real_estate_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib for Real Estate Investment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>id</th>\n",
       "      <th>propertyId</th>\n",
       "      <th>source</th>\n",
       "      <th>neighborhood_id</th>\n",
       "      <th>description</th>\n",
       "      <th>Title</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>nightPrice</th>\n",
       "      <th>numOfBaths</th>\n",
       "      <th>numOfBeds</th>\n",
       "      <th>...</th>\n",
       "      <th>zip</th>\n",
       "      <th>propertyType</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>capacityOfPeople</th>\n",
       "      <th>numOfRooms</th>\n",
       "      <th>reviewsCount</th>\n",
       "      <th>startRating</th>\n",
       "      <th>occupancy</th>\n",
       "      <th>nightsBooked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28612684</td>\n",
       "      <td>11695191.0</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>7877</td>\n",
       "      <td>Beautiful private studio by LAX and the ocean</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90045.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>33.959801</td>\n",
       "      <td>-118.405807</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28612665</td>\n",
       "      <td>15915760.0</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>7877</td>\n",
       "      <td>Beautiful &amp; Private Home with Big Backyard, BB...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>268.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90045.0</td>\n",
       "      <td>House</td>\n",
       "      <td>33.964260</td>\n",
       "      <td>-118.398590</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28612651</td>\n",
       "      <td>1650397.0</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>7877</td>\n",
       "      <td>LAXclose-modern&amp;green guest Suite</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90045.0</td>\n",
       "      <td>House</td>\n",
       "      <td>33.960190</td>\n",
       "      <td>-118.392761</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28612691</td>\n",
       "      <td>16824550.0</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>7877</td>\n",
       "      <td>Studio in Westchester.  Near LAX.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90045.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>33.960659</td>\n",
       "      <td>-118.388420</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28612677</td>\n",
       "      <td>17097814.0</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>7877</td>\n",
       "      <td>Private/Quiet Westside Residential House</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90045.0</td>\n",
       "      <td>House</td>\n",
       "      <td>33.962341</td>\n",
       "      <td>-118.386002</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>355.0</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>3306</th>\n",
       "      <td>5965389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>417433</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143 S Avenue 54 #8</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90042.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>34.106449</td>\n",
       "      <td>-118.196159</td>\n",
       "      <td>NaN</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>3307</th>\n",
       "      <td>5965391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>417433</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4589 Via Marisol #359</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90042.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>34.099129</td>\n",
       "      <td>-118.182632</td>\n",
       "      <td>NaN</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>3308</th>\n",
       "      <td>5968654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>417433</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Condominium - Los Angeles (City), CA</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90042.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>34.097740</td>\n",
       "      <td>-118.185562</td>\n",
       "      <td>NaN</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>3309</th>\n",
       "      <td>5969828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>417433</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4589 Via Marisol #152</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90042.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>34.099129</td>\n",
       "      <td>-118.182632</td>\n",
       "      <td>NaN</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>3310</th>\n",
       "      <td>5977629</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>417433</td>\n",
       "      <td>NaN</td>\n",
       "      <td>835 Temple Terrace</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90042.0</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>34.100342</td>\n",
       "      <td>-118.180588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3311 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  propertyId   source  neighborhood_id  \\\n",
       "0     28612684  11695191.0   Airbnb             7877   \n",
       "1     28612665  15915760.0   Airbnb             7877   \n",
       "2     28612651   1650397.0   Airbnb             7877   \n",
       "3     28612691  16824550.0   Airbnb             7877   \n",
       "4     28612677  17097814.0   Airbnb             7877   \n",
       "...        ...         ...      ...              ...   \n",
       "3306   5965389         NaN  Regular           417433   \n",
       "3307   5965391         NaN  Regular           417433   \n",
       "3308   5968654         NaN  Regular           417433   \n",
       "3309   5969828         NaN  Regular           417433   \n",
       "3310   5977629         NaN  Regular           417433   \n",
       "\n",
       "                                            description  \\\n",
       "0         Beautiful private studio by LAX and the ocean   \n",
       "1     Beautiful & Private Home with Big Backyard, BB...   \n",
       "2                     LAXclose-modern&green guest Suite   \n",
       "3                     Studio in Westchester.  Near LAX.   \n",
       "4              Private/Quiet Westside Residential House   \n",
       "...                                                 ...   \n",
       "3306                                                NaN   \n",
       "3307                                                NaN   \n",
       "3308                                                NaN   \n",
       "3309                                                NaN   \n",
       "3310                                                NaN   \n",
       "\n",
       "                                     Title               neighborhood  \\\n",
       "0                                      NaN  Westchester/Playa Del Rey   \n",
       "1                                      NaN  Westchester/Playa Del Rey   \n",
       "2                                      NaN  Westchester/Playa Del Rey   \n",
       "3                                      NaN  Westchester/Playa Del Rey   \n",
       "4                                      NaN  Westchester/Playa Del Rey   \n",
       "...                                    ...                        ...   \n",
       "3306                    143 S Avenue 54 #8                     Hermon   \n",
       "3307                 4589 Via Marisol #359                     Hermon   \n",
       "3308  Condominium - Los Angeles (City), CA                     Hermon   \n",
       "3309                 4589 Via Marisol #152                     Hermon   \n",
       "3310                    835 Temple Terrace                     Hermon   \n",
       "\n",
       "      nightPrice  numOfBaths  numOfBeds  ...      zip propertyType        lat  \\\n",
       "0          124.0         1.0        2.0  ...  90045.0    Apartment  33.959801   \n",
       "1          268.0         3.0        4.0  ...  90045.0        House  33.964260   \n",
       "2          115.0         0.5        4.0  ...  90045.0        House  33.960190   \n",
       "3           93.0         1.0        2.0  ...  90045.0    Apartment  33.960659   \n",
       "4           90.0         1.0        2.0  ...  90045.0        House  33.962341   \n",
       "...          ...         ...        ...  ...      ...          ...        ...   \n",
       "3306         NaN         1.0        2.0  ...  90042.0    Apartment  34.106449   \n",
       "3307         NaN         2.0        2.0  ...  90042.0    Apartment  34.099129   \n",
       "3308         NaN         2.0        2.0  ...  90042.0    Apartment  34.097740   \n",
       "3309         NaN         2.0        2.0  ...  90042.0    Apartment  34.099129   \n",
       "3310         NaN         3.0        2.0  ...  90042.0    Townhouse  34.100342   \n",
       "\n",
       "             lon capacityOfPeople  numOfRooms  reviewsCount  startRating  \\\n",
       "0    -118.405807              2.0         0.0          87.0          5.0   \n",
       "1    -118.398590              8.0         3.0          37.0          5.0   \n",
       "2    -118.392761              6.0         1.0         437.0          5.0   \n",
       "3    -118.388420              3.0         0.0         302.0          5.0   \n",
       "4    -118.386002              4.0         1.0         139.0          5.0   \n",
       "...          ...              ...         ...           ...          ...   \n",
       "3306 -118.196159              NaN         NaN           NaN          NaN   \n",
       "3307 -118.182632              NaN         NaN           NaN          NaN   \n",
       "3308 -118.185562              NaN         NaN           NaN          NaN   \n",
       "3309 -118.182632              NaN         NaN           NaN          NaN   \n",
       "3310 -118.180588              NaN         NaN           NaN          NaN   \n",
       "\n",
       "      occupancy  nightsBooked  \n",
       "0          89.0         326.0  \n",
       "1          92.0         337.0  \n",
       "2          68.0         248.0  \n",
       "3          99.0         364.0  \n",
       "4          97.0         355.0  \n",
       "...         ...           ...  \n",
       "3306        NaN           NaN  \n",
       "3307        NaN           NaN  \n",
       "3308        NaN           NaN  \n",
       "3309        NaN           NaN  \n",
       "3310        NaN           NaN  \n",
       "\n",
       "[3311 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "real_estate_path = \"data/combined_clean.csv\"\n",
    "\n",
    "# Read the data \n",
    "neighborhood_data = pd.read_csv(real_estate_path)\n",
    "neighborhood_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>id</th>\n",
       "      <th>propertyId</th>\n",
       "      <th>source</th>\n",
       "      <th>neighborhood_id</th>\n",
       "      <th>description</th>\n",
       "      <th>Title</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>nightPrice</th>\n",
       "      <th>numOfBaths</th>\n",
       "      <th>numOfBeds</th>\n",
       "      <th>...</th>\n",
       "      <th>zip</th>\n",
       "      <th>propertyType</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>capacityOfPeople</th>\n",
       "      <th>numOfRooms</th>\n",
       "      <th>reviewsCount</th>\n",
       "      <th>startRating</th>\n",
       "      <th>occupancy</th>\n",
       "      <th>nightsBooked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28612684</td>\n",
       "      <td>11695191</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>7877</td>\n",
       "      <td>Beautiful private studio by LAX and the ocean</td>\n",
       "      <td>0</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>124</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>90045</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>33.959801</td>\n",
       "      <td>-118.405807</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>5</td>\n",
       "      <td>89</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28612665</td>\n",
       "      <td>15915760</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>7877</td>\n",
       "      <td>Beautiful &amp; Private Home with Big Backyard, BB...</td>\n",
       "      <td>0</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>268</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>90045</td>\n",
       "      <td>House</td>\n",
       "      <td>33.964260</td>\n",
       "      <td>-118.398590</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>92</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28612651</td>\n",
       "      <td>1650397</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>7877</td>\n",
       "      <td>LAXclose-modern&amp;green guest Suite</td>\n",
       "      <td>0</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>115</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>90045</td>\n",
       "      <td>House</td>\n",
       "      <td>33.960190</td>\n",
       "      <td>-118.392761</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>437</td>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28612691</td>\n",
       "      <td>16824550</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>7877</td>\n",
       "      <td>Studio in Westchester.  Near LAX.</td>\n",
       "      <td>0</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>93</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>90045</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>33.960659</td>\n",
       "      <td>-118.388420</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>302</td>\n",
       "      <td>5</td>\n",
       "      <td>99</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28612677</td>\n",
       "      <td>17097814</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>7877</td>\n",
       "      <td>Private/Quiet Westside Residential House</td>\n",
       "      <td>0</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>90045</td>\n",
       "      <td>House</td>\n",
       "      <td>33.962341</td>\n",
       "      <td>-118.386002</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>139</td>\n",
       "      <td>5</td>\n",
       "      <td>97</td>\n",
       "      <td>355</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>3306</th>\n",
       "      <td>5965389</td>\n",
       "      <td>0</td>\n",
       "      <td>Regular</td>\n",
       "      <td>417433</td>\n",
       "      <td>0</td>\n",
       "      <td>143 S Avenue 54 #8</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>90042</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>34.106449</td>\n",
       "      <td>-118.196159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>5965391</td>\n",
       "      <td>0</td>\n",
       "      <td>Regular</td>\n",
       "      <td>417433</td>\n",
       "      <td>0</td>\n",
       "      <td>4589 Via Marisol #359</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>90042</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>34.099129</td>\n",
       "      <td>-118.182632</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>5968654</td>\n",
       "      <td>0</td>\n",
       "      <td>Regular</td>\n",
       "      <td>417433</td>\n",
       "      <td>0</td>\n",
       "      <td>Condominium - Los Angeles (City), CA</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>90042</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>34.097740</td>\n",
       "      <td>-118.185562</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>5969828</td>\n",
       "      <td>0</td>\n",
       "      <td>Regular</td>\n",
       "      <td>417433</td>\n",
       "      <td>0</td>\n",
       "      <td>4589 Via Marisol #152</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>90042</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>34.099129</td>\n",
       "      <td>-118.182632</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>5977629</td>\n",
       "      <td>0</td>\n",
       "      <td>Regular</td>\n",
       "      <td>417433</td>\n",
       "      <td>0</td>\n",
       "      <td>835 Temple Terrace</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>90042</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>34.100342</td>\n",
       "      <td>-118.180588</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3311 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  propertyId   source  neighborhood_id  \\\n",
       "0     28612684    11695191   Airbnb             7877   \n",
       "1     28612665    15915760   Airbnb             7877   \n",
       "2     28612651     1650397   Airbnb             7877   \n",
       "3     28612691    16824550   Airbnb             7877   \n",
       "4     28612677    17097814   Airbnb             7877   \n",
       "...        ...         ...      ...              ...   \n",
       "3306   5965389           0  Regular           417433   \n",
       "3307   5965391           0  Regular           417433   \n",
       "3308   5968654           0  Regular           417433   \n",
       "3309   5969828           0  Regular           417433   \n",
       "3310   5977629           0  Regular           417433   \n",
       "\n",
       "                                            description  \\\n",
       "0         Beautiful private studio by LAX and the ocean   \n",
       "1     Beautiful & Private Home with Big Backyard, BB...   \n",
       "2                     LAXclose-modern&green guest Suite   \n",
       "3                     Studio in Westchester.  Near LAX.   \n",
       "4              Private/Quiet Westside Residential House   \n",
       "...                                                 ...   \n",
       "3306                                                  0   \n",
       "3307                                                  0   \n",
       "3308                                                  0   \n",
       "3309                                                  0   \n",
       "3310                                                  0   \n",
       "\n",
       "                                     Title               neighborhood  \\\n",
       "0                                        0  Westchester/Playa Del Rey   \n",
       "1                                        0  Westchester/Playa Del Rey   \n",
       "2                                        0  Westchester/Playa Del Rey   \n",
       "3                                        0  Westchester/Playa Del Rey   \n",
       "4                                        0  Westchester/Playa Del Rey   \n",
       "...                                    ...                        ...   \n",
       "3306                    143 S Avenue 54 #8                     Hermon   \n",
       "3307                 4589 Via Marisol #359                     Hermon   \n",
       "3308  Condominium - Los Angeles (City), CA                     Hermon   \n",
       "3309                 4589 Via Marisol #152                     Hermon   \n",
       "3310                    835 Temple Terrace                     Hermon   \n",
       "\n",
       "      nightPrice  numOfBaths  numOfBeds  ...    zip propertyType        lat  \\\n",
       "0            124         1.0          2  ...  90045    Apartment  33.959801   \n",
       "1            268         3.0          4  ...  90045        House  33.964260   \n",
       "2            115         0.5          4  ...  90045        House  33.960190   \n",
       "3             93         1.0          2  ...  90045    Apartment  33.960659   \n",
       "4             90         1.0          2  ...  90045        House  33.962341   \n",
       "...          ...         ...        ...  ...    ...          ...        ...   \n",
       "3306           0         1.0          2  ...  90042    Apartment  34.106449   \n",
       "3307           0         2.0          2  ...  90042    Apartment  34.099129   \n",
       "3308           0         2.0          2  ...  90042    Apartment  34.097740   \n",
       "3309           0         2.0          2  ...  90042    Apartment  34.099129   \n",
       "3310           0         3.0          2  ...  90042    Townhouse  34.100342   \n",
       "\n",
       "             lon capacityOfPeople  numOfRooms  reviewsCount  startRating  \\\n",
       "0    -118.405807                2           0            87            5   \n",
       "1    -118.398590                8           3            37            5   \n",
       "2    -118.392761                6           1           437            5   \n",
       "3    -118.388420                3           0           302            5   \n",
       "4    -118.386002                4           1           139            5   \n",
       "...          ...              ...         ...           ...          ...   \n",
       "3306 -118.196159                0           0             0            0   \n",
       "3307 -118.182632                0           0             0            0   \n",
       "3308 -118.185562                0           0             0            0   \n",
       "3309 -118.182632                0           0             0            0   \n",
       "3310 -118.180588                0           0             0            0   \n",
       "\n",
       "      occupancy  nightsBooked  \n",
       "0            89           326  \n",
       "1            92           337  \n",
       "2            68           248  \n",
       "3            99           364  \n",
       "4            97           355  \n",
       "...         ...           ...  \n",
       "3306          0             0  \n",
       "3307          0             0  \n",
       "3308          0             0  \n",
       "3309          0             0  \n",
       "3310          0             0  \n",
       "\n",
       "[3311 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (JM code) Replace NaN with 0s; \n",
    "neighborhood_data.fillna(0, inplace = True, downcast = 'infer')\n",
    "neighborhood_data[['nightPrice', 'Price','capacityOfPeople', 'numOfRooms', 'reviewsCount', 'startRating', 'occupancy', 'nightsBooked']].astype(int)\n",
    "neighborhood_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>97</td>\n",
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       "      <th>3306</th>\n",
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       "      <td>0</td>\n",
       "      <td>2495</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>5968654</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0</td>\n",
       "      <td>2950</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>5969828</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0</td>\n",
       "      <td>2395</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>5977629</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3311 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id   source               neighborhood propertyType  nightPrice  \\\n",
       "0     28612684   Airbnb  Westchester/Playa Del Rey    Apartment         124   \n",
       "1     28612665   Airbnb  Westchester/Playa Del Rey        House         268   \n",
       "2     28612651   Airbnb  Westchester/Playa Del Rey        House         115   \n",
       "3     28612691   Airbnb  Westchester/Playa Del Rey    Apartment          93   \n",
       "4     28612677   Airbnb  Westchester/Playa Del Rey        House          90   \n",
       "...        ...      ...                        ...          ...         ...   \n",
       "3306   5965389  Regular                     Hermon    Apartment           0   \n",
       "3307   5965391  Regular                     Hermon    Apartment           0   \n",
       "3308   5968654  Regular                     Hermon    Apartment           0   \n",
       "3309   5969828  Regular                     Hermon    Apartment           0   \n",
       "3310   5977629  Regular                     Hermon    Townhouse           0   \n",
       "\n",
       "      Price  occupancy  \n",
       "0         0         89  \n",
       "1         0         92  \n",
       "2         0         68  \n",
       "3         0         99  \n",
       "4         0         97  \n",
       "...     ...        ...  \n",
       "3306   1800          0  \n",
       "3307   2495          0  \n",
       "3308   2950          0  \n",
       "3309   2395          0  \n",
       "3310   2500          0  \n",
       "\n",
       "[3311 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notes: nightPrice = AirBnb per NIGHT rate  &  Price = Regular rental per MONTH rate\n",
    "impt_neighbh_data = neighborhood_data[['id','source','neighborhood', 'propertyType', 'nightPrice', 'Price', 'occupancy']]\n",
    "impt_neighbh_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-a0c323147029>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  impt_neighbh_data['Final_mthly_price'] = impt_neighbh_data['nightPrice']*multiplier + impt_neighbh_data['Price']\n"
     ]
    },
    {
     "data": {
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       "      <td>2232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28612665</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>House</td>\n",
       "      <td>4824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28612651</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>House</td>\n",
       "      <td>2070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28612691</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28612677</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>House</td>\n",
       "      <td>1620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3306</th>\n",
       "      <td>5965389</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>5965391</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>2495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>5968654</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>2950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>5969828</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>2395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>5977629</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3311 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id   source               neighborhood propertyType  \\\n",
       "0     28612684   Airbnb  Westchester/Playa Del Rey    Apartment   \n",
       "1     28612665   Airbnb  Westchester/Playa Del Rey        House   \n",
       "2     28612651   Airbnb  Westchester/Playa Del Rey        House   \n",
       "3     28612691   Airbnb  Westchester/Playa Del Rey    Apartment   \n",
       "4     28612677   Airbnb  Westchester/Playa Del Rey        House   \n",
       "...        ...      ...                        ...          ...   \n",
       "3306   5965389  Regular                     Hermon    Apartment   \n",
       "3307   5965391  Regular                     Hermon    Apartment   \n",
       "3308   5968654  Regular                     Hermon    Apartment   \n",
       "3309   5969828  Regular                     Hermon    Apartment   \n",
       "3310   5977629  Regular                     Hermon    Townhouse   \n",
       "\n",
       "      Final_mthly_price  \n",
       "0                  2232  \n",
       "1                  4824  \n",
       "2                  2070  \n",
       "3                  1674  \n",
       "4                  1620  \n",
       "...                 ...  \n",
       "3306               1800  \n",
       "3307               2495  \n",
       "3308               2950  \n",
       "3309               2395  \n",
       "3310               2500  \n",
       "\n",
       "[3311 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (JM code) Factor multiplier converts nightly rates into monthly rates. Multiplier is derived from the average yearly occupancy. Assume this rate for a month: (30 days * 60% = 18 days) - only for AirBnb Rentals\n",
    "multiplier = 18\n",
    "impt_neighbh_data['Final_mthly_price'] = impt_neighbh_data['nightPrice']*multiplier + impt_neighbh_data['Price']\n",
    "impt_neighbh_data[['id','source','neighborhood', 'propertyType', 'Final_mthly_price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>source</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>propertyType</th>\n",
       "      <th>nightPrice</th>\n",
       "      <th>Price</th>\n",
       "      <th>occupancy</th>\n",
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       "      <th>0</th>\n",
       "      <td>28612684</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>2232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28612665</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>House</td>\n",
       "      <td>268</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>4824</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28612651</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>House</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>2070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28612691</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28612677</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Westchester/Playa Del Rey</td>\n",
       "      <td>House</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>1620</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3306</th>\n",
       "      <td>5965389</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0</td>\n",
       "      <td>1800</td>\n",
       "      <td>0</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>5965391</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0</td>\n",
       "      <td>2495</td>\n",
       "      <td>0</td>\n",
       "      <td>2495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>5968654</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0</td>\n",
       "      <td>2950</td>\n",
       "      <td>0</td>\n",
       "      <td>2950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>5969828</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0</td>\n",
       "      <td>2395</td>\n",
       "      <td>0</td>\n",
       "      <td>2395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>5977629</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Hermon</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3184 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id   source               neighborhood propertyType  nightPrice  \\\n",
       "0     28612684   Airbnb  Westchester/Playa Del Rey    Apartment         124   \n",
       "1     28612665   Airbnb  Westchester/Playa Del Rey        House         268   \n",
       "2     28612651   Airbnb  Westchester/Playa Del Rey        House         115   \n",
       "3     28612691   Airbnb  Westchester/Playa Del Rey    Apartment          93   \n",
       "4     28612677   Airbnb  Westchester/Playa Del Rey        House          90   \n",
       "...        ...      ...                        ...          ...         ...   \n",
       "3306   5965389  Regular                     Hermon    Apartment           0   \n",
       "3307   5965391  Regular                     Hermon    Apartment           0   \n",
       "3308   5968654  Regular                     Hermon    Apartment           0   \n",
       "3309   5969828  Regular                     Hermon    Apartment           0   \n",
       "3310   5977629  Regular                     Hermon    Townhouse           0   \n",
       "\n",
       "      Price  occupancy  Final_mthly_price  \n",
       "0         0         89               2232  \n",
       "1         0         92               4824  \n",
       "2         0         68               2070  \n",
       "3         0         99               1674  \n",
       "4         0         97               1620  \n",
       "...     ...        ...                ...  \n",
       "3306   1800          0               1800  \n",
       "3307   2495          0               2495  \n",
       "3308   2950          0               2950  \n",
       "3309   2395          0               2395  \n",
       "3310   2500          0               2500  \n",
       "\n",
       "[3184 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (JM Code) Remove \"Other\" from propertyType (not a type of property we want to invest in)\n",
    "impt_neighbh_data = impt_neighbh_data[impt_neighbh_data['propertyType'] != 'Other']\n",
    "impt_neighbh_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>source</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>propertyType</th>\n",
       "      <th>nightPrice</th>\n",
       "      <th>Price</th>\n",
       "      <th>occupancy</th>\n",
       "      <th>Final_mthly_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>28612443</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>House</td>\n",
       "      <td>832</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>14976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>28612467</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>28612449</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>House</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>2304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>28612454</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>House</td>\n",
       "      <td>338</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>6084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>28612432</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>House</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>2754</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2719</th>\n",
       "      <td>5292684</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Silver Lake</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0</td>\n",
       "      <td>2095</td>\n",
       "      <td>0</td>\n",
       "      <td>2095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2720</th>\n",
       "      <td>5292685</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Silver Lake</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2721</th>\n",
       "      <td>5292686</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Silver Lake</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2722</th>\n",
       "      <td>5292689</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Silver Lake</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0</td>\n",
       "      <td>2199</td>\n",
       "      <td>0</td>\n",
       "      <td>2199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2723</th>\n",
       "      <td>5292691</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Silver Lake</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0</td>\n",
       "      <td>2225</td>\n",
       "      <td>0</td>\n",
       "      <td>2225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id   source       neighborhood propertyType  nightPrice  Price  \\\n",
       "54    28612443   Airbnb  Pacific Palisades        House         832      0   \n",
       "55    28612467   Airbnb  Pacific Palisades    Apartment          85      0   \n",
       "56    28612449   Airbnb  Pacific Palisades        House         128      0   \n",
       "57    28612454   Airbnb  Pacific Palisades        House         338      0   \n",
       "58    28612432   Airbnb  Pacific Palisades        House         153      0   \n",
       "...        ...      ...                ...          ...         ...    ...   \n",
       "2719   5292684  Regular        Silver Lake    Apartment           0   2095   \n",
       "2720   5292685  Regular        Silver Lake    Apartment           0   2100   \n",
       "2721   5292686  Regular        Silver Lake    Apartment           0   2100   \n",
       "2722   5292689  Regular        Silver Lake    Apartment           0   2199   \n",
       "2723   5292691  Regular        Silver Lake    Apartment           0   2225   \n",
       "\n",
       "      occupancy  Final_mthly_price  \n",
       "54           88              14976  \n",
       "55           77               1530  \n",
       "56           84               2304  \n",
       "57           43               6084  \n",
       "58           29               2754  \n",
       "...         ...                ...  \n",
       "2719          0               2095  \n",
       "2720          0               2100  \n",
       "2721          0               2100  \n",
       "2722          0               2199  \n",
       "2723          0               2225  \n",
       "\n",
       "[396 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set list of target neighborhoods\n",
    "list_target_neighbhs = [\"Downtown\", \"Encino\", \"Hollywood\", \"Mar Vista\", \"Pacific Palisades\", \"Silver Lake\", \"South Los Angeles\", \"Studio City\", \"Venice\", \"Westwood\"]\n",
    "\n",
    "# FINAL DATAFRAME to use for Matplotlib containing just 10 neighborhoods of interest\n",
    "target_neighbh_df = impt_neighbh_data.loc[\n",
    "                      (impt_neighbh_data['neighborhood'] == list_target_neighbhs[0]) |\n",
    "                      (impt_neighbh_data['neighborhood'] == list_target_neighbhs[1]) |\n",
    "                      (impt_neighbh_data['neighborhood'] == list_target_neighbhs[2]) |\n",
    "                      (impt_neighbh_data['neighborhood'] == list_target_neighbhs[3]) |\n",
    "                      (impt_neighbh_data['neighborhood'] == list_target_neighbhs[4]) |\n",
    "                      (impt_neighbh_data['neighborhood'] == list_target_neighbhs[5]) |\n",
    "                      (impt_neighbh_data['neighborhood'] == list_target_neighbhs[6]) |\n",
    "                      (impt_neighbh_data['neighborhood'] == list_target_neighbhs[7]) |\n",
    "                      (impt_neighbh_data['neighborhood'] == list_target_neighbhs[8]) |\n",
    "                      (impt_neighbh_data['neighborhood'] == list_target_neighbhs[9])]\n",
    "target_neighbh_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shows the number of datapoints for each RENTAL OPTION (value counts)\n",
    "# Use for indexing in generating values for histogram, bar graph 1 (avg income by property type), bar graph 2 (avg income by neighborhood)\n",
    "index_source = target_neighbh_df[\"source\"].value_counts().index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Statistics for Monthly AirBnb Rental Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54      14976\n",
       "55       1530\n",
       "56       2304\n",
       "57       6084\n",
       "58       2754\n",
       "        ...  \n",
       "1277    17820\n",
       "1278     3816\n",
       "1279     2808\n",
       "1282     2106\n",
       "1342     2394\n",
       "Name: Final_mthly_price, Length: 220, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter Airbnb monthly rental price\n",
    "filterAirbnb = target_neighbh_df.loc[target_neighbh_df[\"source\"] == index_source[0], [\"source\", \"Final_mthly_price\"]]\n",
    "\n",
    "# Monthly Rental Income for AirBnb rentals (use in histogram)\n",
    "mthlyRI_Airbnb = filterAirbnb[\"Final_mthly_price\"]\n",
    "mthlyRI_Airbnb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean, median & mode for AirBnb monthly rental income \n",
    "mean_AirBnb = np.mean(mthlyRI_Airbnb)\n",
    "median_AirBnb = np.median(mthlyRI_Airbnb)\n",
    "mode_AirBnb = stats.mode(mthlyRI_Airbnb, axis=0)\n",
    "std_AirBnb = mthlyRI_Airbnb.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram for AirBnb Rental"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set figure size, color and font style\n",
    "plt.figure(figsize=(10, 8), facecolor=\"#9ecae1\")\n",
    "tnr_font = {'fontname':'Times New Roman'}\n",
    "\n",
    "# Plot histogram, set labels, titles, and other formatting elements\n",
    "plt.hist(mthlyRI_Airbnb, color=\"#fdae6b\", alpha=0.5)\n",
    "plt.xlabel('AirBnb Rental Price', **tnr_font, fontsize=14)\n",
    "plt.ylabel('Frequency', **tnr_font, fontsize=14)\n",
    "plt.title(\"Distribution of AirBnb Rental Price\", fontsize=16, **tnr_font, weight=\"bold\")\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save image as png \n",
    "plt.savefig(\"images/airbnb_histogram.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NormaltestResult(statistic=99.03535290421961, pvalue=3.1242541845465294e-22)\n"
     ]
    }
   ],
   "source": [
    "print(stats.normaltest(mthlyRI_Airbnb.sample(100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Dataset may not be normally distributed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-6d0429eccfec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormaltest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmthlyRI_Airbnb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpvalue\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m.05\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Dataset may not be normally distributed\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m: Dataset may not be normally distributed"
     ]
    }
   ],
   "source": [
    "assert stats.normaltest(mthlyRI_Airbnb.sample(100)).pvalue >= .05, \"Dataset may not be normally distributed\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Statistics for Monthly Regular Rental Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1591     4595\n",
       "1593     8500\n",
       "1595    10500\n",
       "1596     3975\n",
       "1599    23950\n",
       "        ...  \n",
       "2719     2095\n",
       "2720     2100\n",
       "2721     2100\n",
       "2722     2199\n",
       "2723     2225\n",
       "Name: Final_mthly_price, Length: 176, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter Regular monthly rental price\n",
    "filterRegular = target_neighbh_df.loc[target_neighbh_df[\"source\"] == index_source[1], [\"source\", \"Final_mthly_price\"]]\n",
    "\n",
    "# Monthly Rental Income for Regular rentals (use in histogram)\n",
    "mthlyRI_Regular = filterRegular[\"Final_mthly_price\"]\n",
    "mthlyRI_Regular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean, median & mode for Regular monthly rental income\n",
    "mean_Regular = np.mean(mthlyRI_Regular)\n",
    "median_Regular = np.median(mthlyRI_Regular)\n",
    "mode_Regular = stats.mode(mthlyRI_Regular)\n",
    "std_Regular = mthlyRI_Regular.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram for Regular Rental"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set figure size, color and font style\n",
    "plt.figure(figsize=(10, 8), facecolor=\"#9ecae1\")\n",
    "\n",
    "# Plot histogram, set labels, titles and other formatting elements\n",
    "plt.hist(mthlyRI_Regular, alpha=0.5, color=\"#fdae6b\")\n",
    "plt.xlabel('Regular Rental Price',  **tnr_font, fontsize=14)\n",
    "plt.ylabel('Frequency', **tnr_font, fontsize=14)\n",
    "plt.title(\"Distribution of Regular Rental Price\", fontsize=16, **tnr_font, weight=\"bold\")\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save image as png \n",
    "plt.savefig(\"images/regular_histogram.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NormaltestResult(statistic=119.58274471331256, pvalue=1.0787900199653828e-26)\n"
     ]
    }
   ],
   "source": [
    "print(stats.normaltest(mthlyRI_Regular.sample(100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Dataset may not be normally distributed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-930a4a4201d0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormaltest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmthlyRI_Regular\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpvalue\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m.05\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Dataset may not be normally distributed\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m: Dataset may not be normally distributed"
     ]
    }
   ],
   "source": [
    "assert stats.normaltest(mthlyRI_Regular.sample(100)).pvalue >= .05, \"Dataset may not be normally distributed\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculation of IQRs of 2 Rental Options: AirBnb vs Regular "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#AirBnb Quartiles & IQR\n",
    "AirBnb_quart = mthlyRI_Airbnb.quantile([.25,.5,.75])\n",
    "AirB_lowerq = AirBnb_quart[0.25]\n",
    "AirB_upperq = AirBnb_quart[0.75]\n",
    "AirBnb_IQR = AirB_upperq-AirB_lowerq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Regular Quartiles & IQR\n",
    "Regular_quart = mthlyRI_Regular.quantile([.25,.5,.75])\n",
    "Reg_lowerq = Regular_quart[0.25]\n",
    "Reg_upperq = Regular_quart[0.75]\n",
    "Regular_IQR = Reg_upperq-Reg_lowerq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creates a list  of results (for summary df use only)\n",
    "Quart1 = [AirB_lowerq, Reg_lowerq]\n",
    "Quart3 = [AirB_upperq, Reg_upperq]\n",
    "IQR = [AirBnb_IQR, Regular_IQR]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rental Options</th>\n",
       "      <th>Average Monthly Rental Price</th>\n",
       "      <th>Median Monthly Rental Price</th>\n",
       "      <th>Popular(mode) Monthly Rental Price</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>1st Quartile</th>\n",
       "      <th>3rd Quartile</th>\n",
       "      <th>IQR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AirBnb</td>\n",
       "      <td>3429.327273</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>([2052], [6])</td>\n",
       "      <td>3127.392200</td>\n",
       "      <td>1831.5</td>\n",
       "      <td>3609.0</td>\n",
       "      <td>1777.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Regular</td>\n",
       "      <td>2815.028409</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>([1895], [5])</td>\n",
       "      <td>2689.289619</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>3212.5</td>\n",
       "      <td>1762.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Rental Options  Average Monthly Rental Price  Median Monthly Rental Price  \\\n",
       "0         AirBnb                   3429.327273                       2430.0   \n",
       "1        Regular                   2815.028409                       1925.0   \n",
       "\n",
       "  Popular(mode) Monthly Rental Price  Standard Deviation  1st Quartile  \\\n",
       "0                      ([2052], [6])         3127.392200        1831.5   \n",
       "1                      ([1895], [5])         2689.289619        1450.0   \n",
       "\n",
       "   3rd Quartile     IQR  \n",
       "0        3609.0  1777.5  \n",
       "1        3212.5  1762.5  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creates a df for the Summary Results of all Quartiles and IQRs of the 4 promising drug regimens (OPTIONAL)\n",
    "rental_options = [\"AirBnb\", \"Regular\"]\n",
    "mean_RI = [mean_AirBnb, mean_Regular]\n",
    "median_RI = [median_AirBnb, median_Regular]\n",
    "mode_RI = [mode_AirBnb, mode_Regular]\n",
    "std_RI = [std_AirBnb, std_Regular]\n",
    "\n",
    "Final_Stats = pd.DataFrame({ \n",
    "    'Rental Options': rental_options,\n",
    "    'Average Monthly Rental Price': mean_RI,\n",
    "    'Median Monthly Rental Price': median_RI,\n",
    "    'Popular(mode) Monthly Rental Price': mode_RI,\n",
    "    'Standard Deviation': std_RI,\n",
    "    '1st Quartile': Quart1,\n",
    "    '3rd Quartile': Quart3,\n",
    "    'IQR': IQR\n",
    "})\n",
    "Final_Stats           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box and Whisker Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lDmurKlVTWaFeA4bohp/8UVWlRe4H8r75+6cUHN40Hca7hZVYzsReX6e3Hv2ZKooKdcl1t7fZ92w+Tyf1ix+phC+nDm1f97EOZ2ZIkgKCrPIPDJLU/F5LC460+9ot1VN4eL+W/OP/1GtgrPuB0PN18vkIk5eX4pMuabWfX0APhfRsenahobZG0ldhtGf/we1+vXP5e25L3PjJCggOUbWtXHnZmaetGX8+2lNrz+iBkqTMDavl+PK3XU6nQ/aGevfX+/5tX60y02hvUEUbv/ECQFgH0E7pK5Y0C7BS05KEJ+eF79n8qSpLiyVJrlN2Nj052tjWr/2djq+msDjsDZKkI3t36vjhptG58V8+qOhsdl1Hs+u39JBaxfECSVJBTpb8ewQ1e+CxJWNnXimTyUtjZ85tNnJdeHi/MlZ9qPA+/TXhiutlMnm1GshOvZeTNQ49ZfOozUvf0rGD2ZKkcTOvlCTtSf1Ma994TllfrNOGJf+VJTBYpQVH9d7jj+iq7/xcM2/5tmbf/h0Fh0e6rx0QHCKzX9PKMifKS92bHzX7u3E5TznmVEl+ripLi1VfU62N77/mfhCzoa5WtpKiL+tv/660p97ryfOm33SPu85lzz/m/kFp3Iyme/30zee1ddWH2r5umfZnbG5Wr9T0OT71c3nyzwFBwZKk+poTqqms0KFdW5W7e1vT6kT79yh16dvuKRh11VWqKi9pfh1Xy1NjGu0Naqz/6gHpQaOTFNKzt0ZNTZGP2ddd09frkaRJV98sSe555MVHD8kvoIc78J/rfX3Z4cu6Xc36t/T1fuq9efuYNWLKLElfLcPYlpOfQ1cbU4fa+ppw1/NlDUlf/vsoK8zTkn/+VjvXf6JPXnpSzsZGjbn0Cnl5eys/J0sfP/s37U39TKtfeUoBQdYz1glczAjrAM5o66oPlbrsbWVtWqu8fbvdx4/s3amaqgpJTXNpP/z3H3Q890CztcR3b1qr0oKj7tVaDmdubbbJzNG9u7Rl5Qfuj1e/9rTef/J3+vBff1Df2OG66v6facLcb0iStq1Z6u6XuXG1DuxI04nyUklSdtoG1Z8cQR8+VvPue1ifvf2i/vefP8lub9Atv3rsjL/GD4nspdjESS3uGLnqlf/ozb/8VNvXLlPKNx9QeJ9+p/X5+r1sWfmB+75Tvvk9+QX0kNPh0Af/+oNys3Zo4KjxSrnzewoKjdC2NR9p9+drNOXa22Ty8tLBnU0P7y762b36y21z9Ojtl+nfD9ykjUteldQ01SLlzu/Jv0eQVrz4j2Z17Nq4Sg11tcrc8FWA377uY0VGD1R03Ej5+vurvrZal958jwJDw1VbZdOJipI2P09ft2fzp8pO3+j+eP27L6v8eIH8LAFK+eYPJEmVpUV6/5+/VfnxAl1y/R1NIdZk0oZ3/6uSvMNKuuJ6uZxObV+3zH2dzA2rtOPT5e6Pd362QpI0cNR4DZ88U47GRi199q/qM3iohoxLVnB4T3l5+8gvoIcumX+nfP0tKjy0T97ePspY85H7Olmb1qqhrrbZPZQWHNHyFx5XTsYXSl+xRPU11TKZTBo3+yr3D1KlBUf12dsvus/JTt/gXpZ0zKVX6NKbFmrLJ+/rw3//US6XSzf/7FEFBIec133t35aqExXlkpp+E1Gwf497OtShzK06UV7a5r01hWKfNn8zUFlarNSlb+tERZkk6fDuDO3asOq0aTZtfU0c2bPDXVfu7m2qtpWr18BYXf3dnyukZ2/l7t6mPV+s06SrbpZfQA9F9I3R1d/9pUKj+mjP5k+1ddWHSrri+natVANczEwvpuWe28Q5AECnqams0Bt//HGLgfmO/3uSrd3RptWvPqXZt3+nq8sA0AFYDQYADGjXhlUyeXnpe/95W/49guRotOvYwX1652+/UGVZic5upXdc6Fwul9KXv6eBo8fL1z9AfWNHnPkkAN0CYR0ADOjYgb3y9bcoIDhEJpNJ3j4+Cu8dLf8egRowYmxXlweDOX44R2vfeFaj83MV3re/Eudc09UlAeggTIMBAAOqLC3Sp2++IIejUT2CQ2Ty8lZjQ70SL7tOPfsN7OryYDD2+jot/tNPVFVWopS7vu9+8BtA90dYBwAAAAyKaTBtmDcgtKtLAFoVGhqg8vKari4DALoN3jdhZEsPl7d4nKUbgW7Kx+fMuyQCAL7C+ya6I8I6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFA+nnyx8uMFWv3a08rLzpSvn7+GTpyu6TfcLR9fXy1f9IR2rFvWrP+s2+5X0uXzJUm5Wdu1+tWnVH68QL0HxWvuPT9UaK++7r5bVn6g1I/eUn1tteKTpmrOnQ/I198iSWq0N2jVK//R3tT18jGblXTF9Zo47ybP3TgAAABwDjwW1h2Ndr372K8V0TdGt//mH6qprNCy5x+TJM269T6V5udqxoJvacSUWe5z/CwBkqTK0iK99/gjmnztrRoybqI+f/81vffEb7Twz8/J5OWl7PQN2vDOS5p3308VGBquj5/7u9a+8awuv/tBSdK6xc8rPydLC37+qCrLSrT06UcVHN5TwyfN8NTtAx3CZqtQ5o5U5eRkqbbeJYufSbGxwzVyTLKs1pCuLg8AAHQwj02DKTiQrfLjBbry2z9RRN8Y9R82RlO/8U1lbVorSSopOKJeA+MUGBLm/s/s5y9J2r5umXr2H6SJ825SRN8Yzb33R6osLVZu1nZJUvqK95WYcq1iEyer96B4XXbXD7Rrw0o11NWqoa5WO9Yt0+zb7levgXGKS5ys5Hk3KmPVh566daBD5OYe0pK3Fynw2CIt7PUP/WrI77Sw1z8UeGyRlry9SLm5h7q6RAAA0ME8FtbDe0frxp/80T01RZJMkhrtdp2oKFPdiSqF945u8dyC/XvUL36U+2Ozn7+iBgxRfk6WnE6HCg9mq9/Qr9r7Dhkmp8Oh44f3q+jIQTU22hUdN9LdHh03UscO7pPT4ej4GwU6gc1WobUrl2hB1CLNDluhMN9yeZmcCvMt1+ywFVoQtUhrVy6RzVbR1aUCAIAO5LFpMAHBIRowMsH9scvp1NZVH6pf/EiV5OfKy9tb6999WQd3pssSZFXS5fM1etplkqTqijIFhoY3u14Pa6iqykpUX12tRnuDAkO+avfy9pYlMFhVZcXy8jHL0iNIPr6+p5wbIkejXTWVFaddFzCizB2pSghOVT9LXovt/Sx5SghKVebOUZoyNcXD1QEAgM7i0QdMT7Xm9Wd0PPeA7vzdv3Vkzw5JUs+YwRp/2XU6smeHPnnxn00PoSZPl72hXt5m32bne5vNamxskL2hzv3xqXzMZjU22uXldLTQ1nStxkZ7mzWGhgbIx8f7vO4T6Aj79+/R3VGpbfZJCErVizmTde386z1UFQB0P5GRQV1dAtCyw+UtHvZ4WHe5XFr96lPatuYjXfv9RxQZPUARfWM0fNIMWQKDJUk9+w9SWWG+MtYs1dDk6fIx+8phb2h2HYfdLktgsDt4O+zNg3ej3S6zr79Mppbamq5l9vVrs9by8przulego9TUORRitrXZx2q2qabeoeLiKg9VBQDdS2RkEO+R6HY8us66y+nUsuf+rm1rluqaB36puMTJkiSTyeQO6idF9OmvE2UlkqTA0HBV25r/tFFtK1NgSJg7sJ/a7nQ4VHuisulB1dAI1VWfkOOUUfRqW7m8zebTXhMwKouflyrs1jb72OxWWXzZOgEAgAuJR7+zr3njWWV9sU7zH/yN4pOmfnX89Wf0zt9/1azv8dz9CuvTT5LUZ8gwHc3OdLfZ6+t0/PAB9RkyTCYvL/UaFN+sPT8nS17e3ooaMERRMYPl7eOjvH1Z7va87Ez1GhArL2+muKB7iI0drowTyW32yahKVmzcCA9VBAAAPMFjYT1/f5a2rFiiS+bfoV4D43Siosz9X2zCJB3cka4tn7yv8uMF2rrqQ2VuXK3kK2+UJI2efrmOHdirTR++rpL8XC17/jEFR/TUgBHjJEkJs69S+vJ3lZ2+QccOZmvly09q9LTL5etvkdnPXyOnztHKl59UwYG9ytm6SWnL3tH4y67z1K0D523kmGRlVCbraG3LKyYdrY1WRlWyRo6e4OHKAABAZzK9mJbr8sQLrX3jWaUte7fFtof/u0LZ6Rv1+Qevqfx4vkIie2vaDd9sNvp+YEea1rz2jCpLi9RnyDBdsfAhhUb1cbdv/uhNpS1/T45Gu+ISpyjlru+756Tb6+v0yUtPKjt9g/wsAUqa+w0lz73hjDXPGxB6nncNdJzc3ENau3KJEoJSlRCUKqvZJpvdqoyqZGVUJWtmynzFxAzs6jIBwLCYsw4jW9rKA6YeC+vdEWEdRmOzVShzZ5py9u1WbYNLFl+TYuNGaOToCexgCgBnQFiHkbUW1rts6UYAZ89qDdGUqSmaMjWFbzoAAFwEWDoCAAAAMCjCOgAAAGBQhHUAAADAoAjrAAAAgEER1gEAAACDIqwDAAAABkVYBwAAAAyKsA4AAAAYFJsiAQCAC5bNVqHMHanKyclSbb1LFj+TYmOHa+SYZHZ+RrdAWAcAABek3NxDWrtyiRKCU7WwV6pCzDZV2K3KOJasJdmZmpkyXzExA7u6TKBNhHUAAHDBsdkqtHblEi2IWqR+ljz38TDfcs0OW6F4S6YWr5Tm37iQEXYYGnPWAQDABSdzR6oSglObBfVT9bPkKSEoVZk70zxcGXB2COsAAOCCk5OTpYTA1Db7JASlKmffbg9VBJwbwjoAALjg1NY7FWK2tdnHaraptsHpoYqAc0NYBwAAFxyLn5cq7NY2+9jsVll8iUIwNr5CAQDABSc2drgyTiS32SejKlmxcSM8VBFwbgjrAADggjNyTLIyKpN1tDa6xfajtdHKqErWyNETPFwZcHZYuhEAAFxwrNYQzUyZr8Urmx4kTQhKldVsk81uVUZVsjKqkjUzZT7LNsLwCOsAAOCCFBMzUPNvXKjMnaO0aN8k1Ta4ZPE1KTZuhOaPnkBQR7dAWAcAABcsqzVEU6amaMrUFEVGBqm4uKqrSwLOCnPWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABiUjydfrPx4gVa/9rTysjPl6+evoROna/oNd8vH11e2kuNa/sLjys/JUnB4pGbeep8Gj5ngPjc3a7tWv/qUyo8XqPegeM2954cK7dXX3b5l5QdK/egt1ddWKz5pqubc+YB8/S2SpEZ7g1a98h/tTV0vH7NZSVdcr4nzbvLkrQMAAABnzWMj645Gu9597Nfy8THr9t/8Q1d952fK2bpJn73zolwul957/BFZAoN15+/+rZGXpOj9f/5OFUXHJEmVpUV67/FHNGLKLH3z9/9RYEiY3nviN3I5nZKk7PQN2vDOS0r55ve04Bd/07FD+7T2jWfdr71u8fPKz8nSgp8/qsvuflCbPnhDWV+s89StAwAAAOfEY2G94EC2yo8X6Mpv/0QRfWPUf9gYTf3GN5W1aa1ys7ar7FieLl/4kCL6xmjS1Terb+xw7fxshSRp+7pl6tl/kCbOu0kRfWM0994fqbK0WLlZ2yVJ6SveV2LKtYpNnKzeg+J12V0/0K4NK9VQV6uGulrtWLdMs2+7X70GxikucbKS592ojFUfeurWAQAAgHPisbAe3jtaN/7kj+6pKZJkktRot6tg/x5FDRgiP0uAuy06bqTyc7IkSQX796hf/Ch3m9nPX1EDhig/J0tOp0OFB7PVb+hX7X2HDJPT4dDxw/tVdOSgGhvtio4b2ezaxw7uk9Ph6MQ7BgAAAM6Px8J6QHCIBoxMcH/scjq1ddWH6hc/UicqyhQYEt6sfw9rqKrKSiRJ1RVlCgxtub2+ulqN9oZm53t5e8sSGKyqsmKdqCiTpUeQfHx9Tzk3RI5Gu2oqKzrhTgEAAICO4dEHTE+15vVndDz3gO783b+Vvvw9eZvNzdq9zWY1NtolSfaGenmbfVtob5C9oc798al8vjzfy+looa3pWiev35rQ0AD5+Hif/c0BHhIZGdTVJQBAt8L7JgzrcHmLhz0e1l0ul1a/+pS2rflI137/EUVGD5CP2az6mupm/Rx2u8y+fk1Fmn3lsDec1m4JDHYHb4e9efButNtl9vWXydRSW9O1Tl6/NeXlNWd/g4CHREYGqbi4qqvLAIBug/dNdEceXWfd5XRq2XN/17Y1S3XNA79UXOJkSVJgaISqbWXN+lbbyhQYEvZle7iqbeUttp8M7Ke2Ox0O1Z6oVGBImAJDI1RXfUKOU0bRq23l8jabZQkM7qxbBQAAAM6bR8P6mjeeVdYX6zT/wd8oPmmq+3ifIcN0PPeAGupq3cfysjPVZ8gwd/vR7Ex3m72+TscPH1CfIcNk8vJSr0Hxzdrzc7Lk5e2tqAFDFBUzWN4+Psrbl9Xs2r0GxMrLmykuAAAAMC6PhfX8/VnasmKJLpl/h3oNjNOJijL3f/2HjZY1IkofP/c3Fecd1uaP3lTBgb0aM2OuJGn09Mt17MBebfrwdZXk52rZ848pOKKnBowYJ0lKmH2V0pe/q+z0DTp2MFsrX35So6ddLl9/i8x+/ho5dY5WvvykCg7sVc7WTUpb9o7GX3adp24dAAAAOCemF9NyXZ54obVvPKu0Ze+22Pbwf1fIVlyoZS88poIDexXas49m3XafBo4a7+5zYEea1rz2jCpLi9RnyDBdsfAhhUb1cbdv/uhNpS1/T45Gu+ISpyjlru+756Tb6+v0yUtPKjt9g/wsAUqa+w0lz73hjDXPGxB6nncNdB7mXgLA2eF9E0a2tJUHTD0W1rsjwjqMjG86AHB2eN+EkbUW1j06Zx0AAABA+xHWAQAAAIMirAMAAAAG1WU7mAI4ezZbhTJ3pConJ0u19S5Z/EyKjR2ukWOSZbWGdHV5AACggxHWgW4iN/eQ1q5cooTgVC3slaoQs00VdqsyjiVrSXamZqbMV0zMwK4uEwAAdCDCOtAN2GwVWrtyiRZELVI/S577eJhvuWaHrVC8JVOLV0rzb1zICDsAABcQ5qwD3UDmjlQlBKc2C+qn6mfJU0JQqjJ3pnm4MgAA0JkI60A3kJOTpYTA1Db7JASlKmffbg9VBAAAPIGwDnQDtfVOhZhtbfaxmm2qbXB6qCIAAOAJhHWgG7D4eanCbm2zj81ulcWXf9IAAFxI+M4OdAOxscOVcSK5zT4ZVcmKjRvhoYoAAIAnENaBbmDkmGRlVCbraG10i+1Ha6OVUZWskaMneLgyAADQmVi6EegGrNYQzUyZr8Urmx4kTQhKldVsk81uVUZVsjKqkjUzZT7LNgIAcIEhrAPdREzMQM2/caEyd47Son2TVNvgksXXpNi4EZo/egJBHQCACxBhHehGrNYQTZmaoilTUxQZGaTi4qquLgkAAHQi5qwDAAAABkVYBwAAAAyKsA4AAAAYFGEdAAAAMCjCOgAAAGBQhHUAAADAoAjrAAAAgEER1gEAAACDIqwDAAAABkVYBwAAAAyKsA4AAAAYFGEdAAAAMCjCOgAAAGBQhHUAAADAoAjrAAAAgEER1gEAAACDIqwDAAAABuXT1QUAaL+8vCPavHGlyspL1ejykY+pUWGh4Zp4SYqio/t3dXkAAKCDEdaBbiI9/Qtt3/KZkkLSlNh/q0LMNlXYrdpamajlH5Vo7PjpSkqa1NVlAgCADkRYB7qBvLwj2r7lM93e9xX1s+S5j4f5lmtOxGoN7bFXr26Revfuywg7AAAXEOasA93A5o0rlRSS1iyon6qfJU9J1nRt/nyVhysDAACdibAOdANl5aVKDN7aZp9E6xaVl5V4qCIAAOAJhHWgG2h0+SjEbGuzj9Vsk93FzDYAAC4khHWgG/AxNarCbm2zj81uldnU6KGKAACAJxDWgW4gLDRcWysT2+yz1TZeoWERHqoIAAB4AmEd6AYmXpKi9IoJOlob3WL70dpopduSNHHKHA9XBgAAOhMTXIFuIDq6v8aOn65Xt0hJ1nQlWrfIarbJZrdqq2280m1JGjt+Oss2AgBwgSGsA91EUtIk9e7dV5s/j1T6kQmyu3xkNjUqNCxCV1w1h6AOAMAFiLAOdCPR0f31jZsWSpIiI4NUXFzVxRUBAIDOxJx1AAAAwKAI6wAAAIBBEdYBAAAAgyKsAwAAAAZFWAcAAAAMirAOAAAAGBRhHQAAADAowjoAAABgUIR1AAAAwKAI6wAAAIBBEdYBAAAAgyKsAwAAAAZFWAcAAAAMirAOAAAAGBRhHQAAADAowjoAAABgUIR1AAAAwKAI6wAAAIBBEdYBAAAAgyKsAwAAAAZFWAcAAAAMirAOAAAAGBRhHQAAADAowjoAAABgUIR1AAAAwKAI6wAAAIBBEdYBAAAAgyKsAwAAAAbl096OhzMzdGjXVpUV5quhtlr+PYIU2X+gYhMmKypmcGfWCAAAAFyUzhjWD+5I16dvL5Kvf4BCo/oorHdfefuY5bDbVV6Yr2XP/V0BwVbNufMBhfWK9kTNAAAAwEWhzbC+bc1SNdTV6vZH/iGzn3+r/apt5cpY/ZEGjRmvvkOGd3iRAAAAwMWo1bBeeHi/eg+KV6+BsWe8SA9rqKZef4f2b9ushrpa+fpbOrRIAAAA4GLUaljvNWDIWV9syLiJ51UMAAAAgK+0OQ3mcGaGjuzdqf7DxmjAiHGSpPLCfH383N9UfPSQYkYmaO49P5J/j0CPFAsAAABcTNpcunHHp8s1buY8d1CvrarUm4/+TGY/f93yq8cUlzhF69950SOFAgAAABebVkfWj+zZIR9fP5UXFai8qECStPmjN1VXfULjL7tO9bU1Cg6PVG5WQ9Po+9DRHisaAAAAuBi0ObLucjq//INL21Z/pNzd23XZXd9vWhnG5fJEfQAAAMBFq9WR9f7Dxmj/tlRlp21QTWWF9qau1+w7vqPhk2a4+9SeqFRddRWj6gAAAEAnaPMB00tvWqhta5fK5XTo+h/+TkPGJbvbjh3I1u4v1mpY8vROLxIAAAC4GLUa1qvKShQUFqHEOde02N57cLx6D45vdqy+tkZeXl5tbqAEAAAAoH1anbPu5e2t7Ws/bveF6qqrlLlxFUEdAAAA6CCtjqz3sIaqb9wIffCvP2jgqETFDB8ra2QvmUwmd5+GulqV5h/RwV1bVF1Rqlm33e+RogEAAICLQZtz1iOjB+jKb/1Yacve1Wu/f0i1JyrlZ+khL29v2evr1FBbq14DY5U87yZNufZWT9UMAAAAXBTaDOuSZPbz15TrbtOU625T8dFDKivMl8PeIEtQsCL7DVJgSJgn6gQAAAAuOmcM66eK7DdQkf0GdlYtAAAAAE5xVmG9ozTaG/Tyr7+r2bfdrwEjEyRJyxc9oR3rljXrN+u2+5V0+XxJUm7Wdq1+9SmVHy9Q70HxmnvPDxXaq6+775aVHyj1o7dUX1ut+KSpmnPnA/L1t7hfb9Ur/9He1PXyMZuVdMX1mjjvJg/dLdBxbLYKZe5IVU5OlmrrXbL4mRQbO1wjxyTLag3p6vIAAEAH83hYb2xo0P+e+pNK8g43O16an6sZC76lEVNmuY/5WQIkSZWlRXrv8Uc0+dpbNWTcRH3+/mt674nfaOGfn5PJy0vZ6Ru04Z2XNO++nyowNFwfP/d3rX3jWV1+94OSpHWLn1d+TpYW/PxRVZaVaOnTjyo4vGezDZ4Ao8vNPaS1K5coIThVC3ulKsRsU4XdqoxjyVqSnamZKfMVE8NvvgAAuJC0unRja1wu1zm/WEl+rl75v++pvOjY6W0FR9RrYJwCQ8Lc/51cBnL7umXq2X+QJs67SRF9YzT33h+psrRYuVnbJUnpK95XYsq1ik2crN6D4nXZXT/Qrg0r1VBXq4a6Wu1Yt0yzb7tfvQbGKS5xspLn3aiMVR+e830AnmazVWjtyiVaELVIs8NWKMy3XF4mp8J8yzU7bIUWRC3S2pVLZLNVdHWpAACgA7U7rJfk5+q/jzygdx9/RJJ07GC21r/zkuqqq9r9Ykf37tLAUeN1x2/+2ez4iYoy1Z2oUnjv6BbPK9i/R/3iR7k/Nvv5K2rAEOXnZMnpdKjwYLb6Df2qve+QYXI6HDp+eL+KjhxUY6Nd0XEj3e3RcSN17OA+OR2OdtcOdKXMHalKCE5VP0tei+39LHlKCEpV5s40D1cGAAA6U7vD+vIXHteAkQmK7BsjSeo9KF59hgzT0mf+2u4XGzdrnmYsuPe0jZNK8nPl5e2t9e++rH9/72Yt+sW3tXP9J+726ooyBYaGNzunhzVUVWUlqq+uVqO9QYEhX7V7eXvLEhisqrJinagok6VHkHx8fU85N0SORrtqKivaXTvQlXJyspQQmNpmn4SgVOXs2+2higAAgCe0e85670Hxmn7j3UpfscR9rNHeoKPZu867iNKCI5KknjGDNf6y63Rkzw598uI/5evnr6HJ02VvqJe32bfZOd5msxobG2RvqHN/fCofs1mNjXZ5OR0ttDVdq7HR3mZdoaEB8vHxPq97AzpCbb1LIWZbm32sZptqG1yKjAzyUFUA0P3wHgnDOlze4uF2h3W/gEA11NW6dzAtOnJQ6xY/rz6Dh513bQmzr9bwSTNkCQyWJPXsP0hlhfnKWLNUQ5Ony8fsK4e9odk5DrtdlsBgd/B22JsH70a7XWZff5lMLbU1Xcvs69dmXeXlNed1X0BHsfiZVGG3Ksy35X/IkmSzW2XxNam4uP1T0wDgYhIZGcR7JLqddof1xDlXa/kLj6vgwF5lrP5I5YX56hs7XHPv/dF5F2EymdxB/aSIPv11eNdWSVJgaLiqbc1DSrWtTJHRA9yBvdpW7l4D3ulwqPZEpQJDwmTy8lJd9Qk5Gu3y9jF/eW65vM3m014TMKrY2OHKOJas2WErWu2TUZWs2LgRHqwKAAB0tnaH9YDgEF3zwC91oqJMlaVFCgwJU3B4zw4pYs3rz6jsWJ5u+PEf3MeO5+5XWJ9+kqQ+Q4bp6N6vptvY6+t0/PABTb7mVpm8vNRrULyOZme612zPz8mSl7e3ogYMkclkkrePj/L2ZSlm+BhJUl52pnoNiJWXN1Nc0D2MHNO0PGO8JbPFh0yP1kYroypZ80dP6ILqAABAZ2n3A6ZVZSVa9+bz8vW3qM/goaosLda+LZ93SBGxCZN0cEe6tnzyvsqPF2jrqg+VuXG1kq+8UZI0evrlOnZgrzZ9+LpK8nO17PnHFBzRUwNGjJMkJcy+SunL31V2+gYdO5itlS8/qdHTLpevv0VmP3+NnDpHK19+UgUH9ipn6yalLXtH4y+7rkNqBzzBag3RzJT5Wnx8oVaXXq6yhlA5XF4qawjV6tLLtfj4Qs1Mmc/GSAAAXGBML6bltmvh9Nf/0DTd5fqH/k/+PZoezti66kPV11Rr8jW3nPUL/+W2Obr5Z4+6R8P3bP5Mn3/wmsqP5ysksrem3fBNxSdNdfc/sCNNa157RpWlReozZJiuWPiQQqP6uNs3f/Sm0pa/J0ejXXGJU5Ry1/fdc9Lt9XX65KUnlZ2+QX6WACXN/YaS595wxhrnDQg96/sCOpPNVqHMnWnK2bdbtQ0uWXxNio0boZGjJxDUAeAMmLMOI1vaygOm7Q7ra994TjNv+VazY/n7s/TuY4/oB0+/e/4VGhBhHUbGNx0AODu8b8LIWgvr7Z4G4+Xt7V5FRZLsDfX64n9vqoeVQAsAAAB0hnY/YDpqaope//0PFRzRU47GRuXty5Rc0vU//F1n1gcAAABctNod1sP79NMtv3pM+zO+kK3kuIYlT9fgscny7xHYmfUBAAAAF612h3WpaROhYRMvbXas4MBe9Rk8tCNrAgAAAKA2wvq7jz+ihFlXadCYJEnSO4/9Wvb6uuadXC6VFhzR9/7zdqcWCQAAAFyMWg3rIybPVET0APfHoT17q4c1VD1CwmSSSZLkkksHtqV2epEAAADAxajVsP716S7JV94ov4Ae8vW3NDs+eAw7JgIAAACdod1LN/73Nw9ob+pnpx1n6UYAAACgc7T7AdOxM65UZPTA047nbN2k2MTJHVoUAABAR7DZKpS5I1U5OVmqrXfJ4mdSbOxwjRyTzM7P6BbaHdYLD+3Tzs+WK6RnH8n05Zx1p0NFRw7qoec+6Kz6AAAAzklu7iGtXblECcGpWtgrVSFmmyrsVmUcS9aS7EzNTJmvmJjTByIBI2l3WA/p2Vt9hgxTYGi4+wFTSdq39fNOKQwAAOBc2WwVWrtyiRZELVI/S577eJhvuWaHrVC8JVOLV0rzb1zICDsMrd1hfeJVN8sSGCxvn+anDBiZ0OFFAQAAnI/MHalKCE5tFtRP1c+Sp4SgVGXuHKUpU1M8XB3Qfm0+YNpob9CKF/+hJ751nV75zQP64qPFp/UJCovotOIAAADORU5OlhIC215eOiEoVTn7dnuoIuDctDmyvm7x8zqwLVUjL5ktSdq2eqmCQiM05tIrPFIcAADAuaitdyrEbGuzj9VsU22D00MVAeemzbBeeGifFj76gvwDekiSJl29QBuXvEJYBwAAhmbx81KF3aow3/JW+9jsVll8272KNdAl2vwKDesV7Q7qkhQYEqbQXn2b9Tm0a2vnVAYAAHCOYmOHK+NEcpt9MqqSFRs3wkMVAeemzZH1vJzdWv/uy82OFR7ap/qaakmSy+HQgR1pGjjq2U4rEAAA4GyNHNO0PGO8JbPFh0yP1kYroypZ80ezEzuMrc2wXmMr15GsHfLy9m52PC87U5LkdDhkKzneedUBAACcA6s1RDNT5mvxyqYHSROCUmU122SzW5VRlayMqmTNTJnPso0wvDbD+mV3P6jhk2a0eYGsTWs7tCAAAICOEBMzUHOuuEGbP7cq9UiiGl0+8jE1KiwsXHOuSFF0dP+uLhE4ozbD+tAJ0854gaHJ0zusGAAAgI5y6g6mCf1P2cH0RLJWLbexgym6hTbD+tenv5xrHwAAAE9iB1NcKFivCAAAXHDav4NpmocrA84OYR0AAFxw2MEUF4rzDuvb1iztiDoAAAA6DDuY4kLR6pz1p35wq6rKSto82SWXTDJp3Kx5HV4YAADAuWIHU1woWg3rE668QZHRA2SN7CWTydRiH5fTqczPV3dacQAAAOciNna4Mo4la3bYilb7sIMpuoNWw3rCrKvOuNJLVXmJkq+8scOLAgAAOB/sYIoLRathvaWgXnh4v+x1tXLJJUlqqK3RttUf6Yaf/LHzKgQAADhL7GCKC0Wb66yf6qOn/qL9276Ql7ePfC0BkqT6mmpFx4/stOIAAADOVUzMQM2/caEyd47Son2TVNvgksXXpNi4EZo/egJBHd1Cu8O6j6+vHnz2fR3K3CprRC+F9+mnggN7VZp/pDPrAwAAOGdWa4imTE3RlKkpiowMUnFxVVeXBJyVdj8CHRBklcnLS4NGJ2n3pjWSpPDe/bThvf92WnEAAADAxazdI+tBYZH6211zddPDf1bM8LF67sd3yV5fpx4hoZ1ZHwAAAHDRandYT5hztUZcMlt+X85Xv/6Hv1PRkYMaNCap04oDAAAALmbtngaz4b3/uoO6JIX36afeg+O15ZP3O6UwAAAA4GJ3xpH17euW6URFqXKzdsjk9WqztroTVdq9aa2mXHtrpxUIAAAAXKzOOLI+auocVZYUqb7mhGxFhc3+a2xo0FX3/9QTdQIAAAAXnTOOrHv7mHXFPT9UbtZ2DRgx7rT2hrraTikMAAAAuNi16wFTk8mkASPGqfx4gaptZXI6nU0NLpey0zdqzh3f7cwaAQAAgItSu1eD+fjZv2n3pjXqYQ2Tl7e3+3hNZQVhHQAAGJLNVqHMHanKyclSbb1LFj+TYmOHa+SYZHYwRbfQ7rCes+0L3f/EawoKi2h2/MD21A4vCgAA4Hzl5h7S2pVLlBCcqoW9UhVitqnCblXGsWQtyc7UzJT5iokZ2NVlAm1qd1gfO+NKORrtpx0P6xXdoQUBAACcL5utQmtXLtGCqEXqZ8lzHw/zLdfssBWKt2Rq8Upp/o0LGWGHobU7rPv4+mr5C4+r37DRXx10SblZ23Xrrx7rjNoAAADOSeaOVCUEpzYL6qfqZ8lTQlCqMneO0pSpKR6uDmi/dm+KVHzkkHwtAaooOuZeurGi6JiqK8o6sz4AAICzlpOTpYTAtqfqJgSlKmffbg9VBJybdo+sT772VvXsN1Amr+b5viQ/t8OLAgAAOB+19U6FmG1t9rGabaptcHqoIuDctHtkPaRnb3369iKte/N5SVLBgb3K3LhKEX1jOq04AACAc2Hx81KF3dpmH5vdKotvu6MQ0CXa/RX68XN/U/6+3aqrPiFJ6jN4qBx2u9a+8VynFQcAAHAuYmOHK+NEcpt9MqqSFRs3wkMVAeem3WHd29tHtz3yj2Yj6cERPbVz/YpOKQwAAOBcjRyTrIzKZB2tbXnVuqO10cqoStbI0RM8XBlwdto9Zz28T39JTbuZSlKjvUGbl76toNCItk4DAADwOKs1RDNT5mvxyqYHSROCUmU122SzW5VRlayMqmTNTJnPso0wvHaH9SHjkvXhv/+ouuoqleTn6sC2VDXU1+kbP/xdZ9YHAABwTmJiBmr+jQuVuXOUFu2bpNoGlyy+JsXGjdD80RMI6ugWTC+m5bra27m+tkYHtqeqsrRIgSFhGjwmWb4Wi7x9zJ1ZY5eZNyC0q0sAWhUZGaTi4qquLgMAug3eN2FkSw+Xt3i83SPrkuRnCdDwSTOaHctY9T8lzLn63CsDAAAA0KJ2PWBaV10lp8Nx2vGK4kJtXvpmhxcFAAAA4Awj65WlxfrgX7/XsQPZ8jabNfmaWzT5mlskSfu3perj5/6mPkOGeaRQAAAA4GLTZlhf9cp/5OXlpVm33ae66hPa8sn76tl/sA7t2qLt6z7WtBvuUvLcGzxVKwAAAHBRaTOs19dW65ZfPSYvL29J0tDkaXr5199VYEiYbv3V4+ozeKhHigQAAAAuRm3OWQ/v3c8d1CUpom+MYhMm6a4/PuMO6oczMzq3QgAAAOAi1ebI+pE927X+3ZebHTtRUab177wkSXI5HTqyZ6fuefSFTisQAAAAuFi1GdZPlJfqSNYOeXl/NbpuMplUfPSQJMnpcKiqvKRzKwQAAAAuUm2G9cvufvC0ddW/LmvT2g4tCAAAAECTNuesD50w7YwXGJo8vcOKAQAAAPCVNsP6qdNfzqcPAAAAgLPXrh1MAQAAAHgeYR0AAAAwqHaH9eWLnlBDXW1n1gIAAADgFG2uBnMql9Op9e+8JJfLpfgJU9V/6OjOrAsAAAC46LU7rM+990eSpMaGBmWnb9DyFx5XcHhPjZyaImtEz04rEAAAALhYtTusOx0OeXl7y+Vyqra6SrlZ2+VyuVRfWyN7fZ0Gj03WkHHJnVkrAAAAcFFpd1j/5OUn5etv0a71KxXRN0aX3nyv4sdPkcmradr7xiWvqvDwPl1y3e2dViwAAABwMWl3WN/x6XINS56umx7+s3oPjj+t3T8wSJ8veZWwDgAAAHSQdof1y775fY2bNa/V9sFjJsgaztx1AAAAoKO0e+nG1oJ6ztZNkqTQqD6KTZzcMVUBAAAAaH1k/b+PPKCaKlubJ7tcLlXbyvSTl5Z1eGEAAADAxa7VsD4kYZKsET0VFNZTJlPLfVwul7LT1ndWbQAAAMBFrdWwnnT5fHn7mOXt0/a09l4D4zq8KAAts9kqlLkjVTk5Waqtd8niZ1Js7HCNHJMsqzWkq8sDAAAdrNUk7utvOe1YRXGhqivK5HQ45JJLcrmUnb5Rc+74bqcWCUDKzT2ktSuXKCE4VQt7pSrEbFOF3aqMY8lakp2pmSnzFRMzsKvLBAAAHajdq8Ese/4xZW5crR7WUHl5e7uP11RWENaBTmazVWjtyiVaELVI/Sx57uNhvuWaHbZC8ZZMLV4pzb9xISPsAABcQNod1vdt/Vz3Pf5fBX9tecb921I7vCgAzWXuSFVCcGqzoH6qfpY8JQSlKnPnKE2ZmuLh6gAAQGdp99KNYy+d696t9FRhvfp2aEEATpeTk6WEwLZ/ME4ISlXOvt0eqggAAHhCqyPrmz58XU6n86uOvn5a9txj6hs3vFm/I1k7dMsv/955FQJQbb1TIea2l1K1mm2qbXC22QcAAHQvrYb1kvwjqiwpUkjP3jJ9uXZjYEiYbEWF7j4uNa2zDqBzWfy8VGG3Ksy3vNU+NrtVFt92/7IMAC4KrKKF7q7VsH7JdbfL1xKgwJAwSVJDXe1pK8TUVlWqsqyocysEoNjY4co4lqzZYSta7ZNRlazYuBEerAoAjI1VtHAhaHUYLqx3tDuoS9IXH75xWh+Tl0nb137cOZUBcBs5JlkZlck6WhvdYvvR2mhlVCVr5OgJHq4MAIzp1FW0ZoetUJhvubxMTvcqWguiFmntyiWy2Sq6ulSgTWdcDWbryg9UeHi/Cg/n6ISt+a/g66qrVLB/ry676wedViAAyWoN0cyU+Vq8sulB0oSgVFnNNtnsVmVUJSujKlkzU+bzK10A+BKraOFCccawnphyrTa894rKC/NljYhq1hbWq6+mXn9npxUH4CsxMQM1/8aFytw5Sov2TVJtg0sWX5Ni40Zo/ugJBHUAOEVOTpYW9jrzKlqL9k0irMPQ2rXO+tTr71Bs4mT1GjDktLaayoqOrglAK6zWEE2ZmqIpU1MUGRmk4uKqri4JAAyJVbRwoWj3pkhR/QfpcGaGqm3lXy3p6HLpwI40Xfu9X3VWfQAAAGeNVbRwoWh3WH/7b7/Q8dwDCusVLS9vb/fxsmNHO6UwAACAc8UqWrhQtDusFx05qPsef+W05Rvz2DER8BjWCwaA9hk5pml5xnhLZosPmZ5cRWs+q2jB4Nod1idedbPKjuWp18DYZse/Ht4BdA7WCwaA9mMVLVwoTC+m5bra03HFi/9Q4aF9iuw36KuDLpeOHczWPY++cFYv2mhv0Mu//q5m33a/BoxMkCTZSo5r+QuPKz8nS8HhkZp5630aPOarn3Zzs7Zr9atPqfx4gXoPitfce36o0F593e1bVn6g1I/eUn1tteKTpmrOnQ+4f5BotDdo1Sv/0d7U9fIxm5V0xfWaOO+mM9Y5b0DoWd0X0FlstgoteXuRrgx/W0fr+iuzapRqHAEK8K7RyKBd6ud/RB+X3qj5Ny7kGw8AnMJmq1DmzjTl7NvdbBWtkayiBYNZerjl5yvaPbLudDgUHTdSfgGBMplMkiSXy6ny4wVnVUhjQ4P+99SfVJJ32H3M5XLpvccfUXif/rrzd/9WztYv9P4/f6d7/vK8Qnr2VmVpkd57/BFNvvZWDRk3UZ+//5ree+I3Wvjn52Ty8lJ2+gZteOclzbvvpwoMDdfHz/1da994Vpff/aAkad3i55Wfk6UFP39UlWUlWvr0owoO76nhk2acVe1AV8nckaqB/tlaVjxPCcEZujt6kXtkfVtlgpYVz9NAS7Yyd6axBBkAnIJVtNDdtTusT7p6gayRUfLy8m52fPT0y9v9YiX5ufrff/6krw/l52ZtV9mxPN366yfkZwlQRN8YHd6doZ2frdC0G+7S9nXL1LP/IPdo+Nx7f6R/ffcm5WZt14CRCUpf8b4SU65VbOJkSdJld/1Ab/7lYc285duSpB3rlukbP/q9eg2MU6+BcUqed6MyVn1IWEe3kb1vt0yN/bWgzxvN5l6G+ZZrVsQaxfXI1uKCW+TKziSsAwBwAWn3ekWhUX2UnbZBi//0Ez3347v07mO/1oEdaadtlNSWo3t3aeCo8brjN/9sdrxg/x5FDRgiP0uA+1h03Ejl52S52/vFj3K3mf38FTVgiPJzsuR0OlR4MFv9hn7V3nfIMDkdDh0/vF9FRw6qsdGu6LiRza597OA+OR2OdtcOdCV7Q6MSrFvb3okveKvsDY0ergwAAHSmdo+sb/7oTaUtf0/DJ89U3PgpcknK+nytqkqLNXbmle26xrhZ81o8fqKiTIEh4c2O9bCGqqqsRJJUXVGmwNCW2+urq9Vob2h2vpe3tyyBwaoqK5aXj1mWHkHy8fU95dwQORrtqqmsOO26gBGZTC4lBGe02SfBmqE020QPVQQAADyh3WH96L7duv8fr8ns6/fVwRTps7dfPO8iGhvq5W02NzvmbTarsdEuSbI31Mvb7NtCe4PsDXXuj0/l8+X5Xk5HC21N1zp5/daEhgbIx8e7zT6AJzhcPu3aia/R5aPIyCAPVQUA3Q/vkTCs833ANCpmcPOg/qXywvxzL+pkEWaz6muqmx1z2O3u1/Mx+8phbzit3RIY7A7eDnvz4N1ot8vs6y+TqaW2pmu1dD+nKi+vOfubATqBv6+pXTvx+ft68fAUALSCB0zRHZ3VHrs71i1TSX6ujuceUNamtVr854fl49d24G2PwNAIVdvKmh2rtpUpMCTsy/ZwVdvKW2w/GdhPbXc6HKo9UanAkDAFhkaorvqEHKeMolfbyuVtNssSGHzetQOeEBc3Qlur2t64Y2vlBMXFsxMfAAAXknaH9SnX3qbyomN67XcP6aVf3a/li55QRN8YXXbXD867iD5Dhul47gE11NW6j+VlZ6rPkGHu9qPZme42e32djh8+oD5Dhsnk5aVeg+KbtefnZMnL21tRA4YoKmawvH18lLcvq9m1ew2IlZc3U1zQPYwck6xtVRN1tDa6xfajtdHadmKiRrITHwAAF5Q2p8E4nQ73Uo3ePj669KaFmn7DXaqpsikgyCqTl5eczvNfUaX/sNGyRkTp4+f+pkvm36ED2zar4MBeXXHvjyQ1LQ+Z9vE72vTh64obf4k+f/81BUf01IAR4yRJCbOv0opFT6hn/4EKDu+plS8/qdHTLndvijRy6hytfPlJXfntn6i6okxpy97R5QsfOu+6AU9hJz4AAC5Obe5gunvTWo2YPLPNC+zZ/KmGTbz0rF/4L7fN0c0/e9S9g2l5Yb6WvfCYCg7sVWjPPpp1230aOGq8u/+BHWla89ozqiwtUp8hw3TFwocUGtXH3X5ytRpHo11xiVOUctf33XPS7fV1+uSlJ5WdvkF+lgAlzf2GkufecMYa2cEURsNOfABw7pizDiNrbQfTNsP64/dcrch+A2Xyanm6iMvpUEneYT30/IcdU6XBENZhZHzTAYCzw/smjKy1sN7mNJjouJE6mr1LwyZequDwnqe1O50O2RvqO6ZCAGeUl3dEmzeuVFl5qRpdPvIxNSosNFwTL0lRdHT/ri4PAAB0sDbD+o0P/0nlxwu0bc1S2UqOa+QlcxQzfEyzPifnjQPoXOnpX2j7ls+UFJKmxP5bFWK2qcJu1dbKRC3/qERjx09XUtKkri4TAAB0oDOusx4a1Uczb/mW7A312r1xtXZ8ukz9h47WiEtmy+zrp/7DxpzpEgDOU17eEW3f8plu7/uK+lny3MfDfMs1J2K1hvbYq1e3SL1792WEHQCAC0i7l240+/pp7MwrdfV3fq6wPv209OlHtfaN51RRdKwz6wMgafPGlUoKSWsW1E/Vz5KnJGu6Nn++ysOVAQCAznRWmyJJUuGhfdr56Qrt375Zuz9fo8zPV3dGXQBOUVZeqsTgrW32SbRuUXlZiYcqAgAAnnDGaTBS04Ok2WkbtOWT95W/f4/6DIrX3Ht+pKHJ0+Xt065LADgPjS4fhZhtbfaxmm2yu/j3CADAhaTN7+w1VTZtX7tU29Z8rJqqCg1Nnq7Zt31HvQfHu/tUlha1uFIMgI7jY2pUhd2qMN+Wl3WSJJvdKrOp0YNVAQCAztZmWH/q+7fI5OWl4ZNmaMyMuephDZMk2UqKJElOR6O2rVmqmbd8q/MrBS5iYaHh2lqZqDkRrU8722obr9CwCA9WBQAAOlubYT04vKeGJk+Tl7ePDu5Mbzp4yhZKTqdDubu3dWZ9ACRNvCRFyz8q0dAee1t8yPRobbTSbUm64qo5XVAdAADoLG2G9ZRvfk8DRia0eQHWWQc6X3R0f40dP12vbpGSrOlKtG6R1WyTzW7VVtt4pduSNHb8dJZtBADgAtNmWD9TUJfEOuuAhyQlTVJwsFWbP/dXmm2CewdTPz8/TZuZovj44V1dIgAA6GAsHQF0E7m5h7Rp/XIlBKcqISrVvYNpxolkbVrvkL+/RTExA7u6TAAA0IEI60A3YLNVaO3KJVoQtei0HUxnh61QvCVTi1dK829cKKs1pOsKBQAAHeqsN0UC4HmZO1KVEJza5g6mCUGpytyZ5uHKAABAZ2JkHegGcnKytLBXqsoaQpVmm6DMqlGqcQQowLtGI4N2aYI1TQlBqVq0b5KmTE3p6nIBAEAHIawD3UBtvVMlDZH6sOgaJQRn6O7oRe4569sqE7Qo7x5d3fND1TY4u7pUAADQgQjrQDfg52vSB8ev1YI+b5w2Z31WxBrF9cjW4oJb5Gc2dWGVAACgozFnHegGggODNc6a0eac9XHB2xQcZPVwZQAAoDMxsg50A5VVlUrss7XNPonWLcoomOKhigCge7DZKpS5I1U5OVmqrXfJ4mdSbOxwjRyTzOpZ6BYI60A3UGd3KcRsa7OP1WxTvd3loYoAwPhycw9p7colSghO1cJep+xPcSxZS7IzNTNlPvtTwPAI60A34GNqVIXdqjDf8lb72OxW+ZgaPVgVABgX+1PgQsGcdaAbcLlMyqhMaLNPhi1BThcPmAKAxP4UuHAwsg50A2ZfH2XYEtXbr0BH6/qfts56P/8jyqhMlNmXf9IAIH21P0Vb2J8C3QHf2YFuID5uhMoOrNCHx69TUkhas3XWt1Ym6sPj16mX33GFxU7o6lIBwBBq653tetaH/SlgdEyDAbqBmEHDVFgXpdv7vqI5EasV5lsuL5NTYb7lmhOxWrf3fUWF9VGKGTi0q0sFAEOw+Hmpwt72crY2u1UWX6IQjI2RdaAbyD24R0mhW9XDu1orii87bRrMBGuakkK2KvdQgqKj+3d1uQDQ5WJjhyvjWLJmh61otU9GVbJi40Z4sCrg7BHWgW4gJydLs6zHtSjvHiUEZzSbBrOtMkGL8u7RrPBV2r5vN3MvAUDSyDFNyzP2Nue2/qxPVbLmj2b6IIyNsA50AzX1Tq0pmaOb+yw+bQmyWRFrFNcjW28WLFCNk7mXACBJVmuIRo6drA+32Ft91mfs+Mks2wjDI6wD3YCft0PjgjPaXIJsXHCGtlRN8nBlAGBMNluFMrdv0jVR7+toXX+9mLew2cj6NVHv6+PtZsXFDSOww9B4qgLoBlwyKSE4o80+CdYMucQ66wAgNa2zPtA/W8uK58lsatTd0Yv0qyF/0N3Ri2Q2NWpZ8TwN9MtmnXUYHmEd6AYaHF7tWoLM7uCfNABIUva+3TpU3V83916sWRFrmq2iNStijW7uvViHavorOzuzq0sF2sR3dqAb8DE1tmsJMh9To4cqAgBjszc0KsG6te0dTIO3yt7A+yaMjbAOdAMul0kZlQlt9smwJcjpYhoMAEiSyeRq1/RBL5PLQxUB54awDnQDZl8fZdgSdbQ2usX2o7XRyqhMlNmXZ8YBQJIaXT7tmj7Y6OJ9E8bGVyjQDcTHjZA99z29UXCLws2lKreHqdZpkcWrVqHmMpXawzUo4IjMA7/R1aUCgCFYfE2qsFsV5lveah+b3Sp/djCFwRHWgW5g5JhkvbMnU16qU0xAruYHL2m2XnCpLUL7a+N1A5t7AIAkKS5uhLYWTNCc8E9a7bO1coLi4tnBFMbGj5NAN+FlcumWPq9rTsTqZqsazIlYrVv6vM68SwA4xcgxydpWNbHN6YPbTkzUSAY5YHCEdaAbyNyRqkRrWpurGiQGp7FeMAB8yWoN0cyU+Vp8fKFWl16usoZQOVxeKmsI1erSy7X4+ELNTJnPhkgwPKbBAN1ATk6WFvZKbbNPQlCqFu2bpClTUzxUFQAYW0zMQM254gZt/tyq1COJanT5yMfUqLCwcM25IkXR0f27ukTgjAjrQDdQW+9s16oGtQ1OD1UEAMaXm3tIa1cuUUJwqhL6p7qf9ck4kaxVy22amTJfMTEDu7pMoE2EdaAb8G/vqgZmZrYBgCTZbBVau3KJFkQtajaFMMy3XLPDVijekqnFK6X5Ny5kKgwMje/sQDcQFBisrZWJbfbZahuvoKBgD1UEAMaWuSNVCcGpbe9gGpTKsz4wPMI60A1UnqjUNltC26saVI5TZVXbU2UA4GKRk5OlhMAzP+uTs2+3hyoCzg1hHegG6htcujbqA715bIFWl8xqvqpBySy9eWyBron6QPV2lm8EAIlnfXDhYM460A1Y/LwU4Vusy8JXaHXpLKVWTHSvamDxqtZl4SsU6VssCzvxAYCkpvfN9jzrw/smjI6wDnQDsbHD9cGB+Sqsi1JSSJoSg7c228F0afFV6uV3XLFx7MQHAFLT+2bGsWTNDlvRap+MqmTeN2F4/DgJdAMRUf1UWBel2/u+0uIOprf3fUWF9VGK6NnynHYAuNiMHJOsjMrkNp/1yahKZgdTGB4j60A3sGvbJiWFtL2DaZI1Xbu2Ryo+friHqwMA43HvYLqy6UHShKBUWc022exWZVQlK6MqmR1M0S0Q1oFuoKy8VIn9t7bZJ9G6RelHGCECgJNiYgZq/o0LlblzlBbtm6TaBpcsvibFxo3Q/NETCOroFgjrQDfQ6PJp16oGdhf/pAHgVFZriKZMTdGUqSmKjAxScXFVV5cEnBXmrAPdgI+pURV2a5t9bHarfEyNHqoIAAB4AmEd6Aa85GrXDqYmD9UDAAA8g7AOdAMNLrPSKya0uapBui2JaTAAAFxg+M4OdAMBfl6K9t6vV/LuUJTfcZXbw1TrtMjiVatQc5mO10dpUMB+5TlYLxgAgAsJYR3oBvr3H6L9++vl4+VQjOWI5vda8tWmSLbxKrVH6GDtEA2JHdLVpQIAgA5EWAe6gQZ7g7xNTt3S5/Vma62H+ZZrTuQqDQ3co9cLblODvaELqwQAAB2NsA50A/lHD2i8Nb3NTZHGB6dryxH+SQMAcCHhAVOgG3A4XUoIzmizT4I1Qw6ny0MVAQAAT2AYDugG2rspkoPVYACgmby8I9q8caXKykvV6PKRj6lRYaHhmnhJiqKj+3d1ecAZ8Z0d6AZ8vZ2qsFsV5lveah+b3Spfb6cHqwIAY0tP/0Lbt3ympJA0Jfbf+tWD+ZWJWv5RicaOn66kpEldXSbQJsI60A0MGhyvLUXjlRK5qtU+W2zjNXDIUA9WBQDGlZd3RNu3fKbb+75y+oP5Eas1tMdevbpF6t27LyPsMDTmrAPdQGLSNG07ManNTZG2nZikxPFTPVwZABjT5o0rlRSS1uaD+UnWdG3+vPVBEMAIGFkHugGrNUSzLrter3/iUkJgqsZb02U122SzW7XFlqSME8mafdn1slpDurpUADCEsvJSJfbf2mafROsWpR+Z4KGKgHNDWAe6iaKiQjkaHTpS2087qkar1hEgi3eNQn3K5Wh0qKioUDExA7u6TAAwhPY+mG/nwXwYHF+hQDdwcu7lHdGvtPgr3aO10cy9BIBT+Jga2/VgvtnU6MGqgLPHnHWgG2DuJQCcnbDQcG2tTGyzz1bbeIWGRXioIuDcENaBbqCsvFSJwWeee1leVuKhigDA2CZekqL0igltPpifbkvSxClzPFwZcHaYBgN0A8y9BICzEx3dX2PHT9erW6Qka7oSrVvcD+ZvtY1Xui1JY8dPZ+ogDI/v7EA30N65lz7MvQQAt6SkSerdu682fx6p9CMTZHf5yGxqVGhYhK64ag5BHd0C02CA7sDlatfcS5fLQ/UAQDdRXX1CNdVVOvn26JJUU12l6uoTXVkW0G6MrAPdgclb6RUTNLTH3lZXg0m3Jclk4udvADhp1aplOrx/p5JC0pQYtVUhZpsq7FZtrUzU+rV1OnLksObMmdvVZQJtIqwD3YCXHIryO6ZX8+9ode5lL99jKmzo09WlAoAhZGdn6fD+nbq9b/Mlb8N8yzUnYrWG9tirV/ffoez+AxQfP7wLKwXaRlgHugGHy6SShkjNCl+pnVVjlW5Lkt1lltlkV6RvkWaFr9RnZTPlZBoMAEiSUj9v75K3FsI6DI2wDnQDDvno2qh39GHRNRoXnKHre73nHlnPqEzQ+vIZuibqA711bEFXlwoAhlBXX6/EqDMveZtmm+ChioBzQ1gHugFfb6cifIu1MPoFpdsm6MW8hapxBCjAu0ajgnZpYfQL7n4AgPYvedvIkrcwOL5CgW6gX/9B2lI+XimRq3RZ5Ce6LPKT0/qsLJ6j6P6DuqA6ADAelrzFhYKlI4BuwOzjq4zKxDZ34suoTJTZ7OvhygDAmPz9/Nq15K2fn7+HKgLODWEd6AaOHNmvlIgVevPYAq0umaWyhlA5XF4qawjV6pJZevPYAqVErNCR3P1dXSoAGELylBSlV0xoc5Aj3ZakiVPmeLgy4OwwDQboBmrrnRobvFMDLLmtzlm3mm1aWnxNV5cKAIYQHz9cR44c1qv7W1/ydsCQ0awEA8MjrAPdgMXPSxV2qyTp66sznvzYZrfK4ssvywDgpDlz5uqLwCClbzcpzTZBjS4f+ZgaZZI0cuxkTZo0tatLBM6IsA50A/1jBmtV/mwdqRughOAM3R29yL0T37bKBC3Ku0f9/Q+r/4AhXV0qABhGevoXytz+edMOpsHNdzBN3+6Sj4+PkpImdXWZQJsI60A3UF1do8Ka2BZ34psVsUZxPbL1av4d6lVd3YVVAoBx5OUd0fYtn7W9g+kWqXfvvoqO7t+FlQJt43fmQDdQWJDbjp340lSYn+vhygDAmDZvbO8Opqs8XBlwdgjrQDfgkpQYfKad+LaeNp8dAC5WZeWl7Xjf3KLyshIPVQScG8I60A20fyc+bw9VBADG1t73TTs7mMLgCOtAN3ByJ762NO3E5/BQRQBgbO1/32QHUxgbYR3oBlwul7bYxrfZZ4ttvFzMgwEASexgigsHYR3oBrzNvtpiS2pzJ74ttiR5m80ergwAjMnucGqbLaHN981tlePU6OA3kjA2JmoBBjBtWrL27t3TavtVV16mq6f01qv5d2i8NV3jT9mJb4ttvLbYktTTXKAPPz+mX/wyuMVrDB06TOvXp3bWLQCAoTTYXbq59wd689gCjQvOUEJwhvt9M6MyQdsqE3RN1Ad6q3BBV5cKtMlQYX33prX66Kk/NzsWmzhZ1z/0W9lKjmv5C48rPydLweGRmnnrfRo8ZoK7X27Wdq1+9SmVHy9Q70HxmnvPDxXaq6+7fcvKD5T60Vuqr61WfNJUzbnzAfn6Wzx2b0BbzhSibbYKLXl7kWaFf6ydVWO1xZYku8sss8muSN8izQpfqc9sV+qJf/xOL74U4pmiAcDA/H1NivAt1sLoF5Rum6AX8xaqxhGgAO8ajQrapYXRLzT1MzPJAMZmqLBemp+ruPFTlPLN77uP+Zh95XK59N7jjyi8T3/d+bt/K2frF3r/n7/TPX95XiE9e6uytEjvPf6IJl97q4aMm6jP339N7z3xGy3883MyeXkpO32DNrzzkubd91MFhobr4+f+rrVvPKvL736w624WOAtWa4hmpszX2pVSQlCqru/13lcjRFXJ+sx2pWamzJfVGtLVpQKAIQQFBmtrZaLmRKzWZZGf6LLIT07rs6p4joKCWv5tJGAUhgrrJfm5iuw3SIEhYc2OH969TWXH8nTrr5+QnyVAEX1jdHh3hnZ+tkLTbrhL29ctU8/+gzRx3k2SpLn3/kj/+u5Nys3argEjE5S+4n0lplyr2MTJkqTL7vqB3vzLw5p5y7cZXUe3ERMzUPNvXKjMnaO0aN8kVdc51MPfW7FxIzR/9ASCOgCcovJEpbY1Jmhoj70tbox0cs66y6ft5R2BrmawsH5EQydMO+14wf49ihowRH6WAPex6LiRysve5W7vFz/K3Wb281fUgCHKz8lS/+FjVHgwW1OuvcXd3nfIMDkdDh0/vF/9hn51HmB0VmuIpkxN0ZSpKerZM1hFRZVdXRIAGFJ9A3PWcWEwTFh3NNpVUVSg/dtTteG9/8rlcmnohGm65Po7dKKiTIEh4c3697CGqurLXceqK8oUGNpye311tRrtDc3O9/L2liUwWFVlxZ1/YwAAwOMsfl7tmrNu8WXOOozNMGG9rDBfTodDZj9/Xfv9R1RRVKDVrz6throaNdrtpy1J5202q7HRLkmyN9TL2+zbQnuD7A117o9P5XPK+a0JDQ2Qjw87QsK4IiODuroEADCkUaPHatvBZM0KW9HqnPXVZZdr9JhxvJfCGA6Xt3jYMGE9MnqAfvD0e7J8+aBHVMxguVzS//7zJ42dcYXqa6qb9XfY7TL7+klqegjVYW84rd0SGCyfL0O8w948mDfa7TL7tr0RQnl5zXndE9DZioururoEAOgybS17Gxoaqu9/5x7FWTJbnbO+sXCUnnzkTpWXtxySJJa9RdczTFiX5A7qJ0X07S+no1GBoeEqOnKwWVu1rcz9IGpgaLiqbeWntUdGD3AH9mpbuSL7DZQkOR0O1Z6oPO1BVgAA0H2cKUTn5h7S4pUBSghKVUJQarNVtDKqkjXv2vn67g9+7aFqgXNjmIla2ekb9OR3bpDjlKkpxw/vl19AoPoMHqbjuQfUUFfrbsvLzlSfIcMkSX2GDNPR7Ex3m72+TscPH1CfIcNk8vJSr0Hxzdrzc7Lk5e2tqAFDPHBnAACgK5xcRetE33u0qPBB/T7nV1pU+KBO9L1H829cqJiYgV1dInBGhhlZ7z90jCRp+aInNPnqW1RWmK91i59T8pU3qP/wMbJGROnj5/6mS+bfoQPbNqvgwF5dce+PJEmjp1+utI/f0aYPX1fc+Ev0+fuvKTiipwaMGCdJSph9lVYsekI9+w9UcHhPrXz5SY2edjnLNgIAcIFjFS10d6YX03JdXV3ESYWH92vt68/o2MFs+QX00NiZV2rKtbfJZDKpvDBfy154TAUH9iq0Zx/Nuu0+DRw13n3ugR1pWvPaM6osLVKfIcN0xcKHFBrVx92++aM3lbb8PTka7YpLnKKUu77vnvPemnkDQjvtXoHzxTcdADg7vG/CyJa28oCpocK60RDWYWR80wGAs8P7JoystbBumDnrAAAAAJojrAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKMI6AAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKJ+uLgC40IweM1yFx/I88lo9ewZ36vV79Y7Wzh1ZnfoaAACgdYR1oIMVHsvTvB9+0NVldIilj1/b1SUAAHBRYxoMAAAAYFCEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqlGwEAQJfz1B4Vnb0/hcQeFehYhHUAANDl2KMCaBnTYAAAAACDIqwDAAAABkVYBwAAAAyKsA4AAAAYFGEdAAAAMCjCOgAAAGBQhHUAAADAoAjrAAAAgEER1gEAAACDIqwDAAAABkVYBwAAAAyKsA4AAAAYFGEdAAAAMCjCOgAAAGBQPl1dAHChSfnbjbL0W9HVZXSIlL/d2NUlAABwUSOsAx1s5U/e1rwfftDVZXSIlY9fK935QleXAQDARYtpMAAAAIBBEdYBAAAAgyKsAwAAAAZFWAcAAAAMirAOAAAAGBRhHQAAADAowjoAAABgUIR1AAAAwKDYFAkAAHQ5dn8GWkZYBwAAXY7dn4GWEdaBDtard7SWPn5tV5fRIXr1ju7qEgAAuKgR1oEOtnNHlkdep2fPYBUVVXrktQAAQNfgAVMAAADAoAjrAAAAgEER1gEAAACDIqwDAAAABkVYBwAAAAyK1WAAAECXY9lboGWEdQAA0OU8sewtS96iO2IaDAAAAGBQhHUAAADAoAjrAAAAgEER1gEAAACDIqwDAAAABkVYBwAAAAyKsA4AAAAYFGEdAAAAMCg2RQIMYNq0ZO3du+esz+vZM7jdfYcOHab161PP+jUAAEDXIawDBnAuIToyMkjFxVWdUA0AADAKpsEAAAAABkVYBwAAAAyKsA4AAAAYFGEdAAAAMCjCOgAAAGBQhHUAAADAoAjrAAAAgEER1gEAAACDYlMkAADQLZ3L7s9ns/OzxO7P6HqEdQAA0C2dbYhm52d0R0yDAQAAAAyKsA4AAAAYFGEdAAAAMKiLZs56o71Bq175j/amrpeP2aykK67XxHk3dXVZAAAAQKsumrC+bvHzys/J0oKfP6rKshItffpRBYf31PBJM7q6NAAAAKBFF8U0mIa6Wu1Yt0yzb7tfvQbGKS5xspLn3aiMVR92dWkAAABAqy6KsF505KAaG+2KjhvpPhYdN1LHDu6T0+HowsoAAACA1l0UYf1ERZksPYLk4+vrPtbDGiJHo101lRVdVxgAAADQhotiznpjQ528zeZmx3zMTcG9sdHe6nmhoQHy8fHu1NqA8xEZGdTVJQBAt8L7JgzrcHmLhy+KsO5j9pXD3jyUN9obJElmX79Wzysvr+nUuoDzwU58AHB2eN9Ed3RRTIMJDI1QXfUJOU4ZRa+2lcvbbJYlMLgLKwMAAABad1GE9aiYwfL28VHeviz3sbzsTPUaECsvb6a5AAAAwJguirBu9vPXyKlztPLlJ1VwYK9ytm5S2rJ3NP6y67q6NAAAAKBVF8WcdUmadet9+uSlJ7X4Tz+RnyVAk6+7TcMmXtrVZQEAAACtumjCutnPX/Pue1jz7nu4q0sBAAAA2uWimAYDAAAAdEeEdQAAAMCgCOsAAACAQRHWAQAAAIMirAMAAAAGRVgHAAAADIqwDgAAABgUYR0AAAAwKNOLabmuri4CAAAAwOkYWQcAAAAMirAOAAAAGBRhHQAAADAowjoAAABgUIR1AAAAwKAI6wAAAIBB+XR1AcDFKGvTWv3vqT9rxi3fUvLcG9zHX//Dj9QvfqSm3XBXi+c99eBtqiw57v7YL6CHBoxI0Jw7H1BgSFi7XvupB2/TlGtu0ZgZc8/vJgCgE3z9fU4mk/wDAhUdP1Ipdz6g4PCenfr6S5/9q1wOp676zs869XWA9iKsA10g64t1Co3qo8wNq5qF9fkP/kbePuY2z515y7c1fPJMuVxO1VRWaO3rz2rpM4/q5p892tllA4BHnHyfkySX06mS/Fx98tI/tfTZv+mWX/yti6sDPItpMICH1VVX6dCurbpk/h0qPnpIhYf3u9ssgcHy9be0eb6fJUCBIWEKCo1QVMwQTbrmFh3OzFBtVWVnlw4AHnHyfS4wJExBYREaOCpRU6+/U0eytquuprqrywM8ipF1wMP2pm2Qt49ZwyZeqs/ff0271n+iXgOGSGo+DWbps3+VXFLR0YOqKi3Wgl/+vcXrBQSHNP3B1PS/pc/+VX6WHqqprND+bZsVGBquSVcv0Ohpl7nPKck/opd+eb9KC46o39DRmnffw+phDe3M2waA8+Jtbvqto5eXl+pqqrX6lf8oZ+vn8vH105CESZp5y7flZwmQJBUe2qeVL/9LRUcOKmrAEMWMGKeje3fp1l89pp3rP9GGd17Wd/+12H3ttqYgbv7oTW1b+7GqykpkCQzWmBlXaNo3vuk+L7LfAB3csUWN9nrd/adnFRBk7fy/DFxUGFkHPCxr01oNGpMkL29vxSZOVtYX6+RobGyx7+7P12jKtbfpxp/8SZF9Y05rb6irVerStzRoTJIsgcHu49vWLFXUgCH65u+f0sBR47XypSdVe+Krkfft6z5W8pU36M7f/VtOR6Pe/+dvO/5GAaCDlBXmacO7/9Wg0Uny9bdo2fN/V22VTbf++gnd8OM/qOzYUX38XNP0mLqaar31118oasAQ3fXHpzV80gxt/ujNc3rdzI2rlbrsXV2x8CF96+8v6ZL5t2nTB6+r4MBed59d61fqym//RNc/9FuCOjoFI+uAB1WVl+jo3l266v6fSpLikqYo9eO3dWBHmuISJ5/WP2rAYMUnXdLs2MpX/q3Vrz0tSbI31Mvbx0c3/fQvzfpE9huoifNukiRNv+EuZaz6UMV5h9V/6GhJ0tiZV7rng15x74/0zEO3q/joIUX2G9ixNwwA5+DU9zmn0yFvb7NiEydp9m3fUfnxAu3b8rl+8PS77kGKeff9VE8/eJsqS4t0cEe6zL6+mnPHA/Ly9lZ4n/7K27dbJyrKzrqOoLAIXfmtH2vAyARJ0rhZV2njktdUkndYfQYPlSQNGp2kfvEjO+jOgdMR1gEP2vPFpzJ5eWnQ2GRJUp/BwxQYGq7MDStbDOvWiF6nHbvkuts1dMI0SVJ9Xa0O7kjV24/+XDf99C/qN3SUJCm0Z293f7+AHpIk5ymj970Hxbv/HBLZS/6BQSotOEJYB2AIJ9/nGuprtXHJa6osOa5pN9wtS1Cw8vdnSS6XnvrBraedV3YsT0VHD6pnzBB5eXu7j/eNHa7s9I1nXUfM8LEq2L9Hn761SKUFR3T88H5V28rkcjrdfayRUed2k0A7EdYBD8r6Yp2cjkb987757mMul0v7t6Wqpsp2Wn8fs+9pxwKCrArt1df9ca8BQ3R07y5tWfm+O6yfaUUZk6n5DDiX0yWvM5wDAJ5y6vvctd/7pV5+5AEteeI3uuO3/5LT4ZDZz193//GZ087rERKm/ds2Sy5Xs+OuUz42nXzA59R2p6PFOnZ8ulyrX31KYy69QnHjL9HMBd/SG3/6SbM+Lb1PAx2JsA54SFlhngoP7dOs2+7XgBHj3McrS4v0zt9/pawv1p3ztV0ul5wO55k7fqn46CENmzjdXVd9zQmF9+53zq8PAJ3F28esuQt/qFf+7/tKX/6uhiRMkr2+Tk6nU+F9mt63ygvzteb1Z3T53Q8qInqA9m35XE6nQ15eTaPrhYdzTrmejxrqauRyuWQymeRyuVRRXKj+w8ac9trb1nykydfcoklXL5Ak1VWfULWtXK7TegKdhwdMAQ/J+mKd/AICNW7mPEX2G+j+b/DYZPWNHa7MDSvbdZ362hqdqCjTiYoyVZYWaevKD5S7e7uGJU9rdy1bPnlfe9M2qOjIQS177u8aPG6i+5seABhN78HxGn3p5dr04Rvys/TQoNFJWvrMoyrYv0fHcw9o6bN/VXVlhQJDwzV80gzZ6+u0+tWnVVpwVDvWLWuagmhqGlHvPShe9bU1Sl36liqKjmnd4udUd6Kqxde1BAbr8O5tKi04qsJD+/Thv/8gp6NRDnuDJ28fFzlG1gEP2fPFOo2YPEM+vqf/ynTcrKu09JlHZQkMPuODSmvfeFZr33hWUtOIU1jvaF129w/cD4y2R/KVN2jjkldUcbxAA0cn6Yp7Hjq7mwEAD5t+493KTtugtW88q3n3/1SrX31Kbz36M8lk0oCRCZpzxwOSJF9/i77xo9/rk5ef1I51y9R7UJxGTJmlE+UlkqTQXn0145ZvKXXp29r04WKNmpaiYRMvbfE1Z9/+HS17/jG99Kv7FRAUrKHJ0+XrH6DjuQc8dduATC+m5fLbHAAAcEGoKDqmqvLSZgMfK19+Ug31dZr37Ye7sDLg3DANBgAAXDDqa6v15p8f1t609bKVHFd2+gZlblyjoROmd3VpwDlhZB0AAFxQdqxbps1L31JlWbGCw3tq4pU3asyMuV1dFnBOCOsAAACAQTENBgAAADAowjoAAABgUIR1AAAAwKAI6wCAdjuanak3//JT7Vrfvk28OkLWF+v0zmO/9tjrAYCRsCkSAHQTxw5k69O3FylvX6aGTbxUTodDpflHNPnaWxWfdIlHaujZf5CqyorlamPD9crSIm364A15eXtJJpNOlJdqxJRZik+aek6v2Td2hPwsAedaMgB0a4R1AOgmeg+O1/CJl6qs4Kh7c5eMVf/TB//6ve7+4zOK7DfwvK6/ZeUHGp9ybZt9/CwBCggOabW9rDBPb/zxx7r6Oz9X/2FjJEl11Sf05l8eVkVxoZLn3nDGOhrtDcrcsEpjZ14pSbJG9JQ1ome77wMALiRMgwGAbsTk7d3s4+FTZsnldOrgri3ndd1Du7Zq68oP2ltFqy1Ln/mrYoaPdQd1SfLvEahpN9ylTxe/oOK8w21e2eV0auXL/1JVeWk7awGACxsj6wDQjdXXnJAkBQRa3cca6mqVvmKJGmprlJu1XTNv/bZ69h+sLSuWaP+2L3Tpzfdq5UtPyuFo1IJf/E1+/gHK/Hy1amzl+vTNFzR88kyZvLyUvvw9hfTsrQPbU3XZ3Q+q5xlG7ovzDqtg/x6NmznvtLb+Q8dIJmnX+k80aHSSNi99S3HjL9GhXVt0JGu7RlwyR3Pu+K5ys7br2IG9Kjt2VC6nUwmzr9IXH72piqJjuuHHf5AkleTnKmP1R/L1t6hg/x5devM96jN4qPZt+VybPnxDk65eoMyNq5W7e5tmLLhX42Y11ZO67B2ZZFJ2+gb1ix+lS2++p6M+DQDQaRhZB4Buyl5fp7VvPKvI6AEaOvGrrdTXLX5OIy+ZrRkL7tXYGXP1v3//Ub5+/orsN1AVRYWqrzmhe/76gsJ6RWvHumWyBAVr9LTL5dcjSJfefI969h+kje+9on5DR2vyNbcoKmawtq/9+Iz1lObnSpICQ8NPa/Px9VVAkFUl+bnqN3SUqm3lys3apsu++X1d/cAvlbH6f9qz+VMNGJmgqIGxihmRoGk3fFOWoGD1sIaqoa5WktTY0KAPnvy9pn3jm7r0poVKmHO13vn7r1RfW6PBYyeoouiYcrO2ad63f6LL7/6BPv/gNUlNP0hUHD+mCXO/oRsf/rP8AgI74lMAAJ2OsA4A3UxdzQmtf+cl/ecHt8jP0kO3/eafMvv6SZJcLpey0zdq35bPlb5iiarKSxTZb5Bqqmzy7xEoX0uA4pOmysvLW70HxanaVt7ia0y65hbFJkxS+fEC2YqPy/5lWG6Ly3Xy/84W201eXjJ5ecvbxyz/HkEaPDZZgaHhGjxmggaMGKcD21JPO8fbx6zAkDD3x/u3p8rH11f+PZrCdvz4S+RyObUvfaO8fczy9bcoPmma/AJ6qPegeFVXNN2f2ddPmRtXKX3FEnn7+Gj09MvOeD8AYARMgwGAbmjaDXepob5O+9I3yuV0uI/XVFaovrZa4y+7TiZT87nlpQVHm31s8vKW85RzTxUcHqnNS99Sn8FDFTUgVpVlRWesKSI6RpJUWVp8WpvT6VDtiUr1jB7Q8rl9Y1R+vKDlC59yH+WF+XI4vqrZ5OUla0QvVZUXn9bX5O3t/sEhpGdvXfntn2jVf/+t9OXv6arv/Fw9rKFnvCcA6GqMrANANzXj5nsUEByi5S884T5mCQqWy+nS/owv3MfKCvNUe6LyrK695B+/1cBRiYobP0Umr/Z9q4iMHqBeA+N0aOfpD7vm5+yRy+nUqGktj2g7GhsV1iv6jK8RHNFTtqJjarQ3fHXQ5VJY735tnldVVqK4xCn61t9eUv/hY/TRU38642sBgBEQ1gGgG3E5HHI5m0aLvX3MuuaBX+pw5lZt+eR9SZKXl7eGJk/Tx8//XTs+W6FDu7Zqy4olsgQGN40yu762PvqXH3r7+KihtkZOh0Nlx/JUlLtfNZU21VVXqfBwjhobGlRRdOyrk75+nS9d+a0f68ieHTq0a6v7mL2+Tp+++bxm3Xqfwnp/FcirK8qaruZyKT9nt8bOnOu+r7rqKlUUHZOj0f7lazW9XlziZJn9LNrzxaeSmpaFbLTXa/DY5C9Lc351UyerdblUVpivfVs/l19AD82544HWygcAw2EaDAB0E8cOZCtr86dqtDdo66oPNXzSDIVG9dHlCx/S//7zZ5UfL1DC7KuUcucDWrHoH1r72tOK7DdI8+57WPW1NcpOW69qW7n2bd2kyL4xOpq9S9UVpSrOO6ye/QcpODxSb/zxx5r/4G+UdMX1WrHoccUmTlFcwiRtXPKqqm3lqq2qVHHeYe3ftlkDR41XUFhEsxoj+w3UHf/3pDa+/6r2pq2Xf0CgKkuLNOnqBRoybmKzvnnZmUpb/p6qyoo16epbFN6nv6Smeej/e/rPsgQFa9zMedq/PVWlBUeVvz9LfYcM1zd+9DutfvUpFR09KEejXdd+79cy+/rpwI40nagoU3baeoVG9dGeL9ZJknau/0Qhkb217Lm/69jBbHl5eWvuvT/yzCcNAM6T6cW0XMYXAAAe9foffqRR01I0upVpMQCAJkyDAQB0gdan0gAAvkJYBwB41P5tqSrJP6L92zbLVnK8q8sBAENjGgwAAABgUIysAwAAAAZFWAcAAAAMirAOAAAAGBRhHQAAADAowjoAAABgUIR1AAAAwKD+H+LH7cnCHAx3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set figure size, color and other aesthetics\n",
    "plt.figure(figsize=(12, 10), facecolor=\"#9ecae1\")\n",
    "flierprops = dict(marker='o', markerfacecolor='orange', markersize=10, linestyle='none', markeredgecolor='grey')\n",
    "\n",
    "# Plots boxplot \n",
    "boxplot_data = [mthlyRI_Airbnb, mthlyRI_Regular]\n",
    "plt.boxplot(boxplot_data, meanline=True, patch_artist=True, whis=1.0, showfliers=True, flierprops=flierprops)\n",
    "\n",
    "# Set labels, titles and other formatting elements\n",
    "plt.title('AirBnb vs Regular Rental Monthly Price', fontsize=16, **tnr_font, weight=\"bold\")\n",
    "plt.ylabel('Monthly Rental Price ($)', fontsize=14, **tnr_font)\n",
    "plt.xlabel('Rental Options', fontsize=14, **tnr_font)\n",
    "#plt.tight_layout()\n",
    "\n",
    "#Modify tick locations and formatting\n",
    "x_axis = np.arange(len(boxplot_data))\n",
    "tick_positions = [1+value for value in x_axis]\n",
    "plt.xticks(tick_positions, rental_options, fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# Save image as png \n",
    "plt.savefig(\"images/boxplot.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### AirBnb & Regular Rental Source Filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Airbnb filter (for use in: outlier analysis & bar plot 1&2)\n",
    "type1 = target_neighbh_df['source']== index_source[0] \n",
    "type1_filter = target_neighbh_df.loc[type1, :]\n",
    "\n",
    "#Filters Regular Rentals (for use in: bar plot 1&2, may use if want to analyze outliers in Regular Rentals)\n",
    "type2 = target_neighbh_df['source']== index_source[1] \n",
    "type2_filter = target_neighbh_df.loc[type2, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis of the Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Monthly Rental Price</th>\n",
       "      <th>Median Monthly Rental Price</th>\n",
       "      <th>Popular(mode) Monthly Rental Price</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>1st Quartile</th>\n",
       "      <th>3rd Quartile</th>\n",
       "      <th>IQR</th>\n",
       "      <th>Q3 Boundary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rental Options</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AirBnb</th>\n",
       "      <td>3429.327273</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>([2052], [6])</td>\n",
       "      <td>3127.392200</td>\n",
       "      <td>1831.5</td>\n",
       "      <td>3609.0</td>\n",
       "      <td>1777.5</td>\n",
       "      <td>6275.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regular</th>\n",
       "      <td>2815.028409</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>([1895], [5])</td>\n",
       "      <td>2689.289619</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>3212.5</td>\n",
       "      <td>1762.5</td>\n",
       "      <td>5856.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Average Monthly Rental Price  Median Monthly Rental Price  \\\n",
       "Rental Options                                                              \n",
       "AirBnb                           3429.327273                       2430.0   \n",
       "Regular                          2815.028409                       1925.0   \n",
       "\n",
       "               Popular(mode) Monthly Rental Price  Standard Deviation  \\\n",
       "Rental Options                                                          \n",
       "AirBnb                              ([2052], [6])         3127.392200   \n",
       "Regular                             ([1895], [5])         2689.289619   \n",
       "\n",
       "                1st Quartile  3rd Quartile     IQR  Q3 Boundary  \n",
       "Rental Options                                                   \n",
       "AirBnb                1831.5        3609.0  1777.5      6275.25  \n",
       "Regular               1450.0        3212.5  1762.5      5856.25  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculates the 3rd Quartile boundary; \n",
    "Final_Stats['Q3 Boundary'] = Final_Stats[\"3rd Quartile\"] + (1.5*Final_Stats[\"IQR\"])\n",
    "Q3_index = Final_Stats.set_index(\"Rental Options\")\n",
    "Q3_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filters AirBnb & Regular Rental outlier boundary\n",
    "AirBnb_upperbound = Q3_index.loc[\"AirBnb\", \"Q3 Boundary\"]\n",
    "Regular_upperbound =  Q3_index.loc[\"Regular\", \"Q3 Boundary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>Final_mthly_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>28612443</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>14976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>28612461</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>8064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>28612436</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>8604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>28612439</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>7524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>28612480</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>10080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>28612492</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>9378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>28612496</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>8892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>28612514</td>\n",
       "      <td>Pacific Palisades</td>\n",
       "      <td>24948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>28613595</td>\n",
       "      <td>Venice</td>\n",
       "      <td>7056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>28613163</td>\n",
       "      <td>Venice</td>\n",
       "      <td>10764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>28612729</td>\n",
       "      <td>Venice</td>\n",
       "      <td>6804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>28613575</td>\n",
       "      <td>Venice</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>28612748</td>\n",
       "      <td>Venice</td>\n",
       "      <td>8694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>28612818</td>\n",
       "      <td>Venice</td>\n",
       "      <td>8316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>28612970</td>\n",
       "      <td>Venice</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>28613476</td>\n",
       "      <td>Studio City</td>\n",
       "      <td>10008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>28613594</td>\n",
       "      <td>Studio City</td>\n",
       "      <td>19260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>28613528</td>\n",
       "      <td>Encino</td>\n",
       "      <td>8136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>28614319</td>\n",
       "      <td>Westwood</td>\n",
       "      <td>6336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>28614422</td>\n",
       "      <td>Mar Vista</td>\n",
       "      <td>7722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>28614125</td>\n",
       "      <td>Mar Vista</td>\n",
       "      <td>9414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>28614395</td>\n",
       "      <td>Westwood</td>\n",
       "      <td>6336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>28615042</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>17820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>28615790</td>\n",
       "      <td>Silver Lake</td>\n",
       "      <td>6750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>28616777</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>17820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id       neighborhood  Final_mthly_price\n",
       "54    28612443  Pacific Palisades              14976\n",
       "59    28612461  Pacific Palisades               8064\n",
       "61    28612436  Pacific Palisades               8604\n",
       "63    28612439  Pacific Palisades               7524\n",
       "65    28612480  Pacific Palisades              10080\n",
       "67    28612492  Pacific Palisades               9378\n",
       "68    28612496  Pacific Palisades               8892\n",
       "71    28612514  Pacific Palisades              24948\n",
       "77    28613595             Venice               7056\n",
       "80    28613163             Venice              10764\n",
       "81    28612729             Venice               6804\n",
       "88    28613575             Venice               7200\n",
       "90    28612748             Venice               8694\n",
       "92    28612818             Venice               8316\n",
       "93    28612970             Venice               7200\n",
       "345   28613476        Studio City              10008\n",
       "351   28613594        Studio City              19260\n",
       "422   28613528             Encino               8136\n",
       "534   28614319           Westwood               6336\n",
       "677   28614422          Mar Vista               7722\n",
       "685   28614125          Mar Vista               9414\n",
       "792   28614395           Westwood               6336\n",
       "829   28615042           Downtown              17820\n",
       "999   28615790        Silver Lake               6750\n",
       "1277  28616777           Downtown              17820"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter all AirBnb avg rental price >= 3rd Quartile boundary\n",
    "AirBnb_outliers = type1_filter['Final_mthly_price']>= AirBnb_upperbound\n",
    "AirB_outlier_filter = type1_filter.loc[AirBnb_outliers, ['id','neighborhood','Final_mthly_price']]\n",
    "AirB_outlier_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighborhood\n",
       "Downtown             2\n",
       "Encino               1\n",
       "Mar Vista            2\n",
       "Pacific Palisades    8\n",
       "Silver Lake          1\n",
       "Studio City          2\n",
       "Venice               7\n",
       "Westwood             2\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check the count of outliers for each neighborhood\n",
    "by_neighbhd_outliers = AirB_outlier_filter.groupby([\"neighborhood\"])[\"id\"].nunique()\n",
    "by_neighbhd_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Downtown',\n",
       " 'Encino',\n",
       " 'Mar Vista',\n",
       " 'Pacific Palisades',\n",
       " 'Silver Lake',\n",
       " 'Studio City',\n",
       " 'Venice',\n",
       " 'Westwood']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For pie graph\n",
    "AirB_outlier_neighbhds =AirB_outlier_filter[\"neighborhood\"].unique()\n",
    "sorted_AirB_outlier_neighbhds = sorted(AirB_outlier_neighbhds)\n",
    "sorted_AirB_outlier_neighbhds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set figure size, color, title and other aesthetics\n",
    "plt.figure(figsize=(10, 7), facecolor=\"#9ecae1\")\n",
    "explode = (0, 0, 0, 0.1, 0, 0.05, 0.05, 0)\n",
    "colors = [\"#bdbdbd\",\"#31a354\",\"#fee6ce\", \"#e38080\",\"#7fcdbb\", \"#3182bd\", \"#fdae6b\", \"#fa9fb5\"]\n",
    "text_props = {'fontsize':13}\n",
    "plt.title('AirBnb Outliers by Neighborhood', fontsize=14, **tnr_font, weight=\"bold\")\n",
    "plt.tight_layout()\n",
    "\n",
    "#Plots  graph\n",
    "plt.pie(by_neighbhd_outliers, labels=sorted_AirB_outlier_neighbhds,\n",
    "        colors=colors, autopct=\"%1.1f%%\", explode=explode,\n",
    "        startangle=90, shadow=True, textprops=text_props)\n",
    "\n",
    "# Save image as png \n",
    "plt.savefig(\"images/outlier_pie.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pie Charts: Airbnb vs Regular Occupancy Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by df by source\n",
    "byRental_options = target_neighbh_df.groupby([\"source\"])\n",
    "\n",
    "# Data to be used in pie chart\n",
    "unique_count_OR = byRental_options[\"id\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set figure size, color, title and other aesthetics\n",
    "plt.figure(figsize=(7, 7), facecolor=\"#9ecae1\")\n",
    "explode = (0.05, 0)\n",
    "colors = [\"#fc8d62\",\"#3182bd\"]\n",
    "text_props = {'fontsize':13}\n",
    "plt.title('Split between AirBnb and Regular Rentals', fontsize=14, **tnr_font, weight=\"bold\")\n",
    "plt.tight_layout()\n",
    "\n",
    "#Plots  graph\n",
    "plt.pie(unique_count_OR, labels=['AirBnb','Regular'],\n",
    "        colors=colors, explode=explode, autopct=\"%1.1f%%\",\n",
    "        startangle=45, shadow=True, textprops=text_props)\n",
    "\n",
    "# Save image as png \n",
    "plt.savefig(\"images/pie.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Plot 1: Compare AirBnb & Regular Average Rental Price by Property Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "propertyType  source \n",
       "Apartment     Airbnb     2312\n",
       "              Regular    2262\n",
       "Condominium   Airbnb     2992\n",
       "House         Airbnb     4318\n",
       "              Regular    5259\n",
       "Townhouse     Airbnb     3087\n",
       "              Regular    3134\n",
       "Name: Final_mthly_price, dtype: int32"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Groups data by property type and source & calculates average rental income \n",
    "#Only used as reference to make sure array generated for the bar graph is correct\n",
    "AvgRI_byptype = target_neighbh_df.groupby(['propertyType','source'])['Final_mthly_price'].mean().astype(int)\n",
    "AvgRI_byptype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2312, 2992, 4318, 3087])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groups data by property type & calculates average monthly price\n",
    "AirB_avgprice = type1_filter.groupby(['propertyType'])['Final_mthly_price'].mean().values.astype(int)\n",
    "AirB_avgprice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2262, 5259, 3134])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groups data by property type & calculates average monthly price\n",
    "Reg_avgprice = type2_filter.groupby(['propertyType'])['Final_mthly_price'].mean().values.astype(int)\n",
    "Reg_avgprice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Apartment', 'Condominium', 'House', 'Townhouse']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shows & sorts target neighborhoods (for bar graph 1 use)\n",
    "unique_ptype = target_neighbh_df['propertyType'].unique()\n",
    "sorted_ptype = sorted(unique_ptype)\n",
    "sorted_ptype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Set figure & color\n",
    "plt.figure(figsize=(10,10), facecolor=\"#9ecae1\")\n",
    "\n",
    "#Use for plotting\n",
    "width_bar = 0.25\n",
    "n_ptypes = 4\n",
    "index = np.arange(n_ptypes)\n",
    "\n",
    "#Fixes value error due to Condominium (Regular)=0; added 0 to match the length of AirBnb bar\n",
    "Reg_avgprice2 = (2262, 0, 5259, 3134)\n",
    "\n",
    "#plotting each bars\n",
    "plt.bar(index, AirB_avgprice, width_bar, edgecolor='black', label=\"AirBnb\", color='#fc8d62')\n",
    "plt.bar(index + width_bar, Reg_avgprice2, width_bar, edgecolor='black', label=\"Regular\", color='#3182bd')\n",
    "\n",
    "#Adds titles, axis labels, and legend with modifications in font styles and size\n",
    "plt.title('Comparison of Average Monthly Rental Price by Property Type', fontsize=20, **tnr_font, weight=\"bold\")\n",
    "plt.xlabel('Property Type', fontsize=18, **tnr_font)\n",
    "plt.ylabel('Average Monthly Rental Price', fontsize=18, **tnr_font)\n",
    "plt.legend(fontsize=17, frameon=True, facecolor=\"#9ecae1\")\n",
    "#plt.tight_layout()\n",
    "\n",
    "#Set tick location to center\n",
    "plt.xticks(index + width_bar, sorted_ptype, rotation=25, fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "\n",
    "# Save image as png \n",
    "plt.savefig(\"images/bar_type.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>Final_mthly_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>5526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>4590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>2160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>2394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>3582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>2430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>2088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>2628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>2430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>2394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      source  Final_mthly_price\n",
       "87    Airbnb               5526\n",
       "360   Airbnb               4590\n",
       "416   Airbnb               2160\n",
       "417   Airbnb               2700\n",
       "426   Airbnb               2394\n",
       "428   Airbnb               3582\n",
       "827   Airbnb               2430\n",
       "840   Airbnb               2088\n",
       "844   Airbnb               2628\n",
       "1267  Airbnb               2430\n",
       "1342  Airbnb               2394"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Confirmation that there are no Regular Rentals for Condo \n",
    "test = target_neighbh_df[\"propertyType\"] == \"Condominium\" \n",
    "Condo_price = target_neighbh_df.loc[test, [\"source\",\"Final_mthly_price\"]]\n",
    "Condo_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>Final_mthly_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>2070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>3636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>1548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>1872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2719</th>\n",
       "      <td>Regular</td>\n",
       "      <td>2095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2720</th>\n",
       "      <td>Regular</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2721</th>\n",
       "      <td>Regular</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2722</th>\n",
       "      <td>Regular</td>\n",
       "      <td>2199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2723</th>\n",
       "      <td>Regular</td>\n",
       "      <td>2225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>229 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       source  Final_mthly_price\n",
       "55     Airbnb               1530\n",
       "64     Airbnb               2070\n",
       "70     Airbnb               3636\n",
       "75     Airbnb               1548\n",
       "78     Airbnb               1872\n",
       "...       ...                ...\n",
       "2719  Regular               2095\n",
       "2720  Regular               2100\n",
       "2721  Regular               2100\n",
       "2722  Regular               2199\n",
       "2723  Regular               2225\n",
       "\n",
       "[229 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Confirmation for Apartment (that there's Airbnb & Regular)\n",
    "test2 = target_neighbh_df[\"propertyType\"] == \"Apartment\" \n",
    "Apt_price = target_neighbh_df.loc[test2, [\"source\",\"Final_mthly_price\"]]\n",
    "Apt_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Plot 2: Compare AirBnb and Regular Average Rental Price by Neighborhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighborhood       source \n",
       "Downtown           Airbnb      2994\n",
       "                   Regular     2805\n",
       "Encino             Airbnb      2892\n",
       "                   Regular     1823\n",
       "Hollywood          Airbnb      2342\n",
       "                   Regular     2203\n",
       "Mar Vista          Airbnb      2582\n",
       "                   Regular     2584\n",
       "Pacific Palisades  Airbnb      6524\n",
       "                   Regular    10235\n",
       "Silver Lake        Airbnb      2803\n",
       "                   Regular     2165\n",
       "South Los Angeles  Regular     1208\n",
       "Studio City        Airbnb      4130\n",
       "                   Regular     3342\n",
       "Venice             Airbnb      4588\n",
       "                   Regular     4384\n",
       "Westwood           Airbnb      2952\n",
       "                   Regular     1758\n",
       "Name: Final_mthly_price, dtype: int32"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Groups data by neighborhood and source & calculates average rental income \n",
    "#Only used as reference to make sure array generated for the bar graph is correct\n",
    "AvgRI_byneighbh = target_neighbh_df.groupby(['neighborhood','source'])['Final_mthly_price'].mean().astype(int)\n",
    "AvgRI_byneighbh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AirBnb Avg Rental Price Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2994, 2892, 2342, 2582, 6524, 2803, 4130, 4588, 2952])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Airbnb (used same filter for AirBnb in bar graph 1)\n",
    "# Groups data by neighborhoods & calculates average monthly price\n",
    "AirB_byneighbh = type1_filter.groupby(['neighborhood'])['Final_mthly_price'].mean().values.astype(int)\n",
    "AirB_byneighbh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2805,  1823,  2203,  2584, 10235,  2165,  1208,  3342,  4384,\n",
       "        1758])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Regular (used same filter for Regular in bar graph 1)\n",
    "# Groups data by neighborhoods & calculates average monthly price\n",
    "Reg_byneighbh = type2_filter.groupby(['neighborhood'])['Final_mthly_price'].mean().values.astype(int)\n",
    "Reg_byneighbh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Downtown',\n",
       " 'Encino',\n",
       " 'Hollywood',\n",
       " 'Mar Vista',\n",
       " 'Pacific Palisades',\n",
       " 'Silver Lake',\n",
       " 'South Los Angeles',\n",
       " 'Studio City',\n",
       " 'Venice',\n",
       " 'Westwood']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use in xticks for bar 2 graph\n",
    "unique_neighbh = target_neighbh_df['neighborhood'].unique()\n",
    "sorted_neighbhlist = sorted(unique_neighbh)\n",
    "sorted_neighbhlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sets figure and bar size first before plotting\n",
    "plt.figure(figsize=(17, 10), facecolor=\"#9ecae1\")\n",
    "width_bar = 0.25\n",
    "n_neighbh = 10\n",
    "index_neighbh = np.arange(n_neighbh)\n",
    "\n",
    "#Fixes value error due to South LA (AirBnb)=0; added 0 to match the length of Regular array  bar\n",
    "AirB_byneighbh2 = (2994, 2892, 2342, 2582, 6524, 2803, 0, 4130, 4588, 2952)     \n",
    "\n",
    "#plotting each bars\n",
    "plt.bar(index_neighbh, AirB_byneighbh2, width_bar, edgecolor='black', label='AirBnb', color='#fc8d62')\n",
    "plt.bar(index_neighbh + width_bar, Reg_byneighbh, width_bar, edgecolor='black', label=\"Regular\", color='#3182bd')\n",
    "\n",
    "#Adds titles, axis labels, and legend with modifications in font style and size\n",
    "plt.title('Comparison of Average Monthly Rental Price by Neighborhood', fontsize=20, **tnr_font, weight=\"bold\")\n",
    "plt.xlabel(\"Neighborhoods\", fontsize=18, **tnr_font)\n",
    "plt.ylabel('Average Monthly Rental Price', fontsize=18, **tnr_font)\n",
    "plt.legend(fontsize=17, frameon=True, facecolor=\"#9ecae1\")\n",
    "#plt.tight_layout()\n",
    "\n",
    "#Set tick location & format\n",
    "plt.xticks(index_neighbh + width_bar, sorted_neighbhlist, rotation=20, fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "\n",
    "# Save image as png \n",
    "plt.savefig(\"images/bar_neighbh.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}