visual.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best honeymoon in Athens with a low budget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation - Final Project\n",
    "### Team P Python Markdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load any necessary libraries and the data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import os\n",
    "import nltk.corpus\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.probability import FreqDist\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "data = pd.read_csv('listings_cleaned.csv',sep = ',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Filter properties with proper double bed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8913, 29)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Entire home/apt', 'Private room', 'Hotel room', 'Shared room'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.room_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_room = data.room_type.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'listing_url', 'neighbourhood', 'latitude', 'longitude',\n",
       "       'property_type', 'room_type', 'bathrooms', 'bedrooms', 'beds',\n",
       "       'bed_type', 'amenities', 'price', 'guests_included', 'extra_people',\n",
       "       'availability_30', 'availability_60', 'availability_90',\n",
       "       'availability_365', 'number_of_reviews', 'number_of_reviews_ltm',\n",
       "       'review_scores_rating', 'review_scores_accuracy',\n",
       "       'review_scores_cleanliness', 'review_scores_checkin',\n",
       "       'review_scores_communication', 'review_scores_location',\n",
       "       'review_scores_value', 'reviews_per_month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "room = ['Entire home/apt']\n",
    "data.room_type.isin(room)\n",
    "\n",
    "df_room = data[data.room_type.isin(room)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8162, 29)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_room.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Real Bed', 'Pull-out Sofa', 'Futon', 'Couch', 'Airbed'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_room.bed_type.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found all properties in Athens that have a proper real bed for couples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_room_bed = df_room[df_room['bed_type'] == 'Real Bed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8101, 29)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_room_bed.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Find Amenities frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "amenities = df_room_bed.amenities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_punctuation(word):\n",
    "    word = word.replace('{',' ')\n",
    "    word = word.replace('/',' ')\n",
    "    word = word.replace('\"',' ')\n",
    "    word = word.replace('}',' ')\n",
    "    word = word.replace(',',' ')\n",
    "    word = word.replace('and', ' ')\n",
    "    word = word.replace('or', ' ')\n",
    "    word = word.replace('to', ' ')\n",
    "    word = word.strip()\n",
    "    return(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = []\n",
    "for items in amenities:\n",
    "    no_pun = remove_punctuation(items)\n",
    "    text.append(no_pun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "text = ','.join(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "token = word_tokenize(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdist = FreqDist(token)\n",
    "fdist1 = fdist.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('friendly', 9312),\n",
       " ('allowed', 8837),\n",
       " ('TV', 8333),\n",
       " (',', 8100),\n",
       " ('Wifi', 7961),\n",
       " ('Essentials', 7909),\n",
       " ('Kitchen', 7841),\n",
       " ('conditioning', 7746),\n",
       " ('Air', 7727),\n",
       " ('Hangers', 7709),\n",
       " ('Hair', 7627),\n",
       " ('dryer', 7627),\n",
       " ('Shampoo', 7328),\n",
       " ('Iron', 7245),\n",
       " ('Heating', 6983),\n",
       " ('entrance', 6723),\n",
       " ('Hot', 6624),\n",
       " ('water', 6453),\n",
       " ('parking', 6446),\n",
       " ('p', 6235)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph below shows the top 10 most common amenities in Athens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 most common amenities in Athens')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "objects = ('Heating','Iron','Shampoo','Hair Dryer','Hangers','Air Conditioning','Kitchen','Essentials','WIFI','TV')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [6983,7245,7328,7627,7709,7746,7841,7909,7961,8333]\n",
    "\n",
    "plt.barh(y_pos, performance, alpha=0.5)\n",
    "plt.yticks(y_pos, objects)\n",
    "plt.xlabel('Amenities frequency')\n",
    "\n",
    "plt.title('Top 10 most common amenities in Athens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Find number of matched properties in each neighbourhood "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "nei = df_room_bed.neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_nei = []\n",
    "for items in nei:\n",
    "    text_nei.append(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(text_nei)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_nei = ','.join([str(i) for i in text_nei])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "token_nei = word_tokenize(text_nei)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdist_nei = FreqDist(token_nei)\n",
    "fdist1_nei = fdist_nei.most_common(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(',', 8100),\n",
       " ('Plaka', 989),\n",
       " ('Koukaki', 763),\n",
       " ('Neos', 754),\n",
       " ('Kosmos', 754),\n",
       " ('Kolonaki', 517),\n",
       " ('Pangrati', 513),\n",
       " ('Larissis', 471),\n",
       " ('Ambelokipi', 437),\n",
       " ('Metaxourgeio', 406),\n",
       " ('Exarcheia', 404),\n",
       " ('Psyri', 389),\n",
       " ('Kerameikos', 311)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist1_nei"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph below shows the top 10 neighbourhood in Athens with double bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 neighbourhood in Athens with double bed')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "objects = ('Kerameikos', 'Psyri','Exarcheia','Metaxourgeio','Ambelokipi','Larissis','Pangrati','Kolonaki','Neos Kosmos','Koukaki')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [311, 389, 404, 406, 437, 471, 513, 517,754, 763]\n",
    "\n",
    "plt.barh(y_pos, performance, alpha=0.5)\n",
    "plt.yticks(y_pos, objects)\n",
    "plt.xlabel('Number of Properties in the neighbourhood')\n",
    "\n",
    "plt.title('Top 10 neighbourhood in Athens with double bed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4）Predict future price for properties in Athens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10595</td>\n",
       "      <td>2019-11-19</td>\n",
       "      <td>f</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10595</td>\n",
       "      <td>2019-11-20</td>\n",
       "      <td>f</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10595</td>\n",
       "      <td>2019-11-21</td>\n",
       "      <td>f</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10595</td>\n",
       "      <td>2019-11-22</td>\n",
       "      <td>f</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10595</td>\n",
       "      <td>2019-11-23</td>\n",
       "      <td>f</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available  price  adjusted_price  minimum_nights  \\\n",
       "0       10595  2019-11-19         f   59.0            59.0               1   \n",
       "1       10595  2019-11-20         f   59.0            59.0               1   \n",
       "2       10595  2019-11-21         f   59.0            59.0               1   \n",
       "3       10595  2019-11-22         f   59.0            59.0               1   \n",
       "4       10595  2019-11-23         f   59.0            59.0               1   \n",
       "\n",
       "   maximum_nights  \n",
       "0              45  \n",
       "1              45  \n",
       "2              45  \n",
       "3              45  \n",
       "4              45  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calendar = pd.read_csv('calendar.csv')\n",
    "#change price type to int\n",
    "calendar['price']=calendar['price'].replace('[\\$,]', '', regex=True).astype(float)\n",
    "calendar['adjusted_price']=calendar['adjusted_price'].replace('[\\$,]', '', regex=True).astype(float)\n",
    "\n",
    "calendar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>listing_id</th>\n",
       "      <th>price</th>\n",
       "      <th>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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",
       "      <td>2019-11-30</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>60.085664</td>\n",
       "      <td>59.791656</td>\n",
       "      <td>3.828199</td>\n",
       "      <td>1667.521590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>65.278832</td>\n",
       "      <td>64.850458</td>\n",
       "      <td>3.836444</td>\n",
       "      <td>1667.986278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>70.968441</td>\n",
       "      <td>70.230816</td>\n",
       "      <td>3.835353</td>\n",
       "      <td>1668.837767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>82.664281</td>\n",
       "      <td>82.431587</td>\n",
       "      <td>3.753848</td>\n",
       "      <td>1668.864482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>85.977294</td>\n",
       "      <td>85.783851</td>\n",
       "      <td>3.581413</td>\n",
       "      <td>1668.838082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>89.088632</td>\n",
       "      <td>88.867034</td>\n",
       "      <td>3.587813</td>\n",
       "      <td>1669.099381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>95.104659</td>\n",
       "      <td>94.855453</td>\n",
       "      <td>3.584469</td>\n",
       "      <td>1669.690159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>96.289168</td>\n",
       "      <td>96.024248</td>\n",
       "      <td>3.582772</td>\n",
       "      <td>1669.702986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-07-31</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>96.465429</td>\n",
       "      <td>96.196155</td>\n",
       "      <td>3.579786</td>\n",
       "      <td>1669.798275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>96.076832</td>\n",
       "      <td>95.810341</td>\n",
       "      <td>3.580912</td>\n",
       "      <td>1669.804756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>100.030149</td>\n",
       "      <td>99.766584</td>\n",
       "      <td>3.576202</td>\n",
       "      <td>1669.800444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>100.571280</td>\n",
       "      <td>100.321830</td>\n",
       "      <td>3.579176</td>\n",
       "      <td>1670.096376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>2.565721e+07</td>\n",
       "      <td>132.746498</td>\n",
       "      <td>131.941453</td>\n",
       "      <td>3.566613</td>\n",
       "      <td>1674.032219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              listing_id       price  adjusted_price  minimum_nights  \\\n",
       "date                                                                   \n",
       "2019-11-30  2.565721e+07   60.085664       59.791656        3.828199   \n",
       "2019-12-31  2.565721e+07   65.278832       64.850458        3.836444   \n",
       "2020-01-31  2.565721e+07   70.968441       70.230816        3.835353   \n",
       "2020-02-29  2.565721e+07   82.664281       82.431587        3.753848   \n",
       "2020-03-31  2.565721e+07   85.977294       85.783851        3.581413   \n",
       "2020-04-30  2.565721e+07   89.088632       88.867034        3.587813   \n",
       "2020-05-31  2.565721e+07   95.104659       94.855453        3.584469   \n",
       "2020-06-30  2.565721e+07   96.289168       96.024248        3.582772   \n",
       "2020-07-31  2.565721e+07   96.465429       96.196155        3.579786   \n",
       "2020-08-31  2.565721e+07   96.076832       95.810341        3.580912   \n",
       "2020-09-30  2.565721e+07  100.030149       99.766584        3.576202   \n",
       "2020-10-31  2.565721e+07  100.571280      100.321830        3.579176   \n",
       "2020-11-30  2.565721e+07  132.746498      131.941453        3.566613   \n",
       "\n",
       "            maximum_nights  \n",
       "date                        \n",
       "2019-11-30     1667.521590  \n",
       "2019-12-31     1667.986278  \n",
       "2020-01-31     1668.837767  \n",
       "2020-02-29     1668.864482  \n",
       "2020-03-31     1668.838082  \n",
       "2020-04-30     1669.099381  \n",
       "2020-05-31     1669.690159  \n",
       "2020-06-30     1669.702986  \n",
       "2020-07-31     1669.798275  \n",
       "2020-08-31     1669.804756  \n",
       "2020-09-30     1669.800444  \n",
       "2020-10-31     1670.096376  \n",
       "2020-11-30     1674.032219  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change date type to year\n",
    "calendar['date'] = pd.to_datetime(calendar['date'])\n",
    "calendar.set_index(['date'], inplace = True) \n",
    "#avg price per month\n",
    "dt = calendar.groupby(pd.Grouper(freq='M')).mean()\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1xxIpkImIiEh2KSiAww+Hjh3jrqTSFMhEREQke8yaBR98kFG9Y6BAJiIiItmkoCDMG7voorgr2SEKZCIiIpIdNm2CoUPhzDOhceO4q9khCmQiIiKSHcaNC1dYZthwJSiQiYiISLYoKICWLeHUU+OuZIcpkImIiEjmW7wYXnkFLr003L8ywyiQiYiISOYbMgSKijJyuBIUyERERCTTuYfhyuOPh3bt4q5mpyQtkJnZADNbZmYfJ7T9zcw+NbOPzOx5M2uYsO13ZjbPzOaYWZdk1SUiIiJZ5u23Yc6cjO0dg+T2kA0EupZomwh0cPdDgbnA7wDMrD3QEzgkes+jZpZ5A8AiIiKSegUFsPvucN55cVey05IWyNx9KrCqRNsEd98SvZwOtIqe9wBGuPtGd/8CmAf8NFm1iYiISJZYvx5GjgxhrEGDuKvZaXHOIesDvBI9bwksTNi2KGoTERERKdvo0bBuXUYPVwLkxPGhZnYLsAUYWtxUym5exnv7Af0AcnNzmTJlSjJKFBERkQzQ8f77qbP33ryzdSukeSbIz88vc1vKA5mZ9QZOB0529+LQtQhonbBbK2Bxae939yeAJwDy8vK8vC8nIiIiWWz+/HAz8b/8hfzOneOuZpekdMjSzLoCNwFnuHthwqaxQE8zq21mbYF2wIxU1iYiIiIZZtAgMAuLwWa4pPWQmdlwIB9oamaLgFsJV1XWBiaaGcB0d7/K3Web2SjgE8JQ5jXuvjVZtYmIiEiGKyqCgQPDbZJat65w93SXtEDm7heW0vxUOfvfCdyZrHpEREQki0yeDAsWwL33xl1JldBK/SIiIpJ5CgqgYUPo0SPuSqqEApmIiIhkljVrwnIXF10EderEXU2VUCATERGRzDJiBGzYkPFrjyVSIBMREZHMUlAAP/4xHHFE3JVUGQUyERERyRyzZ8OMGaF3zEpbVz4zKZCJiIhI5igogJwcuOSSuCupUgpkIiIikhk2b4YhQ6B7d2jWLO5qqpQCmYiIiGSGV16BZcuyajJ/MQUyERERyQwFBZCbC926xV1JlVMgExERkfS3bBm8+CL06hXmkGUZBTIRERFJf08/DVu2ZOVwJSiQiYiISLpzhwED4KijoH37uKtJCgUyERERSW8zZ4b1x7K0dwwUyERERCTdFRSEe1b27Bl3JUmjQCYiIiLpa8MGGD4czjkH9twz7mqSRoFMRERE0teYMbBmTVYPV4ICmYiIiKSzAQOgTRvo3DnuSpJKgUxERETS04IF8NprcNllUCO7I0t2fzsRERHJXIMHhyUveveOu5KkUyATERGR9FNUFK6u7NwZ2raNu5qkUyATERGR9DNtGsyfD336xF1JSiiQiYiISPopKIA99oCzz467kpRQIBMREZH0sm4dPPMMXHAB1KsXdzUpoUAmIiIi6WXUKCgszPq1xxIpkImIiEh6KSiAgw6Co4+Ou5KUSVogM7MBZrbMzD5OaDvPzGabWZGZ5ZXY/3dmNs/M5phZl2TVJSIiImls7lx4883QO2YWdzUpk8wesoFA1xJtHwNnA1MTG82sPdATOCR6z6NmVjOJtYmIiEg6KiiAmjWhV6+4K0mppAUyd58KrCrR9l93n1PK7j2AEe6+0d2/AOYBP01WbSIiIpKGtm4Ni8F26wYtWsRdTUqlyxyylsDChNeLojYRERGpLiZMgMWLq9Vk/mI5cRcQKW2Q2Evd0awf0A8gNzeXKVOmJLEsERERSZX299xDwz335O0GDfAs/P2en59f5rZ0CWSLgNYJr1sBi0vb0d2fAJ4AyMvL8/K+nIiIiGSIlSvh7behf39OPPXUuKtJuXQZshwL9DSz2mbWFmgHzIi5JhEREUmVYcNg06ZqOVwJSewhM7PhQD7Q1MwWAbcSJvk/DDQDXjKzWe7exd1nm9ko4BNgC3CNu29NVm0iIiKSZgoK4PDDoWPHuCuJRdICmbtfWMam58vY/07gzmTVIyIiImlq1iz44AN4+OG4K4lNugxZioiISHVVUAC1asFFF8VdSWwUyERERCQ+mzbB0KFw5pnQuHHc1cRGgUxERETiM25cuMKymk7mL6ZAJiIiIvEZMABatoRquNRFIgUyERERicfixTB+PPTuHe5fWY0pkImIiEg8hgyBoiK47LK4K4mdApmIiIiknnsYrjz+eGjXLu5qYqdAJiIiIqn39tswdy706RN3JWmhwkBmZrlm9pSZvRK9bm9mfZNfmoiIiGStggLYfXc477y4K0kLlekhGwi8CuwdvZ4LXJ+sgkRERCTLrV8PI0eGMFa/ftzVpIXKBLKm7j4KKAJw9y2A7jMpIiIiO+e552Ddumq/9liiygSy9WbWBHAAMzsaWJvUqkRERCR7FRTA/vtDp05xV5I2KnNz8RuBscB+ZvYm0Aw4N6lViYiISHaaPx+mTIG//AXM4q4mbVQYyNz9fTM7ETgQMGCOu29OemUiIiKSfQYODEHs0kvjriStVOYqy2uA+u4+290/Buqb2dXJL01ERESySlERDBoEP/sZtG4ddzVppTJzyK5w9zXFL9x9NXBF8koSERGRrDR5MixYoMn8pahMIKthtm2Q18xqArWSV5KIiIhkpYICaNgQevSIu5K0U5lA9iowysxONrOTgOHA+OSWJSIiIlllzRoYPRouugjq1Im7mrRTmassbwKuBPoTJvVPAJ5MZlEiIiKSZUaMgA0bdKukMpi7x13DTsvLy/OZM2fGXYaIiIhU5Kij4Pvv4cMPq/NyF2V+8TJ7yMxslLufb2b/IVoUNpG7H1pFxYmIiEg2mz0bZsyA+++vzmGsXOUNWV4X/Tw9FYWIiIhIlioogJwcuOSSuCtJW2UGMndfEl1R+ZS7n5LCmkRERCRbbN4MQ4ZA9+7QrFnc1aStcq+ydPetQKGZ7ZmiekRERCSbvPIKLFumtccqUJmrLDcA/zGzicD64kZ3vzZpVYmIiEh2GDAA9toLunWLu5K0VplA9lL02CFmNoAw/2yZu3eI2hoDI4F9gS+B8919dbTw7IPAaUAhcJm7v7+jnykiIiJpZNkyeOkluOGGMIdMylTu2TGznxB6xWa7+3938NgDgUeAwQltNwOT3P1uM7s5en0T0A1oFz2OAh6LfoqIiEiybd0K69dDYWH4WVWPdetgyxYNV1ZCecte/Am4BHgPuNfM/uru/6rsgd19qpntW6K5B5AfPR8ETCEEsh7AYA+Lok03s4Zm1sLdl1T280RERKqFLVtg/vwQdqoqRG3cuGM15OTA7rv/8LHnntCixfZt7dvDwQcn51xkkfJ6yC4ADnP3QjNrQrhdUqUDWRlyi0NWdBVn86i9JbAwYb9FUZsCmYiIyKZN4cbczz0HY8bAihUVv6dOnRCI6tXbPiA1b156mNqRRy3d0rqqlRfINrh7IYC7rzSzytz3cmeVtkpcqbcQMLN+QD+A3NxcpkyZksSyRERE4lFj40Yav/suTadOpelbb5Gzfj1b6tZl5THHsDovj80NGlBUty5b69T53+N/r2vXhpo1d72IrVvh22/DQ3ZZfn5+mdvKC2T7mdnY6LmVeI27n7ETtXxTPBRpZi2AZVH7IqB1wn6tgMWlHcDdnwCegHDrpPK+nIiISEZZtw5efjn0hL38chhObNQIzj0XzjmHnFNPJbdOHXLjrlOqXHmBrEeJ1/dVweeNBXoDd0c/X0ho/6WZjSBM5l+r+WMiIlItrF4NY8eGEDZhQpjP1bx5WNX+nHMgPx922y3uKiXJylup/9+7cmAzG06YwN/UzBYBtxKC2Cgz6wssAM6Ldn+ZsOTFPMKyF7ocQ0REstc334S5YKNHh7lhW7ZA69Zw1VUhhB17bNUMOUrGsHBhY2bKy8vzmTNnxl2GiIhIxRYtCgHsuedg2jRwh/33DwHs7LPhyCN14+3sV+Z/YK3SJiIikiyffx4C2HPPwYwZoa1DB/jjH0MQ+/GPFcIE2IFAZma7u/v6ivcUERGpptzhk09CABs9Gj78MLQfcQTcdVcIYQccEG+NkpYqDGRmdizwJFAf2MfMOgJXuvvVyS5OREQk7bnDBx9s6wmbMyf0eh17LNx/fxiObNMm7iolzVWmh+wBoAvhSkjc/UMzOyGpVYmIiKSzoiKYPn1bT9iXX4ZJ+Pn5cO21cNZZYcV6kUqq1JCluy+07ce4tyanHBERkTS1ZQtMnRpC2PPPw5IlYTmKU08Nc8LOOAOaNo27SslQlQlkC6NhSzezWsC1wI7eaFxERKoj9xBkcnIyc/L6pk0waVIIYS+8EG5ZVLcudOsW5oP9/Ofh/o0iu6gygewq4EHCvSUXAROAa5JZlIiIlLB5c1i7aunS0DOzdCl8/30IO1u2hFvcJP4srW1nt+3KsRKXVqpdO9xfsbRHeduqap/KrutVWAivvhpC2Lhx4bZBDRpA9+4hhHXpEu7nKFKFKgxk7r4CuDgFtYiIVD/r128LWEuWbP9IbFuxYvtwU54aNUKPVM2a4Wfi8/LaSm4rDjG7cozin5s3hxXoN2wo+7F2bQidZe23q3JyKg5tAG+/HUJZkybhlkVnnw2nnBLeJ5IklbnK8qFSmtcCM939hVK2iYhUb+6walXlgta6dT98f04O7LVXmBTepg0cfXR4XvzYa6/wqF+/9ECUiUODFXEPw4fF4ayicFfZfUrut2kT9O4desJOPDGcU5EUqMyftDrAQcAz0etzgNlAXzPr7O7XJ6s4EZG0smVL6MGpKGgtXRp+sZe0++7bQtVhh0HXrj8MWi1ahJ6ZGjVS//3SmVnooapdW3O2JCtVJpDtD5zk7lsAzOwxwjyyU4H/JLE2EZHUmzs3zB8qLWgtX176sGGTJttC1YEHbgtWJcNWgwap/z4ikhEqE8haArsThimJnu/t7lvNbGPSKhMRSaUPPoC//hWefTaErpwcyM0NYWqffeCoo0oPWrm5UKtW3NWLSIarTCC7F5hlZlMIN8U8AbjLzHYHXktibSIiyffGG+GWNq+8AnvsAb/7HVx5JbRqpWFDEUkZ80pctWNmLYCfEgLZDHdfnOzCKiMvL89nzpwZdxkikmncw7DkXXfBtGnQrBnccANcfbXmJ4lIMpV5xU1lLx/ZACwhTPDf38z2d/epVVGZiEjKbN0aVli/664wRNm6NTz0EPTtC/XqxV2diFRjlVn24hfAdUArYBZwNPA2cFJySxMRqSKbNsHQoXD33WHS/gEHwIABcPHFmv8lImmhMhMkrgOOBL5y987AT4DlSa1KRKQqFBbCww/D/vtDnz6hF2zUKPjkE7j8coUxEUkblRmy3ODuG8wMM6vt7p+a2YFJr0xEZGetXQuPPgoPPBCWqujUCZ54ItzyJhsXTRWRjFeZQLbIzBoCY4CJZrYaSItJ/SIi21m2DP7+d/jHP8L9B7t1C1dNduoUd2UiIuWqzL0sz4qe3mZmrwN7AuOTWpWIyI5YsADuuw+efDLc/ubcc0MQ+8lP4q5MRKRSyg1kZlYD+MjdOwC4+79TUpWISGXMmQP33ANDhoTXvXrBTTeF1fJFRDJIuYHM3YvM7EMz28fdF6SqKBGRciWuql+nDvTvD//3f2FFfRGRDFSZOWQtgNlmNgNYX9zo7mckrSoRkdJMmxaCWOKq+tddB82bx12ZiMguqUwguz3pVYiIlMUdxo8Pi7m+8UZYVf+uu7SqvohklcpM6v+3mbUB2rn7a2ZWD6iZ/NJEpFrbuhVGjw7ha9YsraovIlmtwoVhzewK4Fngn1FTS8ISGDvNzK4zs4/NbLaZXR+1NTaziWb2WfSz0a58hohkqE2boKAA2reH888Pi7sOGADz5sGvfqUwJiJZqTIr9V8DHAd8C+DunwE7PWHDzDoAVxBuVt4RON3M2gE3A5PcvR0wKXotItVFYWHoAdOq+iJSDVUmkG10903FL8wsB/Bd+MyDgenuXujuW4B/A2cBPYBB0T6DgDN34TNEJFOsWROGJffdN0zQb9MmTNp//3047zyoqRkSIpL9KhPI/m1mvwfqmtmpwDPAuF34zI+BE8ysSTQf7TSgNZDr7ksAop+6bEokmy1bBr//fQhgt9wCeXkwdWq4krJrV93iSESqFXMvv7MrWhy2L/AzwIBXgSe9ojeWf8y+hKHQ74BPgEmzHOwAABblSURBVO+By929YcI+q939B/PIzKwf0A8gNzf3iBEjRuxsGSISg9rffEPrkSNp8dJL1Ni8meUnnMCCiy/mu3bt4i5NRCSp8vPzy/yXZmUC2VnAy+6+saoLi45/F7AIuA7Id/clZtYCmOLu5S63nZeX5zNnzkxGWSJS1T79NKyq//TT4bVW1ReR6qfMQFaZdcjOAP5uZlOBEcCr0dyvna/GrLm7LzOzfYCzgWOAtkBv4O7o5wu78hkiErPCQnjrLXj99fCYPl2r6ouIlKHCHjIAM9sN6AZcABwPTHT3X+z0h5pNA5oAm4Eb3X2SmTUBRgH7AAuA89x9VXnHUQ+ZSBrZuDGErsQAtmlTmJR/5JFhXlj//lpVX0Sqs13qIcPdN5vZK4SrK+sSrojc6UDm7p1KaVsJnLyzxxSRFNu0Cd59d1sAe+st2LABatSAww8PV0x27gzHHw8NGsRdrYhIWqswkJlZV6An0BmYAjwJnJ/cskQk7WzZEpaiKA5g06aFYUmAjh3hqqtCADvhBGjYsPxjiYjIdirTQ3YZYe7Ylcma2C8iaWjrVvjww+0D2Lffhm3t24fFWzt3hhNPhCZN4q1VRCTDVeZelj0TX5vZccBF7n5N0qoSkdQrKoLZs7cFsH//G1avDtsOOAAuvDAEsPx8yM2NtVQRkWxTqTlkZnYYcBFhqPILYHQyixKRFHCHOXNg8uQQwKZMgRUrwra2beGss0IA69wZWraMtVQRkWxXZiAzswMIc8cuBFYCIwlXZXZOUW0iUpXc4fPPt/WAvf46LF0atrVuDaedti2AtWkTb60iItVMeT1knwLTgO7uPg/AzG5ISVUiUjW++mpb+Jo8GRYtCu177bUtfHXuDPvtp1sViYjEqLxAdg6hh+x1MxtPmNivv7FF0tnXX2/fA/bFF6G9adMw96tzZzjppLA6vgKYiEjaKDOQufvzwPNmtjtwJnADkGtmjwHPu/uEFNUoImVZvnzbHLDXX4e5c0N7o0bh6sfrrw8h7JBDwvpgIiKSlipzleV6YCgw1MwaA+cBNwMKZCKptm4dTJ0KkyaFx0cfhfYGDcL6X/36hQDWsWNYIV9ERDJCpW6dlK506yTJeps2hVsQFQewd94JC7TWrg3HHQcnnxweRxwBOZW6aFpEROKza7dOEpEUKSqCWbO2BbDi1fBr1Aih6ze/CQHs2GOhbt24qxURkSqiQCYSJ3eYNy+Er9deC/PAVq0K2w4+OKyGf/LJYUK+bkckIpK1FMhEUm3Jkm09YJMmwcKFob1VK+jeHU45JVwJuffe8dYpIiIpo0Amkmxr1oTbEBX3gv33v6G9ceMwAf93vwu9YO3aaSkKEZFqSoFMpKpt2ABvvrmtB2zmzDA3rG5d6NQJLr88BLDDDtNSFCIiAiiQiey6rVvhvfdC79ekSSGMbdwYlp046ii45ZYQwI4+OlwdKSIiUoICmciOcg/DjsU9YFOmwNq1YduPfwxXXx0C2AknhPXBREREKqBAJlIZCxZsC2CTJ4eJ+QBt28J554UAdtJJ0Lx5vHWKiEhGUiATKc2qVSF4FYewzz4L7c2aheB1yikhhLVtG2+dIiKSFRTIREqaPBnOOAPWr4f69cM9Ifv3DwGsQwdNxBcRkSqnQCaS6LXXwlpg++0H//wn/PSnsNtucVclIiJZToFMpNiECdCjR1gPbNKkMDwpIiKSAhp7EQEYPz4MUx54YBiyVBgTEZEUUiATefnl0DN28MGhZ6xp07grEhGRakaBTKq3F1+Es84Kk/UnTYImTeKuSEREqqFYApmZ3WBms83sYzMbbmZ1zKytmb1jZp+Z2UgzqxVHbVKNjB0LZ58Nhx4aJvM3bhx3RSIiUk2lPJCZWUvgWiDP3TsANYGewD3AA+7eDlgN9E11bVKNjBkD554LP/kJTJwIjRrFXZGIiFRjcQ1Z5gB1zSwHqAcsAU4Cno22DwLOjKk2yXbPPRdW1z/88HBlZcOGcVckIiLVXMoDmbt/DdwHLCAEsbXAe8Aad98S7bYIaJnq2qQaeOYZuOACOPLIEMb23DPuikRERFK/DpmZNQJ6AG2BNcAzQLdSdvUy3t8P6AeQm5vLlClTklOoZJ1mkyfT/s47+bZ9ez76wx/Y+v77cZckIiLVSH5+fpnb4lgY9hTgC3dfDmBmo4FjgYZmlhP1krUCFpf2Znd/AngCIC8vz8v7ciL/M3w43HknHHcce770Ep0aNIi7IhERkf+JYw7ZAuBoM6tnZgacDHwCvA6cG+3TG3ghhtokGz39NFxyCXTqFNYcUxgTEZE0E8ccsncIk/ffB/4T1fAEcBNwo5nNA5oAT6W6NslCgwfDpZeGG4S/9FK4WbiIiEiaMfdSp2plhLy8PJ85c2bcZUi6GjgQ+vSBk04Ka47Vqxd3RSIiUr1ZWRu0Ur9kpwEDQhg75RQYN05hTERE0poCmWSff/0L+vaFU0+FF16AunXjrkhERKRcCmSSXf75T+jXD7p1UxgTEZGMoUAm2ePRR+Gqq+DnP4fnn4c6deKuSEREpFIUyCQ7PPIIXHMNdO8ebo1Uu3bcFYmIiFSaAplkvgcfhF/9Cnr0gGefVRgTEZGMo0Amme2BB+D66+Gss2DUKKhVK+6KREREdpgCmWSu++6DG2+Ec8+FkSMVxkREJGMpkElmuvde+M1v4PzzYdgw2G23uCsSERHZaQpkknn++le46Sbo2ROGDlUYExGRjKdAJpnlL3+B3/8eLroIhgyBnJy4KxIREdllCmSSOW6/Hf74R+jVK9w0XGFMRESyhAKZpD93uPVWuO026N0bCgqgZs24qxIREakyCmSS3tzhT3+CP/8ZLr8cnnpKYUxERLKOxnwkfbnDLbeESfy/+EW4T2UN/RtCRESyjwKZpCd3uPnmsLxFv37w2GMKYyIikrX0G07Sjzv89rchjPXvrzAmIiJZTz1kkl7c4de/DrdEuuYaePhhMIu7KhERkaRSt4OkD3e44YYQxq69VmFMRESqDfWQSXpwDyHskUfCzcLvv19hTEREqg31kEn8iorgl78MYezXv1YYExGRakeBTOJVVARXXw2PPhom8v/tbwpjIiJS7WjIUuJTVARXXglPPhmWuLjrLoUxERGpltRDJvEoKoIrrghh7JZbFMZERKRaUyCT1NuwAfr2hQEDwm2R7rhDYUxERKq1lAcyMzvQzGYlPL41s+vNrLGZTTSzz6KfjVJdmyTZihXhnpRt2sDAgeFm4bffrjAmIiLVXsoDmbvPcffD3P0w4AigEHgeuBmY5O7tgEnRa8kGc+eGFfdbt4Zbb4W8PJg8OTwXERGR2Cf1nwx87u5fmVkPID9qHwRMAW6KqS7ZVe4wbRr8v/8H48bBbrtBr15w443Qvn3c1YmIiKSVuANZT2B49DzX3ZcAuPsSM2seX1my07ZsgeeeC0Hs3XehSRP4wx/CbZByc+OuTkREJC2Zu8fzwWa1gMXAIe7+jZmtcfeGCdtXu/sP5pGZWT+gH0Bubu4RI0aMSFnNUraahYW0ePllWj37LHW++YbCVq1YdO65LO3ShaI6deIuT0REJHb5+fllTpqOM5D1AK5x959Fr+cA+VHvWAtgirsfWN4x8vLyfObMmSmoVsq0aBE89BA88QSsXQudOoXV9rt3hxq6iFdERCRBmYEszt+YF7JtuBJgLNA7et4beCHlFUnlzZoV5oS1bRuGJ7t0gXfegalToUcPhTEREZEdEMscMjOrB5wKXJnQfDcwysz6AguA8+KoTcrhDuPHw333hask69cP96C87jrYd9+4qxMREclYsQQydy8EmpRoW0m46lLSzYYNMHRouOn3J59Ay5Zwzz3Qrx80bFjx+0VERKRccV9lKels5Up47DF45BH45hvo2BEGD4YLLoBateKuTkREJGsokMkPzZsHDzwABQXw/ffQrVuYqH/SSVpVX0REJAkUyCRwh7feCvPDXnghLOR6ySVhIddDDom7OhERkaymQFbdbdkCzz8frpR85x1o3BhuuSUs5LrXXnFXJyIiUi0okFVX330HAwaEockvv4T994d//AN694bdd4+7OhERkWpFgay6+fprePhh+Oc/Yc0aOO64EMq6d4eaNeOuTkREpFpSIKsuPvooDEsOHw5bt8I554SJ+kcdFXdlIiIi1Z4CWTZzhwkTQhCbODEMRfbvD9dfH1bYFxERkbSgQJaNNm6EYcPCQq4ffwx77w133x0Wcm30g/u1i4iISMwUyLLJqlXw+ONhjtjSpXDooTBoEPTsqYVcRURE0pgCWTZ4//2wov6wYVBYCF27hvlhJ5+shVxFREQygAJZpvr+exg1Ch59FGbMgHr14KKLwo2+O3SIuzoRERHZAQpkmeazz8KwZEEBrF4NBx8MDz0EvXrpRt8iIiIZSoEsE2zZAuPGhWHJiRMhJwfOPjtcMXniiRqWFBERyXAKZOls8WL417/C4+uvoXVruOMO+MUvdFsjERGRLKJAlm7cYfLk0Bs2ZgwUFUGXLmGu2Gmnhd4xERERySr67Z4uVq+GgQPD/LC5c6FJE7jxRrjySthvv7irExERkSRSIIvbu++G3rARI8KVk8ccA0OGwLnnQp06cVcnIiIiKaBAFofCwnBPycceg/feC7c0uvTSMEm/Y8e4qxMREZEUUyBLpU8/DUOSAwfC2rVwyCHwj3/AJZfAHnvEXZ2IiIjERIEs2TZvDpPzH3sMXn8ddtstDEf27w/HH68lK0RERESBLGkWLty2ZMXSpdCmDdx1F/TpA7m5cVcnIiIiaUSBrCoVFYWFWx97LCzk6g7dusHVV4f7S9asGXeFIiIikoYUyKrCypXhVkaPPw6ffw7NmsFvfwv9+kHbtnFXJyIiImlOgWxnucP06aE3bNQo2LgxzAm7445wW6PateOuUERERDJELIHMzBoCTwIdAAf6AHOAkcC+wJfA+e6+Oo76yvXddzBsWFg5/8MPoX596NsXrroKfvzjuKsTERGRDFQjps99EBjv7gcBHYH/AjcDk9y9HTApep0+Zs+GX/4S9t47rJ7vHnrHFi8OS1cojImIiMhOSnkPmZntAZwAXAbg7puATWbWA8iPdhsETAFuSnV929m0CUaPDr1h06ZBrVpw/vlhyYpjjtGSFSIiIlIl4hiy/BGwHCgws47Ae8B1QK67LwFw9yVm1jyG2rb33ntw4YVhYv4998Dll4cJ+yIiIiJVyNw9tR9olgdMB45z93fM7EHgW+BX7t4wYb/V7t6olPf3A/oB5ObmHjFixIjkFetOww8/ZM2hh0KNuEZ3RUREJBvk5+eXObQWRyDbC5ju7vtGrzsR5ovtD+RHvWMtgCnufmB5x8rLy/OZM2cmu2QRERGRqlBmIEt5t4+7LwUWmllx2DoZ+AQYC/SO2noDL6S6NhEREZE4xLUO2a+AoWZWC5gPXE4Ih6PMrC+wADgvptpEREREUiqWQObus4C8UjadnOpaREREROKmmeoiIiIiMVMgExEREYmZApmIiIhIzBTIRERERGKmQCYiIiISMwUyERERkZgpkImIiIjELOW3TqpKZrYc+CruOtJQU2BF3EVkCZ3LqqdzWrV0PpND57Xq6ZzCCnfvWtqGjA5kUjozm+nupS28KztI57Lq6ZxWLZ3P5NB5rXo6p+XTkKWIiIhIzBTIRERERGKmQJadnoi7gCyic1n1dE6rls5ncui8Vj2d03JoDpmIiIhIzNRDJiIiIhIzBbI0YGatzex1M/uvmc02s+ui9sZmNtHMPot+NoraLzazj6LHW2bWMeFYXc1sjpnNM7Oby/nM3tFxPzOz3gntd5rZQjP7LpnfOVnS7FyON7MPozoeN7OayfzuyZJm53RK9P5Z0aN5Mr97MqTL+TSzBgnncZaZrTCzvyf7+ydLupzXqP2C6LizzezeZH7vZIrpnI43szVm9mKJ9l9G73Uza5qs7xwrd9cj5gfQAjg8et4AmAu0B+4Fbo7abwbuiZ4fCzSKnncD3ome1wQ+B34E1AI+BNqX8nmNgfnRz0bR8+LjHR3V813c5yULzuUe0U8DngN6xn1+suCcTgHy4j4n2XI+S+z3HnBC3Ocn088r0ARYADSL9hsEnBz3+cmEcxrtezLQHXixRPtPgH2BL4GmcZ+bZDzUQ5YG3H2Ju78fPV8H/BdoCfQg/M9M9PPMaJ+33H111D4daBU9/ykwz93nu/smYER0jJK6ABPdfVV0nIlA1+jY0919SVV/x1RJs3P5bbRPDuEvoYycsJlO5zQbpOP5NLN2QHNgWtV8y9RLo/P6I2Cuuy+P9nsNOKfqvmnqxHBOcfdJwLpS2j9w9y+r4nulKwWyNGNm+xL+JfAOkFscjqKfpQ3P9AVeiZ63BBYmbFsUtZVU2f0yWjqcSzN7FVhG+Avm2Z34GmklHc4pUBANsf3RzGwnvkbaSJPzCXAhMNKjrohMF/N5nQccZGb7mlkOIay03tnvki5SdE6rtZy4C5BtzKw+YWjrenf/tqLfNWbWmfCH/vjiplJ2K+0v2Mrul7HS5Vy6exczqwMMBU4i/Cs6I6XJOb3Y3b82swZRLb2AwZUoP+2kyfks1pNwLjNe3OfV3VebWX9gJFAEvEXoNctYKTyn1Zp6yNKEme1G+AM/1N1HR83fmFmLaHsLQk9L8f6HAk8CPdx9ZdS8iO3/JdYKWGxmRyVM3D2jrP2S8b3ikG7n0t03AGMpo4s+E6TLOXX3r6Of64BhhKGQjJMu5zM6dkcgx93fq9IvGYN0Oa/uPs7dj3L3Y4A5wGdV/V1TJcXntHpL1WQ1Pcp+EP71MBj4e4n2v7H9xMl7o+f7ELrFjy2xfw5hYmlbtk2cPKSUz2sMfEGYgNooet64xD6ZOqk/Lc4lUB9okXCskcAv4z4/GX5Oc4gm8wK7EYaAr4r7/GTq+UzYfjdwe9znJZvOK9A8+tkImAUcEPf5yYRzmrB/PiUm9Sds+5IsndQfewF6OIRuXQc+iv7nnQWcRrhaZxLhX1eTEv5nfxJYnbDvzIRjnUa4EuZz4JZyPrNP9D/OPODyhPZ7Cf+aKYp+3hb3+cnEcwnkAu9GdcwGHib0QsR+jjL4nO5OuBKw+Jw+CNSM+/xk6vlM2DYfOCju85JN5xUYDnwSPTLy6uoYz+k0YDnwPeF3UJeo/dro9RZCT+STcZ+fqn5opX4RERGRmGkOmYiIiEjMFMhEREREYqZAJiIiIhIzBTIRERGRmCmQiYiIiMRMgUxEspaZuZkNSXidY2bLzezFnTxeQzO7OuF1/s4eS0QkkQKZiGSz9UAHM6sbvT4V+HoXjtcQuLrCvUREdpACmYhku1eAn0fPLyQs2gmAmTU2szFm9pGZTY9u+4KZ3WZmA8xsipnNN7Nro7fcDewX3erlb1FbfTN71sw+NbOhmX7DcxGJhwKZiGS7EUDP6CbvhwLvJGy7HfjA3Q8Ffs/2Nyo/COhCuF/mrdE9/W4GPnf3w9z9N9F+PwGuB9oTbiJ9XDK/jIhkJwUyEclq7v4RsC+hd+zlEpuPB4ZE+00GmpjZntG2l9x9o7uvINw8ObeMj5jh7ovcvYhwu5h9q/YbiEh1kBN3ASIiKTAWuI9w0+ImCe2lDS8W309uY0LbVsr++7Ky+4mIlEk9ZCJSHQwA/uzu/ynRPhW4GMIVk8AKd/+2nOOsAxokpUIRqdb0LzkRyXruvgh4sJRNtwEFZvYRUAj0ruA4K83sTTP7mHCxwEtVXauIVE/m7hXvJSIiIiJJoyFLERERkZgpkImIiIjETIFMREREJGYKZCIiIiIxUyATERERiZkCmYiIiEjMFMhEREREYqZAJiIiIhKz/w9Qqs7ozcUN8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Average price change\n",
    "dt_price_change = dt['price'].pct_change()*100 \n",
    "fig, ax = plt.subplots(figsize=(10,5)) \n",
    "ax.plot(dt.index, dt['price'], color = 'red')\n",
    "ax.set_ylabel('Average Price')  # add the y-axis label\n",
    "ax.set_xlabel('Month')                 # add the x-axis label\n",
    "ax.set_title('2019-2020 Average Price Per Month')\n",
    "\n",
    "ax.spines['right'].set_visible(False) # get ride of the line on the right\n",
    "ax.spines['top'].set_visible(False)   # get rid of the line on top\n",
    "ax.yaxis.grid() # horizontal lines\n",
    "#plt.savefig('2019_2020_price.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>listing_id</th>\n",
       "      <th>price</th>\n",
       "      <th>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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",
       "      <td>2019-11-30</td>\n",
       "      <td>2.494904e+07</td>\n",
       "      <td>59.225299</td>\n",
       "      <td>59.101629</td>\n",
       "      <td>5.092493</td>\n",
       "      <td>2665.909485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>2.440183e+07</td>\n",
       "      <td>66.733469</td>\n",
       "      <td>65.961359</td>\n",
       "      <td>6.088621</td>\n",
       "      <td>3228.845358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2.390317e+07</td>\n",
       "      <td>89.803245</td>\n",
       "      <td>87.625095</td>\n",
       "      <td>7.364602</td>\n",
       "      <td>4086.170807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>2.414775e+07</td>\n",
       "      <td>88.292834</td>\n",
       "      <td>88.196109</td>\n",
       "      <td>6.209030</td>\n",
       "      <td>4372.057227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>2.445869e+07</td>\n",
       "      <td>83.302662</td>\n",
       "      <td>83.157191</td>\n",
       "      <td>4.079808</td>\n",
       "      <td>4219.300508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              listing_id      price  adjusted_price  minimum_nights  \\\n",
       "date                                                                  \n",
       "2019-11-30  2.494904e+07  59.225299       59.101629        5.092493   \n",
       "2019-12-31  2.440183e+07  66.733469       65.961359        6.088621   \n",
       "2020-01-31  2.390317e+07  89.803245       87.625095        7.364602   \n",
       "2020-02-29  2.414775e+07  88.292834       88.196109        6.209030   \n",
       "2020-03-31  2.445869e+07  83.302662       83.157191        4.079808   \n",
       "\n",
       "            maximum_nights  \n",
       "date                        \n",
       "2019-11-30     2665.909485  \n",
       "2019-12-31     3228.845358  \n",
       "2020-01-31     4086.170807  \n",
       "2020-02-29     4372.057227  \n",
       "2020-03-31     4219.300508  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We only want the price from 2019-11-29 to 2020-11-30\n",
    "d = calendar.index >= '2019-11-29'\n",
    "e = calendar.loc[d]\n",
    "\n",
    "# We want to distinguish the availability\n",
    "#ct as the available lists\n",
    "ct = e['available']=='t'    \n",
    "ct = e.loc[ct]\n",
    "\n",
    "#cf as the unavailable lists\n",
    "cf = e['available']=='f'   \n",
    "cf = e.loc[cf]\n",
    "\n",
    "#group by list id and calculate the avg price per month\n",
    "ct =ct.groupby(pd.Grouper(freq='M')).mean()\n",
    "cf =cf.groupby(pd.Grouper(freq='M')).mean()\n",
    "cf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we want to analyse the growth rate of average price\n",
    "#available lists\n",
    "pt_change_2020 = ct['price'].pct_change()*100   #percentage change, growth rate\n",
    "#unavailable lists\n",
    "pf_change_2020 = cf['price'].pct_change()*100   #percentage change, growth rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_props = {'facecolor':'black',  'arrowstyle':'->'}\n",
    "\n",
    "#2020 availability\n",
    "new_fig, new_ax = plt.subplots(figsize = (15,8))\n",
    "\n",
    "new_ax.plot(ct.index,pt_change_2020,color = 'blue',label = \"Available\")\n",
    "new_ax.plot(ct.index,pf_change_2020,'r--',color = 'red',label = \"Not available\")\n",
    "new_ax.axhline(y=0, color='black', linewidth=0.75) \n",
    "new_ax.set_ylabel('Percentage Change of Average Price')  # add the y-axis label\n",
    "new_ax.set_xlabel('Month')                 # add the x-axis label\n",
    "new_ax.set_title('2020 Average Price(%) Per Month')\n",
    "\n",
    "new_ax.spines['right'].set_visible(False) # get ride of the line on the right\n",
    "new_ax.spines['top'].set_visible(False)   # get rid of the line on top\n",
    "\n",
    "new_ax.legend(frameon=False) \n",
    "\n",
    "new_ax.annotate('Increase 34.57%', xy=('2020-01-31', 34.5), xytext=('2020-02-06',34.5), arrowprops=my_props)\n",
    "new_ax.annotate('Decrease 5.13%', xy=('2020-10-31', -5), xytext=('2020-11-06',-5), arrowprops=my_props)\n",
    "new_ax.annotate('Increase 26.41%', xy=('2020-02-29', 26), xytext=('2020-03-06',26), arrowprops=my_props)\n",
    "new_ax.annotate('Increase 12.58%', xy=('2020-05-31', 12.5), xytext=('2020-06-06',12.5), arrowprops=my_props)\n",
    "new_ax.annotate('Increase 45.64%', xy=('2020-11-30', 45.6), xytext=('2020-12-06',45.6), arrowprops=my_props)\n",
    "new_ax.annotate('Increase 22.29%', xy=('2020-11-29', 22), xytext=('2020-12-05',22), arrowprops=my_props)\n",
    "\n",
    "plt.show()\n",
    "#plt.savefig('2020_availability.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Make final recommendation on properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_room_bed\n",
    "\n",
    "#Find the matched properties with criteria on price, review scores, room type, bed type etc...\n",
    "df = df[df['price'] <= 50]\n",
    "df = df[df['availability_365'] == 365]\n",
    "df = df[df['review_scores_rating'] >= 95]\n",
    "df = df[df['neighbourhood'] == 'Koukaki']\n",
    "df = df[df['room_type'] == 'Entire home/apt']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the end we have 3 properties matched our criteria, we provide the airbnb link for them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1702    https://www.airbnb.com/rooms/15627184\n",
       "3436    https://www.airbnb.com/rooms/22916423\n",
       "6942    https://www.airbnb.com/rooms/33465448\n",
       "Name: listing_url, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.listing_url"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.4"
  }
 },
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}