6E0kbKA8tRZpCygPHUWKUt0+vmQMf/5kO8DB3fx2PSzI0uXLmWnnTpdp4iI\nSHmi/3yIjhyJiIiI5Kg4EhEREclRcRRIf39/1U0oTaQsoDx1FikLKE+dRcoSnYqjQGbNmlV1E0oT\nKQsoT51FygLKU2eRskSn4iiQefPmVd2E0kTKAspTZ5GygPLUWaQs0W1QdQOkPKMxim3VqlVdHwqe\nNGlS15fLjzYiT3nqK1IWUJ46i5QlOhVH0tKqVavYYYepPPpod9fm2HjjiVx33XL9npCIiPQUFUc9\nZvny5V09rpujOP39/VlhdBowtcM1LufRRw+mv79fxZGIiPQUFUc943ZgPQ4++OCuHj2yozhT6fxi\nlyOzcOFC3vveTi/OWV/KU1+RsoDy1FmkLNGpQ3bPuA9YQzqKs7TF7Z0tpp/Go48O9NQw0mXLYl1w\nVXnqK1IWUJ46i5QlOh056jlDHcU5cywbMqpOOumkqptQKuWpr0hZQHnqLFKW6HTkSERERCRHxZGI\niIhIjk6rjSOdjnTrdmTcSFV1bSURERFQcRRMH3Buk+kjG+k2lqJeW6mvr49zz2322vSmSHkiZQHl\nqbNIWaJTcRTK7BbT8yPdOrle0fnAZ0faqI6svbbSkcD+HT66vtdWmj271WvTmyLliZQFlKfOImWJ\nTsVRKNOHub/T6xWN/LRa96fy9qfbayuN5YUyob3TgJMmTWo6jLdXTwNOnz7cttY7ImUB5amzSFmi\nU3Eko6SKU3ljf6HMqKcBRUTGMxVHMkqqOJXX7Tqh21Ny+okVEZF4VByFshjYp+pGFHR7Ku+SDh83\nknXm1t71acDh1lnH16Z7ixcvZp99YuSJlAWUp84iZYlOxVEo84nzAfwd4ONjuL7RPg0Y6bWB+fPn\nh9nJR8oCylNnkbJEF644MrPDgU8Ak4GrgQ+7+++rbdVYeXbVDSjRFmO8vtE+DRjptYFnPztOnkhZ\nQHnqLFKW6EIVR2Z2APBV4P3AlcARwIVm9k/u3ju/uioVqv+IPujdUW4iIr0gVHFEKoa+6e6nApjZ\nB4F/A2YBC6psmMi6uj+Vp1FuIiKjJ0xxZGYbAjsDX2pMc3c3s18A0yprmEhL3Z7KS6PcLrvsMqZO\n7XSEXDVHnar4SZh213n//fevcw2qxx57jAkTJnS8zm4fN5LHjqejiN1uR732HI2XnHUWpjgCJgHr\nA3cWpt8J7NBk/o3TP+cDd3e4qutY+9hOT4lc3uVj23nc5cD3x3idZT+28bg/0jzLaKxzJI9t93HN\nXpvGY2/qYH0AfwCs687jG244ga98ZT6TJk3q6HHrrbcea9asAeDyyy/n+99v7/Xp7+9nzpxP8sQT\nj3bcVuiuvZ2uc+eddy5MWY9UuHaq28d1/9ji89PJa5N/TTvR7eO6eWwjz0i2o263eSj3OWrntRlJ\nzo022pizzz6LrbfeuuPHdirXHWDjUV9ZBczdq25DKcxsa+BWYJq7X5GbPh/Y3d2nFeZ/F51/+oqI\niMha73b306tuRNkiHTnqB1YDWxWmbwXc0WT+C4F3AyuB7r7SioiIjE8bA9uSPkvDCXPkCMDMfgdc\n4e4fzf42YBVwvLt/pdLGiYiISE+IdOQI4FjgO2a2lLVD+SeSrigoIiIiMqxQxZG7n2lmk4CjSafT\n/gi80d077XEtIiIi41So02oiIiIiI7Ve1Q0QERERqRMVRyIiIiI547Y4MrPDzewmM3vEzH5nZv9S\ngzbtZmbnmtmtZrbGzPqazHO0md1mZgNm9nMz275w/wQzO8nM+s3sQTM7y8y2LMzzTDP7vpndb2b3\nmtkpZrZJyVk+ZWZXmtkDZnanmZ1jZv/Ui3nM7INmdnW2/PvN7Ddm9qZeyzFEvk9m29uxvZjJzI7K\n2p+/XduLWbL1bGNm38vaMpBtezsV5umJPJb2scXXZo2ZndBrWbL1rGdmXzCzG7P2Xm9mn2kyX09k\nMrOnm9nXzGxl1tYlZrZLL2YpnbuPuxtwAOnaRocALwa+CdwDTKq4XW8idSZ/G+maTX2F+4/M2vlW\n4KXAYuAGYKPcPP9DunbTHsArgd8AlxWW8zNgGbAL8Grgr8BpJWc5H/h30u9ivAw4L2vX03otD+n3\n+d4EvBDYHvgi8BgwtZdytMj2L8CNpMtuH9trr022jqOAPwHPBrbMblv0aJbNSZdMP4X0c0jPB14P\nbNejeZ6Ve022BPYm7dt267Us2Xr+C7iLtD+YAuwLPADM7tHX5wzgGuA1wAuy99J9wNa9lqX017rq\nBlQSGn4HfD33twF/B+ZW3bZcm9awbnF0G3BE7u9NgUeA/XN/Pwa8PTfPDtmyXpX9PTX7+5W5ed4I\nPAlMHsU8k7L1vjZInn8AM3s5B/B00m/hvA64hMHFUc9kIu3Qlw1xfy9l+TLwq2Hm6Zk8Tdr+NeCv\nvZoF+AlwcmHaWcCpvZaJdBHHJ4A3FaZfBRzdS1lG4zbuTqvZ2h+ovbgxzdOrVesfqDWz7YDJDG73\nA8AVrG33LqTLM+TnuY50IczGPLsC97r7H3KL/wXgwL+OVvtJ34id9C2kZ/Nkh9UPJF0/6ze9miNz\nEvATd/9lfmKPZnqRpdPRN5jZaWb2vB7NMgO4yszOtHQ6epmZva9xZw/meUq27303sLCHs/wG2NvM\nXpRleDnpqMv5PZhpA9LvkT5WmP4I8Noey1K6UNc5alOnP1BbF5NJG1Ozdk/O/r8V8Hi2AbeaZzLp\nsPBT3H21md2Tm6dUZmakb4xL3L3RF6Sn8pjZS4Hfkr5tPUj6pnSdmU3rpRwNWYH3CtLOrainXhvS\nkeBDSUfBtgbmAb/OXrNey/IC4EPAV4FjgFcBx5vZY+7+PXovT97bgc2A7+ba0GtZvkw6WrLCzFaT\n+u1+2t1/kGtLT2Ry94fM7LfAZ81sRbb+d5GKmr/1UpbRMB6LIxl73wBeQvqG1atWAC8n7dzfAZxq\nZrtX26TumNlzScXq6939iarbM1Lunv9tpz+b2ZXAzcD+pNetl6wHXOnun83+vjor8j4IfK+6ZpVi\nFvAzd2/2W5e94gBSAXEgcC3pC8bXzey2rHjtNQcD3yL9aPuTpH5Bp5POroxr4+60Gp3/QG1d3EHq\nGzVUu+8ANjKzTYeZpziSYH1gC0Yhv5mdCLwF2NPdb8/d1VN53P1Jd7/R3f/g7p8GrgY+2ms5MjuT\nOi8vM7MnzOwJUmfKj5rZ46Rvfb2W6Snufj+pw+f29N7rczuwvDBtOanzb6MdvZSnsewppI7lJ+cm\n92KWBcCX3f2H7v4Xd/8+cBzwqVxbeiaTu9/k7nsBmwDPc/ddgY1IgzR6KkvZxl1xlH1TXkoaNQE8\nddpnb9L55Fpy95tIG1K+3ZuSztk22r2UVP3n59mBtGP9bTbpt8DmZvbK3OL3Jr0JriizzVlh9DZg\nL3df1et5CtYDJvRojl+QRhC+gnQ07OWkTpinAS9398aOsZcyPcXMnk4qjG7rwdfnctY9vb8D6UhY\nL79vZpGK7vMbE3o0y0TSl+u8NWSfpT2aCXd/xN3vNLNnkjpLL+7VLKWpukd4FTfS4fYBBg/l/wfw\n7IrbtQnpg+oVpDfcx7K/n5fdPzdr5wzSh9ti0rnh/LDKb5CGAu9JOkJwOesOqzyf9GH4L6RTXdcB\n3ys5yzeAe4HdSN8iGreNc/P0RB7gS1mO55OGs/43aYfwul7KMUzG4mi1nskEfAXYPXt9Xg38nPRB\n/KwezLILqYPsp0iXjngXqY/bgb342mTrMdJQ72Oa3NdrWb5N6mz8lmx7ezupP82XejETMJ1UDG0L\nvIF0SY/LgfV7LUvpr3XVDagsOByWvWEfIVW2u9SgTXuQiqLVhdu3cvPMIw2vHAAuBLYvLGMCcALp\n9OGDwA+BLQvzbE46SnA/qYA5GZhYcpZmOVYDhxTmq30e0jVnbsy2lTuAi8gKo17KMUzGX5Irjnop\nE7CIdCmOR0gfXKeTuy5QL2XJ1vMW0nWbBoC/ALOazNNLed5Aeu9v3+L+XsqyCXAsqRh4mFQofB7Y\noBczAe8Ers/eO7cCXwee0YtZyr7ph2dFREREcsZdnyMRERGRoag4EhEREclRcSQiIiKSo+JIRERE\nJGpIhaEAAAZVSURBVEfFkYiIiEiOiiMRERGRHBVHIiIiIjkqjkRERERyVByJiIiI5Kg4EgnEzNaY\nWd8Q999kZh/pYHnvMbN7h5nnKDP7QyftrBszm5A9d9OrbouIVE/FkUgPMbNJZvY/ZnazmT1qZreb\n2QVmNq3NRewC/F+Hq23nN4ba+h2irNhaY2ars3+Lt9VmNqXD9o2Yuz8GTCb9xpyIjHMbVN0AEenI\n2aT37b+TfvxyK2Bv4FntPNjd/zF6TWvLD4Cf5f4+B7gG+Czp19sB7u5mwWa2gbs/WZi2HuDexo9I\nuvtd3axXROLRkSORHmFmmwGvBY5091+7+y3ufpW7z3f381o85vNmdquZvTT7e9BpNTM7wsz+ZGYP\nmdkqMzvJzDZpspy3mdlfzeyR7EjVc4dp6/vM7Nps/mvN7EOQjtC4+12NG/A4MODud+eme7aMD5nZ\nimwZfzGz9+WWv0N2pGk/M7vMzB4B9jWzD2RH0/Y1sxXAo8CzzWyamf3CzPrN7N7s/y/LLW/QabXc\n8meY2a/N7GEzW2ZmO7f5Wm1pZmeY2d+zx/7RzPYtzLOemX3azG7IjgLeaGYfb2f5IjK6VByJ9I6H\nsts+ZrbRcDOb2QnAwcBr3f3PLWZbDXwYeAlwCLAXML8wzybAf2XLejWwObBoiPW+G5gHfAp4cfbY\no83s34drc24Z7wWOBD6RLeNzwAIze2dh1v/O2vti4NJs2ubAR0lH114G3As8HTgZ2DXL8HfgfDOb\nMExTvggcDbwcWAV8v80ITwMuB94EvBT4DvADM9sxN89xWTs/nbX/3XR51ExESubuuummW4/cgLcD\n/cAAsAQ4BnhZ7v41wH6kD/E/A5MLj78J+MgQy98PuCv393tIBdQuuWk7ZOvZJfv7KGBZ7v6/AQcU\nlvtp4PIm67sEOLbJ9FuAtxWmfQG4uNCG9xXm+UDW3u2HeR43zJ7D12V/T8iWN72w/ANzj3lltuwp\nXb52PweOzv7/TNJRs4Oq3qZ00023dW/qcyTSQ9z9HDP7KbAb6SjIm4G5ZvZedz81m+040umkXd39\nnqGWZ2avBz5JOnKxKak/0wQz29jdH81me9Ldr8q14Tozuw+YClxVWN5E4IXAQjM7JXfX+sB97WQ0\nsy2A5wCnmVn+rvWBOwqzL22yiIfc/frCMrcmFZK7AVuSjppvBAzV+dtJ/aEabif1i9qSdBRpqAwb\nkI52vR3YJlvXRsCt2SwvzfKoA7hIDak4Eukx7v44cHF2O8bMTgY+DzSKo4uAg0indE5vtRwzez7w\nE+Ak0qmve0jFwymkD/JHWz12CE/P/n0fcGXhvtUdLuMQ4OrCffkO1w483OTxzaYtIh0tOpx0VOox\n4A+knEN5orA+aK87wmeA/yCdNluetel/c+t7pI1liEhFVByJ9L7lwNtyf59LKnoWmdlqdz+jxeN2\nBszdP9GYYGYHNplvAzPbpXH0yMx2IPXrubY4o7vfZWa3AS909x90F4dbSKcOX+Du53S5jKJpwLvc\n/SIAM9seeEZJy27m1cBZ7n5mtr4NgBeRcgGsIBVeezNEASsi1VBxJNIjstNNPwS+BfwJeBD4F2AO\nsDg/r7v/OOsAfaqZPenuP2qyyOuBDbPRaz8hjYT7QJP5ngROMLOPko7+nAD8xt2bndKC1Afp62b2\nAHABqT/PLsDm7v614XK6u5vZ54EvmdkA8AtgY+BVwMbuflLjKRluWTnXA+8xs2uAScAChj8y1sny\ni/4GvMnMXkXqRD+X1M8IAHd/yMyOBY7LTh3+lnSdpX9y9++OYL0iUgIVRyK94yHgd8DHSP16NiQd\nZfkmadQW5C7G6O4/yq7zc2p2BGlx4f4/mdl/kj64vwT8mtT/qHF6ruFh0oiw00n9Z35NOm3WlLsv\nNLOHs+UuyB5/DdCsMGp6/SF3Pykrrv4TODbL/ifgq8M9toVDSKe1/gisJI2E++YwbWm2/HbXeRTw\nPFJh9yDwDeD8wjyfIRVoXyIVRreRCk8RqZi5d7J/EREREYlN1zkSERERyVFxJCLSITO72MwebHJ7\nwMw+VnX7RGRkdFpNRKRDZrYNqZN4M/3u/sBYtkdEyqXiSERERCRHp9VEREREclQciYiIiOSoOBIR\nERHJUXEkIiIikqPiSERERCRHxZGIiIhIjoojERERkZz/D2TamLbtY0UIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44e031b5f8>"
      ]
     },
     "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": [
    "You now see a rather long tailed distribution. You may wonder about the now most extreme value that is above 8000, but similarly you may also wonder about the value around 7000. If you wanted to spend more time manually checking values you could, but leave this for now. The above distribution is plausible."
   ]
  },
  {
   "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": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    3379.0\n",
       "18    1500.0\n",
       "Name: Snow Making_ac, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>3</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>3379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>360</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</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\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\n",
       "TerrainParks                              3\n",
       "LongestRun_mi                           5.5\n",
       "SkiableTerrain_ac                      4800\n",
       "Snow Making_ac                         3379\n",
       "daysOpenLastYear                        155\n",
       "yearsOpen                                64\n",
       "averageSnowfall                         360\n",
       "AdultWeekday                            NaN\n",
       "AdultWeekend                            NaN\n",
       "projectedDaysOpen                       157\n",
       "NightSkiing_ac                          NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[ski_data['Snow Making_ac'] > 3000].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can adopt a similar approach as for the suspect skiable area value and do some spot checking. To save time, here is a link to the website for [Heavenly Mountain Resort](https://www.skiheavenly.com/the-mountain/about-the-mountain/mountain-info.aspx). From this you can glean that you have values for skiable terrain that agree. Furthermore, you can read that snowmaking covers 60% of the trails."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What, then, is your rough guess for the area covered by snowmaking?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2880.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".6 * 4800"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is less than the value of 3379 in your data so you may have a judgement call to make. However, notice something else. You have no ticket pricing information at all for this resort. Any further effort spent worrying about values for this resort will be wasted. You'll simply be dropping the entire row!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.4.2.3 fastEight<a id='2.6.4.2.3_fastEight'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the different fastEight values more closely:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    163\n",
       "1.0      1\n",
       "Name: fastEight, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.fastEight.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the fastEight column in its entirety; half the values are missing and all but the others are the value zero. There is essentially no information in this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Code task 24#\n",
    "#Drop the 'fastEight' column from ski_data. Use inplace=True\n",
    "#ski_data.drop(['fastEight'], inplace=True)\n",
    "#ski_data.drop('fastEight', inplace=True)\n",
    "del ski_data['fastEight']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about yearsOpen? How many resorts have purportedly been open for more than 100 years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "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>fastSixes</th>\n",
       "      <th>fastQuads</th>\n",
       "      <th>quad</th>\n",
       "      <th>...</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>34</th>\n",
       "      <td>Howelsen Hill</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>7136</td>\n",
       "      <td>440</td>\n",
       "      <td>6696</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Pine Knob Ski Resort</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>1308</td>\n",
       "      <td>300</td>\n",
       "      <td>1009</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name    Region     state  summit_elev  vertical_drop  \\\n",
       "34          Howelsen Hill  Colorado  Colorado         7136            440   \n",
       "115  Pine Knob Ski Resort  Michigan  Michigan         1308            300   \n",
       "\n",
       "     base_elev  trams  fastSixes  fastQuads  quad       ...        \\\n",
       "34        6696      0          0          0     0       ...         \n",
       "115       1009      0          0          0     0       ...         \n",
       "\n",
       "     LongestRun_mi  SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  \\\n",
       "34             6.0               50.0            25.0             100.0   \n",
       "115            1.0               80.0            80.0               NaN   \n",
       "\n",
       "     yearsOpen  averageSnowfall  AdultWeekday  AdultWeekend  \\\n",
       "34       104.0            150.0          25.0          25.0   \n",
       "115     2019.0              NaN          49.0          57.0   \n",
       "\n",
       "     projectedDaysOpen  NightSkiing_ac  \n",
       "34               100.0            10.0  \n",
       "115                NaN             NaN  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 25#\n",
    "#Filter the 'yearsOpen' column for values greater than 100\n",
    "ski_data.loc[ski_data['yearsOpen'] > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, one seems to have been open for 104 years. But beyond that, one is down as having been open for 2019 years. This is wrong! What shall you do about this?"
   ]
  },
  {
   "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": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dQuEoszhnn63c0tK6Fke55YuaExEREckVNSciIiKSK4VoTszsZWa20MweNrOy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\nCQCQIk4AgBRxAgCkiBMAIEWcAAApF6Rcpn/xnh2jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44d90d6668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 26#\n",
    "#Call the hist method on 'yearsOpen' after filtering for values under 1000\n",
    "#Pass the argument bins=30 to hist(), but feel free to explore other values\n",
    "ski_data.yearsOpen[ski_data.yearsOpen < 1000].hist(bins=12, alpha=0.5)\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, including the 104 year value. You can certainly state that no resort will have been open for 2019 years! It likely means the resort opened in 2019. It could also mean the resort is due to open in 2019. You don't know when these data were gathered!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's review the summary statistics for the years under 1000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    328.000000\n",
       "mean      57.695122\n",
       "std       16.841182\n",
       "min        6.000000\n",
       "25%       50.000000\n",
       "50%       58.000000\n",
       "75%       68.250000\n",
       "max      104.000000\n",
       "Name: yearsOpen, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.yearsOpen[ski_data.yearsOpen < 1000].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The smallest number of years open otherwise is 6. You can't be sure whether this resort in question has been open zero years or one year and even whether the numbers are projections or actual. In any case, you would be adding a new youngest resort so it feels best to simply drop this row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ski_data = ski_data[ski_data.yearsOpen < 1000]"
   ]
  },
  {
   "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": [
    "The other features you had mild concern over, you will not investigate further. Perhaps take some care when using these features."
   ]
  },
  {
   "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": [
    "You have, by this point removed one row, but it was for a resort that may not have opened yet, or perhaps in its first season. Using your business knowledge, you know that state-wide supply and demand of certain skiing resources may well factor into pricing strategies. Does a resort dominate the available night skiing in a state? Or does it account for a large proportion of the total skiable terrain or days open?\n",
    "\n",
    "If you want to add any features to your data that captures the state-wide market size, you should do this now, before dropping any more rows. In the next section, you'll drop rows with missing price information. Although you don't know what those resorts charge for their tickets, you do know the resorts exists and have been open for at least six years. Thus, you'll now calculate some state-wide summary statistics for later use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many features in your data pertain to chairlifts, that is for getting people around each resort. These aren't relevant, nor are the features relating to altitudes. Features that you may be interested in are:\n",
    "\n",
    "* TerrainParks\n",
    "* SkiableTerrain_ac\n",
    "* daysOpenLastYear\n",
    "* NightSkiing_ac\n",
    "\n",
    "When you 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. You might consider the total number of ski runs, but understand that the skiable area is more informative than just a number of runs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fairly new groupby behaviour is [named aggregation](https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.25.0.html). This allows us to clearly perform the aggregations you want whilst also creating informative output column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'pandas' has no attribute 'NamedAgg'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-83-2c404f5c63af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#what it does)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m state_summary = ski_data.groupby('state').agg(\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mresorts_per_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNamedAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m#could pick any column here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mstate_total_skiable_area_ac\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNamedAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'SkiableTerrain_ac'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mstate_total_days_open\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNamedAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'daysOpenLastYear'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'NamedAgg'"
     ]
    }
   ],
   "source": [
    "#Code task 27#\n",
    "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n",
    "#call them 'state_total_days_open', 'state_total_terrain_parks', and 'state_total_nightskiing_ac',\n",
    "#respectively\n",
    "#Finally, add a call to the reset_index() method (we recommend you experiment with and without this to see\n",
    "#what it does)\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": [
    "You know there are two columns that refer to price: 'AdultWeekend' and 'AdultWeekday'. You can calculate the number of price values missing per row. This will obviously have to be either 0, 1, or 2, where 0 denotes no price values are missing and 2 denotes that both are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    82.424242\n",
       "2    14.242424\n",
       "1     3.333333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "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. As the price is your target, these rows are of no use. Time to lose them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 283 entries, 0 to 329\n",
      "Data columns (total 26 columns):\n",
      "Name                 283 non-null object\n",
      "Region               283 non-null object\n",
      "state                283 non-null object\n",
      "summit_elev          283 non-null int64\n",
      "vertical_drop        283 non-null int64\n",
      "base_elev            283 non-null int64\n",
      "trams                283 non-null int64\n",
      "fastSixes            283 non-null int64\n",
      "fastQuads            283 non-null int64\n",
      "quad                 283 non-null int64\n",
      "triple               283 non-null int64\n",
      "double               283 non-null int64\n",
      "surface              283 non-null int64\n",
      "total_chairs         283 non-null int64\n",
      "Runs                 280 non-null float64\n",
      "TerrainParks         239 non-null float64\n",
      "LongestRun_mi        278 non-null float64\n",
      "SkiableTerrain_ac    281 non-null float64\n",
      "Snow Making_ac       246 non-null float64\n",
      "daysOpenLastYear     237 non-null float64\n",
      "yearsOpen            282 non-null float64\n",
      "averageSnowfall      272 non-null float64\n",
      "AdultWeekday         276 non-null float64\n",
      "AdultWeekend         279 non-null float64\n",
      "projectedDaysOpen    240 non-null float64\n",
      "NightSkiing_ac       167 non-null float64\n",
      "dtypes: float64(12), int64(11), object(3)\n",
      "memory usage: 59.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#Code task 28#\n",
    "#Use `missing_price` to remove rows from ski_data where both price values are missing\n",
    "ski_data = ski_data[missing_price != 2]\n",
    "ski_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.9 Review distributions<a id='2.9_Review_distributions'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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cp305bqPFN4ArvTgAVHW6iAzEuZ3fDLc+fH9VXRVBeSry1luVZslWYwTV356v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\ncar6bpiy169fC8zB2Uk+VGLPN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CJ8F09ZlVkPeawnmUaYxHl/4tEVZ50zXdnAc3o8FLgv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DM4sox6BGWUqkfwnHA9VOEyL+O2UHy59VCFXyigU6dO1ba2tlVhv/3204svvjgv7vLLL9f1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\niIhIFf5/NfbnBH+s2hgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44d88afa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ski_data.hist(figsize=(15, 10))\n",
    "plt.subplots_adjust(hspace=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These distributions are much better. There are clearly some skewed distributions, so keep an eye on `fastQuads`, `fastSixes`, and perhaps `trams`. These lack much variance away from 0 and may have a small number of relatively extreme values.  Models failing to rate a feature as important when domain knowledge tells you it should be is an issue to look out for, as is a model being overly influenced by some extreme values. If you build a good machine learning pipeline, hopefully it will be robust to such issues, but you may also wish to consider nonlinear transformations of features."
   ]
  },
  {
   "cell_type": "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). Listen, you should have a healthy concern about using data you \"found on the Internet\". Make sure it comes from a reputable source. This table of data is useful because it allows you to easily pull and incorporate an external data set. It also allows you to proceed with an analysis that includes state sizes and populations for your 'first cut' model. Be explicit about your source (we documented it here in this workflow) and ensure it is open to inspection. All steps are subject to review, and it may be that a client has a specific source of data they trust that you should use to rerun the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Code task 29#\n",
    "#Use pandas' `read_html` method to read the table from the URL below\n",
    "states_url = 'https://simple.wikipedia.org/wiki/List_of_U.S._states'\n",
    "usa_states = pd.read_html(states_url)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(usa_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(usa_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Name &amp;postal abbs. [1]</td>\n",
       "      <td>Cities</td>\n",
       "      <td>Established[upper-alpha 1]</td>\n",
       "      <td>Population[upper-alpha 2][3]</td>\n",
       "      <td>Total area[4]</td>\n",
       "      <td>Land area[4]</td>\n",
       "      <td>Water area[4]</td>\n",
       "      <td>Numberof Reps.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Capital</td>\n",
       "      <td>Largest[5]</td>\n",
       "      <td>mi2</td>\n",
       "      <td>km2</td>\n",
       "      <td>mi2</td>\n",
       "      <td>km2</td>\n",
       "      <td>mi2</td>\n",
       "      <td>km2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>50645.0</td>\n",
       "      <td>131171.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>4597.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>570641.0</td>\n",
       "      <td>1477953.0</td>\n",
       "      <td>94743.0</td>\n",
       "      <td>245384.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</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>294207.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0           1                           2   \\\n",
       "0  Name &postal abbs. [1]      Cities  Established[upper-alpha 1]   \n",
       "1                 Capital  Largest[5]                         mi2   \n",
       "2                 Alabama          AL                  Montgomery   \n",
       "3                  Alaska          AK                      Juneau   \n",
       "4                 Arizona          AZ                     Phoenix   \n",
       "\n",
       "                             3              4             5              6   \\\n",
       "0  Population[upper-alpha 2][3]  Total area[4]  Land area[4]  Water area[4]   \n",
       "1                           km2            mi2           km2            mi2   \n",
       "2                    Birmingham   Dec 14, 1819       4903185          52420   \n",
       "3                     Anchorage    Jan 3, 1959        731545         665384   \n",
       "4                  Feb 14, 1912        7278717        113990         295234   \n",
       "\n",
       "               7         8          9        10        11   12  \n",
       "0  Numberof Reps.       NaN        NaN      NaN       NaN  NaN  \n",
       "1             km2       NaN        NaN      NaN       NaN  NaN  \n",
       "2          135767   50645.0   131171.0   1775.0    4597.0  7.0  \n",
       "3         1723337  570641.0  1477953.0  94743.0  245384.0  1.0  \n",
       "4          113594  294207.0      396.0   1026.0       9.0  NaN  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_states = usa_states[0]\n",
    "usa_states.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, in even the last year, the capability of `pd.read_html()` has improved. The merged cells you see in the web table are now handled much more conveniently, with 'Phoenix' now being duplicated so the subsequent columns remain aligned. But check this anyway. If you extract the established date column, you should just get dates. Recall previously you used the `.loc` accessor, because you were using labels. Now you want to refer to a column by its index position and so use `.iloc`. For a discussion on the difference use cases of `.loc` and `.iloc` refer to the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Code task 30#\n",
    "#Use the iloc accessor to get the pandas Series for column number 4 from `usa_states`\n",
    "#It should be a column of dates\n",
    "established = usa_states.iloc[:, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Total area[4]\n",
       "1               mi2\n",
       "2      Dec 14, 1819\n",
       "3       Jan 3, 1959\n",
       "4           7278717\n",
       "5           3017804\n",
       "6       Sep 9, 1850\n",
       "7           5758736\n",
       "8       Jan 9, 1788\n",
       "9       Dec 7, 1787\n",
       "10      Mar 3, 1845\n",
       "11         10617423\n",
       "12          1415872\n",
       "13          1787065\n",
       "14      Dec 3, 1818\n",
       "15          6732219\n",
       "16          3155070\n",
       "17     Jan 29, 1861\n",
       "18      Jun 1, 1792\n",
       "19     Apr 30, 1812\n",
       "20     Mar 15, 1820\n",
       "21     Apr 28, 1788\n",
       "22          6892503\n",
       "23     Jan 26, 1837\n",
       "24     May 11, 1858\n",
       "25          2976149\n",
       "26     Aug 10, 1821\n",
       "27      Nov 8, 1889\n",
       "28      Mar 1, 1867\n",
       "29     Oct 31, 1864\n",
       "30     Jun 21, 1788\n",
       "31     Dec 18, 1787\n",
       "32      Jan 6, 1912\n",
       "33     Jul 26, 1788\n",
       "34     Nov 21, 1789\n",
       "35      Nov 2, 1889\n",
       "36         11689100\n",
       "37          3956971\n",
       "38     Feb 14, 1859\n",
       "39     Dec 12, 1787\n",
       "40          1059361\n",
       "41     May 23, 1788\n",
       "42      Nov 2, 1889\n",
       "43          6829174\n",
       "44     Dec 29, 1845\n",
       "45          3205958\n",
       "46      Mar 4, 1791\n",
       "47     Jun 25, 1788\n",
       "48     Nov 11, 1889\n",
       "49          1792147\n",
       "50     May 29, 1848\n",
       "51           578759\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "established"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the state name, population, and total area (square miles) columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "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>Name &amp;postal abbs. [1]</td>\n",
       "      <td>Land area[4]</td>\n",
       "      <td>Water area[4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Capital</td>\n",
       "      <td>km2</td>\n",
       "      <td>mi2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>4903185</td>\n",
       "      <td>52420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>731545</td>\n",
       "      <td>665384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>113990</td>\n",
       "      <td>295234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    state state_population state_area_sq_miles\n",
       "0  Name &postal abbs. [1]     Land area[4]       Water area[4]\n",
       "1                 Capital              km2                 mi2\n",
       "2                 Alabama          4903185               52420\n",
       "3                  Alaska           731545              665384\n",
       "4                 Arizona           113990              295234"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 31#\n",
    "#Now use the iloc accessor again to extract columns 0, 5, and 6 and the dataframe's `copy()` method\n",
    "#Set the names of these extracted columns to 'state', 'state_population', and 'state_area_sq_miles',\n",
    "#respectively.\n",
    "usa_states_sub = usa_states.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": [
    "Do you have all the ski data states accounted for?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'state_summary' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-82-c08b08420426>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#Find the states in `state_summary` that are not in `usa_states_sub`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#Hint: set(list1) - set(list2) is an easy way to get items in list1 that are not in list2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmissing_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_summary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0musa_states_sub\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mmissing_states\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'state_summary' is not defined"
     ]
    }
   ],
   "source": [
    "#Code task 32#\n",
    "#Find the states in `state_summary` that are not in `usa_states_sub`\n",
    "#Hint: set(list1) - set(list2) is an easy way to get items in list1 that are not in list2\n",
    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
    "missing_states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No?? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you look at the table on the web, you can perhaps start to guess what the problem is. You can confirm your suspicion by pulling out state names that _contain_ 'Massachusetts', 'Pennsylvania', or 'Virginia' from usa_states_sub:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20    Massachusetts[upper-alpha 3]\n",
       "37     Pennsylvania[upper-alpha 3]\n",
       "38     Rhode Island[upper-alpha 4]\n",
       "45         Virginia[upper-alpha 3]\n",
       "47                   West Virginia\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 52,
     "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": [
    "Delete square brackets and their contents and try again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Code task 33#\n",
    "#Use pandas' Series' `replace()` method to replace anything within square brackets (including the brackets)\n",
    "#with the empty string. Do this inplace, so you need to specify the arguments:\n",
    "#to_replace='\\[.*\\]' #literal square bracket followed by anything or nothing followed by literal closing bracket\n",
    "#value='' #empty string as replacement\n",
    "#regex=True #we used a regex in our `to_replace` argument\n",
    "#inplace=True #Do this \"in place\"\n",
    "usa_states_sub.state.___(to_replace=___, value=__, regex=___, inplace=___)\n",
    "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Code task 34#\n",
    "#And now verify none of our states are missing by checking that there are no states in\n",
    "#state_summary that are not in usa_states_sub (as earlier using `set()`)\n",
    "missing_states = ___(state_summary.state) - ___(usa_states_sub.state)\n",
    "missing_states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Better! You have an empty set for missing states now. You can confidently add the population and state area columns to the ski resort data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Code task 35#\n",
    "#Use 'state_summary's `merge()` method to combine our new data in 'usa_states_sub'\n",
    "#specify the arguments how='left' and on='state'\n",
    "state_summary = state_summary.___(usa_states_sub, ___=___, ___=___)\n",
    "state_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having created this data frame of summary statistics for various states, it would seem obvious to join this with the ski resort data to augment it with this additional data. You will do this, but not now. In the next notebook you will be exploring the data, including the relationships between the states. For that you want a separate row for each state, as you have here, and joining the data this soon means you'd need to separate and eliminate redundances in the state data when you wanted it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.11 Target Feature<a id='2.11_Target_Feature'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, what will your target be when modelling ticket price? What relationship is there between weekday and weekend prices?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Code task 36#\n",
    "#Use ski_data's `plot()` method to create a scatterplot (kind='scatter') with 'AdultWeekday' on the x-axis and\n",
    "#'AdultWeekend' on the y-axis\n",
    "ski_data.___(x=___, y=___, kind=___);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A couple of observations can be made. Firstly, there is a clear line where weekend and weekday prices are equal. Weekend prices being higher than weekday prices seem restricted to sub $100 resorts. Recall from the boxplot earlier that the distribution for weekday and weekend prices in Montana seemed equal. Is this confirmed in the actual data for each resort? Big Mountain resort is in Montana, so the relationship between these quantities in this state are particularly relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Code task 37#\n",
    "#Use the loc accessor on ski_data to print the 'AdultWeekend' and 'AdultWeekday' columns for Montana only\n",
    "ski_data.___[ski_data.state == ___, [___, ___]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there any reason to prefer weekend or weekday prices? Which is missing the least?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdultWeekend    4\n",
       "AdultWeekday    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weekend prices have the least missing values of the two, so drop the weekday prices and then keep just the rows that have weekend price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ski_data.drop(columns='AdultWeekday', inplace=True)\n",
    "ski_data.dropna(subset=['AdultWeekend'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(277, 25)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform a final quick check on the data."
   ]
  },
  {
   "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 do you have for the remaining rows?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>6</td>\n",
       "      <td>23.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5</td>\n",
       "      <td>19.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>5</td>\n",
       "      <td>19.230769</td>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5</td>\n",
       "      <td>19.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>5</td>\n",
       "      <td>19.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5</td>\n",
       "      <td>19.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>5</td>\n",
       "      <td>19.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>4</td>\n",
       "      <td>15.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>4</td>\n",
       "      <td>15.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>4</td>\n",
       "      <td>15.384615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count          %\n",
       "329      6  23.076923\n",
       "146      5  19.230769\n",
       "141      5  19.230769\n",
       "86       5  19.230769\n",
       "62       5  19.230769\n",
       "55       5  19.230769\n",
       "74       5  19.230769\n",
       "115      4  15.384615\n",
       "198      4  15.384615\n",
       "186      4  15.384615"
      ]
     },
     "execution_count": 64,
     "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": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   3.84615385,   7.69230769,  11.53846154,\n",
       "        15.38461538,  19.23076923,  23.07692308])"
      ]
     },
     "execution_count": 65,
     "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": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000     104\n",
       "3.846154     100\n",
       "7.692308      44\n",
       "11.538462     17\n",
       "15.384615     11\n",
       "19.230769      6\n",
       "23.076923      1\n",
       "Name: %, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing['%'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is almost as if values have been removed artificially... Nevertheless, what you don't know is how useful the missing features are in predicting ticket price. You shouldn't just drop rows that are missing several useless features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 283 entries, 0 to 329\n",
      "Data columns (total 26 columns):\n",
      "Name                 283 non-null object\n",
      "Region               283 non-null object\n",
      "state                283 non-null object\n",
      "summit_elev          283 non-null int64\n",
      "vertical_drop        283 non-null int64\n",
      "base_elev            283 non-null int64\n",
      "trams                283 non-null int64\n",
      "fastSixes            283 non-null int64\n",
      "fastQuads            283 non-null int64\n",
      "quad                 283 non-null int64\n",
      "triple               283 non-null int64\n",
      "double               283 non-null int64\n",
      "surface              283 non-null int64\n",
      "total_chairs         283 non-null int64\n",
      "Runs                 280 non-null float64\n",
      "TerrainParks         239 non-null float64\n",
      "LongestRun_mi        278 non-null float64\n",
      "SkiableTerrain_ac    281 non-null float64\n",
      "Snow Making_ac       246 non-null float64\n",
      "daysOpenLastYear     237 non-null float64\n",
      "yearsOpen            282 non-null float64\n",
      "averageSnowfall      272 non-null float64\n",
      "AdultWeekday         276 non-null float64\n",
      "AdultWeekend         279 non-null float64\n",
      "projectedDaysOpen    240 non-null float64\n",
      "NightSkiing_ac       167 non-null float64\n",
      "dtypes: float64(12), int64(11), object(3)\n",
      "memory usage: 59.7+ KB\n"
     ]
    }
   ],
   "source": [
    "ski_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are still some missing values, and it's good to be aware of this, but leave them as is for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.12 Save data<a id='2.12_Save_data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(283, 26)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save this to your data directory, separately. Note that you were provided with the data in `raw_data` and you should saving derived data in a separate location. This guards against overwriting our original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datapath = 'data'\n",
    "# renaming the output data directory and re-running this notebook, for example,\n",
    "# will recreate this (empty) directory and resave the data files.\n",
    "# NB this is not a substitute for a modern data pipeline, for which there are\n",
    "# various tools. However, for our purposes here, and often in a \"one off\" analysis,\n",
    "# this is useful because we have to deliberately move/delete our data in order\n",
    "# to overwrite it.\n",
    "if not os.path.exists(datapath):\n",
    "    os.mkdir(datapath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datapath_skidata = os.path.join(datapath, 'ski_data_cleaned.csv')\n",
    "if not os.path.exists(datapath_skidata):\n",
    "    ski_data.to_csv(datapath_skidata, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'state_summary' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-71-7876e083239b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdatapath_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatapath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'state_summary.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatapath_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mstate_summary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatapath_states\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'state_summary' is not defined"
     ]
    }
   ],
   "source": [
    "datapath_states = os.path.join(datapath, 'state_summary.csv')\n",
    "if not os.path.exists(datapath_states):\n",
    "    state_summary.to_csv(datapath_states, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.13 Summary<a id='2.13_Summary'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: 3** Write a summary statement that highlights the key processes and findings from this notebook. This should include information such as the original number of rows in the data, whether our own resort was actually present etc. What columns, if any, have been removed? Any rows? Summarise the reasons why. Were any other issues found? What remedial actions did you take? State where you are in the project. Can you confirm what the target feature is for your desire to predict ticket price? How many rows were left in the data? Hint: this is a great opportunity to reread your notebook, check all cells have been executed in order and from a \"blank slate\" (restarting the kernel will do this), and that your workflow makes sense and follows a logical pattern. As you do this you can pull out salient information for inclusion in this summary. Thus, this section will provide an important overview of \"what\" and \"why\" without having to dive into the \"how\" or any unproductive or inconclusive steps along the way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A: 3** Your answer here"
   ]
  }
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