housing_data_DA.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Project description**: lorem ipsum:  \n",
    "\n",
    "On [this link (Kaggle)](https://www.kaggle.com/anthonypino/melbourne-housing-market/version/27) you can find the original dataset and its description.\n",
    "\n",
    "*Project owner*: ***Gustaf Olofsson***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages and Load Data from CSVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<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>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "      <th>Distance</th>\n",
       "      <th>CouncilArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>49 Lithgow St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1490000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>1/04/2017</td>\n",
       "      <td>3067</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>59A Turner St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1220000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Marshall</td>\n",
       "      <td>1/04/2017</td>\n",
       "      <td>3067</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>119B Yarra St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1420000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>1/04/2017</td>\n",
       "      <td>3067</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aberfeldie</td>\n",
       "      <td>68 Vida St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1515000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Barry</td>\n",
       "      <td>1/04/2017</td>\n",
       "      <td>3040</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>1543</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Moonee Valley City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Airport West</td>\n",
       "      <td>92 Clydesdale Rd</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>1/04/2017</td>\n",
       "      <td>3042</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>3464</td>\n",
       "      <td>10.4</td>\n",
       "      <td>Moonee Valley City Council</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Suburb           Address  Rooms Type      Price Method   SellerG  \\\n",
       "0    Abbotsford     49 Lithgow St      3    h  1490000.0      S    Jellis   \n",
       "1    Abbotsford     59A Turner St      3    h  1220000.0      S  Marshall   \n",
       "2    Abbotsford     119B Yarra St      3    h  1420000.0      S    Nelson   \n",
       "3    Aberfeldie        68 Vida St      3    h  1515000.0      S     Barry   \n",
       "4  Airport West  92 Clydesdale Rd      2    h   670000.0      S    Nelson   \n",
       "\n",
       "        Date  Postcode             Regionname  Propertycount  Distance  \\\n",
       "0  1/04/2017      3067  Northern Metropolitan           4019       3.0   \n",
       "1  1/04/2017      3067  Northern Metropolitan           4019       3.0   \n",
       "2  1/04/2017      3067  Northern Metropolitan           4019       3.0   \n",
       "3  1/04/2017      3040   Western Metropolitan           1543       7.5   \n",
       "4  1/04/2017      3042   Western Metropolitan           3464      10.4   \n",
       "\n",
       "                  CouncilArea  \n",
       "0          Yarra City Council  \n",
       "1          Yarra City Council  \n",
       "2          Yarra City Council  \n",
       "3  Moonee Valley City Council  \n",
       "4  Moonee Valley City Council  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "dir_path = os.path.dirname(os.path.realpath('__file__'))\n",
    "raw_data = pd.read_csv(dir_path+\"\\\\MELBOURNE_HOUSE_PRICES_LESS.csv\")\n",
    "\n",
    "# test that data loaded correctly\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding blanks or unexpected values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 63023 rows in the raw data.\n",
      "There are 14590 rows in the raw data with null values\n"
     ]
    }
   ],
   "source": [
    "print(\"There are {} rows in the raw data.\".format(len(raw_data.index)))\n",
    "\n",
    "def print_unique(df, attribute):\n",
    "    print(\"Unique vales of '{}': {}\".format(getattr(df, attribute).name, getattr(df, attribute).unique()))\n",
    "    return 0\n",
    "\n",
    "print(\"There are {} rows in the raw data with null values\".format(len(raw_data[raw_data.isnull().any(axis=1)].index)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48433"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As we will still have ~50k entries to work with, let's drop the rows with any null values\n",
    "cleaned_data = raw_data.dropna(axis=0, how = 'any', inplace = False)\n",
    "\n",
    "# Testing so the length of our new dataset aligns with expectations\n",
    "len(cleaned_data.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SECTION TITLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SECTION TITLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SECTION TITLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SECTION TITLE"
   ]
  }
 ],
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