analyse_sa_rents_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import duckdb\n",
    "import pandas as pd\n",
    "import plotly.express as px\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to the DuckDB database\n",
    "con = duckdb.connect('clean_data/sa_rents_data.duckdb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_quarter</th>\n",
       "      <th>date_</th>\n",
       "      <th>year_</th>\n",
       "      <th>month_</th>\n",
       "      <th>suburb</th>\n",
       "      <th>br1_flat_count</th>\n",
       "      <th>br1_flat_median_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-06</td>\n",
       "      <td>2008-06-01</td>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>53</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-09</td>\n",
       "      <td>2008-09-01</td>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>68</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>41</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-03</td>\n",
       "      <td>2009-03-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>3</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>212</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-06</td>\n",
       "      <td>2009-06-01</td>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>197</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2023-09</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>9</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>1582</td>\n",
       "      <td>379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2023-12</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>675</td>\n",
       "      <td>389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2024-03</td>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>2470</td>\n",
       "      <td>379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2024-06</td>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>2024</td>\n",
       "      <td>6</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>700</td>\n",
       "      <td>404.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2024-09</td>\n",
       "      <td>2024-09-01</td>\n",
       "      <td>2024</td>\n",
       "      <td>9</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>1565</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 7 columns</p>\n",
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       "   data_quarter      date_  year_  month_    suburb br1_flat_count  \\\n",
       "0       2008-06 2008-06-01   2008       6  Adelaide             53   \n",
       "1       2008-09 2008-09-01   2008       9  Adelaide             68   \n",
       "2       2008-12 2008-12-01   2008      12  Adelaide             41   \n",
       "3       2009-03 2009-03-01   2009       3  Adelaide            212   \n",
       "4       2009-06 2009-06-01   2009       6  Adelaide            197   \n",
       "..          ...        ...    ...     ...       ...            ...   \n",
       "61      2023-09 2023-09-01   2023       9  Adelaide           1582   \n",
       "62      2023-12 2023-12-01   2023      12  Adelaide            675   \n",
       "63      2024-03 2024-03-01   2024       3  Adelaide           2470   \n",
       "64      2024-06 2024-06-01   2024       6  Adelaide            700   \n",
       "65      2024-09 2024-09-01   2024       9  Adelaide           1565   \n",
       "\n",
       "    br1_flat_median_price  \n",
       "0                   255.0  \n",
       "1                   250.0  \n",
       "2                   230.0  \n",
       "3                   270.0  \n",
       "4                   270.0  \n",
       "..                    ...  \n",
       "61                  379.0  \n",
       "62                  389.0  \n",
       "63                  379.0  \n",
       "64                  404.5  \n",
       "65                  380.0  \n",
       "\n",
       "[66 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract data with the query\n",
    "query = \"\"\"\n",
    "SELECT data_quarter, \n",
    "CAST(year || '-' || month || '-01' AS DATE) AS date_,\n",
    "CAST(year AS INTEGER) year_,\n",
    "CAST(month AS INTEGER) month_, \n",
    "suburb, br1_flat_count, \n",
    "CAST(br1_flat_median_price AS DOUBLE) br1_flat_median_price,\n",
    "FROM sa_rents_data\n",
    "WHERE suburb = 'Adelaide' AND cast(year AS INTEGER)  BETWEEN 2008 AND 2024\n",
    "ORDER BY year, month\n",
    ";\n",
    "\"\"\"\n",
    "df = con.execute(query).fetchdf()\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   ],
   "source": [
    "\n",
    "fig = px.line(df, x='date_', y='br1_flat_median_price', title='Trend of Median Price for 1 Bedroom Flats in Adelaide City',\n",
    "               labels={'br1_flat_median_price':'Median Price ($)', 'date_':'Date'}, \n",
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    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write data out to a csv file\n",
    "df.to_csv('clean_data/adelaide_1br_flat_prices.csv', index=False)"
   ]
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