Assignment 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
       "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
       "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
       "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('kc_house_data.csv')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Houses built from 2000 onwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7237550310</td>\n",
       "      <td>20140512T000000</td>\n",
       "      <td>1230000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5420</td>\n",
       "      <td>101930</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>3890</td>\n",
       "      <td>1530</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.6561</td>\n",
       "      <td>-122.005</td>\n",
       "      <td>4760</td>\n",
       "      <td>101930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3793500160</td>\n",
       "      <td>20150312T000000</td>\n",
       "      <td>323000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1890</td>\n",
       "      <td>6560</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1890</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>98038</td>\n",
       "      <td>47.3684</td>\n",
       "      <td>-122.031</td>\n",
       "      <td>2390</td>\n",
       "      <td>7570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1873100390</td>\n",
       "      <td>20150302T000000</td>\n",
       "      <td>719000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2570</td>\n",
       "      <td>7173</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2570</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>98052</td>\n",
       "      <td>47.7073</td>\n",
       "      <td>-122.110</td>\n",
       "      <td>2630</td>\n",
       "      <td>6026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8562750320</td>\n",
       "      <td>20141110T000000</td>\n",
       "      <td>580500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2320</td>\n",
       "      <td>3980</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2320</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>98027</td>\n",
       "      <td>47.5391</td>\n",
       "      <td>-122.070</td>\n",
       "      <td>2580</td>\n",
       "      <td>3980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2426039314</td>\n",
       "      <td>20141201T000000</td>\n",
       "      <td>280000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1190</td>\n",
       "      <td>1265</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1190</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>98133</td>\n",
       "      <td>47.7274</td>\n",
       "      <td>-122.357</td>\n",
       "      <td>1390</td>\n",
       "      <td>1756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>263000018</td>\n",
       "      <td>20140521T000000</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1530</td>\n",
       "      <td>1131</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1530</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.6993</td>\n",
       "      <td>-122.346</td>\n",
       "      <td>1530</td>\n",
       "      <td>1509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>6600060120</td>\n",
       "      <td>20150223T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2310</td>\n",
       "      <td>5813</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2310</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.5107</td>\n",
       "      <td>-122.362</td>\n",
       "      <td>1830</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>1523300141</td>\n",
       "      <td>20140623T000000</td>\n",
       "      <td>402101.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5944</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>291310100</td>\n",
       "      <td>20150116T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1600</td>\n",
       "      <td>2388</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98027</td>\n",
       "      <td>47.5345</td>\n",
       "      <td>-122.069</td>\n",
       "      <td>1410</td>\n",
       "      <td>1287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>1523300157</td>\n",
       "      <td>20141015T000000</td>\n",
       "      <td>325000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1076</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5941</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4541 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id             date      price  bedrooms  bathrooms  \\\n",
       "5      7237550310  20140512T000000  1230000.0         4       4.50   \n",
       "9      3793500160  20150312T000000   323000.0         3       2.50   \n",
       "29     1873100390  20150302T000000   719000.0         4       2.50   \n",
       "30     8562750320  20141110T000000   580500.0         3       2.50   \n",
       "31     2426039314  20141201T000000   280000.0         2       1.50   \n",
       "...           ...              ...        ...       ...        ...   \n",
       "21608   263000018  20140521T000000   360000.0         3       2.50   \n",
       "21609  6600060120  20150223T000000   400000.0         4       2.50   \n",
       "21610  1523300141  20140623T000000   402101.0         2       0.75   \n",
       "21611   291310100  20150116T000000   400000.0         3       2.50   \n",
       "21612  1523300157  20141015T000000   325000.0         2       0.75   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view  ...  grade  \\\n",
       "5             5420    101930     1.0           0     0  ...     11   \n",
       "9             1890      6560     2.0           0     0  ...      7   \n",
       "29            2570      7173     2.0           0     0  ...      8   \n",
       "30            2320      3980     2.0           0     0  ...      8   \n",
       "31            1190      1265     3.0           0     0  ...      7   \n",
       "...            ...       ...     ...         ...   ...  ...    ...   \n",
       "21608         1530      1131     3.0           0     0  ...      8   \n",
       "21609         2310      5813     2.0           0     0  ...      8   \n",
       "21610         1020      1350     2.0           0     0  ...      7   \n",
       "21611         1600      2388     2.0           0     0  ...      8   \n",
       "21612         1020      1076     2.0           0     0  ...      7   \n",
       "\n",
       "       sqft_above  sqft_basement  yr_built  yr_renovated  zipcode      lat  \\\n",
       "5            3890           1530      2001             0    98053  47.6561   \n",
       "9            1890              0      2003             0    98038  47.3684   \n",
       "29           2570              0      2005             0    98052  47.7073   \n",
       "30           2320              0      2003             0    98027  47.5391   \n",
       "31           1190              0      2005             0    98133  47.7274   \n",
       "...           ...            ...       ...           ...      ...      ...   \n",
       "21608        1530              0      2009             0    98103  47.6993   \n",
       "21609        2310              0      2014             0    98146  47.5107   \n",
       "21610        1020              0      2009             0    98144  47.5944   \n",
       "21611        1600              0      2004             0    98027  47.5345   \n",
       "21612        1020              0      2008             0    98144  47.5941   \n",
       "\n",
       "          long  sqft_living15  sqft_lot15  \n",
       "5     -122.005           4760      101930  \n",
       "9     -122.031           2390        7570  \n",
       "29    -122.110           2630        6026  \n",
       "30    -122.070           2580        3980  \n",
       "31    -122.357           1390        1756  \n",
       "...        ...            ...         ...  \n",
       "21608 -122.346           1530        1509  \n",
       "21609 -122.362           1830        7200  \n",
       "21610 -122.299           1020        2007  \n",
       "21611 -122.069           1410        1287  \n",
       "21612 -122.299           1020        1357  \n",
       "\n",
       "[4541 rows x 21 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('yr_built > 2000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Most expensive houses for each year starting 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>yr_built</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9254</th>\n",
       "      <td>9208900037</td>\n",
       "      <td>6890000.0</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4411</th>\n",
       "      <td>2470100110</td>\n",
       "      <td>5570000.0</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>8907500070</td>\n",
       "      <td>5350000.0</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2626</th>\n",
       "      <td>7738500731</td>\n",
       "      <td>4500000.0</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8638</th>\n",
       "      <td>3835500195</td>\n",
       "      <td>4490000.0</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20807</th>\n",
       "      <td>6056100160</td>\n",
       "      <td>182568.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19975</th>\n",
       "      <td>6056100165</td>\n",
       "      <td>175003.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20612</th>\n",
       "      <td>2724049222</td>\n",
       "      <td>163800.0</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20403</th>\n",
       "      <td>6056100150</td>\n",
       "      <td>160797.0</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19650</th>\n",
       "      <td>2626119062</td>\n",
       "      <td>155000.0</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4759 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id      price  yr_built\n",
       "9254   9208900037  6890000.0      2001\n",
       "4411   2470100110  5570000.0      2001\n",
       "1448   8907500070  5350000.0      2009\n",
       "2626   7738500731  4500000.0      2004\n",
       "8638   3835500195  4490000.0      2001\n",
       "...           ...        ...       ...\n",
       "20807  6056100160   182568.0      2014\n",
       "19975  6056100165   175003.0      2014\n",
       "20612  2724049222   163800.0      2004\n",
       "20403  6056100150   160797.0      2012\n",
       "19650  2626119062   155000.0      2013\n",
       "\n",
       "[4759 rows x 3 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.yr_built>=2000,['id','price','yr_built']].sort_values('price',ascending= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Houses with the most bedrooms, bathrooms, and floors for each year starting 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>13314</th>\n",
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       "      <th>21326</th>\n",
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       "      <td>2008</td>\n",
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       "      <th>15168</th>\n",
       "      <td>8835800480</td>\n",
       "      <td>1</td>\n",
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       "      <th>8484</th>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>4759 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  bedrooms  yr_built\n",
       "13314   627300145        10      2008\n",
       "12017   424069271         7      2001\n",
       "15246  5536100005         7      2013\n",
       "8120   4006000183         7      2013\n",
       "18975  1778360150         7      2004\n",
       "...           ...       ...       ...\n",
       "21326  6003000851         1      2008\n",
       "15168  8835800480         1      2001\n",
       "8484   2310060040         0      2003\n",
       "3119   3918400017         0      2006\n",
       "18379  1222029077         0      2003\n",
       "\n",
       "[4759 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['yr_built']>=2000,['id','bedrooms','yr_built']].sort_values('bedrooms', ascending= False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "               id  bathrooms  yr_built\n",
       "9254   9208900037       7.75      2001\n",
       "21506  2524069097       6.50      2010\n",
       "20578   424069279       6.50      2007\n",
       "18302  6072800246       6.25      2001\n",
       "21344  8835770170       6.00      2007\n",
       "...           ...        ...       ...\n",
       "19650  2626119062       1.00      2013\n",
       "21610  1523300141       0.75      2009\n",
       "18379  1222029077       0.75      2003\n",
       "21612  1523300157       0.75      2008\n",
       "3119   3918400017       0.00      2006\n",
       "\n",
       "[4759 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['yr_built']>=2000,['id','bathrooms','yr_built']].sort_values('bathrooms', ascending= False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "               id  floors  yr_built\n",
       "20308  1972200426     3.5      2007\n",
       "10077  1972202010     3.5      2005\n",
       "14885  8673400177     3.5      2006\n",
       "15424  1702900664     3.5      2008\n",
       "20772  1972200428     3.5      2007\n",
       "...           ...     ...       ...\n",
       "1399   8682291840     1.0      2006\n",
       "13314   627300145     1.0      2008\n",
       "13323  8682210650     1.0      2002\n",
       "15777  8682290660     1.0      2007\n",
       "5      7237550310     1.0      2001\n",
       "\n",
       "[4759 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['yr_built']>=2000,['id','floors','yr_built']].sort_values('floors', ascending= False )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Houses with an area larger than the mean area of all houses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Living area:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2079.8997362698374"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liv_avg = df['sqft_living'].mean()\n",
    "liv_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6414100192</td>\n",
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       "    <tr>\n",
       "      <th>21603</th>\n",
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       "      <td>2270</td>\n",
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       "    <tr>\n",
       "      <th>21605</th>\n",
       "      <td>3448900210</td>\n",
       "      <td>2520</td>\n",
       "      <td>2014</td>\n",
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       "    <tr>\n",
       "      <th>21606</th>\n",
       "      <td>7936000429</td>\n",
       "      <td>3510</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>6600060120</td>\n",
       "      <td>2310</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9240 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  sqft_living  yr_built\n",
       "1      6414100192         2570      1951\n",
       "5      7237550310         5420      2001\n",
       "10     1736800520         3560      1965\n",
       "15     9297300055         2950      1979\n",
       "21     2524049179         3050      1968\n",
       "...           ...          ...       ...\n",
       "21600   249000205         4470      2008\n",
       "21603  7852140040         2270      2003\n",
       "21605  3448900210         2520      2014\n",
       "21606  7936000429         3510      2009\n",
       "21609  6600060120         2310      2014\n",
       "\n",
       "[9240 rows x 3 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_liv = df.loc[(df['sqft_living']>liv_avg),['id','sqft_living','yr_built']]\n",
    "df_liv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lot area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15106.967565816869"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lot_avg = df['sqft_lot'].mean()\n",
    "lot_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>3201 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  sqft_lot  yr_built\n",
       "5      7237550310    101930      2001\n",
       "12      114101516     19901      1927\n",
       "21     2524049179     44867      1968\n",
       "36     9435300030     34848      1933\n",
       "41     7766200013     24186      1984\n",
       "...           ...       ...       ...\n",
       "21506  2524069097    130017      2010\n",
       "21520  7237550100     50621      2012\n",
       "21525  2625069038    108865      2014\n",
       "21532   324069112     16526      2013\n",
       "21548  8835770330    184231      2005\n",
       "\n",
       "[3201 rows x 3 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lot = df.loc[(df['sqft_lot']>lot_avg),['id','sqft_lot','yr_built']]\n",
    "df_lot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Similarities / patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['id','sqft_living','yr_built','floors', 'bathrooms', 'bedrooms', 'price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Houses built after the year 2000 with a square footage of living area greater than the average:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4</td>\n",
       "      <td>253000.0</td>\n",
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       "      <th>10569</th>\n",
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       "      <td>2163</td>\n",
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       "      <td>2.0</td>\n",
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       "      <th>5761</th>\n",
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       "      <td>2130</td>\n",
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       "      <td>215000.0</td>\n",
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      "text/plain": [
       "               id  sqft_living  yr_built  floors  bathrooms  bedrooms  \\\n",
       "9254   9208900037         9890      2001     2.0       7.75         6   \n",
       "4411   2470100110         9200      2001     2.0       5.75         5   \n",
       "1448   8907500070         8000      2009     2.0       5.00         5   \n",
       "2626   7738500731         6640      2004     2.0       5.50         5   \n",
       "8638   3835500195         6430      2001     2.0       3.00         4   \n",
       "...           ...          ...       ...     ...        ...       ...   \n",
       "7531   8856004328         2163      2006     2.0       2.50         4   \n",
       "21436  1608000120         2555      2006     2.0       2.50         3   \n",
       "15641  6147650170         2230      2006     2.0       2.50         4   \n",
       "10569  8856004327         2163      2006     2.0       3.00         4   \n",
       "5761   6181400470         2130      2004     2.0       2.50         4   \n",
       "\n",
       "           price  \n",
       "9254   6890000.0  \n",
       "4411   5570000.0  \n",
       "1448   5350000.0  \n",
       "2626   4500000.0  \n",
       "8638   4490000.0  \n",
       "...          ...  \n",
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       "21436   255000.0  \n",
       "15641   253000.0  \n",
       "10569   248000.0  \n",
       "5761    215000.0  \n",
       "\n",
       "[2818 rows x 7 columns]"
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     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['sqft_living']>liv_avg) & (df['yr_built']>=2000),cols].sort_values('price', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Houses built before the year 2000 with a square footage of living area greater than the average:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>4</td>\n",
       "      <td>198000.0</td>\n",
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       "      <th>15442</th>\n",
       "      <td>6121800050</td>\n",
       "      <td>2170</td>\n",
       "      <td>1952</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>4</td>\n",
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       "      <th>18590</th>\n",
       "      <td>2154900040</td>\n",
       "      <td>2190</td>\n",
       "      <td>1987</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3</td>\n",
       "      <td>194250.0</td>\n",
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      "text/plain": [
       "               id  sqft_living  yr_built  floors  bathrooms  bedrooms  \\\n",
       "7252   6762700020        12050      1910     2.5       8.00         6   \n",
       "3914   9808700762        10040      1940     2.0       4.50         5   \n",
       "1315   7558700030         7390      1991     2.0       6.00         6   \n",
       "1164   1247600105         8010      1999     2.0       5.25         5   \n",
       "8092   1924059029         9640      1983     1.0       6.75         5   \n",
       "...           ...          ...       ...     ...        ...       ...   \n",
       "7948    257000037         2120      1942     1.0       1.00         3   \n",
       "6523   8661000089         2270      1948     1.5       2.75         3   \n",
       "5128   1311500020         2080      1966     1.0       1.75         4   \n",
       "15442  6121800050         2170      1952     2.0       1.50         4   \n",
       "18590  2154900040         2190      1987     1.0       2.25         3   \n",
       "\n",
       "           price  \n",
       "7252   7700000.0  \n",
       "3914   7060000.0  \n",
       "1315   5300000.0  \n",
       "1164   5110000.0  \n",
       "8092   4670000.0  \n",
       "...          ...  \n",
       "7948    200000.0  \n",
       "6523    199950.0  \n",
       "5128    198000.0  \n",
       "15442   195000.0  \n",
       "18590   194250.0  \n",
       "\n",
       "[6422 rows x 7 columns]"
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     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['sqft_living']>liv_avg) & (df['yr_built']<2000),cols].sort_values('price', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There are more houses with a square footage greater than the avg square footage of living area built before the year 2000 than those built after the year 2000. Moreover, the prices are somewhat similar even though the number of bedrooms, bathrooms, and floors are somewhat close in the two scenarios.<br>\n",
    "So, we might conclude that the year in which the house is built does not make a big difference in price based on the variables used in this analysis.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Houses built after the year 2000 with a square footage of total area greater than the average:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.50</td>\n",
       "      <td>4</td>\n",
       "      <td>265000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>357 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  sqft_living  yr_built  floors  bathrooms  bedrooms  \\\n",
       "9254   9208900037         9890      2001     2.0       7.75         6   \n",
       "4411   2470100110         9200      2001     2.0       5.75         5   \n",
       "1448   8907500070         8000      2009     2.0       5.00         5   \n",
       "2626   7738500731         6640      2004     2.0       5.50         5   \n",
       "8638   3835500195         6430      2001     2.0       3.00         4   \n",
       "...           ...          ...       ...     ...        ...       ...   \n",
       "15168  8835800480         1780      2001     2.0       2.00         1   \n",
       "7957   3793501390         1690      2003     2.0       2.50         3   \n",
       "13289  3750605674         1808      2005     1.0       2.50         3   \n",
       "18379  1222029077          384      2003     1.0       0.75         0   \n",
       "9644   3222059206         1820      2004     2.0       2.50         4   \n",
       "\n",
       "           price  \n",
       "9254   6890000.0  \n",
       "4411   5570000.0  \n",
       "1448   5350000.0  \n",
       "2626   4500000.0  \n",
       "8638   4490000.0  \n",
       "...          ...  \n",
       "15168   316000.0  \n",
       "7957    293000.0  \n",
       "13289   270000.0  \n",
       "18379   265000.0  \n",
       "9644    265000.0  \n",
       "\n",
       "[357 rows x 7 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['sqft_lot']>lot_avg) & (df['yr_built']>=2000),cols].sort_values('price', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Houses built before the year 2000 with a square footage of total area greater than the average:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>floors</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7252</th>\n",
       "      <td>6762700020</td>\n",
       "      <td>12050</td>\n",
       "      <td>1910</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>6</td>\n",
       "      <td>7700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>9808700762</td>\n",
       "      <td>10040</td>\n",
       "      <td>1940</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5</td>\n",
       "      <td>7060000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>7558700030</td>\n",
       "      <td>7390</td>\n",
       "      <td>1991</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6</td>\n",
       "      <td>5300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164</th>\n",
       "      <td>1247600105</td>\n",
       "      <td>8010</td>\n",
       "      <td>1999</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>5</td>\n",
       "      <td>5110000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2085</th>\n",
       "      <td>8106100105</td>\n",
       "      <td>5770</td>\n",
       "      <td>1980</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4</td>\n",
       "      <td>3850000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10585</th>\n",
       "      <td>6198400218</td>\n",
       "      <td>1070</td>\n",
       "      <td>1948</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>95000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3767</th>\n",
       "      <td>1523049188</td>\n",
       "      <td>700</td>\n",
       "      <td>1949</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>84000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2141</th>\n",
       "      <td>1623049041</td>\n",
       "      <td>520</td>\n",
       "      <td>1951</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>82500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15293</th>\n",
       "      <td>40000362</td>\n",
       "      <td>780</td>\n",
       "      <td>1942</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>78000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>3421079032</td>\n",
       "      <td>670</td>\n",
       "      <td>1966</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>75000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2844 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  sqft_living  yr_built  floors  bathrooms  bedrooms  \\\n",
       "7252   6762700020        12050      1910     2.5       8.00         6   \n",
       "3914   9808700762        10040      1940     2.0       4.50         5   \n",
       "1315   7558700030         7390      1991     2.0       6.00         6   \n",
       "1164   1247600105         8010      1999     2.0       5.25         5   \n",
       "2085   8106100105         5770      1980     2.0       4.25         4   \n",
       "...           ...          ...       ...     ...        ...       ...   \n",
       "10585  6198400218         1070      1948     1.0       1.00         2   \n",
       "3767   1523049188          700      1949     1.0       1.00         2   \n",
       "2141   1623049041          520      1951     1.0       1.00         2   \n",
       "15293    40000362          780      1942     1.0       1.00         2   \n",
       "1149   3421079032          670      1966     1.0       0.00         1   \n",
       "\n",
       "           price  \n",
       "7252   7700000.0  \n",
       "3914   7060000.0  \n",
       "1315   5300000.0  \n",
       "1164   5110000.0  \n",
       "2085   3850000.0  \n",
       "...          ...  \n",
       "10585    95000.0  \n",
       "3767     84000.0  \n",
       "2141     82500.0  \n",
       "15293    78000.0  \n",
       "1149     75000.0  \n",
       "\n",
       "[2844 rows x 7 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['sqft_lot']>lot_avg) & (df['yr_built']<2000),cols].sort_values('price', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Same conclusion can be drawn when looking at the total square footage area.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id      lat     long\n",
       "1  6414100192  47.7210 -122.319\n",
       "2  5631500400  47.7379 -122.233\n",
       "3  2487200875  47.5208 -122.393\n",
       "4  1954400510  47.6168 -122.045"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1:4,['id','lat','long']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance Function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sin, cos, acos, radians\n",
    "def dist(lat1,lon1,lat2, lon2):\n",
    "    lat11 = radians(lat1)\n",
    "    lat22 = radians(lat2)\n",
    "    lon11 = radians(lon1)\n",
    "    lon22 = radians(lon2)\n",
    "    return acos(sin(lat11)*sin(lat22)+cos(lat11)*cos(lat22)*cos(lon22-lon11))*6371\n",
    "#testing\n",
    "#print(dist(47.7210,-122.319,47.7379,-122.233))\n",
    "#Output: 6.7011090149458985"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Distance by id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.7011090149458985\n"
     ]
    }
   ],
   "source": [
    "id_list = list(df['id'])\n",
    "\n",
    "def dist_id(id1,id2):\n",
    "    coordinates1 = list(df.loc[(df.id== id1),['lat','long']].values)\n",
    "    coordinates2 = list(df.loc[(df.id== id2),['lat','long']].values)\n",
    "    distance = dist(coordinates1[0][0],coordinates1[0][1],coordinates2[0][0],coordinates2[0][1])\n",
    "    return distance\n",
    "    \n",
    "id1 = int(input('Enter id1: '))\n",
    "while id1 not in id_list:\n",
    "    id1 = int(input('Enter valid id: '))\n",
    "id2 = int(input('Enter id2: '))\n",
    "while id2 not in id_list:\n",
    "    id2 = int(input('Enter valid id: '))\n",
    "\n",
    "print(dist_id(id1,id2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at id's of most expensive houses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\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>id</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7252</th>\n",
       "      <td>6762700020</td>\n",
       "      <td>7700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>9808700762</td>\n",
       "      <td>7060000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9254</th>\n",
       "      <td>9208900037</td>\n",
       "      <td>6890000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4411</th>\n",
       "      <td>2470100110</td>\n",
       "      <td>5570000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>8907500070</td>\n",
       "      <td>5350000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>7558700030</td>\n",
       "      <td>5300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164</th>\n",
       "      <td>1247600105</td>\n",
       "      <td>5110000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8092</th>\n",
       "      <td>1924059029</td>\n",
       "      <td>4670000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2626</th>\n",
       "      <td>7738500731</td>\n",
       "      <td>4500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8638</th>\n",
       "      <td>3835500195</td>\n",
       "      <td>4490000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id      price\n",
       "7252  6762700020  7700000.0\n",
       "3914  9808700762  7060000.0\n",
       "9254  9208900037  6890000.0\n",
       "4411  2470100110  5570000.0\n",
       "1448  8907500070  5350000.0\n",
       "1315  7558700030  5300000.0\n",
       "1164  1247600105  5110000.0\n",
       "8092  1924059029  4670000.0\n",
       "2626  7738500731  4500000.0\n",
       "8638  3835500195  4490000.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[['id','price']].sort_values('price', ascending = False).copy()\n",
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance between most expensive house and second most expensive house:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.469737942043475\n"
     ]
    }
   ],
   "source": [
    "print(dist_id(df2.iloc[0,0],df2.iloc[1,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance between most expensive house and third most expensive house:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.2201568982235305\n"
     ]
    }
   ],
   "source": [
    "print(dist_id(df2.iloc[0,0],df2.iloc[2,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at id's of cheapest houses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <td>75000.0</td>\n",
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       "    <tr>\n",
       "      <th>15293</th>\n",
       "      <td>40000362</td>\n",
       "      <td>78000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>8658300340</td>\n",
       "      <td>80000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16198</th>\n",
       "      <td>3028200080</td>\n",
       "      <td>81000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8274</th>\n",
       "      <td>3883800011</td>\n",
       "      <td>82000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2141</th>\n",
       "      <td>1623049041</td>\n",
       "      <td>82500.0</td>\n",
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       "    <tr>\n",
       "      <th>18468</th>\n",
       "      <td>7999600180</td>\n",
       "      <td>83000.0</td>\n",
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       "    <tr>\n",
       "      <th>3767</th>\n",
       "      <td>1523049188</td>\n",
       "      <td>84000.0</td>\n",
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       "    <tr>\n",
       "      <th>16714</th>\n",
       "      <td>1322049150</td>\n",
       "      <td>85000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10253</th>\n",
       "      <td>2422049104</td>\n",
       "      <td>85000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id    price\n",
       "1149   3421079032  75000.0\n",
       "15293    40000362  78000.0\n",
       "465    8658300340  80000.0\n",
       "16198  3028200080  81000.0\n",
       "8274   3883800011  82000.0\n",
       "2141   1623049041  82500.0\n",
       "18468  7999600180  83000.0\n",
       "3767   1523049188  84000.0\n",
       "16714  1322049150  85000.0\n",
       "10253  2422049104  85000.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df[['id','price']].sort_values('price').copy()\n",
    "df3.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance between most expensive house and cheapest house:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51.37665546669276\n"
     ]
    }
   ],
   "source": [
    "print(dist_id(df2.iloc[0,0],df3.iloc[0,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance between most expensive house and third cheapest house:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.098038528174147\n"
     ]
    }
   ],
   "source": [
    "print(dist_id(df2.iloc[0,0],df3.iloc[2,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance between cheapest house and second cheapest house:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.593620618619276\n"
     ]
    }
   ],
   "source": [
    "print(dist_id(df3.iloc[0,0],df3.iloc[1,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance between cheapest house and third cheapest house:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42.93295364867862\n"
     ]
    }
   ],
   "source": [
    "print(dist_id(df3.iloc[0,0],df3.iloc[2,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance between most cheapest house and fourth cheapest house:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39.125865939206314\n"
     ]
    }
   ],
   "source": [
    "print(dist_id(df3.iloc[0,0],df3.iloc[3,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```It seems like the most expensive houses are situated close to each other and the cheapest houses are much farther away from them. Moreover and interestingly enough, the cheaper houses are also far from each other, at least farther from each other than the expensive houses are from each other.```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## .iat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "        DataFrame.iat\n",
    "```\n",
    "**Access a single value for a row/column pair by integer position.<br>\n",
    "Similar to iloc, in that both provide integer-based lookups. Use iat if you only need to get or set a single value in a DataFrame or Series.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iloc also works for that purpose, but iat is faster, while iloc can handle more complex scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6762700020"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.iat[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6762700020"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.iloc[0,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code raises an error, that's because the integer position is out of bound.\n",
    "```\n",
    "        df3.iat[4,7]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98059"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example on original dataframe\n",
    "df.iat[2352,16]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## .at"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "        DataFrame.at\n",
    "```\n",
    "**Access a single value for a row/column label pair.<br>\n",
    "Similar to loc, in that both provide label-based lookups. Use **at** if you only need to get or set a single value in a DataFrame or Series.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1954400510"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[4, 'id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      1955             0    98178  47.5112 -122.257           1340   \n",
       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
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       "  <thead>\n",
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       "      <th>bedrooms</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      price  bedrooms\n",
       "1  538000.0         3\n",
       "2  180000.0         2\n",
       "3  604000.0         4\n",
       "4  510000.0         3"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df.iloc[1:5,2:4].copy()\n",
    "df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use **at** to add new columns to a dataframe and set a value for each row, and even change an existing value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>bedrooms</th>\n",
       "      <th>Stars</th>\n",
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      ],
      "text/plain": [
       "      price  bedrooms  Stars\n",
       "1  538000.0         3    5.0\n",
       "2  180000.0         2    NaN\n",
       "3  604000.0         4    NaN\n",
       "4  510000.0         3    NaN"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.at[1,'Stars'] = 5\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>bedrooms</th>\n",
       "      <th>Stars</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>510000.0</td>\n",
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      "text/plain": [
       "       price  bedrooms  Stars\n",
       "1  1000000.0         3    5.0\n",
       "2   180000.0         2    NaN\n",
       "3   604000.0         4    NaN\n",
       "4   510000.0         3    NaN"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.at[1,'price'] = 1000000\n",
    "df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      1955             0    98178  47.5112 -122.257           1340   \n",
       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_date = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        20141013T000000\n",
       "1        20141209T000000\n",
       "2        20150225T000000\n",
       "3        20141209T000000\n",
       "4        20150218T000000\n",
       "              ...       \n",
       "21608    20140521T000000\n",
       "21609    20150223T000000\n",
       "21610    20140623T000000\n",
       "21611    20150116T000000\n",
       "21612    20141015T000000\n",
       "Name: date, Length: 21613, dtype: object"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date['date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To convert the date object type column to Datetime, we can use the pd. to_datetime() function in pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2014-10-13\n",
       "1       2014-12-09\n",
       "2       2015-02-25\n",
       "3       2014-12-09\n",
       "4       2015-02-18\n",
       "           ...    \n",
       "21608   2014-05-21\n",
       "21609   2015-02-23\n",
       "21610   2014-06-23\n",
       "21611   2015-01-16\n",
       "21612   2014-10-15\n",
       "Name: date, Length: 21613, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date['date'] = pd.to_datetime(df_date['date'])\n",
    "df_date['date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference: https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html"
   ]
  }
 ],
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