Part 2. Outliers Visualization and Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 49352 entries, 4 to 124009\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   bathrooms        49352 non-null  float64\n",
      " 1   bedrooms         49352 non-null  int64  \n",
      " 2   building_id      49352 non-null  object \n",
      " 3   created          49352 non-null  object \n",
      " 4   description      49352 non-null  object \n",
      " 5   display_address  49352 non-null  object \n",
      " 6   features         49352 non-null  object \n",
      " 7   latitude         49352 non-null  float64\n",
      " 8   listing_id       49352 non-null  int64  \n",
      " 9   longitude        49352 non-null  float64\n",
      " 10  manager_id       49352 non-null  object \n",
      " 11  photos           49352 non-null  object \n",
      " 12  price            49352 non-null  int64  \n",
      " 13  street_address   49352 non-null  object \n",
      " 14  interest_level   49352 non-null  object \n",
      "dtypes: float64(3), int64(3), object(9)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.read_json(\"./datasets/train.json\")\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Identify outliers for the \"Bathrooms\" field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the renthop website, users are given the following options to select for features of their properties as shown below:\n",
    "\n",
    "![image.png](https://media.discordapp.net/attachments/664927198523686912/674363960048353300/unknown.png?width=400&height=248)\n",
    "\n",
    "For the number of bedrooms, selecting one of \"Loft\", \"Room/Share\" and \"Studio\" seems to set the number of bedrooms to 0 in our dataset. It is pre-processed by the company, so we make an assumption here. \n",
    "\n",
    "However, the number of bathrooms must be 1+ bath and not contain any decimal place according to their options.\n",
    "\n",
    "Therefore, any listing that has the number of bathrooms equal to 0 is a faulty data and defined as an outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df2 = df2.loc[df2['bathrooms'] > 0]\n",
    "indexes = list(new_df2.index.values.tolist())\n",
    "values = list(new_df2['bathrooms'][indexes])\n",
    "\n",
    "# for index in indexes:\n",
    "#     print(index, \",\", df2['bathrooms'][index])\n",
    "\n",
    "# #testing\n",
    "# new_df2.loc[new_df2['bathrooms'] == 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    49039.000000\n",
       "mean         1.219917\n",
       "std          0.493546\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          1.000000\n",
       "max         10.000000\n",
       "Name: bathrooms, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.scatter(indexes, new_df2['bathrooms'][indexes], edgecolor = 'black')\n",
    "plt.xlabel('indexes')\n",
    "plt.ylabel('Number of Bathrooms')\n",
    "plt.show()\n",
    "new_df2['bathrooms'].describe() # 49352 previously /w outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Is the one datapoint with 10 bathrooms an outlier?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>listing_id</th>\n",
       "      <th>longitude</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.7633</td>\n",
       "      <td>6849204.0</td>\n",
       "      <td>-73.9849</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.7633</td>\n",
       "      <td>6849204.0</td>\n",
       "      <td>-73.9849</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.7633</td>\n",
       "      <td>6849204.0</td>\n",
       "      <td>-73.9849</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.7633</td>\n",
       "      <td>6849204.0</td>\n",
       "      <td>-73.9849</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.7633</td>\n",
       "      <td>6849204.0</td>\n",
       "      <td>-73.9849</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.7633</td>\n",
       "      <td>6849204.0</td>\n",
       "      <td>-73.9849</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bathrooms  bedrooms  latitude  listing_id  longitude   price\n",
       "count        1.0       1.0    1.0000         1.0     1.0000     1.0\n",
       "mean        10.0       2.0   40.7633   6849204.0   -73.9849  3600.0\n",
       "std          NaN       NaN       NaN         NaN        NaN     NaN\n",
       "min         10.0       2.0   40.7633   6849204.0   -73.9849  3600.0\n",
       "25%         10.0       2.0   40.7633   6849204.0   -73.9849  3600.0\n",
       "50%         10.0       2.0   40.7633   6849204.0   -73.9849  3600.0\n",
       "75%         10.0       2.0   40.7633   6849204.0   -73.9849  3600.0\n",
       "max         10.0       2.0   40.7633   6849204.0   -73.9849  3600.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df2[new_df2['bathrooms'] == 10].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>listing_id</th>\n",
       "      <th>longitude</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>38.723515</td>\n",
       "      <td>7.024429e+06</td>\n",
       "      <td>-70.273665</td>\n",
       "      <td>27214.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.598243</td>\n",
       "      <td>9.114611</td>\n",
       "      <td>1.383369e+05</td>\n",
       "      <td>16.540716</td>\n",
       "      <td>16313.099522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.812493e+06</td>\n",
       "      <td>-74.011300</td>\n",
       "      <td>9995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.751325</td>\n",
       "      <td>6.951518e+06</td>\n",
       "      <td>-73.976475</td>\n",
       "      <td>18624.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.775950</td>\n",
       "      <td>7.036624e+06</td>\n",
       "      <td>-73.964600</td>\n",
       "      <td>26750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>40.777500</td>\n",
       "      <td>7.114054e+06</td>\n",
       "      <td>-73.963100</td>\n",
       "      <td>28500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>40.786000</td>\n",
       "      <td>7.232801e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bathrooms   bedrooms   latitude    listing_id  longitude         price\n",
       "count       20.0  20.000000  20.000000  2.000000e+01  20.000000     20.000000\n",
       "mean         5.0   4.400000  38.723515  7.024429e+06 -70.273665  27214.700000\n",
       "std          0.0   0.598243   9.114611  1.383369e+05  16.540716  16313.099522\n",
       "min          5.0   4.000000   0.000000  6.812493e+06 -74.011300   9995.000000\n",
       "25%          5.0   4.000000  40.751325  6.951518e+06 -73.976475  18624.250000\n",
       "50%          5.0   4.000000  40.775950  7.036624e+06 -73.964600  26750.000000\n",
       "75%          5.0   5.000000  40.777500  7.114054e+06 -73.963100  28500.000000\n",
       "max          5.0   6.000000  40.786000  7.232801e+06   0.000000  85000.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df2[new_df2['bathrooms'] == 5].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the listing with \"10 bathrooms\" is a lot cheaper than the min of the listings with \"5 bathrooms\". It looks odd. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df2 = df2.loc[ (df2['bathrooms'] > 0) & (df2['bathrooms'] != 10)]\n",
    "indexes = list(new_df2.index.values.tolist())\n",
    "values = list(new_df2['bathrooms'][indexes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    49038.000000\n",
       "mean         1.219738\n",
       "std          0.491956\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          1.000000\n",
       "max          7.000000\n",
       "Name: bathrooms, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.scatter(indexes, new_df2['bathrooms'][indexes], edgecolor = 'black')\n",
    "plt.xlabel('indexes')\n",
    "plt.ylabel('Number of Bathrooms')\n",
    "plt.show()\n",
    "new_df2['bathrooms'].describe() # 49352 previously /w outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 314\n"
     ]
    }
   ],
   "source": [
    "x = df2['bathrooms'].loc[(df2['bathrooms'] == 0) | (df2['bathrooms'] == 10)].count()\n",
    "print(\"Number of outliers:\", x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Using Z-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 1035\n"
     ]
    }
   ],
   "source": [
    "z = np.abs(stats.zscore(df2['bathrooms']))\n",
    "threshold = 3\n",
    "y = np.where(z>3)\n",
    "print(\"Number of outliers:\", y[0].size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Identify outliers for the \"bedrooms\" field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As discussed above, we assume that selecting any one of \"Roft\", \"Room/Share\" and \"Studio\" will set the number of bedrooms to 0. So, we keep any row that contains 0 bedroom. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    49352.000000\n",
       "mean         1.541640\n",
       "std          1.115018\n",
       "min          0.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          2.000000\n",
       "max          8.000000\n",
       "Name: bedrooms, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexes = list(df2.index.values.tolist())\n",
    "values = list(df2['bedrooms'][indexes])\n",
    "    \n",
    "plt.scatter(indexes, df2['bedrooms'][indexes], edgecolor = 'black')\n",
    "plt.xlabel('indexes')\n",
    "plt.ylabel('Number of Bedrooms')\n",
    "plt.show()\n",
    "df2['bedrooms'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Try different types of graph to see the distribution of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f67ba13cdd8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df2['bedrooms'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As discussed earlier, we assume selecting any one of \"Roft\", \"Room/Share\" and \"Studio\" will set the number of bedrooms to 0. In the graph above, 0 is in the quartiles of our dataset which means 0 bedroom are valid data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3 = df2.loc[df2['bedrooms'] > 3]\n",
    "sns.boxplot(x=df3['bedrooms'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see most of our bedroom data entries lie between 1 and 2. Any other points above 3 may be outliers as they are no where near the quartiles. We investigated those points by ploting another graph to see their quartiles. It shows a single vertical line, which means they don't have relevance to each other. However, we don't know if they are meaningful - they are factual/valid data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Compare prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      2226.000000\n",
       "mean       7446.745732\n",
       "std        5977.921077\n",
       "min        1025.000000\n",
       "25%        5000.000000\n",
       "50%        6000.000000\n",
       "75%        7495.000000\n",
       "max      100000.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['price'].loc[df2['bedrooms'] > 3].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.712600e+04\n",
       "mean     3.659345e+03\n",
       "std      2.253029e+04\n",
       "min      4.300000e+01\n",
       "25%      2.470000e+03\n",
       "50%      3.095000e+03\n",
       "75%      3.961000e+03\n",
       "max      4.490000e+06\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['price'].loc[df2['bedrooms'] <= 3].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean price of listings with the number of bedrooms above 3 is greater than the mean price of listings with the number of bedrooms below 3. This assures that all \"bedrooms\" values are reasonable, so we should keep all of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of outliers:\", 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Using Z-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 297\n"
     ]
    }
   ],
   "source": [
    "z = np.abs(stats.zscore(df2['bedrooms']))\n",
    "threshold = 3\n",
    "y = np.where(z>3)\n",
    "print(\"Number of outliers:\", y[0].size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Identify outliers for the \"building_id\" field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f67ba40f438>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['building_id'].str.len()\n",
    "sns.boxplot(x=df2['building_id'].str.len())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 8286\n"
     ]
    }
   ],
   "source": [
    "# print(df2['building_id'].count())\n",
    "print(\"Number of outliers:\",\n",
    "df2['building_id'].loc[df2['building_id'].str.len() != 32].count())\n",
    "\n",
    "# testing\n",
    "# df2.loc[df2['building_id'].str.len() != 32]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Identify outliers for the \"created\" field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f67ba40d3c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df2['created'].str.len())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f67ba0cf978>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df2['created'].loc[df2['created'].str.len() == 19]\n",
    "x['dates'] = pd.to_datetime(x, format='%Y-%m-%d %H:%M:%S')\n",
    "y = x['dates']\n",
    "plt.plot(x['dates'].dt.date, x['dates'].dt.hour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing the ourlier detection for 'created' attribute is not meaningful. As shown in two graphs above, all dates follow the same format and are of 19 characters in legnth. In addition to that, listings are distingushed by milisecond which means each listing will likely have its own timestamp differentiated by a millisecond. So there's not much meaning of 'created' attribute with respect to the objective of the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Identify outliers for other non-numeric attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4         Spacious 1 Bedroom 1 Bathroom in Williamsburg!...\n",
       "6         BRAND NEW GUT RENOVATED TRUE 2 BEDROOMFind you...\n",
       "9         **FLEX 2 BEDROOM WITH FULL PRESSURIZED WALL**L...\n",
       "10        A Brand New 3 Bedroom 1.5 bath ApartmentEnjoy ...\n",
       "15        Over-sized Studio w abundant closets. Availabl...\n",
       "                                ...                        \n",
       "124000    There is 700 square feet of recently renovated...\n",
       "124002    2 bedroom apartment with updated kitchen, rece...\n",
       "124004    No Brokers Fee * Never Lived 1 Bedroom 1 Bathr...\n",
       "124008    Wonderful Bright Chelsea 2 Bedroom apartment o...\n",
       "124009    ***PRIME MIDTOWN EAST OFF PARK AVE***TRUE 3 BE...\n",
       "Name: description, Length: 49352, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['description']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4          145 Borinquen Place\n",
       "6                    East 44th\n",
       "9             East 56th Street\n",
       "10         Metropolitan Avenue\n",
       "15            East 34th Street\n",
       "                  ...         \n",
       "124000            W 171 Street\n",
       "124002                Broadway\n",
       "124004    210 Brighton 15th St\n",
       "124008        West 21st Street\n",
       "124009               E 54th St\n",
       "Name: display_address, Length: 49352, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['display_address']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4         [Dining Room, Pre-War, Laundry in Building, Di...\n",
       "6         [Doorman, Elevator, Laundry in Building, Dishw...\n",
       "9         [Doorman, Elevator, Laundry in Building, Laund...\n",
       "10                                                       []\n",
       "15        [Doorman, Elevator, Fitness Center, Laundry in...\n",
       "                                ...                        \n",
       "124000              [Elevator, Dishwasher, Hardwood Floors]\n",
       "124002    [Common Outdoor Space, Cats Allowed, Dogs Allo...\n",
       "124004    [Dining Room, Elevator, Pre-War, Laundry in Bu...\n",
       "124008    [Pre-War, Laundry in Unit, Dishwasher, No Fee,...\n",
       "124009    [Dining Room, Elevator, Laundry in Building, D...\n",
       "Name: features, Length: 49352, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['features']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4         a10db4590843d78c784171a107bdacb4\n",
       "6         955db33477af4f40004820b4aed804a0\n",
       "9         c8b10a317b766204f08e613cef4ce7a0\n",
       "10        5ba989232d0489da1b5f2c45f6688adc\n",
       "15        2c3b41f588fbb5234d8a1e885a436cfa\n",
       "                        ...               \n",
       "124000    a61e21da3ba18c7a3d54cfdcc247e1f8\n",
       "124002    8f90e5e10e8a2d7cf997f016d89230eb\n",
       "124004    a10db4590843d78c784171a107bdacb4\n",
       "124008    c3cd45f4381ac371507090e9ffabea80\n",
       "124009    e90f2ded843cdb2efd65ef47d9fc8029\n",
       "Name: manager_id, Length: 49352, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['manager_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4         [https://photos.renthop.com/2/7170325_3bb5ac84...\n",
       "6         [https://photos.renthop.com/2/7092344_7663c19a...\n",
       "9         [https://photos.renthop.com/2/7158677_c897a134...\n",
       "10        [https://photos.renthop.com/2/7211212_1ed4542e...\n",
       "15        [https://photos.renthop.com/2/7225292_901f1984...\n",
       "                                ...                        \n",
       "124000    [https://photos.renthop.com/2/6824800_0682be16...\n",
       "124002    [https://photos.renthop.com/2/6813268_1e6fcc32...\n",
       "124004    [https://photos.renthop.com/2/6927093_93a52104...\n",
       "124008    [https://photos.renthop.com/2/6892816_1a8d087a...\n",
       "124009    [https://photos.renthop.com/2/6901023_02052d90...\n",
       "Name: photos, Length: 49352, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['photos']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4             145 Borinquen Place\n",
       "6                   230 East 44th\n",
       "9            405 East 56th Street\n",
       "10        792 Metropolitan Avenue\n",
       "15           340 East 34th Street\n",
       "                   ...           \n",
       "124000           620 W 171 Street\n",
       "124002              3333 Broadway\n",
       "124004       210 Brighton 15th St\n",
       "124008       350 West 21st Street\n",
       "124009              123 E 54th St\n",
       "Name: street_address, Length: 49352, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['street_address']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4         medium\n",
       "6            low\n",
       "9         medium\n",
       "10        medium\n",
       "15           low\n",
       "           ...  \n",
       "124000       low\n",
       "124002    medium\n",
       "124004    medium\n",
       "124008    medium\n",
       "124009      high\n",
       "Name: interest_level, Length: 49352, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['interest_level']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Non-numeric attributes in our dataset are all unique. There's no way to identify one as an outlier as every single one of them has unique values. Even if 'interest_level' has only 3 categories, there's no way to tell any one of those categories is an outlier. There's no boundary among different values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Identify outliers for the \"latitude\" field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lat-long coorditates for cities in United States are in range: \n",
    "- Latitude from 19.50139 to 64.85694\n",
    "- longitude from -161.75583 to -68.01197"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    49340.000000\n",
       "mean        40.751454\n",
       "std          0.063417\n",
       "min         34.012600\n",
       "25%         40.728300\n",
       "50%         40.751800\n",
       "75%         40.774300\n",
       "max         44.883500\n",
       "Name: latitude, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df2['latitude'].loc[\n",
    "    (df2['latitude'] >= 19.50139) & \n",
    "    (df2['latitude'] <= 64.85694)\n",
    "    ]\n",
    "\n",
    "indexes = list(x.index.values.tolist())\n",
    "values = list(x[indexes])\n",
    "\n",
    "plt.scatter(values,indexes, edgecolor = 'black')\n",
    "plt.xlabel('indexes')\n",
    "plt.ylabel('Number of Bathrooms')\n",
    "plt.show()\n",
    "\n",
    "x.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apart from one data point that is afar, other data points are difficult to identify as outliers. So, we can use z-score to find extreme outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 18\n"
     ]
    }
   ],
   "source": [
    "# Use Z-score to find outers\n",
    "z = np.abs(stats.zscore(df2['latitude']))\n",
    "threshold = 3\n",
    "y = np.where(z>3)\n",
    "print(\"Number of outliers:\", y[0].size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulted arry contains the list of row numbers that have a Z-score higher than 3, which are outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Identify outliers for the \"longitude\" field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    49340.000000\n",
       "mean       -73.973702\n",
       "std          0.239243\n",
       "min       -118.271000\n",
       "25%        -73.991700\n",
       "50%        -73.977900\n",
       "75%        -73.954800\n",
       "max        -70.984600\n",
       "Name: longitude, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df2['longitude'].loc[\n",
    "    (df2['longitude'] >= -161.75583) & \n",
    "    (df2['longitude'] <= -68.01197)\n",
    "    ]\n",
    "\n",
    "indexes = list(x.index.values.tolist())\n",
    "values = list(x[indexes])\n",
    "\n",
    "plt.scatter(values,indexes, edgecolor = 'black')\n",
    "plt.xlabel('indexes')\n",
    "plt.ylabel('Number of Bathrooms')\n",
    "plt.show()\n",
    "\n",
    "x.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 16\n"
     ]
    }
   ],
   "source": [
    "z = np.abs(stats.zscore(df2['longitude']))\n",
    "threshold = 3\n",
    "y = np.where(z>3)\n",
    "print(\"Number of outliers:\", y[0].size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f67ba0c65f8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df2['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    4.935200e+04\n",
       "mean     3.830174e+03\n",
       "std      2.206687e+04\n",
       "min      4.300000e+01\n",
       "25%      2.500000e+03\n",
       "50%      3.150000e+03\n",
       "75%      4.100000e+03\n",
       "max      4.490000e+06\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df2['price']\n",
    "indexes = list(x.index.values.tolist())\n",
    "values = list(x[indexes])\n",
    "\n",
    "plt.scatter(values,indexes, edgecolor = 'black')\n",
    "plt.xlabel('indexes')\n",
    "plt.ylabel('Number of Bathrooms')\n",
    "plt.show()\n",
    "\n",
    "x.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 10\n"
     ]
    }
   ],
   "source": [
    "z = np.abs(stats.zscore(df2['price']))\n",
    "threshold = 3\n",
    "y = np.where(z>3)\n",
    "print(\"Number of outliers:\", y[0].size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Identify outliers for the \"listing_id\" field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4         7170325\n",
       "6         7092344\n",
       "9         7158677\n",
       "10        7211212\n",
       "15        7225292\n",
       "           ...   \n",
       "124000    6824800\n",
       "124002    6813268\n",
       "124004    6927093\n",
       "124008    6892816\n",
       "124009    6901023\n",
       "Name: listing_id, Length: 49352, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['listing_id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Listing_id is also uniquely assigned for each listing. Therefore, outlier detection is meaningless for this attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
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