Data Analysis.ipynb

{
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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
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       "            paths: {\n",
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       "        require(['plotly'], function(Plotly) {\n",
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       "        </script>\n",
       "        "
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "#importing our libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import cufflinks as cf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.cluster import KMeans\n",
    "%matplotlib inline\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>500 €</td>\n",
       "      <td>Superficie (mq): 123</td>\n",
       "      <td>Centro</td>\n",
       "      <td>28 marzo, 03:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>900 €</td>\n",
       "      <td>Superficie (mq): 50</td>\n",
       "      <td>Città Studi / Lambrate</td>\n",
       "      <td>Oggi, 11:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>635 €</td>\n",
       "      <td>Superficie (mq): 60</td>\n",
       "      <td>Lodi / Ripamonti / Mecenate</td>\n",
       "      <td>Oggi, 09:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Contatta l'utente</td>\n",
       "      <td>Superficie (mq): 40</td>\n",
       "      <td>Bicocca / Greco / Viale Monza</td>\n",
       "      <td>Oggi, 08:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.150 €</td>\n",
       "      <td>Superficie (mq): 50</td>\n",
       "      <td>Porta Romana / Monte Nero</td>\n",
       "      <td>Oggi, 05:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price                  area                       location  \\\n",
       "0              500 €  Superficie (mq): 123                         Centro   \n",
       "1              900 €   Superficie (mq): 50         Città Studi / Lambrate   \n",
       "2              635 €   Superficie (mq): 60    Lodi / Ripamonti / Mecenate   \n",
       "3  Contatta l'utente   Superficie (mq): 40  Bicocca / Greco / Viale Monza   \n",
       "4            1.150 €   Superficie (mq): 50      Porta Romana / Monte Nero   \n",
       "\n",
       "              date  \n",
       "0  28 marzo, 03:23  \n",
       "1      Oggi, 11:18  \n",
       "2      Oggi, 09:16  \n",
       "3      Oggi, 08:06  \n",
       "4      Oggi, 05:05  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing our dataset \n",
    "df=pd.read_csv('kijiji.csv',index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1-Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#deleting any values without prices and any values before january \n",
    "df = df[df.price != \"Contatta l'utente\"]\n",
    "df = df[df.date != \"22 dicembre, 02:39\"]\n",
    "df = df[df.date != \"11 dicembre, 02:56\"]\n",
    "df = df[df.date != \"10 dicembre, 15:44\"]\n",
    "df = df[df.date != \"10 dicembre, 12:45\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to clean and organize our data for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>580.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2020-03-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>258</td>\n",
       "      <td>700.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2020-02-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>337</td>\n",
       "      <td>500.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2020-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>580.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2020-03-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>580.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2020-03-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>252</td>\n",
       "      <td>500.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Centro</td>\n",
       "      <td>2020-03-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>369</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2020-02-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>590.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>~Altre zone</td>\n",
       "      <td>2020-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2020-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>304</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2020-02-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>363 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      price   area     location        date\n",
       "144   580.0   20.0       Milano  2020-03-10\n",
       "258   700.0   20.0       Milano  2020-02-29\n",
       "337   500.0   20.0       Milano  2020-02-19\n",
       "149   580.0   22.0       Milano  2020-03-09\n",
       "156   580.0   22.0       Milano  2020-03-09\n",
       "..      ...    ...          ...         ...\n",
       "252   500.0  123.0       Centro  2020-03-28\n",
       "369  2100.0  125.0       Milano  2020-02-13\n",
       "62    590.0  125.0  ~Altre zone  2020-03-31\n",
       "104  2150.0  130.0       Milano  2020-03-13\n",
       "304  2100.0  150.0       Milano  2020-02-23\n",
       "\n",
       "[363 rows x 4 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#take a copy of the dataset\n",
    "df = df.copy()\n",
    "#removing any signs or strings not needed converting the strings into floats\n",
    "df['price'] = df['price'].apply(lambda x: x.replace('€', '').replace('.', '')\n",
    "                                if isinstance(x, str) else x).astype(float)\n",
    "df['area'] = df['area'].apply(lambda x: x.replace('Superficie (mq): ', '').replace('.', '')\n",
    "                                if isinstance(x, str) else x).astype(float)\n",
    "#converting some italian words of today and yesterday to dates  \n",
    "df['date'] = df['date'].apply(lambda x: x.replace('Oggi', '15 aprile').replace(',', '')\n",
    "                                if isinstance(x, str) else x).astype(str)\n",
    "df['date'] = df['date'].apply(lambda x: x.replace('Ieri', '14 aprile').replace(',', '')\n",
    "                                if isinstance(x, str) else x).astype(str)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will try to work on the date column, we need to convert the data to a datetime format in order to be readable by python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'gennaio':1, 'febbraio':2, 'marzo':3, 'aprile':4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>time</th>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>03:23</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>11:18</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>09:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>05:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>452</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>03:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>454</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>09:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>456</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>02:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>457</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>21:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>458</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>08:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>388 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    day  month   time\n",
       "0    28      3  03:23\n",
       "1    15      4  11:18\n",
       "2    15      4  09:16\n",
       "4    15      4  05:05\n",
       "5    15      4  04:59\n",
       "..   ..    ...    ...\n",
       "452  12      1  03:59\n",
       "454  11      1  09:14\n",
       "456   8      1  02:18\n",
       "457   7      1  21:43\n",
       "458   7      1  08:05\n",
       "\n",
       "[388 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#split each elemnet in the column\n",
    "date = df.date.str.split(expand=True)\n",
    "#rename the columns\n",
    "date = date.rename(columns={0: \"day\", 1: \"month\",2:'time'})\n",
    "#converting months to numbers\n",
    "date.month = date.month.map(d)\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Centro</td>\n",
       "      <td>2020-03-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>900.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Città Studi / Lambrate</td>\n",
       "      <td>2020-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>635.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Lodi / Ripamonti / Mecenate</td>\n",
       "      <td>2020-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Porta Romana / Monte Nero</td>\n",
       "      <td>2020-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Città Studi / Lambrate</td>\n",
       "      <td>2020-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>452</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>Centro</td>\n",
       "      <td>2020-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>454</td>\n",
       "      <td>350.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Porta Genova / Navigli / Corso Italia</td>\n",
       "      <td>2020-01-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>456</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2020-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>457</td>\n",
       "      <td>1166.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2020-01-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>458</td>\n",
       "      <td>450.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Porta Genova / Navigli / Corso Italia</td>\n",
       "      <td>2020-01-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>388 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      price   area                               location       date\n",
       "0     500.0  123.0                                 Centro 2020-03-28\n",
       "1     900.0   50.0                 Città Studi / Lambrate 2020-04-15\n",
       "2     635.0   60.0            Lodi / Ripamonti / Mecenate 2020-04-15\n",
       "4    1150.0   50.0              Porta Romana / Monte Nero 2020-04-15\n",
       "5    1400.0   80.0                 Città Studi / Lambrate 2020-04-15\n",
       "..      ...    ...                                    ...        ...\n",
       "452  4800.0  255.0                                 Centro 2020-01-12\n",
       "454   350.0   40.0  Porta Genova / Navigli / Corso Italia 2020-01-11\n",
       "456  3100.0  120.0                                 Milano 2020-01-08\n",
       "457  1166.0   50.0                                 Milano 2020-01-07\n",
       "458   450.0   70.0  Porta Genova / Navigli / Corso Italia 2020-01-07\n",
       "\n",
       "[388 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add a year column\n",
    "date['year'] = '2020'\n",
    "#join day month year column\n",
    "ndate=pd.to_datetime(date[['year', 'month', 'day']])\n",
    "df['date'] = ndate\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's time to work on the outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Centro</td>\n",
       "      <td>2020-03-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>900.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Città Studi / Lambrate</td>\n",
       "      <td>2020-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>635.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Lodi / Ripamonti / Mecenate</td>\n",
       "      <td>2020-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Porta Romana / Monte Nero</td>\n",
       "      <td>2020-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Città Studi / Lambrate</td>\n",
       "      <td>2020-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>447</td>\n",
       "      <td>760.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Barona / Giambellino / Lorenteggio</td>\n",
       "      <td>2020-01-12</td>\n",
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       "    <tr>\n",
       "      <td>451</td>\n",
       "      <td>750.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Centro</td>\n",
       "      <td>2020-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>454</td>\n",
       "      <td>350.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Porta Genova / Navigli / Corso Italia</td>\n",
       "      <td>2020-01-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>457</td>\n",
       "      <td>1166.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2020-01-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>458</td>\n",
       "      <td>450.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Porta Genova / Navigli / Corso Italia</td>\n",
       "      <td>2020-01-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>363 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      price   area                               location       date\n",
       "0     500.0  123.0                                 Centro 2020-03-28\n",
       "1     900.0   50.0                 Città Studi / Lambrate 2020-04-15\n",
       "2     635.0   60.0            Lodi / Ripamonti / Mecenate 2020-04-15\n",
       "4    1150.0   50.0              Porta Romana / Monte Nero 2020-04-15\n",
       "5    1400.0   80.0                 Città Studi / Lambrate 2020-04-15\n",
       "..      ...    ...                                    ...        ...\n",
       "447   760.0   70.0     Barona / Giambellino / Lorenteggio 2020-01-12\n",
       "451   750.0   44.0                                 Centro 2020-01-12\n",
       "454   350.0   40.0  Porta Genova / Navigli / Corso Italia 2020-01-11\n",
       "457  1166.0   50.0                                 Milano 2020-01-07\n",
       "458   450.0   70.0  Porta Genova / Navigli / Corso Italia 2020-01-07\n",
       "\n",
       "[363 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#setting the parameters\n",
    "q1 = df['price'].quantile(0.25)\n",
    "q3 = df['price'].quantile(0.75)\n",
    "#Interquartile range\n",
    "iqr = q3-q1 \n",
    "fence_low  = q1-1.5*iqr\n",
    "fence_high = q3+1.5*iqr\n",
    "#data without ouliers\n",
    "df = df.loc[(df['price'] > fence_low) & (df['price'] < fence_high)]\n",
    "df\n",
    "#for reference: https://www.itl.nist.gov/div898/handbook/prc/section1/prc16.htm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can notice 25 values has been removed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2-Data Exploring & Visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now before splitting the data set to before and after coronavirus outbreak, let's explore some data exploring to be familier with our data first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "955.4214876033058\n",
      "          price      area\n",
      "price  1.000000  0.261033\n",
      "area   0.261033  1.000000\n"
     ]
    }
   ],
   "source": [
    "#Checking the price mean and the correlation between price and area \n",
    "print(df['price'].mean())\n",
    "print(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "26% correlation is a very weak one, and it's worth investigating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#first let's sort the data frame by the ascending area values\n",
    "df = df.sort_values(by='area', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th>price</th>\n",
       "      <th>area</th>\n",
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       "      <th>location</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Affori / Bovisa</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Baggio / Forze Armate / Quinto Romano</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>72.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Barona / Giambellino / Lorenteggio</td>\n",
       "      <td>806.875000</td>\n",
       "      <td>60.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bicocca / Greco / Viale Monza</td>\n",
       "      <td>940.000000</td>\n",
       "      <td>59.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bonola / Molino Dorino / Lampugnano</td>\n",
       "      <td>1195.000000</td>\n",
       "      <td>72.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Centro</td>\n",
       "      <td>629.310345</td>\n",
       "      <td>109.620690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Città Studi / Lambrate</td>\n",
       "      <td>564.384615</td>\n",
       "      <td>47.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Garibaldi / Stazione Centrale / Isola</td>\n",
       "      <td>1033.333333</td>\n",
       "      <td>52.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lodi / Ripamonti / Mecenate</td>\n",
       "      <td>978.600000</td>\n",
       "      <td>60.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Loreto / Porta Venezia / Indipendenza</td>\n",
       "      <td>871.500000</td>\n",
       "      <td>54.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Maciachini / Zara / Niguarda</td>\n",
       "      <td>1071.428571</td>\n",
       "      <td>57.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Milano</td>\n",
       "      <td>1042.532258</td>\n",
       "      <td>55.940860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Porta Genova / Navigli / Corso Italia</td>\n",
       "      <td>1049.722222</td>\n",
       "      <td>68.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Porta Romana / Monte Nero</td>\n",
       "      <td>1031.428571</td>\n",
       "      <td>54.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rogoredo / Chiesa Rossa</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>San Siro / Fiera</td>\n",
       "      <td>1193.750000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sempione / Certosa</td>\n",
       "      <td>776.666667</td>\n",
       "      <td>48.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>~Altre zone</td>\n",
       "      <td>761.000000</td>\n",
       "      <td>65.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             price        area\n",
       "location                                                      \n",
       "Affori / Bovisa                         816.000000   53.000000\n",
       "Baggio / Forze Armate / Quinto Romano   800.000000   72.142857\n",
       "Barona / Giambellino / Lorenteggio      806.875000   60.625000\n",
       "Bicocca / Greco / Viale Monza           940.000000   59.700000\n",
       "Bonola / Molino Dorino / Lampugnano    1195.000000   72.500000\n",
       "Centro                                  629.310345  109.620690\n",
       "Città Studi / Lambrate                  564.384615   47.846154\n",
       "Garibaldi / Stazione Centrale / Isola  1033.333333   52.166667\n",
       "Lodi / Ripamonti / Mecenate             978.600000   60.040000\n",
       "Loreto / Porta Venezia / Indipendenza   871.500000   54.100000\n",
       "Maciachini / Zara / Niguarda           1071.428571   57.571429\n",
       "Milano                                 1042.532258   55.940860\n",
       "Porta Genova / Navigli / Corso Italia  1049.722222   68.833333\n",
       "Porta Romana / Monte Nero              1031.428571   54.285714\n",
       "Rogoredo / Chiesa Rossa                 750.000000   50.000000\n",
       "San Siro / Fiera                       1193.750000   53.000000\n",
       "Sempione / Certosa                      776.666667   48.333333\n",
       "~Altre zone                             761.000000   65.300000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's explore the mean price and area for each area in milan\n",
    "df.groupby('location').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"font\": {\"color\": \"#4D5663\"}}, \"xaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"\"}, \"zerolinecolor\": \"#E1E5ED\"}, \"yaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"\"}, \"zerolinecolor\": \"#E1E5ED\"}},\n",
       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('4bcdfd07-f0ae-4026-840d-854e331b8636');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('location').mean().iplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can notice that the most expensive areas are (Bonola / Molino Dorino / Lampugnano) (San Siro / Fiera) while the cheapest ones are (Città Studi / Lambrate) and very strangely (Centro), that will be under investigation in the next step when we split the data.\n",
    "#Note: the milano area is based on user submission of not specifying the zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the dataframe\n",
    "dfb = df[(df['date'] < '2020-02-21')]\n",
    "dfa = df[(df['date'] >= '2020-02-21')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 85 entries, 337 to 369\n",
      "Data columns (total 4 columns):\n",
      "price       85 non-null float64\n",
      "area        85 non-null float64\n",
      "location    85 non-null object\n",
      "date        85 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(2), object(1)\n",
      "memory usage: 3.3+ KB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 278 entries, 144 to 304\n",
      "Data columns (total 4 columns):\n",
      "price       278 non-null float64\n",
      "area        278 non-null float64\n",
      "location    278 non-null object\n",
      "date        278 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(2), object(1)\n",
      "memory usage: 10.9+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(dfb.info())\n",
    "print('\\n')\n",
    "print(dfa.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 85 online rent postings before the outbreak while strangely we have more than triple the number after for the same time period. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1141.8235294117646\n",
      "898.4280575539568\n"
     ]
    }
   ],
   "source": [
    "print(dfb['price'].mean())\n",
    "print(dfa['price'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average prices before the outbreak are higher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          price      area\n",
      "price  1.000000  0.555945\n",
      "area   0.555945  1.000000\n",
      "          price      area\n",
      "price  1.000000  0.176422\n",
      "area   0.176422  1.000000\n"
     ]
    }
   ],
   "source": [
    "print(dfb.corr())\n",
    "print(dfa.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1374f5ed0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='price',y='area',data=dfb,height=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x13770bc50>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='price',y='area',data=dfa,height=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A positive relationship exists with 55% correlation between prices and areas before the outbreak while on the other hand it's almost zero after the outbreak, which suggests that people's pricing behaviors have changed. It's also worth to mention that 55% correlation between price and area is not that strong and it shows how in a lot of tenants can be charged for a lot more than what they get, it's also worth mentioning that house prices doesn't only depend on those features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
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         "mode": "lines",
         "name": "price",
         "text": "",
         "type": "scatter",
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          "Lodi / Ripamonti / Mecenate",
          "Loreto / Porta Venezia / Indipendenza",
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          "Porta Romana / Monte Nero",
          "San Siro / Fiera",
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    "Here we can clearly see the change in trends, before the outbreak prices in the center was the highest along with sansiro area (~1200), however after the outbreak the prices dropped to almost half in the center and loreto area to reach ~600 with small to medium drops in other areas, however we can notice an increase in the pricesin areas like Bonola / Molino Dorino / Lampugnano"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "dfb.groupby(['location','area']).mean().iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1365a92d0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 791x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('price','area',data=dfb, hue='location',\n",
    "           palette='coolwarm',size=8,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can explore the change in prices based on apartment area and location, it should be normal to have the same price for the same features in the same location like what we see in (Baggio / Forze Armate / Quinto Romano) or in the center, however there are a lot of variations in the prices in areas like (Porta Genova / Navigli / Corso Italia) or (San Siro / Fiera), that's definitely explains the low correlation number from before, and we should expect higher variations in the after data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "line": {
          "color": "rgba(255, 153, 51, 1.0)",
          "dash": "solid",
          "shape": "linear",
          "width": 1.3
         },
         "mode": "lines",
         "name": "price",
         "text": "",
         "type": "scatter",
         "x": [
          "(Affori / Bovisa,50.0)",
          "(Affori / Bovisa,55.0)",
          "(Affori / Bovisa,65.0)",
          "(Affori / Bovisa,70.0)",
          "(Baggio / Forze Armate / Quinto Romano,55.0)",
          "(Barona / Giambellino / Lorenteggio,40.0)",
          "(Barona / Giambellino / Lorenteggio,65.0)",
          "(Barona / Giambellino / Lorenteggio,70.0)",
          "(Bicocca / Greco / Viale Monza,25.0)",
          "(Bicocca / Greco / Viale Monza,50.0)",
          "(Bicocca / Greco / Viale Monza,65.0)",
          "(Bicocca / Greco / Viale Monza,70.0)",
          "(Bicocca / Greco / Viale Monza,80.0)",
          "(Bicocca / Greco / Viale Monza,102.0)",
          "(Bonola / Molino Dorino / Lampugnano,60.0)",
          "(Bonola / Molino Dorino / Lampugnano,85.0)",
          "(Centro,40.0)",
          "(Centro,65.0)",
          "(Centro,123.0)",
          "(Città Studi / Lambrate,25.0)",
          "(Città Studi / Lambrate,27.0)",
          "(Città Studi / Lambrate,28.0)",
          "(Città Studi / Lambrate,30.0)",
          "(Città Studi / Lambrate,35.0)",
          "(Città Studi / Lambrate,50.0)",
          "(Città Studi / Lambrate,52.0)",
          "(Città Studi / Lambrate,80.0)",
          "(Città Studi / Lambrate,95.0)",
          "(Città Studi / Lambrate,110.0)",
          "(Garibaldi / Stazione Centrale / Isola,28.0)",
          "(Garibaldi / Stazione Centrale / Isola,45.0)",
          "(Garibaldi / Stazione Centrale / Isola,54.0)",
          "(Garibaldi / Stazione Centrale / Isola,55.0)",
          "(Garibaldi / Stazione Centrale / Isola,65.0)",
          "(Lodi / Ripamonti / Mecenate,28.0)",
          "(Lodi / Ripamonti / Mecenate,30.0)",
          "(Lodi / Ripamonti / Mecenate,50.0)",
          "(Lodi / Ripamonti / Mecenate,55.0)",
          "(Lodi / Ripamonti / Mecenate,58.0)",
          "(Lodi / Ripamonti / Mecenate,60.0)",
          "(Lodi / Ripamonti / Mecenate,65.0)",
          "(Lodi / Ripamonti / Mecenate,70.0)",
          "(Loreto / Porta Venezia / Indipendenza,28.0)",
          "(Loreto / Porta Venezia / Indipendenza,30.0)",
          "(Loreto / Porta Venezia / Indipendenza,45.0)",
          "(Loreto / Porta Venezia / Indipendenza,75.0)",
          "(Maciachini / Zara / Niguarda,35.0)",
          "(Maciachini / Zara / Niguarda,55.0)",
          "(Maciachini / Zara / Niguarda,60.0)",
          "(Maciachini / Zara / Niguarda,65.0)",
          "(Maciachini / Zara / Niguarda,75.0)",
          "(Milano,20.0)",
          "(Milano,22.0)",
          "(Milano,25.0)",
          "(Milano,27.0)",
          "(Milano,28.0)",
          "(Milano,30.0)",
          "(Milano,32.0)",
          "(Milano,35.0)",
          "(Milano,39.0)",
          "(Milano,40.0)",
          "(Milano,45.0)",
          "(Milano,46.0)",
          "(Milano,47.0)",
          "(Milano,50.0)",
          "(Milano,52.0)",
          "(Milano,55.0)",
          "(Milano,60.0)",
          "(Milano,65.0)",
          "(Milano,66.0)",
          "(Milano,67.0)",
          "(Milano,69.0)",
          "(Milano,70.0)",
          "(Milano,72.0)",
          "(Milano,75.0)",
          "(Milano,78.0)",
          "(Milano,80.0)",
          "(Milano,85.0)",
          "(Milano,90.0)",
          "(Milano,95.0)",
          "(Milano,100.0)",
          "(Milano,105.0)",
          "(Milano,108.0)",
          "(Milano,110.0)",
          "(Milano,120.0)",
          "(Milano,130.0)",
          "(Milano,150.0)",
          "(Porta Genova / Navigli / Corso Italia,26.0)",
          "(Porta Genova / Navigli / Corso Italia,40.0)",
          "(Porta Genova / Navigli / Corso Italia,45.0)",
          "(Porta Genova / Navigli / Corso Italia,55.0)",
          "(Porta Genova / Navigli / Corso Italia,58.0)",
          "(Porta Genova / Navigli / Corso Italia,60.0)",
          "(Porta Genova / Navigli / Corso Italia,70.0)",
          "(Porta Genova / Navigli / Corso Italia,80.0)",
          "(Porta Genova / Navigli / Corso Italia,90.0)",
          "(Porta Genova / Navigli / Corso Italia,100.0)",
          "(Porta Romana / Monte Nero,40.0)",
          "(Porta Romana / Monte Nero,50.0)",
          "(Porta Romana / Monte Nero,70.0)",
          "(Rogoredo / Chiesa Rossa,50.0)",
          "(San Siro / Fiera,42.0)",
          "(San Siro / Fiera,45.0)",
          "(San Siro / Fiera,65.0)",
          "(Sempione / Certosa,27.0)",
          "(Sempione / Certosa,45.0)",
          "(Sempione / Certosa,50.0)",
          "(Sempione / Certosa,70.0)",
          "(~Altre zone,25.0)",
          "(~Altre zone,28.0)",
          "(~Altre zone,35.0)",
          "(~Altre zone,45.0)",
          "(~Altre zone,50.0)",
          "(~Altre zone,110.0)",
          "(~Altre zone,120.0)",
          "(~Altre zone,125.0)"
         ],
         "y": [
          850,
          800,
          500,
          1300,
          662.5,
          1000,
          1050,
          760,
          700,
          875,
          1183.3333333333333,
          1000,
          500,
          1200,
          1200,
          1190,
          900,
          1150,
          500,
          625,
          570,
          600,
          52,
          650,
          900,
          825,
          1400,
          338,
          50,
          850,
          950,
          1150,
          1300,
          950,
          580,
          650,
          850,
          950,
          1500,
          867.5,
          1050,
          600,
          655,
          900,
          80,
          1300,
          900,
          800,
          1250,
          1250,
          1100,
          640,
          580,
          717.5,
          756.6666666666666,
          757.5,
          587.6666666666666,
          730,
          714.4444444444445,
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     "metadata": {},
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   ],
   "source": [
    "dfa.groupby(['location','area']).mean().iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1365f5110>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 791x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('price','area',data=dfa, hue='location', palette='coolwarm',size=8,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, prices disruption, can be due to fear from the country lockdown as workers stay home, and due to business shutdowns, quarantines and curfews. Huge numbers of layoffs will lead to further contraction in consumer spending, which will force landlords to decrease their prices, some has already started to adapt to the new situation and some are still offering the same normal prices, having in mind also that the market for short-term renting is slowing down massively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3-Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part we will try to divide the market into distinct subsets of apartments based on their area and price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract the price and area data \n",
    "dfbn=dfb[['price','area']]\n",
    "dfan=dfa[['price','area']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MinMaxScaler(feature_range = (0, 1)) will transform each value in the column proportionally within the range [0,1]. Use this as the first scaler choice to transform a feature, as it will preserve the shape of the dataset (no distortion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#starting with the data from before the outbreak\n",
    "scaler = MinMaxScaler()\n",
    "scaled_df = scaler.fit_transform(dfbn)\n",
    "#chossing 4 clusters\n",
    "kmeans = KMeans(n_clusters=4, random_state=0).fit(scaled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>price</th>\n",
       "      <th>area</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>337</td>\n",
       "      <td>500.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>343</td>\n",
       "      <td>630.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>355</td>\n",
       "      <td>700.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>327</td>\n",
       "      <td>450.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>329</td>\n",
       "      <td>750.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     price  area  cluster\n",
       "337  500.0  20.0        3\n",
       "343  630.0  25.0        3\n",
       "355  700.0  28.0        3\n",
       "327  450.0  28.0        3\n",
       "329  750.0  30.0        3"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_dfb = dfbn.copy(deep=True)\n",
    "cluster_dfb['cluster'] = kmeans.labels_\n",
    "cluster_dfb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1382527d0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 617.625x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing the clusters\n",
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('price','area',data=cluster_dfb, hue='cluster',\n",
    "           palette='coolwarm',size=8,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 0: prices(1500-2250)€, area(60-105)mq, this cluster has small apartments with high prices, those usually are concentrated in expensive neighborhood or in the center.\n",
    "\n",
    "Cluster 1: prices(625-1500)€, area(35-86)mq, this is the medium cluster, you can have small apartments with higher prices than cluster 3 for the sake of a better neighborhood.\n",
    "\n",
    "Cluster 2: prices(1000-2200)€, area(100-130)mq, this cluster kinda have a correlated relationship, big apartments with high prices.\n",
    "\n",
    "Cluster 3: prices(375-1100)€, area(20-70)mq, this cluster has also a positive relationship, small apartments with low price range, that is usually in areas far from the center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#join the location to the cluster data\n",
    "cluster_dfb['location'] = dfb['location']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x137aaa5d0>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 791x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let us print the prices clustered based on the location \n",
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('price','cluster',data=cluster_dfb, hue='location',\n",
    "           palette='coolwarm',size=8,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clusters are as before only here we can see each cluster contins postings in which areas in the city, cluster 0 as predicted has apartments in the center, we can also assume from this results that the Milano segment is associated with the center and expensive neighborhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#doing the same thing for the other dataset\n",
    "scaler = MinMaxScaler()\n",
    "scaled_df = scaler.fit_transform(dfan)\n",
    "kmeans = KMeans(n_clusters=4, random_state=0).fit(scaled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>580.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>258</td>\n",
       "      <td>700.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>580.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>580.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>580.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     price  area  cluster\n",
       "144  580.0  20.0        0\n",
       "258  700.0  20.0        0\n",
       "149  580.0  22.0        0\n",
       "156  580.0  22.0        0\n",
       "120  580.0  22.0        0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_dfa = dfan.copy(deep=True)\n",
    "cluster_dfa['cluster'] = kmeans.labels_\n",
    "cluster_dfa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1372939d0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 617.625x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('price','area',data=cluster_dfa, hue='cluster',\n",
    "           palette='coolwarm',size=8,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 0: prices(100-1000)€, area(20-70)mq, this cluster has small apartments with very low price range, that is usually in areas far from the center.\n",
    "\n",
    "Cluster 1: prices(100-1000)€, area(80-125)mq, this cluster has bigger apartments with the same low price range from before, which indicates that maybe some apartments had to lower their range of prices after the outbreak.\n",
    "\n",
    "Cluster 2: prices(750-1500)€, area(40-86)mq, this is the medium cluster, you can have small apartments with higher prices than cluster 3 for the sake of a better neighborhoods.\n",
    "\n",
    "Cluster 3: prices(1200-2250)€, area(55-150)mq, this cluster has big range from small apartments to bigger ones, however with high prices, those usually are concentrated in expensive neighborhoods or in the center.\n",
    "\n",
    "We can notice the price range went down in comparison from the dataset before the outbreak, 500€ drop shows the fall in prices, and maybe its not as big as the drop in sales. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1370e2050>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 791x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#join the location to the cluster data\n",
    "cluster_dfa['location'] = dfa['location']\n",
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('price','cluster',data=cluster_dfa, hue='location',\n",
    "           palette='coolwarm',size=8,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this last representation, cluster 3 as shown before has the highest prices, however, the prices are not the center anymore as before the outbreak, they are in areas like Biccoca and Citta Studi, concentrated near universities, while the center prices has shown a huge drop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4-Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data and our analysis showed that the market bevaiour has definietly changed, some landlords has already started to lower their renting price for their apartments, the data also showed that the number of postings after the outbreat is triple the number before which indicated a huge drop in sales. \n",
    "\n",
    "Property renting and buying platform Idealista.com reported in early April that the effects of the coronavirus crisis have yet to be noticed on the market, although they warned the next quarter will probably shed more light on the situation.\n",
    "\n",
    "According to their data from the first quarter of 2020, house prices across Italy dropped by 0.4 percent, with the average price for second-hand properties now standing at €1,699 per square meter (annual decrease of 2 percent)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you like the idea of investigating the market trends, having more features of each apartment is definitely recommended, playing with dummy variables to turn the location to a measurable variable can be an interesting idea, collecting more data after couple of month to have a deeper look is also recommended. "
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}