# data[].attributes.body
Yay! Another assignment completed :)

# data[].attributes.body
Another milestone!

# data[].attributes.body
would be grateful for advice re: Q5 - not sure why hdbdf does not have any missing values after merging the dataframes :(

# data[].attributes.body
hi all, grateful if any one can advise on my Q7a and Q7b. Can't seem to figure out what I did wrong.  Any help/advice is much appreciated.
TIA!!!

# data[].attributes.attachment_link.readable_text
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bsxvpzCMZTfG",
    "tags": []
   },
   "source": [
    "#  Capstone Assignment 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "L5SAGf4TZTfI",
    "tags": [],
    "trust": true
   },
   "source": [
    "## Background\n",
    "You are a HDB officer who has just completed the DC Bootcamp <br/>\n",
    "As your first data task, your director has asked you to tackle some of the data processing tasks and analyses.<br/>\n",
    "\n",
    "\n",
    "To aid you in your task, you have been provided with the following **hdb_resale.csv** dataset. <br/>\n",
    "See the description below for the fields given. <br/>\n",
    "<br/>\n",
    "<table>\n",
    "    <tr><th>Field</th><th>Description</th></tr>\n",
    "    <tr><td>Trans_YearMonth</td><td>year-month (YYYY-MM) for transaction</td></tr>\n",
    "    <tr><td>year</td><td>year (YYYY) for transaction</td></tr>\n",
    "    <tr><td>town</td><td>town for the resale flat</td></tr>\n",
    "    <tr><td>flat_type</td><td>flat type for the resale flat</td></tr>\n",
    "    <tr><td>block</td><td>block for the resale flat</td></tr>\n",
    "    <tr><td>street_name</td><td>street for the resale flat</td></tr>\n",
    "    <tr><td>storey_range</td><td>storey range for the resale flat</td></tr>\n",
    "    <tr><td>floor_area_sqm</td><td>floor area in square meter</td></tr>\n",
    "    <tr><td>flat_model</td><td>model of resale flat</td></tr>\n",
    "    <tr><td>lease_commence_date</td><td>year of lease commencement</td></tr>\n",
    "    <tr><td>resale_price</td><td>price of the resale flat</td></tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "4Zk5YNN8ZTfJ",
    "tags": [],
    "trust": true
   },
   "source": [
    "---\n",
    "⚠️**Notebook Instruction:**\n",
    "- This is an open-book test. You may refer to previous notes or internet sources at any point during the test.\n",
    "- You are forbidden to share or discuss any questions/solutions with other students.\n",
    "- If you need to seek for any clarifications, you may privately message the trainers.  \n",
    "\n",
    "\n",
    "- Feel free to insert additional cell(s) as you need.\n",
    "- You may import any packages that you need. \n",
    "- You may create additional variables, columns, or object in this notebook to derive at your solution. \n",
    "- If not specifed, you may use any name for your variable or column.\n",
    "- If any expected output is provided, you are ought to remove any additional columns that is not included in the expected output. Failure in doing so, will result in reduction of marks.\n",
    "\n",
    "\n",
    "- ⬇️ Enter your name as a string in cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "RcDEZJkkZTfK"
   },
   "outputs": [],
   "source": [
    "Name = \"Wong Huan Leong\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "A4uPByClZTfL",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Import Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "LpsR5VyVZTfL",
    "tags": [],
    "trust": true
   },
   "source": [
    "> Cell below is for you to write the codes for importing the packages that you need to use in this notebook.\\\n",
    "> You can also import the package(s) elsewhere within this notebook and will not be penalized for that.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "b7iSVLk-ZTfL"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wong_huan_leong\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\compat\\_optional.py:138: UserWarning: Pandas requires version '2.7.0' or newer of 'numexpr' (version '2.6.8' currently installed).\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "wHgmbYW8ZTfL",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 1 - Importing and Checking the Data\n",
    "You decided to import the data into Pandas and do some exploration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yuXuEA-CZTfM"
   },
   "source": [
    "> **[ Question 1a ]** Read in **hdb_resale.csv** as a dataframe and store it in a variable called **hdbdf**. <br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "1JP9k-E9ZTfM"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded\n"
     ]
    }
   ],
   "source": [
    "hdbdf = pd.read_csv(\"hdb_resale.csv\")\n",
    "print('loaded')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IHM2kFYWZTfM"
   },
   "source": [
    "> **[ Question 1b ]** Check if there are any missing values in all of the columns.<br/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "6scoREKGZTfN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 185968 entries, 0 to 185967\n",
      "Data columns (total 11 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   Tranc_YearMonth      185968 non-null  object \n",
      " 1   year                 185968 non-null  int64  \n",
      " 2   town                 185968 non-null  object \n",
      " 3   flat_type            185968 non-null  object \n",
      " 4   block                185968 non-null  object \n",
      " 5   street_name          185968 non-null  object \n",
      " 6   storey_range         185968 non-null  object \n",
      " 7   floor_area_sqm       185968 non-null  float64\n",
      " 8   flat_model           185968 non-null  object \n",
      " 9   lease_commence_date  185968 non-null  int64  \n",
      " 10  resale_price         185968 non-null  float64\n",
      "dtypes: float64(2), int64(2), object(7)\n",
      "memory usage: 15.6+ MB\n"
     ]
    }
   ],
   "source": [
    "hdbdf.info()\n",
    "# no missing value found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3.5\n"
     ]
    }
   ],
   "source": [
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.21.6\n"
     ]
    }
   ],
   "source": [
    "print(np.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: numpy in c:\\users\\wong_huan_leong\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (1.21.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# run this in needed to upgrade numpy e.g. if df.info() gives errors\n",
    "pip install --upgrade numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Downloading https://files.pythonhosted.org/packages/b2/56/f886ed6f1777ffa9d54c6e80231b69db8a1f52dcc33f5967b06a105dcfe0/pandas-1.3.5-cp37-cp37m-win_amd64.whl (10.0MB)\n",
      "Requirement already satisfied, skipping upgrade: pytz>=2017.3 in c:\\users\\wong_huan_leong\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from pandas) (2018.9)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in c:\\users\\wong_huan_leong\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from pandas) (2.8.0)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.17.3; platform_machine != \"aarch64\" and platform_machine != \"arm64\" and python_version < \"3.10\" in c:\\users\\wong_huan_leong\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from pandas) (1.21.6)\n",
      "Requirement already satisfied, skipping upgrade: six>=1.5 in c:\\users\\wong_huan_leong\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.12.0)\n",
      "Installing collected packages: pandas\n",
      "  Found existing installation: pandas 1.0.3\n",
      "    Uninstalling pandas-1.0.3:\n",
      "      Successfully uninstalled pandas-1.0.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not install packages due to an EnvironmentError: [WinError 5] Access is denied: 'c:\\\\users\\\\wong_huan_leong\\\\appdata\\\\roaming\\\\python\\\\python37\\\\site-packages\\\\~andas\\\\_libs\\\\algos.cp37-win_amd64.pyd'\n",
      "Consider using the `--user` option or check the permissions.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# run this if needed to upgrade pandas e.g. if df.info() gives errors\n",
    "pip install --upgrade pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZWmYUI70ZTfN"
   },
   "source": [
    "> **[Question 1c]** Display the top 10 rows of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "FyuXvKUwZTfN"
   },
   "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>Tranc_YearMonth</th>\n",
       "      <th>year</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>block</th>\n",
       "      <th>street_name</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>174</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>1986</td>\n",
       "      <td>255000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>541</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1981</td>\n",
       "      <td>275000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>163</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>69.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>285000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>446</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1979</td>\n",
       "      <td>290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>557</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>603</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>709</td>\n",
       "      <td>ANG MO KIO AVE 8</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>333</td>\n",
       "      <td>ANG MO KIO AVE 1</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1981</td>\n",
       "      <td>293000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>109</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1978</td>\n",
       "      <td>300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>564</td>\n",
       "      <td>ANG MO KIO AVE 3</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1985</td>\n",
       "      <td>307500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Tranc_YearMonth  year        town flat_type block        street_name  \\\n",
       "0         2015-01  2015  ANG MO KIO    3 ROOM   174   ANG MO KIO AVE 4   \n",
       "1         2015-01  2015  ANG MO KIO    3 ROOM   541  ANG MO KIO AVE 10   \n",
       "2         2015-01  2015  ANG MO KIO    3 ROOM   163   ANG MO KIO AVE 4   \n",
       "3         2015-01  2015  ANG MO KIO    3 ROOM   446  ANG MO KIO AVE 10   \n",
       "4         2015-01  2015  ANG MO KIO    3 ROOM   557  ANG MO KIO AVE 10   \n",
       "5         2015-01  2015  ANG MO KIO    3 ROOM   603   ANG MO KIO AVE 5   \n",
       "6         2015-01  2015  ANG MO KIO    3 ROOM   709   ANG MO KIO AVE 8   \n",
       "7         2015-01  2015  ANG MO KIO    3 ROOM   333   ANG MO KIO AVE 1   \n",
       "8         2015-01  2015  ANG MO KIO    3 ROOM   109   ANG MO KIO AVE 4   \n",
       "9         2015-01  2015  ANG MO KIO    3 ROOM   564   ANG MO KIO AVE 3   \n",
       "\n",
       "  storey_range  floor_area_sqm      flat_model  lease_commence_date  \\\n",
       "0     07 TO 09            60.0        Improved                 1986   \n",
       "1     01 TO 03            68.0  New Generation                 1981   \n",
       "2     01 TO 03            69.0  New Generation                 1980   \n",
       "3     01 TO 03            68.0  New Generation                 1979   \n",
       "4     07 TO 09            68.0  New Generation                 1980   \n",
       "5     07 TO 09            67.0  New Generation                 1980   \n",
       "6     01 TO 03            68.0  New Generation                 1980   \n",
       "7     01 TO 03            68.0  New Generation                 1981   \n",
       "8     01 TO 03            67.0  New Generation                 1978   \n",
       "9     13 TO 15            68.0  New Generation                 1985   \n",
       "\n",
       "   resale_price  \n",
       "0      255000.0  \n",
       "1      275000.0  \n",
       "2      285000.0  \n",
       "3      290000.0  \n",
       "4      290000.0  \n",
       "5      290000.0  \n",
       "6      290000.0  \n",
       "7      293000.0  \n",
       "8      300000.0  \n",
       "9      307500.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "RjXBvUzlZTfN",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 2 - Accessing and Filtering DataFrame\n",
    "You decided to do further exploration on the crucial fields in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "sLVvakU5ZTfN",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[Question 2a]**  Find out how many unique values are there in the **town** column. Your code should show  **26** as the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "ZW3f9L84ZTfO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf['town'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "mu8Z370EZTfO",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[Question 2b]** Select all the records that are \"3 ROOM\" and also \"Model A\". \\\n",
    "> No need to assign the output to any variable, just show the filtered dataframe on the notebook.\\\n",
    "> Your output should looks this:\n",
    ">> ![](markdown_resources/q002b.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "fXNm3-SnZTfO"
   },
   "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>Tranc_YearMonth</th>\n",
       "      <th>year</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>block</th>\n",
       "      <th>street_name</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>BEDOK</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>221B</td>\n",
       "      <td>BEDOK CTRL</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2010</td>\n",
       "      <td>458000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>BEDOK</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>219A</td>\n",
       "      <td>BEDOK CTRL</td>\n",
       "      <td>16 TO 18</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2010</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>BUKIT BATOK</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>414</td>\n",
       "      <td>BT BATOK WEST AVE 4</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>1987</td>\n",
       "      <td>285000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>BUKIT BATOK</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>540</td>\n",
       "      <td>BT BATOK ST 52</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>1987</td>\n",
       "      <td>298000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>BUKIT BATOK</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>249</td>\n",
       "      <td>BT BATOK EAST AVE 5</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>1985</td>\n",
       "      <td>300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185763</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>SENGKANG</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>473A</td>\n",
       "      <td>FERNVALE ST</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2016</td>\n",
       "      <td>360988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185764</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>SENGKANG</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>164B</td>\n",
       "      <td>RIVERVALE CRES</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2017</td>\n",
       "      <td>340000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185816</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>TOA PAYOH</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>9</td>\n",
       "      <td>JOO SENG RD</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2017</td>\n",
       "      <td>520000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185817</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>TOA PAYOH</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>7</td>\n",
       "      <td>UPP ALJUNIED LANE</td>\n",
       "      <td>16 TO 18</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2017</td>\n",
       "      <td>495000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185820</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>WOODLANDS</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>302A</td>\n",
       "      <td>WOODLANDS ST 31</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2016</td>\n",
       "      <td>352000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10478 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tranc_YearMonth  year         town flat_type block  \\\n",
       "102            2015-01  2015        BEDOK    3 ROOM  221B   \n",
       "103            2015-01  2015        BEDOK    3 ROOM  219A   \n",
       "185            2015-01  2015  BUKIT BATOK    3 ROOM   414   \n",
       "192            2015-01  2015  BUKIT BATOK    3 ROOM   540   \n",
       "194            2015-01  2015  BUKIT BATOK    3 ROOM   249   \n",
       "...                ...   ...          ...       ...   ...   \n",
       "185763         2021-04  2021     SENGKANG    3 ROOM  473A   \n",
       "185764         2021-04  2021     SENGKANG    3 ROOM  164B   \n",
       "185816         2021-04  2021    TOA PAYOH    3 ROOM     9   \n",
       "185817         2021-04  2021    TOA PAYOH    3 ROOM     7   \n",
       "185820         2021-04  2021    WOODLANDS    3 ROOM  302A   \n",
       "\n",
       "                street_name storey_range  floor_area_sqm flat_model  \\\n",
       "102              BEDOK CTRL     04 TO 06            69.0    Model A   \n",
       "103              BEDOK CTRL     16 TO 18            69.0    Model A   \n",
       "185     BT BATOK WEST AVE 4     07 TO 09            73.0    Model A   \n",
       "192          BT BATOK ST 52     10 TO 12            73.0    Model A   \n",
       "194     BT BATOK EAST AVE 5     07 TO 09            73.0    Model A   \n",
       "...                     ...          ...             ...        ...   \n",
       "185763          FERNVALE ST     04 TO 06            68.0    Model A   \n",
       "185764       RIVERVALE CRES     04 TO 06            68.0    Model A   \n",
       "185816          JOO SENG RD     13 TO 15            68.0    Model A   \n",
       "185817    UPP ALJUNIED LANE     16 TO 18            68.0    Model A   \n",
       "185820      WOODLANDS ST 31     04 TO 06            67.0    Model A   \n",
       "\n",
       "        lease_commence_date  resale_price  \n",
       "102                    2010      458000.0  \n",
       "103                    2010      500000.0  \n",
       "185                    1987      285000.0  \n",
       "192                    1987      298000.0  \n",
       "194                    1985      300000.0  \n",
       "...                     ...           ...  \n",
       "185763                 2016      360988.0  \n",
       "185764                 2017      340000.0  \n",
       "185816                 2017      520000.0  \n",
       "185817                 2017      495000.0  \n",
       "185820                 2016      352000.0  \n",
       "\n",
       "[10478 rows x 11 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf[(hdbdf['flat_type'] == '3 ROOM') & (hdbdf['flat_model'] == 'Model A')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "hXT-f1mZZTfO",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[Question 2c]** Find out the minimum, average, and maximum **floor_area_sqm** and print the following exact statements:\n",
    ">> Minimum floor area = 31.0 squared meters\\\n",
    ">> Average floor area = 97.2 squared meters\\\n",
    ">> Maximum floor area = 280.0 squared meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "oxeCjdNdZTfO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum floor area =  31.0 squared meters\\ Average floor area =  97.2 squared meters\\ Maximum floor area =  280.0 squared meters\n"
     ]
    }
   ],
   "source": [
    "print('Minimum floor area = ', hdbdf['floor_area_sqm'].min(),'squared meters\\\\' ,'Average floor area = ',np.round(hdbdf['floor_area_sqm'].mean(), decimals = 1),'squared meters\\\\' , 'Maximum floor area = ',hdbdf['floor_area_sqm'].max(),'squared meters')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "kWr_g5nwZTfO",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[Question 2d]** Display a sorted dataframe, to have the records ordered from the highest **resale_price** to the lowest **resale_price**. \\\n",
    "> No need to assign the sorted dataframe to any variable, just display the output on the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "6idkpDdWZTfP"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Tranc_YearMonth</th>\n",
       "      <th>year</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>block</th>\n",
       "      <th>street_name</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114935</th>\n",
       "      <td>2020-09</td>\n",
       "      <td>2020</td>\n",
       "      <td>CENTRAL AREA</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>1B</td>\n",
       "      <td>CANTONMENT RD</td>\n",
       "      <td>43 TO 45</td>\n",
       "      <td>107.0</td>\n",
       "      <td>Type S2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1258000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129659</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>BISHAN</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>273B</td>\n",
       "      <td>BISHAN ST 24</td>\n",
       "      <td>31 TO 33</td>\n",
       "      <td>120.0</td>\n",
       "      <td>DBSS</td>\n",
       "      <td>2011</td>\n",
       "      <td>1250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119417</th>\n",
       "      <td>2020-11</td>\n",
       "      <td>2020</td>\n",
       "      <td>CENTRAL AREA</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>1A</td>\n",
       "      <td>CANTONMENT RD</td>\n",
       "      <td>49 TO 51</td>\n",
       "      <td>105.0</td>\n",
       "      <td>Type S2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1248000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105194</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>2020</td>\n",
       "      <td>CENTRAL AREA</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>1B</td>\n",
       "      <td>CANTONMENT RD</td>\n",
       "      <td>40 TO 42</td>\n",
       "      <td>107.0</td>\n",
       "      <td>Type S2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1232000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127590</th>\n",
       "      <td>2021-03</td>\n",
       "      <td>2021</td>\n",
       "      <td>BISHAN</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>273B</td>\n",
       "      <td>BISHAN ST 24</td>\n",
       "      <td>28 TO 30</td>\n",
       "      <td>120.0</td>\n",
       "      <td>DBSS</td>\n",
       "      <td>2011</td>\n",
       "      <td>1220000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110484</th>\n",
       "      <td>2020-07</td>\n",
       "      <td>2020</td>\n",
       "      <td>GEYLANG</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>72</td>\n",
       "      <td>CIRCUIT RD</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1969</td>\n",
       "      <td>160000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103312</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>2020</td>\n",
       "      <td>BUKIT MERAH</td>\n",
       "      <td>1 ROOM</td>\n",
       "      <td>7</td>\n",
       "      <td>TELOK BLANGAH CRES</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>1975</td>\n",
       "      <td>157000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95866</th>\n",
       "      <td>2019-10</td>\n",
       "      <td>2019</td>\n",
       "      <td>GEYLANG</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>68</td>\n",
       "      <td>CIRCUIT RD</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1969</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102699</th>\n",
       "      <td>2020-01</td>\n",
       "      <td>2020</td>\n",
       "      <td>TOA PAYOH</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>52</td>\n",
       "      <td>LOR 6 TOA PAYOH</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1983</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104450</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>2020</td>\n",
       "      <td>TOA PAYOH</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>26</td>\n",
       "      <td>TOA PAYOH EAST</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>1971</td>\n",
       "      <td>140000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185968 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tranc_YearMonth  year          town flat_type block  \\\n",
       "114935         2020-09  2020  CENTRAL AREA    5 ROOM    1B   \n",
       "129659         2021-04  2021        BISHAN    5 ROOM  273B   \n",
       "119417         2020-11  2020  CENTRAL AREA    5 ROOM    1A   \n",
       "105194         2020-03  2020  CENTRAL AREA    5 ROOM    1B   \n",
       "127590         2021-03  2021        BISHAN    5 ROOM  273B   \n",
       "...                ...   ...           ...       ...   ...   \n",
       "110484         2020-07  2020       GEYLANG    2 ROOM    72   \n",
       "103312         2020-02  2020   BUKIT MERAH    1 ROOM     7   \n",
       "95866          2019-10  2019       GEYLANG    2 ROOM    68   \n",
       "102699         2020-01  2020     TOA PAYOH    2 ROOM    52   \n",
       "104450         2020-02  2020     TOA PAYOH    3 ROOM    26   \n",
       "\n",
       "               street_name storey_range  floor_area_sqm flat_model  \\\n",
       "114935       CANTONMENT RD     43 TO 45           107.0    Type S2   \n",
       "129659        BISHAN ST 24     31 TO 33           120.0       DBSS   \n",
       "119417       CANTONMENT RD     49 TO 51           105.0    Type S2   \n",
       "105194       CANTONMENT RD     40 TO 42           107.0    Type S2   \n",
       "127590        BISHAN ST 24     28 TO 30           120.0       DBSS   \n",
       "...                    ...          ...             ...        ...   \n",
       "110484          CIRCUIT RD     10 TO 12            42.0   Standard   \n",
       "103312  TELOK BLANGAH CRES     10 TO 12            31.0   Improved   \n",
       "95866           CIRCUIT RD     04 TO 06            45.0   Standard   \n",
       "102699     LOR 6 TOA PAYOH     01 TO 03            43.0   Standard   \n",
       "104450      TOA PAYOH EAST     10 TO 12            67.0   Improved   \n",
       "\n",
       "        lease_commence_date  resale_price  \n",
       "114935                 2011     1258000.0  \n",
       "129659                 2011     1250000.0  \n",
       "119417                 2011     1248000.0  \n",
       "105194                 2011     1232000.0  \n",
       "127590                 2011     1220000.0  \n",
       "...                     ...           ...  \n",
       "110484                 1969      160000.0  \n",
       "103312                 1975      157000.0  \n",
       "95866                  1969      150000.0  \n",
       "102699                 1983      150000.0  \n",
       "104450                 1971      140000.0  \n",
       "\n",
       "[185968 rows x 11 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf.sort_values('resale_price', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "ZiFFQP0KZTfP",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[Question 2e]** Display a sorted dataframe, with the following three combined sorting criteria in the exact order:\n",
    "> 1. **year**, with the latest year to the earliest year\n",
    "> 2. **town**, with alphabetical order (A to Z)\n",
    "> 3. **resale_price**, with the highest to the lowest value\n",
    ">\n",
    "> No need to assign the sorted dataframe to any variable, just display the sorted dataframe on the notebook. Your output should look like this:\n",
    ">>![](markdown_resources/q002e.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "5XilS3RgZTfP"
   },
   "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>Tranc_YearMonth</th>\n",
       "      <th>year</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>block</th>\n",
       "      <th>street_name</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>123297</th>\n",
       "      <td>2021-01</td>\n",
       "      <td>2021</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>EXECUTIVE</td>\n",
       "      <td>556</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>163.0</td>\n",
       "      <td>Adjoined flat</td>\n",
       "      <td>1980</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129518</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>310B</td>\n",
       "      <td>ANG MO KIO AVE 1</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>119.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2012</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129530</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>588C</td>\n",
       "      <td>ANG MO KIO ST 52</td>\n",
       "      <td>28 TO 30</td>\n",
       "      <td>112.0</td>\n",
       "      <td>DBSS</td>\n",
       "      <td>2011</td>\n",
       "      <td>935000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127411</th>\n",
       "      <td>2021-03</td>\n",
       "      <td>2021</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>310B</td>\n",
       "      <td>ANG MO KIO AVE 1</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>119.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2012</td>\n",
       "      <td>908000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125527</th>\n",
       "      <td>2021-02</td>\n",
       "      <td>2021</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>588A</td>\n",
       "      <td>ANG MO KIO ST 52</td>\n",
       "      <td>16 TO 18</td>\n",
       "      <td>112.0</td>\n",
       "      <td>DBSS</td>\n",
       "      <td>2011</td>\n",
       "      <td>890000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133311</th>\n",
       "      <td>2012-03</td>\n",
       "      <td>2012</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>271</td>\n",
       "      <td>YISHUN ST 22</td>\n",
       "      <td>01 TO 05</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Simplified</td>\n",
       "      <td>1985</td>\n",
       "      <td>278000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144028</th>\n",
       "      <td>2012-08</td>\n",
       "      <td>2012</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>165</td>\n",
       "      <td>YISHUN RING RD</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Simplified</td>\n",
       "      <td>1988</td>\n",
       "      <td>276000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144027</th>\n",
       "      <td>2012-08</td>\n",
       "      <td>2012</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>264</td>\n",
       "      <td>YISHUN ST 22</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Simplified</td>\n",
       "      <td>1986</td>\n",
       "      <td>274000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135483</th>\n",
       "      <td>2012-04</td>\n",
       "      <td>2012</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>261</td>\n",
       "      <td>YISHUN ST 22</td>\n",
       "      <td>01 TO 05</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Simplified</td>\n",
       "      <td>1985</td>\n",
       "      <td>270000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147734</th>\n",
       "      <td>2012-10</td>\n",
       "      <td>2012</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>4 ROOOM</td>\n",
       "      <td>279</td>\n",
       "      <td>YISHUN ST 22</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>104.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>1985</td>\n",
       "      <td>247000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185968 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tranc_YearMonth  year        town  flat_type block        street_name  \\\n",
       "123297         2021-01  2021  ANG MO KIO  EXECUTIVE   556  ANG MO KIO AVE 10   \n",
       "129518         2021-04  2021  ANG MO KIO     5 ROOM  310B   ANG MO KIO AVE 1   \n",
       "129530         2021-04  2021  ANG MO KIO     5 ROOM  588C   ANG MO KIO ST 52   \n",
       "127411         2021-03  2021  ANG MO KIO     5 ROOM  310B   ANG MO KIO AVE 1   \n",
       "125527         2021-02  2021  ANG MO KIO     5 ROOM  588A   ANG MO KIO ST 52   \n",
       "...                ...   ...         ...        ...   ...                ...   \n",
       "133311         2012-03  2012      YISHUN     3 ROOM   271       YISHUN ST 22   \n",
       "144028         2012-08  2012      YISHUN     3 ROOM   165     YISHUN RING RD   \n",
       "144027         2012-08  2012      YISHUN     3 ROOM   264       YISHUN ST 22   \n",
       "135483         2012-04  2012      YISHUN     3 ROOM   261       YISHUN ST 22   \n",
       "147734         2012-10  2012      YISHUN    4 ROOOM   279       YISHUN ST 22   \n",
       "\n",
       "       storey_range  floor_area_sqm     flat_model  lease_commence_date  \\\n",
       "123297     04 TO 06           163.0  Adjoined flat                 1980   \n",
       "129518     07 TO 09           119.0       Improved                 2012   \n",
       "129530     28 TO 30           112.0           DBSS                 2011   \n",
       "127411     04 TO 06           119.0       Improved                 2012   \n",
       "125527     16 TO 18           112.0           DBSS                 2011   \n",
       "...             ...             ...            ...                  ...   \n",
       "133311     01 TO 05            64.0     Simplified                 1985   \n",
       "144028     01 TO 03            64.0     Simplified                 1988   \n",
       "144027     04 TO 06            64.0     Simplified                 1986   \n",
       "135483     01 TO 05            64.0     Simplified                 1985   \n",
       "147734     10 TO 12           104.0        Model A                 1985   \n",
       "\n",
       "        resale_price  \n",
       "123297      950000.0  \n",
       "129518      950000.0  \n",
       "129530      935000.0  \n",
       "127411      908000.0  \n",
       "125527      890000.0  \n",
       "...              ...  \n",
       "133311      278000.0  \n",
       "144028      276000.0  \n",
       "144027      274000.0  \n",
       "135483      270000.0  \n",
       "147734      247000.0  \n",
       "\n",
       "[185968 rows x 11 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf.sort_values(['year','town','resale_price'], ascending = [False, True, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "t6fkQLWaZTfP",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 3 - Modifying Values in DataFrame\n",
    "While you were doing your exploration, your supervisor tells you to focus on last 5 years of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "eLb5OFO4ZTfQ",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 3a ]** Filter **hdbdf** so that it will only contain records from **year** 2017 and onward.\\\n",
    "> The remaining of this notebook will use **hdbdf** with the filtered data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "vYZDw5CGZTfQ"
   },
   "outputs": [],
   "source": [
    "hdbdf = hdbdf[hdbdf['year'] >= 2017]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "9OIDUursZTfQ",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 3b ]** Show the number of records for each of the **year** in your **hdbdf** dataframe.\n",
    "> Your output should look like this:\n",
    ">> ![](markdown_resources/q003b.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "oplL5nZDZTfR",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "groupby_year = hdbdf.groupby('year')['year'].count().sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2020    23340\n",
       "2019    22186\n",
       "2018    21561\n",
       "2017    20509\n",
       "2021     9020\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After filtering your data, you continue to explore and clean data..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "_MTpv2__ZTfR",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 3c ]** Check **flat_type** for any typo errors and clean them accordingly. <br/>\n",
    ">💡<b>Hint </b> : There is only **one** category with error.<br/>\n",
    ">💡<b>Hint </b> : Don't worry if you get a **warning message**, starting with \" ... SettingWithCopyWarning:\" in the first line.<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "L6w0jrzPZTfR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 ROOM', '3 ROOM', '4 ROOOM', '5 ROOM', 'EXECUTIVE', '1 ROOM',\n",
       "       'MULTI-GENERATION'], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf['flat_type'].unique()\n",
    "# '4 ROOOM' should be corrected as '4 ROOM'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 ROOM', '3 ROOM', '4 ROOM', '5 ROOM', 'EXECUTIVE', '1 ROOM',\n",
       "       'MULTI-GENERATION'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf['flat_type'] = hdbdf['flat_type'].replace('4 ROOOM', '4 ROOM' )\n",
    "hdbdf['flat_type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "KDumnSWnZTfS",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 4 - Groupby and Aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "uKJUTqUzZTfS",
    "tags": [],
    "trust": true
   },
   "source": [
    "> MYTH: HDB flat prices have been increasing over the years.\n",
    "\n",
    "After all the exploration and cleaning, you start to work on your task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "33CXMFkVZTfS",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 4a ]** Find the average of resale price for each year. <br/>\n",
    "> No need to assign the output to any variable, just show the output on the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "4BNperRXZTfS"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2017    443888.520571\n",
       "2018    441282.063703\n",
       "2019    432137.912902\n",
       "2020    452243.739911\n",
       "2021    492365.273588\n",
       "Name: resale_price, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf.groupby('year')['resale_price'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "gg8FJanmZTfT",
    "tags": [],
    "trust": true
   },
   "source": [
    "Your supervisor advised that you shouldn't use mean resale price as it doesn't take into account the flat size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "_fUeKQ8yZTfT",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 4b ]**  Create a new column **price_per_sqm** by dividing **resale_price** with **floor_area_sqm**. \n",
    "<br/> Then, find the average of **price_per_sqm** (resale price per squared meter) for every year instead. No need to assign the output to any variable, just show the output on the notebook. </br>\n",
    "\n",
    ">💡<b>Hint </b> : Don't worry if you get a **warning message**, starting with \" ... SettingWithCopyWarning:\" in the first line of the message.<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "F2R3ui9NZTfT"
   },
   "outputs": [],
   "source": [
    "hdbdf['price_per_sqm'] = hdbdf['resale_price'] / hdbdf['floor_area_sqm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "NMY2PNzzZTfT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2017    4578.877692\n",
       "2018    4509.672396\n",
       "2019    4476.800287\n",
       "2020    4669.830753\n",
       "2021    5045.132410\n",
       "Name: price_per_sqm, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf.groupby('year')['price_per_sqm'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "cbvhUB54ZTfT",
    "tags": [],
    "trust": true
   },
   "source": [
    "You presented this result to your director.<br/>\n",
    "He commended you on the use of price_per_sqm instead of resale price for a better analysis.<br/>\n",
    "He suggested to add in regions for a more complete analysis. This is because different regions may have varying prices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "ofst-XmNZTfT",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 5 - Merge and Join Tables\n",
    "\n",
    "The data engineer has helped to scrap the information you need from https://en.wikipedia.org/wiki/Planning_Areas_of_Singapore, and stored the list of towns and their respective region in a csv file called **regions.csv**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "MYVykJNNZTfT",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 5a ]** Read in **regions.csv** as DataFrame and merge it to your main dataset **hdbdf**. Store the merged table in the same **hdbdf** variable.<br/>\n",
    "<br/>\n",
    ">💡 **Hint:** Note that not all the 26 towns from your **hdbdf** are in the CSV file **regions.csv**.<br/>\n",
    ">💡 **Hint:** Town values in both **regions.csv** and your **hdbdf** are in different capitalisations (uppercase and lowercase)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "FKR3NBZ2ZTfU"
   },
   "outputs": [],
   "source": [
    "regiondf = pd.read_csv(\"regions.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "regiondf['town'] = regiondf['town'].apply(lambda x: x.upper())  # convert to all upper case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ANG MO KIO', 'BEDOK', 'BISHAN', 'BOON LAY', 'BUKIT BATOK',\n",
       "       'BUKIT MERAH', 'BUKIT PANJANG', 'BUKIT TIMAH',\n",
       "       'CENTRAL WATER CATCHMENT', 'CHANGI', 'CHANGI BAY', 'CHOA CHU KANG',\n",
       "       'CLEMENTI', 'DOWNTOWN CORE', 'GEYLANG', 'HOUGANG', 'JURONG EAST',\n",
       "       'JURONG WEST', 'KALLANG', 'LIM CHU KANG', 'MANDAI', 'MARINA EAST',\n",
       "       'MARINA SOUTH', 'MARINE PARADE', 'MUSEUM', 'NEWTON',\n",
       "       'NORTH-EASTERN ISLANDS', 'NOVENA', 'ORCHARD', 'OUTRAM',\n",
       "       'PASIR RIS', 'PAYA LEBAR', 'PIONEER', 'PUNGGOL', 'QUEENSTOWN',\n",
       "       'RIVER VALLEY', 'ROCHOR', 'SELETAR', 'SEMBAWANG', 'SENGKANG',\n",
       "       'SERANGOON', 'SIMPANG', 'SINGAPORE RIVER', 'SOUTHERN ISLANDS',\n",
       "       'STRAITS VIEW', 'SUNGEI KADUT', 'TAMPINES', 'TANGLIN', 'TENGAH',\n",
       "       'TOA PAYOH', 'TUAS', 'WESTERN ISLANDS', 'WESTERN WATER CATCHMENT',\n",
       "       'WOODLANDS', 'YISHUN'], dtype=object)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiondf['town'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "OEmx--4zZTfU"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ANG MO KIO', 'BEDOK', 'BISHAN', 'BUKIT BATOK', 'BUKIT MERAH',\n",
       "       'BUKIT PANJANG', 'BUKIT TIMAH', 'CENTRAL AREA', 'CHOA CHU KANG',\n",
       "       'CLEMENTI', 'GEYLANG', 'HOUGANG', 'JURONG EAST', 'JURONG WEST',\n",
       "       'KALLANG/WHAMPOA', 'MARINE PARADE', 'PASIR RIS', 'PUNGGOL',\n",
       "       'QUEENSTOWN', 'SEMBAWANG', 'SENGKANG', 'SERANGOON', 'TAMPINES',\n",
       "       'TOA PAYOH', 'WOODLANDS', 'YISHUN'], dtype=object)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf['town'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdbdf = hdbdf.merge( regiondf,how='left',left_on= 'town', right_on='town')  # left merge, not all town can be found in region.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 96616 entries, 0 to 96615\n",
      "Data columns (total 13 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Tranc_YearMonth      96616 non-null  object \n",
      " 1   year                 96616 non-null  int64  \n",
      " 2   town                 96616 non-null  object \n",
      " 3   flat_type            96616 non-null  object \n",
      " 4   block                96616 non-null  object \n",
      " 5   street_name          96616 non-null  object \n",
      " 6   storey_range         96616 non-null  object \n",
      " 7   floor_area_sqm       96616 non-null  float64\n",
      " 8   flat_model           96616 non-null  object \n",
      " 9   lease_commence_date  96616 non-null  int64  \n",
      " 10  resale_price         96616 non-null  float64\n",
      " 11  price_per_sqm        96616 non-null  float64\n",
      " 12  region               93096 non-null  object \n",
      "dtypes: float64(3), int64(2), object(8)\n",
      "memory usage: 10.3+ MB\n"
     ]
    }
   ],
   "source": [
    "hdbdf.info()\n",
    "# matched 93096 towns,  3520 towns without regoin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "gjXliYUPZTfU",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 5b ]** There are some records with missing values in the \"region\" column.<br/>\n",
    "> 1) To examine more into these missing values, create a new dataframe named **nulldf** which contains only records with missing data in the **region** column. <br/>\n",
    "> 2) Find the unique values of <b>town</b> in **nulldf**.\n",
    ">\n",
    ">💡<b>Hint 1</b> : You may use df['*column*'].**isnull()** https://pandas.pydata.org/docs/reference/api/pandas.Series.isnull.html or other ways to help you to generate nulldf.<br/>\n",
    ">💡<b>Hint 2 </b>: Sometimes (actually most of the times), Google and StackOverflow is your best friend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "PkAH42FPZTfU"
   },
   "outputs": [],
   "source": [
    "nulldf = hdbdf[hdbdf['region'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CENTRAL AREA', 'KALLANG/WHAMPOA'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldf['town'].unique()\n",
    "# two towns are withou region info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "a22w0J9qZTfU",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 5c ]** You have been informed that the records with missing values in the **region** column are belong to the **same region**.<br/>\n",
    "> Fill up the missing values in the **region** column of the **hdbdf** dataframe based on this information.<br/><br/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "id": "xI9s9AMFZTfV"
   },
   "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>town</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>North-East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BEDOK</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BISHAN</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BOON LAY</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BUKIT BATOK</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BUKIT MERAH</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BUKIT PANJANG</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BUKIT TIMAH</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CENTRAL WATER CATCHMENT</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHANGI</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CHANGI BAY</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CHOA CHU KANG</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CLEMENTI</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DOWNTOWN CORE</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GEYLANG</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HOUGANG</td>\n",
       "      <td>North-East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>JURONG WEST</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KALLANG</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LIM CHU KANG</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MANDAI</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MARINA EAST</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MARINA SOUTH</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MARINE PARADE</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MUSEUM</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NEWTON</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NORTH-EASTERN ISLANDS</td>\n",
       "      <td>North-East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NOVENA</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ORCHARD</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>OUTRAM</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PASIR RIS</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PAYA LEBAR</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PIONEER</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PUNGGOL</td>\n",
       "      <td>North-East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>QUEENSTOWN</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>RIVER VALLEY</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ROCHOR</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>SELETAR</td>\n",
       "      <td>North-East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>SEMBAWANG</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>SENGKANG</td>\n",
       "      <td>North-East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>SERANGOON</td>\n",
       "      <td>North-East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>SIMPANG</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>SINGAPORE RIVER</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>SOUTHERN ISLANDS</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>STRAITS VIEW</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>SUNGEI KADUT</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>TAMPINES</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>TANGLIN</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>TENGAH</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>TOA PAYOH</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>TUAS</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>WESTERN ISLANDS</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>WESTERN WATER CATCHMENT</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>WOODLANDS</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>YISHUN</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       town      region\n",
       "0                ANG MO KIO  North-East\n",
       "1                     BEDOK        East\n",
       "2                    BISHAN     Central\n",
       "3                  BOON LAY        West\n",
       "4               BUKIT BATOK        West\n",
       "5               BUKIT MERAH     Central\n",
       "6             BUKIT PANJANG        West\n",
       "7               BUKIT TIMAH     Central\n",
       "8   CENTRAL WATER CATCHMENT       North\n",
       "9                    CHANGI        East\n",
       "10               CHANGI BAY        East\n",
       "11            CHOA CHU KANG        West\n",
       "12                 CLEMENTI        West\n",
       "13            DOWNTOWN CORE     Central\n",
       "14                  GEYLANG     Central\n",
       "15                  HOUGANG  North-East\n",
       "16              JURONG EAST        West\n",
       "17              JURONG WEST        West\n",
       "18                  KALLANG     Central\n",
       "19             LIM CHU KANG       North\n",
       "20                   MANDAI       North\n",
       "21              MARINA EAST     Central\n",
       "22             MARINA SOUTH     Central\n",
       "23            MARINE PARADE     Central\n",
       "24                   MUSEUM     Central\n",
       "25                   NEWTON     Central\n",
       "26    NORTH-EASTERN ISLANDS  North-East\n",
       "27                   NOVENA     Central\n",
       "28                  ORCHARD     Central\n",
       "29                   OUTRAM     Central\n",
       "30                PASIR RIS        East\n",
       "31               PAYA LEBAR        East\n",
       "32                  PIONEER        West\n",
       "33                  PUNGGOL  North-East\n",
       "34               QUEENSTOWN     Central\n",
       "35             RIVER VALLEY     Central\n",
       "36                   ROCHOR     Central\n",
       "37                  SELETAR  North-East\n",
       "38                SEMBAWANG       North\n",
       "39                 SENGKANG  North-East\n",
       "40                SERANGOON  North-East\n",
       "41                  SIMPANG       North\n",
       "42          SINGAPORE RIVER     Central\n",
       "43         SOUTHERN ISLANDS     Central\n",
       "44             STRAITS VIEW     Central\n",
       "45             SUNGEI KADUT       North\n",
       "46                 TAMPINES        East\n",
       "47                  TANGLIN     Central\n",
       "48                   TENGAH        West\n",
       "49                TOA PAYOH     Central\n",
       "50                     TUAS        West\n",
       "51          WESTERN ISLANDS        West\n",
       "52  WESTERN WATER CATCHMENT        West\n",
       "53                WOODLANDS       North\n",
       "54                   YISHUN       North"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiondf\n",
    "# missing values should be 'Central' region (ref: Kallang)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace missing region for town = 'CENTRAL AREA', 'KALLANG/WHAMPOA' \n",
    "def check_region(row):\n",
    "    if row['town'] == 'CENTRAL AREA' or row['town'] == 'KALLANG/WHAMPOA':\n",
    "        return 'Central' \n",
    "    else:\n",
    "        return row['region'] \n",
    "\n",
    "hdbdf['region'] = hdbdf.apply (lambda row: check_region(row), axis=1)\n"
   ]
  },
  {
   "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>Tranc_YearMonth</th>\n",
       "      <th>year</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>block</th>\n",
       "      <th>street_name</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>price_per_sqm</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>406</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>1979</td>\n",
       "      <td>232000.0</td>\n",
       "      <td>5272.727273</td>\n",
       "      <td>North-East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>108</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1978</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>3731.343284</td>\n",
       "      <td>North-East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>602</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>262000.0</td>\n",
       "      <td>3910.447761</td>\n",
       "      <td>North-East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>465</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>3897.058824</td>\n",
       "      <td>North-East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>601</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>3955.223881</td>\n",
       "      <td>North-East</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96611</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>WOODLANDS</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>890A</td>\n",
       "      <td>WOODLANDS DR 50</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>505000.0</td>\n",
       "      <td>4469.026549</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96612</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>WOODLANDS</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>889B</td>\n",
       "      <td>WOODLANDS DR 50</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>565000.0</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96613</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>4 ROOM</td>\n",
       "      <td>506C</td>\n",
       "      <td>YISHUN AVE 4</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2018</td>\n",
       "      <td>460888.0</td>\n",
       "      <td>4955.784946</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96614</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>511B</td>\n",
       "      <td>YISHUN ST 51</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>5309.734513</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96615</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>511A</td>\n",
       "      <td>YISHUN ST 51</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>575000.0</td>\n",
       "      <td>5088.495575</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96616 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Tranc_YearMonth  year        town flat_type block        street_name  \\\n",
       "0             2017-01  2017  ANG MO KIO    2 ROOM   406  ANG MO KIO AVE 10   \n",
       "1             2017-01  2017  ANG MO KIO    3 ROOM   108   ANG MO KIO AVE 4   \n",
       "2             2017-01  2017  ANG MO KIO    3 ROOM   602   ANG MO KIO AVE 5   \n",
       "3             2017-01  2017  ANG MO KIO    3 ROOM   465  ANG MO KIO AVE 10   \n",
       "4             2017-01  2017  ANG MO KIO    3 ROOM   601   ANG MO KIO AVE 5   \n",
       "...               ...   ...         ...       ...   ...                ...   \n",
       "96611         2021-04  2021   WOODLANDS    5 ROOM  890A    WOODLANDS DR 50   \n",
       "96612         2021-04  2021   WOODLANDS    5 ROOM  889B    WOODLANDS DR 50   \n",
       "96613         2021-04  2021      YISHUN    4 ROOM  506C       YISHUN AVE 4   \n",
       "96614         2021-04  2021      YISHUN    5 ROOM  511B       YISHUN ST 51   \n",
       "96615         2021-04  2021      YISHUN    5 ROOM  511A       YISHUN ST 51   \n",
       "\n",
       "      storey_range  floor_area_sqm      flat_model  lease_commence_date  \\\n",
       "0         10 TO 12            44.0        Improved                 1979   \n",
       "1         01 TO 03            67.0  New Generation                 1978   \n",
       "2         01 TO 03            67.0  New Generation                 1980   \n",
       "3         04 TO 06            68.0  New Generation                 1980   \n",
       "4         01 TO 03            67.0  New Generation                 1980   \n",
       "...            ...             ...             ...                  ...   \n",
       "96611     01 TO 03           113.0        Improved                 2017   \n",
       "96612     04 TO 06           113.0        Improved                 2017   \n",
       "96613     07 TO 09            93.0         Model A                 2018   \n",
       "96614     13 TO 15           113.0        Improved                 2017   \n",
       "96615     07 TO 09           113.0        Improved                 2017   \n",
       "\n",
       "       resale_price  price_per_sqm      region  \n",
       "0          232000.0    5272.727273  North-East  \n",
       "1          250000.0    3731.343284  North-East  \n",
       "2          262000.0    3910.447761  North-East  \n",
       "3          265000.0    3897.058824  North-East  \n",
       "4          265000.0    3955.223881  North-East  \n",
       "...             ...            ...         ...  \n",
       "96611      505000.0    4469.026549       North  \n",
       "96612      565000.0    5000.000000       North  \n",
       "96613      460888.0    4955.784946       North  \n",
       "96614      600000.0    5309.734513       North  \n",
       "96615      575000.0    5088.495575       North  \n",
       "\n",
       "[96616 rows x 13 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "bbpHwsHUZTfV",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 6 - Groupby multiple columns\n",
    "With the new **region** column, you now have all the data needed to handle your director's suggestion to add in regions into your analysis of the average **price_per_sqm** over years. <br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "GUKRxxmMZTfV",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 6a ]** Find the average **price_per_sqm** for each **region** and **year**, and store the result into a variable called **psmbyregionyeardf**.<br/>\n",
    "<br/>\n",
    ">💡<b>Hint </b>: if you are unable to solve question 5, you can read in data in **hdbdf_q6.csv** and store the data in the **hdbdf** variable before you continue on question 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "GJgwFDOmZTfV"
   },
   "outputs": [],
   "source": [
    "psmbyregionyeardf = hdbdf.groupby(['region','year'])['price_per_sqm'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region      year\n",
       "Central     2017    5994.979040\n",
       "            2018    6008.892984\n",
       "            2019    5818.252724\n",
       "            2020    5872.648071\n",
       "            2021    6278.227623\n",
       "East        2017    4433.345711\n",
       "            2018    4414.984528\n",
       "            2019    4433.248145\n",
       "            2020    4594.932519\n",
       "            2021    4883.278453\n",
       "North       2017    3737.509673\n",
       "            2018    3627.454360\n",
       "            2019    3768.022205\n",
       "            2020    3909.595354\n",
       "            2021    4281.412830\n",
       "North-East  2017    4510.156947\n",
       "            2018    4498.681734\n",
       "            2019    4561.177684\n",
       "            2020    4715.106345\n",
       "            2021    4947.307695\n",
       "West        2017    4091.234506\n",
       "            2018    3993.434201\n",
       "            2019    3960.708361\n",
       "            2020    4256.923288\n",
       "            2021    4593.481741\n",
       "Name: price_per_sqm, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psmbyregionyeardf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "hMTyf-ROZTfV",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 6b ]** Reset the index of the **psmbyregionyeardf**. <br/>\n",
    "Rename the column  **price_per_sqm** in **psmbyregionyeardf** to become **avg_region_price_per_sqm_by_year**. <br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "jygorDLAZTfV"
   },
   "outputs": [],
   "source": [
    "psmbyregionyeardf = psmbyregionyeardf.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "c_fyc29PZTfW"
   },
   "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>region</th>\n",
       "      <th>year</th>\n",
       "      <th>price_per_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>2017</td>\n",
       "      <td>5994.979040</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central</td>\n",
       "      <td>2018</td>\n",
       "      <td>6008.892984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Central</td>\n",
       "      <td>2019</td>\n",
       "      <td>5818.252724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Central</td>\n",
       "      <td>2020</td>\n",
       "      <td>5872.648071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Central</td>\n",
       "      <td>2021</td>\n",
       "      <td>6278.227623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>East</td>\n",
       "      <td>2017</td>\n",
       "      <td>4433.345711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>East</td>\n",
       "      <td>2018</td>\n",
       "      <td>4414.984528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>East</td>\n",
       "      <td>2019</td>\n",
       "      <td>4433.248145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>East</td>\n",
       "      <td>2020</td>\n",
       "      <td>4594.932519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>East</td>\n",
       "      <td>2021</td>\n",
       "      <td>4883.278453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>North</td>\n",
       "      <td>2017</td>\n",
       "      <td>3737.509673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>North</td>\n",
       "      <td>2018</td>\n",
       "      <td>3627.454360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>North</td>\n",
       "      <td>2019</td>\n",
       "      <td>3768.022205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>North</td>\n",
       "      <td>2020</td>\n",
       "      <td>3909.595354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>North</td>\n",
       "      <td>2021</td>\n",
       "      <td>4281.412830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2017</td>\n",
       "      <td>4510.156947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2018</td>\n",
       "      <td>4498.681734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2019</td>\n",
       "      <td>4561.177684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2020</td>\n",
       "      <td>4715.106345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2021</td>\n",
       "      <td>4947.307695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>West</td>\n",
       "      <td>2017</td>\n",
       "      <td>4091.234506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>West</td>\n",
       "      <td>2018</td>\n",
       "      <td>3993.434201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>West</td>\n",
       "      <td>2019</td>\n",
       "      <td>3960.708361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>West</td>\n",
       "      <td>2020</td>\n",
       "      <td>4256.923288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>West</td>\n",
       "      <td>2021</td>\n",
       "      <td>4593.481741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region  year  price_per_sqm\n",
       "0      Central  2017    5994.979040\n",
       "1      Central  2018    6008.892984\n",
       "2      Central  2019    5818.252724\n",
       "3      Central  2020    5872.648071\n",
       "4      Central  2021    6278.227623\n",
       "5         East  2017    4433.345711\n",
       "6         East  2018    4414.984528\n",
       "7         East  2019    4433.248145\n",
       "8         East  2020    4594.932519\n",
       "9         East  2021    4883.278453\n",
       "10       North  2017    3737.509673\n",
       "11       North  2018    3627.454360\n",
       "12       North  2019    3768.022205\n",
       "13       North  2020    3909.595354\n",
       "14       North  2021    4281.412830\n",
       "15  North-East  2017    4510.156947\n",
       "16  North-East  2018    4498.681734\n",
       "17  North-East  2019    4561.177684\n",
       "18  North-East  2020    4715.106345\n",
       "19  North-East  2021    4947.307695\n",
       "20        West  2017    4091.234506\n",
       "21        West  2018    3993.434201\n",
       "22        West  2019    3960.708361\n",
       "23        West  2020    4256.923288\n",
       "24        West  2021    4593.481741"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psmbyregionyeardf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "psmbyregionyeardf.rename(columns = {'price_per_sqm' : 'avg_region_price_per_sqm_by_year'}, inplace = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5994.979040</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central</td>\n",
       "      <td>2018</td>\n",
       "      <td>6008.892984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Central</td>\n",
       "      <td>2019</td>\n",
       "      <td>5818.252724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Central</td>\n",
       "      <td>2020</td>\n",
       "      <td>5872.648071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Central</td>\n",
       "      <td>2021</td>\n",
       "      <td>6278.227623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>East</td>\n",
       "      <td>2017</td>\n",
       "      <td>4433.345711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>East</td>\n",
       "      <td>2018</td>\n",
       "      <td>4414.984528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>East</td>\n",
       "      <td>2019</td>\n",
       "      <td>4433.248145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>East</td>\n",
       "      <td>2020</td>\n",
       "      <td>4594.932519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>East</td>\n",
       "      <td>2021</td>\n",
       "      <td>4883.278453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>North</td>\n",
       "      <td>2017</td>\n",
       "      <td>3737.509673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>North</td>\n",
       "      <td>2018</td>\n",
       "      <td>3627.454360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>North</td>\n",
       "      <td>2019</td>\n",
       "      <td>3768.022205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>North</td>\n",
       "      <td>2020</td>\n",
       "      <td>3909.595354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>North</td>\n",
       "      <td>2021</td>\n",
       "      <td>4281.412830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2017</td>\n",
       "      <td>4510.156947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2018</td>\n",
       "      <td>4498.681734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2019</td>\n",
       "      <td>4561.177684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2020</td>\n",
       "      <td>4715.106345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2021</td>\n",
       "      <td>4947.307695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>West</td>\n",
       "      <td>2017</td>\n",
       "      <td>4091.234506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>West</td>\n",
       "      <td>2018</td>\n",
       "      <td>3993.434201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>West</td>\n",
       "      <td>2019</td>\n",
       "      <td>3960.708361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>West</td>\n",
       "      <td>2020</td>\n",
       "      <td>4256.923288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>West</td>\n",
       "      <td>2021</td>\n",
       "      <td>4593.481741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region  year  avg_region_price_per_sqm_by_year\n",
       "0      Central  2017                       5994.979040\n",
       "1      Central  2018                       6008.892984\n",
       "2      Central  2019                       5818.252724\n",
       "3      Central  2020                       5872.648071\n",
       "4      Central  2021                       6278.227623\n",
       "5         East  2017                       4433.345711\n",
       "6         East  2018                       4414.984528\n",
       "7         East  2019                       4433.248145\n",
       "8         East  2020                       4594.932519\n",
       "9         East  2021                       4883.278453\n",
       "10       North  2017                       3737.509673\n",
       "11       North  2018                       3627.454360\n",
       "12       North  2019                       3768.022205\n",
       "13       North  2020                       3909.595354\n",
       "14       North  2021                       4281.412830\n",
       "15  North-East  2017                       4510.156947\n",
       "16  North-East  2018                       4498.681734\n",
       "17  North-East  2019                       4561.177684\n",
       "18  North-East  2020                       4715.106345\n",
       "19  North-East  2021                       4947.307695\n",
       "20        West  2017                       4091.234506\n",
       "21        West  2018                       3993.434201\n",
       "22        West  2019                       3960.708361\n",
       "23        West  2020                       4256.923288\n",
       "24        West  2021                       4593.481741"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psmbyregionyeardf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "ZUMWwqofZTfW",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 6c ]** Plot a line graph with the **avg_region_price_per_sqm_by_year** (y axis) against **year** (x axis) with 5 lines, each representing a **region**. <br/>\n",
    "\n",
    "> 💡 **Hint** : If you are unable to solve questions 6a and 6b, you can read in data in **psmbyregionyeardf.csv** and store the data in the **psmbyregionyeardf** variable before you continue on question 6c."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "Xw7pzEALZTfW"
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e9ad659208>"
      ]
     },
     "execution_count": 30,
     "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": [
    "psmbyregionyeardf.pivot_table('avg_region_price_per_sqm_by_year', 'year', 'region' ).plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "RnDSPpDGZTfW",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 7 - Further Analyses\n",
    "Intrigued by the result, you decided to explore more on the relationship of **avg_region_price_per_sqm_by_year** with the other columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "K7ciLC9PZTfW",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 7a ]** Merge the **psmbyregionyeardf** into your main table **hdbdf**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "IrkN6b1gZTfW"
   },
   "outputs": [],
   "source": [
    "hdbdf = hdbdf.merge(psmbyregionyeardf, how = 'left', left_on= ['region','year'], right_on= ['region', 'year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Tranc_YearMonth</th>\n",
       "      <th>year</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>block</th>\n",
       "      <th>street_name</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>price_per_sqm</th>\n",
       "      <th>region</th>\n",
       "      <th>avg_region_price_per_sqm_by_year</th>\n",
       "      <th>price_per_sqm_diff_to_region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>406</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>1979</td>\n",
       "      <td>232000.0</td>\n",
       "      <td>5272.727273</td>\n",
       "      <td>North-East</td>\n",
       "      <td>4510.156947</td>\n",
       "      <td>762.570326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>108</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1978</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>3731.343284</td>\n",
       "      <td>North-East</td>\n",
       "      <td>4510.156947</td>\n",
       "      <td>-778.813663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>602</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>262000.0</td>\n",
       "      <td>3910.447761</td>\n",
       "      <td>North-East</td>\n",
       "      <td>4510.156947</td>\n",
       "      <td>-599.709186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>465</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>3897.058824</td>\n",
       "      <td>North-East</td>\n",
       "      <td>4510.156947</td>\n",
       "      <td>-613.098124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>601</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>3955.223881</td>\n",
       "      <td>North-East</td>\n",
       "      <td>4510.156947</td>\n",
       "      <td>-554.933066</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96611</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>WOODLANDS</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>890A</td>\n",
       "      <td>WOODLANDS DR 50</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>505000.0</td>\n",
       "      <td>4469.026549</td>\n",
       "      <td>North</td>\n",
       "      <td>4281.412830</td>\n",
       "      <td>187.613719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96612</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>WOODLANDS</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>889B</td>\n",
       "      <td>WOODLANDS DR 50</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>565000.0</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>North</td>\n",
       "      <td>4281.412830</td>\n",
       "      <td>718.587170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96613</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>4 ROOM</td>\n",
       "      <td>506C</td>\n",
       "      <td>YISHUN AVE 4</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2018</td>\n",
       "      <td>460888.0</td>\n",
       "      <td>4955.784946</td>\n",
       "      <td>North</td>\n",
       "      <td>4281.412830</td>\n",
       "      <td>674.372117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96614</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>511B</td>\n",
       "      <td>YISHUN ST 51</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>5309.734513</td>\n",
       "      <td>North</td>\n",
       "      <td>4281.412830</td>\n",
       "      <td>1028.321684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96615</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>511A</td>\n",
       "      <td>YISHUN ST 51</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>575000.0</td>\n",
       "      <td>5088.495575</td>\n",
       "      <td>North</td>\n",
       "      <td>4281.412830</td>\n",
       "      <td>807.082746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96616 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Tranc_YearMonth  year        town flat_type block        street_name  \\\n",
       "0             2017-01  2017  ANG MO KIO    2 ROOM   406  ANG MO KIO AVE 10   \n",
       "1             2017-01  2017  ANG MO KIO    3 ROOM   108   ANG MO KIO AVE 4   \n",
       "2             2017-01  2017  ANG MO KIO    3 ROOM   602   ANG MO KIO AVE 5   \n",
       "3             2017-01  2017  ANG MO KIO    3 ROOM   465  ANG MO KIO AVE 10   \n",
       "4             2017-01  2017  ANG MO KIO    3 ROOM   601   ANG MO KIO AVE 5   \n",
       "...               ...   ...         ...       ...   ...                ...   \n",
       "96611         2021-04  2021   WOODLANDS    5 ROOM  890A    WOODLANDS DR 50   \n",
       "96612         2021-04  2021   WOODLANDS    5 ROOM  889B    WOODLANDS DR 50   \n",
       "96613         2021-04  2021      YISHUN    4 ROOM  506C       YISHUN AVE 4   \n",
       "96614         2021-04  2021      YISHUN    5 ROOM  511B       YISHUN ST 51   \n",
       "96615         2021-04  2021      YISHUN    5 ROOM  511A       YISHUN ST 51   \n",
       "\n",
       "      storey_range  floor_area_sqm      flat_model  lease_commence_date  \\\n",
       "0         10 TO 12            44.0        Improved                 1979   \n",
       "1         01 TO 03            67.0  New Generation                 1978   \n",
       "2         01 TO 03            67.0  New Generation                 1980   \n",
       "3         04 TO 06            68.0  New Generation                 1980   \n",
       "4         01 TO 03            67.0  New Generation                 1980   \n",
       "...            ...             ...             ...                  ...   \n",
       "96611     01 TO 03           113.0        Improved                 2017   \n",
       "96612     04 TO 06           113.0        Improved                 2017   \n",
       "96613     07 TO 09            93.0         Model A                 2018   \n",
       "96614     13 TO 15           113.0        Improved                 2017   \n",
       "96615     07 TO 09           113.0        Improved                 2017   \n",
       "\n",
       "       resale_price  price_per_sqm      region  \\\n",
       "0          232000.0    5272.727273  North-East   \n",
       "1          250000.0    3731.343284  North-East   \n",
       "2          262000.0    3910.447761  North-East   \n",
       "3          265000.0    3897.058824  North-East   \n",
       "4          265000.0    3955.223881  North-East   \n",
       "...             ...            ...         ...   \n",
       "96611      505000.0    4469.026549       North   \n",
       "96612      565000.0    5000.000000       North   \n",
       "96613      460888.0    4955.784946       North   \n",
       "96614      600000.0    5309.734513       North   \n",
       "96615      575000.0    5088.495575       North   \n",
       "\n",
       "       avg_region_price_per_sqm_by_year  price_per_sqm_diff_to_region  \n",
       "0                           4510.156947                    762.570326  \n",
       "1                           4510.156947                   -778.813663  \n",
       "2                           4510.156947                   -599.709186  \n",
       "3                           4510.156947                   -613.098124  \n",
       "4                           4510.156947                   -554.933066  \n",
       "...                                 ...                           ...  \n",
       "96611                       4281.412830                    187.613719  \n",
       "96612                       4281.412830                    718.587170  \n",
       "96613                       4281.412830                    674.372117  \n",
       "96614                       4281.412830                   1028.321684  \n",
       "96615                       4281.412830                    807.082746  \n",
       "\n",
       "[96616 rows x 15 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "dK03COE7ZTfX",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 7b ]** Create a new column called **price_per_sqm_diff_to_region**, to store the differences between i) **price_per_sqm** of the unit and ii) the average price_per_sqm of the region and year of which the unit belongs to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "TrF2JttFZTfX"
   },
   "outputs": [],
   "source": [
    "hdbdf['price_per_sqm_diff_to_region'] = hdbdf['price_per_sqm'] - hdbdf['avg_region_price_per_sqm_by_year']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "BhEc3o9xZTfX",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 7c ]** Display a **histogram** to show the distribution of the differences in prices stored in the **price_per_sqm_diff_to_region** column. \\\n",
    "> Set the number of bins in the histogram to **20**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "YrmotTp1ZTfX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e9ae1a4be0>"
      ]
     },
     "execution_count": 40,
     "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": [
    "hdbdf['price_per_sqm_diff_to_region'].plot.hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "gGIlx0C8ZTfY",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 7d ]**\n",
    "Create a column **mid_storey** to store the midpoint of the unit's **storey_range**. <br/> Round the result to the nearest integer. <br/> For example, if the unit **storey_range** is \"07 TO 09\", the midpoint is **8**.<br/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "c1tB7eR2ZTfZ"
   },
   "outputs": [],
   "source": [
    "def mid_storey(row):\n",
    "    lower = int(row['storey_range'][0:2])\n",
    "    upper = int(row['storey_range'][6:8])\n",
    "    return (lower + upper)/2\n",
    "\n",
    "hdbdf['mid_storey'] = hdbdf.apply(lambda row: mid_storey(row), axis=1 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Tranc_YearMonth</th>\n",
       "      <th>year</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>block</th>\n",
       "      <th>street_name</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>price_per_sqm</th>\n",
       "      <th>region</th>\n",
       "      <th>avg_region_price_per_sqm_by_year</th>\n",
       "      <th>price_per_sqm_diff_to_region</th>\n",
       "      <th>mid_storey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>406</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>1979</td>\n",
       "      <td>232000.0</td>\n",
       "      <td>5272.727273</td>\n",
       "      <td>North-East</td>\n",
       "      <td>4510.156947</td>\n",
       "      <td>762.570326</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>108</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1978</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>3731.343284</td>\n",
       "      <td>North-East</td>\n",
       "      <td>4510.156947</td>\n",
       "      <td>-778.813663</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>602</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>262000.0</td>\n",
       "      <td>3910.447761</td>\n",
       "      <td>North-East</td>\n",
       "      <td>4510.156947</td>\n",
       "      <td>-599.709186</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>465</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>3897.058824</td>\n",
       "      <td>North-East</td>\n",
       "      <td>4510.156947</td>\n",
       "      <td>-613.098124</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>601</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>3955.223881</td>\n",
       "      <td>North-East</td>\n",
       "      <td>4510.156947</td>\n",
       "      <td>-554.933066</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>96611</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>WOODLANDS</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>890A</td>\n",
       "      <td>WOODLANDS DR 50</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>505000.0</td>\n",
       "      <td>4469.026549</td>\n",
       "      <td>North</td>\n",
       "      <td>4281.412830</td>\n",
       "      <td>187.613719</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96612</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>WOODLANDS</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>889B</td>\n",
       "      <td>WOODLANDS DR 50</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>565000.0</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>North</td>\n",
       "      <td>4281.412830</td>\n",
       "      <td>718.587170</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96613</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>4 ROOM</td>\n",
       "      <td>506C</td>\n",
       "      <td>YISHUN AVE 4</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2018</td>\n",
       "      <td>460888.0</td>\n",
       "      <td>4955.784946</td>\n",
       "      <td>North</td>\n",
       "      <td>4281.412830</td>\n",
       "      <td>674.372117</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96614</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>511B</td>\n",
       "      <td>YISHUN ST 51</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>5309.734513</td>\n",
       "      <td>North</td>\n",
       "      <td>4281.412830</td>\n",
       "      <td>1028.321684</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96615</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>511A</td>\n",
       "      <td>YISHUN ST 51</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2017</td>\n",
       "      <td>575000.0</td>\n",
       "      <td>5088.495575</td>\n",
       "      <td>North</td>\n",
       "      <td>4281.412830</td>\n",
       "      <td>807.082746</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96616 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Tranc_YearMonth  year        town flat_type block        street_name  \\\n",
       "0             2017-01  2017  ANG MO KIO    2 ROOM   406  ANG MO KIO AVE 10   \n",
       "1             2017-01  2017  ANG MO KIO    3 ROOM   108   ANG MO KIO AVE 4   \n",
       "2             2017-01  2017  ANG MO KIO    3 ROOM   602   ANG MO KIO AVE 5   \n",
       "3             2017-01  2017  ANG MO KIO    3 ROOM   465  ANG MO KIO AVE 10   \n",
       "4             2017-01  2017  ANG MO KIO    3 ROOM   601   ANG MO KIO AVE 5   \n",
       "...               ...   ...         ...       ...   ...                ...   \n",
       "96611         2021-04  2021   WOODLANDS    5 ROOM  890A    WOODLANDS DR 50   \n",
       "96612         2021-04  2021   WOODLANDS    5 ROOM  889B    WOODLANDS DR 50   \n",
       "96613         2021-04  2021      YISHUN    4 ROOM  506C       YISHUN AVE 4   \n",
       "96614         2021-04  2021      YISHUN    5 ROOM  511B       YISHUN ST 51   \n",
       "96615         2021-04  2021      YISHUN    5 ROOM  511A       YISHUN ST 51   \n",
       "\n",
       "      storey_range  floor_area_sqm      flat_model  lease_commence_date  \\\n",
       "0         10 TO 12            44.0        Improved                 1979   \n",
       "1         01 TO 03            67.0  New Generation                 1978   \n",
       "2         01 TO 03            67.0  New Generation                 1980   \n",
       "3         04 TO 06            68.0  New Generation                 1980   \n",
       "4         01 TO 03            67.0  New Generation                 1980   \n",
       "...            ...             ...             ...                  ...   \n",
       "96611     01 TO 03           113.0        Improved                 2017   \n",
       "96612     04 TO 06           113.0        Improved                 2017   \n",
       "96613     07 TO 09            93.0         Model A                 2018   \n",
       "96614     13 TO 15           113.0        Improved                 2017   \n",
       "96615     07 TO 09           113.0        Improved                 2017   \n",
       "\n",
       "       resale_price  price_per_sqm      region  \\\n",
       "0          232000.0    5272.727273  North-East   \n",
       "1          250000.0    3731.343284  North-East   \n",
       "2          262000.0    3910.447761  North-East   \n",
       "3          265000.0    3897.058824  North-East   \n",
       "4          265000.0    3955.223881  North-East   \n",
       "...             ...            ...         ...   \n",
       "96611      505000.0    4469.026549       North   \n",
       "96612      565000.0    5000.000000       North   \n",
       "96613      460888.0    4955.784946       North   \n",
       "96614      600000.0    5309.734513       North   \n",
       "96615      575000.0    5088.495575       North   \n",
       "\n",
       "       avg_region_price_per_sqm_by_year  price_per_sqm_diff_to_region  \\\n",
       "0                           4510.156947                    762.570326   \n",
       "1                           4510.156947                   -778.813663   \n",
       "2                           4510.156947                   -599.709186   \n",
       "3                           4510.156947                   -613.098124   \n",
       "4                           4510.156947                   -554.933066   \n",
       "...                                 ...                           ...   \n",
       "96611                       4281.412830                    187.613719   \n",
       "96612                       4281.412830                    718.587170   \n",
       "96613                       4281.412830                    674.372117   \n",
       "96614                       4281.412830                   1028.321684   \n",
       "96615                       4281.412830                    807.082746   \n",
       "\n",
       "       mid_storey  \n",
       "0            11.0  \n",
       "1             2.0  \n",
       "2             2.0  \n",
       "3             5.0  \n",
       "4             2.0  \n",
       "...           ...  \n",
       "96611         2.0  \n",
       "96612         5.0  \n",
       "96613         8.0  \n",
       "96614        14.0  \n",
       "96615         8.0  \n",
       "\n",
       "[96616 rows x 16 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "2wHlieTIZTfZ",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 7e ]**\n",
    "> Display a **scatter plot** which looks similar to the one given below:\n",
    ">> ![](markdown_resources/q007.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "gB-Hwx7HZTfa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e9b25dd2b0>"
      ]
     },
     "execution_count": 59,
     "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": [
    "hdbdf.plot.scatter('mid_storey','price_per_sqm_diff_to_region')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "6z_zKJBsZTfa",
    "tags": [],
    "trust": true
   },
   "source": [
    "---\n",
    "## END OF CAPSTONE ASSIGNMENT 2\n",
    "\n",
    "- Well done everyone!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "DC_Bootcamp_2021_Milestone_Test_2_v2.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

# data[].attributes.attachment_link.readable_text
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bsxvpzCMZTfG",
    "tags": []
   },
   "source": [
    "#  Capstone Assignment 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "L5SAGf4TZTfI",
    "tags": [],
    "trust": true
   },
   "source": [
    "## Background\n",
    "You are a HDB officer who has just completed the DC Bootcamp <br/>\n",
    "As your first data task, your director has asked you to tackle some of the data processing tasks and analyses.<br/>\n",
    "\n",
    "\n",
    "To aid you in your task, you have been provided with the following **hdb_resale.csv** dataset. <br/>\n",
    "See the description below for the fields given. <br/>\n",
    "<br/>\n",
    "<table>\n",
    "    <tr><th>Field</th><th>Description</th></tr>\n",
    "    <tr><td>Trans_YearMonth</td><td>year-month (YYYY-MM) for transaction</td></tr>\n",
    "    <tr><td>year</td><td>year (YYYY) for transaction</td></tr>\n",
    "    <tr><td>town</td><td>town for the resale flat</td></tr>\n",
    "    <tr><td>flat_type</td><td>flat type for the resale flat</td></tr>\n",
    "    <tr><td>block</td><td>block for the resale flat</td></tr>\n",
    "    <tr><td>street_name</td><td>street for the resale flat</td></tr>\n",
    "    <tr><td>storey_range</td><td>storey range for the resale flat</td></tr>\n",
    "    <tr><td>floor_area_sqm</td><td>floor area in square meter</td></tr>\n",
    "    <tr><td>flat_model</td><td>model of resale flat</td></tr>\n",
    "    <tr><td>lease_commence_date</td><td>year of lease commencement</td></tr>\n",
    "    <tr><td>resale_price</td><td>price of the resale flat</td></tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "4Zk5YNN8ZTfJ",
    "tags": [],
    "trust": true
   },
   "source": [
    "---\n",
    "⚠️**Notebook Instruction:**\n",
    "- This is an open-book test. You may refer to previous notes or internet sources at any point during the test.\n",
    "- You are forbidden to share or discuss any questions/solutions with other students.\n",
    "- If you need to seek for any clarifications, you may privately message the trainers.  \n",
    "\n",
    "\n",
    "- Feel free to insert additional cell(s) as you need.\n",
    "- You may import any packages that you need. \n",
    "- You may create additional variables, columns, or object in this notebook to derive at your solution. \n",
    "- If not specifed, you may use any name for your variable or column.\n",
    "- If any expected output is provided, you are ought to remove any additional columns that is not included in the expected output. Failure in doing so, will result in reduction of marks.\n",
    "\n",
    "\n",
    "- ⬇️ Enter your name as a string in cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "RcDEZJkkZTfK"
   },
   "outputs": [],
   "source": [
    "Name = \"Yeo Khee Yong\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "A4uPByClZTfL",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Import Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "LpsR5VyVZTfL",
    "tags": [],
    "trust": true
   },
   "source": [
    "> Cell below is for you to write the codes for importing the packages that you need to use in this notebook.\\\n",
    "> You can also import the package(s) elsewhere within this notebook and will not be penalized for that.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "b7iSVLk-ZTfL"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rc('figure', figsize=(16,9))\n",
    "sns.set(rc={\"figure.figsize\": (16,9)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "wHgmbYW8ZTfL",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 1 - Importing and Checking the Data\n",
    "You decided to import the data into Pandas and do some exploration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yuXuEA-CZTfM"
   },
   "source": [
    "> **[ Question 1a ]** Read in **hdb_resale.csv** as a dataframe and store it in a variable called **hdbdf**. <br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "1JP9k-E9ZTfM"
   },
   "outputs": [],
   "source": [
    "hdbdf = pd.read_csv(\"hdb_resale.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IHM2kFYWZTfM"
   },
   "source": [
    "> **[ Question 1b ]** Check if there are any missing values in all of the columns.<br/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "6scoREKGZTfN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 185968 entries, 0 to 185967\n",
      "Data columns (total 11 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   Tranc_YearMonth      185968 non-null  object \n",
      " 1   year                 185968 non-null  int64  \n",
      " 2   town                 185968 non-null  object \n",
      " 3   flat_type            185968 non-null  object \n",
      " 4   block                185968 non-null  object \n",
      " 5   street_name          185968 non-null  object \n",
      " 6   storey_range         185968 non-null  object \n",
      " 7   floor_area_sqm       185968 non-null  float64\n",
      " 8   flat_model           185968 non-null  object \n",
      " 9   lease_commence_date  185968 non-null  int64  \n",
      " 10  resale_price         185968 non-null  float64\n",
      "dtypes: float64(2), int64(2), object(7)\n",
      "memory usage: 15.6+ MB\n"
     ]
    }
   ],
   "source": [
    "hdbdf.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 185,968 records in this dataset, and all 11 variables have 185,968 non-null values based on above output. Hence, there are no missing values in all of the columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZWmYUI70ZTfN"
   },
   "source": [
    "> **[Question 1c]** Display the top 10 rows of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "FyuXvKUwZTfN"
   },
   "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>Tranc_YearMonth</th>\n",
       "      <th>year</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>block</th>\n",
       "      <th>street_name</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>174</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>1986</td>\n",
       "      <td>255000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>541</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1981</td>\n",
       "      <td>275000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>163</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>69.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>285000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>446</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1979</td>\n",
       "      <td>290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>557</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>603</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>709</td>\n",
       "      <td>ANG MO KIO AVE 8</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1980</td>\n",
       "      <td>290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>333</td>\n",
       "      <td>ANG MO KIO AVE 1</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1981</td>\n",
       "      <td>293000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>109</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1978</td>\n",
       "      <td>300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>564</td>\n",
       "      <td>ANG MO KIO AVE 3</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>68.0</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>1985</td>\n",
       "      <td>307500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Tranc_YearMonth  year        town flat_type block        street_name  \\\n",
       "0         2015-01  2015  ANG MO KIO    3 ROOM   174   ANG MO KIO AVE 4   \n",
       "1         2015-01  2015  ANG MO KIO    3 ROOM   541  ANG MO KIO AVE 10   \n",
       "2         2015-01  2015  ANG MO KIO    3 ROOM   163   ANG MO KIO AVE 4   \n",
       "3         2015-01  2015  ANG MO KIO    3 ROOM   446  ANG MO KIO AVE 10   \n",
       "4         2015-01  2015  ANG MO KIO    3 ROOM   557  ANG MO KIO AVE 10   \n",
       "5         2015-01  2015  ANG MO KIO    3 ROOM   603   ANG MO KIO AVE 5   \n",
       "6         2015-01  2015  ANG MO KIO    3 ROOM   709   ANG MO KIO AVE 8   \n",
       "7         2015-01  2015  ANG MO KIO    3 ROOM   333   ANG MO KIO AVE 1   \n",
       "8         2015-01  2015  ANG MO KIO    3 ROOM   109   ANG MO KIO AVE 4   \n",
       "9         2015-01  2015  ANG MO KIO    3 ROOM   564   ANG MO KIO AVE 3   \n",
       "\n",
       "  storey_range  floor_area_sqm      flat_model  lease_commence_date  \\\n",
       "0     07 TO 09            60.0        Improved                 1986   \n",
       "1     01 TO 03            68.0  New Generation                 1981   \n",
       "2     01 TO 03            69.0  New Generation                 1980   \n",
       "3     01 TO 03            68.0  New Generation                 1979   \n",
       "4     07 TO 09            68.0  New Generation                 1980   \n",
       "5     07 TO 09            67.0  New Generation                 1980   \n",
       "6     01 TO 03            68.0  New Generation                 1980   \n",
       "7     01 TO 03            68.0  New Generation                 1981   \n",
       "8     01 TO 03            67.0  New Generation                 1978   \n",
       "9     13 TO 15            68.0  New Generation                 1985   \n",
       "\n",
       "   resale_price  \n",
       "0      255000.0  \n",
       "1      275000.0  \n",
       "2      285000.0  \n",
       "3      290000.0  \n",
       "4      290000.0  \n",
       "5      290000.0  \n",
       "6      290000.0  \n",
       "7      293000.0  \n",
       "8      300000.0  \n",
       "9      307500.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "RjXBvUzlZTfN",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 2 - Accessing and Filtering DataFrame\n",
    "You decided to do further exploration on the crucial fields in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "sLVvakU5ZTfN",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[Question 2a]**  Find out how many unique values are there in the **town** column. Your code should show  **26** as the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "ZW3f9L84ZTfO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf['town'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "mu8Z370EZTfO",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[Question 2b]** Select all the records that are \"3 ROOM\" and also \"Model A\". \\\n",
    "> No need to assign the output to any variable, just show the filtered dataframe on the notebook.\\\n",
    "> Your output should looks this:\n",
    ">> ![](markdown_resources/q002b.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "fXNm3-SnZTfO"
   },
   "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>Tranc_YearMonth</th>\n",
       "      <th>year</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>block</th>\n",
       "      <th>street_name</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>BEDOK</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>221B</td>\n",
       "      <td>BEDOK CTRL</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2010</td>\n",
       "      <td>458000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>BEDOK</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>219A</td>\n",
       "      <td>BEDOK CTRL</td>\n",
       "      <td>16 TO 18</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2010</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>BUKIT BATOK</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>414</td>\n",
       "      <td>BT BATOK WEST AVE 4</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>1987</td>\n",
       "      <td>285000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>BUKIT BATOK</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>540</td>\n",
       "      <td>BT BATOK ST 52</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>1987</td>\n",
       "      <td>298000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>BUKIT BATOK</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>249</td>\n",
       "      <td>BT BATOK EAST AVE 5</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>1985</td>\n",
       "      <td>300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185763</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>SENGKANG</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>473A</td>\n",
       "      <td>FERNVALE ST</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2016</td>\n",
       "      <td>360988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185764</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>SENGKANG</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>164B</td>\n",
       "      <td>RIVERVALE CRES</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2017</td>\n",
       "      <td>340000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185816</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>TOA PAYOH</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>9</td>\n",
       "      <td>JOO SENG RD</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2017</td>\n",
       "      <td>520000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185817</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>TOA PAYOH</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>7</td>\n",
       "      <td>UPP ALJUNIED LANE</td>\n",
       "      <td>16 TO 18</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2017</td>\n",
       "      <td>495000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185820</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>WOODLANDS</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>302A</td>\n",
       "      <td>WOODLANDS ST 31</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>2016</td>\n",
       "      <td>352000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10478 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tranc_YearMonth  year         town flat_type block  \\\n",
       "102            2015-01  2015        BEDOK    3 ROOM  221B   \n",
       "103            2015-01  2015        BEDOK    3 ROOM  219A   \n",
       "185            2015-01  2015  BUKIT BATOK    3 ROOM   414   \n",
       "192            2015-01  2015  BUKIT BATOK    3 ROOM   540   \n",
       "194            2015-01  2015  BUKIT BATOK    3 ROOM   249   \n",
       "...                ...   ...          ...       ...   ...   \n",
       "185763         2021-04  2021     SENGKANG    3 ROOM  473A   \n",
       "185764         2021-04  2021     SENGKANG    3 ROOM  164B   \n",
       "185816         2021-04  2021    TOA PAYOH    3 ROOM     9   \n",
       "185817         2021-04  2021    TOA PAYOH    3 ROOM     7   \n",
       "185820         2021-04  2021    WOODLANDS    3 ROOM  302A   \n",
       "\n",
       "                street_name storey_range  floor_area_sqm flat_model  \\\n",
       "102              BEDOK CTRL     04 TO 06            69.0    Model A   \n",
       "103              BEDOK CTRL     16 TO 18            69.0    Model A   \n",
       "185     BT BATOK WEST AVE 4     07 TO 09            73.0    Model A   \n",
       "192          BT BATOK ST 52     10 TO 12            73.0    Model A   \n",
       "194     BT BATOK EAST AVE 5     07 TO 09            73.0    Model A   \n",
       "...                     ...          ...             ...        ...   \n",
       "185763          FERNVALE ST     04 TO 06            68.0    Model A   \n",
       "185764       RIVERVALE CRES     04 TO 06            68.0    Model A   \n",
       "185816          JOO SENG RD     13 TO 15            68.0    Model A   \n",
       "185817    UPP ALJUNIED LANE     16 TO 18            68.0    Model A   \n",
       "185820      WOODLANDS ST 31     04 TO 06            67.0    Model A   \n",
       "\n",
       "        lease_commence_date  resale_price  \n",
       "102                    2010      458000.0  \n",
       "103                    2010      500000.0  \n",
       "185                    1987      285000.0  \n",
       "192                    1987      298000.0  \n",
       "194                    1985      300000.0  \n",
       "...                     ...           ...  \n",
       "185763                 2016      360988.0  \n",
       "185764                 2017      340000.0  \n",
       "185816                 2017      520000.0  \n",
       "185817                 2017      495000.0  \n",
       "185820                 2016      352000.0  \n",
       "\n",
       "[10478 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf[(hdbdf['flat_type'] == \"3 ROOM\") & (hdbdf['flat_model'] == \"Model A\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "hXT-f1mZZTfO",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[Question 2c]** Find out the minimum, average, and maximum **floor_area_sqm** and print the following exact statements:\n",
    ">> Minimum floor area = 31.0 squared meters\\\n",
    ">> Average floor area = 97.2 squared meters\\\n",
    ">> Maximum floor area = 280.0 squared meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "oxeCjdNdZTfO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum floor area = 31.0 squared meters\n",
      "Average floor area = 97.2 squared meters\n",
      "Maximum floor area = 280.0 squared meters\n"
     ]
    }
   ],
   "source": [
    "min_floor_area = hdbdf['floor_area_sqm'].min()\n",
    "mean_floor_area = hdbdf['floor_area_sqm'].mean()\n",
    "mean_floor_area = round(mean_floor_area, 1)\n",
    "max_floor_area = hdbdf['floor_area_sqm'].max()\n",
    "\n",
    "print(\"Minimum floor area =\", min_floor_area, \"squared meters\")\n",
    "print(\"Average floor area =\", mean_floor_area, \"squared meters\")\n",
    "print(\"Maximum floor area =\", max_floor_area, \"squared meters\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "kWr_g5nwZTfO",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[Question 2d]** Display a sorted dataframe, to have the records ordered from the highest **resale_price** to the lowest **resale_price**. \\\n",
    "> No need to assign the sorted dataframe to any variable, just display the output on the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "6idkpDdWZTfP"
   },
   "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>Tranc_YearMonth</th>\n",
       "      <th>year</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>block</th>\n",
       "      <th>street_name</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114935</th>\n",
       "      <td>2020-09</td>\n",
       "      <td>2020</td>\n",
       "      <td>CENTRAL AREA</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>1B</td>\n",
       "      <td>CANTONMENT RD</td>\n",
       "      <td>43 TO 45</td>\n",
       "      <td>107.0</td>\n",
       "      <td>Type S2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1258000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129659</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>BISHAN</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>273B</td>\n",
       "      <td>BISHAN ST 24</td>\n",
       "      <td>31 TO 33</td>\n",
       "      <td>120.0</td>\n",
       "      <td>DBSS</td>\n",
       "      <td>2011</td>\n",
       "      <td>1250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119417</th>\n",
       "      <td>2020-11</td>\n",
       "      <td>2020</td>\n",
       "      <td>CENTRAL AREA</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>1A</td>\n",
       "      <td>CANTONMENT RD</td>\n",
       "      <td>49 TO 51</td>\n",
       "      <td>105.0</td>\n",
       "      <td>Type S2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1248000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105194</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>2020</td>\n",
       "      <td>CENTRAL AREA</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>1B</td>\n",
       "      <td>CANTONMENT RD</td>\n",
       "      <td>40 TO 42</td>\n",
       "      <td>107.0</td>\n",
       "      <td>Type S2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1232000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127590</th>\n",
       "      <td>2021-03</td>\n",
       "      <td>2021</td>\n",
       "      <td>BISHAN</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>273B</td>\n",
       "      <td>BISHAN ST 24</td>\n",
       "      <td>28 TO 30</td>\n",
       "      <td>120.0</td>\n",
       "      <td>DBSS</td>\n",
       "      <td>2011</td>\n",
       "      <td>1220000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110484</th>\n",
       "      <td>2020-07</td>\n",
       "      <td>2020</td>\n",
       "      <td>GEYLANG</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>72</td>\n",
       "      <td>CIRCUIT RD</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1969</td>\n",
       "      <td>160000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103312</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>2020</td>\n",
       "      <td>BUKIT MERAH</td>\n",
       "      <td>1 ROOM</td>\n",
       "      <td>7</td>\n",
       "      <td>TELOK BLANGAH CRES</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>1975</td>\n",
       "      <td>157000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95866</th>\n",
       "      <td>2019-10</td>\n",
       "      <td>2019</td>\n",
       "      <td>GEYLANG</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>68</td>\n",
       "      <td>CIRCUIT RD</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1969</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102699</th>\n",
       "      <td>2020-01</td>\n",
       "      <td>2020</td>\n",
       "      <td>TOA PAYOH</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>52</td>\n",
       "      <td>LOR 6 TOA PAYOH</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1983</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104450</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>2020</td>\n",
       "      <td>TOA PAYOH</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>26</td>\n",
       "      <td>TOA PAYOH EAST</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>1971</td>\n",
       "      <td>140000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185968 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tranc_YearMonth  year          town flat_type block  \\\n",
       "114935         2020-09  2020  CENTRAL AREA    5 ROOM    1B   \n",
       "129659         2021-04  2021        BISHAN    5 ROOM  273B   \n",
       "119417         2020-11  2020  CENTRAL AREA    5 ROOM    1A   \n",
       "105194         2020-03  2020  CENTRAL AREA    5 ROOM    1B   \n",
       "127590         2021-03  2021        BISHAN    5 ROOM  273B   \n",
       "...                ...   ...           ...       ...   ...   \n",
       "110484         2020-07  2020       GEYLANG    2 ROOM    72   \n",
       "103312         2020-02  2020   BUKIT MERAH    1 ROOM     7   \n",
       "95866          2019-10  2019       GEYLANG    2 ROOM    68   \n",
       "102699         2020-01  2020     TOA PAYOH    2 ROOM    52   \n",
       "104450         2020-02  2020     TOA PAYOH    3 ROOM    26   \n",
       "\n",
       "               street_name storey_range  floor_area_sqm flat_model  \\\n",
       "114935       CANTONMENT RD     43 TO 45           107.0    Type S2   \n",
       "129659        BISHAN ST 24     31 TO 33           120.0       DBSS   \n",
       "119417       CANTONMENT RD     49 TO 51           105.0    Type S2   \n",
       "105194       CANTONMENT RD     40 TO 42           107.0    Type S2   \n",
       "127590        BISHAN ST 24     28 TO 30           120.0       DBSS   \n",
       "...                    ...          ...             ...        ...   \n",
       "110484          CIRCUIT RD     10 TO 12            42.0   Standard   \n",
       "103312  TELOK BLANGAH CRES     10 TO 12            31.0   Improved   \n",
       "95866           CIRCUIT RD     04 TO 06            45.0   Standard   \n",
       "102699     LOR 6 TOA PAYOH     01 TO 03            43.0   Standard   \n",
       "104450      TOA PAYOH EAST     10 TO 12            67.0   Improved   \n",
       "\n",
       "        lease_commence_date  resale_price  \n",
       "114935                 2011     1258000.0  \n",
       "129659                 2011     1250000.0  \n",
       "119417                 2011     1248000.0  \n",
       "105194                 2011     1232000.0  \n",
       "127590                 2011     1220000.0  \n",
       "...                     ...           ...  \n",
       "110484                 1969      160000.0  \n",
       "103312                 1975      157000.0  \n",
       "95866                  1969      150000.0  \n",
       "102699                 1983      150000.0  \n",
       "104450                 1971      140000.0  \n",
       "\n",
       "[185968 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf.sort_values('resale_price', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "ZiFFQP0KZTfP",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[Question 2e]** Display a sorted dataframe, with the following three combined sorting criteria in the exact order:\n",
    "> 1. **year**, with the latest year to the earliest year\n",
    "> 2. **town**, with alphabetical order (A to Z)\n",
    "> 3. **resale_price**, with the highest to the lowest value\n",
    ">\n",
    "> No need to assign the sorted dataframe to any variable, just display the sorted dataframe on the notebook. Your output should look like this:\n",
    ">>![](markdown_resources/q002e.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "5XilS3RgZTfP"
   },
   "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>Tranc_YearMonth</th>\n",
       "      <th>year</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>block</th>\n",
       "      <th>street_name</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>123297</th>\n",
       "      <td>2021-01</td>\n",
       "      <td>2021</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>EXECUTIVE</td>\n",
       "      <td>556</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>163.0</td>\n",
       "      <td>Adjoined flat</td>\n",
       "      <td>1980</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129518</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>310B</td>\n",
       "      <td>ANG MO KIO AVE 1</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>119.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2012</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129530</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>2021</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>588C</td>\n",
       "      <td>ANG MO KIO ST 52</td>\n",
       "      <td>28 TO 30</td>\n",
       "      <td>112.0</td>\n",
       "      <td>DBSS</td>\n",
       "      <td>2011</td>\n",
       "      <td>935000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127411</th>\n",
       "      <td>2021-03</td>\n",
       "      <td>2021</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>310B</td>\n",
       "      <td>ANG MO KIO AVE 1</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>119.0</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2012</td>\n",
       "      <td>908000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125527</th>\n",
       "      <td>2021-02</td>\n",
       "      <td>2021</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>588A</td>\n",
       "      <td>ANG MO KIO ST 52</td>\n",
       "      <td>16 TO 18</td>\n",
       "      <td>112.0</td>\n",
       "      <td>DBSS</td>\n",
       "      <td>2011</td>\n",
       "      <td>890000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133311</th>\n",
       "      <td>2012-03</td>\n",
       "      <td>2012</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>271</td>\n",
       "      <td>YISHUN ST 22</td>\n",
       "      <td>01 TO 05</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Simplified</td>\n",
       "      <td>1985</td>\n",
       "      <td>278000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144028</th>\n",
       "      <td>2012-08</td>\n",
       "      <td>2012</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>165</td>\n",
       "      <td>YISHUN RING RD</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Simplified</td>\n",
       "      <td>1988</td>\n",
       "      <td>276000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144027</th>\n",
       "      <td>2012-08</td>\n",
       "      <td>2012</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>264</td>\n",
       "      <td>YISHUN ST 22</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Simplified</td>\n",
       "      <td>1986</td>\n",
       "      <td>274000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135483</th>\n",
       "      <td>2012-04</td>\n",
       "      <td>2012</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>261</td>\n",
       "      <td>YISHUN ST 22</td>\n",
       "      <td>01 TO 05</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Simplified</td>\n",
       "      <td>1985</td>\n",
       "      <td>270000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147734</th>\n",
       "      <td>2012-10</td>\n",
       "      <td>2012</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>4 ROOOM</td>\n",
       "      <td>279</td>\n",
       "      <td>YISHUN ST 22</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>104.0</td>\n",
       "      <td>Model A</td>\n",
       "      <td>1985</td>\n",
       "      <td>247000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185968 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tranc_YearMonth  year        town  flat_type block        street_name  \\\n",
       "123297         2021-01  2021  ANG MO KIO  EXECUTIVE   556  ANG MO KIO AVE 10   \n",
       "129518         2021-04  2021  ANG MO KIO     5 ROOM  310B   ANG MO KIO AVE 1   \n",
       "129530         2021-04  2021  ANG MO KIO     5 ROOM  588C   ANG MO KIO ST 52   \n",
       "127411         2021-03  2021  ANG MO KIO     5 ROOM  310B   ANG MO KIO AVE 1   \n",
       "125527         2021-02  2021  ANG MO KIO     5 ROOM  588A   ANG MO KIO ST 52   \n",
       "...                ...   ...         ...        ...   ...                ...   \n",
       "133311         2012-03  2012      YISHUN     3 ROOM   271       YISHUN ST 22   \n",
       "144028         2012-08  2012      YISHUN     3 ROOM   165     YISHUN RING RD   \n",
       "144027         2012-08  2012      YISHUN     3 ROOM   264       YISHUN ST 22   \n",
       "135483         2012-04  2012      YISHUN     3 ROOM   261       YISHUN ST 22   \n",
       "147734         2012-10  2012      YISHUN    4 ROOOM   279       YISHUN ST 22   \n",
       "\n",
       "       storey_range  floor_area_sqm     flat_model  lease_commence_date  \\\n",
       "123297     04 TO 06           163.0  Adjoined flat                 1980   \n",
       "129518     07 TO 09           119.0       Improved                 2012   \n",
       "129530     28 TO 30           112.0           DBSS                 2011   \n",
       "127411     04 TO 06           119.0       Improved                 2012   \n",
       "125527     16 TO 18           112.0           DBSS                 2011   \n",
       "...             ...             ...            ...                  ...   \n",
       "133311     01 TO 05            64.0     Simplified                 1985   \n",
       "144028     01 TO 03            64.0     Simplified                 1988   \n",
       "144027     04 TO 06            64.0     Simplified                 1986   \n",
       "135483     01 TO 05            64.0     Simplified                 1985   \n",
       "147734     10 TO 12           104.0        Model A                 1985   \n",
       "\n",
       "        resale_price  \n",
       "123297      950000.0  \n",
       "129518      950000.0  \n",
       "129530      935000.0  \n",
       "127411      908000.0  \n",
       "125527      890000.0  \n",
       "...              ...  \n",
       "133311      278000.0  \n",
       "144028      276000.0  \n",
       "144027      274000.0  \n",
       "135483      270000.0  \n",
       "147734      247000.0  \n",
       "\n",
       "[185968 rows x 11 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf.sort_values(['year', 'town', 'resale_price'], ascending=[False, True, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "t6fkQLWaZTfP",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 3 - Modifying Values in DataFrame\n",
    "While you were doing your exploration, your supervisor tells you to focus on last 5 years of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "eLb5OFO4ZTfQ",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 3a ]** Filter **hdbdf** so that it will only contain records from **year** 2017 and onward.\\\n",
    "> The remaining of this notebook will use **hdbdf** with the filtered data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "vYZDw5CGZTfQ"
   },
   "outputs": [],
   "source": [
    "hdbdf = hdbdf[hdbdf['year'] >= 2017]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "9OIDUursZTfQ",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 3b ]** Show the number of records for each of the **year** in your **hdbdf** dataframe.\n",
    "> Your output should look like this:\n",
    ">> ![](markdown_resources/q003b.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "oplL5nZDZTfR",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020    23340\n",
       "2019    22186\n",
       "2018    21561\n",
       "2017    20509\n",
       "2021     9020\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf['year'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After filtering your data, you continue to explore and clean data..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "_MTpv2__ZTfR",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 3c ]** Check **flat_type** for any typo errors and clean them accordingly. <br/>\n",
    ">💡<b>Hint </b> : There is only **one** category with error.<br/>\n",
    ">💡<b>Hint </b> : Don't worry if you get a **warning message**, starting with \" ... SettingWithCopyWarning:\" in the first line.<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "L6w0jrzPZTfR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 ROOM', '3 ROOM', '4 ROOOM', '5 ROOM', 'EXECUTIVE', '1 ROOM',\n",
       "       'MULTI-GENERATION'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf['flat_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdbdf['flat_type'] = hdbdf['flat_type'].replace('4 ROOOM', '4 ROOM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 ROOM', '3 ROOM', '4 ROOM', '5 ROOM', 'EXECUTIVE', '1 ROOM',\n",
       "       'MULTI-GENERATION'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf['flat_type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "KDumnSWnZTfS",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 4 - Groupby and Aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "uKJUTqUzZTfS",
    "tags": [],
    "trust": true
   },
   "source": [
    "> MYTH: HDB flat prices have been increasing over the years.\n",
    "\n",
    "After all the exploration and cleaning, you start to work on your task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "33CXMFkVZTfS",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 4a ]** Find the average of resale price for each year. <br/>\n",
    "> No need to assign the output to any variable, just show the output on the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "4BNperRXZTfS"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2017    443888.520571\n",
       "2018    441282.063703\n",
       "2019    432137.912902\n",
       "2020    452243.739911\n",
       "2021    492365.273588\n",
       "Name: resale_price, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf.groupby('year')['resale_price'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "gg8FJanmZTfT",
    "tags": [],
    "trust": true
   },
   "source": [
    "Your supervisor advised that you shouldn't use mean resale price as it doesn't take into account the flat size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "_fUeKQ8yZTfT",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 4b ]**  Create a new column **price_per_sqm** by dividing **resale_price** with **floor_area_sqm**. \n",
    "<br/> Then, find the average of **price_per_sqm** (resale price per squared meter) for every year instead. No need to assign the output to any variable, just show the output on the notebook. </br>\n",
    "\n",
    ">💡<b>Hint </b> : Don't worry if you get a **warning message**, starting with \" ... SettingWithCopyWarning:\" in the first line of the message.<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "F2R3ui9NZTfT"
   },
   "outputs": [],
   "source": [
    "hdbdf['price_per_sqm'] = hdbdf['resale_price'] / hdbdf['floor_area_sqm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "NMY2PNzzZTfT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2017    4578.877692\n",
       "2018    4509.672396\n",
       "2019    4476.800287\n",
       "2020    4669.830753\n",
       "2021    5045.132410\n",
       "Name: price_per_sqm, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdbdf.groupby('year')['price_per_sqm'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "cbvhUB54ZTfT",
    "tags": [],
    "trust": true
   },
   "source": [
    "You presented this result to your director.<br/>\n",
    "He commended you on the use of price_per_sqm instead of resale price for a better analysis.<br/>\n",
    "He suggested to add in regions for a more complete analysis. This is because different regions may have varying prices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "ofst-XmNZTfT",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 5 - Merge and Join Tables\n",
    "\n",
    "The data engineer has helped to scrap the information you need from https://en.wikipedia.org/wiki/Planning_Areas_of_Singapore, and stored the list of towns and their respective region in a csv file called **regions.csv**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "MYVykJNNZTfT",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 5a ]** Read in **regions.csv** as DataFrame and merge it to your main dataset **hdbdf**. Store the merged table in the same **hdbdf** variable.<br/>\n",
    "<br/>\n",
    ">💡 **Hint:** Note that not all the 26 towns from your **hdbdf** are in the CSV file **regions.csv**.<br/>\n",
    ">💡 **Hint:** Town values in both **regions.csv** and your **hdbdf** are in different capitalisations (uppercase and lowercase)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "FKR3NBZ2ZTfU"
   },
   "outputs": [],
   "source": [
    "regions = pd.read_csv('regions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "QZ69HFbLZTfU"
   },
   "outputs": [],
   "source": [
    "regions['town'] = regions['town'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "OEmx--4zZTfU"
   },
   "outputs": [],
   "source": [
    "hdbdf = pd.merge(hdbdf, regions, how='left', on='town')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "gjXliYUPZTfU",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 5b ]** There are some records with missing values in the \"region\" column.<br/>\n",
    "> 1) To examine more into these missing values, create a new dataframe named **nulldf** which contains only records with missing data in the **region** column. <br/>\n",
    "> 2) Find the unique values of <b>town</b> in **nulldf**.\n",
    ">\n",
    ">💡<b>Hint 1</b> : You may use df['*column*'].**isnull()** https://pandas.pydata.org/docs/reference/api/pandas.Series.isnull.html or other ways to help you to generate nulldf.<br/>\n",
    ">💡<b>Hint 2 </b>: Sometimes (actually most of the times), Google and StackOverflow is your best friend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "PkAH42FPZTfU"
   },
   "outputs": [],
   "source": [
    "hdbdf['region_null'] = hdbdf['region'].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "nulldf = hdbdf[hdbdf['region_null'] == True].drop('region_null', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CENTRAL AREA', 'KALLANG/WHAMPOA'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulldf['town'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "a22w0J9qZTfU",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 5c ]** You have been informed that the records with missing values in the **region** column are belong to the **same region**.<br/>\n",
    "> Fill up the missing values in the **region** column of the **hdbdf** dataframe based on this information.<br/><br/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "xI9s9AMFZTfV"
   },
   "outputs": [],
   "source": [
    "hdbdf['region'] = hdbdf['region'].fillna('Central')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "bbpHwsHUZTfV",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 6 - Groupby multiple columns\n",
    "With the new **region** column, you now have all the data needed to handle your director's suggestion to add in regions into your analysis of the average **price_per_sqm** over years. <br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "GUKRxxmMZTfV",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 6a ]** Find the average **price_per_sqm** for each **region** and **year**, and store the result into a variable called **psmbyregionyeardf**.<br/>\n",
    "<br/>\n",
    ">💡<b>Hint </b>: if you are unable to solve question 5, you can read in data in **hdbdf_q6.csv** and store the data in the **hdbdf** variable before you continue on question 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "GJgwFDOmZTfV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region      year\n",
       "Central     2017    5994.979040\n",
       "            2018    6008.892984\n",
       "            2019    5818.252724\n",
       "            2020    5872.648071\n",
       "            2021    6278.227623\n",
       "East        2017    4433.345711\n",
       "            2018    4414.984528\n",
       "            2019    4433.248145\n",
       "            2020    4594.932519\n",
       "            2021    4883.278453\n",
       "North       2017    3737.509673\n",
       "            2018    3627.454360\n",
       "            2019    3768.022205\n",
       "            2020    3909.595354\n",
       "            2021    4281.412830\n",
       "North-East  2017    4510.156947\n",
       "            2018    4498.681734\n",
       "            2019    4561.177684\n",
       "            2020    4715.106345\n",
       "            2021    4947.307695\n",
       "West        2017    4091.234506\n",
       "            2018    3993.434201\n",
       "            2019    3960.708361\n",
       "            2020    4256.923288\n",
       "            2021    4593.481741\n",
       "Name: price_per_sqm, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_region_year = hdbdf.groupby(['region', 'year'])\n",
    "psmbyregionyeardf = groupby_region_year['price_per_sqm'].mean()\n",
    "\n",
    "psmbyregionyeardf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "hMTyf-ROZTfV",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 6b ]** Reset the index of the **psmbyregionyeardf**. <br/>\n",
    "Rename the column  **price_per_sqm** in **psmbyregionyeardf** to become **avg_region_price_per_sqm_by_year**. <br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "jygorDLAZTfV"
   },
   "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>region</th>\n",
       "      <th>year</th>\n",
       "      <th>price_per_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>2017</td>\n",
       "      <td>5994.979040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central</td>\n",
       "      <td>2018</td>\n",
       "      <td>6008.892984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Central</td>\n",
       "      <td>2019</td>\n",
       "      <td>5818.252724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Central</td>\n",
       "      <td>2020</td>\n",
       "      <td>5872.648071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Central</td>\n",
       "      <td>2021</td>\n",
       "      <td>6278.227623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>East</td>\n",
       "      <td>2017</td>\n",
       "      <td>4433.345711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>East</td>\n",
       "      <td>2018</td>\n",
       "      <td>4414.984528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>East</td>\n",
       "      <td>2019</td>\n",
       "      <td>4433.248145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>East</td>\n",
       "      <td>2020</td>\n",
       "      <td>4594.932519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>East</td>\n",
       "      <td>2021</td>\n",
       "      <td>4883.278453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>North</td>\n",
       "      <td>2017</td>\n",
       "      <td>3737.509673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>North</td>\n",
       "      <td>2018</td>\n",
       "      <td>3627.454360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>North</td>\n",
       "      <td>2019</td>\n",
       "      <td>3768.022205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>North</td>\n",
       "      <td>2020</td>\n",
       "      <td>3909.595354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>North</td>\n",
       "      <td>2021</td>\n",
       "      <td>4281.412830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2017</td>\n",
       "      <td>4510.156947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2018</td>\n",
       "      <td>4498.681734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2019</td>\n",
       "      <td>4561.177684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2020</td>\n",
       "      <td>4715.106345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2021</td>\n",
       "      <td>4947.307695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>West</td>\n",
       "      <td>2017</td>\n",
       "      <td>4091.234506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>West</td>\n",
       "      <td>2018</td>\n",
       "      <td>3993.434201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>West</td>\n",
       "      <td>2019</td>\n",
       "      <td>3960.708361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>West</td>\n",
       "      <td>2020</td>\n",
       "      <td>4256.923288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>West</td>\n",
       "      <td>2021</td>\n",
       "      <td>4593.481741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region  year  price_per_sqm\n",
       "0      Central  2017    5994.979040\n",
       "1      Central  2018    6008.892984\n",
       "2      Central  2019    5818.252724\n",
       "3      Central  2020    5872.648071\n",
       "4      Central  2021    6278.227623\n",
       "5         East  2017    4433.345711\n",
       "6         East  2018    4414.984528\n",
       "7         East  2019    4433.248145\n",
       "8         East  2020    4594.932519\n",
       "9         East  2021    4883.278453\n",
       "10       North  2017    3737.509673\n",
       "11       North  2018    3627.454360\n",
       "12       North  2019    3768.022205\n",
       "13       North  2020    3909.595354\n",
       "14       North  2021    4281.412830\n",
       "15  North-East  2017    4510.156947\n",
       "16  North-East  2018    4498.681734\n",
       "17  North-East  2019    4561.177684\n",
       "18  North-East  2020    4715.106345\n",
       "19  North-East  2021    4947.307695\n",
       "20        West  2017    4091.234506\n",
       "21        West  2018    3993.434201\n",
       "22        West  2019    3960.708361\n",
       "23        West  2020    4256.923288\n",
       "24        West  2021    4593.481741"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psmbyregionyeardf = psmbyregionyeardf.reset_index()\n",
    "psmbyregionyeardf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "c_fyc29PZTfW"
   },
   "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>region</th>\n",
       "      <th>year</th>\n",
       "      <th>avg_region_price_per_sqm_by_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>2017</td>\n",
       "      <td>5994.979040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central</td>\n",
       "      <td>2018</td>\n",
       "      <td>6008.892984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Central</td>\n",
       "      <td>2019</td>\n",
       "      <td>5818.252724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Central</td>\n",
       "      <td>2020</td>\n",
       "      <td>5872.648071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Central</td>\n",
       "      <td>2021</td>\n",
       "      <td>6278.227623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>East</td>\n",
       "      <td>2017</td>\n",
       "      <td>4433.345711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>East</td>\n",
       "      <td>2018</td>\n",
       "      <td>4414.984528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>East</td>\n",
       "      <td>2019</td>\n",
       "      <td>4433.248145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>East</td>\n",
       "      <td>2020</td>\n",
       "      <td>4594.932519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>East</td>\n",
       "      <td>2021</td>\n",
       "      <td>4883.278453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>North</td>\n",
       "      <td>2017</td>\n",
       "      <td>3737.509673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>North</td>\n",
       "      <td>2018</td>\n",
       "      <td>3627.454360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>North</td>\n",
       "      <td>2019</td>\n",
       "      <td>3768.022205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>North</td>\n",
       "      <td>2020</td>\n",
       "      <td>3909.595354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>North</td>\n",
       "      <td>2021</td>\n",
       "      <td>4281.412830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2017</td>\n",
       "      <td>4510.156947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2018</td>\n",
       "      <td>4498.681734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2019</td>\n",
       "      <td>4561.177684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2020</td>\n",
       "      <td>4715.106345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>North-East</td>\n",
       "      <td>2021</td>\n",
       "      <td>4947.307695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>West</td>\n",
       "      <td>2017</td>\n",
       "      <td>4091.234506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>West</td>\n",
       "      <td>2018</td>\n",
       "      <td>3993.434201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>West</td>\n",
       "      <td>2019</td>\n",
       "      <td>3960.708361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>West</td>\n",
       "      <td>2020</td>\n",
       "      <td>4256.923288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>West</td>\n",
       "      <td>2021</td>\n",
       "      <td>4593.481741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region  year  avg_region_price_per_sqm_by_year\n",
       "0      Central  2017                       5994.979040\n",
       "1      Central  2018                       6008.892984\n",
       "2      Central  2019                       5818.252724\n",
       "3      Central  2020                       5872.648071\n",
       "4      Central  2021                       6278.227623\n",
       "5         East  2017                       4433.345711\n",
       "6         East  2018                       4414.984528\n",
       "7         East  2019                       4433.248145\n",
       "8         East  2020                       4594.932519\n",
       "9         East  2021                       4883.278453\n",
       "10       North  2017                       3737.509673\n",
       "11       North  2018                       3627.454360\n",
       "12       North  2019                       3768.022205\n",
       "13       North  2020                       3909.595354\n",
       "14       North  2021                       4281.412830\n",
       "15  North-East  2017                       4510.156947\n",
       "16  North-East  2018                       4498.681734\n",
       "17  North-East  2019                       4561.177684\n",
       "18  North-East  2020                       4715.106345\n",
       "19  North-East  2021                       4947.307695\n",
       "20        West  2017                       4091.234506\n",
       "21        West  2018                       3993.434201\n",
       "22        West  2019                       3960.708361\n",
       "23        West  2020                       4256.923288\n",
       "24        West  2021                       4593.481741"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psmbyregionyeardf = psmbyregionyeardf.rename(columns = {'price_per_sqm' : 'avg_region_price_per_sqm_by_year'})\n",
    "psmbyregionyeardf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "ZUMWwqofZTfW",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 6c ]** Plot a line graph with the **avg_region_price_per_sqm_by_year** (y axis) against **year** (x axis) with 5 lines, each representing a **region**. <br/>\n",
    "\n",
    "> 💡 **Hint** : If you are unable to solve questions 6a and 6b, you can read in data in **psmbyregionyeardf.csv** and store the data in the **psmbyregionyeardf** variable before you continue on question 6c."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "Xw7pzEALZTfW"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "psmbyregionyeardf.pivot_table('avg_region_price_per_sqm_by_year', index='year', columns='region', aggfunc='mean').plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "RnDSPpDGZTfW",
    "tags": [],
    "trust": true
   },
   "source": [
    "# Question 7 - Further Analyses\n",
    "Intrigued by the result, you decided to explore more on the relationship of **avg_region_price_per_sqm_by_year** with the other columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "K7ciLC9PZTfW",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 7a ]** Merge the **psmbyregionyeardf** into your main table **hdbdf**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "IrkN6b1gZTfW"
   },
   "outputs": [],
   "source": [
    "hdbdf = hdbdf.drop('region_null', axis=1)\n",
    "hdbdf = pd.merge(hdbdf, psmbyregionyeardf, how='left', on=['region', 'year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "dK03COE7ZTfX",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 7b ]** Create a new column called **price_per_sqm_diff_to_region**, to store the differences between i) **price_per_sqm** of the unit and ii) the average price_per_sqm of the region and year of which the unit belongs to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "TrF2JttFZTfX"
   },
   "outputs": [],
   "source": [
    "hdbdf['price_per_sqm_diff_to_region'] = hdbdf['price_per_sqm'] - hdbdf['avg_region_price_per_sqm_by_year']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "BhEc3o9xZTfX",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 7c ]** Display a **histogram** to show the distribution of the differences in prices stored in the **price_per_sqm_diff_to_region** column. \\\n",
    "> Set the number of bins in the histogram to **20**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "YrmotTp1ZTfX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price_per_sqm_diff_to_region', ylabel='Count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(hdbdf['price_per_sqm_diff_to_region'], kde=False, bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "gGIlx0C8ZTfY",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 7d ]**\n",
    "Create a column **mid_storey** to store the midpoint of the unit's **storey_range**. <br/> Round the result to the nearest integer. <br/> For example, if the unit **storey_range** is \"07 TO 09\", the midpoint is **8**.<br/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "c1tB7eR2ZTfZ"
   },
   "outputs": [],
   "source": [
    "def get_mid_storey(row):\n",
    "    top = int(row['storey_range'].split(' TO ')[1])\n",
    "    bottom = int(row['storey_range'].split(' TO ')[0])\n",
    "    mid = int((top + bottom) / 2)\n",
    "    return mid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdbdf['mid_storey'] = hdbdf.apply(get_mid_storey, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "2wHlieTIZTfZ",
    "tags": [],
    "trust": true
   },
   "source": [
    "> **[ Question 7e ]**\n",
    "> Display a **scatter plot** which looks similar to the one given below:\n",
    ">> ![](markdown_resources/q007.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "gB-Hwx7HZTfa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mid_storey', ylabel='price_per_sqm_diff_to_region'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='mid_storey', y='price_per_sqm_diff_to_region', data=hdbdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "id": "6z_zKJBsZTfa",
    "tags": [],
    "trust": true
   },
   "source": [
    "---\n",
    "## END OF CAPSTONE ASSIGNMENT 2\n",
    "\n",
    "- Well done everyone!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "DC_Bootcamp_2021_Milestone_Test_2_v2.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

# data[].attributes.body
Revised version