group1_housing_solution.ipynb

{
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  {
   "cell_type": "code",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REF_DATE</th>\n",
       "      <th>GEO</th>\n",
       "      <th>DGUID</th>\n",
       "      <th>New housing price indexes</th>\n",
       "      <th>UOM</th>\n",
       "      <th>UOM_ID</th>\n",
       "      <th>SCALAR_FACTOR</th>\n",
       "      <th>SCALAR_ID</th>\n",
       "      <th>VECTOR</th>\n",
       "      <th>COORDINATE</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>SYMBOL</th>\n",
       "      <th>TERMINATED</th>\n",
       "      <th>DECIMALS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61075</th>\n",
       "      <td>2023-05</td>\n",
       "      <td>Vancouver, British Columbia</td>\n",
       "      <td>2011S0503933</td>\n",
       "      <td>House only</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>347</td>\n",
       "      <td>units</td>\n",
       "      <td>0</td>\n",
       "      <td>v111955557</td>\n",
       "      <td>39.2</td>\n",
       "      <td>126.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61076</th>\n",
       "      <td>2023-05</td>\n",
       "      <td>Vancouver, British Columbia</td>\n",
       "      <td>2011S0503933</td>\n",
       "      <td>Land only</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>347</td>\n",
       "      <td>units</td>\n",
       "      <td>0</td>\n",
       "      <td>v111955558</td>\n",
       "      <td>39.3</td>\n",
       "      <td>121.0</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61077</th>\n",
       "      <td>2023-05</td>\n",
       "      <td>Victoria, British Columbia</td>\n",
       "      <td>2011S0503935</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>347</td>\n",
       "      <td>units</td>\n",
       "      <td>0</td>\n",
       "      <td>v111955559</td>\n",
       "      <td>40.1</td>\n",
       "      <td>121.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61078</th>\n",
       "      <td>2023-05</td>\n",
       "      <td>Victoria, British Columbia</td>\n",
       "      <td>2011S0503935</td>\n",
       "      <td>House only</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>347</td>\n",
       "      <td>units</td>\n",
       "      <td>0</td>\n",
       "      <td>v111955560</td>\n",
       "      <td>40.2</td>\n",
       "      <td>127.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61079</th>\n",
       "      <td>2023-05</td>\n",
       "      <td>Victoria, British Columbia</td>\n",
       "      <td>2011S0503935</td>\n",
       "      <td>Land only</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>347</td>\n",
       "      <td>units</td>\n",
       "      <td>0</td>\n",
       "      <td>v111955561</td>\n",
       "      <td>40.3</td>\n",
       "      <td>110.6</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      REF_DATE                          GEO         DGUID  \\\n",
       "61075  2023-05  Vancouver, British Columbia  2011S0503933   \n",
       "61076  2023-05  Vancouver, British Columbia  2011S0503933   \n",
       "61077  2023-05   Victoria, British Columbia  2011S0503935   \n",
       "61078  2023-05   Victoria, British Columbia  2011S0503935   \n",
       "61079  2023-05   Victoria, British Columbia  2011S0503935   \n",
       "\n",
       "      New housing price indexes                UOM  UOM_ID SCALAR_FACTOR  \\\n",
       "61075                House only  Index, 201612=100     347         units   \n",
       "61076                 Land only  Index, 201612=100     347         units   \n",
       "61077    Total (house and land)  Index, 201612=100     347         units   \n",
       "61078                House only  Index, 201612=100     347         units   \n",
       "61079                 Land only  Index, 201612=100     347         units   \n",
       "\n",
       "       SCALAR_ID      VECTOR  COORDINATE  VALUE STATUS  SYMBOL  TERMINATED  \\\n",
       "61075          0  v111955557        39.2  126.4    NaN     NaN         NaN   \n",
       "61076          0  v111955558        39.3  121.0      E     NaN         NaN   \n",
       "61077          0  v111955559        40.1  121.9    NaN     NaN         NaN   \n",
       "61078          0  v111955560        40.2  127.9    NaN     NaN         NaN   \n",
       "61079          0  v111955561        40.3  110.6      E     NaN         NaN   \n",
       "\n",
       "       DECIMALS  \n",
       "61075         1  \n",
       "61076         1  \n",
       "61077         1  \n",
       "61078         1  \n",
       "61079         1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dependencies\n",
    "from pathlib import Path\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "Main_csv_path = Path(\"Resources/18100205.csv\")\n",
    "Metadata_csv_path = Path(\"Resources/18100205_MetaData.csv\")\n",
    "\n",
    "Main_StatsCan_IndexDF = pd.read_csv(Main_csv_path, encoding=\"UTF-8-Sig\")\n",
    "Metadata_DF = pd.read_csv(Metadata_csv_path, encoding=\"UTF-8-Sig\")\n",
    "\n",
    "Main_StatsCan_IndexDF.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "099a3466",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cube Title</th>\n",
       "      <th>Product Id</th>\n",
       "      <th>CANSIM Id</th>\n",
       "      <th>URL</th>\n",
       "      <th>Cube Notes</th>\n",
       "      <th>Archive Status</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Start Reference Period</th>\n",
       "      <th>End Reference Period</th>\n",
       "      <th>Total number of dimensions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New housing price index, monthly</th>\n",
       "      <td>18100205</td>\n",
       "      <td>327-0056</td>\n",
       "      <td>https://www150.statcan.gc.ca/t1/tbl1/en/tv.act...</td>\n",
       "      <td>1;2;3;13;14</td>\n",
       "      <td>CURRENT - a cube available to the public and t...</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>1981-01-01</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension ID</th>\n",
       "      <td>Dimension name</td>\n",
       "      <td>Dimension Notes</td>\n",
       "      <td>Dimension Definitions</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Geography</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New housing price indexes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension ID</th>\n",
       "      <td>Member Name</td>\n",
       "      <td>Classification Code</td>\n",
       "      <td>Member ID</td>\n",
       "      <td>Parent Member ID</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>Member Notes</td>\n",
       "      <td>Member Definitions</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                                 Cube Title  \\\n",
       "New housing price index, monthly                   18100205   \n",
       "Dimension ID                                 Dimension name   \n",
       "1                                                 Geography   \n",
       "2                                 New housing price indexes   \n",
       "Dimension ID                                    Member Name   \n",
       "\n",
       "                                           Product Id  \\\n",
       "New housing price index, monthly             327-0056   \n",
       "Dimension ID                          Dimension Notes   \n",
       "1                                                 NaN   \n",
       "2                                                 NaN   \n",
       "Dimension ID                      Classification Code   \n",
       "\n",
       "                                                                          CANSIM Id  \\\n",
       "New housing price index, monthly  https://www150.statcan.gc.ca/t1/tbl1/en/tv.act...   \n",
       "Dimension ID                                                  Dimension Definitions   \n",
       "1                                                                               NaN   \n",
       "2                                                                               NaN   \n",
       "Dimension ID                                                              Member ID   \n",
       "\n",
       "                                               URL  \\\n",
       "New housing price index, monthly       1;2;3;13;14   \n",
       "Dimension ID                                   NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "Dimension ID                      Parent Member ID   \n",
       "\n",
       "                                                                         Cube Notes  \\\n",
       "New housing price index, monthly  CURRENT - a cube available to the public and t...   \n",
       "Dimension ID                                                                    NaN   \n",
       "1                                                                               NaN   \n",
       "2                                                                               NaN   \n",
       "Dimension ID                                                             Terminated   \n",
       "\n",
       "                                 Archive Status           Frequency  \\\n",
       "New housing price index, monthly        Monthly          1981-01-01   \n",
       "Dimension ID                                NaN                 NaN   \n",
       "1                                           NaN                 NaN   \n",
       "2                                           NaN                 NaN   \n",
       "Dimension ID                       Member Notes  Member Definitions   \n",
       "\n",
       "                                 Start Reference Period  End Reference Period  \\\n",
       "New housing price index, monthly             2023-05-01                   2.0   \n",
       "Dimension ID                                        NaN                   NaN   \n",
       "1                                                   NaN                   NaN   \n",
       "2                                                   NaN                   NaN   \n",
       "Dimension ID                                        NaN                   NaN   \n",
       "\n",
       "                                  Total number of dimensions  \n",
       "New housing price index, monthly                         NaN  \n",
       "Dimension ID                                             NaN  \n",
       "1                                                        NaN  \n",
       "2                                                        NaN  \n",
       "Dimension ID                                             NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Metadata_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e773a9d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REF_DATE</th>\n",
       "      <th>GEO</th>\n",
       "      <th>NewHousePrice_Index_Type</th>\n",
       "      <th>UOM</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>DECIMALS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61075</th>\n",
       "      <td>2023-05</td>\n",
       "      <td>Vancouver, British Columbia</td>\n",
       "      <td>House only</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>126.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61076</th>\n",
       "      <td>2023-05</td>\n",
       "      <td>Vancouver, British Columbia</td>\n",
       "      <td>Land only</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>121.0</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61077</th>\n",
       "      <td>2023-05</td>\n",
       "      <td>Victoria, British Columbia</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>121.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61078</th>\n",
       "      <td>2023-05</td>\n",
       "      <td>Victoria, British Columbia</td>\n",
       "      <td>House only</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>127.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61079</th>\n",
       "      <td>2023-05</td>\n",
       "      <td>Victoria, British Columbia</td>\n",
       "      <td>Land only</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>110.6</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      REF_DATE                          GEO NewHousePrice_Index_Type  \\\n",
       "61075  2023-05  Vancouver, British Columbia               House only   \n",
       "61076  2023-05  Vancouver, British Columbia                Land only   \n",
       "61077  2023-05   Victoria, British Columbia   Total (house and land)   \n",
       "61078  2023-05   Victoria, British Columbia               House only   \n",
       "61079  2023-05   Victoria, British Columbia                Land only   \n",
       "\n",
       "                     UOM  VALUE STATUS  DECIMALS  \n",
       "61075  Index, 201612=100  126.4    NaN         1  \n",
       "61076  Index, 201612=100  121.0      E         1  \n",
       "61077  Index, 201612=100  121.9    NaN         1  \n",
       "61078  Index, 201612=100  127.9    NaN         1  \n",
       "61079  Index, 201612=100  110.6      E         1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select necessary columns for initial data cleaning processes.\n",
    "reduced_main_df = Main_StatsCan_IndexDF[[\"REF_DATE\",\"GEO\",\"New housing price indexes\",\"UOM\",\"VALUE\",\"STATUS\",\"DECIMALS\"]]\t\t\n",
    "\n",
    "#Rename columns for code readability.\n",
    "\n",
    "reduced_main_renamed_df =reduced_main_df.rename(columns={\"New housing price indexes\": \"NewHousePrice_Index_Type\" })\n",
    "\n",
    "#Initial Rename Approach Below:\n",
    "#reduced_main_df.columns.values[2] = \"NewHousePrice_Index_Type\" \n",
    "#Replaced column name of \"New housing price indexes\".\n",
    "\n",
    "reduced_main_renamed_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85a7b3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REF_DATE                     object\n",
       "GEO                          object\n",
       "NewHousePrice_Index_Type     object\n",
       "UOM                          object\n",
       "VALUE                       float64\n",
       "STATUS                       object\n",
       "DECIMALS                      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Validate the data types within our reduced_main_renamed_df\n",
    "#Confirms that we can split the object columns as text.\n",
    "\n",
    "reduced_main_renamed_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b0240861",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                 Canada\n",
      "1                 Canada\n",
      "2                 Canada\n",
      "3        Atlantic Region\n",
      "4        Atlantic Region\n",
      "              ...       \n",
      "61075          Vancouver\n",
      "61076          Vancouver\n",
      "61077           Victoria\n",
      "61078           Victoria\n",
      "61079           Victoria\n",
      "Name: GEO, Length: 61080, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Create VAR of column as a series to add an index to each row in preparation of split text by Delimiter function.\n",
    "\n",
    "Orig_Geo_series = pd.Series(reduced_main_df[\"GEO\"]) #TEST of Initial Output via => print(Orig_Geo_series)\n",
    "\n",
    "#SPLIT data by delimiter \n",
    "Geo_Split = Orig_Geo_series.str.split(',')\n",
    "\n",
    "#Fetch by column index the first part of the original VAR column.\n",
    "Geo_CityRow = Geo_Split.str.get(0) #access the first element for eventual \"column1\"\n",
    "\n",
    "print(Geo_CityRow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "088ce3ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                      NaN\n",
      "1                      NaN\n",
      "2                      NaN\n",
      "3                      NaN\n",
      "4                      NaN\n",
      "               ...        \n",
      "61075     British Columbia\n",
      "61076     British Columbia\n",
      "61077     British Columbia\n",
      "61078     British Columbia\n",
      "61079     British Columbia\n",
      "Name: GEO, Length: 61080, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Fetch by column index the second part of the original VAR column.\n",
    "Geo_ProvRow = Geo_Split.str.get(1) #access the first element for eventual \"column2\"\n",
    "\n",
    "print(Geo_ProvRow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ddc45c34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           City_Region           Province\n",
      "0               Canada                NaN\n",
      "1               Canada                NaN\n",
      "2               Canada                NaN\n",
      "3      Atlantic Region                NaN\n",
      "4      Atlantic Region                NaN\n",
      "...                ...                ...\n",
      "61075        Vancouver   British Columbia\n",
      "61076        Vancouver   British Columbia\n",
      "61077         Victoria   British Columbia\n",
      "61078         Victoria   British Columbia\n",
      "61079         Victoria   British Columbia\n",
      "\n",
      "[61080 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "#Leverage pd.series() index on Original DF column to join the delimited data back together.\n",
    "#Accomplished by the usage of \"axis=1\"\n",
    "\n",
    "Manual_Merge_Geo = pd.concat([Geo_CityRow,Geo_ProvRow], axis=1) #axis=1 is using \"column 1\" to merge. Being the column created by pd.series() + #TEST of Initial Output via => print(Manual_Merge_Geo)\n",
    "\n",
    "#Rename Columns from both duplicating \"GEO\" using the column index.\n",
    "#Column Index Approach and Rename Options presented via => https://www.geeksforgeeks.org/rename-specific-columns-in-pandas/\n",
    "#Team decision => BEST PRACTICE TO RENAME RIGHT AWAY \"HERE\" ... to save coding time later.\n",
    "Manual_Merge_Geo.columns.values[0] = \"City_Region\"\n",
    "Manual_Merge_Geo.columns.values[1] = \"Province\"\n",
    "\n",
    "print(Manual_Merge_Geo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ddc50dc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        1981\n",
      "1        1981\n",
      "2        1981\n",
      "3        1981\n",
      "4        1981\n",
      "         ... \n",
      "61075    2023\n",
      "61076    2023\n",
      "61077    2023\n",
      "61078    2023\n",
      "61079    2023\n",
      "Name: REF_DATE, Length: 61080, dtype: object\n",
      "0        01\n",
      "1        01\n",
      "2        01\n",
      "3        01\n",
      "4        01\n",
      "         ..\n",
      "61075    05\n",
      "61076    05\n",
      "61077    05\n",
      "61078    05\n",
      "61079    05\n",
      "Name: REF_DATE, Length: 61080, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Create VAR of column as a series to add an index to each row in preparation of split text by Delimiter function.\n",
    "Orig_Year_series = pd.Series(reduced_main_df[\"REF_DATE\"])\n",
    "\n",
    "#SPLIT data by delimiter \n",
    "Year_Split = Orig_Year_series.str.split('-')\n",
    "\n",
    "#Fetch by column index the second part of the original VAR column.\n",
    "Row_Year = Year_Split.str.get(0) #access the first element for eventual \"column1\"\n",
    "Row_Month = Year_Split.str.get(1) #access the second element for eventual \"column2\"\n",
    "\n",
    "#REMOVED BELOW as a script test. These outputs end up \"inserting to row#1 (as index=0)\" ... rather than naming the column as expected.\n",
    "#Row_Year.values[0] = \"Year\"  #NOTE => \n",
    "#Row_Month.values[0] = \"Month\" #NOTE => \n",
    "\n",
    "print(Row_Year)\n",
    "print(Row_Month)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "071dd970",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Year Month\n",
      "0      1981    01\n",
      "1      1981    01\n",
      "2      1981    01\n",
      "3      1981    01\n",
      "4      1981    01\n",
      "...     ...   ...\n",
      "61075  2023    05\n",
      "61076  2023    05\n",
      "61077  2023    05\n",
      "61078  2023    05\n",
      "61079  2023    05\n",
      "\n",
      "[61080 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "#Leverage initial pd.series() index on Original DF column to join the delimited data back together.\n",
    "#Accomplished by the usage of \"axis=1\"\n",
    "\n",
    "Manual_Merge_date = pd.concat([Row_Year,Row_Month], axis=1) #axis=1 is using \"column 1\" to merge.\n",
    "\n",
    "#Rename Columns from both duplicating \"GEO\" using the column index.\n",
    "#Column Index Approach and Rename Options presented via => https://www.geeksforgeeks.org/rename-specific-columns-in-pandas/\n",
    "#Team decision => BEST PRACTICE TO RENAME RIGHT AWAY \"HERE\" ... to save coding time later.\n",
    "Manual_Merge_date.columns.values[0] = \"Year\"\n",
    "Manual_Merge_date.columns.values[1] = \"Month\"\n",
    "\n",
    "#KEY REMINDER ... if we need the month to be transformed into \"1\" intstead of \"01\" (IE: INT from TEXT/VARCHAR) ... here is probably the best place as well.\n",
    "\n",
    "print(Manual_Merge_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "deb061e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      REF_DATE                          GEO NewHousePrice_Index_Type  \\\n",
      "0      1981-01                       Canada   Total (house and land)   \n",
      "1      1981-01                       Canada               House only   \n",
      "2      1981-01                       Canada                Land only   \n",
      "3      1981-01              Atlantic Region   Total (house and land)   \n",
      "4      1981-01              Atlantic Region               House only   \n",
      "...        ...                          ...                      ...   \n",
      "61075  2023-05  Vancouver, British Columbia               House only   \n",
      "61076  2023-05  Vancouver, British Columbia                Land only   \n",
      "61077  2023-05   Victoria, British Columbia   Total (house and land)   \n",
      "61078  2023-05   Victoria, British Columbia               House only   \n",
      "61079  2023-05   Victoria, British Columbia                Land only   \n",
      "\n",
      "                     UOM  VALUE STATUS  DECIMALS      City_Region  \\\n",
      "0      Index, 201612=100   38.2    NaN         1           Canada   \n",
      "1      Index, 201612=100   36.1    NaN         1           Canada   \n",
      "2      Index, 201612=100   40.6      E         1           Canada   \n",
      "3      Index, 201612=100    NaN     ..         1  Atlantic Region   \n",
      "4      Index, 201612=100    NaN     ..         1  Atlantic Region   \n",
      "...                  ...    ...    ...       ...              ...   \n",
      "61075  Index, 201612=100  126.4    NaN         1        Vancouver   \n",
      "61076  Index, 201612=100  121.0      E         1        Vancouver   \n",
      "61077  Index, 201612=100  121.9    NaN         1         Victoria   \n",
      "61078  Index, 201612=100  127.9    NaN         1         Victoria   \n",
      "61079  Index, 201612=100  110.6      E         1         Victoria   \n",
      "\n",
      "                Province  \n",
      "0                    NaN  \n",
      "1                    NaN  \n",
      "2                    NaN  \n",
      "3                    NaN  \n",
      "4                    NaN  \n",
      "...                  ...  \n",
      "61075   British Columbia  \n",
      "61076   British Columbia  \n",
      "61077   British Columbia  \n",
      "61078   British Columbia  \n",
      "61079   British Columbia  \n",
      "\n",
      "[61080 rows x 9 columns]\n",
      "      REF_DATE                          GEO NewHousePrice_Index_Type  \\\n",
      "0      1981-01                       Canada   Total (house and land)   \n",
      "1      1981-01                       Canada               House only   \n",
      "2      1981-01                       Canada                Land only   \n",
      "3      1981-01              Atlantic Region   Total (house and land)   \n",
      "4      1981-01              Atlantic Region               House only   \n",
      "...        ...                          ...                      ...   \n",
      "61075  2023-05  Vancouver, British Columbia               House only   \n",
      "61076  2023-05  Vancouver, British Columbia                Land only   \n",
      "61077  2023-05   Victoria, British Columbia   Total (house and land)   \n",
      "61078  2023-05   Victoria, British Columbia               House only   \n",
      "61079  2023-05   Victoria, British Columbia                Land only   \n",
      "\n",
      "                     UOM  VALUE STATUS  DECIMALS      City_Region  \\\n",
      "0      Index, 201612=100   38.2    NaN         1           Canada   \n",
      "1      Index, 201612=100   36.1    NaN         1           Canada   \n",
      "2      Index, 201612=100   40.6      E         1           Canada   \n",
      "3      Index, 201612=100    NaN     ..         1  Atlantic Region   \n",
      "4      Index, 201612=100    NaN     ..         1  Atlantic Region   \n",
      "...                  ...    ...    ...       ...              ...   \n",
      "61075  Index, 201612=100  126.4    NaN         1        Vancouver   \n",
      "61076  Index, 201612=100  121.0      E         1        Vancouver   \n",
      "61077  Index, 201612=100  121.9    NaN         1         Victoria   \n",
      "61078  Index, 201612=100  127.9    NaN         1         Victoria   \n",
      "61079  Index, 201612=100  110.6      E         1         Victoria   \n",
      "\n",
      "                Province  Year Month  \n",
      "0                    NaN  1981    01  \n",
      "1                    NaN  1981    01  \n",
      "2                    NaN  1981    01  \n",
      "3                    NaN  1981    01  \n",
      "4                    NaN  1981    01  \n",
      "...                  ...   ...   ...  \n",
      "61075   British Columbia  2023    05  \n",
      "61076   British Columbia  2023    05  \n",
      "61077   British Columbia  2023    05  \n",
      "61078   British Columbia  2023    05  \n",
      "61079   British Columbia  2023    05  \n",
      "\n",
      "[61080 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "#via => https://appdividend.com/2020/06/02/pandas-concat-method-example-in-python/\n",
    "#JOIN Split GEO Columns to Original DF\n",
    "GeoUpdated_DF = pd.concat([reduced_main_renamed_df,Manual_Merge_Geo],axis=1,join='outer') # ,join_axes=None,ignore_index=False)\n",
    "print(GeoUpdated_DF)\n",
    "\n",
    "#JOIN Split Year+Month Columns to GeoUpdated_DF\n",
    "DateUpdated_DF = pd.concat([GeoUpdated_DF,Manual_Merge_date],axis=1,join='outer')\n",
    "print(DateUpdated_DF)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "993e2980",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>City_Region</th>\n",
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       "      <th>NewHousePrice_Index_Type</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>British Columbia</td>\n",
       "      <td>House only</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>126.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61076</th>\n",
       "      <td>2023</td>\n",
       "      <td>05</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Land only</td>\n",
       "      <td>Index, 201612=100</td>\n",
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       "      <th>61077</th>\n",
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       "      <td>05</td>\n",
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       "      <td>British Columbia</td>\n",
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       "      <td>121.9</td>\n",
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       "      <td>Victoria</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>House only</td>\n",
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       "      <td>Victoria</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Land only</td>\n",
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       "      <td>E</td>\n",
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       "<p>61080 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "       Year Month      City_Region           Province  \\\n",
       "0      1981    01           Canada                NaN   \n",
       "1      1981    01           Canada                NaN   \n",
       "2      1981    01           Canada                NaN   \n",
       "3      1981    01  Atlantic Region                NaN   \n",
       "4      1981    01  Atlantic Region                NaN   \n",
       "...     ...   ...              ...                ...   \n",
       "61075  2023    05        Vancouver   British Columbia   \n",
       "61076  2023    05        Vancouver   British Columbia   \n",
       "61077  2023    05         Victoria   British Columbia   \n",
       "61078  2023    05         Victoria   British Columbia   \n",
       "61079  2023    05         Victoria   British Columbia   \n",
       "\n",
       "      NewHousePrice_Index_Type                UOM  VALUE STATUS  DECIMALS  \n",
       "0       Total (house and land)  Index, 201612=100   38.2    NaN         1  \n",
       "1                   House only  Index, 201612=100   36.1    NaN         1  \n",
       "2                    Land only  Index, 201612=100   40.6      E         1  \n",
       "3       Total (house and land)  Index, 201612=100    NaN     ..         1  \n",
       "4                   House only  Index, 201612=100    NaN     ..         1  \n",
       "...                        ...                ...    ...    ...       ...  \n",
       "61075               House only  Index, 201612=100  126.4    NaN         1  \n",
       "61076                Land only  Index, 201612=100  121.0      E         1  \n",
       "61077   Total (house and land)  Index, 201612=100  121.9    NaN         1  \n",
       "61078               House only  Index, 201612=100  127.9    NaN         1  \n",
       "61079                Land only  Index, 201612=100  110.6      E         1  \n",
       "\n",
       "[61080 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select newly merged columns and reorder untouched columns from the initial reduced_main_renamed_df and our new concat DFs to establish a DF for Analysis purposes.\n",
    "\n",
    "Analysis_DF = DateUpdated_DF[[\"Year\",\"Month\",\"City_Region\",\"Province\",\"NewHousePrice_Index_Type\",\"UOM\",\"VALUE\",\"STATUS\",\"DECIMALS\"]]\n",
    "\n",
    "Analysis_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "01861d10",
   "metadata": {},
   "outputs": [
    {
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       "      <th>9</th>\n",
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       "      <td>St. John's</td>\n",
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       "      <th>33</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <th>42</th>\n",
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       "      <td>01</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>Quebec</td>\n",
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       "      <td>1</td>\n",
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       "      <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>61065</th>\n",
       "      <td>2023</td>\n",
       "      <td>05</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>108.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61068</th>\n",
       "      <td>2023</td>\n",
       "      <td>05</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>127.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61071</th>\n",
       "      <td>2023</td>\n",
       "      <td>05</td>\n",
       "      <td>Kelowna</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>123.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61074</th>\n",
       "      <td>2023</td>\n",
       "      <td>05</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>127.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61077</th>\n",
       "      <td>2023</td>\n",
       "      <td>05</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>121.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16614 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year Month       City_Region                    Province  \\\n",
       "0      1981    01            Canada                         NaN   \n",
       "9      1981    01        St. John's   Newfoundland and Labrador   \n",
       "27     1981    01        Saint John                 Fredericton   \n",
       "33     1981    01            Québec                      Quebec   \n",
       "42     1981    01          Montréal                      Quebec   \n",
       "...     ...   ...               ...                         ...   \n",
       "61065  2023    05          Edmonton                     Alberta   \n",
       "61068  2023    05  British Columbia                         NaN   \n",
       "61071  2023    05           Kelowna            British Columbia   \n",
       "61074  2023    05         Vancouver            British Columbia   \n",
       "61077  2023    05          Victoria            British Columbia   \n",
       "\n",
       "      NewHousePrice_Index_Type                UOM  VALUE STATUS  DECIMALS  \n",
       "0       Total (house and land)  Index, 201612=100   38.2    NaN         1  \n",
       "9       Total (house and land)  Index, 201612=100   36.1    NaN         1  \n",
       "27      Total (house and land)  Index, 201612=100   55.9    NaN         1  \n",
       "33      Total (house and land)  Index, 201612=100   30.1    NaN         1  \n",
       "42      Total (house and land)  Index, 201612=100   28.5    NaN         1  \n",
       "...                        ...                ...    ...    ...       ...  \n",
       "61065   Total (house and land)  Index, 201612=100  108.2    NaN         1  \n",
       "61068   Total (house and land)  Index, 201612=100  127.0    NaN         1  \n",
       "61071   Total (house and land)  Index, 201612=100  123.8    NaN         1  \n",
       "61074   Total (house and land)  Index, 201612=100  127.8    NaN         1  \n",
       "61077   Total (house and land)  Index, 201612=100  121.9    NaN         1  \n",
       "\n",
       "[16614 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove \"junk data\" columns from the Analysis DF based on StatsCan Legend and our analysis needs.\n",
    "\n",
    "StatusClean_Analysis_DF = Analysis_DF.query(\"STATUS not in ['E', 'x', '..']\") #QUERY fuction delivers to python \"memory\" a \"true/false\" outcome. Via URL => https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html\n",
    "\n",
    "TotalPriceIndex_Analysis_DF = StatusClean_Analysis_DF.query(\"NewHousePrice_Index_Type not in ['House only','Land only']\")\n",
    "\n",
    "TotalPriceIndex_Analysis_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83a9034a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for Toronto:\n",
      "       Year Month City_Region  Province NewHousePrice_Index_Type  \\\n",
      "57     1981    01     Toronto   Ontario   Total (house and land)   \n",
      "177    1981    02     Toronto   Ontario   Total (house and land)   \n",
      "297    1981    03     Toronto   Ontario   Total (house and land)   \n",
      "417    1981    04     Toronto   Ontario   Total (house and land)   \n",
      "537    1981    05     Toronto   Ontario   Total (house and land)   \n",
      "...     ...   ...         ...       ...                      ...   \n",
      "60537  2023    01     Toronto   Ontario   Total (house and land)   \n",
      "60657  2023    02     Toronto   Ontario   Total (house and land)   \n",
      "60777  2023    03     Toronto   Ontario   Total (house and land)   \n",
      "60897  2023    04     Toronto   Ontario   Total (house and land)   \n",
      "61017  2023    05     Toronto   Ontario   Total (house and land)   \n",
      "\n",
      "                     UOM  VALUE STATUS  DECIMALS  \n",
      "57     Index, 201612=100   28.0    NaN         1  \n",
      "177    Index, 201612=100   28.5    NaN         1  \n",
      "297    Index, 201612=100   29.8    NaN         1  \n",
      "417    Index, 201612=100   31.2    NaN         1  \n",
      "537    Index, 201612=100   32.3    NaN         1  \n",
      "...                  ...    ...    ...       ...  \n",
      "60537  Index, 201612=100  115.6    NaN         1  \n",
      "60657  Index, 201612=100  115.2    NaN         1  \n",
      "60777  Index, 201612=100  115.2    NaN         1  \n",
      "60897  Index, 201612=100  115.2    NaN         1  \n",
      "61017  Index, 201612=100  115.1    NaN         1  \n",
      "\n",
      "[509 rows x 9 columns]\n",
      "\n",
      "\n",
      "Data for Vancouver:\n",
      "       Year Month City_Region           Province NewHousePrice_Index_Type  \\\n",
      "114    1981    01   Vancouver   British Columbia   Total (house and land)   \n",
      "234    1981    02   Vancouver   British Columbia   Total (house and land)   \n",
      "354    1981    03   Vancouver   British Columbia   Total (house and land)   \n",
      "474    1981    04   Vancouver   British Columbia   Total (house and land)   \n",
      "594    1981    05   Vancouver   British Columbia   Total (house and land)   \n",
      "...     ...   ...         ...                ...                      ...   \n",
      "60594  2023    01   Vancouver   British Columbia   Total (house and land)   \n",
      "60714  2023    02   Vancouver   British Columbia   Total (house and land)   \n",
      "60834  2023    03   Vancouver   British Columbia   Total (house and land)   \n",
      "60954  2023    04   Vancouver   British Columbia   Total (house and land)   \n",
      "61074  2023    05   Vancouver   British Columbia   Total (house and land)   \n",
      "\n",
      "                     UOM  VALUE STATUS  DECIMALS  \n",
      "114    Index, 201612=100   90.5    NaN         1  \n",
      "234    Index, 201612=100   92.2    NaN         1  \n",
      "354    Index, 201612=100   92.2    NaN         1  \n",
      "474    Index, 201612=100   91.8    NaN         1  \n",
      "594    Index, 201612=100   91.8    NaN         1  \n",
      "...                  ...    ...    ...       ...  \n",
      "60594  Index, 201612=100  127.7    NaN         1  \n",
      "60714  Index, 201612=100  127.6    NaN         1  \n",
      "60834  Index, 201612=100  127.6    NaN         1  \n",
      "60954  Index, 201612=100  127.6    NaN         1  \n",
      "61074  Index, 201612=100  127.8    NaN         1  \n",
      "\n",
      "[509 rows x 9 columns]\n",
      "\n",
      "\n",
      "Data for Montréal:\n",
      "       Year Month City_Region Province NewHousePrice_Index_Type  \\\n",
      "42     1981    01    Montréal   Quebec   Total (house and land)   \n",
      "162    1981    02    Montréal   Quebec   Total (house and land)   \n",
      "282    1981    03    Montréal   Quebec   Total (house and land)   \n",
      "402    1981    04    Montréal   Quebec   Total (house and land)   \n",
      "522    1981    05    Montréal   Quebec   Total (house and land)   \n",
      "...     ...   ...         ...      ...                      ...   \n",
      "60522  2023    01    Montréal   Quebec   Total (house and land)   \n",
      "60642  2023    02    Montréal   Quebec   Total (house and land)   \n",
      "60762  2023    03    Montréal   Quebec   Total (house and land)   \n",
      "60882  2023    04    Montréal   Quebec   Total (house and land)   \n",
      "61002  2023    05    Montréal   Quebec   Total (house and land)   \n",
      "\n",
      "                     UOM  VALUE STATUS  DECIMALS  \n",
      "42     Index, 201612=100   28.5    NaN         1  \n",
      "162    Index, 201612=100   28.8    NaN         1  \n",
      "282    Index, 201612=100   29.0    NaN         1  \n",
      "402    Index, 201612=100   29.3    NaN         1  \n",
      "522    Index, 201612=100   29.7    NaN         1  \n",
      "...                  ...    ...    ...       ...  \n",
      "60522  Index, 201612=100  151.7    NaN         1  \n",
      "60642  Index, 201612=100  151.7    NaN         1  \n",
      "60762  Index, 201612=100  151.7    NaN         1  \n",
      "60882  Index, 201612=100  152.5    NaN         1  \n",
      "61002  Index, 201612=100  152.5    NaN         1  \n",
      "\n",
      "[509 rows x 9 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Esablish list of cities to be filtered from the TotalPriceIndex_Analysis_DF\n",
    "desired_cities = ['Toronto', 'Vancouver', 'Montréal']\n",
    "\n",
    "#Create empty array to enable Dictionary to be populated with data from Established list of cities.\n",
    "filtered_city_data = {}\n",
    "\n",
    "#Iterate through the TotalPriceIndex_Analysis_DF['City_Region] column with established list of cities.\n",
    "for city in desired_cities:\n",
    "    city_filtered_df = TotalPriceIndex_Analysis_DF[TotalPriceIndex_Analysis_DF['City_Region'] == city]\n",
    "\n",
    "#Place iterated/fetched list from TotalPriceIndex_Analysis_DF['City_Region] column into city_filtered_df\n",
    "    filtered_city_data[city] = city_filtered_df\n",
    "\n",
    "# Review Expected Output by transforming items within the array/dictionary to Print the data for all cities and break print() output up for user readability.\n",
    "for city, city_df in filtered_city_data.items():\n",
    "    print(f\"Data for {city}:\")\n",
    "    print(city_df)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2c91d94a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract sample data to validate that the STATUS not in funciton above was successful and to store within the Repo as a data source.\n",
    "\n",
    "#Establish Path VARs to enable Output produced to be mapped correctly within the Repo.\n",
    "Output_Folder = 'Output/'\n",
    "Output_file = Output_Folder + 'city_filtered_df.csv'\n",
    "\n",
    "# Concatenate the 3 city filtered dataframes from the desired list of cities and reset the index of the output.\n",
    "combined_data = pd.concat(filtered_city_data.values(), ignore_index=True)\n",
    "\n",
    "# Save the 3 combined city_filtered_data dataframes into a single CSV file\n",
    "combined_data.to_csv(Output_file, index=False, encoding='utf-8-sig') # for \"Montréal\" producing issues ... ref for variation in export encoding https://stackoverflow.com/questions/57152985/what-is-the-difference-between-utf-8-and-utf-8-sig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2ed58c50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Year Month City_Region  Province NewHousePrice_Index_Type  \\\n",
      "0     1981    01     Toronto   Ontario   Total (house and land)   \n",
      "1     1981    02     Toronto   Ontario   Total (house and land)   \n",
      "2     1981    03     Toronto   Ontario   Total (house and land)   \n",
      "3     1981    04     Toronto   Ontario   Total (house and land)   \n",
      "4     1981    05     Toronto   Ontario   Total (house and land)   \n",
      "...    ...   ...         ...       ...                      ...   \n",
      "1522  2023    01    Montréal    Quebec   Total (house and land)   \n",
      "1523  2023    02    Montréal    Quebec   Total (house and land)   \n",
      "1524  2023    03    Montréal    Quebec   Total (house and land)   \n",
      "1525  2023    04    Montréal    Quebec   Total (house and land)   \n",
      "1526  2023    05    Montréal    Quebec   Total (house and land)   \n",
      "\n",
      "                    UOM  VALUE STATUS  DECIMALS  \n",
      "0     Index, 201612=100   28.0    NaN         1  \n",
      "1     Index, 201612=100   28.5    NaN         1  \n",
      "2     Index, 201612=100   29.8    NaN         1  \n",
      "3     Index, 201612=100   31.2    NaN         1  \n",
      "4     Index, 201612=100   32.3    NaN         1  \n",
      "...                 ...    ...    ...       ...  \n",
      "1522  Index, 201612=100  151.7    NaN         1  \n",
      "1523  Index, 201612=100  151.7    NaN         1  \n",
      "1524  Index, 201612=100  151.7    NaN         1  \n",
      "1525  Index, 201612=100  152.5    NaN         1  \n",
      "1526  Index, 201612=100  152.5    NaN         1  \n",
      "\n",
      "[1527 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "#Validate output for Notebook user.\n",
    "Final_City_Filtered_DF = combined_data\n",
    "print(Final_City_Filtered_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e565fd03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     month_Year Vancouver   Toronto Montreal\n",
      "0    2012-01-01    660,600  420,950  284,000\n",
      "1    2012-02-01    670,900  456,537      NaN\n",
      "2    2012-03-01    679,000  450,750      NaN\n",
      "3    2012-04-01    683,800  464,075      NaN\n",
      "4    2012-05-01    625,100  472,839      NaN\n",
      "..          ...        ...      ...      ...\n",
      "127  2022-08-01  1,180,500  939,625  523,700\n",
      "128  2022-09-01  1,155,300  957,500  511,400\n",
      "129  2022-10-01  1,148,900  964,125  504,800\n",
      "130  2022-11-01  1,131,600  964,125  501,000\n",
      "131  2022-12-01  1,114,300  939,125  516,400\n",
      "\n",
      "[132 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "#Use Path to import comparative price data built by the team to enable comparative analysis on the storytelling from the StatsCan dataset. \n",
    "\n",
    "Housing_Prices_by_year = Path(\"Resources/Housing_Prices_by_year.csv\")\n",
    "Housing_Prices_by_year_df = pd.read_csv(Housing_Prices_by_year, encoding=\"UTF-8-Sig\")\n",
    "\n",
    "#Specifically this data is to establish observations assoicated to the generalized quality of Canadian data on the Housing Market. For example, it's expected that from a regression perspective the average prices are compare against the StatsCan Index.\n",
    "\n",
    "print(Housing_Prices_by_year_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dad0f166",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      2012-01-01\n",
      "1      2012-02-01\n",
      "2      2012-03-01\n",
      "3      2012-04-01\n",
      "4      2012-05-01\n",
      "          ...    \n",
      "127    2022-08-01\n",
      "128    2022-09-01\n",
      "129    2022-10-01\n",
      "130    2022-11-01\n",
      "131    2022-12-01\n",
      "Name: month_Year, Length: 132, dtype: object\n",
      "month_Year    object\n",
      "Vancouver     object\n",
      "Toronto       object\n",
      "Montreal      object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Validating the Structure and Data Type of the \"date\" data from the .csv as Date Format input from \"user\" (the csv data set) to Python's/Panda's processing needs.\n",
    "#Panda's format requirements found via => https://www.w3schools.com/python/python_datetime.asp\n",
    "print(Housing_Prices_by_year_df['month_Year'])\n",
    "\n",
    "#Validating what was loaded as DataTypes from the .csv of our new Housing Price DataSet.\n",
    "\n",
    "print(Housing_Prices_by_year_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b215fd88",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename columns for code error appearing associated to \"Vancouver\" (Likely a \"text trim\")  issue from the team's .csv.\n",
    "\n",
    "Housing_Prices_by_year_df.columns.values[1] = \"Vancouver\"\n",
    "Housing_Prices_by_year_df.columns.values[2] = \"Toronto\"\n",
    "Housing_Prices_by_year_df.columns.values[3] = \"Montreal\"\n",
    "\n",
    "#print(Housing_Prices_by_year_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e4d3b49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: Timestamp('2012-01-01 00:00:00'), 1: Timestamp('2012-02-01 00:00:00'), 2: Timestamp('2012-03-01 00:00:00'), 3: Timestamp('2012-04-01 00:00:00'), 4: Timestamp('2012-05-01 00:00:00'), 5: Timestamp('2012-06-01 00:00:00'), 6: Timestamp('2012-07-01 00:00:00'), 7: Timestamp('2012-08-01 00:00:00'), 8: Timestamp('2012-09-01 00:00:00'), 9: Timestamp('2012-10-01 00:00:00'), 10: Timestamp('2012-11-01 00:00:00'), 11: Timestamp('2012-12-01 00:00:00'), 12: Timestamp('2013-01-01 00:00:00'), 13: Timestamp('2013-02-01 00:00:00'), 14: Timestamp('2013-03-01 00:00:00'), 15: Timestamp('2013-04-01 00:00:00'), 16: Timestamp('2013-05-01 00:00:00'), 17: Timestamp('2013-06-01 00:00:00'), 18: Timestamp('2013-07-01 00:00:00'), 19: Timestamp('2013-08-01 00:00:00'), 20: Timestamp('2013-09-01 00:00:00'), 21: Timestamp('2013-10-01 00:00:00'), 22: Timestamp('2013-11-01 00:00:00'), 23: Timestamp('2013-12-01 00:00:00'), 24: Timestamp('2014-01-01 00:00:00'), 25: Timestamp('2014-02-01 00:00:00'), 26: Timestamp('2014-03-01 00:00:00'), 27: Timestamp('2014-04-01 00:00:00'), 28: Timestamp('2014-05-01 00:00:00'), 29: Timestamp('2014-06-01 00:00:00'), 30: Timestamp('2014-07-01 00:00:00'), 31: Timestamp('2014-08-01 00:00:00'), 32: Timestamp('2014-09-01 00:00:00'), 33: Timestamp('2014-10-01 00:00:00'), 34: Timestamp('2014-11-01 00:00:00'), 35: Timestamp('2014-12-01 00:00:00'), 36: Timestamp('2015-01-01 00:00:00'), 37: Timestamp('2015-02-01 00:00:00'), 38: Timestamp('2015-03-01 00:00:00'), 39: Timestamp('2015-04-01 00:00:00'), 40: Timestamp('2015-05-01 00:00:00'), 41: Timestamp('2015-06-01 00:00:00'), 42: Timestamp('2015-07-01 00:00:00'), 43: Timestamp('2015-08-01 00:00:00'), 44: Timestamp('2015-09-01 00:00:00'), 45: Timestamp('2015-10-01 00:00:00'), 46: Timestamp('2015-11-01 00:00:00'), 47: Timestamp('2015-12-01 00:00:00'), 48: Timestamp('2016-01-01 00:00:00'), 49: Timestamp('2016-02-01 00:00:00'), 50: Timestamp('2016-03-01 00:00:00'), 51: Timestamp('2016-04-01 00:00:00'), 52: Timestamp('2016-05-01 00:00:00'), 53: Timestamp('2016-06-01 00:00:00'), 54: Timestamp('2016-07-01 00:00:00'), 55: Timestamp('2016-08-01 00:00:00'), 56: Timestamp('2016-09-01 00:00:00'), 57: Timestamp('2016-10-01 00:00:00'), 58: Timestamp('2016-11-01 00:00:00'), 59: Timestamp('2016-12-01 00:00:00'), 60: Timestamp('2017-01-01 00:00:00'), 61: Timestamp('2017-02-01 00:00:00'), 62: Timestamp('2017-03-01 00:00:00'), 63: Timestamp('2017-04-01 00:00:00'), 64: Timestamp('2017-05-01 00:00:00'), 65: Timestamp('2017-06-01 00:00:00'), 66: Timestamp('2017-07-01 00:00:00'), 67: Timestamp('2017-08-01 00:00:00'), 68: Timestamp('2017-09-01 00:00:00'), 69: Timestamp('2017-10-01 00:00:00'), 70: Timestamp('2017-11-01 00:00:00'), 71: Timestamp('2017-12-01 00:00:00'), 72: Timestamp('2018-01-01 00:00:00'), 73: Timestamp('2018-02-01 00:00:00'), 74: Timestamp('2018-03-01 00:00:00'), 75: Timestamp('2018-04-01 00:00:00'), 76: Timestamp('2018-05-01 00:00:00'), 77: Timestamp('2018-06-01 00:00:00'), 78: Timestamp('2018-07-01 00:00:00'), 79: Timestamp('2018-08-01 00:00:00'), 80: Timestamp('2018-09-01 00:00:00'), 81: Timestamp('2018-10-01 00:00:00'), 82: Timestamp('2018-11-01 00:00:00'), 83: Timestamp('2018-12-01 00:00:00'), 84: Timestamp('2019-01-01 00:00:00'), 85: Timestamp('2019-02-01 00:00:00'), 86: Timestamp('2019-03-01 00:00:00'), 87: Timestamp('2019-04-01 00:00:00'), 88: Timestamp('2019-05-01 00:00:00'), 89: Timestamp('2019-06-01 00:00:00'), 90: Timestamp('2019-07-01 00:00:00'), 91: Timestamp('2019-08-01 00:00:00'), 92: Timestamp('2019-09-01 00:00:00'), 93: Timestamp('2019-10-01 00:00:00'), 94: Timestamp('2019-11-01 00:00:00'), 95: Timestamp('2019-12-01 00:00:00'), 96: Timestamp('2020-01-01 00:00:00'), 97: Timestamp('2020-02-01 00:00:00'), 98: Timestamp('2020-03-01 00:00:00'), 99: Timestamp('2020-04-01 00:00:00'), 100: Timestamp('2020-05-01 00:00:00'), 101: Timestamp('2020-06-01 00:00:00'), 102: Timestamp('2020-07-01 00:00:00'), 103: Timestamp('2020-08-01 00:00:00'), 104: Timestamp('2020-09-01 00:00:00'), 105: Timestamp('2020-10-01 00:00:00'), 106: Timestamp('2020-11-01 00:00:00'), 107: Timestamp('2020-12-01 00:00:00'), 108: Timestamp('2021-01-01 00:00:00'), 109: Timestamp('2021-02-01 00:00:00'), 110: Timestamp('2021-03-01 00:00:00'), 111: Timestamp('2021-04-01 00:00:00'), 112: Timestamp('2021-05-01 00:00:00'), 113: Timestamp('2021-06-01 00:00:00'), 114: Timestamp('2021-07-01 00:00:00'), 115: Timestamp('2021-08-01 00:00:00'), 116: Timestamp('2021-09-01 00:00:00'), 117: Timestamp('2021-10-01 00:00:00'), 118: Timestamp('2021-11-01 00:00:00'), 119: Timestamp('2021-12-01 00:00:00'), 120: Timestamp('2022-01-01 00:00:00'), 121: Timestamp('2022-02-01 00:00:00'), 122: Timestamp('2022-03-01 00:00:00'), 123: Timestamp('2022-04-01 00:00:00'), 124: Timestamp('2022-05-01 00:00:00'), 125: Timestamp('2022-06-01 00:00:00'), 126: Timestamp('2022-07-01 00:00:00'), 127: Timestamp('2022-08-01 00:00:00'), 128: Timestamp('2022-09-01 00:00:00'), 129: Timestamp('2022-10-01 00:00:00'), 130: Timestamp('2022-11-01 00:00:00'), 131: Timestamp('2022-12-01 00:00:00')}\n",
      "0     2012-01-01\n",
      "1     2012-02-01\n",
      "2     2012-03-01\n",
      "3     2012-04-01\n",
      "4     2012-05-01\n",
      "         ...    \n",
      "127   2022-08-01\n",
      "128   2022-09-01\n",
      "129   2022-10-01\n",
      "130   2022-11-01\n",
      "131   2022-12-01\n",
      "Name: month_Year, Length: 132, dtype: datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "#1 => Goal of next step is to \"establish\" a column being similar to StatsCan original data of \"YYYY-MM\".\n",
    "#2 => Leveraged following URL to establish what our \"format=\" parameter code has to be ... based on OUR USER INPUT DATA (IE: the .csv.) URL => https://www.w3schools.com/python/python_datetime.asp\n",
    "\n",
    "DateInsert_Month_Year_DF = pd.Series(pd.to_datetime(Housing_Prices_by_year_df['month_Year'], format='%Y-%m-%d', errors = 'coerce'))\n",
    "\n",
    "#Validate conversion was successful.\n",
    "print(DateInsert_Month_Year_DF.to_dict())\n",
    "print(DateInsert_Month_Year_DF)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "69a8c9fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    OLD_AsTXT_month_Year  Vancouver  Toronto Montreal\n",
      "0             2012-01-01    660,600  420,950  284,000\n",
      "1             2012-02-01    670,900  456,537      NaN\n",
      "2             2012-03-01    679,000  450,750      NaN\n",
      "3             2012-04-01    683,800  464,075      NaN\n",
      "4             2012-05-01    625,100  472,839      NaN\n",
      "..                   ...        ...      ...      ...\n",
      "127           2022-08-01  1,180,500  939,625  523,700\n",
      "128           2022-09-01  1,155,300  957,500  511,400\n",
      "129           2022-10-01  1,148,900  964,125  504,800\n",
      "130           2022-11-01  1,131,600  964,125  501,000\n",
      "131           2022-12-01  1,114,300  939,125  516,400\n",
      "\n",
      "[132 rows x 4 columns]\n",
      "OLD_AsTXT_month_Year    object\n",
      "Vancouver               object\n",
      "Toronto                 object\n",
      "Montreal                object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Rename original text date column for eventual deletion and maintain TXT column in new version of Housing_Prices_by_year_df.\n",
    "TempRename_Housing_Prices_df = Housing_Prices_by_year_df.rename(columns={'month_Year': 'OLD_AsTXT_month_Year' })\n",
    "print(TempRename_Housing_Prices_df)\n",
    "print(TempRename_Housing_Prices_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d859137a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month_Year_asDate       datetime64[ns]\n",
      "OLD_AsTXT_month_Year            object\n",
      "Vancouver                       object\n",
      "Toronto                         object\n",
      "Montreal                        object\n",
      "dtype: object\n",
      "{'month_Year_asDate': {0: Timestamp('2012-01-01 00:00:00'), 1: Timestamp('2012-02-01 00:00:00'), 2: Timestamp('2012-03-01 00:00:00'), 3: Timestamp('2012-04-01 00:00:00'), 4: Timestamp('2012-05-01 00:00:00'), 5: Timestamp('2012-06-01 00:00:00'), 6: Timestamp('2012-07-01 00:00:00'), 7: Timestamp('2012-08-01 00:00:00'), 8: Timestamp('2012-09-01 00:00:00'), 9: Timestamp('2012-10-01 00:00:00'), 10: Timestamp('2012-11-01 00:00:00'), 11: Timestamp('2012-12-01 00:00:00'), 12: Timestamp('2013-01-01 00:00:00'), 13: Timestamp('2013-02-01 00:00:00'), 14: Timestamp('2013-03-01 00:00:00'), 15: Timestamp('2013-04-01 00:00:00'), 16: Timestamp('2013-05-01 00:00:00'), 17: Timestamp('2013-06-01 00:00:00'), 18: Timestamp('2013-07-01 00:00:00'), 19: Timestamp('2013-08-01 00:00:00'), 20: Timestamp('2013-09-01 00:00:00'), 21: Timestamp('2013-10-01 00:00:00'), 22: Timestamp('2013-11-01 00:00:00'), 23: Timestamp('2013-12-01 00:00:00'), 24: Timestamp('2014-01-01 00:00:00'), 25: Timestamp('2014-02-01 00:00:00'), 26: Timestamp('2014-03-01 00:00:00'), 27: Timestamp('2014-04-01 00:00:00'), 28: Timestamp('2014-05-01 00:00:00'), 29: Timestamp('2014-06-01 00:00:00'), 30: Timestamp('2014-07-01 00:00:00'), 31: Timestamp('2014-08-01 00:00:00'), 32: Timestamp('2014-09-01 00:00:00'), 33: Timestamp('2014-10-01 00:00:00'), 34: Timestamp('2014-11-01 00:00:00'), 35: Timestamp('2014-12-01 00:00:00'), 36: Timestamp('2015-01-01 00:00:00'), 37: Timestamp('2015-02-01 00:00:00'), 38: Timestamp('2015-03-01 00:00:00'), 39: Timestamp('2015-04-01 00:00:00'), 40: Timestamp('2015-05-01 00:00:00'), 41: Timestamp('2015-06-01 00:00:00'), 42: Timestamp('2015-07-01 00:00:00'), 43: Timestamp('2015-08-01 00:00:00'), 44: Timestamp('2015-09-01 00:00:00'), 45: Timestamp('2015-10-01 00:00:00'), 46: Timestamp('2015-11-01 00:00:00'), 47: Timestamp('2015-12-01 00:00:00'), 48: Timestamp('2016-01-01 00:00:00'), 49: Timestamp('2016-02-01 00:00:00'), 50: Timestamp('2016-03-01 00:00:00'), 51: Timestamp('2016-04-01 00:00:00'), 52: Timestamp('2016-05-01 00:00:00'), 53: Timestamp('2016-06-01 00:00:00'), 54: Timestamp('2016-07-01 00:00:00'), 55: Timestamp('2016-08-01 00:00:00'), 56: Timestamp('2016-09-01 00:00:00'), 57: Timestamp('2016-10-01 00:00:00'), 58: Timestamp('2016-11-01 00:00:00'), 59: Timestamp('2016-12-01 00:00:00'), 60: Timestamp('2017-01-01 00:00:00'), 61: Timestamp('2017-02-01 00:00:00'), 62: Timestamp('2017-03-01 00:00:00'), 63: Timestamp('2017-04-01 00:00:00'), 64: Timestamp('2017-05-01 00:00:00'), 65: Timestamp('2017-06-01 00:00:00'), 66: Timestamp('2017-07-01 00:00:00'), 67: Timestamp('2017-08-01 00:00:00'), 68: Timestamp('2017-09-01 00:00:00'), 69: Timestamp('2017-10-01 00:00:00'), 70: Timestamp('2017-11-01 00:00:00'), 71: Timestamp('2017-12-01 00:00:00'), 72: Timestamp('2018-01-01 00:00:00'), 73: Timestamp('2018-02-01 00:00:00'), 74: Timestamp('2018-03-01 00:00:00'), 75: Timestamp('2018-04-01 00:00:00'), 76: Timestamp('2018-05-01 00:00:00'), 77: Timestamp('2018-06-01 00:00:00'), 78: Timestamp('2018-07-01 00:00:00'), 79: Timestamp('2018-08-01 00:00:00'), 80: Timestamp('2018-09-01 00:00:00'), 81: Timestamp('2018-10-01 00:00:00'), 82: Timestamp('2018-11-01 00:00:00'), 83: Timestamp('2018-12-01 00:00:00'), 84: Timestamp('2019-01-01 00:00:00'), 85: Timestamp('2019-02-01 00:00:00'), 86: Timestamp('2019-03-01 00:00:00'), 87: Timestamp('2019-04-01 00:00:00'), 88: Timestamp('2019-05-01 00:00:00'), 89: Timestamp('2019-06-01 00:00:00'), 90: Timestamp('2019-07-01 00:00:00'), 91: Timestamp('2019-08-01 00:00:00'), 92: Timestamp('2019-09-01 00:00:00'), 93: Timestamp('2019-10-01 00:00:00'), 94: Timestamp('2019-11-01 00:00:00'), 95: Timestamp('2019-12-01 00:00:00'), 96: Timestamp('2020-01-01 00:00:00'), 97: Timestamp('2020-02-01 00:00:00'), 98: Timestamp('2020-03-01 00:00:00'), 99: Timestamp('2020-04-01 00:00:00'), 100: Timestamp('2020-05-01 00:00:00'), 101: Timestamp('2020-06-01 00:00:00'), 102: Timestamp('2020-07-01 00:00:00'), 103: Timestamp('2020-08-01 00:00:00'), 104: Timestamp('2020-09-01 00:00:00'), 105: Timestamp('2020-10-01 00:00:00'), 106: Timestamp('2020-11-01 00:00:00'), 107: Timestamp('2020-12-01 00:00:00'), 108: Timestamp('2021-01-01 00:00:00'), 109: Timestamp('2021-02-01 00:00:00'), 110: Timestamp('2021-03-01 00:00:00'), 111: Timestamp('2021-04-01 00:00:00'), 112: Timestamp('2021-05-01 00:00:00'), 113: Timestamp('2021-06-01 00:00:00'), 114: Timestamp('2021-07-01 00:00:00'), 115: Timestamp('2021-08-01 00:00:00'), 116: Timestamp('2021-09-01 00:00:00'), 117: Timestamp('2021-10-01 00:00:00'), 118: Timestamp('2021-11-01 00:00:00'), 119: Timestamp('2021-12-01 00:00:00'), 120: Timestamp('2022-01-01 00:00:00'), 121: Timestamp('2022-02-01 00:00:00'), 122: Timestamp('2022-03-01 00:00:00'), 123: Timestamp('2022-04-01 00:00:00'), 124: Timestamp('2022-05-01 00:00:00'), 125: Timestamp('2022-06-01 00:00:00'), 126: Timestamp('2022-07-01 00:00:00'), 127: Timestamp('2022-08-01 00:00:00'), 128: Timestamp('2022-09-01 00:00:00'), 129: Timestamp('2022-10-01 00:00:00'), 130: Timestamp('2022-11-01 00:00:00'), 131: Timestamp('2022-12-01 00:00:00')}, 'OLD_AsTXT_month_Year': {0: '2012-01-01', 1: '2012-02-01', 2: '2012-03-01', 3: '2012-04-01', 4: '2012-05-01', 5: '2012-06-01', 6: '2012-07-01', 7: '2012-08-01', 8: '2012-09-01', 9: '2012-10-01', 10: '2012-11-01', 11: '2012-12-01', 12: '2013-01-01', 13: '2013-02-01', 14: '2013-03-01', 15: '2013-04-01', 16: '2013-05-01', 17: '2013-06-01', 18: '2013-07-01', 19: '2013-08-01', 20: '2013-09-01', 21: '2013-10-01', 22: '2013-11-01', 23: '2013-12-01', 24: '2014-01-01', 25: '2014-02-01', 26: '2014-03-01', 27: '2014-04-01', 28: '2014-05-01', 29: '2014-06-01', 30: '2014-07-01', 31: '2014-08-01', 32: '2014-09-01', 33: '2014-10-01', 34: '2014-11-01', 35: '2014-12-01', 36: '2015-01-01', 37: '2015-02-01', 38: '2015-03-01', 39: '2015-04-01', 40: '2015-05-01', 41: '2015-06-01', 42: '2015-07-01', 43: '2015-08-01', 44: '2015-09-01', 45: '2015-10-01', 46: '2015-11-01', 47: '2015-12-01', 48: '2016-01-01', 49: '2016-02-01', 50: '2016-03-01', 51: '2016-04-01', 52: '2016-05-01', 53: '2016-06-01', 54: '2016-07-01', 55: '2016-08-01', 56: '2016-09-01', 57: '2016-10-01', 58: '2016-11-01', 59: '2016-12-01', 60: '2017-01-01', 61: '2017-02-01', 62: '2017-03-01', 63: '2017-04-01', 64: '2017-05-01', 65: '2017-06-01', 66: '2017-07-01', 67: '2017-08-01', 68: '2017-09-01', 69: '2017-10-01', 70: '2017-11-01', 71: '2017-12-01', 72: '2018-01-01', 73: '2018-02-01', 74: '2018-03-01', 75: '2018-04-01', 76: '2018-05-01', 77: '2018-06-01', 78: '2018-07-01', 79: '2018-08-01', 80: '2018-09-01', 81: '2018-10-01', 82: '2018-11-01', 83: '2018-12-01', 84: '2019-01-01', 85: '2019-02-01', 86: '2019-03-01', 87: '2019-04-01', 88: '2019-05-01', 89: '2019-06-01', 90: '2019-07-01', 91: '2019-08-01', 92: '2019-09-01', 93: '2019-10-01', 94: '2019-11-01', 95: '2019-12-01', 96: '2020-01-01', 97: '2020-02-01', 98: '2020-03-01', 99: '2020-04-01', 100: '2020-05-01', 101: '2020-06-01', 102: '2020-07-01', 103: '2020-08-01', 104: '2020-09-01', 105: '2020-10-01', 106: '2020-11-01', 107: '2020-12-01', 108: '2021-01-01', 109: '2021-02-01', 110: '2021-03-01', 111: '2021-04-01', 112: '2021-05-01', 113: '2021-06-01', 114: '2021-07-01', 115: '2021-08-01', 116: '2021-09-01', 117: '2021-10-01', 118: '2021-11-01', 119: '2021-12-01', 120: '2022-01-01', 121: '2022-02-01', 122: '2022-03-01', 123: '2022-04-01', 124: '2022-05-01', 125: '2022-06-01', 126: '2022-07-01', 127: '2022-08-01', 128: '2022-09-01', 129: '2022-10-01', 130: '2022-11-01', 131: '2022-12-01'}, 'Vancouver': {0: '660,600', 1: '670,900', 2: '679,000', 3: '683,800', 4: '625,100', 5: '624,600', 6: '616,000', 7: '609,500', 8: '606,100', 9: '603,800', 10: '596,900', 11: '590,800', 12: '588,100', 13: '590,400', 14: '593,100', 15: '597,300', 16: '598,400', 17: '601,900', 18: '601,900', 19: '601,500', 20: '601,900', 21: '600,700', 22: '603,700', 23: '603,400', 24: '606,800', 25: '609,100', 26: '615,200', 27: '619,000', 28: '624,000', 29: '628,200', 30: '628,600', 31: '631,600', 32: '633,500', 33: '637,000', 34: '637,300', 35: '638,500', 36: '641,600', 37: '649,700', 38: '660,700', 39: '673,000', 40: '684,400', 41: '694,000', 42: '700,500', 43: '708,500', 44: '722,300', 45: '736,000', 46: '752,500', 47: '760,900', 48: '770,689', 49: '781,979', 50: '793,268', 51: '804,557', 52: '815,846', 53: '827,136', 54: '838,425', 55: '849,714', 56: '861,004', 57: '872,293', 58: '883,582', 59: '894,871', 60: '896,000', 61: '906,700', 62: '919,300', 63: '941,100', 64: '967,500', 65: '998,700', 66: '1,019,400', 67: '1,029,700', 68: '1,037,300', 69: '1,042,300', 70: '1,046,900', 71: '1,050,300', 72: '1,056,500', 73: '1,602,000', 74: '1,084,000', 75: '1,092,000', 76: '1,094,000', 77: '1,093,600', 78: '1,087,500', 79: '1,083,400', 80: '1,070,600', 81: '1,524,000', 82: '1,042,100', 83: '1,479,000', 84: '1,019,600', 85: '1,016,600', 86: '1,011,200', 87: '1,008,400', 88: '1,006,400', 89: '998,700', 90: '995,200', 91: '993,300', 92: '990,600', 93: '992,900', 94: '993,700', 95: '1,001,000', 96: '1,008,700', 97: '1,020,600', 98: '1,033,700', 99: '1,036,000', 100: '1,036,000', 101: '1,025,300', 102: '1,031,400', 103: '1,038,700', 104: '1,041,300', 105: '1,045,100', 106: '1,044,000', 107: '1,044,000', 108: '1,056,600', 109: '1,052,800', 110: '1,128,600', 111: '1,128,600', 112: '1,158,000', 113: '1,152,600', 114: '1,175,500', 115: '1,176,600', 116: '1,186,100', 117: '1,199,400', 118: '1,870,000', 119: '1,230,200', 120: '1,255,200', 121: '1,313,400', 122: '1,360,500', 123: '1,374,500', 124: '1,261,100', 125: '1,235,900', 126: '1,207,400', 127: '1,180,500', 128: '1,155,300', 129: '1,148,900', 130: '1,131,600', 131: '1,114,300'}, 'Toronto': {0: '420,950', 1: '456,537', 2: '450,750', 3: '464,075', 4: '472,839', 5: '449,963', 6: '430,375', 7: '421,000', 8: '449,862', 9: '454,500', 10: '440,768', 11: '421,875', 12: '448,800', 13: '468,062', 14: '472,812', 15: '473,750', 16: '479,475', 17: '472,018', 18: '461,250', 19: '440,187', 20: '474,225', 21: '486,750', 22: '482,600', 23: '469,375', 24: '472,437', 25: '528,750', 26: '502,400', 27: '519,212', 28: '519,000', 29: '517,875', 30: '501,343', 31: '486,062', 32: '523,750', 33: '529,500', 34: '526,372', 35: '488,575', 36: '528,437', 37: '547,437', 38: '563,625', 39: '567,450', 40: '578,500', 41: '566,787', 42: '546,750', 43: '539,125', 44: '566,562', 45: '576,375', 46: '566,762', 47: '547,000', 48: '555,875', 49: '625,000', 50: '616,750', 51: '648,750', 52: '654,000', 53: '654,375', 54: '624,750', 55: '614,250', 56: '661,225', 57: '680,437', 58: '685,025', 59: '656,750', 60: '705,502', 61: '796,000', 62: '827,325', 63: '839,125', 64: '781,187', 65: '742,562', 66: '704,373', 67: '688,625', 68: '721,500', 69: '739,250', 70: '701,281', 71: '681,611', 72: '717,750', 73: '748,249', 74: '754,200', 75: '784,562', 76: '777,125', 77: '774,925', 78: '729,225', 79: '706,000', 80: '759,625', 81: '772,625', 82: '765,500', 83: '709,687', 84: '735,250', 85: '790,625', 86: '767,562', 87: '784,000', 88: '807,500', 89: '801,562', 90: '761,250', 91: '756,250', 92: '813,475', 93: '811,875', 94: '798,562', 95: '780,064', 96: '799,250', 97: '912,784', 98: '878,600', 99: '818,750', 100: '864,450', 101: '918,437', 102: '904,125', 103: '877,475', 104: '875,595', 105: '859,500', 106: '870,225', 107: '875,000', 108: '936,037', 109: '981,100', 110: '996,500', 111: '1,001,975', 112: '988,500', 113: '976,937', 114: '955,812', 115: '952,000', 116: '1,008,375', 117: '1,027,000', 118: '1,034,812', 119: '1,033,750', 120: '1,142,625', 121: '1,188,250', 122: '1,162,272', 123: '1,121,717', 124: '1,084,562', 125: '1,023,000', 126: '955,026', 127: '939,625', 128: '957,500', 129: '964,125', 130: '964,125', 131: '939,125'}, 'Montreal': {0: '284,000', 1: nan, 2: nan, 3: nan, 4: nan, 5: nan, 6: nan, 7: nan, 8: nan, 9: nan, 10: nan, 11: nan, 12: nan, 13: nan, 14: nan, 15: nan, 16: nan, 17: nan, 18: nan, 19: nan, 20: nan, 21: nan, 22: nan, 23: nan, 24: nan, 25: nan, 26: nan, 27: nan, 28: nan, 29: nan, 30: nan, 31: nan, 32: nan, 33: nan, 34: nan, 35: nan, 36: nan, 37: nan, 38: nan, 39: nan, 40: nan, 41: nan, 42: nan, 43: nan, 44: nan, 45: nan, 46: nan, 47: nan, 48: nan, 49: '303,600', 50: nan, 51: nan, 52: nan, 53: nan, 54: nan, 55: nan, 56: nan, 57: nan, 58: nan, 59: nan, 60: '314,300', 61: '315,200', 62: '318,500', 63: '321,000', 64: '322,400', 65: '323,900', 66: '326,200', 67: '326,400', 68: '327,600', 69: '329,300', 70: '327,800', 71: '330,900', 72: '328,600', 73: '332,600', 74: '336,300', 75: '339,300', 76: '341,900', 77: '342,600', 78: '342,800', 79: '343,600', 80: '346,100', 81: '348,000', 82: '348,400', 83: '349,200', 84: '349,600', 85: '353,400', 86: '357,800', 87: '397,730', 88: '347,200', 89: '350,500', 90: '351,700', 91: '350,700', 92: '357,600', 93: '361,300', 94: '360,300', 95: '360,900', 96: '372,000', 97: '380,500', 98: '387,200', 99: '385,500', 100: '386,200', 101: '391,600', 102: '402,000', 103: '409,200', 104: '413,600', 105: '418,000', 106: '424,000', 107: '426,900', 108: '434,600', 109: '453,700', 110: '475,000', 111: '488,300', 112: '482,900', 113: '485,200', 114: '482,200', 115: '482,600', 116: '483,800', 117: '491,000', 118: '496,900', 119: '501,100', 120: '527,000', 121: '536,600', 122: '546,600', 123: '552,400', 124: '549,700', 125: '541,100', 126: '533,300', 127: '523,700', 128: '511,400', 129: '504,800', 130: '501,000', 131: '516,400'}}\n",
      "    month_Year_asDate OLD_AsTXT_month_Year  Vancouver  Toronto Montreal\n",
      "0          2012-01-01           2012-01-01    660,600  420,950  284,000\n",
      "1          2012-02-01           2012-02-01    670,900  456,537      NaN\n",
      "2          2012-03-01           2012-03-01    679,000  450,750      NaN\n",
      "3          2012-04-01           2012-04-01    683,800  464,075      NaN\n",
      "4          2012-05-01           2012-05-01    625,100  472,839      NaN\n",
      "..                ...                  ...        ...      ...      ...\n",
      "127        2022-08-01           2022-08-01  1,180,500  939,625  523,700\n",
      "128        2022-09-01           2022-09-01  1,155,300  957,500  511,400\n",
      "129        2022-10-01           2022-10-01  1,148,900  964,125  504,800\n",
      "130        2022-11-01           2022-11-01  1,131,600  964,125  501,000\n",
      "131        2022-12-01           2022-12-01  1,114,300  939,125  516,400\n",
      "\n",
      "[132 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "#Add Date Column to TempRename_Housing_Prices_df to enable statistical analysis via \"date between\" functions if necessary.\n",
    "DateClean_Housing_Prices_by_year_df = pd.concat((DateInsert_Month_Year_DF,TempRename_Housing_Prices_df), axis=1, join='outer')\n",
    "#Noted Syntax Error => https://stackoverflow.com/questions/45175469/typeerror-concat-got-multiple-values-for-argument-axis\n",
    "\n",
    "#Rename Column to include \"asDate\"\n",
    "DateClean_Housing_Prices_by_year_df.columns.values[0] = 'month_Year_asDate'\n",
    "\n",
    "#Validate Data Types and Output of above code.\n",
    "print(DateClean_Housing_Prices_by_year_df.dtypes)\n",
    "print(DateClean_Housing_Prices_by_year_df.to_dict())\n",
    "print(DateClean_Housing_Prices_by_year_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "60ab7e06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      2012\n",
      "1      2012\n",
      "2      2012\n",
      "3      2012\n",
      "4      2012\n",
      "       ... \n",
      "127    2022\n",
      "128    2022\n",
      "129    2022\n",
      "130    2022\n",
      "131    2022\n",
      "Name: OLD_AsTXT_month_Year, Length: 132, dtype: object\n",
      "0      01\n",
      "1      02\n",
      "2      03\n",
      "3      04\n",
      "4      05\n",
      "       ..\n",
      "127    08\n",
      "128    09\n",
      "129    10\n",
      "130    11\n",
      "131    12\n",
      "Name: OLD_AsTXT_month_Year, Length: 132, dtype: object\n",
      "0      01\n",
      "1      01\n",
      "2      01\n",
      "3      01\n",
      "4      01\n",
      "       ..\n",
      "127    01\n",
      "128    01\n",
      "129    01\n",
      "130    01\n",
      "131    01\n",
      "Name: OLD_AsTXT_month_Year, Length: 132, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Parsing the OLD_asTXT_month_Year column into a data frame to enable the Concat to StatsCan's YYYY-MM data formatting.\n",
    "#Below executes spliting the column by the \"-\"\n",
    "\n",
    "HP_Month_Year_DF = DateClean_Housing_Prices_by_year_df['OLD_AsTXT_month_Year'].str.split('-')\n",
    "\n",
    "HP_Year_Row = HP_Month_Year_DF.str.get(0) #access the first element for eventual \"column1\"\n",
    "HP_Month_Row = HP_Month_Year_DF.str.get(1) #access the 2nd element for eventual \"column2\"\n",
    "HP_Day_Row = HP_Month_Year_DF.str.get(2) #access the 3rd element for eventual \"column3\"\n",
    "\n",
    "print(HP_Year_Row)\n",
    "print(HP_Month_Row)\n",
    "print(HP_Day_Row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7c972257",
   "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>HousePrice_Year</th>\n",
       "      <th>HousePrice_Month</th>\n",
       "      <th>HousePrice_Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>03</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>04</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>05</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2022</td>\n",
       "      <td>08</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2022</td>\n",
       "      <td>09</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2022</td>\n",
       "      <td>10</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    HousePrice_Year HousePrice_Month HousePrice_Day\n",
       "0              2012               01             01\n",
       "1              2012               02             01\n",
       "2              2012               03             01\n",
       "3              2012               04             01\n",
       "4              2012               05             01\n",
       "..              ...              ...            ...\n",
       "127            2022               08             01\n",
       "128            2022               09             01\n",
       "129            2022               10             01\n",
       "130            2022               11             01\n",
       "131            2022               12             01\n",
       "\n",
       "[132 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging the parsed data and recreating into a single DataFrame.\n",
    "\n",
    "Manual_Merge_HP_Date =  pd.concat([HP_Year_Row,HP_Month_Row,HP_Day_Row], axis=1)\n",
    "#Manual_Merge_HP_Date\n",
    "Manual_Merge_HP_Date.columns.values[0] = \"HousePrice_Year\"\n",
    "Manual_Merge_HP_Date.columns.values[1] = \"HousePrice_Month\"\n",
    "Manual_Merge_HP_Date.columns.values[2] = \"HousePrice_Day\"\n",
    "\n",
    "Manual_Merge_HP_Date\n",
    "\n",
    "#EXAMPLE Code Reference from above.\n",
    "#Manual_Merge_date = pd.concat([Row_Year,Row_Month], axis=1) #axis=1 is using \"column 1\" to merge.\n",
    "#Manual_Merge_date\n",
    "#Rename Options presented via => https://www.geeksforgeeks.org/rename-specific-columns-in-pandas/\n",
    "#Manual_Merge_date.columns.values[0] = \"Year\"\n",
    "#Manual_Merge_date.columns.values[1] = \"Month\"\n",
    "#Manual_Merge_date\n",
    "#BEST PRACTICE MIGHT BE TO RENAME RIGHT AWAY \"HERE\" ... to save coding time later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c2457674",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    HousePrice_Year HousePrice_Month HousePrice_Day month_Year_asDate  \\\n",
      "0              2012               01             01        2012-01-01   \n",
      "1              2012               02             01        2012-02-01   \n",
      "2              2012               03             01        2012-03-01   \n",
      "3              2012               04             01        2012-04-01   \n",
      "4              2012               05             01        2012-05-01   \n",
      "..              ...              ...            ...               ...   \n",
      "127            2022               08             01        2022-08-01   \n",
      "128            2022               09             01        2022-09-01   \n",
      "129            2022               10             01        2022-10-01   \n",
      "130            2022               11             01        2022-11-01   \n",
      "131            2022               12             01        2022-12-01   \n",
      "\n",
      "    OLD_AsTXT_month_Year  Vancouver  Toronto Montreal  \n",
      "0             2012-01-01    660,600  420,950  284,000  \n",
      "1             2012-02-01    670,900  456,537      NaN  \n",
      "2             2012-03-01    679,000  450,750      NaN  \n",
      "3             2012-04-01    683,800  464,075      NaN  \n",
      "4             2012-05-01    625,100  472,839      NaN  \n",
      "..                   ...        ...      ...      ...  \n",
      "127           2022-08-01  1,180,500  939,625  523,700  \n",
      "128           2022-09-01  1,155,300  957,500  511,400  \n",
      "129           2022-10-01  1,148,900  964,125  504,800  \n",
      "130           2022-11-01  1,131,600  964,125  501,000  \n",
      "131           2022-12-01  1,114,300  939,125  516,400  \n",
      "\n",
      "[132 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "#This cell is merging back into the DateClean_Housing_Prices_by_year_df \"user data\" \n",
    "\n",
    "Full_HP_by_Year_DF = pd.concat([Manual_Merge_HP_Date, DateClean_Housing_Prices_by_year_df], axis=1,join='outer')\n",
    "print(Full_HP_by_Year_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c5000bc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    HousePrice_Year HousePrice_Month month_Year_asDate  Vancouver  Toronto  \\\n",
      "0              2012               01        2012-01-01    660,600  420,950   \n",
      "1              2012               02        2012-02-01    670,900  456,537   \n",
      "2              2012               03        2012-03-01    679,000  450,750   \n",
      "3              2012               04        2012-04-01    683,800  464,075   \n",
      "4              2012               05        2012-05-01    625,100  472,839   \n",
      "..              ...              ...               ...        ...      ...   \n",
      "127            2022               08        2022-08-01  1,180,500  939,625   \n",
      "128            2022               09        2022-09-01  1,155,300  957,500   \n",
      "129            2022               10        2022-10-01  1,148,900  964,125   \n",
      "130            2022               11        2022-11-01  1,131,600  964,125   \n",
      "131            2022               12        2022-12-01  1,114,300  939,125   \n",
      "\n",
      "    Montreal  \n",
      "0    284,000  \n",
      "1        NaN  \n",
      "2        NaN  \n",
      "3        NaN  \n",
      "4        NaN  \n",
      "..       ...  \n",
      "127  523,700  \n",
      "128  511,400  \n",
      "129  504,800  \n",
      "130  501,000  \n",
      "131  516,400  \n",
      "\n",
      "[132 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "#Removing columns no longer needed to enable House Price Analysis.\n",
    "#Purpose = to prepare to merge to the \"master Analysis data sheet\" on a city by city basis.\n",
    "\n",
    "reduced_HP_by_year_df = Full_HP_by_Year_DF[[\"HousePrice_Year\",\"HousePrice_Month\",\"month_Year_asDate\",\"Vancouver\",\"Toronto\",\"Montreal\"]]\n",
    "print(reduced_HP_by_year_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8ed64b3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          DATE HousePrice_Year HousePrice_Month Avg_HousePrice\n",
      "0   2012-01-01            2012               01        660,600\n",
      "1   2012-02-01            2012               02        670,900\n",
      "2   2012-03-01            2012               03        679,000\n",
      "3   2012-04-01            2012               04        683,800\n",
      "4   2012-05-01            2012               05        625,100\n",
      "..         ...             ...              ...            ...\n",
      "127 2022-08-01            2022               08      1,180,500\n",
      "128 2022-09-01            2022               09      1,155,300\n",
      "129 2022-10-01            2022               10      1,148,900\n",
      "130 2022-11-01            2022               11      1,131,600\n",
      "131 2022-12-01            2022               12      1,114,300\n",
      "\n",
      "[132 rows x 4 columns]\n",
      "          DATE HousePrice_Year HousePrice_Month Avg_HousePrice HP_City_Region\n",
      "0   2012-01-01            2012               01        660,600      Vancouver\n",
      "1   2012-02-01            2012               02        670,900      Vancouver\n",
      "2   2012-03-01            2012               03        679,000      Vancouver\n",
      "3   2012-04-01            2012               04        683,800      Vancouver\n",
      "4   2012-05-01            2012               05        625,100      Vancouver\n",
      "..         ...             ...              ...            ...            ...\n",
      "127 2022-08-01            2022               08      1,180,500      Vancouver\n",
      "128 2022-09-01            2022               09      1,155,300      Vancouver\n",
      "129 2022-10-01            2022               10      1,148,900      Vancouver\n",
      "130 2022-11-01            2022               11      1,131,600      Vancouver\n",
      "131 2022-12-01            2022               12      1,114,300      Vancouver\n",
      "\n",
      "[132 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "#Using Loc to select all rows for \"HousePrice_Year\", \"HousePrice_Month\",\"Vancouver\".\n",
    "Vancouver_Avg_HP_df = reduced_HP_by_year_df.loc[:,[\"month_Year_asDate\",\"HousePrice_Year\", \"HousePrice_Month\",\"Vancouver\"]]\n",
    "Vancouver_Avg_HP_df.columns.values[0] = 'DATE'\n",
    "Vancouver_Avg_HP_df.columns.values[3] = 'Avg_HousePrice'\n",
    "print(Vancouver_Avg_HP_df)\n",
    "\n",
    "#Creating new column named \"City_Region\" = \"Vancouver\"\n",
    "Vancouver_Avg_HP_df[\"HP_City_Region\"] = \"Vancouver\"\n",
    "print(Vancouver_Avg_HP_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "79d081ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          DATE HousePrice_Year HousePrice_Month Avg_HousePrice\n",
      "0   2012-01-01            2012               01        420,950\n",
      "1   2012-02-01            2012               02        456,537\n",
      "2   2012-03-01            2012               03        450,750\n",
      "3   2012-04-01            2012               04        464,075\n",
      "4   2012-05-01            2012               05        472,839\n",
      "..         ...             ...              ...            ...\n",
      "127 2022-08-01            2022               08        939,625\n",
      "128 2022-09-01            2022               09        957,500\n",
      "129 2022-10-01            2022               10        964,125\n",
      "130 2022-11-01            2022               11        964,125\n",
      "131 2022-12-01            2022               12        939,125\n",
      "\n",
      "[132 rows x 4 columns]\n",
      "          DATE HousePrice_Year HousePrice_Month Avg_HousePrice HP_City_Region\n",
      "0   2012-01-01            2012               01        420,950        Toronto\n",
      "1   2012-02-01            2012               02        456,537        Toronto\n",
      "2   2012-03-01            2012               03        450,750        Toronto\n",
      "3   2012-04-01            2012               04        464,075        Toronto\n",
      "4   2012-05-01            2012               05        472,839        Toronto\n",
      "..         ...             ...              ...            ...            ...\n",
      "127 2022-08-01            2022               08        939,625        Toronto\n",
      "128 2022-09-01            2022               09        957,500        Toronto\n",
      "129 2022-10-01            2022               10        964,125        Toronto\n",
      "130 2022-11-01            2022               11        964,125        Toronto\n",
      "131 2022-12-01            2022               12        939,125        Toronto\n",
      "\n",
      "[132 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "#Using Loc to select all rows for \"HousePrice_Year\", \"HousePrice_Month\",\"Vancouver\".\n",
    "Toronto_Avg_HP_df = reduced_HP_by_year_df.loc[:,[\"month_Year_asDate\",\"HousePrice_Year\", \"HousePrice_Month\",\"Toronto\"]]\n",
    "Toronto_Avg_HP_df.columns.values[0] = 'DATE'\n",
    "Toronto_Avg_HP_df.columns.values[3] = 'Avg_HousePrice'\n",
    "print(Toronto_Avg_HP_df)\n",
    "\n",
    "#Creating new column named \"City_Region\" = \"Vancouver\"\n",
    "Toronto_Avg_HP_df[\"HP_City_Region\"] = \"Toronto\"\n",
    "print(Toronto_Avg_HP_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6272b387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          DATE HousePrice_Year HousePrice_Month Avg_HousePrice\n",
      "0   2012-01-01            2012               01        420,950\n",
      "1   2012-02-01            2012               02        456,537\n",
      "2   2012-03-01            2012               03        450,750\n",
      "3   2012-04-01            2012               04        464,075\n",
      "4   2012-05-01            2012               05        472,839\n",
      "..         ...             ...              ...            ...\n",
      "127 2022-08-01            2022               08        939,625\n",
      "128 2022-09-01            2022               09        957,500\n",
      "129 2022-10-01            2022               10        964,125\n",
      "130 2022-11-01            2022               11        964,125\n",
      "131 2022-12-01            2022               12        939,125\n",
      "\n",
      "[132 rows x 4 columns]\n",
      "          DATE HousePrice_Year HousePrice_Month Avg_HousePrice HP_City_Region\n",
      "0   2012-01-01            2012               01        420,950       Montréal\n",
      "1   2012-02-01            2012               02        456,537       Montréal\n",
      "2   2012-03-01            2012               03        450,750       Montréal\n",
      "3   2012-04-01            2012               04        464,075       Montréal\n",
      "4   2012-05-01            2012               05        472,839       Montréal\n",
      "..         ...             ...              ...            ...            ...\n",
      "127 2022-08-01            2022               08        939,625       Montréal\n",
      "128 2022-09-01            2022               09        957,500       Montréal\n",
      "129 2022-10-01            2022               10        964,125       Montréal\n",
      "130 2022-11-01            2022               11        964,125       Montréal\n",
      "131 2022-12-01            2022               12        939,125       Montréal\n",
      "\n",
      "[132 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "#Using Loc to select all rows for \"HousePrice_Year\", \"HousePrice_Month\",\"Vancouver\".\n",
    "Montreal_Avg_HP_df = reduced_HP_by_year_df.loc[:,[\"month_Year_asDate\",\"HousePrice_Year\", \"HousePrice_Month\",\"Toronto\"]]\n",
    "Montreal_Avg_HP_df.columns.values[0] = 'DATE'\n",
    "Montreal_Avg_HP_df.columns.values[3] = 'Avg_HousePrice'\n",
    "print(Montreal_Avg_HP_df)\n",
    "\n",
    "#Creating new column named \"City_Region\" = \"Vancouver\"\n",
    "Montreal_Avg_HP_df[\"HP_City_Region\"] = \"Montréal\"\n",
    "print(Montreal_Avg_HP_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6f75e8e",
   "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>DATE</th>\n",
       "      <th>HousePrice_Year</th>\n",
       "      <th>HousePrice_Month</th>\n",
       "      <th>Avg_HousePrice</th>\n",
       "      <th>HP_City_Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>420,950</td>\n",
       "      <td>Montréal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>420,950</td>\n",
       "      <td>Toronto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>660,600</td>\n",
       "      <td>Vancouver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>456,537</td>\n",
       "      <td>Montréal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>456,537</td>\n",
       "      <td>Toronto</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE HousePrice_Year HousePrice_Month Avg_HousePrice HP_City_Region\n",
       "0 2012-01-01            2012               01        420,950       Montréal\n",
       "1 2012-01-01            2012               01        420,950        Toronto\n",
       "2 2012-01-01            2012               01        660,600      Vancouver\n",
       "3 2012-02-01            2012               02        456,537       Montréal\n",
       "4 2012-02-01            2012               02        456,537        Toronto"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use pd.concat to union the data rather than to join it on an axis.\n",
    "\n",
    "avg_hp_df = pd.concat([Vancouver_Avg_HP_df,Toronto_Avg_HP_df,Montreal_Avg_HP_df])\n",
    "\n",
    "#Sort Values\n",
    "avg_hp_df = avg_hp_df.sort_values(by=[\"DATE\",\"HousePrice_Year\", \"HousePrice_Month\", \"HP_City_Region\"], ascending=True)\n",
    "\n",
    "#reset the index so that the index is now based on the sort.\n",
    "avg_hp_df = avg_hp_df.reset_index(drop=True)\n",
    "\n",
    "avg_hp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "688422cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Year Month City_Region  Province NewHousePrice_Index_Type  \\\n",
      "0     1981    01     Toronto   Ontario   Total (house and land)   \n",
      "1     1981    02     Toronto   Ontario   Total (house and land)   \n",
      "2     1981    03     Toronto   Ontario   Total (house and land)   \n",
      "3     1981    04     Toronto   Ontario   Total (house and land)   \n",
      "4     1981    05     Toronto   Ontario   Total (house and land)   \n",
      "...    ...   ...         ...       ...                      ...   \n",
      "1522  2023    01    Montréal    Quebec   Total (house and land)   \n",
      "1523  2023    02    Montréal    Quebec   Total (house and land)   \n",
      "1524  2023    03    Montréal    Quebec   Total (house and land)   \n",
      "1525  2023    04    Montréal    Quebec   Total (house and land)   \n",
      "1526  2023    05    Montréal    Quebec   Total (house and land)   \n",
      "\n",
      "                    UOM  VALUE STATUS  DECIMALS DATE HousePrice_Year  \\\n",
      "0     Index, 201612=100   28.0    NaN         1  NaT             NaN   \n",
      "1     Index, 201612=100   28.5    NaN         1  NaT             NaN   \n",
      "2     Index, 201612=100   29.8    NaN         1  NaT             NaN   \n",
      "3     Index, 201612=100   31.2    NaN         1  NaT             NaN   \n",
      "4     Index, 201612=100   32.3    NaN         1  NaT             NaN   \n",
      "...                 ...    ...    ...       ...  ...             ...   \n",
      "1522  Index, 201612=100  151.7    NaN         1  NaT             NaN   \n",
      "1523  Index, 201612=100  151.7    NaN         1  NaT             NaN   \n",
      "1524  Index, 201612=100  151.7    NaN         1  NaT             NaN   \n",
      "1525  Index, 201612=100  152.5    NaN         1  NaT             NaN   \n",
      "1526  Index, 201612=100  152.5    NaN         1  NaT             NaN   \n",
      "\n",
      "     HousePrice_Month Avg_HousePrice HP_City_Region  \n",
      "0                 NaN            NaN            NaN  \n",
      "1                 NaN            NaN            NaN  \n",
      "2                 NaN            NaN            NaN  \n",
      "3                 NaN            NaN            NaN  \n",
      "4                 NaN            NaN            NaN  \n",
      "...               ...            ...            ...  \n",
      "1522              NaN            NaN            NaN  \n",
      "1523              NaN            NaN            NaN  \n",
      "1524              NaN            NaN            NaN  \n",
      "1525              NaN            NaN            NaN  \n",
      "1526              NaN            NaN            NaN  \n",
      "\n",
      "[1527 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "#Creating a test cell for the \"if + elif\" row ... as For (?) LOOP to populate a data frame. I'm not 100% certain the cells above are executing 100% of everything we need.\n",
    "\n",
    "HP_to_Final_City_Filtered_DF = pd.merge( Final_City_Filtered_DF ,avg_hp_df, how='left',\n",
    "left_on=['Year','Month','City_Region'], right_on=['HousePrice_Year','HousePrice_Month','HP_City_Region'])\n",
    "\n",
    "print(HP_to_Final_City_Filtered_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cfea5006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Year Month City_Region  Province Avg_HousePrice\n",
      "0     1981    01     Toronto   Ontario            NaN\n",
      "1     1981    02     Toronto   Ontario            NaN\n",
      "2     1981    03     Toronto   Ontario            NaN\n",
      "3     1981    04     Toronto   Ontario            NaN\n",
      "4     1981    05     Toronto   Ontario            NaN\n",
      "...    ...   ...         ...       ...            ...\n",
      "1522  2023    01    Montréal    Quebec            NaN\n",
      "1523  2023    02    Montréal    Quebec            NaN\n",
      "1524  2023    03    Montréal    Quebec            NaN\n",
      "1525  2023    04    Montréal    Quebec            NaN\n",
      "1526  2023    05    Montréal    Quebec            NaN\n",
      "\n",
      "[1527 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# remove duplicated data columns HP_to_Final_City_Filtered_DF\n",
    "StatsCan_Final_City_Filtered_DF = HP_to_Final_City_Filtered_DF[['Year','Month','City_Region','Province','Avg_HousePrice']]\n",
    "print(StatsCan_Final_City_Filtered_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f88515a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Year Month City_Region           Province NewHousePrice_Index_Type  \\\n",
      "0    2012    01    Montréal             Quebec   Total (house and land)   \n",
      "1    2012    01     Toronto            Ontario   Total (house and land)   \n",
      "2    2012    01   Vancouver   British Columbia   Total (house and land)   \n",
      "3    2012    02    Montréal             Quebec   Total (house and land)   \n",
      "4    2012    02     Toronto            Ontario   Total (house and land)   \n",
      "..    ...   ...         ...                ...                      ...   \n",
      "391  2022    11     Toronto            Ontario   Total (house and land)   \n",
      "392  2022    11   Vancouver   British Columbia   Total (house and land)   \n",
      "393  2022    12    Montréal             Quebec   Total (house and land)   \n",
      "394  2022    12     Toronto            Ontario   Total (house and land)   \n",
      "395  2022    12   Vancouver   British Columbia   Total (house and land)   \n",
      "\n",
      "                   UOM  VALUE STATUS  DECIMALS       DATE HousePrice_Year  \\\n",
      "0    Index, 201612=100   96.5    NaN         1 2012-01-01            2012   \n",
      "1    Index, 201612=100   82.4    NaN         1 2012-01-01            2012   \n",
      "2    Index, 201612=100   96.0    NaN         1 2012-01-01            2012   \n",
      "3    Index, 201612=100   96.6    NaN         1 2012-02-01            2012   \n",
      "4    Index, 201612=100   82.7    NaN         1 2012-02-01            2012   \n",
      "..                 ...    ...    ...       ...        ...             ...   \n",
      "391  Index, 201612=100  115.6    NaN         1 2022-11-01            2022   \n",
      "392  Index, 201612=100  128.2    NaN         1 2022-11-01            2022   \n",
      "393  Index, 201612=100  151.7    NaN         1 2022-12-01            2022   \n",
      "394  Index, 201612=100  115.6    NaN         1 2022-12-01            2022   \n",
      "395  Index, 201612=100  128.2    NaN         1 2022-12-01            2022   \n",
      "\n",
      "    HousePrice_Month Avg_HousePrice HP_City_Region  \n",
      "0                 01        420,950       Montréal  \n",
      "1                 01        420,950        Toronto  \n",
      "2                 01        660,600      Vancouver  \n",
      "3                 02        456,537       Montréal  \n",
      "4                 02        456,537        Toronto  \n",
      "..               ...            ...            ...  \n",
      "391               11        964,125        Toronto  \n",
      "392               11      1,131,600      Vancouver  \n",
      "393               12        939,125       Montréal  \n",
      "394               12        939,125        Toronto  \n",
      "395               12      1,114,300      Vancouver  \n",
      "\n",
      "[396 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "Inner_to_Final_City_Filtered_DF = pd.merge( Final_City_Filtered_DF ,avg_hp_df, \n",
    "how='right',\n",
    "left_on=['Year','Month',\"City_Region\"],\n",
    "right_on=['HousePrice_Year','HousePrice_Month',\"HP_City_Region\"])\n",
    "\n",
    "print(Inner_to_Final_City_Filtered_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "27f90007",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SET UP the basepoints for the calculation with historical prices ...\n",
    "#TEXT Concat notes if necessary #via URL => https://stackoverflow.com/questions/39291499/how-to-concatenate-multiple-column-values-into-a-single-column-in-pandas-datafra\n",
    "\n",
    "#Create VARs to enable them to run against the cleaned data set ...\n",
    "\n",
    "#Determine how to complete the analysis ... and fit into how they can be placed into slides.\n",
    "\n",
    "#Confirm the types of \"plots\" ... we're confident in to speak to the story of the data analysis we're able to produce.\n",
    "#Remember to comment that our $ costs are \"approximations\" ... due to needing the baseprice assumptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "577d2a91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        0\n",
      "1        0\n",
      "2        0\n",
      "3        1\n",
      "4        1\n",
      "      ... \n",
      "391    131\n",
      "392    131\n",
      "393    132\n",
      "394    132\n",
      "395    132\n",
      "Name: DATE, Length: 396, dtype: int64\n",
      "0       0\n",
      "1       0\n",
      "2       0\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "391    10\n",
      "392    10\n",
      "393    10\n",
      "394    10\n",
      "395    10\n",
      "Name: DATE, Length: 396, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Max + Min date columns created here for the \"RIGHT\" dataframe['DATE'] column ... \n",
    "right_max_date = Inner_to_Final_City_Filtered_DF['DATE'].max()\n",
    "right_min_date = Inner_to_Final_City_Filtered_DF['DATE'].min()\n",
    "\n",
    "#Establishes key variable for axis plotting on our INDEX and Avg_Price trend diagrams.\n",
    "date_diff = Inner_to_Final_City_Filtered_DF['DATE'] - right_min_date\n",
    "\n",
    "#Leverages the above calculation to transform the datapoint into Months rather than the literal Days.\n",
    "date_diff_months = date_diff.dt.days // 30\n",
    "date_diff_years = date_diff.dt.days // 365\n",
    "\n",
    "print(date_diff_months)\n",
    "print(date_diff_years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "67255b6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>City_Region</th>\n",
       "      <th>Province</th>\n",
       "      <th>NewHousePrice_Index_Type</th>\n",
       "      <th>UOM</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>DECIMALS</th>\n",
       "      <th>DATE</th>\n",
       "      <th>HousePrice_Year</th>\n",
       "      <th>HousePrice_Month</th>\n",
       "      <th>Avg_HousePrice</th>\n",
       "      <th>HP_City_Region</th>\n",
       "      <th>right_max_date</th>\n",
       "      <th>right_min_date</th>\n",
       "      <th>date_diff_months</th>\n",
       "      <th>date_diff_years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>96.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>420,950</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>82.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>420,950</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>660,600</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>96.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>456,537</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>82.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>456,537</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>115.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>964,125</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>131</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>128.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>1,131,600</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>131</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>151.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>939,125</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>132</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>115.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>939,125</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>132</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>128.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>1,114,300</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>132</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year Month City_Region           Province NewHousePrice_Index_Type  \\\n",
       "0    2012    01    Montréal             Quebec   Total (house and land)   \n",
       "1    2012    01     Toronto            Ontario   Total (house and land)   \n",
       "2    2012    01   Vancouver   British Columbia   Total (house and land)   \n",
       "3    2012    02    Montréal             Quebec   Total (house and land)   \n",
       "4    2012    02     Toronto            Ontario   Total (house and land)   \n",
       "..    ...   ...         ...                ...                      ...   \n",
       "391  2022    11     Toronto            Ontario   Total (house and land)   \n",
       "392  2022    11   Vancouver   British Columbia   Total (house and land)   \n",
       "393  2022    12    Montréal             Quebec   Total (house and land)   \n",
       "394  2022    12     Toronto            Ontario   Total (house and land)   \n",
       "395  2022    12   Vancouver   British Columbia   Total (house and land)   \n",
       "\n",
       "                   UOM  VALUE STATUS  DECIMALS       DATE HousePrice_Year  \\\n",
       "0    Index, 201612=100   96.5    NaN         1 2012-01-01            2012   \n",
       "1    Index, 201612=100   82.4    NaN         1 2012-01-01            2012   \n",
       "2    Index, 201612=100   96.0    NaN         1 2012-01-01            2012   \n",
       "3    Index, 201612=100   96.6    NaN         1 2012-02-01            2012   \n",
       "4    Index, 201612=100   82.7    NaN         1 2012-02-01            2012   \n",
       "..                 ...    ...    ...       ...        ...             ...   \n",
       "391  Index, 201612=100  115.6    NaN         1 2022-11-01            2022   \n",
       "392  Index, 201612=100  128.2    NaN         1 2022-11-01            2022   \n",
       "393  Index, 201612=100  151.7    NaN         1 2022-12-01            2022   \n",
       "394  Index, 201612=100  115.6    NaN         1 2022-12-01            2022   \n",
       "395  Index, 201612=100  128.2    NaN         1 2022-12-01            2022   \n",
       "\n",
       "    HousePrice_Month Avg_HousePrice HP_City_Region right_max_date  \\\n",
       "0                 01        420,950       Montréal     2022-12-01   \n",
       "1                 01        420,950        Toronto     2022-12-01   \n",
       "2                 01        660,600      Vancouver     2022-12-01   \n",
       "3                 02        456,537       Montréal     2022-12-01   \n",
       "4                 02        456,537        Toronto     2022-12-01   \n",
       "..               ...            ...            ...            ...   \n",
       "391               11        964,125        Toronto     2022-12-01   \n",
       "392               11      1,131,600      Vancouver     2022-12-01   \n",
       "393               12        939,125       Montréal     2022-12-01   \n",
       "394               12        939,125        Toronto     2022-12-01   \n",
       "395               12      1,114,300      Vancouver     2022-12-01   \n",
       "\n",
       "    right_min_date  date_diff_months  date_diff_years  \n",
       "0       2012-01-01                 0                0  \n",
       "1       2012-01-01                 0                0  \n",
       "2       2012-01-01                 0                0  \n",
       "3       2012-01-01                 1                0  \n",
       "4       2012-01-01                 1                0  \n",
       "..             ...               ...              ...  \n",
       "391     2012-01-01               131               10  \n",
       "392     2012-01-01               131               10  \n",
       "393     2012-01-01               132               10  \n",
       "394     2012-01-01               132               10  \n",
       "395     2012-01-01               132               10  \n",
       "\n",
       "[396 rows x 18 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Firstly, Max + Min date columns created here for the \"RIGHT\" dataframe['DATE'] column ... \n",
    "Inner_to_Final_City_Filtered_DF['right_max_date'] = right_max_date\n",
    "Inner_to_Final_City_Filtered_DF['right_min_date'] = right_min_date\n",
    "\n",
    "#Secondly, another column for a DateDiff by MIN year to ['DATE'] column\n",
    "Inner_to_Final_City_Filtered_DF['date_diff_months'] = date_diff_months #+ 1    #Removed \"plus 1\" to keep data true and the \"+1\" can be added within the plotting code blocks if needed.\n",
    "Inner_to_Final_City_Filtered_DF['date_diff_years'] = date_diff_years\n",
    "\n",
    "#Confirm that the data can successfully be observed within the Core Analysis_DF to enable our plotting.\n",
    "Inner_to_Final_City_Filtered_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dfe229b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year                                object\n",
      "Month                               object\n",
      "City_Region                         object\n",
      "Province                            object\n",
      "NewHousePrice_Index_Type            object\n",
      "UOM                                 object\n",
      "VALUE                              float64\n",
      "STATUS                              object\n",
      "DECIMALS                             int64\n",
      "DATE                        datetime64[ns]\n",
      "HousePrice_Year                     object\n",
      "HousePrice_Month                    object\n",
      "Avg_HousePrice                      object\n",
      "HP_City_Region                      object\n",
      "right_max_date              datetime64[ns]\n",
      "right_min_date              datetime64[ns]\n",
      "date_diff_months                     int64\n",
      "date_diff_years                      int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Validate the Dtypes within our Analysis_DF to confirm if anything has to be converted to float.\n",
    "print(Inner_to_Final_City_Filtered_DF.dtypes)\n",
    "\n",
    "#Confirmed1 > [\"VALUE\"] (StatsCan \"scale of price\") is already float64\n",
    "#Confirmed2 > [\"date_diff_months\"] (Our CUSTOM \"scale\" of dates for plotting) is int64\n",
    "#Confirmed3 > ['Avg_HousePrice'] (Our COMPARATIVE data as the \"real\" scale of price) is an object\n",
    "#Confirmed4 > ['DATE] is in DateTime format\n",
    "\n",
    "#Linregress requires float as the datatype to execute.\n",
    "# via URL => https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a841f056",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AsType_Validations =      date_diff_months  Avg_HousePrice Avg_HousePrice_Display\n",
      "0                 0.0        420950.0            $420,950.00\n",
      "1                 0.0        420950.0            $420,950.00\n",
      "2                 0.0        660600.0            $660,600.00\n",
      "3                 1.0        456537.0            $456,537.00\n",
      "4                 1.0        456537.0            $456,537.00\n",
      "..                ...             ...                    ...\n",
      "391             131.0        964125.0            $964,125.00\n",
      "392             131.0       1131600.0          $1,131,600.00\n",
      "393             132.0        939125.0            $939,125.00\n",
      "394             132.0        939125.0            $939,125.00\n",
      "395             132.0       1114300.0          $1,114,300.00\n",
      "\n",
      "[396 rows x 3 columns]\n",
      "Year                               float64\n",
      "Month                              float64\n",
      "City_Region                         object\n",
      "Province                            object\n",
      "NewHousePrice_Index_Type            object\n",
      "UOM                                 object\n",
      "VALUE                              float64\n",
      "STATUS                              object\n",
      "DECIMALS                             int64\n",
      "DATE                        datetime64[ns]\n",
      "HousePrice_Year                     object\n",
      "HousePrice_Month                    object\n",
      "Avg_HousePrice                     float64\n",
      "HP_City_Region                      object\n",
      "right_max_date              datetime64[ns]\n",
      "right_min_date              datetime64[ns]\n",
      "date_diff_months                   float64\n",
      "date_diff_years                    float64\n",
      "Avg_HousePrice_Display              object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Converting the Avg_HousePrice to Float\n",
    "#1st need to replace any \",\" values from the string to enable to_numeric or float conversions.\n",
    "#Approach via URL => https://stackoverflow.com/questions/28986489/how-to-replace-text-in-a-string-column-of-a-pandas-dataframe\n",
    "Inner_to_Final_City_Filtered_DF['Avg_HousePrice'] = Inner_to_Final_City_Filtered_DF['Avg_HousePrice'].astype('string').str.replace(',', '',regex=True)\n",
    "\n",
    "#leveraging pd.Series() approach via => https://stackoverflow.com/questions/16729483/converting-strings-to-floats-in-a-dataframe\n",
    "\n",
    "#Execute the conversion to float for the plotting needs.\n",
    "#NOTE for Team => making the code more easy to follow and robust by adding the \".astype('string')\" even though as already know it is ... it is very easy to forget since the \"int64\"(date_diff_months) .. wouldn't go to float without me setting it to \"string\".\n",
    "Inner_to_Final_City_Filtered_DF['Year'] = pd.Series(Inner_to_Final_City_Filtered_DF['Year'].astype('string')).astype(float)\n",
    "Inner_to_Final_City_Filtered_DF['Month'] = pd.Series(Inner_to_Final_City_Filtered_DF['Month'].astype('string')).astype(float)\n",
    "Inner_to_Final_City_Filtered_DF['Avg_HousePrice'] = pd.Series(Inner_to_Final_City_Filtered_DF['Avg_HousePrice'].astype('string')).astype(float)\n",
    "Inner_to_Final_City_Filtered_DF['date_diff_months'] = pd.Series(Inner_to_Final_City_Filtered_DF['date_diff_months'].astype('string')).astype(float)\n",
    "Inner_to_Final_City_Filtered_DF['date_diff_years'] = pd.Series(Inner_to_Final_City_Filtered_DF['date_diff_years'].astype('string')).astype(float)\n",
    "\n",
    "#Create Display column separate to \"calculate plots with\" Column.\n",
    "Inner_to_Final_City_Filtered_DF['Avg_HousePrice_Display'] = pd.Series(Inner_to_Final_City_Filtered_DF['Avg_HousePrice']).astype(float).map(\"${:,.2f}\".format)\n",
    "\n",
    "#Print output to discuss with team. Code leveraged via URL => https://stackoverflow.com/questions/50406466/print-2-columns-in-python\n",
    "#print(RIGHT_to_Final_City_Filtered_DF['Avg_HousePrice'])\n",
    "#print(RIGHT_to_Final_City_Filtered_DF['Avg_HousePrice_Display'])\n",
    "\n",
    "print('AsType_Validations = {}'.format(Inner_to_Final_City_Filtered_DF.loc[:, ['date_diff_months','Avg_HousePrice', 'Avg_HousePrice_Display']]))\n",
    "print(Inner_to_Final_City_Filtered_DF.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0ab4c5c3",
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>City_Region</th>\n",
       "      <th>Province</th>\n",
       "      <th>NewHousePrice_Index_Type</th>\n",
       "      <th>UOM</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>DECIMALS</th>\n",
       "      <th>DATE</th>\n",
       "      <th>HousePrice_Year</th>\n",
       "      <th>HousePrice_Month</th>\n",
       "      <th>Avg_HousePrice</th>\n",
       "      <th>HP_City_Region</th>\n",
       "      <th>right_max_date</th>\n",
       "      <th>right_min_date</th>\n",
       "      <th>date_diff_months</th>\n",
       "      <th>date_diff_years</th>\n",
       "      <th>Avg_HousePrice_Display</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>96.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>420950.0</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>$420,950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>82.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>420950.0</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>$420,950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>660600.0</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>$660,600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>96.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>456537.0</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>$456,537.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Total (house and land)</td>\n",
       "      <td>Index, 201612=100</td>\n",
       "      <td>82.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>456537.0</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>$456,537.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Month City_Region           Province NewHousePrice_Index_Type  \\\n",
       "0  2012.0    1.0    Montréal             Quebec   Total (house and land)   \n",
       "1  2012.0    1.0     Toronto            Ontario   Total (house and land)   \n",
       "2  2012.0    1.0   Vancouver   British Columbia   Total (house and land)   \n",
       "3  2012.0    2.0    Montréal             Quebec   Total (house and land)   \n",
       "4  2012.0    2.0     Toronto            Ontario   Total (house and land)   \n",
       "\n",
       "                 UOM  VALUE STATUS  DECIMALS       DATE HousePrice_Year  \\\n",
       "0  Index, 201612=100   96.5    NaN         1 2012-01-01            2012   \n",
       "1  Index, 201612=100   82.4    NaN         1 2012-01-01            2012   \n",
       "2  Index, 201612=100   96.0    NaN         1 2012-01-01            2012   \n",
       "3  Index, 201612=100   96.6    NaN         1 2012-02-01            2012   \n",
       "4  Index, 201612=100   82.7    NaN         1 2012-02-01            2012   \n",
       "\n",
       "  HousePrice_Month  Avg_HousePrice HP_City_Region right_max_date  \\\n",
       "0               01        420950.0       Montréal     2022-12-01   \n",
       "1               01        420950.0        Toronto     2022-12-01   \n",
       "2               01        660600.0      Vancouver     2022-12-01   \n",
       "3               02        456537.0       Montréal     2022-12-01   \n",
       "4               02        456537.0        Toronto     2022-12-01   \n",
       "\n",
       "  right_min_date  date_diff_months  date_diff_years Avg_HousePrice_Display  \n",
       "0     2012-01-01               0.0              0.0            $420,950.00  \n",
       "1     2012-01-01               0.0              0.0            $420,950.00  \n",
       "2     2012-01-01               0.0              0.0            $660,600.00  \n",
       "3     2012-01-01               1.0              0.0            $456,537.00  \n",
       "4     2012-01-01               1.0              0.0            $456,537.00  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Confirm outputs Captured within full DF\n",
    "\n",
    "#print(RIGHT_to_Final_City_Filtered_DF)\n",
    "Inner_to_Final_City_Filtered_DF.head()\n",
    "\n",
    "#print(RIGHT_to_Final_City_Filtered_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2d37c170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Toronto\"),\"Year\"]     # & (vehicle_data[\"engine\"]==\"Petrol-Electric\"),\"year\"]\n",
    "y_values = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Toronto\"),\"VALUE\"] \n",
    "(slope, intercept, rvalue, pvalue, stderr) = stats.linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(5.8,0.8),fontsize=15,color=\"red\")\n",
    "plt.xlabel('Year of House Sale')\n",
    "plt.ylabel('StatsCan - House Price Index Value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9c406d06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1      2012.0\n",
      "4      2012.0\n",
      "7      2012.0\n",
      "10     2012.0\n",
      "13     2012.0\n",
      "        ...  \n",
      "382    2022.0\n",
      "385    2022.0\n",
      "388    2022.0\n",
      "391    2022.0\n",
      "394    2022.0\n",
      "Name: Year, Length: 132, dtype: float64\n",
      "1        0.0\n",
      "4        1.0\n",
      "7        2.0\n",
      "10       3.0\n",
      "13       4.0\n",
      "       ...  \n",
      "382    128.0\n",
      "385    129.0\n",
      "388    130.0\n",
      "391    131.0\n",
      "394    132.0\n",
      "Name: date_diff_months, Length: 132, dtype: float64\n",
      "1       82.4\n",
      "4       82.7\n",
      "7       83.2\n",
      "10      83.5\n",
      "13      84.0\n",
      "       ...  \n",
      "382    115.6\n",
      "385    115.6\n",
      "388    115.6\n",
      "391    115.6\n",
      "394    115.6\n",
      "Name: VALUE, Length: 132, dtype: float64\n",
      "1      420950.0\n",
      "4      456537.0\n",
      "7      450750.0\n",
      "10     464075.0\n",
      "13     472839.0\n",
      "         ...   \n",
      "382    939625.0\n",
      "385    957500.0\n",
      "388    964125.0\n",
      "391    964125.0\n",
      "394    939125.0\n",
      "Name: Avg_HousePrice, Length: 132, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Establish Year and VALUE  Variable for Plot purposes for each City_Region Independently.\n",
    "\n",
    "Toronto_Year = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Toronto\"),\"Year\"].astype(float) #.drop_duplicates(keep='last').sort_values( ascending=True).reset_index()\n",
    "print(Toronto_Year)\n",
    "Toronto_xValue = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Toronto\"),\"date_diff_months\"].astype(float)\n",
    "print(Toronto_xValue)\n",
    "StatCan_Index_Toronto = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Toronto\"),\"VALUE\"].astype(float) #not dropping dupes or sorting the data.\n",
    "print(StatCan_Index_Toronto)\n",
    "Avg_HP_Toronto = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Toronto\"),\"Avg_HousePrice\"].astype(float) #not dropping dupes or sorting the data.\n",
    "print(Avg_HP_Toronto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "57f1864a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      2012.0\n",
      "3      2012.0\n",
      "6      2012.0\n",
      "9      2012.0\n",
      "12     2012.0\n",
      "        ...  \n",
      "381    2022.0\n",
      "384    2022.0\n",
      "387    2022.0\n",
      "390    2022.0\n",
      "393    2022.0\n",
      "Name: Year, Length: 132, dtype: float64\n",
      "0        0.0\n",
      "3        1.0\n",
      "6        2.0\n",
      "9        3.0\n",
      "12       4.0\n",
      "       ...  \n",
      "381    128.0\n",
      "384    129.0\n",
      "387    130.0\n",
      "390    131.0\n",
      "393    132.0\n",
      "Name: date_diff_months, Length: 132, dtype: float64\n",
      "0       96.5\n",
      "3       96.6\n",
      "6       96.7\n",
      "9       96.7\n",
      "12      96.8\n",
      "       ...  \n",
      "381    151.5\n",
      "384    151.4\n",
      "387    152.0\n",
      "390    151.3\n",
      "393    151.7\n",
      "Name: VALUE, Length: 132, dtype: float64\n",
      "0      420950.0\n",
      "3      456537.0\n",
      "6      450750.0\n",
      "9      464075.0\n",
      "12     472839.0\n",
      "         ...   \n",
      "381    939625.0\n",
      "384    957500.0\n",
      "387    964125.0\n",
      "390    964125.0\n",
      "393    939125.0\n",
      "Name: Avg_HousePrice, Length: 132, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Montreal_Year = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Montréal\"),\"Year\"].astype(float)#.drop_duplicates(keep='last').sort_values( ascending=True).reset_index()\n",
    "print(Montreal_Year)\n",
    "Montreal_xValue = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Montréal\"),\"date_diff_months\"].astype(float)\n",
    "print(Montreal_xValue)\n",
    "StatCan_Index_Montreal = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Montréal\"),\"VALUE\"].astype(float) #not dropping dupes or sorting the data.\n",
    "print(StatCan_Index_Montreal)\n",
    "Avg_HP_Montreal = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Montréal\"),\"Avg_HousePrice\"].astype(float) #not dropping dupes or sorting the data.\n",
    "print(Avg_HP_Montreal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "10f5aca0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], Name: Year, dtype: float64)\n",
      "2        0.0\n",
      "5        1.0\n",
      "8        2.0\n",
      "11       3.0\n",
      "14       4.0\n",
      "       ...  \n",
      "383    128.0\n",
      "386    129.0\n",
      "389    130.0\n",
      "392    131.0\n",
      "395    132.0\n",
      "Name: date_diff_months, Length: 132, dtype: float64\n",
      "Series([], Name: VALUE, dtype: float64)\n",
      "Series([], Name: Avg_HousePrice, dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "Vancouver_Year = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Vancouver\") & (Inner_to_Final_City_Filtered_DF[\"Month\"]==\"05\"),\"Year\"]#.drop_duplicates(keep='last').sort_values( ascending=True).reset_index()\n",
    "print(Vancouver_Year)\n",
    "Vancouver_xValue = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Vancouver\"),\"date_diff_months\"].astype(float)\n",
    "print(Vancouver_xValue)\n",
    "StatCan_Index_Vancouver = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Vancouver\") & (Inner_to_Final_City_Filtered_DF[\"Month\"]==\"05\"),\"VALUE\"] #not dropping dupes or sorting the data.\n",
    "print(StatCan_Index_Vancouver)\n",
    "Avg_HP_Vancouver = Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Vancouver\") & (Inner_to_Final_City_Filtered_DF[\"Month\"]==\"05\"),\"Avg_HousePrice\"] #not dropping dupes or sorting the data.\n",
    "print(Avg_HP_Vancouver)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "593c506a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "StatsCan_Toronto_Plot = plt.plot(\n",
    "    Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"NewHousePrice_Index_Type\"]==\"Total (house and land)\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Toronto\"),'date_diff_years' ]  # \"Year\"]\n",
    "     ,\n",
    "     Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Toronto\"),\"VALUE\"])\n",
    "\n",
    "#Simpler approach taken using URL => https://realpython.com/pandas-plot-python/\n",
    "#Very code heavy and likely not as dynamic as expected to plot all 3 lines together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6118ba7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert \"date_diff_years\" to integer type (if not already)\n",
    "Inner_to_Final_City_Filtered_DF[\"date_diff_months\"] = pd.to_numeric(Inner_to_Final_City_Filtered_DF[\"date_diff_months\"], errors=\"coerce\")\n",
    "\n",
    "# Group the data by \"date_diff_years\" and calculate the average house price for each year\n",
    "average_house_prices = Inner_to_Final_City_Filtered_DF.groupby(\"date_diff_years\")[\"Avg_HousePrice\"].mean()\n",
    "\n",
    "# Plot the data for Toronto\n",
    "StatsCan_Toronto_Plot2 = plt.plot(\n",
    "    average_house_prices.index,  # Years on the x-axis\n",
    "    average_house_prices.values  # Average house prices on the y-axis\n",
    ")\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Average House Price\")\n",
    "plt.title(\"Average House Price for Toronto\")\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility\n",
    "plt.tight_layout()  # Adjust layout to prevent overlapping labels\n",
    "plt.show()\n",
    "\n",
    "#Simpler approach taken using URL => https://realpython.com/pandas-plot-python/\n",
    "#Very code heavy and likely not as dynamic as expected to plot all 3 lines together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7b9b4137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #line plot\n",
    "# # # Extract the 'Year' and 'VALUE' columns for the line plot\n",
    "#years = TotalPriceIndex_Analysis_DF['Year']\n",
    "# housing_price_index_values = TotalPriceIndex_Analysis_DF['VALUE']\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "\n",
    "# # Plot the line plot\n",
    "plt.plot(Toronto_xValue, StatCan_Index_Toronto, marker='o', linestyle='-')\n",
    "\n",
    "\n",
    "# # # Add labels and title\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('New Housing Price Index')\n",
    "plt.title('Toronto - Line Plot of New Housing Price Index Over Time')\n",
    "\n",
    "# # Display the line plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4675bb0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "StatsCan_Montreal_Plot = plt.plot(\n",
    "     Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"NewHousePrice_Index_Type\"]==\"Total (house and land)\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Montréal\"),\"Year\"]\n",
    "    ,\n",
    "    Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Montréal\"),\"VALUE\"])\n",
    "\n",
    "#Simpler approach taken using URL => https://realpython.com/pandas-plot-python/\n",
    "#Very code heavy and likely not as dynamic as expected to plot all 3 lines together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aa18f99c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "StatsCan_Vancouver_Plot = plt.plot(\n",
    "    Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"NewHousePrice_Index_Type\"]==\"Total (house and land)\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Vancouver\"),\"Year\"]\n",
    "    ,\n",
    "    Inner_to_Final_City_Filtered_DF.loc[(Inner_to_Final_City_Filtered_DF[\"UOM\"]==\"Index, 201612=100\") & (Inner_to_Final_City_Filtered_DF[\"City_Region\"]==\"Vancouver\"),\"VALUE\"])\n",
    "\n",
    "#Simpler approach taken using URL => https://realpython.com/pandas-plot-python/\n",
    "#Very code heavy and likely not as dynamic as expected to plot all 3 lines together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "db50c1c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = Inner_to_Final_City_Filtered_DF\n",
    "\n",
    "#Filter data for the last 10 years\n",
    "df = df[df['Year'] >= 2012]\n",
    "\n",
    "#Pivot the DataFrame to have separate columns for each city\n",
    "df_pivot = df.pivot(index=['Year','Month'], columns='City_Region', values='VALUE').reset_index()\n",
    "\n",
    "#Plot the chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "#Plot lines for each city\n",
    "plt.plot(df_pivot['Year'], df_pivot['Montréal'], label='Montréal')\n",
    "plt.plot(df_pivot['Year'], df_pivot['Toronto'], label='Toronto')\n",
    "plt.plot(df_pivot['Year'], df_pivot['Vancouver'], label='Vancouver')\n",
    "\n",
    "#Customize the chart\n",
    "plt.title('House Price Index Over the Last 10 Years')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Price Index')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "#Show the chart\n",
    "plt.savefig(\"output/plots/House Price Index Over the Last 10 Years.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5e697a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mm1S7dm3169dPkvXHecGCBWrevLkcDoe8vb3l4+OjNWvWOB+ri91+++3y8fFxaWvVqpV++OEHDRs2THFxcUpJSclX7YAnI0gBHq5+/fpq2bJljlt2F5hsx48fl7e3typWrOjSbrPZFBoa6tIdL79eeeUVPfXUU1q5cqU6dOigoKAg3XHHHdq3b58kOUcLGz16tHx8fFxu2SHo2LFjV9xOr169tHnzZsXHx2vPnj06fvy4JkyYkGO+i7uB5SX7/I7Ldcs6fvy4zp8/r7lz5+aou1u3bvmuOzfBwcEu97O7+GQHi+zn4dKuOrm1/fHHHzp16pR8fX1z1JmUlJSjxhtvvFENGzZUamqqhg4dmmf3K6uyv+CGhYXlOc+VXiv5kZ/n92J5PYZX81q3Inv9udUbFhaWY/uXviakC6+LK4XNatWqSZIOHDiQ5zzZ55iFh4c72wYPHqzff//d2TXw/fffV1pamkvws/rasvrcXM6V3iMF9blyOadPn9Ytt9yi7777TlOmTNH69eu1efNmLV++3KUWm82mNWvWKCoqStOnT1fz5s1VsWJFPfHEE/rrr7/+Vg35ERwcnOvr+cyZM0pPT89zoInc2O12+fr6Ou9beZxnzZqloUOH6sYbb9SHH36ob7/9Vps3b9Ztt92W6+s4t9fLuHHj9NJLL+nbb79V165dFRwcrI4dOxboZQyAa41zpIBiIjg4WOfPn9eff/7pEqaMMUpKSnKeTC9d+IealpaWYx2X/sP29/fXpEmTNGnSJP3xxx/OIw49e/bU7t27neeBjBs3zuUk9ovVrVv3irVXrFhRLVu2vOJ8+fn1NXvfLz3p/2Lly5eXl5eXHnjgAT366KO5zlOjRo0rbutqZH+JzO3crkvbKlSooODg4DxPKC9btqzL/YkTJ2rnzp1q0aKFnn32WfXo0UM1a9b8W/UaY/TJJ5/I39//ss/RlV4r+WH1Wk15PYYXj0TmcDhyfa0fO3bM5TwmK7Kfw8TExByB/ciRI1e93kt17txZ//znP7Vy5Urddtttuc6zcuVK57zZoqKiFBYWpoULFyoqKkoLFy7UjTfeqAYNGjjnsfraupbX0Sqoz5XLWbt2rY4cOaL169c7j0JJyvV8nYiICL399tuSLhzx/te//qWYmBilp6cX2FHfvDRu3FjLli1TUlKSyw8H2QPINGrU6KrXbeVxXrp0qdq3b6/XXnvNZXpeYTK314u3t7dGjhypkSNH6tSpU/riiy/0z3/+U1FRUTp06JBKly591fsCuAtHpIBiomPHjpIu/MO72IcffqgzZ844p0sXRpnasWOHy3xr167V6dOn81x/SEiIBg4cqPvvv1979uzR2bNnVbduXdWuXVs//PBDrkfNWrZsmeMLWWGrU6eOatWqpXfeeSfXL9CSVLp0aXXo0EHbtm1TkyZNcq07t6MIBaFu3bqqXLmy3n//fZcucwkJCdq4caPLvD169NDx48eVmZmZa40Xf5lcvXq1pk2bpvHjx2v16tUKDAxU7969lZ6e/rfqnTRpkn7++Wc9+eSTeXaVvFRurxUp55GHvys2Ntbl/saNG5WQkOBy4ntur/W9e/fm6BpmpbZbb71VUs732ubNm7Vr1y6X99rf0bJlS3Xp0kVvv/22vvnmmxzTN2zYoHfeeUe33XabWrRo4WzP/pEge/CO+Ph4DR482GVZK68tq/JztO1yrsXnSvYX/UsHhXj99dcvu1ydOnU0fvx4NW7cWFu3bnW2/919zkt0dLRsNptzBMtsixYtkp+fX54BOz+sPM42my3HY7Vjx46rvr5auXLldM899+jRRx/ViRMnnEdWgaKGI1JAMdG5c2dFRUXpqaeeUkpKitq0aeMcta9Zs2Z64IEHnPM+8MADmjBhgp599lm1a9dOP//8s+bNm5eju+CNN96oHj16qEmTJipfvrx27dqlJUuWqHXr1s5fD19//XV17dpVUVFRGjhwoKpUqaITJ05o165d2rp1q/79739f08dBujBKVc+ePXXTTTdpxIgRqlatmn777TfFxcU5v3y//PLLuvnmm3XLLbdo6NChql69uv766y/t379fn3zyicuoXQWpVKlSmjx5sh588EHdeeedeuihh3Tq1CnFxMTk6Kp23333KTY2Vt26ddOTTz6pVq1aycfHR4cPH9a6desUHR2tO++8U4mJierXr5/atWuniRMnqlSpUvrggw/Utm1bjR07Nl8XRz116pTzvLAzZ85oz549WrZsmb7++mv16tXrihfSzc9rJXv0wBdffFFdu3aVl5eXmjRp4tLdyIr4+Hg9+OCDuvfee3Xo0CE988wzqlKlisu5dQ888ID69eunYcOG6e6771ZCQoKmT5+eowtsrVq15Ofnp9jYWNWvX19lypRRWFhYrt0Z69atq3/84x+aO3euSpUqpa5duzpH7QsPD9eIESOuan9y8+6776pTp07q0qWLnnjiCWdIW7t2rV5++WXVq1cv1wvBDh48WC+++KL69OkjPz8/9e7d22V6fl9bV6Nx48Zavny5XnvtNbVo0UKlSpXK1xHnixXE58ovv/yi//znPznaGzRooMjISJUvX16PPPKIJk6cKB8fH8XGxuqHH35wmXfHjh167LHHdO+996p27dry9fXV2rVrtWPHDj399NMu+7xs2TJ98MEHqlmzphwOx2VHy0xISNDmzZuddUpy1lq9enXn49WwYUMNGTJEEydOlJeXl2644QatWrVKb7zxhqZMmWKpa19u8vs49+jRQ5MnT9bEiRPVrl077dmzR88995xq1KiR66iyuenZs6caNWqkli1bOi9vMWfOHEVEROQYoRUoMtw61AWAPGWPTLZ58+Zcp+c2Mta5c+fMU089ZSIiIoyPj4+pXLmyGTp0qDl58qTLfGlpaWbs2LEmPDzc+Pn5mXbt2pnt27fnGOHs6aefNi1btjTly5c3drvd1KxZ04wYMcIcO3bMZX0//PCD6dWrl6lUqZLx8fExoaGh5tZbbzULFiy44n4qH6NrZY/M9+9//zvPaReP2meMMZs2bTJdu3Y1gYGBxm63m1q1apkRI0a4zHPgwAEzePBgU6VKFePj42MqVqxoIiMjXUZ9y0teo/bNmDEj1328dJTEt956y9SuXdv4+vqaOnXqmHfeeccMGDAgx3OakZFhXnrpJdO0aVPjcDhMmTJlTL169czDDz9s9u3bZ86fP2/atWtnQkJCTGJiosuyM2bMMJLMihUrrrgv+v8jetlsNlOmTBlTt25d88ADD5i4uLhcl7l0n/LzWklLSzMPPvigqVixorHZbEaSOXDggHN9eb0OLt1W9ntj1apV5oEHHjDlypUzfn5+plu3bmbfvn0uy2ZlZZnp06ebmjVrGofDYVq2bGnWrl2bY9Q+Yy6MZFevXj3j4+Pjss1LR+0z5sLoiy+++KKpU6eO8fHxMRUqVDD9+vUzhw4dcpmvXbt2pmHDhjn2KbfnOi+nT582U6dONddff70pXbq0KV26tGnSpImZMmWKOX36dJ7LRUZGGkmmb9++uU6/0msr25VGdbzUiRMnzD333GPKlSvnfJ6Nsf4e+bufK3ndsrezceNG07p1a1O6dGlTsWJF8+CDD5qtW7e6jN74xx9/mIEDB5p69eoZf39/U6ZMGdOkSRMze/Zsl5EHDx48aLp06WLKli2bY0TV3FxuVNZLR5lMT083EydONNWqVXN+XrzyyitXfAwulddrMT+Pc1pamhk9erSpUqWKcTgcpnnz5mblypU5XseXe45nzpxpIiMjTYUKFYyvr6+pVq2aGTJkiDl48KDlfQE8hc2YfAzHBAAAAABw4hwpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBEX5JWUlZWlI0eOqGzZss6rnQMAAAAoeYwx+uuvvxQWFqZSpfI+7kSQknTkyBGFh4e7uwwAAAAAHuLQoUOqWrVqntMJUpLKli0r6cKDFRAQ4OZqAAAAALhLSkqKwsPDnRkhLwQpydmdLyAggCAFAAAA4Iqn/DDYBAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKUDSW2+9pfbt2+utt95ydykAAAAoAghSKPFOnTqlpUuXKisrS0uXLtWpU6fcXRIAAAA8HEEKJd4zzzyjrKwsSVJWVpbGjx/v5ooAAADg6QhSKNHi4+O1c+dOl7YdO3YoPj7eTRUBAACgKCBIocTKyspSTExMrtNiYmKcR6kAAACASxGkUGJt2rRJKSkpuU5LSUnRpk2brnFFAAAAKCoIUiixbrzxxr81HQAAACUXQQol1pWOOHFECgAAAHkhSKHEOnr06N+aDgAAgJKLIIUSq1mzZn9rOgAAAEoughRKrOrVq8vhcOQ6zeFwqHr16te2IAAAABQZBCmUWL/99ptSU1NznZaamqrffvvtGlcEAACAooIghRIrIiJCjRs3znVakyZNFBERcY0rAgAAQFHh1iD11VdfqWfPngoLC5PNZtPKlStdpg8cOFA2m83ldtNNN7nMk5aWpscff1wVKlSQv7+/br/9dh0+fPga7gWKMpvN5u4SAAAAUAS5NUidOXNGTZs21bx58/Kc57bbblNiYqLz9tlnn7lMHz58uFasWKFly5Zpw4YNOn36tHr06KHMzMzCLh9FXEJCgnbs2JHrtB07dighIeEaVwQAAICiwtudG+/atau6du162XnsdrtCQ0NznZacnKy3335bS5YsUadOnSRJS5cuVXh4uL744gtFRUUVeM0oPiIiInTDDTdoy5YtysrKcrZ7eXmpRYsWdO0DAABAnjz+HKn169erUqVKqlOnjh566CGXa/ts2bJFGRkZ6tKli7MtLCxMjRo10saNG/NcZ1pamlJSUlxuKHlsNptGjBiRo3tfXu0AAABANo8OUl27dlVsbKzWrl2rmTNnavPmzbr11luVlpYmSUpKSpKvr6/Kly/vslxISIiSkpLyXO+0adMUGBjovIWHhxfqfsBzVa1aVX379nWGJpvNpr59+6pKlSpurgwAAACezKODVO/evdW9e3c1atRIPXv21P/+9z/t3btX//3vfy+7nDHmskcTxo0bp+TkZOft0KFDBV06ipB+/fopODhYklShQgX17dvXzRUBAADA03l0kLpU5cqVFRERoX379kmSQkNDlZ6erpMnT7rMd/ToUYWEhOS5HrvdroCAAJcbSi6Hw6FRo0YpJCREI0eOzPMivQAAAEC2IhWkjh8/rkOHDqly5cqSpBYtWsjHx0erV692zpOYmKgff/xRkZGR7ioTRVCbNm3073//W23atHF3KQAAACgC3Dpq3+nTp7V//37n/QMHDmj79u0KCgpSUFCQYmJidPfdd6ty5co6ePCg/vnPf6pChQq68847JUmBgYEaMmSIRo0apeDgYAUFBWn06NFq3LixcxQ/AAAAAChobg1S8fHx6tChg/P+yJEjJUkDBgzQa6+9pp07d+rdd9/VqVOnVLlyZXXo0EEffPCBypYt61xm9uzZ8vb2Vq9evXTu3Dl17NhRixYtkpeX1zXfHwAAAAAlg80YY9xdhLulpKQoMDBQycnJnC8FAAAAlGD5zQZF6hwpAAAAAPAEBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAVI+uabb3Tvvffqm2++cXcpAAAAKAIIUijxUlNTNXPmTP3xxx+aOXOmUlNT3V0SAAAAPBxBCiXe0qVLdfz4cUnS8ePHFRsb6+aKAAAA4OkIUijRDh8+rNjYWBljJEnGGMXGxurw4cNurgwAAACejCCFEssYo9mzZ+fZnh2uAAAAgEsRpFBiJSQkaPPmzcrMzHRpz8zM1ObNm5WQkOCmygAAAODpCFIosSIiInTDDTfIy8vLpd3Ly0utWrVSRESEmyoDAACApyNIocSy2WwaMWJEnu02m80NVQEAAKAoIEihRKtatar69u3rDE02m019+/ZVlSpV3FwZAAAAPBlBCiVev379FBwcLEmqUKGC+vbt6+aKAAAA4OkIUijxHA6HRo0apZCQEI0cOVIOh8PdJQEAAMDD2QxjPCslJUWBgYFKTk5WQECAu8sBAAAA4Cb5zQYckQIkvfXWW2rfvr3eeustd5cCAACAIoAghRLv1KlTWrp0qbKysrR06VKdOnXK3SUBAADAwxGkUOI988wzysrKkiRlZWVp/Pjxbq4IAAAAno4ghRItPj5eO3fudGnbsWOH4uPj3VQRAAAAigKCFEqsrKwsxcTE5DotJibGeZQKAAAAuBRBCiXWpk2blJKSkuu0lJQUbdq06RpXBAAAgKKCIIUSq3Xr1ipTpkyu08qUKaPWrVtf44oAAABQVBCkUGLZbDaFhYXlOi0sLEw2m+0aVwQAAICigiCFEishIUF79+7NddrevXuVkJBwjSsCAABAUUGQQokVERGhG264IceRJ5vNplatWikiIsJNlQEAAMDTEaRQYtlsNo0YMUKlSrm+Dby8vDRixAi69gEAACBPBCmUaFWrVlXfvn2doclms6lv376qUqWKmysDAACAJyNIocTr16+fypYtK0kKCAhQ37593VwRAAAAPB1BCpBkjJEkLsILAACAfCFIocRbunSpTp8+LUk6ffq0YmNj3VwRAAAAPB1BCiXa4cOHFRsb6zwiZYxRbGysDh8+7ObKAAAA4MkIUiixjDGaPXt2ju58mZmZmj17tjNcAQAAAJciSKHESkhI0ObNm3MEJmOMNm/ezAV5AQAAkCeCFEqsatWqKSAgINdpAQEBqlat2jWuCAAAAEUFQQol1m+//aaUlJRcp6WkpOi33367xhUBAACgqCBIocSKiIjQDTfc4LwYbzabzaZWrVopIiLCTZUBAADA0xGkUGLZbDaNGDEiR5AqVapUru0AAABANoIUSrSqVauqX79+Lm39+vVTlSpV3FQRAAAAigKCFEq8fv36qUKFCpKkihUrqm/fvm6uCAAAAJ6OIIUSz+FwaNSoUQoJCdHIkSPlcDjcXRIAAAA8nM1w1VGlpKQoMDBQycnJeQ6HDQAAAKD4y2824IgUAAAAAFjk1iD11VdfqWfPngoLC5PNZtPKlSvznPfhhx+WzWbTnDlzXNrT0tL0+OOPq0KFCvL399ftt9+uw4cPF27hAAAAAEo0twapM2fOqGnTppo3b95l51u5cqW+++47hYWF5Zg2fPhwrVixQsuWLdOGDRt0+vRp9ejRQ5mZmYVVNgAAAIASzq1BqmvXrpoyZYruuuuuPOf5/fff9dhjjyk2NlY+Pj4u05KTk/X2229r5syZ6tSpk5o1a6alS5dq586d+uKLLwq7fBQj33zzje69915988037i4FAAAARYBHnyOVlZWlBx54QGPGjFHDhg1zTN+yZYsyMjLUpUsXZ1tYWJgaNWqkjRs35rnetLQ0paSkuNxQcqWmpmrmzJn6448/NHPmTKWmprq7JAAAAHg4jw5SL774ory9vfXEE0/kOj0pKUm+vr4qX768S3tISIiSkpLyXO+0adMUGBjovIWHhxdo3Shali5dquPHj0uSjh8/rtjYWDdXBAAAAE/nsUFqy5Ytevnll7Vo0SLZbDZLyxpjLrvMuHHjlJyc7LwdOnTo75aLIurw4cOKjY1V9lUAjDGKjY1lwBIAAABclscGqa+//lpHjx5VtWrV5O3tLW9vbyUkJGjUqFGqXr26JCk0NFTp6ek6efKky7JHjx5VSEhInuu22+0KCAhwuaHkMcZo9uzZebZziTUAAADkxWOD1AMPPKAdO3Zo+/btzltYWJjGjBmjuLg4SVKLFi3k4+Oj1atXO5dLTEzUjz/+qMjISHeVjiIiISFBmzdvzjHCY2ZmpjZv3qyEhAQ3VQYAAABP5+3OjZ8+fVr79+933j9w4IC2b9+uoKAgVatWTcHBwS7z+/j4KDQ0VHXr1pUkBQYGasiQIRo1apSCg4MVFBSk0aNHq3HjxurUqdM13RcUPREREbrhhhu0detWlzDl5eWlFi1aKCIiwo3VAQAAwJO59YhUfHy8mjVrpmbNmkmSRo4cqWbNmunZZ5/N9zpmz56tO+64Q7169VKbNm1UunRpffLJJ/Ly8iqsslFM2Gw2jRgxIs92q+fmAQAAoOSwGU4EUUpKigIDA5WcnMz5UiXQW2+9pSVLljgHKenfv7+GDBni7rIAAADgBvnNBh57jhRwrfTr18/ZjbRChQrq27evmysCAACApyNIocRzOBzq1q2bSpUqpa5du8rhcLi7JAAAAHg4ghRKvNTUVH322WfKysrSZ599ptTUVHeXBAAAAA9HkEKJt3TpUh0/flySdPz4ccXGxrq5IgAAAHg6ghRKtMOHDys2NtZ58V1jjGJjY3X48GE3VwYAAABPRpBCiWWM0ezZs/NsZ0BLAAAA5IUghRIrISFBmzdvdrkYryRlZmZq8+bNSkhIcFNlAAAA8HQEKZRYERERuuGGG3JcvNnLy0utWrVSRESEmyoDAACApyNIocSy2WwaMWJEnu02m80NVQEAAKAoIEihRKtataoaNGjg0tagQQNVqVLFTRUBAACgKCBIoUQ7fPiwfvrpJ5e2n376iVH7AAAAcFkEKZRY2aPz5daFj1H7AAAAcDkEKZRYjNoHAACAq0WQQonFqH0AAAC4WgQplFiM2gcAAICrRZBCiVa1alX17dvXGZpsNpv69u3LqH0AAAC4LG93F4Cixxij1NRUd5dRYO666y7997//1fHjxxUcHKy77rpL586dc3dZBcLhcHBkDQAAoBAQpGBZamqqoqKi3F1GoTh27Jiio6PdXUaBiYuLk5+fn7vLAAAAKHbo2gcAAAAAFnFECpY5HA7FxcW5u4wCk5qa6jwK9dFHH8nhcLi5ooJTnPYFAADAkxCkYJnNZiu23cUcDkex3TcAAAAUHLr2AQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABY5NYg9dVXX6lnz54KCwuTzWbTypUrXabHxMSoXr168vf3V/ny5dWpUyd99913LvOkpaXp8ccfV4UKFeTv76/bb79dhw8fvoZ7AQAAAKCkcWuQOnPmjJo2bap58+blOr1OnTqaN2+edu7cqQ0bNqh69erq0qWL/vzzT+c8w4cP14oVK7Rs2TJt2LBBp0+fVo8ePZSZmXmtdgMAAABACWMzxhh3FyFJNptNK1as0B133JHnPCkpKQoMDNQXX3yhjh07Kjk5WRUrVtSSJUvUu3dvSdKRI0cUHh6uzz77TFFRUbmuJy0tTWlpaS7rDQ8PV3JysgICAgp0v+D5zp0753ytxMXFyc/Pz80VAQAAwF2yM8eVskGROUcqPT1db7zxhgIDA9W0aVNJ0pYtW5SRkaEuXbo45wsLC1OjRo20cePGPNc1bdo0BQYGOm/h4eGFXj8AAACA4sPjg9Snn36qMmXKyOFwaPbs2Vq9erUqVKggSUpKSpKvr6/Kly/vskxISIiSkpLyXOe4ceOUnJzsvB06dKhQ9wEAAABA8eLt7gKupEOHDtq+fbuOHTumN998U7169dJ3332nSpUq5bmMMUY2my3P6Xa7XXa7vTDKBQAAKDGMMUpNTXV3GQXGGOM8/cNut1/2+2RR4nA4is2+eBKPD1L+/v667rrrdN111+mmm25S7dq19fbbb2vcuHEKDQ1Venq6Tp486XJU6ujRo4qMjHRj1QAAAMVfampqnuekw3NwDnjh8PiufZe6+JeCFi1ayMfHR6tXr3ZOT0xM1I8//kiQAgAAAFBo3HpE6vTp09q/f7/z/oEDB7R9+3YFBQUpODhYzz//vG6//XZVrlxZx48f1/z583X48GHde++9kqTAwEANGTJEo0aNUnBwsIKCgjR69Gg1btxYnTp1ctduAQAAlAgOh0NxcXHuLqPApKamKjo6WpL00UcfyeFwuLmiglFc9sPTuDVIxcfHq0OHDs77I0eOlCQNGDBACxYs0O7du7V48WIdO3ZMwcHBuuGGG/T111+rYcOGzmVmz54tb29v9erVS+fOnVPHjh21aNEieXl5XfP9AQAAKElsNlux7TLmcDiK7b6hYLg1SLVv316Xu4zV8uXLr7gOh8OhuXPnau7cuQVZGgAAAADkqcidIwUAAAAA7kaQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiywHqQkTJigzMzNHe3Jysu6///4CKQoAAAAAPJnlIPXuu++qTZs2+uWXX5xt69evV+PGjXXw4MGCrA0AAAAAPJLlILVjxw5Vr15d119/vd58802NGTNGXbp00cCBA7Vhw4bCqBEAAAAAPIq31QUCAwO1bNkyPfPMM3r44Yfl7e2t//3vf+rYsWNh1AcAAAAAHueqBpuYO3euZs+erfvvv181a9bUE088oR9++KGgawMAAAAAj2Q5SHXt2lWTJk3Su+++q9jYWG3btk1t27bVTTfdpOnTpxdGjQAAAADgUSwHqfPnz2vHjh265557JEl+fn567bXX9J///EezZ88u8AIBAAAAwNNYPkdq9erVubZ3795dO3fu/NsFAQAAAICnu6pzpL7++mv169dPrVu31u+//y5JWrJkiXbv3l2gxQEAAACAJ7IcpD788ENFRUXJz89P27ZtU1pamiTpr7/+0tSpUwu8QAAAAADwNJaD1JQpU7RgwQK9+eab8vHxcbZHRkZq69atBVocAAAAAHgiy0Fqz549atu2bY72gIAAnTp1qiBqAgAAAACPZjlIVa5cWfv378/RvmHDBtWsWbNAigIAAAAAT2Y5SD388MN68skn9d1338lms+nIkSOKjY3V6NGjNWzYsMKoEQAAAAA8iuXhz8eOHavk5GR16NBBqampatu2rex2u0aPHq3HHnusMGoEAAAAAI9yVcOfP//88zp27Ji+//57ffvtt/rzzz81efJky+v56quv1LNnT4WFhclms2nlypXOaRkZGXrqqafUuHFj+fv7KywsTP3799eRI0dc1pGWlqbHH39cFSpUkL+/v26//XYdPnz4anYLAAAAAPLlqoKUJJUuXVotW7ZUq1atVKZMmatax5kzZ9S0aVPNmzcvx7SzZ89q69atmjBhgrZu3arly5dr7969uv32213mGz58uFasWKFly5Zpw4YNOn36tHr06KHMzMyrqgkAAAAAriRfXfvuuuuufK9w+fLl+Z63a9eu6tq1a67TAgMDtXr1ape2uXPnqlWrVvrtt99UrVo1JScn6+2339aSJUvUqVMnSdLSpUsVHh6uL774QlFRUfmuBQAAAADyK19HpAIDA523gIAArVmzRvHx8c7pW7Zs0Zo1axQYGFhohUpScnKybDabypUr59xuRkaGunTp4pwnLCxMjRo10saNG/NcT1pamlJSUlxuAAAAAJBf+ToitXDhQuffTz31lHr16qUFCxbIy8tLkpSZmalhw4YpICCgcKqUlJqaqqefflp9+vRxbicpKUm+vr4qX768y7whISFKSkrKc13Tpk3TpEmTCq1WAAAAAMWb5XOk3nnnHY0ePdoZoiTJy8tLI0eO1DvvvFOgxWXLyMjQfffdp6ysLM2fP/+K8xtjZLPZ8pw+btw4JScnO2+HDh0qyHIBAAAAFHOWg9T58+e1a9euHO27du1SVlZWgRR1sYyMDPXq1UsHDhzQ6tWrXY56hYaGKj09XSdPnnRZ5ujRowoJCclznXa7XQEBAS43AAAAAMgvy9eRGjRokAYPHqz9+/frpptukiR9++23euGFFzRo0KACLS47RO3bt0/r1q1TcHCwy/QWLVrIx8dHq1evVq9evSRJiYmJ+vHHHzV9+vQCrQUAAAAAslkOUi+99JJCQ0M1e/ZsJSYmSpIqV66ssWPHatSoUZbWdfr0ae3fv995/8CBA9q+fbuCgoIUFhame+65R1u3btWnn36qzMxM53lPQUFB8vX1VWBgoIYMGaJRo0YpODhYQUFBGj16tBo3buwcxQ8AAAAACprlIFWqVCmNHTtWY8eOdY52d7Vd4+Lj49WhQwfn/ZEjR0qSBgwYoJiYGH388ceSpOuvv95luXXr1ql9+/aSpNmzZ8vb21u9evXSuXPn1LFjRy1atMjlHC4AAAAAKEiWg9TF/u65Re3bt5cxJs/pl5uWzeFwaO7cuZo7d+7fqgUAAAAA8svyYBN//PGHHnjgAYWFhcnb21teXl4uNwAAAAAo7iwfkRo4cKB+++03TZgwQZUrV77sMOMAAAAAUBxZDlIbNmzQ119/neO8JQAAAAAoKSx37QsPD8/XuUsAAAAAUFxZDlJz5szR008/rYMHDxZCOQAAAADg+Sx37evdu7fOnj2rWrVqqXTp0vLx8XGZfuLEiQIrDgAAAAA8keUgNWfOnEIoAwAAAACKDstBasCAAYVRBwAAAAAUGfkOUikpKfma7+9epBcAAAAAPF2+g1S5cuUue80oY4xsNpsyMzMLpDAAAAAA8FT5DlLr1q0rzDoAAAAAoMjId5Bq165dYdYBAAAAAEWG5etIAQAAAEBJR5ACAAAAAIsIUgAAAABgEUEKAAAAACy66iC1f/9+xcXF6dy5c5IuDH8OAAAAACVBvkfty3b8+HH17t1ba9eulc1m0759+1SzZk09+OCDKleunGbOnFkYdQIAPIwxRqmpqe4uo0AYY5SWliZJstvtl71uYlHjcDiK1f4AgKewHKRGjBghb29v/fbbb6pfv76zvXfv3hoxYgRBCgBKiNTUVEVFRbm7DFxBXFyc/Pz83F0GABQ7loPUqlWrFBcXp6pVq7q0165dWwkJCQVWGAAAAAB4KstB6syZMypdunSO9mPHjslutxdIUQAAz+dwOBQXF+fuMgpEamqqoqOjJUkfffSRHA6HmysqOMVpXwDAk1gOUm3bttW7776ryZMnS5JsNpuysrI0Y8YMdejQocALBAB4JpvNViy7jDkcjmK5XwCAgmU5SM2YMUPt27dXfHy80tPTNXbsWP300086ceKEvvnmm8KoEQAAAAA8iuXhzxs0aKAdO3aoVatW6ty5s86cOaO77rpL27ZtU61atQqjRgAAAADwKJaPSElSaGioJk2aVNC1AAAAAECRYPmI1MKFC/Xvf/87R/u///1vLV68uECKAgAAAABPZjlIvfDCC6pQoUKO9kqVKmnq1KkFUhQAAAAAeDLLQSohIUE1atTI0R4REaHffvutQIoCAAAAAE9mOUhVqlRJO3bsyNH+ww8/KDg4uECKAgAAAABPZnmwifvuu09PPPGEypYtq7Zt20qSvvzySz355JO67777CrzA4sIYo9TUVHeXgVxc/LzwHHkuh8Mhm83m7jIAAAAkXUWQmjJlihISEtSxY0d5e19YPCsrS/379+ccqctITU1VVFSUu8vAFURHR7u7BOQhLi6Oi6QCAACPYTlI+fr66oMPPtDkyZP1ww8/yM/PT40bN1ZERERh1AcAAAAAHueqriMlSXXq1FGdOnUKspYS40zzvlKpq37oUdCMkbLOX/i7lLdE9zHPkXVe/ltj3V0FAABADvn6Nj9y5EhNnjxZ/v7+Gjly5GXnnTVrVoEUVqyV8pa8fNxdBVz4ursAAAAAFCH5ClLbtm1TRkaGJGnr1q15nvDNieAAAAAASoJ8Bal169Y5/16/fn1h1QIAAAAARYKl60idP39e3t7e+vHHHwurHgAAAADweJaClLe3tyIiIpSZmVlY9QAAAACAx7MUpCRp/PjxGjdunE6cOFEY9QAAAACAx7M8Bvcrr7yi/fv3KywsTBEREfL393eZvnXr1gIrDgAAAAA8keUgFR0dzeh8AAAAAEo0y0EqJiamEMoAAAAAgKIj3+dInT17Vo8++qiqVKmiSpUqqU+fPjp27Fhh1gYAAAAAHinfQWrixIlatGiRunfvrvvuu0+rV6/W0KFDC7M2AAAAAPBI+e7at3z5cr399tu67777JEn9+vVTmzZtlJmZKS8vr0IrEAAAAAA8Tb6PSB06dEi33HKL836rVq3k7e2tI0eOFEphAAAAAOCp8h2kMjMz5evr69Lm7e2t8+fPF3hRAAAAAODJ8t21zxijgQMHym63O9tSU1P1yCOPuFxLavny5QVbIQAAAAB4mHwHqQEDBuRo69evX4EWAwAAAABFQb6D1MKFCwt841999ZVmzJihLVu2KDExUStWrNAdd9zhnL58+XK9/vrr2rJli44fP65t27bp+uuvd1lHWlqaRo8erffff1/nzp1Tx44dNX/+fFWtWrXA6wUAAAAAycI5UoXhzJkzatq0qebNm5fn9DZt2uiFF17Icx3Dhw/XihUrtGzZMm3YsEGnT59Wjx49lJmZWVhlAwAAACjh8n1EqjB07dpVXbt2zXP6Aw88IEk6ePBgrtOTk5P19ttva8mSJerUqZMkaenSpQoPD9cXX3yhqKioAq8ZAAAAANx6ROrv2rJlizIyMtSlSxdnW1hYmBo1aqSNGzfmuVxaWppSUlJcbgAAAACQX0U6SCUlJcnX11fly5d3aQ8JCVFSUlKey02bNk2BgYHOW3h4eGGXCgAAAKAYKdJBKi/GGNlstjynjxs3TsnJyc7boUOHrmF1AAAAAIq6Ih2kQkNDlZ6erpMnT7q0Hz16VCEhIXkuZ7fbFRAQ4HIDAAAAgPwq0kGqRYsW8vHx0erVq51tiYmJ+vHHHxUZGenGygAAAAAUZ24dte/06dPav3+/8/6BAwe0fft2BQUFqVq1ajpx4oR+++03HTlyRJK0Z88eSReORIWGhiowMFBDhgzRqFGjFBwcrKCgII0ePVqNGzd2juIHAAAAAAXNrUEqPj5eHTp0cN4fOXKkJGnAgAFatGiRPv74Yw0aNMg5/b777pMkTZw4UTExMZKk2bNny9vbW7169XJekHfRokXy8vK6djsCAACQD8YYpaamursM5OHi54bnyXM5HI7LjodwrdiMMcbdRbhbSkqKAgMDlZycXGjnS507d855XaszLQdIXj6Fsh2gWMnMkH/8YklSXFyc/Pz83FwQiquLP6N5raEwXfxaA3B1CvtzOr/ZoEifIwUAAAAA7uDWrn0AAAAlVWbPTL6JeRojKfP//+0lyf29x5DtvOT1iWedusPbFwAAwB28xTcxT8TZF8gnuvYBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIu83V0AAABASWGM+b87591XB1DkXPR+cXkfuRFBCgAA4BpJS0tz/u31iZcbKwGKrrS0NJUuXdrdZdC1DwAAAACs4ogUAADANWK3251/Z/bM5JsYkF/n/+8o7sXvI3fi7QsAAHCN2Gy2/7vjLb6JAVfB5X3kRnTtAwAAAACLCFIAAAAAYBEHlAHgGjHGKDU11d1lIBcXPy88R57N4XB4TLceACUbQQoArpHU1FRFRUW5uwxcQXR0tLtLwGXExcXJz8/P3WUAAF37AAAAAMAqjkgBgBu82vaU7F6ecWV2SMZI6VkX/vYtJdFzzLOkZdr06Ffl3F0GALggSAGAG9i9jBxe7q4CF6OzmCfjRwcAnoeufQAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYJG3uwsAgJLCGOP8Oy3TjYUARczF75eL30cA4E4EKQC4RtLS0px/P/pVeTdWAhRdaWlpKl26tLvLAAC69gEAAACAVRyRAoBrxG63O/9+te1J2b3cWAxQhKRl/t9R3IvfRwDgTgQpALhGbDab82+7l+QgSAGWXfw+AgB3omsfAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALGKwiWvE5QKCmRnuKwQoSi56r3ARTgAA4EkIUtfIxRfi9N/2nhsrAYomLsIJAAA8CV37AAAAAMAitx6R+uqrrzRjxgxt2bJFiYmJWrFihe644w7ndGOMJk2apDfeeEMnT57UjTfeqFdffVUNGzZ0zpOWlqbRo0fr/fff17lz59SxY0fNnz9fVatWdcMe5e3iCwieadZH8vJxYzVAEZGZ4TyCy0U4AQCAJ3FrkDpz5oyaNm2qQYMG6e67784xffr06Zo1a5YWLVqkOnXqaMqUKercubP27NmjsmXLSpKGDx+uTz75RMuWLVNwcLBGjRqlHj16aMuWLfLy8pyrXbpcQNDLhyAFWMRFOAEAgCdxa5Dq2rWrunbtmus0Y4zmzJmjZ555RnfddZckafHixQoJCdF7772nhx9+WMnJyXr77be1ZMkSderUSZK0dOlShYeH64svvlBUVNQ12xcAAAAAJYfHniN14MABJSUlqUuXLs42u92udu3aaePGjZKkLVu2KCMjw2WesLAwNWrUyDlPbtLS0pSSkuJyAwAAAID88tgglZSUJEkKCQlxaQ8JCXFOS0pKkq+vr8qXL5/nPLmZNm2aAgMDnbfw8PACrh4AAABAceaxQSrbpedFGGOueK7EleYZN26ckpOTnbdDhw4VSK0AAAAASgaPDVKhoaGSlOPI0tGjR51HqUJDQ5Wenq6TJ0/mOU9u7Ha7AgICXG4AAAAAkF8eG6Rq1Kih0NBQrV692tmWnp6uL7/8UpGRkZKkFi1ayMfHx2WexMRE/fjjj855AAAAAKCguXXUvtOnT2v//v3O+wcOHND27dsVFBSkatWqafjw4Zo6dapq166t2rVra+rUqSpdurT69OkjSQoMDNSQIUM0atQoBQcHKygoSKNHj1bjxo2do/gBAAAAQEFza5CKj49Xhw4dnPdHjhwpSRowYIAWLVqksWPH6ty5cxo2bJjzgryrVq1yXkNKkmbPni1vb2/16tXLeUHeRYsWedQ1pAAAAAAUL24NUu3bt5cxJs/pNptNMTExiomJyXMeh8OhuXPnau7cuYVQIQAAAADk5LHnSAEAAACAp3LrEakSK+u8uyvAxYz5v+eklLd0heH1cQ3xXgEAAB6KIOUG/ltj3V0CAAAAgL+Brn0AAAAAYBFHpK4Rh8OhuLg4d5eBXKSmpio6OlqS9NFHH8nhcLi5IuSG5wUAAHgSgtQ1YrPZ5Ofn5+4ycAUOh4PnCQAAAFdE1z4AAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEcOfwzJjjFJTU91dRoG5eF+K035JF4Zzt9ls7i4DAACg2CFIwbLU1FRFRUW5u4xCkX1h3uIiLi6O62IBAAAUArr2AQAAAIBFHJGCZQ6HQ3Fxce4uo8AYY5SWliZJstvtxaornMPhcHcJAAAAxRJBCpbZbLZi112sdOnS7i4BAAAARQhd+wAAAADAIoIUAAAAAFhE1z4AAAB3OO/uApCDkZT5///2klR8Tpsu+jzw/UKQAgAAcAOvT7zcXQKAv4GufQAAAABgEUekAAAArpHidgmR4iY1NVXR0dGSpI8++ojLiHgoT3leCFIAAADXSHG8hEhx5XA4eK5wWXTtAwAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFjEBXkBAABwVYwxSk1NdXcZBebifSlO++VwOGSz2dxdRrFDkAIAAMBVSU1NVVRUlLvLKBTR0dHuLqHAxMXFyc/Pz91lFDt07QMAAAAAizgiBQAAgKvicDgUFxfn7jIKjDFGaWlpkiS73V5susM5HA53l1AsEaQAAABwVWw2W7HrMla6dGl3l4Aigq59AAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALDI44PUX3/9peHDhysiIkJ+fn6KjIzU5s2bndONMYqJiVFYWJj8/PzUvn17/fTTT26sGAAAAEBx5/FB6sEHH9Tq1au1ZMkS7dy5U126dFGnTp30+++/S5KmT5+uWbNmad68edq8ebNCQ0PVuXNn/fXXX26uHAAAAEBx5dFB6ty5c/rwww81ffp0tW3bVtddd51iYmJUo0YNvfbaazLGaM6cOXrmmWd01113qVGjRlq8eLHOnj2r9957z93lAwAAACimPDpInT9/XpmZmXI4HC7tfn5+2rBhgw4cOKCkpCR16dLFOc1ut6tdu3bauHFjnutNS0tTSkqKyw0AAAAA8sujg1TZsmXVunVrTZ48WUeOHFFmZqaWLl2q7777TomJiUpKSpIkhYSEuCwXEhLinJabadOmKTAw0HkLDw8v1P0AAAAAULx4dJCSpCVLlsgYoypVqshut+uVV15Rnz595OXl5ZzHZrO5LGOMydF2sXHjxik5Odl5O3ToUKHVDwAAAKD48fggVatWLX355Zc6ffq0Dh06pO+//14ZGRmqUaOGQkNDJSnH0aejR4/mOEp1MbvdroCAAJcbAAAAAOSXxwepbP7+/qpcubJOnjypuLg4RUdHO8PU6tWrnfOlp6fryy+/VGRkpBurBQAAAFCcebu7gCuJi4uTMUZ169bV/v37NWbMGNWtW1eDBg2SzWbT8OHDNXXqVNWuXVu1a9fW1KlTVbp0afXp08fdpQMAAAAopjw+SCUnJ2vcuHE6fPiwgoKCdPfdd+v555+Xj4+PJGns2LE6d+6chg0bppMnT+rGG2/UqlWrVLZsWTdXDgAAAKC4shljjLuLcLeUlBQFBgYqOTmZ86UAFJpz584pKipKkvRq21Oye5X4j1+PYYyUnnXhb99S0mXGK4IbpGXa9OhX5SRd6Kni5+fn3oIAFGv5zQYef0QKAIqj7C+FAACgaCoyg00AAAAAgKfgiBQAXCMOh0NxcXHuLgO5SE1NVXR0tCTpo48+ksPhcHNFyAvPDQBPQZACgGvEZrNxbkcR4HA4eJ4AAFdE1z4AAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALDI290FAACKJmOMUlNT3V1Ggbh4P4rLPmVzOByy2WzuLgMAih2CFADgqqSmpioqKsrdZRS46Ohod5dQoOLi4uTn5+fuMgCg2KFrHwAAAABYxBEpAMBVcTgciouLc3cZBcIYo7S0NEmS3W4vVl3hHA6Hu0sAgGKJIAUAuCo2m61YdRkrXbq0u0sAABQhdO0DAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAijw5S58+f1/jx41WjRg35+fmpZs2aeu6555SVleWcxxijmJgYhYWFyc/PT+3bt9dPP/3kxqoBAAAAFHceHaRefPFFLViwQPPmzdOuXbs0ffp0zZgxQ3PnznXOM336dM2aNUvz5s3T5s2bFRoaqs6dO+uvv/5yY+UAAAAAijOPDlKbNm1SdHS0unfvrurVq+uee+5Rly5dFB8fL+nC0ag5c+bomWee0V133aVGjRpp8eLFOnv2rN577z03Vw8AAACguPLoIHXzzTdrzZo12rt3ryTphx9+0IYNG9StWzdJ0oEDB5SUlKQuXbo4l7Hb7WrXrp02btyY53rT0tKUkpLicgMAAACA/PJ2dwGX89RTTyk5OVn16tWTl5eXMjMz9fzzz+v++++XJCUlJUmSQkJCXJYLCQlRQkJCnuudNm2aJk2aVHiFAwAAACjWPPqI1AcffKClS5fqvffe09atW7V48WK99NJLWrx4sct8NpvN5b4xJkfbxcaNG6fk5GTn7dChQ4VSPwAAAIDiyaOPSI0ZM0ZPP/207rvvPklS48aNlZCQoGnTpmnAgAEKDQ2VdOHIVOXKlZ3LHT16NMdRqovZ7XbZ7fbCLR4AAABAseXRR6TOnj2rUqVcS/Ty8nIOf16jRg2FhoZq9erVzunp6en68ssvFRkZeU1rBQAAAFByePQRqZ49e+r5559XtWrV1LBhQ23btk2zZs3S4MGDJV3o0jd8+HBNnTpVtWvXVu3atTV16lSVLl1affr0cXP1AAAAAIorjw5Sc+fO1YQJEzRs2DAdPXpUYWFhevjhh/Xss8865xk7dqzOnTunYcOG6eTJk7rxxhu1atUqlS1b1o2VAwAAACjObMYY4+4i3C05OVnlypXToUOHFBAQ4O5yAAAAALhJSkqKwsPDderUKQUGBuY5n0cfkbpW/vrrL0lSeHi4mysBAAAA4An++uuvywYpjkhJysrK0pEjR1S2bNnLDpuO4iv7lweOSgIlF58DACQ+C3DhUkp//fWXwsLCcgx8dzGOSEkqVaqUqlat6u4y4AECAgL40ARKOD4HAEh8FpR0lzsSlc2jhz8HAAAAAE9EkAIAAAAAiwhSgCS73a6JEyfKbre7uxQAbsLnAACJzwLkH4NNAAAAAIBFHJECAAAAAIsIUgAAAABgEUEK+JtWrlyp999/391lAAAA4BoiSAF/w3fffacnnnhCrVu3trTcokWLVK5cucIpCgAAAIWOIAWPNHDgQNlsNj3yyCM5pg0bNkw2m00DBw4ssO3FxMTo+uuvt7TMiRMnNGTIEK1cuVLVq1cvsFoA5M5ms132VpCfCVasX79eNptNp06dcsv2gaKuZ8+e6tSpU67TNm3aJJvNpq1bt17jqoArI0jBY4WHh2vZsmU6d+6csy01NVXvv/++qlWr5paaMjIynH8HBQXpxx9/VPPmzd1SC1DSJCYmOm9z5sxRQECAS9vLL79saX0Xv58BuM+QIUO0du1aJSQk5Jj2zjvv6Prrry+x/2szMzOVlZXl7jKQB4IUPFbz5s1VrVo1LV++3Nm2fPlyhYeHq1mzZs62tLQ0PfHEE6pUqZIcDoduvvlmbd682Tk9+9fiNWvWqGXLlipdurQiIyO1Z88eSRe62U2aNEk//PCD85ftRYsWSbrwC/iCBQsUHR0tf39/TZkyRZL0ySefqEWLFnI4HKpZs6YmTZqk8+fPO7c5a9YsNW7cWP7+/goPD9ewYcN0+vTpwny4gGIvNDTUeQsMDJTNZnNpe++991SrVi35+vqqbt26WrJkicvyeb2fX3vttSsu99Zbb+nOO+9U6dKlVbt2bX388ceSpIMHD6pDhw6SpPLly7scGbvSZxOAC3r06KFKlSo5//dmO3v2rD744APdcccduv/++1W1alWVLl1ajRs3znFucvv27fXEE09o7NixCgoKUmhoqGJiYlzmOXXqlP7xj38oJCREDodDjRo10qeffuqc/uGHH6phw4ay2+2qXr26Zs6c6bK8zWbTypUrXdrKlSvnrLt169Z6+umnXab/+eef8vHx0bp16yRJ6enpGjt2rKpUqSJ/f3/deOONWr9+vXP+7K7/n376qRo0aCC73Z5rwISHMIAHGjBggImOjjazZs0yHTt2dLZ37NjRzJ4920RHR5sBAwYYY4x54oknTFhYmPnss8/MTz/9ZAYMGGDKly9vjh8/bowxZt26dUaSufHGG8369evNTz/9ZG655RYTGRlpjDHm7NmzZtSoUaZhw4YmMTHRJCYmmrNnzxpjjJFkKlWqZN5++23zyy+/mIMHD5rPP//cBAQEmEWLFplffvnFrFq1ylSvXt3ExMQ465w9e7ZZu3at+fXXX82aNWtM3bp1zdChQ53TFy5caAIDAwv5UQSKr0vfQ8uXLzc+Pj7m1VdfNXv27DEzZ840Xl5eZu3atc55cns/53e5qlWrmvfee8/s27fPPPHEE6ZMmTLm+PHj5vz58+bDDz80ksyePXtMYmKiOXXqlDHmyp9NAP7PmDFjTPXq1U1WVpazbdGiRcZut5vDhw+bGTNmmG3btplffvnFvPLKK8bLy8t8++23znnbtWtnAgICTExMjNm7d69ZvHixsdlsZtWqVcYYYzIzM81NN91kGjZsaFatWmV++eUX88knn5jPPvvMGGNMfHy8KVWqlHnuuefMnj17zMKFC42fn59ZuHChcxuSzIoVK1zqDgwMdM4zd+5cU61aNZd9mDt3rqlSpYrJzMw0xhjTp08fExkZab766iuzf/9+M2PGDGO3283evXuNMRc+23x8fExkZKT55ptvzO7du83p06cL7HFGwSJIwSNlB6k///zT2O12c+DAAXPw4EHjcDjMn3/+6QxSp0+fNj4+PiY2Nta5bHp6ugkLCzPTp083xvxfkPriiy+c8/z3v/81ksy5c+eMMcZMnDjRNG3aNEcdkszw4cNd2m655RYzdepUl7YlS5aYypUr57k///rXv0xwcLDzPkEK+HsufQ9FRkaahx56yGWee++913Tr1s15P7f3c36XGz9+vPP+6dOnjc1mM//73/+MMf/3GXPy5EmXea702QTg/+zatctIcvkRo23btub+++/Pdf5u3bqZUaNGOe+3a9fO3HzzzS7z3HDDDeapp54yxhgTFxdnSpUqZfbs2ZPr+vr06WM6d+7s0jZmzBjToEED5/0rBamjR48ab29v89VXXzmnt27d2owZM8YYY8z+/fuNzWYzv//+u8s6OnbsaMaNG2eMufDZJsls37491zrhWejaB49WoUIFde/eXYsXL9bChQvVvXt3VahQwTn9l19+UUZGhtq0aeNs8/HxUatWrbRr1y6XdTVp0sT5d+XKlSVJR48evWINLVu2dLm/ZcsWPffccypTpozz9tBDDykxMVFnz56VJK1bt06dO3dWlSpVVLZsWfXv31/Hjx/XmTNnrD8IAK5o165dLp8DktSmTZscnwOXvp/zu9zFnx/+/v4qW7bsZT8/rHw2AZDq1aunyMhIvfPOO5IuvIe+/vprDR48WJmZmXr++efVpEkTBQcHq0yZMlq1apV+++03l3Vc/D6VLvyvz36fbt++XVWrVlWdOnVy3X5enwX79u1TZmZmvvahYsWK6ty5s2JjYyVJBw4c0KZNm9S3b19J0tatW2WMUZ06dVy+Q3z55Zf65ZdfnOvx9fXNsS/wTN7uLgC4ksGDB+uxxx6TJL366qsu04wxki70W760/dI2Hx8f59/Z0/JzAqe/v7/L/aysLE2aNEl33XVXjnkdDocSEhLUrVs3PfLII5o8ebKCgoK0YcMGDRkyhJPbgUKUn8+BS9/P+V3u4s+P7GUu9/lh5bMJwAVDhgzRY489pldffVULFy5URESEOnbsqBkzZmj27NmaM2eO8/zj4cOHKz093WX5y71P/fz8Lrvt3N6b2e/ji9d3adul/9f79u2rJ598UnPnztV7772nhg0bqmnTppIufH/w8vLSli1b5OXl5bJcmTJlnH/7+fnxOVFEcEQKHu+2225Tenq60tPTFRUV5TLtuuuuk6+vrzZs2OBsy8jIUHx8vOrXr5/vbfj6+ub7F6fmzZtrz549uu6663LcSpUqpfj4eJ0/f14zZ87UTTfdpDp16ujIkSP5rgWAdfXr13f5HJCkjRs3XvFz4GqXu5ivr68kuXyGFNRnE1CS9OrVS15eXnrvvfe0ePFiDRo0SDabTV9//bWio6PVr18/NW3aVDVr1tS+ffssrbtJkyY6fPiw9u7dm+v0Bg0a5PpZUKdOHWfoqVixohITE53T9+3b5+yJku2OO+5QamqqPv/8c7333nvq16+fc1qzZs2UmZmpo0eP5vj+EBoaaml/4Bk4IgWP5+Xl5ewKc+kvOP7+/ho6dKjGjBmjoKAgVatWTdOnT9fZs2c1ZMiQfG+jevXqOnDggPPQf9myZWW323Od99lnn1WPHj0UHh6ue++9V6VKldKOHTu0c+dOTZkyRbVq1dL58+c1d+5c9ezZU998840WLFhw9Q8AgCsaM2aMevXqpebNm6tjx4765JNPtHz5cn3xxReFstzFIiIiZLPZ9Omnn6pbt27y8/NTmTJlCuSzCShJypQpo969e+uf//ynkpOTnSNgXnfddfrwww+1ceNGlS9fXrNmzVJSUpKlHyXatWuntm3b6u6779asWbN03XXXaffu3bLZbLrttts0atQo3XDDDZo8ebJ69+6tTZs2ad68eZo/f75zHbfeeqvmzZunm266SVlZWXrqqadyHAXz9/dXdHS0JkyYoF27dqlPnz7OaXXq1FHfvn3Vv39/zZw5U82aNdOxY8e0du1aNW7cWN26dft7DyCuPXednAVcTvZgE3m5eNS+c+fOmccff9xUqFDB2O1206ZNG/P99987583tRPBt27YZSebAgQPGGGNSU1PN3XffbcqVK2ckOU8cVS4nlhpjzOeff24iIyONn5+fCQgIMK1atTJvvPGGc/qsWbNM5cqVjZ+fn4mKijLvvvuuSw0MNgH8Pbm9h+bPn29q1qxpfHx8TJ06dcy7777rMj2v9/PVLHfxCebGGPPcc8+Z0NBQY7PZ8v3ZBCCnjRs3GkmmS5cuzrbjx4+b6OhoU6ZMGVOpUiUzfvx4079/f5fvCe3atTNPPvmky7ou/q6QvZ5BgwaZ4OBg43A4TKNGjcynn37qnP6f//zHNGjQwPj4+Jhq1aqZGTNmuKzv999/N126dDH+/v6mdu3a5rPPPsvxWWDM/w1o1bZt2xz7l56ebp599llTvXp14+PjY0JDQ82dd95pduzYYYzh+0FRYzPmks6eAAAAAIDL4hwpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQCAR7LZbFq5cqW7y7hq69evl81m06lTp9xdCgCgEBCkAABukZSUpMcff1w1a9aU3W5XeHi4evbsqTVr1kiSEhMT1bVrV0nSwYMHZbPZtH379qva1vr161W5cmUZYzRw4EDZbDbZbDZ5e3urWrVqGjp0qE6ePFlQuyZJioyMVGJiogIDAwt0vQAAz+Dt7gIAACXPwYMH1aZNG5UrV07Tp09XkyZNlJGRobi4OD366KPavXu3QkNDC2x7H3/8sW6//XbZbDZJ0m233aaFCxfq/Pnz+vnnnzV48GCdOnVK77//foFt09fXt0D3AQDgWTgiBQC45oYNGyabzabvv/9e99xzj+rUqaOGDRtq5MiR+vbbbyW5du2rUaOGJKlZs2ay2Wxq3769vvrqK/n4+CgpKcll3aNGjVLbtm1d2rKDVDa73a7Q0FBVrVpVXbp0Ue/evbVq1SqXZRYuXKj69evL4XCoXr16mj9/vsv0jRs36vrrr5fD4VDLli21cuVKl6NmuXXt+/DDD9WwYUPZ7XZVr15dM2fOdFln9erVNXXqVA0ePFhly5ZVtWrV9MYbb1h7cAEA1wRBCgBwTZ04cUKff/65Hn30Ufn7++eYXq5cuRxt33//vSTpiy++UGJiopYvX662bduqZs2aWrJkiXO+8+fPa+nSpRo0aJCz7aefflJSUpI6duyYaz2//vqrPv/8c/n4+Djb3nzzTT3zzDN6/vnntWvXLk2dOlUTJkzQ4sWLJUl//fWXevbsqcaNG2vr1q2aPHmynnrqqcvu95YtW9SrVy/dd9992rlzp2JiYjRhwgQtWrTIZb6ZM2eqZcuW2rZtm4YNG6ahQ4dq9+7dl103AODao2sfAOCa2r9/v4wxqlevXr6XqVixoiQpODjYpbvckCFDtHDhQo0ZM0aS9N///ldnz55Vr169nPN89NFHioqKksPhcLZ9+umnKlOmjDIzM5WamipJmjVrlnP65MmTNXPmTN11112SLhwR+/nnn/X6669rwIABio2Nlc1m05tvvimHw6EGDRro999/10MPPZTnPsyaNUsdO3bUhAkTJEl16tTRzz//rBkzZmjgwIHO+bp166Zhw4ZJkp566inNnj1b69evt/R4AQAKH0ekAADXlDFGkpznK/0dAwcO1P79+53dAd955x316tXL5UjXRx995NKtT5I6dOig7du367vvvtPjjz+uqKgoPf7445KkP//8U4cOHdKQIUNUpkwZ523KlCn65ZdfJEl79uxRkyZNXMJZq1atLlvrrl271KZNG5e2Nm3aaN++fcrMzHS2NWnSxPm3zWZTaGiojh49auVhAQBcAwQpAMA1Vbt2bdlsNu3atetvr6tSpUrq2bOnFi5cqKNHj+qzzz7T4MGDndOTkpK0detWde/e3WU5f39/XXfddWrSpIleeeUVpaWladKkSZKkrKwsSRe6923fvt15+/HHH52BzRiTIwhmB8S85HeZi7sYShfCVHZNAADPQZACAFxTQUFBioqK0quvvqozZ87kmJ7bdZd8fX0lyeXITbYHH3xQy5Yt0+uvv65atWq5HPX5+OOP1bp1a1WoUOGyNU2cOFEvvfSSjhw5opCQEFWpUkW//vqrrrvuOpdb9qAX9erV044dO5SWluZcR3x8/GW30aBBA23YsMGlbePGjapTp468vLwuuywAwPMQpAAA19z8+fOVmZmpVq1a6cMPP9S+ffu0a9cuvfLKK2rdunWO+StVqiQ/Pz99/vnn+uOPP5ScnOycFhUVpcDAQE2ZMsVlkAnpQpCKjo6+Yj3t27dXw4YNNXXqVElSTEyMpk2bppdffll79+7Vzp07tXDhQud5VH369FFWVpb+8Y9/aNeuXYqLi9NLL70kKe8ui6NGjdKaNWs0efJk7d27V4sXL9a8efM0evTo/D1oAACPQpACAFxzNWrU0NatW9WhQweNGjVKjRo1UufOnbVmzRq99tprOeb39vbWK6+8otdff11hYWEu4ahUqVIaOHCgMjMz1b9/f2f7mTNntGbNmhznR+Vl5MiRevPNN3Xo0CE9+OCDeuutt7Ro0SI1btxY7dq106JFi5xHpAICAvTJJ59o+/btuv766/XMM8/o2WeflSSX86Yu1rx5c/3rX//SsmXL1KhRIz377LN67rnnXAaaAAAUHTZzpU7dAAB4uIceekh//PGHPv74Y2fb8uXLNX78eP3888/XpIbY2FgNGjRIycnJ8vPzuybbBAC4D8OfAwCKrOTkZG3evFmxsbH66KOPXKaVKVNGL774YqFt+91331XNmjVVpUoV/fDDD3rqqafUq1cvQhQAlBAEKQBAkRUdHa3vv/9eDz/8sDp37uwyrUuXLoW67aSkJD377LNKSkpS5cqVde+99+r5558v1G0CADwHXfsAAAAAwCIGmwAAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABY9P8AjvwkPAUmw0oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df = Inner_to_Final_City_Filtered_DF\n",
    "\n",
    "#Filter data for the last 10 years\n",
    "df = df[df['Year'] >= 2012]\n",
    "\n",
    "#Create the box and whisker plot using Seaborn\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='City_Region', y='VALUE', data=df)\n",
    "\n",
    "#Customize the plot\n",
    "plt.title('House Price Index Distribution Over the Last 10 Years')\n",
    "plt.xlabel('City/Region')\n",
    "plt.ylabel('Price Index')\n",
    "\n",
    "#Show the plot\n",
    "plt.savefig(\"output/plots/House Price Index Distribution Over the Last 10 Years.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0f1f1f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = Inner_to_Final_City_Filtered_DF\n",
    "\n",
    "#Filter data for the last 10 years\n",
    "df = df[df['Year'] >= 2012]\n",
    "\n",
    "#Pivot the DataFrame to have separate columns for each city\n",
    "df_pivot = df.pivot(index=['Year','Month'], columns='City_Region', values='Avg_HousePrice').reset_index()\n",
    "\n",
    "#Plot the chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "#Plot lines for each city\n",
    "plt.plot(df_pivot['Year'], df_pivot['Montréal'], label='Montréal')\n",
    "plt.plot(df_pivot['Year'], df_pivot['Toronto'], label='Toronto')\n",
    "plt.plot(df_pivot['Year'], df_pivot['Vancouver'], label='Vancouver')\n",
    "\n",
    "#Customize the chart\n",
    "plt.title('Average Sales Price Over the Last 10 Years')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average House Price per Month')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "#Show the chart\n",
    "plt.savefig(\"output/plots/Average Sales Price Over the Last 10 Years.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f6b64d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df = Inner_to_Final_City_Filtered_DF\n",
    "\n",
    "#Filter data for the last 10 years\n",
    "df = df[df['Year'] >= 2012]\n",
    "\n",
    "#Create the box and whisker plot using Seaborn\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='City_Region', y='Avg_HousePrice', data=df)\n",
    "\n",
    "#Customize the plot\n",
    "plt.title('Average Sales Price Distribution Over the Last 10 Years')\n",
    "plt.xlabel('City/Region')\n",
    "plt.ylabel('Average Sales Price per Month')\n",
    "\n",
    "#Show the plot\n",
    "plt.savefig(\"output/plots/Average Sales Price Distribution Over the Last 10 Years.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "52586899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert \"DATE\" column to datetime type\n",
    "Inner_to_Final_City_Filtered_DF[\"DATE\"] = pd.to_datetime(Inner_to_Final_City_Filtered_DF[\"DATE\"])\n",
    "\n",
    "# Filter the data for the desired date range (January 2020 to December 2021)\n",
    "start_date = \"2020-01-01\"\n",
    "end_date = \"2021-12-31\"\n",
    "filtered_data = Inner_to_Final_City_Filtered_DF.loc[\n",
    "    (Inner_to_Final_City_Filtered_DF[\"DATE\"] >= start_date) &\n",
    "    (Inner_to_Final_City_Filtered_DF[\"DATE\"] <= end_date)\n",
    "]\n",
    "\n",
    "# Group the data by month and calculate the average house price for each month\n",
    "average_house_prices = filtered_data.groupby(filtered_data[\"DATE\"].dt.to_period(\"M\"))[\"Avg_HousePrice\"].mean()\n",
    "\n",
    "# Convert the Period index to a string representation for plotting\n",
    "average_house_prices.index = average_house_prices.index.to_timestamp().strftime(\"%b %Y\")\n",
    "\n",
    "# Plot the data as a line chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(average_house_prices.index, average_house_prices.values, marker='o', linestyle='-', color='b')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Average House Price\")\n",
    "plt.title(\"Housing market during Covid\")\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility\n",
    "plt.tight_layout()  # Adjust layout to prevent overlapping labels\n",
    "\n",
    "# Save the plot to a file\n",
    "output_directory = \"output/plots\"  # Replace \"output\" with the actual directory path\n",
    "filename = \"Average_House_Price_2020_2021.png\"\n",
    "plt.savefig(f\"{output_directory}/{filename}\")\n",
    "\n",
    "# Display the plot\n",
    "plt.savefig(\"output/plots/Housing market during Covid.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3bf3d7e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert \"DATE\" column to datetime type\n",
    "Inner_to_Final_City_Filtered_DF[\"DATE\"] = pd.to_datetime(Inner_to_Final_City_Filtered_DF[\"DATE\"])\n",
    "\n",
    "# Filter the data for the desired date range (January 2017 to November 2019)\n",
    "start_date = \"2017-01-01\"\n",
    "end_date = \"2019-11-30\"\n",
    "filtered_data = Inner_to_Final_City_Filtered_DF.loc[\n",
    "    (Inner_to_Final_City_Filtered_DF[\"DATE\"] >= start_date) &\n",
    "    (Inner_to_Final_City_Filtered_DF[\"DATE\"] <= end_date)\n",
    "]\n",
    "\n",
    "# Group the data by month and city, and calculate the average house price for each month\n",
    "average_house_prices = filtered_data.groupby([filtered_data[\"DATE\"].dt.to_period(\"M\"), \"City_Region\"])[\"Avg_HousePrice\"].mean()\n",
    "\n",
    "# Convert the MultiIndex to a DataFrame and pivot it to have cities as columns\n",
    "average_house_prices_df = average_house_prices.unstack(level=1)\n",
    "average_house_prices_df.index = average_house_prices_df.index.to_timestamp().strftime(\"%b %Y\")\n",
    "\n",
    "# Plot the data as a horizontal bar chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "average_house_prices_df.plot(kind=\"barh\", color=['blue', 'green', 'red'])\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Average House Price\")\n",
    "plt.ylabel(\"Month\")\n",
    "plt.title(\"Housing Market Pre-Covid\")\n",
    "plt.legend(title=\"City\", loc=\"upper left\")\n",
    "plt.tight_layout()  # Adjust layout to prevent overlapping labels\n",
    "\n",
    "# Save the plot to a file\n",
    "output_directory = \"output/plots\"  # Replace \"output\" with the actual directory path\n",
    "filename = \"Housing Market Pre-Covid.png\"\n",
    "plt.savefig(f\"{output_directory}/{filename}\")\n",
    "\n",
    "# Display the plot\n",
    "plt.savefig(\"output/plots/Housing market pre-Covid.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f71ecb88",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract sample data to validate that the STATUS not in funciton above was successful.\n",
    "Output_Folder = 'Output/'\n",
    "Output_file = Output_Folder + 'HP_to_Final_City_Filtered_DF.csv'\n",
    "\n",
    "# Save the combined data to a single CSV file\n",
    "HP_to_Final_City_Filtered_DF.to_csv(Output_file, index=False, encoding='utf-8-sig') # ref for variation in export encoding https://stackoverflow.com/questions/57152985/what-is-the-difference-between-utf-8-and-utf-8-sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "549a0d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract sample data to validate that the STATUS not in funciton above was successful.\n",
    "Output_Folder = 'Output/'\n",
    "Output_file = Output_Folder + 'RIGHT_to_Final_City_Filtered_DF.csv'\n",
    "\n",
    "# Save the combined data to a single CSV file\n",
    "Inner_to_Final_City_Filtered_DF.to_csv(Output_file, index=False, encoding='utf-8-sig') # ref for variation in export encoding https://stackoverflow.com/questions/57152985/what-is-the-difference-between-utf-8-and-utf-8-sig"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dev",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}