jupyter-house-rocket.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **HOUSE ROCKET INSIGHTS PROJECT**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. **House Rocket, Business Problem and Solution Plan**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "House Rocket is a real estate company whose business model consists in identifying good deals, so that those properties could be bought for an interesting price and futurely sold for a higher price, so that the company could turn in a profit. For this particular case, House Rocket will be operating in King County, which includes Seattle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Obs: The  company and business problem are both fictitious, although the data is real.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Problem 1: Which properties should House Rocket buy and for which suggested price?**\n",
    "\n",
    "To solve this problem, we firstly need to analyse the properties by their location, because in real estate, location is undoubtedly a decisive factor in price evaluation. One interesting metric in this case is to calculate the price median by each zipcode, as the median isn't influenced by extreme values (outliers) in the data. \n",
    "\n",
    "The properties that will receive a buy suggestion will be the ones that fulfill the two following rules:\n",
    "\n",
    "- Their asked price has to be lower than the median price for that region\n",
    "- The property needs to be in good conditions, which means condition >= 3\n",
    "\n",
    "As for the suggested price, the density by region will be taken into account, which means that for properties in regions that have a higher number of real estate ads it's viable to offer lower prices, and vice-versa. Therefore the rule for suggested buy prices is:\n",
    "\n",
    "- From 0 to 204 properties in the region => Offer the asked price \n",
    "- From 205 to 282 properties in the region => Offer 3% less than the asked price\n",
    "- From 283 to 408 properties in the region => Offer 4% less than the asked price\n",
    "- From 409 properties upwards => Offer 5% less than the asked price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Problem 2: Once a property is bought, for which price should it be sold?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To suggest a sell price, again the density by region will be considered, since for regions where the number of real estate ads is lower it's possible to sell the property for a higher price, and vice-versa. Hence, a reasonable rule for suggested sell prices is:\n",
    "\n",
    "- From 0 to 204 properties in the region => Sell for 16% than the suggested buy price \n",
    "- From 205 to 282 properties in the region => Sell for 14% than the suggested buy price\n",
    "- From 283 to 408 properties in the region => Sell for 12% than the suggested buy price\n",
    "- From 409 properties upwards => Sell for 10% than the suggested buy price\n",
    "\n",
    "*It's important to point out that selling a property for 10-16% more than the paid price is just a suggestion, so that the selling prices are realistic, since selling a property on a short run for say 30-40% more, although it can happen, it seems unlikely (or it would take too long to sell).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. **Importing Libraries, Creating Functions and Loading Data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lbraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "from geopandas import GeoDataFrame, read_file\n",
    "import folium\n",
    "from streamlit_folium import folium_static\n",
    "pd.set_option('display.float_format', lambda x: '%.5f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Creating functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_identifier(df: pd.DataFrame, col: str, a: int = 10, b: int = 5, c: int = 5, asc: bool = False) -> pd.DataFrame:  \n",
    "    \"\"\" \n",
    "    Returns a boxplot for the selected DataFrame, in order to help detecting outliers. \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pd.DataFrame\n",
    "    DataFrame on which the function will be applied.\n",
    "\n",
    "    col: str\n",
    "    The column on which the function will look for outliers in.\n",
    "\n",
    "    a: int, default = 10\n",
    "    Figsize parameter.\n",
    "    \n",
    "    b: int, default = 5\n",
    "    Figsize parameter.\n",
    "\n",
    "    c: int, default = 5\n",
    "    Pandas' .head() values.\n",
    "\n",
    "    asc: bool, default = False\n",
    "    Ascending or descending order.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    sorted_values : pd.DataFrame\n",
    " \n",
    "    \"\"\"   \n",
    "    boxplot = df[[col]].boxplot(figsize=(a,b));\n",
    "    sorted_values = df.sort_values(col,ascending=asc).head(c) \n",
    "    return sorted_values\n",
    "\n",
    "def get_data(path: str, convert: bool = True) -> pd.DataFrame:\n",
    "    \"\"\" \n",
    "    Loads data in csv file.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    path : str\n",
    "    File location.\n",
    "\n",
    "    convert: bool, default = True\n",
    "    Whether to convert the \"date\" column to datetime.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    data : pd.DataFrame\n",
    "    Loaded data.\n",
    " \n",
    "    \"\"\"     \n",
    "    if convert==True:\n",
    "        data = pd.read_csv(path).rename({'price':'asked_price'},axis=1) \n",
    "        data['date'] = pd.to_datetime(data['date'], format='%Y-%m-%d') \n",
    "    else:\n",
    "        data = pd.read_csv(path).rename({'price':'asked_price'},axis=1)\n",
    "    return data\n",
    "\n",
    "def get_geofile(url: str) -> GeoDataFrame:\n",
    "    \"\"\" \n",
    "    Loads data and returns geofile.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    url : str\n",
    "    File url.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    data : GeoDataFrame\n",
    "    Loaded data.\n",
    " \n",
    "    \"\"\"      \n",
    "    geofile = read_file(url)\n",
    "    return geofile\n",
    "\n",
    "def avg_profit_func(df_sell: pd.DataFrame, col: list[str], group_by_col: str) -> pd.DataFrame:\n",
    "    \"\"\" \n",
    "    Returns DataFrame with both profit mean and median, grouped by the selected columns.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df_sell : pd.DataFrame\n",
    "    DataFrame.\n",
    "\n",
    "    col : list[str]\n",
    "    Selected cols.\n",
    "\n",
    "    group_by_col : str\n",
    "    Which col to groupby.\n",
    "\n",
    "    Returns    \n",
    "    -------\n",
    "    avg_profit_df : pd.DataFrame\n",
    "    DataFrame with metrics.\n",
    " \n",
    "    \"\"\"   \n",
    "    avg_profit_by_ = df_sell[col].groupby(group_by_col).mean().reset_index().rename({'profit':'avg_profit'},axis=1)\n",
    "    median_profit_by_ = df_sell[col].groupby(group_by_col).median().reset_index().rename({'profit':'median_profit'},axis=1)\n",
    "    avg_profit_df = avg_profit_by_.join(median_profit_by_['median_profit'])\n",
    "    return avg_profit_df\n",
    "\n",
    "def setting_buy_price_suggestion(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\" \n",
    "    Returns DataFrame with the suggested buy price for each property. \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pd.DataFrame\n",
    "    DataFrame.\n",
    "\n",
    "    Returns    \n",
    "    -------\n",
    "    df : pd.DataFrame\n",
    "    DataFrame with metrics.\n",
    " \n",
    "    \"\"\"    \n",
    "    suggested_price_conditions = [((df['properties_in_region'] <= 204) & (df['buy_suggestion']=='Yes')) , \n",
    "                                  ((df['properties_in_region'] >= 205) & (df['properties_in_region'] <= 282) & (df['buy_suggestion']=='Yes')),\n",
    "                                  ((df['properties_in_region'] >= 283) & (df['properties_in_region'] <= 408) & (df['buy_suggestion']=='Yes')),\n",
    "                                  ((df['properties_in_region'] >= 409) & (df['buy_suggestion']=='Yes'))]\n",
    "    choicelist = [df['asked_price'], df['asked_price']*0.97, df['asked_price']*0.96, df['asked_price']*0.95]\n",
    "    df['suggested_buy_price'] = np.select(suggested_price_conditions, choicelist, default='Not suggested')\n",
    "    return df\n",
    "\n",
    "def setting_sell_price_suggestion(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\" \n",
    "    Returns DataFrame with the suggested sell price for each property. \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pd.DataFrame\n",
    "    DataFrame.\n",
    "\n",
    "    Returns    \n",
    "    -------\n",
    "    df : pd.DataFrame\n",
    "    DataFrame with metrics.\n",
    " \n",
    "    \"\"\"   \n",
    "    suggested_price_conditions = [(df['properties_in_region'] <= 204) , \n",
    "                                  (df['properties_in_region'] >= 205) & (df['properties_in_region'] <= 282),\n",
    "                                  (df['properties_in_region'] >= 283) & (df['properties_in_region'] <= 408),\n",
    "                                  (df['properties_in_region'] >= 409)]\n",
    "    choicelist = [df['suggested_buy_price']*1.16, df['suggested_buy_price']*1.14, df['suggested_buy_price']*1.12, df['suggested_buy_price']*1.10]\n",
    "    df['suggested_sell_price'] = np.select(suggested_price_conditions, choicelist)\n",
    "    df['profit'] = df['suggested_sell_price'] - df['suggested_buy_price']    \n",
    "    return df\n",
    "\n",
    "\n",
    "def period(X: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\" \n",
    "    Returns DataFrame with the ranged year built. \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    X : pd.DataFrame\n",
    "    DataFrame.\n",
    "\n",
    "    Returns    \n",
    "    -------\n",
    "    df_yrbuilt : pd.DataFrame\n",
    "    DataFrame.\n",
    " \n",
    "    \"\"\"  \n",
    "    novo_yearbuilt = pd.Series([], dtype='object')\n",
    "    for i in range(len(X)):\n",
    "        if (X['yr_built'][i] <=1940):\n",
    "            novo_yearbuilt[i]='1900-1940' \n",
    "        elif ((X['yr_built'][i] > 1940) and (X['yr_built'][i] <= 1955)):\n",
    "            novo_yearbuilt[i]='1941-1955' \n",
    "        elif ((X['yr_built'][i] > 1955) and (X['yr_built'][i] <= 1968)):\n",
    "            novo_yearbuilt[i]='1956-1968'\n",
    "        elif ((X['yr_built'][i] > 1968) and (X['yr_built'][i] <= 1983)):\n",
    "            novo_yearbuilt[i]='1969-1983'\n",
    "        elif ((X['yr_built'][i] > 1983) and (X['yr_built'][i] <= 1997)):\n",
    "            novo_yearbuilt[i]='1984-1997'\n",
    "        elif ((X['yr_built'][i] > 1997) and (X['yr_built'][i] <= 2006)):\n",
    "            novo_yearbuilt[i]='1998-2006'\n",
    "        else:\n",
    "            novo_yearbuilt[i]='2007-2015'\n",
    "\n",
    "    df_yrbuilt = X[['asked_price','yr_built']]\n",
    "    df_yrbuilt.insert(2, 'Period', novo_yearbuilt)\n",
    "    df_yrbuilt = df_yrbuilt[['asked_price','Period']].groupby('Period').mean().reset_index()\n",
    "    return df_yrbuilt\n",
    "\n",
    "def bar_plot(df: pd.DataFrame, x, xlabel, y, ylabel) -> None:\n",
    "    \"\"\" \n",
    "    Returns DataFrame with the ranged year built. \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pd.DataFrame\n",
    "    DataFrame.\n",
    "\n",
    "    x : str\n",
    "    x values.\n",
    "\n",
    "    xlabel : str\n",
    "    x label.\n",
    "\n",
    "    y : str\n",
    "    y values.\n",
    "\n",
    "    ylabel : str\n",
    "    y label.\n",
    "\n",
    "    Returns    \n",
    "    -------\n",
    "    None\n",
    "    \"\"\" \n",
    "    df.plot(kind='bar',\n",
    "            x=x,\n",
    "            y=y,\n",
    "            color='#1CA03E',\n",
    "            legend=False,\n",
    "            figsize=(10,5), \n",
    "            xlabel=xlabel,\n",
    "            ylabel=ylabel,\n",
    "            rot=1)\n",
    "    plt.ticklabel_format(style='plain', axis='y')\n",
    "    \n",
    "    return None   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the geofile data\n",
    "url = 'https://opendata.arcgis.com/datasets/83fc2e72903343aabff6de8cb445b81c_2.geojson'\n",
    "geofile = get_geofile(url)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>2015-02-23</td>\n",
       "      <td>400000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>2310</td>\n",
       "      <td>5813</td>\n",
       "      <td>2.00000</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>47.51070</td>\n",
       "      <td>-122.36200</td>\n",
       "      <td>1830</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>1523300141</td>\n",
       "      <td>2014-06-23</td>\n",
       "      <td>402101.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>1020</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.00000</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>47.59440</td>\n",
       "      <td>-122.29900</td>\n",
       "      <td>1020</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>291310100</td>\n",
       "      <td>2015-01-16</td>\n",
       "      <td>400000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>1600</td>\n",
       "      <td>2388</td>\n",
       "      <td>2.00000</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>47.53450</td>\n",
       "      <td>-122.06900</td>\n",
       "      <td>1410</td>\n",
       "      <td>1287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>1523300157</td>\n",
       "      <td>2014-10-15</td>\n",
       "      <td>325000.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>1020</td>\n",
       "      <td>1076</td>\n",
       "      <td>2.00000</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>47.59410</td>\n",
       "      <td>-122.29900</td>\n",
       "      <td>1020</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21613 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id       date  asked_price  bedrooms  bathrooms  sqft_living  \\\n",
       "0      7129300520 2014-10-13 221900.00000         3    1.00000         1180   \n",
       "1      6414100192 2014-12-09 538000.00000         3    2.25000         2570   \n",
       "2      5631500400 2015-02-25 180000.00000         2    1.00000          770   \n",
       "3      2487200875 2014-12-09 604000.00000         4    3.00000         1960   \n",
       "4      1954400510 2015-02-18 510000.00000         3    2.00000         1680   \n",
       "...           ...        ...          ...       ...        ...          ...   \n",
       "21608   263000018 2014-05-21 360000.00000         3    2.50000         1530   \n",
       "21609  6600060120 2015-02-23 400000.00000         4    2.50000         2310   \n",
       "21610  1523300141 2014-06-23 402101.00000         2    0.75000         1020   \n",
       "21611   291310100 2015-01-16 400000.00000         3    2.50000         1600   \n",
       "21612  1523300157 2014-10-15 325000.00000         2    0.75000         1020   \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  ...  grade  sqft_above  \\\n",
       "0          5650 1.00000           0     0  ...      7        1180   \n",
       "1          7242 2.00000           0     0  ...      7        2170   \n",
       "2         10000 1.00000           0     0  ...      6         770   \n",
       "3          5000 1.00000           0     0  ...      7        1050   \n",
       "4          8080 1.00000           0     0  ...      8        1680   \n",
       "...         ...     ...         ...   ...  ...    ...         ...   \n",
       "21608      1131 3.00000           0     0  ...      8        1530   \n",
       "21609      5813 2.00000           0     0  ...      8        2310   \n",
       "21610      1350 2.00000           0     0  ...      7        1020   \n",
       "21611      2388 2.00000           0     0  ...      8        1600   \n",
       "21612      1076 2.00000           0     0  ...      7        1020   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat       long  \\\n",
       "0                  0      1955             0    98178 47.51120 -122.25700   \n",
       "1                400      1951          1991    98125 47.72100 -122.31900   \n",
       "2                  0      1933             0    98028 47.73790 -122.23300   \n",
       "3                910      1965             0    98136 47.52080 -122.39300   \n",
       "4                  0      1987             0    98074 47.61680 -122.04500   \n",
       "...              ...       ...           ...      ...      ...        ...   \n",
       "21608              0      2009             0    98103 47.69930 -122.34600   \n",
       "21609              0      2014             0    98146 47.51070 -122.36200   \n",
       "21610              0      2009             0    98144 47.59440 -122.29900   \n",
       "21611              0      2004             0    98027 47.53450 -122.06900   \n",
       "21612              0      2008             0    98144 47.59410 -122.29900   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "0               1340        5650  \n",
       "1               1690        7639  \n",
       "2               2720        8062  \n",
       "3               1360        5000  \n",
       "4               1800        7503  \n",
       "...              ...         ...  \n",
       "21608           1530        1509  \n",
       "21609           1830        7200  \n",
       "21610           1020        2007  \n",
       "21611           1410        1287  \n",
       "21612           1020        1357  \n",
       "\n",
       "[21613 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the House Rocket dataset\n",
    "path = 'data/kc_house_data.csv'\n",
    "path_address = \"data/address.csv\"\n",
    "df_address = get_data(path_address, convert=False)\n",
    "df = get_data(path)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obs: The address information for these properties was gathered by using the two created modules: address.py and defs.py (both available in the modules folder). It's important to notice that all properties are located in Seattle, US, therefore only the road name and property number were collected. Later on section 3.3 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4. Checking the data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        int64\n",
       "date             datetime64[ns]\n",
       "asked_price             float64\n",
       "bedrooms                  int64\n",
       "bathrooms               float64\n",
       "sqft_living               int64\n",
       "sqft_lot                  int64\n",
       "floors                  float64\n",
       "waterfront                int64\n",
       "view                      int64\n",
       "condition                 int64\n",
       "grade                     int64\n",
       "sqft_above                int64\n",
       "sqft_basement             int64\n",
       "yr_built                  int64\n",
       "yr_renovated              int64\n",
       "zipcode                   int64\n",
       "lat                     float64\n",
       "long                    float64\n",
       "sqft_living15             int64\n",
       "sqft_lot15                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5. Definitions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Feature | Definition |\n",
    "|---|---|\n",
    "| id | Unique ID for each home sold |\n",
    "| date | Date of the home sale |\n",
    "| price | Price of each home sold |\n",
    "| bedrooms | Number of bedrooms |\n",
    "| bathrooms | Number of bathrooms, where .5 accounts for a room with a toilet but no shower |\n",
    "| sqft_living | Square footage of the apartments interior living space |\n",
    "| sqft_lot | Square footage of the land space |\n",
    "| floors | Number of floors |\n",
    "| waterfront | A dummy variable for whether the apartment was overlooking the waterfront or not |\n",
    "| view | An index from 0 to 4 of how good the view of the property was |\n",
    "| condition  | An index from 1 to 5 on the condition of the apartment |\n",
    "| grade  | An index from 1 to 13, where 1-3 falls short of building construction and design, 7 has an average level of construction and design, and 11-13 have a high quality level of construction and design. |\n",
    "| sqft_above | The square footage of the interior housing space that is above ground level |\n",
    "| sqft_basement | The square footage of the interior housing space that is below ground level |\n",
    "| yr_built | The year the house was initially built |\n",
    "| yr_renovated | The year of the house’s last renovation |\n",
    "| zipcode | What zipcode area the house is in |\n",
    "| lat | Lattitude |\n",
    "| long | Longitude |\n",
    "| sqft_living15 | The square footage of interior housing living space for the nearest 15 neighbors |\n",
    "| sqft_lot15 | The square footage of the land lots of the nearest 15 neighbors |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. **Data Cleaning** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Checking for missing values (NaN's) in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id               0\n",
       "date             0\n",
       "asked_price      0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft_living      0\n",
       "sqft_lot         0\n",
       "floors           0\n",
       "waterfront       0\n",
       "view             0\n",
       "condition        0\n",
       "grade            0\n",
       "sqft_above       0\n",
       "sqft_basement    0\n",
       "yr_built         0\n",
       "yr_renovated     0\n",
       "zipcode          0\n",
       "lat              0\n",
       "long             0\n",
       "sqft_living15    0\n",
       "sqft_lot15       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Checking for duplicated id's in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "795000620     3\n",
       "8651510380    2\n",
       "2568300045    2\n",
       "9353300600    2\n",
       "4139480200    2\n",
       "             ..\n",
       "2767603649    1\n",
       "1446403617    1\n",
       "5602000275    1\n",
       "1786200010    1\n",
       "1523300157    1\n",
       "Name: id, Length: 21436, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(subset=['id'], inplace=True, keep='first') # Dropping the duplicated values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Checking for outliers in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.2.1 Outliers in Asked Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\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>date</th>\n",
       "      <th>asked_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>7252</th>\n",
       "      <td>6762700020</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>7700000.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>12050</td>\n",
       "      <td>27600</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>8570</td>\n",
       "      <td>3480</td>\n",
       "      <td>1910</td>\n",
       "      <td>1987</td>\n",
       "      <td>98102</td>\n",
       "      <td>47.62980</td>\n",
       "      <td>-122.32300</td>\n",
       "      <td>3940</td>\n",
       "      <td>8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>9808700762</td>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>7062500.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.50000</td>\n",
       "      <td>10040</td>\n",
       "      <td>37325</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>7680</td>\n",
       "      <td>2360</td>\n",
       "      <td>1940</td>\n",
       "      <td>2001</td>\n",
       "      <td>98004</td>\n",
       "      <td>47.65000</td>\n",
       "      <td>-122.21400</td>\n",
       "      <td>3930</td>\n",
       "      <td>25449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9254</th>\n",
       "      <td>9208900037</td>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>6885000.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>7.75000</td>\n",
       "      <td>9890</td>\n",
       "      <td>31374</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>8860</td>\n",
       "      <td>1030</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>98039</td>\n",
       "      <td>47.63050</td>\n",
       "      <td>-122.24000</td>\n",
       "      <td>4540</td>\n",
       "      <td>42730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4411</th>\n",
       "      <td>2470100110</td>\n",
       "      <td>2014-08-04</td>\n",
       "      <td>5570000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.75000</td>\n",
       "      <td>9200</td>\n",
       "      <td>35069</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>6200</td>\n",
       "      <td>3000</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>98039</td>\n",
       "      <td>47.62890</td>\n",
       "      <td>-122.23300</td>\n",
       "      <td>3560</td>\n",
       "      <td>24345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>8907500070</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>5350000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>8000</td>\n",
       "      <td>23985</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>6720</td>\n",
       "      <td>1280</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98004</td>\n",
       "      <td>47.62320</td>\n",
       "      <td>-122.22000</td>\n",
       "      <td>4600</td>\n",
       "      <td>21750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>7558700030</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>5300000.00000</td>\n",
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       "      <td>7390</td>\n",
       "      <td>24829</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>5000</td>\n",
       "      <td>2390</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>98040</td>\n",
       "      <td>47.56310</td>\n",
       "      <td>-122.21000</td>\n",
       "      <td>4320</td>\n",
       "      <td>24619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164</th>\n",
       "      <td>1247600105</td>\n",
       "      <td>2014-10-20</td>\n",
       "      <td>5110800.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.25000</td>\n",
       "      <td>8010</td>\n",
       "      <td>45517</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>5990</td>\n",
       "      <td>2020</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>98033</td>\n",
       "      <td>47.67670</td>\n",
       "      <td>-122.21100</td>\n",
       "      <td>3430</td>\n",
       "      <td>26788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8092</th>\n",
       "      <td>1924059029</td>\n",
       "      <td>2014-06-17</td>\n",
       "      <td>4668000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>6.75000</td>\n",
       "      <td>9640</td>\n",
       "      <td>13068</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>4820</td>\n",
       "      <td>4820</td>\n",
       "      <td>1983</td>\n",
       "      <td>2009</td>\n",
       "      <td>98040</td>\n",
       "      <td>47.55700</td>\n",
       "      <td>-122.21000</td>\n",
       "      <td>3270</td>\n",
       "      <td>10454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2626</th>\n",
       "      <td>7738500731</td>\n",
       "      <td>2014-08-15</td>\n",
       "      <td>4500000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.50000</td>\n",
       "      <td>6640</td>\n",
       "      <td>40014</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>6350</td>\n",
       "      <td>290</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98155</td>\n",
       "      <td>47.74930</td>\n",
       "      <td>-122.28000</td>\n",
       "      <td>3030</td>\n",
       "      <td>23408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8638</th>\n",
       "      <td>3835500195</td>\n",
       "      <td>2014-06-18</td>\n",
       "      <td>4489000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>6430</td>\n",
       "      <td>27517</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>6430</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>98004</td>\n",
       "      <td>47.62080</td>\n",
       "      <td>-122.21900</td>\n",
       "      <td>3720</td>\n",
       "      <td>14592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12370</th>\n",
       "      <td>6065300370</td>\n",
       "      <td>2015-05-06</td>\n",
       "      <td>4208000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>7440</td>\n",
       "      <td>21540</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>5550</td>\n",
       "      <td>1890</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>98006</td>\n",
       "      <td>47.56920</td>\n",
       "      <td>-122.18900</td>\n",
       "      <td>4740</td>\n",
       "      <td>19329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4149</th>\n",
       "      <td>6447300265</td>\n",
       "      <td>2014-10-14</td>\n",
       "      <td>4000000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>5.50000</td>\n",
       "      <td>7080</td>\n",
       "      <td>16573</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>5760</td>\n",
       "      <td>1320</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98039</td>\n",
       "      <td>47.61510</td>\n",
       "      <td>-122.22400</td>\n",
       "      <td>3140</td>\n",
       "      <td>15996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2085</th>\n",
       "      <td>8106100105</td>\n",
       "      <td>2014-11-14</td>\n",
       "      <td>3850000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.25000</td>\n",
       "      <td>5770</td>\n",
       "      <td>21300</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>5770</td>\n",
       "      <td>0</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>98040</td>\n",
       "      <td>47.58500</td>\n",
       "      <td>-122.22200</td>\n",
       "      <td>4620</td>\n",
       "      <td>22748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19017</th>\n",
       "      <td>2303900100</td>\n",
       "      <td>2014-09-11</td>\n",
       "      <td>3800000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>4.25000</td>\n",
       "      <td>5510</td>\n",
       "      <td>35000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>4910</td>\n",
       "      <td>600</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>98177</td>\n",
       "      <td>47.72960</td>\n",
       "      <td>-122.37000</td>\n",
       "      <td>3430</td>\n",
       "      <td>45302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7035</th>\n",
       "      <td>853200010</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>3800000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.50000</td>\n",
       "      <td>7050</td>\n",
       "      <td>42840</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>4320</td>\n",
       "      <td>2730</td>\n",
       "      <td>1978</td>\n",
       "      <td>0</td>\n",
       "      <td>98004</td>\n",
       "      <td>47.62290</td>\n",
       "      <td>-122.22000</td>\n",
       "      <td>5070</td>\n",
       "      <td>20570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id       date   asked_price  bedrooms  bathrooms  sqft_living  \\\n",
       "7252   6762700020 2014-10-13 7700000.00000         6    8.00000        12050   \n",
       "3914   9808700762 2014-06-11 7062500.00000         5    4.50000        10040   \n",
       "9254   9208900037 2014-09-19 6885000.00000         6    7.75000         9890   \n",
       "4411   2470100110 2014-08-04 5570000.00000         5    5.75000         9200   \n",
       "1448   8907500070 2015-04-13 5350000.00000         5    5.00000         8000   \n",
       "1315   7558700030 2015-04-13 5300000.00000         6    6.00000         7390   \n",
       "1164   1247600105 2014-10-20 5110800.00000         5    5.25000         8010   \n",
       "8092   1924059029 2014-06-17 4668000.00000         5    6.75000         9640   \n",
       "2626   7738500731 2014-08-15 4500000.00000         5    5.50000         6640   \n",
       "8638   3835500195 2014-06-18 4489000.00000         4    3.00000         6430   \n",
       "12370  6065300370 2015-05-06 4208000.00000         5    6.00000         7440   \n",
       "4149   6447300265 2014-10-14 4000000.00000         4    5.50000         7080   \n",
       "2085   8106100105 2014-11-14 3850000.00000         4    4.25000         5770   \n",
       "19017  2303900100 2014-09-11 3800000.00000         3    4.25000         5510   \n",
       "7035    853200010 2014-07-01 3800000.00000         5    5.50000         7050   \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  ...  grade  sqft_above  \\\n",
       "7252      27600 2.50000           0     3  ...     13        8570   \n",
       "3914      37325 2.00000           1     2  ...     11        7680   \n",
       "9254      31374 2.00000           0     4  ...     13        8860   \n",
       "4411      35069 2.00000           0     0  ...     13        6200   \n",
       "1448      23985 2.00000           0     4  ...     12        6720   \n",
       "1315      24829 2.00000           1     4  ...     12        5000   \n",
       "1164      45517 2.00000           1     4  ...     12        5990   \n",
       "8092      13068 1.00000           1     4  ...     12        4820   \n",
       "2626      40014 2.00000           1     4  ...     12        6350   \n",
       "8638      27517 2.00000           0     0  ...     12        6430   \n",
       "12370     21540 2.00000           0     0  ...     12        5550   \n",
       "4149      16573 2.00000           0     0  ...     12        5760   \n",
       "2085      21300 2.00000           1     4  ...     11        5770   \n",
       "19017     35000 2.00000           0     4  ...     13        4910   \n",
       "7035      42840 1.00000           0     2  ...     13        4320   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat       long  \\\n",
       "7252            3480      1910          1987    98102 47.62980 -122.32300   \n",
       "3914            2360      1940          2001    98004 47.65000 -122.21400   \n",
       "9254            1030      2001             0    98039 47.63050 -122.24000   \n",
       "4411            3000      2001             0    98039 47.62890 -122.23300   \n",
       "1448            1280      2009             0    98004 47.62320 -122.22000   \n",
       "1315            2390      1991             0    98040 47.56310 -122.21000   \n",
       "1164            2020      1999             0    98033 47.67670 -122.21100   \n",
       "8092            4820      1983          2009    98040 47.55700 -122.21000   \n",
       "2626             290      2004             0    98155 47.74930 -122.28000   \n",
       "8638               0      2001             0    98004 47.62080 -122.21900   \n",
       "12370           1890      2003             0    98006 47.56920 -122.18900   \n",
       "4149            1320      2008             0    98039 47.61510 -122.22400   \n",
       "2085               0      1980             0    98040 47.58500 -122.22200   \n",
       "19017            600      1997             0    98177 47.72960 -122.37000   \n",
       "7035            2730      1978             0    98004 47.62290 -122.22000   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "7252            3940        8800  \n",
       "3914            3930       25449  \n",
       "9254            4540       42730  \n",
       "4411            3560       24345  \n",
       "1448            4600       21750  \n",
       "1315            4320       24619  \n",
       "1164            3430       26788  \n",
       "8092            3270       10454  \n",
       "2626            3030       23408  \n",
       "8638            3720       14592  \n",
       "12370           4740       19329  \n",
       "4149            3140       15996  \n",
       "2085            4620       22748  \n",
       "19017           3430       45302  \n",
       "7035            5070       20570  \n",
       "\n",
       "[15 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outlier_identifier(df, 'asked_price',c=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Price wise, none of these observations can be considered outliers, as these properties with higher prices have features that match their price, such as high number of bathrooms, bedrooms, sqft living area, grade quality (which is the building construction and design level), condition, view, and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.2.2 Outliers in Bedrooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>asked_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>3119</th>\n",
       "      <td>3918400017</td>\n",
       "      <td>2015-02-05</td>\n",
       "      <td>380000.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1470</td>\n",
       "      <td>979</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1470</td>\n",
       "      <td>0</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>98133</td>\n",
       "      <td>47.71450</td>\n",
       "      <td>-122.35600</td>\n",
       "      <td>1470</td>\n",
       "      <td>1399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8484</th>\n",
       "      <td>2310060040</td>\n",
       "      <td>2014-09-25</td>\n",
       "      <td>240000.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>1810</td>\n",
       "      <td>5669</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1810</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>98038</td>\n",
       "      <td>47.34930</td>\n",
       "      <td>-122.05300</td>\n",
       "      <td>1810</td>\n",
       "      <td>5685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467</th>\n",
       "      <td>1453602309</td>\n",
       "      <td>2014-08-05</td>\n",
       "      <td>288000.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1430</td>\n",
       "      <td>1650</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1430</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.72220</td>\n",
       "      <td>-122.29000</td>\n",
       "      <td>1430</td>\n",
       "      <td>1650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4868</th>\n",
       "      <td>6896300380</td>\n",
       "      <td>2014-10-02</td>\n",
       "      <td>228000.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>390</td>\n",
       "      <td>5900</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>390</td>\n",
       "      <td>0</td>\n",
       "      <td>1953</td>\n",
       "      <td>0</td>\n",
       "      <td>98118</td>\n",
       "      <td>47.52600</td>\n",
       "      <td>-122.26100</td>\n",
       "      <td>2170</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18379</th>\n",
       "      <td>1222029077</td>\n",
       "      <td>2014-10-29</td>\n",
       "      <td>265000.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>384</td>\n",
       "      <td>213444</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>384</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>98070</td>\n",
       "      <td>47.41770</td>\n",
       "      <td>-122.49100</td>\n",
       "      <td>1920</td>\n",
       "      <td>224341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9773</th>\n",
       "      <td>3374500520</td>\n",
       "      <td>2015-04-29</td>\n",
       "      <td>355000.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2460</td>\n",
       "      <td>8049</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2460</td>\n",
       "      <td>0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>98031</td>\n",
       "      <td>47.40950</td>\n",
       "      <td>-122.16800</td>\n",
       "      <td>2520</td>\n",
       "      <td>8050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12653</th>\n",
       "      <td>7849202299</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>320000.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>1490</td>\n",
       "      <td>7111</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1490</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>98065</td>\n",
       "      <td>47.52610</td>\n",
       "      <td>-121.82600</td>\n",
       "      <td>1500</td>\n",
       "      <td>4675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9854</th>\n",
       "      <td>7849202190</td>\n",
       "      <td>2014-12-23</td>\n",
       "      <td>235000.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1470</td>\n",
       "      <td>4800</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1470</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>98065</td>\n",
       "      <td>47.52650</td>\n",
       "      <td>-121.82800</td>\n",
       "      <td>1060</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6994</th>\n",
       "      <td>2954400190</td>\n",
       "      <td>2014-06-24</td>\n",
       "      <td>1295650.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4810</td>\n",
       "      <td>28008</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>4810</td>\n",
       "      <td>0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.66420</td>\n",
       "      <td>-122.06900</td>\n",
       "      <td>4740</td>\n",
       "      <td>35061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14423</th>\n",
       "      <td>9543000205</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>139950.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>844</td>\n",
       "      <td>4269</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>844</td>\n",
       "      <td>0</td>\n",
       "      <td>1913</td>\n",
       "      <td>0</td>\n",
       "      <td>98001</td>\n",
       "      <td>47.27810</td>\n",
       "      <td>-122.25000</td>\n",
       "      <td>1380</td>\n",
       "      <td>9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19452</th>\n",
       "      <td>3980300371</td>\n",
       "      <td>2014-09-26</td>\n",
       "      <td>142000.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>290</td>\n",
       "      <td>20875</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>290</td>\n",
       "      <td>0</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>98024</td>\n",
       "      <td>47.53080</td>\n",
       "      <td>-121.88800</td>\n",
       "      <td>1620</td>\n",
       "      <td>22850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>6306400140</td>\n",
       "      <td>2014-06-12</td>\n",
       "      <td>1095000.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3064</td>\n",
       "      <td>4764</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>3064</td>\n",
       "      <td>0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>98102</td>\n",
       "      <td>47.63620</td>\n",
       "      <td>-122.32200</td>\n",
       "      <td>2360</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8477</th>\n",
       "      <td>2569500210</td>\n",
       "      <td>2014-11-17</td>\n",
       "      <td>339950.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>2290</td>\n",
       "      <td>8319</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2290</td>\n",
       "      <td>0</td>\n",
       "      <td>1985</td>\n",
       "      <td>0</td>\n",
       "      <td>98042</td>\n",
       "      <td>47.34730</td>\n",
       "      <td>-122.15100</td>\n",
       "      <td>2500</td>\n",
       "      <td>8751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>2123039032</td>\n",
       "      <td>2014-10-27</td>\n",
       "      <td>369900.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>760</td>\n",
       "      <td>10079</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>760</td>\n",
       "      <td>0</td>\n",
       "      <td>1936</td>\n",
       "      <td>0</td>\n",
       "      <td>98070</td>\n",
       "      <td>47.46830</td>\n",
       "      <td>-122.43800</td>\n",
       "      <td>1230</td>\n",
       "      <td>14267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9518</th>\n",
       "      <td>6450301220</td>\n",
       "      <td>2014-09-16</td>\n",
       "      <td>264000.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>710</td>\n",
       "      <td>4725</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>710</td>\n",
       "      <td>0</td>\n",
       "      <td>1939</td>\n",
       "      <td>0</td>\n",
       "      <td>98133</td>\n",
       "      <td>47.73280</td>\n",
       "      <td>-122.34000</td>\n",
       "      <td>900</td>\n",
       "      <td>5250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id       date   asked_price  bedrooms  bathrooms  sqft_living  \\\n",
       "3119   3918400017 2015-02-05  380000.00000         0    0.00000         1470   \n",
       "8484   2310060040 2014-09-25  240000.00000         0    2.50000         1810   \n",
       "3467   1453602309 2014-08-05  288000.00000         0    1.50000         1430   \n",
       "4868   6896300380 2014-10-02  228000.00000         0    1.00000          390   \n",
       "18379  1222029077 2014-10-29  265000.00000         0    0.75000          384   \n",
       "9773   3374500520 2015-04-29  355000.00000         0    0.00000         2460   \n",
       "12653  7849202299 2015-02-18  320000.00000         0    2.50000         1490   \n",
       "9854   7849202190 2014-12-23  235000.00000         0    0.00000         1470   \n",
       "6994   2954400190 2014-06-24 1295650.00000         0    0.00000         4810   \n",
       "14423  9543000205 2015-04-13  139950.00000         0    0.00000          844   \n",
       "19452  3980300371 2014-09-26  142000.00000         0    0.00000          290   \n",
       "875    6306400140 2014-06-12 1095000.00000         0    0.00000         3064   \n",
       "8477   2569500210 2014-11-17  339950.00000         0    2.50000         2290   \n",
       "264    2123039032 2014-10-27  369900.00000         1    0.75000          760   \n",
       "9518   6450301220 2014-09-16  264000.00000         1    1.00000          710   \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  ...  grade  sqft_above  \\\n",
       "3119        979 3.00000           0     2  ...      8        1470   \n",
       "8484       5669 2.00000           0     0  ...      7        1810   \n",
       "3467       1650 3.00000           0     0  ...      7        1430   \n",
       "4868       5900 1.00000           0     0  ...      4         390   \n",
       "18379    213444 1.00000           0     0  ...      4         384   \n",
       "9773       8049 2.00000           0     0  ...      8        2460   \n",
       "12653      7111 2.00000           0     0  ...      7        1490   \n",
       "9854       4800 2.00000           0     0  ...      7        1470   \n",
       "6994      28008 2.00000           0     0  ...     12        4810   \n",
       "14423      4269 1.00000           0     0  ...      7         844   \n",
       "19452     20875 1.00000           0     0  ...      1         290   \n",
       "875        4764 3.50000           0     2  ...      7        3064   \n",
       "8477       8319 2.00000           0     0  ...      8        2290   \n",
       "264       10079 1.00000           1     4  ...      5         760   \n",
       "9518       4725 1.00000           0     0  ...      6         710   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat       long  \\\n",
       "3119               0      2006             0    98133 47.71450 -122.35600   \n",
       "8484               0      2003             0    98038 47.34930 -122.05300   \n",
       "3467               0      1999             0    98125 47.72220 -122.29000   \n",
       "4868               0      1953             0    98118 47.52600 -122.26100   \n",
       "18379              0      2003             0    98070 47.41770 -122.49100   \n",
       "9773               0      1990             0    98031 47.40950 -122.16800   \n",
       "12653              0      1999             0    98065 47.52610 -121.82600   \n",
       "9854               0      1996             0    98065 47.52650 -121.82800   \n",
       "6994               0      1990             0    98053 47.66420 -122.06900   \n",
       "14423              0      1913             0    98001 47.27810 -122.25000   \n",
       "19452              0      1963             0    98024 47.53080 -121.88800   \n",
       "875                0      1990             0    98102 47.63620 -122.32200   \n",
       "8477               0      1985             0    98042 47.34730 -122.15100   \n",
       "264                0      1936             0    98070 47.46830 -122.43800   \n",
       "9518               0      1939             0    98133 47.73280 -122.34000   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "3119            1470        1399  \n",
       "8484            1810        5685  \n",
       "3467            1430        1650  \n",
       "4868            2170        6000  \n",
       "18379           1920      224341  \n",
       "9773            2520        8050  \n",
       "12653           1500        4675  \n",
       "9854            1060        7200  \n",
       "6994            4740       35061  \n",
       "14423           1380        9600  \n",
       "19452           1620       22850  \n",
       "875             2360        4000  \n",
       "8477            2500        8751  \n",
       "264             1230       14267  \n",
       "9518             900        5250  \n",
       "\n",
       "[15 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outlier_identifier(df, 'bedrooms', asc=True, c=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>asked_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>15870</th>\n",
       "      <td>2402100895</td>\n",
       "      <td>2014-06-25</td>\n",
       "      <td>640000.00000</td>\n",
       "      <td>33</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>1620</td>\n",
       "      <td>6000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1040</td>\n",
       "      <td>580</td>\n",
       "      <td>1947</td>\n",
       "      <td>0</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.68780</td>\n",
       "      <td>-122.33100</td>\n",
       "      <td>1330</td>\n",
       "      <td>4700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id       date  asked_price  bedrooms  bathrooms  sqft_living  \\\n",
       "15870  2402100895 2014-06-25 640000.00000        33    1.75000         1620   \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  ...  grade  sqft_above  \\\n",
       "15870      6000 1.00000           0     0  ...      7        1040   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat       long  \\\n",
       "15870            580      1947             0    98103 47.68780 -122.33100   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "15870           1330        4700  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('bedrooms',ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This observation is definitely a typo, as a house with 33 bedrooms would probably cost more than $650.000, and would be a lot bigger than 1620 sqft living area. Therefore it will be removed.\n",
    "The observations that have no bedrooms will also be removed, as those are probably typos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[(df['bedrooms']!= 33) & (df['bedrooms']!= 0)] # Removing the observation with 33 bedrooms and the ones with 0 bedrooms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.2.3 Outliers in Bathrooms, Sqft Living, Sqft Lot and Floors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>asked_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>12777</th>\n",
       "      <td>1225069038</td>\n",
       "      <td>2014-05-05</td>\n",
       "      <td>2280000.00000</td>\n",
       "      <td>7</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>13540</td>\n",
       "      <td>307752</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>9410</td>\n",
       "      <td>4130</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.66750</td>\n",
       "      <td>-121.98600</td>\n",
       "      <td>4850</td>\n",
       "      <td>217800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7252</th>\n",
       "      <td>6762700020</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>7700000.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>12050</td>\n",
       "      <td>27600</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>8570</td>\n",
       "      <td>3480</td>\n",
       "      <td>1910</td>\n",
       "      <td>1987</td>\n",
       "      <td>98102</td>\n",
       "      <td>47.62980</td>\n",
       "      <td>-122.32300</td>\n",
       "      <td>3940</td>\n",
       "      <td>8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9254</th>\n",
       "      <td>9208900037</td>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>6885000.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>7.75000</td>\n",
       "      <td>9890</td>\n",
       "      <td>31374</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>8860</td>\n",
       "      <td>1030</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>98039</td>\n",
       "      <td>47.63050</td>\n",
       "      <td>-122.24000</td>\n",
       "      <td>4540</td>\n",
       "      <td>42730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8546</th>\n",
       "      <td>424049043</td>\n",
       "      <td>2014-08-11</td>\n",
       "      <td>450000.00000</td>\n",
       "      <td>9</td>\n",
       "      <td>7.50000</td>\n",
       "      <td>4050</td>\n",
       "      <td>6504</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>4050</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.59230</td>\n",
       "      <td>-122.30100</td>\n",
       "      <td>1448</td>\n",
       "      <td>3866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4024</th>\n",
       "      <td>9175600025</td>\n",
       "      <td>2014-10-07</td>\n",
       "      <td>800000.00000</td>\n",
       "      <td>7</td>\n",
       "      <td>6.75000</td>\n",
       "      <td>7480</td>\n",
       "      <td>41664</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>5080</td>\n",
       "      <td>2400</td>\n",
       "      <td>1953</td>\n",
       "      <td>0</td>\n",
       "      <td>98166</td>\n",
       "      <td>47.46430</td>\n",
       "      <td>-122.36800</td>\n",
       "      <td>2810</td>\n",
       "      <td>33190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8092</th>\n",
       "      <td>1924059029</td>\n",
       "      <td>2014-06-17</td>\n",
       "      <td>4668000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>6.75000</td>\n",
       "      <td>9640</td>\n",
       "      <td>13068</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>4820</td>\n",
       "      <td>4820</td>\n",
       "      <td>1983</td>\n",
       "      <td>2009</td>\n",
       "      <td>98040</td>\n",
       "      <td>47.55700</td>\n",
       "      <td>-122.21000</td>\n",
       "      <td>3270</td>\n",
       "      <td>10454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20578</th>\n",
       "      <td>424069279</td>\n",
       "      <td>2015-03-28</td>\n",
       "      <td>1180000.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>6.50000</td>\n",
       "      <td>6260</td>\n",
       "      <td>10955</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>4840</td>\n",
       "      <td>1420</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>98075</td>\n",
       "      <td>47.59470</td>\n",
       "      <td>-122.03900</td>\n",
       "      <td>2710</td>\n",
       "      <td>12550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21506</th>\n",
       "      <td>2524069097</td>\n",
       "      <td>2014-05-09</td>\n",
       "      <td>2238890.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>6.50000</td>\n",
       "      <td>7270</td>\n",
       "      <td>130017</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>6420</td>\n",
       "      <td>850</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>98027</td>\n",
       "      <td>47.53710</td>\n",
       "      <td>-121.98200</td>\n",
       "      <td>1800</td>\n",
       "      <td>44890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14556</th>\n",
       "      <td>2303900035</td>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>2888000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>6.25000</td>\n",
       "      <td>8670</td>\n",
       "      <td>64033</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>6120</td>\n",
       "      <td>2550</td>\n",
       "      <td>1965</td>\n",
       "      <td>2003</td>\n",
       "      <td>98177</td>\n",
       "      <td>47.72950</td>\n",
       "      <td>-122.37200</td>\n",
       "      <td>4140</td>\n",
       "      <td>81021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18302</th>\n",
       "      <td>6072800246</td>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>3300000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>6.25000</td>\n",
       "      <td>8020</td>\n",
       "      <td>21738</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>8020</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>98006</td>\n",
       "      <td>47.56750</td>\n",
       "      <td>-122.18900</td>\n",
       "      <td>4160</td>\n",
       "      <td>18969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id       date   asked_price  bedrooms  bathrooms  sqft_living  \\\n",
       "12777  1225069038 2014-05-05 2280000.00000         7    8.00000        13540   \n",
       "7252   6762700020 2014-10-13 7700000.00000         6    8.00000        12050   \n",
       "9254   9208900037 2014-09-19 6885000.00000         6    7.75000         9890   \n",
       "8546    424049043 2014-08-11  450000.00000         9    7.50000         4050   \n",
       "4024   9175600025 2014-10-07  800000.00000         7    6.75000         7480   \n",
       "8092   1924059029 2014-06-17 4668000.00000         5    6.75000         9640   \n",
       "20578   424069279 2015-03-28 1180000.00000         6    6.50000         6260   \n",
       "21506  2524069097 2014-05-09 2238890.00000         5    6.50000         7270   \n",
       "14556  2303900035 2014-06-11 2888000.00000         5    6.25000         8670   \n",
       "18302  6072800246 2014-07-02 3300000.00000         5    6.25000         8020   \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  ...  grade  sqft_above  \\\n",
       "12777    307752 3.00000           0     4  ...     12        9410   \n",
       "7252      27600 2.50000           0     3  ...     13        8570   \n",
       "9254      31374 2.00000           0     4  ...     13        8860   \n",
       "8546       6504 2.00000           0     0  ...      7        4050   \n",
       "4024      41664 2.00000           0     2  ...     11        5080   \n",
       "8092      13068 1.00000           1     4  ...     12        4820   \n",
       "20578     10955 2.00000           0     0  ...     11        4840   \n",
       "21506    130017 2.00000           0     0  ...     12        6420   \n",
       "14556     64033 2.00000           0     4  ...     13        6120   \n",
       "18302     21738 2.00000           0     0  ...     11        8020   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat       long  \\\n",
       "12777           4130      1999             0    98053 47.66750 -121.98600   \n",
       "7252            3480      1910          1987    98102 47.62980 -122.32300   \n",
       "9254            1030      2001             0    98039 47.63050 -122.24000   \n",
       "8546               0      1996             0    98144 47.59230 -122.30100   \n",
       "4024            2400      1953             0    98166 47.46430 -122.36800   \n",
       "8092            4820      1983          2009    98040 47.55700 -122.21000   \n",
       "20578           1420      2007             0    98075 47.59470 -122.03900   \n",
       "21506            850      2010             0    98027 47.53710 -121.98200   \n",
       "14556           2550      1965          2003    98177 47.72950 -122.37200   \n",
       "18302              0      2001             0    98006 47.56750 -122.18900   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "12777           4850      217800  \n",
       "7252            3940        8800  \n",
       "9254            4540       42730  \n",
       "8546            1448        3866  \n",
       "4024            2810       33190  \n",
       "8092            3270       10454  \n",
       "20578           2710       12550  \n",
       "21506           1800       44890  \n",
       "14556           4140       81021  \n",
       "18302           4160       18969  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outlier_identifier(df, 'bathrooms', c=10, asc=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>asked_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>12777</th>\n",
       "      <td>1225069038</td>\n",
       "      <td>2014-05-05</td>\n",
       "      <td>2280000.00000</td>\n",
       "      <td>7</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>13540</td>\n",
       "      <td>307752</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>9410</td>\n",
       "      <td>4130</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.66750</td>\n",
       "      <td>-121.98600</td>\n",
       "      <td>4850</td>\n",
       "      <td>217800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7252</th>\n",
       "      <td>6762700020</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>7700000.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>12050</td>\n",
       "      <td>27600</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>8570</td>\n",
       "      <td>3480</td>\n",
       "      <td>1910</td>\n",
       "      <td>1987</td>\n",
       "      <td>98102</td>\n",
       "      <td>47.62980</td>\n",
       "      <td>-122.32300</td>\n",
       "      <td>3940</td>\n",
       "      <td>8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>9808700762</td>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>7062500.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.50000</td>\n",
       "      <td>10040</td>\n",
       "      <td>37325</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>7680</td>\n",
       "      <td>2360</td>\n",
       "      <td>1940</td>\n",
       "      <td>2001</td>\n",
       "      <td>98004</td>\n",
       "      <td>47.65000</td>\n",
       "      <td>-122.21400</td>\n",
       "      <td>3930</td>\n",
       "      <td>25449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9254</th>\n",
       "      <td>9208900037</td>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>6885000.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>7.75000</td>\n",
       "      <td>9890</td>\n",
       "      <td>31374</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>8860</td>\n",
       "      <td>1030</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>98039</td>\n",
       "      <td>47.63050</td>\n",
       "      <td>-122.24000</td>\n",
       "      <td>4540</td>\n",
       "      <td>42730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8092</th>\n",
       "      <td>1924059029</td>\n",
       "      <td>2014-06-17</td>\n",
       "      <td>4668000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>6.75000</td>\n",
       "      <td>9640</td>\n",
       "      <td>13068</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>4820</td>\n",
       "      <td>4820</td>\n",
       "      <td>1983</td>\n",
       "      <td>2009</td>\n",
       "      <td>98040</td>\n",
       "      <td>47.55700</td>\n",
       "      <td>-122.21000</td>\n",
       "      <td>3270</td>\n",
       "      <td>10454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4411</th>\n",
       "      <td>2470100110</td>\n",
       "      <td>2014-08-04</td>\n",
       "      <td>5570000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.75000</td>\n",
       "      <td>9200</td>\n",
       "      <td>35069</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>6200</td>\n",
       "      <td>3000</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>98039</td>\n",
       "      <td>47.62890</td>\n",
       "      <td>-122.23300</td>\n",
       "      <td>3560</td>\n",
       "      <td>24345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14556</th>\n",
       "      <td>2303900035</td>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>2888000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>6.25000</td>\n",
       "      <td>8670</td>\n",
       "      <td>64033</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>6120</td>\n",
       "      <td>2550</td>\n",
       "      <td>1965</td>\n",
       "      <td>2003</td>\n",
       "      <td>98177</td>\n",
       "      <td>47.72950</td>\n",
       "      <td>-122.37200</td>\n",
       "      <td>4140</td>\n",
       "      <td>81021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18302</th>\n",
       "      <td>6072800246</td>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>3300000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>6.25000</td>\n",
       "      <td>8020</td>\n",
       "      <td>21738</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>8020</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>98006</td>\n",
       "      <td>47.56750</td>\n",
       "      <td>-122.18900</td>\n",
       "      <td>4160</td>\n",
       "      <td>18969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164</th>\n",
       "      <td>1247600105</td>\n",
       "      <td>2014-10-20</td>\n",
       "      <td>5110800.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.25000</td>\n",
       "      <td>8010</td>\n",
       "      <td>45517</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>5990</td>\n",
       "      <td>2020</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>98033</td>\n",
       "      <td>47.67670</td>\n",
       "      <td>-122.21100</td>\n",
       "      <td>3430</td>\n",
       "      <td>26788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>8907500070</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>5350000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>8000</td>\n",
       "      <td>23985</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>6720</td>\n",
       "      <td>1280</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98004</td>\n",
       "      <td>47.62320</td>\n",
       "      <td>-122.22000</td>\n",
       "      <td>4600</td>\n",
       "      <td>21750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id       date   asked_price  bedrooms  bathrooms  sqft_living  \\\n",
       "12777  1225069038 2014-05-05 2280000.00000         7    8.00000        13540   \n",
       "7252   6762700020 2014-10-13 7700000.00000         6    8.00000        12050   \n",
       "3914   9808700762 2014-06-11 7062500.00000         5    4.50000        10040   \n",
       "9254   9208900037 2014-09-19 6885000.00000         6    7.75000         9890   \n",
       "8092   1924059029 2014-06-17 4668000.00000         5    6.75000         9640   \n",
       "4411   2470100110 2014-08-04 5570000.00000         5    5.75000         9200   \n",
       "14556  2303900035 2014-06-11 2888000.00000         5    6.25000         8670   \n",
       "18302  6072800246 2014-07-02 3300000.00000         5    6.25000         8020   \n",
       "1164   1247600105 2014-10-20 5110800.00000         5    5.25000         8010   \n",
       "1448   8907500070 2015-04-13 5350000.00000         5    5.00000         8000   \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  ...  grade  sqft_above  \\\n",
       "12777    307752 3.00000           0     4  ...     12        9410   \n",
       "7252      27600 2.50000           0     3  ...     13        8570   \n",
       "3914      37325 2.00000           1     2  ...     11        7680   \n",
       "9254      31374 2.00000           0     4  ...     13        8860   \n",
       "8092      13068 1.00000           1     4  ...     12        4820   \n",
       "4411      35069 2.00000           0     0  ...     13        6200   \n",
       "14556     64033 2.00000           0     4  ...     13        6120   \n",
       "18302     21738 2.00000           0     0  ...     11        8020   \n",
       "1164      45517 2.00000           1     4  ...     12        5990   \n",
       "1448      23985 2.00000           0     4  ...     12        6720   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat       long  \\\n",
       "12777           4130      1999             0    98053 47.66750 -121.98600   \n",
       "7252            3480      1910          1987    98102 47.62980 -122.32300   \n",
       "3914            2360      1940          2001    98004 47.65000 -122.21400   \n",
       "9254            1030      2001             0    98039 47.63050 -122.24000   \n",
       "8092            4820      1983          2009    98040 47.55700 -122.21000   \n",
       "4411            3000      2001             0    98039 47.62890 -122.23300   \n",
       "14556           2550      1965          2003    98177 47.72950 -122.37200   \n",
       "18302              0      2001             0    98006 47.56750 -122.18900   \n",
       "1164            2020      1999             0    98033 47.67670 -122.21100   \n",
       "1448            1280      2009             0    98004 47.62320 -122.22000   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "12777           4850      217800  \n",
       "7252            3940        8800  \n",
       "3914            3930       25449  \n",
       "9254            4540       42730  \n",
       "8092            3270       10454  \n",
       "4411            3560       24345  \n",
       "14556           4140       81021  \n",
       "18302           4160       18969  \n",
       "1164            3430       26788  \n",
       "1448            4600       21750  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outlier_identifier(df, 'sqft_living', c=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>asked_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>1719</th>\n",
       "      <td>1020069017</td>\n",
       "      <td>2015-03-27</td>\n",
       "      <td>700000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1300</td>\n",
       "      <td>1651359</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>0</td>\n",
       "      <td>98022</td>\n",
       "      <td>47.23130</td>\n",
       "      <td>-122.02300</td>\n",
       "      <td>2560</td>\n",
       "      <td>425581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17319</th>\n",
       "      <td>3326079016</td>\n",
       "      <td>2015-05-04</td>\n",
       "      <td>190000.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>710</td>\n",
       "      <td>1164794</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>710</td>\n",
       "      <td>0</td>\n",
       "      <td>1915</td>\n",
       "      <td>0</td>\n",
       "      <td>98014</td>\n",
       "      <td>47.68880</td>\n",
       "      <td>-121.90900</td>\n",
       "      <td>1680</td>\n",
       "      <td>16730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7647</th>\n",
       "      <td>2623069031</td>\n",
       "      <td>2014-05-21</td>\n",
       "      <td>542500.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>3.25000</td>\n",
       "      <td>3010</td>\n",
       "      <td>1074218</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "      <td>1000</td>\n",
       "      <td>1931</td>\n",
       "      <td>0</td>\n",
       "      <td>98027</td>\n",
       "      <td>47.45640</td>\n",
       "      <td>-122.00400</td>\n",
       "      <td>2450</td>\n",
       "      <td>68825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7769</th>\n",
       "      <td>2323089009</td>\n",
       "      <td>2015-01-19</td>\n",
       "      <td>855000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>4030</td>\n",
       "      <td>1024068</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>4030</td>\n",
       "      <td>0</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>98045</td>\n",
       "      <td>47.46190</td>\n",
       "      <td>-121.74400</td>\n",
       "      <td>1830</td>\n",
       "      <td>11700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>722069232</td>\n",
       "      <td>2014-09-05</td>\n",
       "      <td>998000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25000</td>\n",
       "      <td>3770</td>\n",
       "      <td>982998</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>3770</td>\n",
       "      <td>0</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>98058</td>\n",
       "      <td>47.41400</td>\n",
       "      <td>-122.08700</td>\n",
       "      <td>2290</td>\n",
       "      <td>37141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4441</th>\n",
       "      <td>3626079040</td>\n",
       "      <td>2014-07-30</td>\n",
       "      <td>790000.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2560</td>\n",
       "      <td>982278</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2560</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98014</td>\n",
       "      <td>47.69550</td>\n",
       "      <td>-121.86100</td>\n",
       "      <td>1620</td>\n",
       "      <td>40946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6691</th>\n",
       "      <td>2624089007</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>1998000.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>3900</td>\n",
       "      <td>920423</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>3900</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98065</td>\n",
       "      <td>47.53710</td>\n",
       "      <td>-121.75600</td>\n",
       "      <td>2720</td>\n",
       "      <td>411962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7077</th>\n",
       "      <td>2724079090</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>1650000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25000</td>\n",
       "      <td>3920</td>\n",
       "      <td>881654</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>3920</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "      <td>98024</td>\n",
       "      <td>47.53850</td>\n",
       "      <td>-121.89600</td>\n",
       "      <td>2970</td>\n",
       "      <td>112384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20452</th>\n",
       "      <td>1125079111</td>\n",
       "      <td>2015-04-15</td>\n",
       "      <td>1600000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>5.50000</td>\n",
       "      <td>6530</td>\n",
       "      <td>871200</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>6530</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98014</td>\n",
       "      <td>47.66400</td>\n",
       "      <td>-121.87800</td>\n",
       "      <td>1280</td>\n",
       "      <td>858132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9714</th>\n",
       "      <td>225079036</td>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>937500.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>5545</td>\n",
       "      <td>871200</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>3605</td>\n",
       "      <td>1940</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>98014</td>\n",
       "      <td>47.67600</td>\n",
       "      <td>-121.88200</td>\n",
       "      <td>3420</td>\n",
       "      <td>871200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id       date   asked_price  bedrooms  bathrooms  sqft_living  \\\n",
       "1719   1020069017 2015-03-27  700000.00000         4    1.00000         1300   \n",
       "17319  3326079016 2015-05-04  190000.00000         2    1.00000          710   \n",
       "7647   2623069031 2014-05-21  542500.00000         5    3.25000         3010   \n",
       "7769   2323089009 2015-01-19  855000.00000         4    3.50000         4030   \n",
       "3949    722069232 2014-09-05  998000.00000         4    3.25000         3770   \n",
       "4441   3626079040 2014-07-30  790000.00000         2    3.00000         2560   \n",
       "6691   2624089007 2015-03-20 1998000.00000         2    2.50000         3900   \n",
       "7077   2724079090 2015-01-05 1650000.00000         4    3.25000         3920   \n",
       "20452  1125079111 2015-04-15 1600000.00000         4    5.50000         6530   \n",
       "9714    225079036 2015-01-07  937500.00000         4    4.00000         5545   \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  ...  grade  sqft_above  \\\n",
       "1719    1651359 1.00000           0     3  ...      6        1300   \n",
       "17319   1164794 1.00000           0     0  ...      5         710   \n",
       "7647    1074218 1.50000           0     0  ...      8        2010   \n",
       "7769    1024068 2.00000           0     0  ...     10        4030   \n",
       "3949     982998 2.00000           0     0  ...     10        3770   \n",
       "4441     982278 1.00000           0     0  ...      8        2560   \n",
       "6691     920423 2.00000           0     0  ...     12        3900   \n",
       "7077     881654 3.00000           0     3  ...     11        3920   \n",
       "20452    871200 2.00000           0     2  ...     11        6530   \n",
       "9714     871200 2.00000           0     0  ...     11        3605   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat       long  \\\n",
       "1719               0      1920             0    98022 47.23130 -122.02300   \n",
       "17319              0      1915             0    98014 47.68880 -121.90900   \n",
       "7647            1000      1931             0    98027 47.45640 -122.00400   \n",
       "7769               0      2006             0    98045 47.46190 -121.74400   \n",
       "3949               0      1992             0    98058 47.41400 -122.08700   \n",
       "4441               0      2004             0    98014 47.69550 -121.86100   \n",
       "6691               0      2009             0    98065 47.53710 -121.75600   \n",
       "7077               0      2002             0    98024 47.53850 -121.89600   \n",
       "20452              0      2008             0    98014 47.66400 -121.87800   \n",
       "9714            1940      2003             0    98014 47.67600 -121.88200   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "1719            2560      425581  \n",
       "17319           1680       16730  \n",
       "7647            2450       68825  \n",
       "7769            1830       11700  \n",
       "3949            2290       37141  \n",
       "4441            1620       40946  \n",
       "6691            2720      411962  \n",
       "7077            2970      112384  \n",
       "20452           1280      858132  \n",
       "9714            3420      871200  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outlier_identifier(df, 'sqft_lot', c=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>asked_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>15424</th>\n",
       "      <td>1702900664</td>\n",
       "      <td>2015-04-16</td>\n",
       "      <td>479000.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>1730</td>\n",
       "      <td>1037</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1730</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98118</td>\n",
       "      <td>47.55940</td>\n",
       "      <td>-122.28500</td>\n",
       "      <td>1280</td>\n",
       "      <td>1026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11594</th>\n",
       "      <td>3180100023</td>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>544000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>1760</td>\n",
       "      <td>1755</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1760</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>0</td>\n",
       "      <td>98105</td>\n",
       "      <td>47.66880</td>\n",
       "      <td>-122.27900</td>\n",
       "      <td>1700</td>\n",
       "      <td>1721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20308</th>\n",
       "      <td>1972200426</td>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>525000.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.75000</td>\n",
       "      <td>1310</td>\n",
       "      <td>1268</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1310</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.65340</td>\n",
       "      <td>-122.35500</td>\n",
       "      <td>1350</td>\n",
       "      <td>1288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20772</th>\n",
       "      <td>1972200428</td>\n",
       "      <td>2014-06-25</td>\n",
       "      <td>563500.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>1400</td>\n",
       "      <td>1312</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1400</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.65340</td>\n",
       "      <td>-122.35500</td>\n",
       "      <td>1350</td>\n",
       "      <td>1312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14885</th>\n",
       "      <td>8673400177</td>\n",
       "      <td>2015-04-02</td>\n",
       "      <td>525000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>1730</td>\n",
       "      <td>1074</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1730</td>\n",
       "      <td>0</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>98107</td>\n",
       "      <td>47.66920</td>\n",
       "      <td>-122.39200</td>\n",
       "      <td>1370</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id       date  asked_price  bedrooms  bathrooms  sqft_living  \\\n",
       "15424  1702900664 2015-04-16 479000.00000         2    2.50000         1730   \n",
       "11594  3180100023 2015-01-30 544000.00000         3    2.50000         1760   \n",
       "20308  1972200426 2014-09-18 525000.00000         2    2.75000         1310   \n",
       "20772  1972200428 2014-06-25 563500.00000         3    2.50000         1400   \n",
       "14885  8673400177 2015-04-02 525000.00000         3    3.00000         1730   \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  ...  grade  sqft_above  \\\n",
       "15424      1037 3.50000           0     0  ...      8        1730   \n",
       "11594      1755 3.50000           0     0  ...      8        1760   \n",
       "20308      1268 3.50000           0     0  ...      8        1310   \n",
       "20772      1312 3.50000           0     0  ...      8        1400   \n",
       "14885      1074 3.50000           0     0  ...      8        1730   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat       long  \\\n",
       "15424              0      2008             0    98118 47.55940 -122.28500   \n",
       "11594              0      1998             0    98105 47.66880 -122.27900   \n",
       "20308              0      2007             0    98103 47.65340 -122.35500   \n",
       "20772              0      2007             0    98103 47.65340 -122.35500   \n",
       "14885              0      2006             0    98107 47.66920 -122.39200   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "15424           1280        1026  \n",
       "11594           1700        1721  \n",
       "20308           1350        1288  \n",
       "20772           1350        1312  \n",
       "14885           1370        1185  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lqi5I8n9Jbs3RD7gDnBS/gA4AMMBtPgCAAWIKAGCAmAIAGCCmAAAGiCkAgAFiCgBggJgCABggpgAABvw/6QTJoSC7ZhIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outlier_identifier(df, 'floors')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All these properties with very high sqft living values have reasonable features for such area, for instance the price, grade, number of bathrooms, bedrooms, etc. While for properties with very high sqft lot these seem to be located in rural areas, therefore they don't appear to be outliers. In terms of floors and bathrooms, there's nothing out of the ordinary for this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Merging the Address Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data is cleaned we can merge the dataset with the address data. A few properties will show as NaN in their address, since for those properties this information wasn't available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>asked_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>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",
       "      <th>road</th>\n",
       "      <th>house_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>221900.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.51120</td>\n",
       "      <td>-122.25700</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "      <td>61st Avenue South</td>\n",
       "      <td>10012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>538000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25000</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.72100</td>\n",
       "      <td>-122.31900</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "      <td>Northeast 127th Street</td>\n",
       "      <td>837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>180000.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.73790</td>\n",
       "      <td>-122.23300</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "      <td>81st Avenue Northeast</td>\n",
       "      <td>15109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>604000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.52080</td>\n",
       "      <td>-122.39300</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "      <td>Fauntleroy Way Southwest</td>\n",
       "      <td>9236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>510000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.61680</td>\n",
       "      <td>-122.04500</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "      <td>221st Avenue Northeast</td>\n",
       "      <td>907</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>21417</th>\n",
       "      <td>263000018</td>\n",
       "      <td>2014-05-21</td>\n",
       "      <td>360000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>1530</td>\n",
       "      <td>1131</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.69930</td>\n",
       "      <td>-122.34600</td>\n",
       "      <td>1530</td>\n",
       "      <td>1509</td>\n",
       "      <td>North 97th Street</td>\n",
       "      <td>927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21418</th>\n",
       "      <td>6600060120</td>\n",
       "      <td>2015-02-23</td>\n",
       "      <td>400000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>2310</td>\n",
       "      <td>5813</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.51070</td>\n",
       "      <td>-122.36200</td>\n",
       "      <td>1830</td>\n",
       "      <td>7200</td>\n",
       "      <td>Southwest 103rd Place</td>\n",
       "      <td>2211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21419</th>\n",
       "      <td>1523300141</td>\n",
       "      <td>2014-06-23</td>\n",
       "      <td>402101.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>1020</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.59440</td>\n",
       "      <td>-122.29900</td>\n",
       "      <td>1020</td>\n",
       "      <td>2007</td>\n",
       "      <td>26th Avenue South</td>\n",
       "      <td>902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21420</th>\n",
       "      <td>291310100</td>\n",
       "      <td>2015-01-16</td>\n",
       "      <td>400000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>1600</td>\n",
       "      <td>2388</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98027</td>\n",
       "      <td>47.53450</td>\n",
       "      <td>-122.06900</td>\n",
       "      <td>1410</td>\n",
       "      <td>1287</td>\n",
       "      <td>Northwest Boulder Way Drive</td>\n",
       "      <td>2136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21421</th>\n",
       "      <td>1523300157</td>\n",
       "      <td>2014-10-15</td>\n",
       "      <td>325000.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75000</td>\n",
       "      <td>1020</td>\n",
       "      <td>1076</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.59410</td>\n",
       "      <td>-122.29900</td>\n",
       "      <td>1020</td>\n",
       "      <td>1357</td>\n",
       "      <td>26th Avenue South</td>\n",
       "      <td>916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21422 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id       date  asked_price  bedrooms  bathrooms  sqft_living  \\\n",
       "0      7129300520 2014-10-13 221900.00000         3    1.00000         1180   \n",
       "1      6414100192 2014-12-09 538000.00000         3    2.25000         2570   \n",
       "2      5631500400 2015-02-25 180000.00000         2    1.00000          770   \n",
       "3      2487200875 2014-12-09 604000.00000         4    3.00000         1960   \n",
       "4      1954400510 2015-02-18 510000.00000         3    2.00000         1680   \n",
       "...           ...        ...          ...       ...        ...          ...   \n",
       "21417   263000018 2014-05-21 360000.00000         3    2.50000         1530   \n",
       "21418  6600060120 2015-02-23 400000.00000         4    2.50000         2310   \n",
       "21419  1523300141 2014-06-23 402101.00000         2    0.75000         1020   \n",
       "21420   291310100 2015-01-16 400000.00000         3    2.50000         1600   \n",
       "21421  1523300157 2014-10-15 325000.00000         2    0.75000         1020   \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  ...  sqft_basement  yr_built  \\\n",
       "0          5650 1.00000           0     0  ...              0      1955   \n",
       "1          7242 2.00000           0     0  ...            400      1951   \n",
       "2         10000 1.00000           0     0  ...              0      1933   \n",
       "3          5000 1.00000           0     0  ...            910      1965   \n",
       "4          8080 1.00000           0     0  ...              0      1987   \n",
       "...         ...     ...         ...   ...  ...            ...       ...   \n",
       "21417      1131 3.00000           0     0  ...              0      2009   \n",
       "21418      5813 2.00000           0     0  ...              0      2014   \n",
       "21419      1350 2.00000           0     0  ...              0      2009   \n",
       "21420      2388 2.00000           0     0  ...              0      2004   \n",
       "21421      1076 2.00000           0     0  ...              0      2008   \n",
       "\n",
       "       yr_renovated  zipcode      lat       long  sqft_living15  sqft_lot15  \\\n",
       "0                 0    98178 47.51120 -122.25700           1340        5650   \n",
       "1              1991    98125 47.72100 -122.31900           1690        7639   \n",
       "2                 0    98028 47.73790 -122.23300           2720        8062   \n",
       "3                 0    98136 47.52080 -122.39300           1360        5000   \n",
       "4                 0    98074 47.61680 -122.04500           1800        7503   \n",
       "...             ...      ...      ...        ...            ...         ...   \n",
       "21417             0    98103 47.69930 -122.34600           1530        1509   \n",
       "21418             0    98146 47.51070 -122.36200           1830        7200   \n",
       "21419             0    98144 47.59440 -122.29900           1020        2007   \n",
       "21420             0    98027 47.53450 -122.06900           1410        1287   \n",
       "21421             0    98144 47.59410 -122.29900           1020        1357   \n",
       "\n",
       "                              road  house_number  \n",
       "0                61st Avenue South         10012  \n",
       "1           Northeast 127th Street           837  \n",
       "2            81st Avenue Northeast         15109  \n",
       "3         Fauntleroy Way Southwest          9236  \n",
       "4           221st Avenue Northeast           907  \n",
       "...                            ...           ...  \n",
       "21417            North 97th Street           927  \n",
       "21418        Southwest 103rd Place          2211  \n",
       "21419            26th Avenue South           902  \n",
       "21420  Northwest Boulder Way Drive          2136  \n",
       "21421            26th Avenue South           916  \n",
       "\n",
       "[21422 rows x 23 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, df_address, on='id', how='inner').drop(columns=['query'],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Descriptive Analysis - Cleaned Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>asked_price</th>\n",
       "      <td>540604.15694</td>\n",
       "      <td>367696.64143</td>\n",
       "      <td>75000.00000</td>\n",
       "      <td>322500.00000</td>\n",
       "      <td>450000.00000</td>\n",
       "      <td>645000.00000</td>\n",
       "      <td>7700000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>3.37223</td>\n",
       "      <td>0.90339</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>11.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>2.11815</td>\n",
       "      <td>0.76909</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>2.25000</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>8.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>2082.95402</td>\n",
       "      <td>918.91596</td>\n",
       "      <td>370.00000</td>\n",
       "      <td>1430.00000</td>\n",
       "      <td>1920.00000</td>\n",
       "      <td>2550.00000</td>\n",
       "      <td>13540.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>15130.59920</td>\n",
       "      <td>41529.34601</td>\n",
       "      <td>520.00000</td>\n",
       "      <td>5040.00000</td>\n",
       "      <td>7615.00000</td>\n",
       "      <td>10696.75000</td>\n",
       "      <td>1651359.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>1.49594</td>\n",
       "      <td>0.54008</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>0.00761</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>0.23509</td>\n",
       "      <td>0.76713</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>3.41065</td>\n",
       "      <td>0.65000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>7.66236</td>\n",
       "      <td>1.17278</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>13.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>1791.04593</td>\n",
       "      <td>828.74790</td>\n",
       "      <td>370.00000</td>\n",
       "      <td>1200.00000</td>\n",
       "      <td>1560.00000</td>\n",
       "      <td>2220.00000</td>\n",
       "      <td>9410.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>291.90809</td>\n",
       "      <td>442.86393</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>560.00000</td>\n",
       "      <td>4820.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>1971.09182</td>\n",
       "      <td>29.38611</td>\n",
       "      <td>1900.00000</td>\n",
       "      <td>1952.00000</td>\n",
       "      <td>1975.00000</td>\n",
       "      <td>1997.00000</td>\n",
       "      <td>2015.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>84.78517</td>\n",
       "      <td>402.55667</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2015.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>98077.86789</td>\n",
       "      <td>53.47681</td>\n",
       "      <td>98001.00000</td>\n",
       "      <td>98033.00000</td>\n",
       "      <td>98065.00000</td>\n",
       "      <td>98117.00000</td>\n",
       "      <td>98199.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>47.56018</td>\n",
       "      <td>0.13860</td>\n",
       "      <td>47.15590</td>\n",
       "      <td>47.47120</td>\n",
       "      <td>47.57205</td>\n",
       "      <td>47.67800</td>\n",
       "      <td>47.77760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>-122.21373</td>\n",
       "      <td>0.14084</td>\n",
       "      <td>-122.51900</td>\n",
       "      <td>-122.32800</td>\n",
       "      <td>-122.23000</td>\n",
       "      <td>-122.12500</td>\n",
       "      <td>-121.31500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>1988.31561</td>\n",
       "      <td>685.55063</td>\n",
       "      <td>399.00000</td>\n",
       "      <td>1490.00000</td>\n",
       "      <td>1840.00000</td>\n",
       "      <td>2370.00000</td>\n",
       "      <td>6210.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>12778.26085</td>\n",
       "      <td>27345.18092</td>\n",
       "      <td>651.00000</td>\n",
       "      <td>5100.00000</td>\n",
       "      <td>7620.00000</td>\n",
       "      <td>10087.75000</td>\n",
       "      <td>871200.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean          std         min          25%          50%  \\\n",
       "asked_price   540604.15694 367696.64143 75000.00000 322500.00000 450000.00000   \n",
       "bedrooms           3.37223      0.90339     1.00000      3.00000      3.00000   \n",
       "bathrooms          2.11815      0.76909     0.00000      1.75000      2.25000   \n",
       "sqft_living     2082.95402    918.91596   370.00000   1430.00000   1920.00000   \n",
       "sqft_lot       15130.59920  41529.34601   520.00000   5040.00000   7615.00000   \n",
       "floors             1.49594      0.54008     1.00000      1.00000      1.50000   \n",
       "waterfront         0.00761      0.08690     0.00000      0.00000      0.00000   \n",
       "view               0.23509      0.76713     0.00000      0.00000      0.00000   \n",
       "condition          3.41065      0.65000     1.00000      3.00000      3.00000   \n",
       "grade              7.66236      1.17278     3.00000      7.00000      7.00000   \n",
       "sqft_above      1791.04593    828.74790   370.00000   1200.00000   1560.00000   \n",
       "sqft_basement    291.90809    442.86393     0.00000      0.00000      0.00000   \n",
       "yr_built        1971.09182     29.38611  1900.00000   1952.00000   1975.00000   \n",
       "yr_renovated      84.78517    402.55667     0.00000      0.00000      0.00000   \n",
       "zipcode        98077.86789     53.47681 98001.00000  98033.00000  98065.00000   \n",
       "lat               47.56018      0.13860    47.15590     47.47120     47.57205   \n",
       "long            -122.21373      0.14084  -122.51900   -122.32800   -122.23000   \n",
       "sqft_living15   1988.31561    685.55063   399.00000   1490.00000   1840.00000   \n",
       "sqft_lot15     12778.26085  27345.18092   651.00000   5100.00000   7620.00000   \n",
       "\n",
       "                       75%           max  \n",
       "asked_price   645000.00000 7700000.00000  \n",
       "bedrooms           4.00000      11.00000  \n",
       "bathrooms          2.50000       8.00000  \n",
       "sqft_living     2550.00000   13540.00000  \n",
       "sqft_lot       10696.75000 1651359.00000  \n",
       "floors             2.00000       3.50000  \n",
       "waterfront         0.00000       1.00000  \n",
       "view               0.00000       4.00000  \n",
       "condition          4.00000       5.00000  \n",
       "grade              8.00000      13.00000  \n",
       "sqft_above      2220.00000    9410.00000  \n",
       "sqft_basement    560.00000    4820.00000  \n",
       "yr_built        1997.00000    2015.00000  \n",
       "yr_renovated       0.00000    2015.00000  \n",
       "zipcode        98117.00000   98199.00000  \n",
       "lat               47.67800      47.77760  \n",
       "long            -122.12500    -121.31500  \n",
       "sqft_living15   2370.00000    6210.00000  \n",
       "sqft_lot15     10087.75000  871200.00000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive Analysis \n",
    "df[['asked_price','bedrooms','bathrooms', 'sqft_living','sqft_lot',\n",
    "    'floors','waterfront','view','condition','grade','sqft_above',\n",
    "    'sqft_basement','yr_built','yr_renovated','zipcode','lat','long',\n",
    "    'sqft_living15','sqft_lot15','road','house_number']].describe().drop(['count']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. **Feature Engineering** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can solve the business problems it's important to create a new feature:\n",
    "- ad_season: Column that shows on which season the property was announced in the real estate market (spring, summer, fall or winter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.assign(month_day=df['date'].dt.strftime('%m-%d')) # Getting only the month and day\n",
    "season_conditions = [(df['month_day']>='03-22') & (df['month_day']<='06-21'), \n",
    "                     (df['month_day']>='06-22') & (df['month_day']<='09-23'),\n",
    "                     (df['month_day']>='09-24') & (df['month_day']<='12-20')]\n",
    "seasons = ['spring','summer','fall']\n",
    "df = df.assign(ad_season=np.select(season_conditions, seasons, default='winter')) # Creating a column named ad_season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. **Solving both Business Problems** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Problem 1: Which properties should House Rocket buy and for which suggested price?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>properties_in_region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>70.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>306.02857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>140.98720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>49.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>202.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>281.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>403.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>599.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       properties_in_region\n",
       "count              70.00000\n",
       "mean              306.02857\n",
       "std               140.98720\n",
       "min                49.00000\n",
       "25%               202.50000\n",
       "50%               281.50000\n",
       "75%               403.50000\n",
       "max               599.00000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_density_by_zipcode = df[['id','zipcode']].groupby('zipcode').count().reset_index().rename({'id':'properties_in_region'},axis=1) # Getting the density by region\n",
    "df_density_by_zipcode[['properties_in_region']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>asked_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>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>road</th>\n",
       "      <th>house_number</th>\n",
       "      <th>month_day</th>\n",
       "      <th>ad_season</th>\n",
       "      <th>median_price_zipcode_season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>221900.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1180</td>\n",
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       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>98178</td>\n",
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       "      <td>-122.25700</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "      <td>61st Avenue South</td>\n",
       "      <td>10012</td>\n",
       "      <td>10-13</td>\n",
       "      <td>fall</td>\n",
       "      <td>284000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2976800796</td>\n",
       "      <td>2014-09-25</td>\n",
       "      <td>236000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1300</td>\n",
       "      <td>5898</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>98178</td>\n",
       "      <td>47.50530</td>\n",
       "      <td>-122.25500</td>\n",
       "      <td>1320</td>\n",
       "      <td>7619</td>\n",
       "      <td>63rd Avenue South</td>\n",
       "      <td>10658</td>\n",
       "      <td>09-25</td>\n",
       "      <td>fall</td>\n",
       "      <td>284000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3352402236</td>\n",
       "      <td>2014-12-15</td>\n",
       "      <td>252500.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1150</td>\n",
       "      <td>6000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>98178</td>\n",
       "      <td>47.49800</td>\n",
       "      <td>-122.26300</td>\n",
       "      <td>1980</td>\n",
       "      <td>6360</td>\n",
       "      <td>Beacon Avenue South</td>\n",
       "      <td>11708</td>\n",
       "      <td>12-15</td>\n",
       "      <td>fall</td>\n",
       "      <td>284000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1180002378</td>\n",
       "      <td>2014-09-26</td>\n",
       "      <td>299000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>1950</td>\n",
       "      <td>3000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.49770</td>\n",
       "      <td>-122.22600</td>\n",
       "      <td>1170</td>\n",
       "      <td>6000</td>\n",
       "      <td>South 117th Place</td>\n",
       "      <td>8438</td>\n",
       "      <td>09-26</td>\n",
       "      <td>fall</td>\n",
       "      <td>284000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1180500070</td>\n",
       "      <td>2014-11-24</td>\n",
       "      <td>335000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>2330</td>\n",
       "      <td>7050</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.50010</td>\n",
       "      <td>-122.23100</td>\n",
       "      <td>1810</td>\n",
       "      <td>5424</td>\n",
       "      <td>81st Place South</td>\n",
       "      <td>11431</td>\n",
       "      <td>11-24</td>\n",
       "      <td>fall</td>\n",
       "      <td>284000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21417</th>\n",
       "      <td>3262300485</td>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>2250000.00000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.25000</td>\n",
       "      <td>3410</td>\n",
       "      <td>8118</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98039</td>\n",
       "      <td>47.62950</td>\n",
       "      <td>-122.23600</td>\n",
       "      <td>3410</td>\n",
       "      <td>16236</td>\n",
       "      <td>78th Avenue Northeast</td>\n",
       "      <td>2091</td>\n",
       "      <td>04-21</td>\n",
       "      <td>spring</td>\n",
       "      <td>1800000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21418</th>\n",
       "      <td>9558200210</td>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>290000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1240</td>\n",
       "      <td>9300</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98148</td>\n",
       "      <td>47.43630</td>\n",
       "      <td>-122.33300</td>\n",
       "      <td>1250</td>\n",
       "      <td>9300</td>\n",
       "      <td>South 186th Street</td>\n",
       "      <td>205</td>\n",
       "      <td>02-17</td>\n",
       "      <td>winter</td>\n",
       "      <td>282750.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21419</th>\n",
       "      <td>854000165</td>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>285000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1400</td>\n",
       "      <td>7582</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98148</td>\n",
       "      <td>47.45360</td>\n",
       "      <td>-122.33000</td>\n",
       "      <td>1280</td>\n",
       "      <td>7872</td>\n",
       "      <td>3rd Avenue South</td>\n",
       "      <td>16711</td>\n",
       "      <td>01-07</td>\n",
       "      <td>winter</td>\n",
       "      <td>282750.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21420</th>\n",
       "      <td>5245400030</td>\n",
       "      <td>2015-02-23</td>\n",
       "      <td>255000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1250</td>\n",
       "      <td>9102</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98148</td>\n",
       "      <td>47.42580</td>\n",
       "      <td>-122.32700</td>\n",
       "      <td>1260</td>\n",
       "      <td>9180</td>\n",
       "      <td>South 198th Street</td>\n",
       "      <td>620</td>\n",
       "      <td>02-23</td>\n",
       "      <td>winter</td>\n",
       "      <td>282750.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21421</th>\n",
       "      <td>4438000075</td>\n",
       "      <td>2015-03-18</td>\n",
       "      <td>280500.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1670</td>\n",
       "      <td>6988</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98148</td>\n",
       "      <td>47.42830</td>\n",
       "      <td>-122.32300</td>\n",
       "      <td>1090</td>\n",
       "      <td>7753</td>\n",
       "      <td>South 194th Street</td>\n",
       "      <td>907</td>\n",
       "      <td>03-18</td>\n",
       "      <td>winter</td>\n",
       "      <td>282750.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21422 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id       date   asked_price  bedrooms  bathrooms  sqft_living  \\\n",
       "0      7129300520 2014-10-13  221900.00000         3    1.00000         1180   \n",
       "1      2976800796 2014-09-25  236000.00000         3    1.00000         1300   \n",
       "2      3352402236 2014-12-15  252500.00000         3    2.00000         1150   \n",
       "3      1180002378 2014-09-26  299000.00000         4    2.50000         1950   \n",
       "4      1180500070 2014-11-24  335000.00000         4    2.50000         2330   \n",
       "...           ...        ...           ...       ...        ...          ...   \n",
       "21417  3262300485 2015-04-21 2250000.00000         5    5.25000         3410   \n",
       "21418  9558200210 2015-02-17  290000.00000         3    1.00000         1240   \n",
       "21419   854000165 2015-01-07  285000.00000         3    1.50000         1400   \n",
       "21420  5245400030 2015-02-23  255000.00000         4    1.00000         1250   \n",
       "21421  4438000075 2015-03-18  280500.00000         3    1.50000         1670   \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  ...  zipcode      lat       long  \\\n",
       "0          5650 1.00000           0     0  ...    98178 47.51120 -122.25700   \n",
       "1          5898 1.00000           0     0  ...    98178 47.50530 -122.25500   \n",
       "2          6000 1.00000           0     0  ...    98178 47.49800 -122.26300   \n",
       "3          3000 2.00000           0     0  ...    98178 47.49770 -122.22600   \n",
       "4          7050 2.00000           0     0  ...    98178 47.50010 -122.23100   \n",
       "...         ...     ...         ...   ...  ...      ...      ...        ...   \n",
       "21417      8118 2.00000           0     0  ...    98039 47.62950 -122.23600   \n",
       "21418      9300 1.00000           0     0  ...    98148 47.43630 -122.33300   \n",
       "21419      7582 1.00000           0     0  ...    98148 47.45360 -122.33000   \n",
       "21420      9102 1.00000           0     0  ...    98148 47.42580 -122.32700   \n",
       "21421      6988 1.00000           0     0  ...    98148 47.42830 -122.32300   \n",
       "\n",
       "       sqft_living15  sqft_lot15                   road  house_number  \\\n",
       "0               1340        5650      61st Avenue South         10012   \n",
       "1               1320        7619      63rd Avenue South         10658   \n",
       "2               1980        6360    Beacon Avenue South         11708   \n",
       "3               1170        6000      South 117th Place          8438   \n",
       "4               1810        5424       81st Place South         11431   \n",
       "...              ...         ...                    ...           ...   \n",
       "21417           3410       16236  78th Avenue Northeast          2091   \n",
       "21418           1250        9300     South 186th Street           205   \n",
       "21419           1280        7872       3rd Avenue South         16711   \n",
       "21420           1260        9180     South 198th Street           620   \n",
       "21421           1090        7753     South 194th Street           907   \n",
       "\n",
       "       month_day  ad_season  median_price_zipcode_season  \n",
       "0          10-13       fall                 284000.00000  \n",
       "1          09-25       fall                 284000.00000  \n",
       "2          12-15       fall                 284000.00000  \n",
       "3          09-26       fall                 284000.00000  \n",
       "4          11-24       fall                 284000.00000  \n",
       "...          ...        ...                          ...  \n",
       "21417      04-21     spring                1800000.00000  \n",
       "21418      02-17     winter                 282750.00000  \n",
       "21419      01-07     winter                 282750.00000  \n",
       "21420      02-23     winter                 282750.00000  \n",
       "21421      03-18     winter                 282750.00000  \n",
       "\n",
       "[21422 rows x 26 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_medians_buy = df[['zipcode', 'asked_price','ad_season']].groupby(['zipcode','ad_season']).median().reset_index().rename({'asked_price': 'median_price_zipcode_season'}, axis=1) # Getting the price median for each zipcode \n",
    "df_suggestions = pd.merge(df, df_medians_buy, on=['zipcode','ad_season'], how='inner') # Creating a new DataFrame by adding the median_price column to the original DataFrame\n",
    "df_suggestions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's necessary to determine the buy suggestions based on conditions:\n",
    "- Their asked price has to be lower than the median price for that region\n",
    "- The property needs to be in good conditions, which means condition >= 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-122.23400</td>\n",
       "      <td>1800000.00000</td>\n",
       "      <td>3710000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
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       "      <th>21418</th>\n",
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       "      <td>98039</td>\n",
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       "      <td>47.63350</td>\n",
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       "      <td>1800000.00000</td>\n",
       "      <td>1200000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
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       "      <th>21419</th>\n",
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       "      <td>98039</td>\n",
       "      <td>49</td>\n",
       "      <td>47.62450</td>\n",
       "      <td>-122.23600</td>\n",
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       "      <td>1880000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>21420</th>\n",
       "      <td>2425049066</td>\n",
       "      <td>2014-06-16</td>\n",
       "      <td>spring</td>\n",
       "      <td>98039</td>\n",
       "      <td>49</td>\n",
       "      <td>47.64000</td>\n",
       "      <td>-122.24000</td>\n",
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       "      <td>47.62950</td>\n",
       "      <td>-122.23600</td>\n",
       "      <td>1800000.00000</td>\n",
       "      <td>2250000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
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       "<p>21422 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "               id       date ad_season  zipcode  properties_in_region  \\\n",
       "0      7129300520 2014-10-13      fall    98178                   258   \n",
       "1      2976800796 2014-09-25      fall    98178                   258   \n",
       "2      3352402236 2014-12-15      fall    98178                   258   \n",
       "3      1180002378 2014-09-26      fall    98178                   258   \n",
       "4      1180500070 2014-11-24      fall    98178                   258   \n",
       "...           ...        ...       ...      ...                   ...   \n",
       "21417  7397300170 2014-05-30    spring    98039                    49   \n",
       "21418  3262300920 2015-04-08    spring    98039                    49   \n",
       "21419  2470200020 2014-05-14    spring    98039                    49   \n",
       "21420  2425049066 2014-06-16    spring    98039                    49   \n",
       "21421  3262300485 2015-04-21    spring    98039                    49   \n",
       "\n",
       "           lat       long  median_price_zipcode_season   asked_price  \\\n",
       "0     47.51120 -122.25700                 284000.00000  221900.00000   \n",
       "1     47.50530 -122.25500                 284000.00000  236000.00000   \n",
       "2     47.49800 -122.26300                 284000.00000  252500.00000   \n",
       "3     47.49770 -122.22600                 284000.00000  299000.00000   \n",
       "4     47.50010 -122.23100                 284000.00000  335000.00000   \n",
       "...        ...        ...                          ...           ...   \n",
       "21417 47.63950 -122.23400                1800000.00000 3710000.00000   \n",
       "21418 47.63350 -122.23600                1800000.00000 1200000.00000   \n",
       "21419 47.62450 -122.23600                1800000.00000 1880000.00000   \n",
       "21420 47.64000 -122.24000                1800000.00000 1920000.00000   \n",
       "21421 47.62950 -122.23600                1800000.00000 2250000.00000   \n",
       "\n",
       "       condition buy_suggestion  \n",
       "0              3            Yes  \n",
       "1              3            Yes  \n",
       "2              5            Yes  \n",
       "3              3             No  \n",
       "4              3             No  \n",
       "...          ...            ...  \n",
       "21417          4             No  \n",
       "21418          3            Yes  \n",
       "21419          4             No  \n",
       "21420          4             No  \n",
       "21421          3             No  \n",
       "\n",
       "[21422 rows x 11 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buy_condition = [((df_suggestions['asked_price'] < df_suggestions['median_price_zipcode_season']) & (df_suggestions['condition']>=3))] # Conditions\n",
    "df_suggestions['buy_suggestion'] = np.select(buy_condition, ['Yes'], default='No') # Using np.select to create the buy suggestion column\n",
    "df_buy = pd.merge(df_suggestions, df_density_by_zipcode, on='zipcode', how='inner')[['id','date','ad_season','zipcode','properties_in_region','lat','long','median_price_zipcode_season','asked_price','condition','buy_suggestion']].rename({'price':'asked_price'},axis=1)\n",
    "df_buy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the suggested properties to be bought are determined, the suggested price for each one of them needs to be set, based on the density by region. The rule for suggested buy prices is:\n",
    "\n",
    "- From 0 to 204 properties in the region => Offer the asked price \n",
    "- From 205 to 282 properties in the region => Offer 3% less than the asked price\n",
    "- From 283 to 408 properties in the region => Offer 4% less than the asked price\n",
    "- From 409 properties upwards => Offer 5% less than the asked price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
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       "      <th>3</th>\n",
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       "      <td>3</td>\n",
       "      <td>No</td>\n",
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       "      <td>-122.23100</td>\n",
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       "      <td>335000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Not suggested</td>\n",
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       "      <th>...</th>\n",
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       "      <td>4</td>\n",
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       "      <td>3262300920</td>\n",
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       "      <td>98039</td>\n",
       "      <td>49</td>\n",
       "      <td>47.63350</td>\n",
       "      <td>-122.23600</td>\n",
       "      <td>1800000.00000</td>\n",
       "      <td>1200000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1200000.0</td>\n",
       "    </tr>\n",
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       "      <th>21419</th>\n",
       "      <td>2470200020</td>\n",
       "      <td>2014-05-14</td>\n",
       "      <td>spring</td>\n",
       "      <td>98039</td>\n",
       "      <td>49</td>\n",
       "      <td>47.62450</td>\n",
       "      <td>-122.23600</td>\n",
       "      <td>1800000.00000</td>\n",
       "      <td>1880000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>Not suggested</td>\n",
       "    </tr>\n",
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       "      <th>21420</th>\n",
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       "      <td>49</td>\n",
       "      <td>47.64000</td>\n",
       "      <td>-122.24000</td>\n",
       "      <td>1800000.00000</td>\n",
       "      <td>1920000.00000</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>Not suggested</td>\n",
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       "    <tr>\n",
       "      <th>21421</th>\n",
       "      <td>3262300485</td>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>spring</td>\n",
       "      <td>98039</td>\n",
       "      <td>49</td>\n",
       "      <td>47.62950</td>\n",
       "      <td>-122.23600</td>\n",
       "      <td>1800000.00000</td>\n",
       "      <td>2250000.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Not suggested</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21422 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id       date ad_season  zipcode  properties_in_region  \\\n",
       "0      7129300520 2014-10-13      fall    98178                   258   \n",
       "1      2976800796 2014-09-25      fall    98178                   258   \n",
       "2      3352402236 2014-12-15      fall    98178                   258   \n",
       "3      1180002378 2014-09-26      fall    98178                   258   \n",
       "4      1180500070 2014-11-24      fall    98178                   258   \n",
       "...           ...        ...       ...      ...                   ...   \n",
       "21417  7397300170 2014-05-30    spring    98039                    49   \n",
       "21418  3262300920 2015-04-08    spring    98039                    49   \n",
       "21419  2470200020 2014-05-14    spring    98039                    49   \n",
       "21420  2425049066 2014-06-16    spring    98039                    49   \n",
       "21421  3262300485 2015-04-21    spring    98039                    49   \n",
       "\n",
       "           lat       long  median_price_zipcode_season   asked_price  \\\n",
       "0     47.51120 -122.25700                 284000.00000  221900.00000   \n",
       "1     47.50530 -122.25500                 284000.00000  236000.00000   \n",
       "2     47.49800 -122.26300                 284000.00000  252500.00000   \n",
       "3     47.49770 -122.22600                 284000.00000  299000.00000   \n",
       "4     47.50010 -122.23100                 284000.00000  335000.00000   \n",
       "...        ...        ...                          ...           ...   \n",
       "21417 47.63950 -122.23400                1800000.00000 3710000.00000   \n",
       "21418 47.63350 -122.23600                1800000.00000 1200000.00000   \n",
       "21419 47.62450 -122.23600                1800000.00000 1880000.00000   \n",
       "21420 47.64000 -122.24000                1800000.00000 1920000.00000   \n",
       "21421 47.62950 -122.23600                1800000.00000 2250000.00000   \n",
       "\n",
       "       condition buy_suggestion suggested_buy_price  \n",
       "0              3            Yes            215243.0  \n",
       "1              3            Yes            228920.0  \n",
       "2              5            Yes            244925.0  \n",
       "3              3             No       Not suggested  \n",
       "4              3             No       Not suggested  \n",
       "...          ...            ...                 ...  \n",
       "21417          4             No       Not suggested  \n",
       "21418          3            Yes           1200000.0  \n",
       "21419          4             No       Not suggested  \n",
       "21420          4             No       Not suggested  \n",
       "21421          3             No       Not suggested  \n",
       "\n",
       "[21422 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "setting_buy_price_suggestion(df_buy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "               id       date  zipcode  median_price_zipcode_season  \\\n",
       "0      7129300520 2014-10-13    98178                 284000.00000   \n",
       "1      2976800796 2014-09-25    98178                 284000.00000   \n",
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       "21420  2425049066 2014-06-16    98039                1800000.00000   \n",
       "21421  3262300485 2015-04-21    98039                1800000.00000   \n",
       "\n",
       "        asked_price buy_suggestion suggested_buy_price                   road  \\\n",
       "0      221900.00000            Yes            215243.0      61st Avenue South   \n",
       "1      236000.00000            Yes            228920.0      63rd Avenue South   \n",
       "2      252500.00000            Yes            244925.0    Beacon Avenue South   \n",
       "3      299000.00000             No       Not suggested      South 117th Place   \n",
       "4      335000.00000             No       Not suggested       81st Place South   \n",
       "...             ...            ...                 ...                    ...   \n",
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       "\n",
       "      house_number  \n",
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       "...            ...  \n",
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       "\n",
       "[21422 rows x 9 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_buy_final = df_buy[['id','date','zipcode','median_price_zipcode_season','asked_price','buy_suggestion','suggested_buy_price']]\n",
    "df_buy_final = pd.merge(df_buy_final, df_address, on='id', how='inner').drop(columns=['query'],axis=1)\n",
    "df_buy_final # Final data set with buy suggestions and suggested buy prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Problem 2: Once a property is bought, for which price should it be sold?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rule for suggesting a sell price for those properties is:\n",
    "- From 0 to 204 properties in the region => Sell for 16% than the suggested buy price \n",
    "- From 205 to 282 properties in the region => Sell for 14% than the suggested buy price\n",
    "- From 283 to 408 properties in the region => Sell for 12% than the suggested buy price\n",
    "- From 409 properties upwards => Sell for 10% than the suggested buy price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sell = df_buy[['id','date','ad_season','zipcode','properties_in_region','lat','long','asked_price','buy_suggestion','suggested_buy_price']] # Setting the sell DataFrame1\n",
    "df_sell = df_sell.loc[df_sell['buy_suggestion']=='Yes'].drop(columns=['buy_suggestion', 'asked_price'],axis=1) # Getting only the properties that were suggested to be bought\n",
    "df_sell['suggested_buy_price'] = pd.to_numeric(df_sell['suggested_buy_price']) # Converting suggested_buy_price to float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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       "               id       date ad_season  zipcode  properties_in_region  \\\n",
       "0      7129300520 2014-10-13      fall    98178                   258   \n",
       "1      2976800796 2014-09-25      fall    98178                   258   \n",
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       "5       179000350 2014-11-05      fall    98178                   258   \n",
       "8       179003055 2014-11-13      fall    98178                   258   \n",
       "...           ...        ...       ...      ...                   ...   \n",
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       "21416  3025300250 2015-05-13    spring    98039                    49   \n",
       "21418  3262300920 2015-04-08    spring    98039                    49   \n",
       "\n",
       "           lat       long  suggested_buy_price  suggested_sell_price  \\\n",
       "0     47.51120 -122.25700         215243.00000          245377.02000   \n",
       "1     47.50530 -122.25500         228920.00000          260968.80000   \n",
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       "5     47.49250 -122.27800         188180.00000          214525.20000   \n",
       "8     47.49210 -122.27500         203700.00000          232218.00000   \n",
       "...        ...        ...                  ...                   ...   \n",
       "21409 47.61910 -122.23400        1398000.00000         1621680.00000   \n",
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       "21416 47.62320 -122.23600        1620000.00000         1879200.00000   \n",
       "21418 47.63350 -122.23600        1200000.00000         1392000.00000   \n",
       "\n",
       "            profit  \n",
       "0      30134.02000  \n",
       "1      32048.80000  \n",
       "2      34289.50000  \n",
       "5      26345.20000  \n",
       "8      28518.00000  \n",
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       "\n",
       "[10435 rows x 10 columns]"
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     },
     "execution_count": 31,
     "metadata": {},
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   ],
   "source": [
    "setting_sell_price_suggestion(df_sell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <td>30134.02000</td>\n",
       "      <td>61st Avenue South</td>\n",
       "      <td>10012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2976800796</td>\n",
       "      <td>fall</td>\n",
       "      <td>98178</td>\n",
       "      <td>228920.00000</td>\n",
       "      <td>260968.80000</td>\n",
       "      <td>32048.80000</td>\n",
       "      <td>63rd Avenue South</td>\n",
       "      <td>10658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3352402236</td>\n",
       "      <td>fall</td>\n",
       "      <td>98178</td>\n",
       "      <td>244925.00000</td>\n",
       "      <td>279214.50000</td>\n",
       "      <td>34289.50000</td>\n",
       "      <td>Beacon Avenue South</td>\n",
       "      <td>11708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>179000350</td>\n",
       "      <td>fall</td>\n",
       "      <td>98178</td>\n",
       "      <td>188180.00000</td>\n",
       "      <td>214525.20000</td>\n",
       "      <td>26345.20000</td>\n",
       "      <td>44th Avenue South</td>\n",
       "      <td>12253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>179003055</td>\n",
       "      <td>fall</td>\n",
       "      <td>98178</td>\n",
       "      <td>203700.00000</td>\n",
       "      <td>232218.00000</td>\n",
       "      <td>28518.00000</td>\n",
       "      <td>South 124th Street</td>\n",
       "      <td>4623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10430</th>\n",
       "      <td>2525049133</td>\n",
       "      <td>spring</td>\n",
       "      <td>98039</td>\n",
       "      <td>1398000.00000</td>\n",
       "      <td>1621680.00000</td>\n",
       "      <td>223680.00000</td>\n",
       "      <td>Northeast 10th Street</td>\n",
       "      <td>7903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10431</th>\n",
       "      <td>3262300322</td>\n",
       "      <td>spring</td>\n",
       "      <td>98039</td>\n",
       "      <td>1651000.00000</td>\n",
       "      <td>1915160.00000</td>\n",
       "      <td>264160.00000</td>\n",
       "      <td>77th Avenue Northeast</td>\n",
       "      <td>2027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10432</th>\n",
       "      <td>5427110040</td>\n",
       "      <td>spring</td>\n",
       "      <td>98039</td>\n",
       "      <td>1225000.00000</td>\n",
       "      <td>1421000.00000</td>\n",
       "      <td>196000.00000</td>\n",
       "      <td>Northeast 28th Street</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10433</th>\n",
       "      <td>3025300250</td>\n",
       "      <td>spring</td>\n",
       "      <td>98039</td>\n",
       "      <td>1620000.00000</td>\n",
       "      <td>1879200.00000</td>\n",
       "      <td>259200.00000</td>\n",
       "      <td>Northeast 14th Street</td>\n",
       "      <td>7744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10434</th>\n",
       "      <td>3262300920</td>\n",
       "      <td>spring</td>\n",
       "      <td>98039</td>\n",
       "      <td>1200000.00000</td>\n",
       "      <td>1392000.00000</td>\n",
       "      <td>192000.00000</td>\n",
       "      <td>78th Avenue Northeast</td>\n",
       "      <td>2511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10435 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id ad_season  zipcode  suggested_buy_price  \\\n",
       "0      7129300520      fall    98178         215243.00000   \n",
       "1      2976800796      fall    98178         228920.00000   \n",
       "2      3352402236      fall    98178         244925.00000   \n",
       "3       179000350      fall    98178         188180.00000   \n",
       "4       179003055      fall    98178         203700.00000   \n",
       "...           ...       ...      ...                  ...   \n",
       "10430  2525049133    spring    98039        1398000.00000   \n",
       "10431  3262300322    spring    98039        1651000.00000   \n",
       "10432  5427110040    spring    98039        1225000.00000   \n",
       "10433  3025300250    spring    98039        1620000.00000   \n",
       "10434  3262300920    spring    98039        1200000.00000   \n",
       "\n",
       "       suggested_sell_price       profit                   road house_number  \n",
       "0              245377.02000  30134.02000      61st Avenue South        10012  \n",
       "1              260968.80000  32048.80000      63rd Avenue South        10658  \n",
       "2              279214.50000  34289.50000    Beacon Avenue South        11708  \n",
       "3              214525.20000  26345.20000      44th Avenue South        12253  \n",
       "4              232218.00000  28518.00000     South 124th Street         4623  \n",
       "...                     ...          ...                    ...          ...  \n",
       "10430         1621680.00000 223680.00000  Northeast 10th Street         7903  \n",
       "10431         1915160.00000 264160.00000  77th Avenue Northeast         2027  \n",
       "10432         1421000.00000 196000.00000  Northeast 28th Street          NaN  \n",
       "10433         1879200.00000 259200.00000  Northeast 14th Street         7744  \n",
       "10434         1392000.00000 192000.00000  78th Avenue Northeast         2511  \n",
       "\n",
       "[10435 rows x 8 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sell = df_sell[['id','ad_season','zipcode','lat','long','suggested_buy_price','suggested_sell_price','profit']]\n",
    "df_sell_final = df_sell[['id','ad_season','zipcode','suggested_buy_price','suggested_sell_price','profit']]\n",
    "df_sell_final = pd.merge(df_sell_final, df_address, on='id', how='inner').drop(columns=['query'],axis=1)\n",
    "df_sell_final  # Final data set with suggested sell prices and profit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. **Financial Results**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three interesting metrics to evaluate the financial performance for this solution is the profit mean and median (grouped by ad_season, zipcode and ad_season with zipcode), as well as the total profit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>473094328.48000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>45337.26195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>39995.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22772.81950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8217.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30210.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39995.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54144.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>350036.80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                profit\n",
       "total  473094328.48000\n",
       "mean       45337.26195\n",
       "median     39995.00000\n",
       "std        22772.81950\n",
       "min         8217.50000\n",
       "25%        30210.00000\n",
       "50%        39995.00000\n",
       "75%        54144.00000\n",
       "max       350036.80000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df_sell[['profit']].describe().drop(['count'])\n",
    "median_profit = pd.DataFrame(df_sell[['profit']].apply(np.median)).rename(columns={0:'median'}).transpose() # Adding the median\n",
    "total_profit = pd.DataFrame(df_sell[['profit']].apply(np.sum)).rename(columns={0:'total'}).transpose() # Adding the sum\n",
    "df_profit = pd.concat([df1, median_profit, total_profit]).reindex(['total','mean','median','std','min','25%','50%','75%','max'])\n",
    "\n",
    "df_profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sell[['profit']].boxplot(figsize=(10,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>ad_season</th>\n",
       "      <th>avg_profit</th>\n",
       "      <th>median_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fall</td>\n",
       "      <td>44130.53530</td>\n",
       "      <td>38475.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>spring</td>\n",
       "      <td>46985.94344</td>\n",
       "      <td>41538.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>summer</td>\n",
       "      <td>45211.13089</td>\n",
       "      <td>40375.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>winter</td>\n",
       "      <td>44085.70760</td>\n",
       "      <td>38000.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ad_season  avg_profit  median_profit\n",
       "0      fall 44130.53530    38475.00000\n",
       "1    spring 46985.94344    41538.75000\n",
       "2    summer 45211.13089    40375.00000\n",
       "3    winter 44085.70760    38000.00000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_avg_profit_by_season = avg_profit_func(df_sell,['ad_season','profit'], 'ad_season')    \n",
    "med_avg_profit_by_season # Average and median profit by ad_season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>avg_profit</th>\n",
       "      <th>median_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98001</td>\n",
       "      <td>47.30207</td>\n",
       "      <td>-122.26829</td>\n",
       "      <td>24334.99432</td>\n",
       "      <td>24768.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98002</td>\n",
       "      <td>47.30402</td>\n",
       "      <td>-122.21578</td>\n",
       "      <td>30863.81224</td>\n",
       "      <td>31756.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98003</td>\n",
       "      <td>47.31872</td>\n",
       "      <td>-122.31096</td>\n",
       "      <td>30262.66820</td>\n",
       "      <td>30616.11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98004</td>\n",
       "      <td>47.61274</td>\n",
       "      <td>-122.20072</td>\n",
       "      <td>97809.52985</td>\n",
       "      <td>97257.60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98005</td>\n",
       "      <td>47.60147</td>\n",
       "      <td>-122.16810</td>\n",
       "      <td>100777.83901</td>\n",
       "      <td>101600.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>98177</td>\n",
       "      <td>47.74495</td>\n",
       "      <td>-122.36565</td>\n",
       "      <td>57869.29143</td>\n",
       "      <td>57850.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>98178</td>\n",
       "      <td>47.49737</td>\n",
       "      <td>-122.25069</td>\n",
       "      <td>29500.48929</td>\n",
       "      <td>30351.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>98188</td>\n",
       "      <td>47.44769</td>\n",
       "      <td>-122.28250</td>\n",
       "      <td>34510.77091</td>\n",
       "      <td>36000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>98198</td>\n",
       "      <td>47.39384</td>\n",
       "      <td>-122.31342</td>\n",
       "      <td>28484.95196</td>\n",
       "      <td>28687.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>98199</td>\n",
       "      <td>47.64987</td>\n",
       "      <td>-122.39502</td>\n",
       "      <td>60939.85052</td>\n",
       "      <td>61632.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    zipcode      lat       long   avg_profit  median_profit\n",
       "0     98001 47.30207 -122.26829  24334.99432    24768.00000\n",
       "1     98002 47.30402 -122.21578  30863.81224    31756.00000\n",
       "2     98003 47.31872 -122.31096  30262.66820    30616.11000\n",
       "3     98004 47.61274 -122.20072  97809.52985    97257.60000\n",
       "4     98005 47.60147 -122.16810 100777.83901   101600.00000\n",
       "..      ...      ...        ...          ...            ...\n",
       "65    98177 47.74495 -122.36565  57869.29143    57850.80000\n",
       "66    98178 47.49737 -122.25069  29500.48929    30351.30000\n",
       "67    98188 47.44769 -122.28250  34510.77091    36000.00000\n",
       "68    98198 47.39384 -122.31342  28484.95196    28687.75000\n",
       "69    98199 47.64987 -122.39502  60939.85052    61632.00000\n",
       "\n",
       "[70 rows x 5 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_avg_profit_by_zipcode = avg_profit_func(df_sell,['zipcode','profit','lat','long'], 'zipcode')    \n",
    "med_avg_profit_by_zipcode = med_avg_profit_by_zipcode[['zipcode','lat','long','avg_profit','median_profit']]\n",
    "med_avg_profit_by_zipcode # Average and median profit by zipcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>zipcode</th>\n",
       "      <th>ad_season</th>\n",
       "      <th>avg_profit</th>\n",
       "      <th>median_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98001</td>\n",
       "      <td>fall</td>\n",
       "      <td>23510.84012</td>\n",
       "      <td>24192.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98001</td>\n",
       "      <td>spring</td>\n",
       "      <td>24139.57045</td>\n",
       "      <td>24768.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98001</td>\n",
       "      <td>summer</td>\n",
       "      <td>24648.50824</td>\n",
       "      <td>25228.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98001</td>\n",
       "      <td>winter</td>\n",
       "      <td>25126.49193</td>\n",
       "      <td>26092.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98002</td>\n",
       "      <td>fall</td>\n",
       "      <td>30588.63333</td>\n",
       "      <td>30800.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>98198</td>\n",
       "      <td>winter</td>\n",
       "      <td>28661.92375</td>\n",
       "      <td>27903.50500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>98199</td>\n",
       "      <td>fall</td>\n",
       "      <td>56809.97053</td>\n",
       "      <td>58792.32000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>98199</td>\n",
       "      <td>spring</td>\n",
       "      <td>63535.24357</td>\n",
       "      <td>64961.28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>98199</td>\n",
       "      <td>summer</td>\n",
       "      <td>63336.10423</td>\n",
       "      <td>61632.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>98199</td>\n",
       "      <td>winter</td>\n",
       "      <td>57793.14432</td>\n",
       "      <td>62208.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>280 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     zipcode ad_season  avg_profit  median_profit\n",
       "0      98001      fall 23510.84012    24192.00000\n",
       "1      98001    spring 24139.57045    24768.00000\n",
       "2      98001    summer 24648.50824    25228.80000\n",
       "3      98001    winter 25126.49193    26092.80000\n",
       "4      98002      fall 30588.63333    30800.00000\n",
       "..       ...       ...         ...            ...\n",
       "275    98198    winter 28661.92375    27903.50500\n",
       "276    98199      fall 56809.97053    58792.32000\n",
       "277    98199    spring 63535.24357    64961.28000\n",
       "278    98199    summer 63336.10423    61632.00000\n",
       "279    98199    winter 57793.14432    62208.00000\n",
       "\n",
       "[280 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_avg_profit_by_zipcode_season = avg_profit_func(df_sell,['zipcode','ad_season','profit'], ['zipcode','ad_season'])    \n",
    "med_avg_profit_by_zipcode_season # Average and median profit by ad_season and zipcode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. **Hypotheses, Business Insights and Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All these hypothesis have the potential to become a business insight. An insight is a discovery that can be turned into a useful doable action, which means those have an **Usage**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### **H1** - Properties that possess waterfront view are, on average, considerably more expensive in comparison to the ones that do not have such feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: Properties that possess waterfront view are, on average, 212.38% more expensive in comparison to the ones that do not have such feature.\n"
     ]
    }
   ],
   "source": [
    "no_waterfront_avg_price = df['asked_price'][df['waterfront']==0].mean()\n",
    "waterfront_avg_price = df['asked_price'][df['waterfront']==1].mean()\n",
    "\n",
    "price_comparison = ((waterfront_avg_price/no_waterfront_avg_price) - 1)*100\n",
    "\n",
    "print(f'True: Properties that possess waterfront view are, on average, {round(price_comparison,2)}% more expensive in comparison to the ones that do not have such feature.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_water = df[['asked_price', 'waterfront']].groupby('waterfront').mean().reset_index()\n",
    "df_water['waterfront'] = df_water['waterfront'].apply(lambda x: \"No\" if x==0 else \"Yes\")\n",
    "\n",
    "bar_plot(df_water,'waterfront','Waterfront View','asked_price','Average Price (US$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSIGHT**\n",
    "\n",
    "**Usage**: House Rocket could focus on buying and selling waterfront view properties, since the profit will be higher in absolut values.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### H2 - A property year built is directly proportional to its price, on average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False: Properties with the lowest prices (on average) were built from 1941 to 1983. This means that houses built from 1900-1940 have higher prices than the ones built from 1941-1983\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_yrbuilt = period(df)\n",
    "bar_plot(df_yrbuilt,'Period','Period','asked_price','Average Price (US$)')\n",
    "# plt.savefig('images/periods.pdf', bbox_inches='tight') \n",
    "print(f'False: Properties with the lowest prices (on average) were built from 1941 to 1983. This means that houses built from 1900-1940 have higher prices than the ones built from 1941-1983')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSIGHT**\n",
    "\n",
    "**Usage**: House Rocket would have higher profits buying and selling properties built from the mid 1980's upwards, as well as the ones built from 1900 to 1940."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### H3 - Properties that do not have a basement are, on average, around 30% cheaper in comparison to the ones that have such feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: Properties that do not have a basement are, on average, 27.71% cheaper in comparison to the ones that have such feature.\n"
     ]
    }
   ],
   "source": [
    "price_no_basement = df.loc[df['sqft_basement']==0, 'asked_price'].mean()\n",
    "price_basement = df.loc[df['sqft_basement']!=0, 'asked_price'].mean()\n",
    "sqft_comparison = ((price_basement/price_no_basement) - 1)*100\n",
    "print(f'True: Properties that do not have a basement are, on average, {round(sqft_comparison,2)}% cheaper in comparison to the ones that have such feature.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_basement = pd.DataFrame({'asked_price':[price_basement,price_no_basement]}, index=['Yes','No']).reset_index().rename({'index':'basement'},axis=1)\n",
    "bar_plot(df_basement,'basement', 'Basement','asked_price','Average Price (US$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSIGHT**\n",
    "\n",
    "**Usage**: House Rocket could look to buy properties without a basement that have the potential to possess one. Therefore these properties can be sold for a lot higher price. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### H4 - The average price growth YoY considering all properties in the dataset is 5%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False: The average price growth YoY considering all properties in the dataset is 1.09%.\n"
     ]
    }
   ],
   "source": [
    "df_price = df[['asked_price','yr_built']].groupby('yr_built').mean().reset_index()\n",
    "df_price['year_growth'] = (df_price['asked_price'].pct_change())*100\n",
    "df_avg_price = df_price['year_growth'].mean()\n",
    "\n",
    "print(f'False: The average price growth YoY considering all properties in the dataset is {round(df_avg_price,2)}%.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### H5 - The average price growth MoM (month over month) for properties with three bathrooms is 1%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we can also consider a property with three bathrooms the ones in which this value is equal to 2.5 or 2.75, as .5 represents a room with a toilet but no shower or bath, and .75 represents a room with a toilet and shower or bath, not both. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False: The average price growth MoM for properties with three bathrooms is -0.609%.\n"
     ]
    }
   ],
   "source": [
    "df_3_bathrooms = df[['id','date','asked_price','bathrooms']].loc[(df['bathrooms']>=2.5) & (df['bathrooms']<=3.00)]\n",
    "df_3_bathrooms['month'] = df_3_bathrooms['date'].dt.strftime('%Y-%m')\n",
    "df_3_bathrooms = df_3_bathrooms[['asked_price','month']].groupby('month').mean().reset_index()\n",
    "\n",
    "df_3_bathrooms['month_growth'] = (df_3_bathrooms['asked_price'].pct_change())*100\n",
    "df_3_bathrooms_avg_price = df_3_bathrooms['month_growth'].mean()\n",
    "\n",
    "print(f'False: The average price growth MoM for properties with three bathrooms is {round(df_3_bathrooms_avg_price,3)}%.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### H6 - Properties with good views are, on average, around two times more expensive than the ones with not so good views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: Properties with good views are, on average, 1.89 times more expensive than the ones with not so good views\n"
     ]
    }
   ],
   "source": [
    "df_bad_view = df.loc[df['view']==0, 'asked_price'].mean()\n",
    "df_good_view = df.loc[df['view']!=0, 'asked_price'].mean()\n",
    "\n",
    "view_comparison = df_good_view/df_bad_view\n",
    "\n",
    "print(f'True: Properties with good views are, on average, {round(view_comparison,2)} times more expensive than the ones with not so good views')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_view = pd.DataFrame({'asked_price':[df_good_view,df_bad_view]}, index=['Yes','No']).reset_index().rename({'index':'good_view'},axis=1)\n",
    "bar_plot(df_view,'good_view', 'Good View','asked_price','Average Price (US$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSIGHT**\n",
    "\n",
    "**Usage**: House Rocket could focus on buying and selling properties with good views, since the profit will be higher in absolut values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### H7 - If a property has a waterfront view those necessarily have a good view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: 100.0% of properties with waterfront view also have a good view, necessarily.\n"
     ]
    }
   ],
   "source": [
    "water_and_good_view = len(df.loc[(df['view']!=0)&(df['waterfront']==1)]) # all properties with waterfront view and good views\n",
    "water_view = len(df.loc[(df['waterfront']==1)]) # all properties with waterfront view\n",
    "\n",
    "water_view_comparison = (water_and_good_view/water_view)*100\n",
    "\n",
    "print(f'True: {round(water_view_comparison,2)}% of properties with waterfront view also have a good view, necessarily.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### H8 - Properties that were renovated are, on average, in better conditions in comparison to the ones that weren't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False: The properties that have better conditions are, on average, the not renovated ones.\n"
     ]
    }
   ],
   "source": [
    "not_renovated = df.loc[(df['yr_renovated']==0), 'condition'].mean()\n",
    "renovated = df.loc[(df['yr_renovated']!=0), 'condition'].mean()\n",
    "dict_renovated = {'renovated':renovated, 'not renovated':not_renovated}\n",
    "\n",
    "print(f'False: The properties that have better conditions are, on average, the {max(dict_renovated, key=dict_renovated.get)} ones.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### H9 - Regions bordering Lake Washington produce, on average, considerably more profit in comparison to other regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: Regions bordering Lake Washington produce, on average, 36.23% more profit in comparison to other regions.\n"
     ]
    }
   ],
   "source": [
    "zipcodes_lake = [98178,98118,98144,98112,98122,98105,98115,98125,98155,98028,98034,98033,98004,98039,98040,98006,98056]\n",
    "avg_profit_lake = med_avg_profit_by_zipcode.loc[med_avg_profit_by_zipcode['zipcode'].isin(zipcodes_lake), 'avg_profit'].mean()\n",
    "avg_profit_not_lake = med_avg_profit_by_zipcode.loc[~med_avg_profit_by_zipcode['zipcode'].isin(zipcodes_lake), 'avg_profit'].mean()\n",
    "comparison_lake = ((avg_profit_lake/avg_profit_not_lake) - 1)*100\n",
    "print(f'True: Regions bordering Lake Washington produce, on average, {round(comparison_lake,2)}% more profit in comparison to other regions.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_lake = pd.DataFrame({'avg_profit':[avg_profit_lake,avg_profit_not_lake]}, index=['Yes','No']).reset_index().rename({'index':'around_lake'},axis=1)\n",
    "bar_plot(df_lake,'around_lake', 'Around Lake Washington','avg_profit','Average Profit (US$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSIGHT**\n",
    "\n",
    "**Usage**: House Rocket could focus on buying and selling properties around Lake Washington, since the profit will be higher in absolut values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. **Conclusion**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project the two main objectives were accomplished:\n",
    "\n",
    " - A feasible solution was found for both business problems, leading to profitable results.\n",
    " - Five interesting and useful insights were found through Exploratory Data Analysis (EDA).\n",
    "\n",
    " We also managed to deliver tables with in-depth financial results, as well as buy and sell suggestion tables. All this information can be filtered by using the [House Rocket Cloud App](https://brunodifranco-house-rocket-app-house-rocket-app-4dn0re.streamlitapp.com/), that also has the five business insights and a interactive Buy Suggestion Map.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. **Next Steps**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further on, this solution could be improved by using [regression models](https://www.imsl.com/blog/what-is-regression-model) to determine wheter a property is in a good buying price, and for which price it could be bought and sold. Another interesting study would be to produce a market research, so that data about clients could be collected. Then, a [clustering algorithm](https://machinelearningmastery.com/clustering-algorithms-with-python/) could be applied to identify what types of property features each group of customers would prefer. "
   ]
  }
 ],
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