London Housing Tier 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd356851",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72454677",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_LondonHousePrices= \"https://data.london.gov.uk/download/uk-house-price-index/70ac0766-8902-4eb5-aab5-01951aaed773/UK%20House%20price%20index.xls\"\n",
    "properties=pd.read_excel(url_LondonHousePrices, sheet_name='Average price', index_col=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4781a237",
   "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>Unnamed: 0</th>\n",
       "      <th>City of London</th>\n",
       "      <th>Barking &amp; Dagenham</th>\n",
       "      <th>Barnet</th>\n",
       "      <th>Bexley</th>\n",
       "      <th>Brent</th>\n",
       "      <th>Bromley</th>\n",
       "      <th>Camden</th>\n",
       "      <th>Croydon</th>\n",
       "      <th>Ealing</th>\n",
       "      <th>...</th>\n",
       "      <th>NORTH WEST</th>\n",
       "      <th>YORKS &amp; THE HUMBER</th>\n",
       "      <th>EAST MIDLANDS</th>\n",
       "      <th>WEST MIDLANDS</th>\n",
       "      <th>EAST OF ENGLAND</th>\n",
       "      <th>LONDON</th>\n",
       "      <th>SOUTH EAST</th>\n",
       "      <th>SOUTH WEST</th>\n",
       "      <th>Unnamed: 47</th>\n",
       "      <th>England</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaT</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>E09000002</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>E09000006</td>\n",
       "      <td>E09000007</td>\n",
       "      <td>E09000008</td>\n",
       "      <td>E09000009</td>\n",
       "      <td>...</td>\n",
       "      <td>E12000002</td>\n",
       "      <td>E12000003</td>\n",
       "      <td>E12000004</td>\n",
       "      <td>E12000005</td>\n",
       "      <td>E12000006</td>\n",
       "      <td>E12000007</td>\n",
       "      <td>E12000008</td>\n",
       "      <td>E12000009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E92000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>91448.98487</td>\n",
       "      <td>50460.2266</td>\n",
       "      <td>93284.51832</td>\n",
       "      <td>64958.09036</td>\n",
       "      <td>71306.56698</td>\n",
       "      <td>81671.47692</td>\n",
       "      <td>120932.8881</td>\n",
       "      <td>69158.16225</td>\n",
       "      <td>79885.89069</td>\n",
       "      <td>...</td>\n",
       "      <td>43958.48001</td>\n",
       "      <td>44803.42878</td>\n",
       "      <td>45544.52227</td>\n",
       "      <td>48527.52339</td>\n",
       "      <td>56701.5961</td>\n",
       "      <td>74435.76052</td>\n",
       "      <td>64018.87894</td>\n",
       "      <td>54705.1579</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53202.77128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>82202.77314</td>\n",
       "      <td>51085.77983</td>\n",
       "      <td>93190.16963</td>\n",
       "      <td>64787.92069</td>\n",
       "      <td>72022.26197</td>\n",
       "      <td>81657.55944</td>\n",
       "      <td>119508.8622</td>\n",
       "      <td>68951.09542</td>\n",
       "      <td>80897.06551</td>\n",
       "      <td>...</td>\n",
       "      <td>43925.42289</td>\n",
       "      <td>44528.80721</td>\n",
       "      <td>46051.57066</td>\n",
       "      <td>49341.29029</td>\n",
       "      <td>56593.59475</td>\n",
       "      <td>72777.93709</td>\n",
       "      <td>63715.02399</td>\n",
       "      <td>54356.14843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53096.1549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995-03-01</td>\n",
       "      <td>79120.70256</td>\n",
       "      <td>51268.96956</td>\n",
       "      <td>92247.52435</td>\n",
       "      <td>64367.49344</td>\n",
       "      <td>72015.76274</td>\n",
       "      <td>81449.31143</td>\n",
       "      <td>120282.2131</td>\n",
       "      <td>68712.44341</td>\n",
       "      <td>81379.86288</td>\n",
       "      <td>...</td>\n",
       "      <td>44434.8681</td>\n",
       "      <td>45200.46775</td>\n",
       "      <td>45383.82395</td>\n",
       "      <td>49442.17973</td>\n",
       "      <td>56171.18278</td>\n",
       "      <td>73896.84204</td>\n",
       "      <td>64113.60858</td>\n",
       "      <td>53583.07667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53201.2843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995-04-01</td>\n",
       "      <td>77101.20804</td>\n",
       "      <td>53133.50526</td>\n",
       "      <td>90762.87492</td>\n",
       "      <td>64277.66881</td>\n",
       "      <td>72965.63094</td>\n",
       "      <td>81124.41227</td>\n",
       "      <td>120097.899</td>\n",
       "      <td>68610.04641</td>\n",
       "      <td>82188.90498</td>\n",
       "      <td>...</td>\n",
       "      <td>44267.7796</td>\n",
       "      <td>45614.34341</td>\n",
       "      <td>46124.23045</td>\n",
       "      <td>49455.93299</td>\n",
       "      <td>56567.89582</td>\n",
       "      <td>74455.28754</td>\n",
       "      <td>64623.22395</td>\n",
       "      <td>54786.01938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53590.8548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0 City of London Barking & Dagenham       Barnet       Bexley  \\\n",
       "0        NaT      E09000001          E09000002    E09000003    E09000004   \n",
       "1 1995-01-01    91448.98487         50460.2266  93284.51832  64958.09036   \n",
       "2 1995-02-01    82202.77314        51085.77983  93190.16963  64787.92069   \n",
       "3 1995-03-01    79120.70256        51268.96956  92247.52435  64367.49344   \n",
       "4 1995-04-01    77101.20804        53133.50526  90762.87492  64277.66881   \n",
       "\n",
       "         Brent      Bromley       Camden      Croydon       Ealing  ...  \\\n",
       "0    E09000005    E09000006    E09000007    E09000008    E09000009  ...   \n",
       "1  71306.56698  81671.47692  120932.8881  69158.16225  79885.89069  ...   \n",
       "2  72022.26197  81657.55944  119508.8622  68951.09542  80897.06551  ...   \n",
       "3  72015.76274  81449.31143  120282.2131  68712.44341  81379.86288  ...   \n",
       "4  72965.63094  81124.41227   120097.899  68610.04641  82188.90498  ...   \n",
       "\n",
       "    NORTH WEST YORKS & THE HUMBER EAST MIDLANDS WEST MIDLANDS EAST OF ENGLAND  \\\n",
       "0    E12000002          E12000003     E12000004     E12000005       E12000006   \n",
       "1  43958.48001        44803.42878   45544.52227   48527.52339      56701.5961   \n",
       "2  43925.42289        44528.80721   46051.57066   49341.29029     56593.59475   \n",
       "3   44434.8681        45200.46775   45383.82395   49442.17973     56171.18278   \n",
       "4   44267.7796        45614.34341   46124.23045   49455.93299     56567.89582   \n",
       "\n",
       "        LONDON   SOUTH EAST   SOUTH WEST Unnamed: 47      England  \n",
       "0    E12000007    E12000008    E12000009         NaN    E92000001  \n",
       "1  74435.76052  64018.87894   54705.1579         NaN  53202.77128  \n",
       "2  72777.93709  63715.02399  54356.14843         NaN   53096.1549  \n",
       "3  73896.84204  64113.60858  53583.07667         NaN   53201.2843  \n",
       "4  74455.28754  64623.22395  54786.01938         NaN   53590.8548  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e98f997",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 333 entries, 0 to 332\n",
      "Data columns (total 49 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   Unnamed: 0            332 non-null    datetime64[ns]\n",
      " 1   City of London        333 non-null    object        \n",
      " 2   Barking & Dagenham    333 non-null    object        \n",
      " 3   Barnet                333 non-null    object        \n",
      " 4   Bexley                333 non-null    object        \n",
      " 5   Brent                 333 non-null    object        \n",
      " 6   Bromley               333 non-null    object        \n",
      " 7   Camden                333 non-null    object        \n",
      " 8   Croydon               333 non-null    object        \n",
      " 9   Ealing                333 non-null    object        \n",
      " 10  Enfield               333 non-null    object        \n",
      " 11  Greenwich             333 non-null    object        \n",
      " 12  Hackney               333 non-null    object        \n",
      " 13  Hammersmith & Fulham  333 non-null    object        \n",
      " 14  Haringey              333 non-null    object        \n",
      " 15  Harrow                333 non-null    object        \n",
      " 16  Havering              333 non-null    object        \n",
      " 17  Hillingdon            333 non-null    object        \n",
      " 18  Hounslow              333 non-null    object        \n",
      " 19  Islington             333 non-null    object        \n",
      " 20  Kensington & Chelsea  333 non-null    object        \n",
      " 21  Kingston upon Thames  333 non-null    object        \n",
      " 22  Lambeth               333 non-null    object        \n",
      " 23  Lewisham              333 non-null    object        \n",
      " 24  Merton                333 non-null    object        \n",
      " 25  Newham                333 non-null    object        \n",
      " 26  Redbridge             333 non-null    object        \n",
      " 27  Richmond upon Thames  333 non-null    object        \n",
      " 28  Southwark             333 non-null    object        \n",
      " 29  Sutton                333 non-null    object        \n",
      " 30  Tower Hamlets         333 non-null    object        \n",
      " 31  Waltham Forest        333 non-null    object        \n",
      " 32  Wandsworth            333 non-null    object        \n",
      " 33  Westminster           333 non-null    object        \n",
      " 34  Unnamed: 34           0 non-null      float64       \n",
      " 35  Inner London          333 non-null    object        \n",
      " 36  Outer London          333 non-null    object        \n",
      " 37  Unnamed: 37           0 non-null      float64       \n",
      " 38  NORTH EAST            333 non-null    object        \n",
      " 39  NORTH WEST            333 non-null    object        \n",
      " 40  YORKS & THE HUMBER    333 non-null    object        \n",
      " 41  EAST MIDLANDS         333 non-null    object        \n",
      " 42  WEST MIDLANDS         333 non-null    object        \n",
      " 43  EAST OF ENGLAND       333 non-null    object        \n",
      " 44  LONDON                333 non-null    object        \n",
      " 45  SOUTH EAST            333 non-null    object        \n",
      " 46  SOUTH WEST            333 non-null    object        \n",
      " 47  Unnamed: 47           0 non-null      float64       \n",
      " 48  England               333 non-null    object        \n",
      "dtypes: datetime64[ns](1), float64(3), object(45)\n",
      "memory usage: 127.6+ KB\n"
     ]
    }
   ],
   "source": [
    "properties.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4dd15de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "properties.drop(labels=['Unnamed: 34','Unnamed: 37','Unnamed: 47'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c93f82f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'City of London', 'Barking & Dagenham', 'Barnet',\n",
       "       'Bexley', 'Brent', 'Bromley', 'Camden', 'Croydon', 'Ealing', 'Enfield',\n",
       "       'Greenwich', 'Hackney', 'Hammersmith & Fulham', 'Haringey', 'Harrow',\n",
       "       'Havering', 'Hillingdon', 'Hounslow', 'Islington',\n",
       "       'Kensington & Chelsea', 'Kingston upon Thames', 'Lambeth', 'Lewisham',\n",
       "       'Merton', 'Newham', 'Redbridge', 'Richmond upon Thames', 'Southwark',\n",
       "       'Sutton', 'Tower Hamlets', 'Waltham Forest', 'Wandsworth',\n",
       "       'Westminster', 'Inner London', 'Outer London', 'NORTH EAST',\n",
       "       'NORTH WEST', 'YORKS & THE HUMBER', 'EAST MIDLANDS', 'WEST MIDLANDS',\n",
       "       'EAST OF ENGLAND', 'LONDON', 'SOUTH EAST', 'SOUTH WEST', 'England'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties_T=properties.T\n",
    "properties_T.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31d473cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=46, step=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties_T= properties_T.reset_index()\n",
    "properties_T.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a17d8fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "properties_T.columns=properties_T.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c848255",
   "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>Unnamed: 0</th>\n",
       "      <th>NaT</th>\n",
       "      <th>1995-01-01 00:00:00</th>\n",
       "      <th>1995-02-01 00:00:00</th>\n",
       "      <th>1995-03-01 00:00:00</th>\n",
       "      <th>1995-04-01 00:00:00</th>\n",
       "      <th>1995-05-01 00:00:00</th>\n",
       "      <th>1995-06-01 00:00:00</th>\n",
       "      <th>1995-07-01 00:00:00</th>\n",
       "      <th>1995-08-01 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2021-11-01 00:00:00</th>\n",
       "      <th>2021-12-01 00:00:00</th>\n",
       "      <th>2022-01-01 00:00:00</th>\n",
       "      <th>2022-02-01 00:00:00</th>\n",
       "      <th>2022-03-01 00:00:00</th>\n",
       "      <th>2022-04-01 00:00:00</th>\n",
       "      <th>2022-05-01 00:00:00</th>\n",
       "      <th>2022-06-01 00:00:00</th>\n",
       "      <th>2022-07-01 00:00:00</th>\n",
       "      <th>2022-08-01 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1995-01-01 00:00:00</td>\n",
       "      <td>1995-02-01 00:00:00</td>\n",
       "      <td>1995-03-01 00:00:00</td>\n",
       "      <td>1995-04-01 00:00:00</td>\n",
       "      <td>1995-05-01 00:00:00</td>\n",
       "      <td>1995-06-01 00:00:00</td>\n",
       "      <td>1995-07-01 00:00:00</td>\n",
       "      <td>1995-08-01 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2021-11-01 00:00:00</td>\n",
       "      <td>2021-12-01 00:00:00</td>\n",
       "      <td>2022-01-01 00:00:00</td>\n",
       "      <td>2022-02-01 00:00:00</td>\n",
       "      <td>2022-03-01 00:00:00</td>\n",
       "      <td>2022-04-01 00:00:00</td>\n",
       "      <td>2022-05-01 00:00:00</td>\n",
       "      <td>2022-06-01 00:00:00</td>\n",
       "      <td>2022-07-01 00:00:00</td>\n",
       "      <td>2022-08-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>City of London</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>91448.98487</td>\n",
       "      <td>82202.77314</td>\n",
       "      <td>79120.70256</td>\n",
       "      <td>77101.20804</td>\n",
       "      <td>84409.14932</td>\n",
       "      <td>94900.51244</td>\n",
       "      <td>110128.0423</td>\n",
       "      <td>112329.4376</td>\n",
       "      <td>...</td>\n",
       "      <td>799674.7093</td>\n",
       "      <td>809376.0744</td>\n",
       "      <td>795130.20381</td>\n",
       "      <td>841835.34232</td>\n",
       "      <td>811271.13574</td>\n",
       "      <td>842758.40961</td>\n",
       "      <td>813520.74676</td>\n",
       "      <td>840302.71474</td>\n",
       "      <td>820438.51245</td>\n",
       "      <td>810105.5166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>E09000002</td>\n",
       "      <td>50460.2266</td>\n",
       "      <td>51085.77983</td>\n",
       "      <td>51268.96956</td>\n",
       "      <td>53133.50526</td>\n",
       "      <td>53042.24852</td>\n",
       "      <td>53700.34831</td>\n",
       "      <td>52113.12157</td>\n",
       "      <td>52232.19868</td>\n",
       "      <td>...</td>\n",
       "      <td>328530.925</td>\n",
       "      <td>330850.6123</td>\n",
       "      <td>337172.7301</td>\n",
       "      <td>340876.55884</td>\n",
       "      <td>341939.60089</td>\n",
       "      <td>336872.40725</td>\n",
       "      <td>338983.75876</td>\n",
       "      <td>347835.52623</td>\n",
       "      <td>352359.05665</td>\n",
       "      <td>354051.98905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.51832</td>\n",
       "      <td>93190.16963</td>\n",
       "      <td>92247.52435</td>\n",
       "      <td>90762.87492</td>\n",
       "      <td>90258.00033</td>\n",
       "      <td>90107.23471</td>\n",
       "      <td>91441.24768</td>\n",
       "      <td>92361.31512</td>\n",
       "      <td>...</td>\n",
       "      <td>572810.1795</td>\n",
       "      <td>574877.2915</td>\n",
       "      <td>579683.00638</td>\n",
       "      <td>577030.36727</td>\n",
       "      <td>581310.81733</td>\n",
       "      <td>590638.40858</td>\n",
       "      <td>597510.51644</td>\n",
       "      <td>603261.46212</td>\n",
       "      <td>601111.39405</td>\n",
       "      <td>608725.72939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.09036</td>\n",
       "      <td>64787.92069</td>\n",
       "      <td>64367.49344</td>\n",
       "      <td>64277.66881</td>\n",
       "      <td>63997.13588</td>\n",
       "      <td>64252.32335</td>\n",
       "      <td>63722.70055</td>\n",
       "      <td>64432.60005</td>\n",
       "      <td>...</td>\n",
       "      <td>377659.1213</td>\n",
       "      <td>378364.5877</td>\n",
       "      <td>381100.58195</td>\n",
       "      <td>384340.63511</td>\n",
       "      <td>388679.76934</td>\n",
       "      <td>387431.61795</td>\n",
       "      <td>386861.33254</td>\n",
       "      <td>390232.14841</td>\n",
       "      <td>396541.15377</td>\n",
       "      <td>404141.23999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 334 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0          Unnamed: 0        NaT  1995-01-01 00:00:00  1995-02-01 00:00:00  \\\n",
       "0          Unnamed: 0        NaT  1995-01-01 00:00:00  1995-02-01 00:00:00   \n",
       "1      City of London  E09000001          91448.98487          82202.77314   \n",
       "2  Barking & Dagenham  E09000002           50460.2266          51085.77983   \n",
       "3              Barnet  E09000003          93284.51832          93190.16963   \n",
       "4              Bexley  E09000004          64958.09036          64787.92069   \n",
       "\n",
       "0  1995-03-01 00:00:00  1995-04-01 00:00:00  1995-05-01 00:00:00  \\\n",
       "0  1995-03-01 00:00:00  1995-04-01 00:00:00  1995-05-01 00:00:00   \n",
       "1          79120.70256          77101.20804          84409.14932   \n",
       "2          51268.96956          53133.50526          53042.24852   \n",
       "3          92247.52435          90762.87492          90258.00033   \n",
       "4          64367.49344          64277.66881          63997.13588   \n",
       "\n",
       "0  1995-06-01 00:00:00  1995-07-01 00:00:00  1995-08-01 00:00:00  ...  \\\n",
       "0  1995-06-01 00:00:00  1995-07-01 00:00:00  1995-08-01 00:00:00  ...   \n",
       "1          94900.51244          110128.0423          112329.4376  ...   \n",
       "2          53700.34831          52113.12157          52232.19868  ...   \n",
       "3          90107.23471          91441.24768          92361.31512  ...   \n",
       "4          64252.32335          63722.70055          64432.60005  ...   \n",
       "\n",
       "0  2021-11-01 00:00:00  2021-12-01 00:00:00  2022-01-01 00:00:00  \\\n",
       "0  2021-11-01 00:00:00  2021-12-01 00:00:00  2022-01-01 00:00:00   \n",
       "1          799674.7093          809376.0744         795130.20381   \n",
       "2           328530.925          330850.6123          337172.7301   \n",
       "3          572810.1795          574877.2915         579683.00638   \n",
       "4          377659.1213          378364.5877         381100.58195   \n",
       "\n",
       "0  2022-02-01 00:00:00  2022-03-01 00:00:00  2022-04-01 00:00:00  \\\n",
       "0  2022-02-01 00:00:00  2022-03-01 00:00:00  2022-04-01 00:00:00   \n",
       "1         841835.34232         811271.13574         842758.40961   \n",
       "2         340876.55884         341939.60089         336872.40725   \n",
       "3         577030.36727         581310.81733         590638.40858   \n",
       "4         384340.63511         388679.76934         387431.61795   \n",
       "\n",
       "0  2022-05-01 00:00:00  2022-06-01 00:00:00  2022-07-01 00:00:00  \\\n",
       "0  2022-05-01 00:00:00  2022-06-01 00:00:00  2022-07-01 00:00:00   \n",
       "1         813520.74676         840302.71474         820438.51245   \n",
       "2         338983.75876         347835.52623         352359.05665   \n",
       "3         597510.51644         603261.46212         601111.39405   \n",
       "4         386861.33254         390232.14841         396541.15377   \n",
       "\n",
       "0  2022-08-01 00:00:00  \n",
       "0  2022-08-01 00:00:00  \n",
       "1          810105.5166  \n",
       "2         354051.98905  \n",
       "3         608725.72939  \n",
       "4         404141.23999  \n",
       "\n",
       "[5 rows x 334 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d71c1eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=46, step=1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties_T.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b69f1bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "properties_T.drop(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "93419225",
   "metadata": {},
   "outputs": [],
   "source": [
    "properties_T=properties_T.reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "537e1a65",
   "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>Unnamed: 0</th>\n",
       "      <th>NaT</th>\n",
       "      <th>1995-01-01 00:00:00</th>\n",
       "      <th>1995-02-01 00:00:00</th>\n",
       "      <th>1995-03-01 00:00:00</th>\n",
       "      <th>1995-04-01 00:00:00</th>\n",
       "      <th>1995-05-01 00:00:00</th>\n",
       "      <th>1995-06-01 00:00:00</th>\n",
       "      <th>1995-07-01 00:00:00</th>\n",
       "      <th>1995-08-01 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2021-11-01 00:00:00</th>\n",
       "      <th>2021-12-01 00:00:00</th>\n",
       "      <th>2022-01-01 00:00:00</th>\n",
       "      <th>2022-02-01 00:00:00</th>\n",
       "      <th>2022-03-01 00:00:00</th>\n",
       "      <th>2022-04-01 00:00:00</th>\n",
       "      <th>2022-05-01 00:00:00</th>\n",
       "      <th>2022-06-01 00:00:00</th>\n",
       "      <th>2022-07-01 00:00:00</th>\n",
       "      <th>2022-08-01 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>City of London</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>91448.98487</td>\n",
       "      <td>82202.77314</td>\n",
       "      <td>79120.70256</td>\n",
       "      <td>77101.20804</td>\n",
       "      <td>84409.14932</td>\n",
       "      <td>94900.51244</td>\n",
       "      <td>110128.0423</td>\n",
       "      <td>112329.4376</td>\n",
       "      <td>...</td>\n",
       "      <td>799674.7093</td>\n",
       "      <td>809376.0744</td>\n",
       "      <td>795130.20381</td>\n",
       "      <td>841835.34232</td>\n",
       "      <td>811271.13574</td>\n",
       "      <td>842758.40961</td>\n",
       "      <td>813520.74676</td>\n",
       "      <td>840302.71474</td>\n",
       "      <td>820438.51245</td>\n",
       "      <td>810105.5166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>E09000002</td>\n",
       "      <td>50460.2266</td>\n",
       "      <td>51085.77983</td>\n",
       "      <td>51268.96956</td>\n",
       "      <td>53133.50526</td>\n",
       "      <td>53042.24852</td>\n",
       "      <td>53700.34831</td>\n",
       "      <td>52113.12157</td>\n",
       "      <td>52232.19868</td>\n",
       "      <td>...</td>\n",
       "      <td>328530.925</td>\n",
       "      <td>330850.6123</td>\n",
       "      <td>337172.7301</td>\n",
       "      <td>340876.55884</td>\n",
       "      <td>341939.60089</td>\n",
       "      <td>336872.40725</td>\n",
       "      <td>338983.75876</td>\n",
       "      <td>347835.52623</td>\n",
       "      <td>352359.05665</td>\n",
       "      <td>354051.98905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.51832</td>\n",
       "      <td>93190.16963</td>\n",
       "      <td>92247.52435</td>\n",
       "      <td>90762.87492</td>\n",
       "      <td>90258.00033</td>\n",
       "      <td>90107.23471</td>\n",
       "      <td>91441.24768</td>\n",
       "      <td>92361.31512</td>\n",
       "      <td>...</td>\n",
       "      <td>572810.1795</td>\n",
       "      <td>574877.2915</td>\n",
       "      <td>579683.00638</td>\n",
       "      <td>577030.36727</td>\n",
       "      <td>581310.81733</td>\n",
       "      <td>590638.40858</td>\n",
       "      <td>597510.51644</td>\n",
       "      <td>603261.46212</td>\n",
       "      <td>601111.39405</td>\n",
       "      <td>608725.72939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.09036</td>\n",
       "      <td>64787.92069</td>\n",
       "      <td>64367.49344</td>\n",
       "      <td>64277.66881</td>\n",
       "      <td>63997.13588</td>\n",
       "      <td>64252.32335</td>\n",
       "      <td>63722.70055</td>\n",
       "      <td>64432.60005</td>\n",
       "      <td>...</td>\n",
       "      <td>377659.1213</td>\n",
       "      <td>378364.5877</td>\n",
       "      <td>381100.58195</td>\n",
       "      <td>384340.63511</td>\n",
       "      <td>388679.76934</td>\n",
       "      <td>387431.61795</td>\n",
       "      <td>386861.33254</td>\n",
       "      <td>390232.14841</td>\n",
       "      <td>396541.15377</td>\n",
       "      <td>404141.23999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>71306.56698</td>\n",
       "      <td>72022.26197</td>\n",
       "      <td>72015.76274</td>\n",
       "      <td>72965.63094</td>\n",
       "      <td>73704.04743</td>\n",
       "      <td>74310.48167</td>\n",
       "      <td>74127.03788</td>\n",
       "      <td>73547.0411</td>\n",
       "      <td>...</td>\n",
       "      <td>519123.411</td>\n",
       "      <td>522416.266</td>\n",
       "      <td>522521.52277</td>\n",
       "      <td>525847.50963</td>\n",
       "      <td>530939.02353</td>\n",
       "      <td>538639.04596</td>\n",
       "      <td>539195.09598</td>\n",
       "      <td>528814.72945</td>\n",
       "      <td>548565.47794</td>\n",
       "      <td>565440.54799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 334 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0          Unnamed: 0        NaT 1995-01-01 00:00:00 1995-02-01 00:00:00  \\\n",
       "0      City of London  E09000001         91448.98487         82202.77314   \n",
       "1  Barking & Dagenham  E09000002          50460.2266         51085.77983   \n",
       "2              Barnet  E09000003         93284.51832         93190.16963   \n",
       "3              Bexley  E09000004         64958.09036         64787.92069   \n",
       "4               Brent  E09000005         71306.56698         72022.26197   \n",
       "\n",
       "0 1995-03-01 00:00:00 1995-04-01 00:00:00 1995-05-01 00:00:00  \\\n",
       "0         79120.70256         77101.20804         84409.14932   \n",
       "1         51268.96956         53133.50526         53042.24852   \n",
       "2         92247.52435         90762.87492         90258.00033   \n",
       "3         64367.49344         64277.66881         63997.13588   \n",
       "4         72015.76274         72965.63094         73704.04743   \n",
       "\n",
       "0 1995-06-01 00:00:00 1995-07-01 00:00:00 1995-08-01 00:00:00  ...  \\\n",
       "0         94900.51244         110128.0423         112329.4376  ...   \n",
       "1         53700.34831         52113.12157         52232.19868  ...   \n",
       "2         90107.23471         91441.24768         92361.31512  ...   \n",
       "3         64252.32335         63722.70055         64432.60005  ...   \n",
       "4         74310.48167         74127.03788          73547.0411  ...   \n",
       "\n",
       "0 2021-11-01 00:00:00 2021-12-01 00:00:00 2022-01-01 00:00:00  \\\n",
       "0         799674.7093         809376.0744        795130.20381   \n",
       "1          328530.925         330850.6123         337172.7301   \n",
       "2         572810.1795         574877.2915        579683.00638   \n",
       "3         377659.1213         378364.5877        381100.58195   \n",
       "4          519123.411          522416.266        522521.52277   \n",
       "\n",
       "0 2022-02-01 00:00:00 2022-03-01 00:00:00 2022-04-01 00:00:00  \\\n",
       "0        841835.34232        811271.13574        842758.40961   \n",
       "1        340876.55884        341939.60089        336872.40725   \n",
       "2        577030.36727        581310.81733        590638.40858   \n",
       "3        384340.63511        388679.76934        387431.61795   \n",
       "4        525847.50963        530939.02353        538639.04596   \n",
       "\n",
       "0 2022-05-01 00:00:00 2022-06-01 00:00:00 2022-07-01 00:00:00  \\\n",
       "0        813520.74676        840302.71474        820438.51245   \n",
       "1        338983.75876        347835.52623        352359.05665   \n",
       "2        597510.51644        603261.46212        601111.39405   \n",
       "3        386861.33254        390232.14841        396541.15377   \n",
       "4        539195.09598        528814.72945        548565.47794   \n",
       "\n",
       "0 2022-08-01 00:00:00  \n",
       "0         810105.5166  \n",
       "1        354051.98905  \n",
       "2        608725.72939  \n",
       "3        404141.23999  \n",
       "4        565440.54799  \n",
       "\n",
       "[5 rows x 334 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92ed206e",
   "metadata": {},
   "outputs": [],
   "source": [
    "properties_T.rename(columns={'Unnamed: 0': 'Boroughs', pd.NaT:'ID'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4d61de38",
   "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>Boroughs</th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>City of London</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>91448.98487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>E09000002</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>50460.2266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>93284.51832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>64958.09036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>71306.56698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Boroughs         ID       Date Average_price\n",
       "0      City of London  E09000001 1995-01-01   91448.98487\n",
       "1  Barking & Dagenham  E09000002 1995-01-01    50460.2266\n",
       "2              Barnet  E09000003 1995-01-01   93284.51832\n",
       "3              Bexley  E09000004 1995-01-01   64958.09036\n",
       "4               Brent  E09000005 1995-01-01   71306.56698"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_properties=pd.melt(properties_T, id_vars=['Boroughs','ID'], var_name=['Date'], value_name='Average_price')\n",
    "clean_properties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "18b76d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Boroughs                 object\n",
       "ID                       object\n",
       "Date             datetime64[ns]\n",
       "Average_price            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_properties.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8691dbe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_properties['Average_price']=clean_properties['Average_price'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "43fff403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Boroughs                 object\n",
       "ID                       object\n",
       "Date             datetime64[ns]\n",
       "Average_price           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_properties.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b495dfc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Boroughs         False\n",
       "ID               False\n",
       "Date             False\n",
       "Average_price    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_properties.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7475d85f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['City of London', 'Barking & Dagenham', 'Barnet', 'Bexley',\n",
       "       'Brent', 'Bromley', 'Camden', 'Croydon', 'Ealing', 'Enfield',\n",
       "       'Greenwich', 'Hackney', 'Hammersmith & Fulham', 'Haringey',\n",
       "       'Harrow', 'Havering', 'Hillingdon', 'Hounslow', 'Islington',\n",
       "       'Kensington & Chelsea', 'Kingston upon Thames', 'Lambeth',\n",
       "       'Lewisham', 'Merton', 'Newham', 'Redbridge',\n",
       "       'Richmond upon Thames', 'Southwark', 'Sutton', 'Tower Hamlets',\n",
       "       'Waltham Forest', 'Wandsworth', 'Westminster', 'Inner London',\n",
       "       'Outer London', 'NORTH EAST', 'NORTH WEST', 'YORKS & THE HUMBER',\n",
       "       'EAST MIDLANDS', 'WEST MIDLANDS', 'EAST OF ENGLAND', 'LONDON',\n",
       "       'SOUTH EAST', 'SOUTH WEST', 'England'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_properties['Boroughs'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "157a7821",
   "metadata": {},
   "outputs": [],
   "source": [
    "NotB=['Inner London', 'Outer London', \n",
    "               'NORTH EAST', 'NORTH WEST', 'YORKS & THE HUMBER', \n",
    "               'EAST MIDLANDS', 'WEST MIDLANDS',\n",
    "              'EAST OF ENGLAND', 'LONDON', 'SOUTH EAST', \n",
    "              'SOUTH WEST', 'England']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "47c57e30",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_properties= clean_properties[~clean_properties.Boroughs.isin(NotB)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cfaa5f3b",
   "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>Boroughs</th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>City of London</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>91448.98487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>E09000002</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>50460.22660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>93284.51832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>64958.09036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>71306.56698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Boroughs         ID       Date  Average_price\n",
       "0      City of London  E09000001 1995-01-01    91448.98487\n",
       "1  Barking & Dagenham  E09000002 1995-01-01    50460.22660\n",
       "2              Barnet  E09000003 1995-01-01    93284.51832\n",
       "3              Bexley  E09000004 1995-01-01    64958.09036\n",
       "4               Brent  E09000005 1995-01-01    71306.56698"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_properties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f7dc0668",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=clean_properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "18d64137",
   "metadata": {},
   "outputs": [],
   "source": [
    "LondonP= df[df['Boroughs']=='City of London']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6db2b4fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LP= LondonP.plot(kind='line',x='Date', y= 'Average_price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a7aaea25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(4.444444444444452, 0.5, 'Price')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LP.set_ylabel('Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4da56a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Year']=df['Date'].apply(lambda t: t.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5a7891a2",
   "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>Boroughs</th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Average_price</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14923</th>\n",
       "      <td>Sutton</td>\n",
       "      <td>E09000029</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>446106.79905</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14924</th>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>E09000030</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>493330.14981</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14925</th>\n",
       "      <td>Waltham Forest</td>\n",
       "      <td>E09000031</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>524277.09279</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14926</th>\n",
       "      <td>Wandsworth</td>\n",
       "      <td>E09000032</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>656762.17930</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14927</th>\n",
       "      <td>Westminster</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>964078.73061</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Boroughs         ID       Date  Average_price  Year\n",
       "14923          Sutton  E09000029 2022-08-01   446106.79905  2022\n",
       "14924   Tower Hamlets  E09000030 2022-08-01   493330.14981  2022\n",
       "14925  Waltham Forest  E09000031 2022-08-01   524277.09279  2022\n",
       "14926      Wandsworth  E09000032 2022-08-01   656762.17930  2022\n",
       "14927     Westminster  E09000033 2022-08-01   964078.73061  2022"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "51c55ea0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfg=df.groupby(by=['Boroughs','Year']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b32f7f99",
   "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>Boroughs</th>\n",
       "      <th>Year</th>\n",
       "      <th>Average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>1995</td>\n",
       "      <td>51817.969390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>1996</td>\n",
       "      <td>51718.192690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>1997</td>\n",
       "      <td>55974.262309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>1998</td>\n",
       "      <td>60285.821083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>1999</td>\n",
       "      <td>65320.934441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Boroughs  Year  Average_price\n",
       "0  Barking & Dagenham  1995   51817.969390\n",
       "1  Barking & Dagenham  1996   51718.192690\n",
       "2  Barking & Dagenham  1997   55974.262309\n",
       "3  Barking & Dagenham  1998   60285.821083\n",
       "4  Barking & Dagenham  1999   65320.934441"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfg=dfg.reset_index()\n",
    "dfg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "75b5dd42",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_price_ratio(x):\n",
    "    y1998= float(x['Average_price'][x['Year']==1998])\n",
    "    y2018=float(x['Average_price'][x['Year']==2018])\n",
    "    ratio=[y2018/y1998]\n",
    "    return ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cc6084b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Barking & Dagenham': [4.89661861291754], 'Barnet': [4.358195917538044], 'Bexley': [4.248977046127877], 'Brent': [4.8945544971392865], 'Bromley': [4.094784685333876], 'Camden': [4.935353408884261], 'City of London': [5.30162037758761], 'Croydon': [4.201100280024766], 'Ealing': [4.311450902121834], 'Enfield': [4.263471583495811], 'Greenwich': [4.7630363473291935], 'Hackney': [6.198285561008663], 'Hammersmith & Fulham': [4.13779810193623], 'Haringey': [5.134624964136042], 'Harrow': [4.0591964329643195], 'Havering': [4.325230371335307], 'Hillingdon': [4.2002730803844575], 'Hounslow': [3.976409106143329], 'Islington': [4.844048012802297], 'Kensington & Chelsea': [5.082465066092464], 'Kingston upon Thames': [4.270549521484271], 'Lambeth': [4.957751163514062], 'Lewisham': [5.449221041059686], 'Merton': [4.741273313294603], 'Newham': [5.305390437201879], 'Redbridge': [4.471182006097364], 'Richmond upon Thames': [4.005161895721457], 'Southwark': [5.516485302379378], 'Sutton': [4.118522608573157], 'Tower Hamlets': [4.62670104006116], 'Waltham Forest': [5.83475580932281], 'Wandsworth': [4.75770934773927], 'Westminster': [5.353565392605412]}\n"
     ]
    }
   ],
   "source": [
    "final={}\n",
    "for x in dfg['Boroughs'].unique():\n",
    "    borough= dfg[dfg['Boroughs']==x]\n",
    "    final[x]=create_price_ratio(borough)\n",
    "print(final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d5f45000",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratio= pd.DataFrame(final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ca499cba",
   "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>index</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>4.896619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>4.358196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>4.248977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brent</td>\n",
       "      <td>4.894554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bromley</td>\n",
       "      <td>4.094785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                index         0\n",
       "0  Barking & Dagenham  4.896619\n",
       "1              Barnet  4.358196\n",
       "2              Bexley  4.248977\n",
       "3               Brent  4.894554\n",
       "4             Bromley  4.094785"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratios_T= df_ratio.T\n",
    "df_ratios= df_ratios_T.reset_index()\n",
    "df_ratios.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "243e882e",
   "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>Borough</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>4.896619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>4.358196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>4.248977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brent</td>\n",
       "      <td>4.894554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bromley</td>\n",
       "      <td>4.094785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Borough      2018\n",
       "0  Barking & Dagenham  4.896619\n",
       "1              Barnet  4.358196\n",
       "2              Bexley  4.248977\n",
       "3               Brent  4.894554\n",
       "4             Bromley  4.094785"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratios.rename(columns={'index':'Borough', 0:'2018'}, inplace=True)\n",
    "df_ratios.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "227b7f14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Borough      2018\n",
      "11               Hackney  6.198286\n",
      "30        Waltham Forest  5.834756\n",
      "27             Southwark  5.516485\n",
      "22              Lewisham  5.449221\n",
      "32           Westminster  5.353565\n",
      "24                Newham  5.305390\n",
      "6         City of London  5.301620\n",
      "13              Haringey  5.134625\n",
      "19  Kensington & Chelsea  5.082465\n",
      "21               Lambeth  4.957751\n",
      "5                 Camden  4.935353\n",
      "0     Barking & Dagenham  4.896619\n",
      "3                  Brent  4.894554\n",
      "18             Islington  4.844048\n",
      "10             Greenwich  4.763036\n"
     ]
    }
   ],
   "source": [
    "top15=df_ratios.sort_values(by='2018', ascending=False).head(15)\n",
    "print(top15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d239ab14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Hackney'),\n",
       " Text(1, 0, 'Waltham Forest'),\n",
       " Text(2, 0, 'Southwark'),\n",
       " Text(3, 0, 'Lewisham'),\n",
       " Text(4, 0, 'Westminster'),\n",
       " Text(5, 0, 'Newham'),\n",
       " Text(6, 0, 'City of London'),\n",
       " Text(7, 0, 'Haringey'),\n",
       " Text(8, 0, 'Kensington & Chelsea'),\n",
       " Text(9, 0, 'Lambeth'),\n",
       " Text(10, 0, 'Camden'),\n",
       " Text(11, 0, 'Barking & Dagenham'),\n",
       " Text(12, 0, 'Brent'),\n",
       " Text(13, 0, 'Islington'),\n",
       " Text(14, 0, 'Greenwich')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAImCAYAAAAPPcN+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABu90lEQVR4nO3ddVjV5/8/8OchRDpUEJUUUVFQnDqTmoU9N7trUyfmnDprOrtr5mag+9gdwwRMZiBgoxg4J7agqKhw//7gx/lyBJxs7wB5Pq7rXJP3OXq/BodznudOjRBCgIiIiEgCemoXQERERJ8OBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQMlG4wLS0Nf//9N8zNzaHRaJRunoiIiP4FIQSeP3+OEiVKQE8v534JxYPF33//DQcHB6WbJSIiIgncuXMHpUqVyvF+xYOFubk5gPTCLCwslG6eiIiI/oWkpCQ4ODho38dzoniwyBj+sLCwYLAgIiLKZ/5pGgMnbxIREZFkGCyIiIhIMgwWREREJBnF51gQERFJKTU1FW/fvlW7jHzP0NAQ+vr6//nfYbAgIqJ8SQiBhIQEPHv2TO1SPhlWVlYoXrz4f9pnisGCiIjypYxQYWtrCxMTE266+B8IIfDy5Us8ePAAAGBvb/+v/y0GCyIiyndSU1O1oaJIkSJql/NJMDY2BgA8ePAAtra2/3pYhJM3iYgo38mYU2FiYqJyJZ+WjO/nf5mzwmBBRET5Foc/pCXF95PBgoiIiCTDYEFERESS4eRNIiL6pDiP2KNYW7emNlGsrfyCPRZEREQKmjJlCqpVqwZzc3PY2tqiZcuWuHr1qs5jhBD46aefUKJECRgbG8PPzw8XL17UecyyZcvg5+cHCwsLaDSabPfziI2NRYsWLVC0aFFYWFigdu3aCA0NlfN/j8GCiIhISeHh4fjuu+8QERGBAwcO4N27d2jQoAGSk5O1j5k+fTpmz56NhQsX4vTp0yhevDjq16+P58+fax/z8uVLNGrUCD/++GOObTVp0gTv3r3D4cOHcfbsWVSuXBlNmzZFQkKCbP9/GiGEkO1fz0ZSUhIsLS2RmJjIY9OJiOhfef36NW7evAkXFxcULlxY5778NhTy8OFD2NraIjw8HD4+PhBCoESJEhg0aBCGDx8OAEhJSYGdnR2mTZuGb7/9Vufvh4WFwd/fH0+fPoWVlZX2+qNHj1CsWDEcOXIEdevWBQA8f/4cFhYWOHjwIL744osstXzo+/qx7995co7Ff31ScMyLiIjyi8TERACAjY0NAODmzZtISEhAgwYNtI8xMjKCr68vTpw4kSVY5KRIkSIoX748goODUaVKFRgZGWHp0qWws7PDZ599Jv3/yP+XJ4MFERFRQSCEwJAhQ1CnTh1UrFgRALTDFHZ2djqPtbOzw+3btz/639ZoNDhw4ABatGgBc3Nz6Onpwc7ODiEhITo9G1LjHAsiIiKV9O/fHzExMVi3bl2W+97frEoIkasNrIQQ6NevH2xtbXH06FGcOnUKLVq0QNOmTXHv3r3/XHtOGCyIiIhUEBQUhJ07dyI0NBSlSpXSXi9evDgAZJlg+eDBgyy9GB9y+PBh7N69G+vXr0ft2rVRpUoVLFq0CMbGxli9erU0/xPZYLAgIiJSkBAC/fv3x9atW3H48GG4uLjo3O/i4oLixYvjwIED2mtv3rxBeHg4atWq9dHtvHz5EgCgp6f7Vq+np4e0tLT/8H/wYZxjQUREpKDvvvsO//vf/7Bjxw6Ym5treyYsLS1hbGwMjUaDQYMGYfLkyShTpgzKlCmDyZMnw8TEBB06dND+OwkJCUhISMD169cBAOfPn4e5uTkcHR1hY2ODmjVrwtraGl27dsXYsWNhbGyM5cuX4+bNm2jSRL5FDgwWRET0ScnrKwMXL14MAPDz89O5vnLlSnTr1g0A8MMPP+DVq1fo168fnj59is8//xz79++Hubm59vFLlizB+PHjtV/7+Pjo/DtFixZFSEgIRo0ahYCAALx9+xYVKlTAjh07UKlSJdn+/3K9j8Xdu3cxfPhw/PHHH3j16hXc3d3x22+/ffTSlY9ZB8vlpkRE9CEf2m+B/j3F97F4+vQpateuDX9/f/zxxx+wtbVFXFycrMtWiIiIKP/IVbCYNm0aHBwcsHLlSu01Z2dnqWsiIiKifCpXq0J27tyJqlWronXr1rC1tYW3tzeWL1/+wb+TkpKCpKQknRsRERF9mnIVLG7cuIHFixejTJky2LdvH/r06YMBAwYgODg4x78zZcoUWFpaam8ODg7/uWgiIiIgfekmSUeK72eugkVaWhqqVKmCyZMnw9vbG99++y169+6tneGanZEjRyIxMVF7u3Pnzn8umoiICjZDQ0MA/7dXA0kj4/uZ8f39N3I1x8Le3h4eHh4618qXL48tW7bk+HeMjIxgZGT076ojIiLKhr6+PqysrPDgwQMAgImJSa62uyZdQgi8fPkSDx48gJWVFfT19f/1v5WrYFG7dm1cvXpV51psbCycnJz+dQFERET/RsbW1xnhgv47Kysr7ff138pVsBg8eDBq1aqFyZMno02bNjh16hSWLVuGZcuW/aciiIiIckuj0cDe3h62trZ4+/at2uXke4aGhv+ppyJDroJFtWrVsG3bNowcORITJkyAi4sL5s6di44dO/7nQoiIiP4NfX19Sd4QSRq53tK7adOmaNq0qRy1EBERUT7H002JiIhIMgwWREREJBmebpoDHoRGRESUe+yxICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyRioXQDlzHnEnv/8b9ya2kSCSoiIiD4OeyyIiIhIMgwWREREJBkGCyIiIpIM51jQB3GeBxER5QZ7LIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw1UhlOdxZQoRUf7BHgsiIiKSDHssiD4Ce02IiD4OgwVRPsFwQ0T5AYdCiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWS4QRYRfTRu0kVE/4Q9FkRERCQZBgsiIiKSDIdCiChf4XAMUd6Wqx6Ln376CRqNRudWvHhxuWojIiKifCbXPRYVKlTAwYMHtV/r6+tLWhARERHlX7kOFgYGBuylICIiomzlevLmtWvXUKJECbi4uKBdu3a4cePGBx+fkpKCpKQknRsRERF9mnIVLD7//HMEBwdj3759WL58ORISElCrVi08fvw4x78zZcoUWFpaam8ODg7/uWgiIiLKm3IVLAIDA/HVV1/B09MT9erVw5496bOzV69enePfGTlyJBITE7W3O3fu/LeKiYiIKM/6T8tNTU1N4enpiWvXruX4GCMjIxgZGf2XZoiI8hQueSXK2X8KFikpKbh8+TLq1q0rVT1ERPSR/mvAYbghOeRqKOT7779HeHg4bt68iT///BNff/01kpKS0LVrV7nqIyIionwkVz0Wf/31F9q3b49Hjx6hWLFiqFGjBiIiIuDk5CRXfURERJSP5CpYrF+/Xq46iIgoH8oLwzF5oQb6PzyEjIiIiCTDYEFERESS4emmRERE/xGHY/4PeyyIiIhIMgwWREREJBkGCyIiIpIM51gQERF9AvLKPA/2WBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESS+U/BYsqUKdBoNBg0aJBE5RAREVF+9q+DxenTp7Fs2TJ4eXlJWQ8RERHlY/8qWLx48QIdO3bE8uXLYW1t/cHHpqSkICkpSedGREREn6Z/FSy+++47NGnSBPXq1fvHx06ZMgWWlpbam4ODw79pkoiIiPKBXAeL9evXIzIyElOmTPmox48cORKJiYna2507d3JdJBEREeUPBrl58J07dzBw4EDs378fhQsX/qi/Y2RkBCMjo39VHBEREeUvuQoWZ8+exYMHD/DZZ59pr6WmpuLIkSNYuHAhUlJSoK+vL3mRRERElD/kKlh88cUXOH/+vM617t27o1y5chg+fDhDBRERUQGXq2Bhbm6OihUr6lwzNTVFkSJFslwnIiKigoc7bxIREZFkctVjkZ2wsDAJyiAiIqJPAXssiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJLJVbBYvHgxvLy8YGFhAQsLC9SsWRN//PGHXLURERFRPpOrYFGqVClMnToVZ86cwZkzZxAQEIAWLVrg4sWLctVHRERE+YhBbh7crFkzna8nTZqExYsXIyIiAhUqVMj276SkpCAlJUX7dVJS0r8ok4iIiPKDfz3HIjU1FevXr0dycjJq1qyZ4+OmTJkCS0tL7c3BweHfNklERER5XK6Dxfnz52FmZgYjIyP06dMH27Ztg4eHR46PHzlyJBITE7W3O3fu/KeCiYiIKO/K1VAIAJQtWxZRUVF49uwZtmzZgq5duyI8PDzHcGFkZAQjI6P/XCgRERHlfbkOFoUKFYKbmxsAoGrVqjh9+jTmzZuHpUuXSl4cERER5S//eR8LIYTO5EwiIiIquHLVY/Hjjz8iMDAQDg4OeP78OdavX4+wsDCEhITIVR8RERHlI7kKFvfv30fnzp1x7949WFpawsvLCyEhIahfv75c9REREVE+kqtg8dtvv8lVBxEREX0CeFYIERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDK5ChZTpkxBtWrVYG5uDltbW7Rs2RJXr16VqzYiIiLKZ3IVLMLDw/Hdd98hIiICBw4cwLt379CgQQMkJyfLVR8RERHlIwa5eXBISIjO1ytXroStrS3Onj0LHx+fbP9OSkoKUlJStF8nJSX9izKJiIgoP/hPcywSExMBADY2Njk+ZsqUKbC0tNTeHBwc/kuTRERElIf962AhhMCQIUNQp04dVKxYMcfHjRw5EomJidrbnTt3/m2TRERElMflaigks/79+yMmJgbHjh374OOMjIxgZGT0b5shIiKifORfBYugoCDs3LkTR44cQalSpaSuiYiIiPKpXAULIQSCgoKwbds2hIWFwcXFRa66iIiIKB/KVbD47rvv8L///Q87duyAubk5EhISAACWlpYwNjaWpUAiIiLKP3I1eXPx4sVITEyEn58f7O3ttbcNGzbIVR8RERHlI7keCiEiIiLKCc8KISIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikkyug8WRI0fQrFkzlChRAhqNBtu3b5ehLCIiIsqPch0skpOTUalSJSxcuFCOeoiIiCgfM8jtXwgMDERgYKActRAREVE+l+tgkVspKSlISUnRfp2UlCR3k0RERKQS2SdvTpkyBZaWltqbg4OD3E0SERGRSmQPFiNHjkRiYqL2dufOHbmbJCIiIpXIPhRiZGQEIyMjuZshIiKiPID7WBAREZFkct1j8eLFC1y/fl379c2bNxEVFQUbGxs4OjpKWhwRERHlL7kOFmfOnIG/v7/26yFDhgAAunbtilWrVklWGBEREeU/uQ4Wfn5+EELIUQsRERHlc5xjQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJJhsCAiIiLJMFgQERGRZBgsiIiISDIMFkRERCQZBgsiIiKSDIMFERERSYbBgoiIiCTDYEFERESSYbAgIiIiyTBYEBERkWQYLIiIiEgyDBZEREQkGQYLIiIikgyDBREREUmGwYKIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpLMvwoWixYtgouLCwoXLozPPvsMR48elbouIiIiyodyHSw2bNiAQYMGYdSoUTh37hzq1q2LwMBAxMfHy1EfERER5SO5DhazZ89Gz5490atXL5QvXx5z586Fg4MDFi9eLEd9RERElI8Y5ObBb968wdmzZzFixAid6w0aNMCJEyey/TspKSlISUnRfp2YmAgASEpKyrGdtJSXuSkriw/92x/rU6hBijpYA2tgDXmzBinqYA2sITc1ZNwvhPjwPyRy4e7duwKAOH78uM71SZMmCXd392z/zrhx4wQA3njjjTfeeOPtE7jduXPng1khVz0WGTQajc7XQogs1zKMHDkSQ4YM0X6dlpaGJ0+eoEiRIjn+nQ9JSkqCg4MD7ty5AwsLi1z/fSmwBtaQ12rIK3WwBtbAGj7dGoQQeP78OUqUKPHBx+UqWBQtWhT6+vpISEjQuf7gwQPY2dll+3eMjIxgZGSkc83Kyio3zWbLwsJC1Rdx1sAa8mINeaUO1sAaWMOnWYOlpeU/PiZXkzcLFSqEzz77DAcOHNC5fuDAAdSqVSt31REREdEnJ9dDIUOGDEHnzp1RtWpV1KxZE8uWLUN8fDz69OkjR31ERESUj+Q6WLRt2xaPHz/GhAkTcO/ePVSsWBF79+6Fk5OTHPVlYWRkhHHjxmUZXlESa2ANea2GvFIHa2ANrIE1aMQ/rhshIiIi+jg8K4SIiIgkw2BBREREkmGwICIiIskwWBAREZFkGCw+QnBwsM55JxnevHmD4OBgFSoiIiLKm7gq5CPo6+vj3r17sLW11bn++PFj2NraIjU1VZE61q5di06dOmV737BhwzBjxgxF6lDTu3fvULhwYURFRaFixYpql6O6Z8+e4dSpU3jw4AHS0tJ07uvSpYtKVanj0qVLiI+Px5s3b3SuN2/eXKWKCqbXr18jJiYm2+ckfxYFw786K6SgyekslL/++uujtjeVSv/+/WFlZYWmTZvqXB88eDDWr19fIIKFgYEBnJycFAtzedmuXbvQsWNHJCcnw9zcXOc5qtFoFAsWq1atQps2bWBiYqJIe++7ceMGvvzyS5w/fx4ajUZ78mLG96MgPVeSk5MxdepUHDp0KNs39hs3bsjafkhICLp06YJHjx5luU+j0Sj2s5gwYQK+//77LM/JV69eYcaMGRg7dqwideQFsbGxCAsLy/b5INf3Id/0WHTr1g09evSAj4+PYm16e3tDo9EgOjoaFSpUgIHB/+Ww1NRU3Lx5E40aNcLGjRsVqSckJATt2rXDzp07td+HoKAgbN26FYcOHUK5cuUUqSOzFy9eZHmyyr0X/sqVK7Fp0yasXbsWNjY2srb1IY8fP8bYsWMRGhqa7S/tkydPZG3f3d0djRs3xuTJk1V7UwcAe3t7JCcno3Xr1ujZs6fi2/s3a9YM+vr6WL58OVxdXXHq1Ck8fvwYQ4cOxcyZM1G3bl3FalG7B6l9+/YIDw9H586dYW9vn+UD0cCBA2Vt383NDQ0bNsTYsWNzPD9KCXmllxlIHzLP7vng6Ogoe9vLly9H3759UbRoURQvXjzLh4/IyEhZ2s03weKrr77Cnj174ODggO7du6Nr164oWbKkrG2OHz9e+9+hQ4fCzMxMe1+hQoXg7OyMr776CoUKFZK1jszWr1+Pfv36Yf/+/VixYgV27NiB0NBQuLu7K1bDzZs30b9/f4SFheH169fa6xk9O3L/0np7e+P69et4+/YtnJycYGpqqnO/XL8s7wsMDERcXBx69uwJOzu7LC/iXbt2lbV9U1NTnD9/Hq6urrK2809SU1OxZ88erFq1Cnv27IGLi4v2d7R48eKyt1+0aFEcPnwYXl5esLS0xKlTp1C2bFkcPnwYQ4cOxblz52SvAfjnHiS5gyaQfsDjnj17ULt2bdnbyo6FhQXOnTuH0qVLq9J+Bj09Pdy/fx/FihXTuX748GG0bdsWDx8+lL2Ga9euoUePHjhx4oTOdaVeJwHAyckJ/fr1w/Dhw2VvK7N8MxSyZcsWPH78GGvXrsWqVaswbtw41KtXDz179kSLFi1gaGgoeZvjxo0DADg7O6Ndu3aqb9kMAO3atcPTp09Rp04dFCtWDOHh4XBzc1O0ho4dOwIAVqxYke0bqtxatmypaHs5OXbsGI4dO4ZKlSqp0n7Dhg1x5swZ1YOFvr4+mjdvjubNm+PBgwfa39ExY8agUaNG6NmzJ5o1awY9PXnmiqempmpDf9GiRfH333+jbNmycHJywtWrV2VpMztDhw5Fjx49VO1Bsra2VrUX7+uvv0ZYWJhqwcLa2hoajQYajQbu7u46r02pqal48eKFYudadevWDQYGBti9e3e2vUdKePr0KVq3bq14u/mmx+J9586dw4oVK/Drr7/CzMwMnTp1Qr9+/VCmTBnJ27pz5w40Gg1KlSoFADh16hT+97//wcPDA998843k7WU2ZMiQbK9v3rwZ3t7eOr/As2fPlrWWDGZmZjh79izKli2rSHt5VbVq1bBgwQLUqFFDlfZ/++03TJgwAd27d4enp2eWcK3WRLk///wTK1aswOrVq2Fvb49nz57BysoKK1euhJ+fn+Tt1a1bF0OHDkXLli3RoUMHPH36FKNHj8ayZctw9uxZXLhwQfI2s5MXepDWrl2LHTt2YPXq1aqEm5cvX6J169YoVqxYts/JAQMGyNr+6tWrIYRAjx49MHfuXJ05cBm9zDVr1pS1hgympqY4e/asKkPUGXr27Ilq1aopfkhovgwW9+7dQ3BwMFasWIG7d+/iq6++wr179xAaGorp06dj8ODBkrZXt25dfPPNN+jcuTMSEhLg7u6OihUrIjY2FgMGDJB1IpC/v/9HPU6j0eDw4cOy1ZGZv78/Ro0ahXr16inSXnaePXuGzZs3Iy4uDsOGDYONjQ0iIyNhZ2cn+xBZhtOnT2PEiBEYO3YsKlasmOVFVO65Jh/qAVByohwA3L9/H2vWrMHKlStx48YNtGzZEj179kS9evXw6tUrjB49Gps3b8bt27clb3vfvn1ITk5Gq1atcOPGDTRt2hRXrlxBkSJFsGHDBgQEBEjeZnZatWqFdu3aoU2bNoq0lx1vb2/ExcVBCAFnZ+csz0m5hwl//fVX9OnTB8bGxihSpEiW4SC5J49mCA8PR61atWTpyf5Y1apVw5w5c1CnTh1F250/f772z8nJyZg9ezaaNGmiaNDLN8Hi7du32LlzJ1auXIn9+/fDy8sLvXr1QseOHWFubg4gff5B37598fTpU0nbtra2RkREBMqWLYv58+djw4YNOH78OPbv348+ffoo9suSV8TFxaFPnz7o1KlTtm+oXl5esrYfExODevXqwdLSErdu3cLVq1fh6uqKMWPG4Pbt24rtLXLt2jW0b98+yxi+kmOoeUGzZs2wb98+uLu7o1evXujSpUuW7vi///4bpUqVyjKBTS5PnjzRdovLaefOndo/P3z4UPUepIx5YTnJGN6VS/HixTFgwACMGDFCtqGvj5WWlobr169nO3FSiUUAhw8fxujRozF58uRsnw9yffBwcXH5qMfJGfTyTbAoWrQo0tLS0L59e/Tu3RuVK1fO8pinT5+iSpUquHnzpqRtm5mZ4cKFC3B2dkbz5s1Ru3ZtDB8+HPHx8ShbtixevXolaXvZyUv7N0RERKBDhw64deuW9lrGMj8l3lDr1auHKlWqYPr06TA3N0d0dDRcXV1x4sSJLHXJqXr16jAwMMDAgQOznWvi6+urSB1q69mzJ3r16vXBLmYhBOLj4+Hk5CRbHdevX0dcXBx8fHxgbGyc4zJxKX3sm2dBCZo2NjY4ffq06pM3M16jbt++jfff4pT6WWQ8N95/DhaEDx75ZvLmnDlz0Lp1axQuXDjHx1hbW0seKgCgQoUKWLJkCZo0aYIDBw7g559/BpD+KaxIkSKSt5edvLR/Q48ePeDt7Y1169apMnnz9OnTWLp0aZbrJUuWREJCgmJ1XLhwAefOnVN1rkl4eDhmzpyJy5cvQ6PRoHz58hg2bJiiSyx/++037Z9fv36d7e+oRqORLVQ8fvwYbdq0QWhoKDQaDa5duwZXV1f06tULVlZWmDVrliztAlCsByY31Bwm7Nq1KzZs2IAff/xR1nb+SZ8+fVC1alXs2bNHtYmToaGhireZZ4h85tq1ayIkJES8fPlSCCFEWlqa7G2GhoYKKysroaenJ7p37669PnLkSPHll1/K3n6GFStWiMDAQPH48WPF2syOiYmJuHbtmmrt29raisjISCGEEGZmZiIuLk4IIcS+fftEqVKlFKujbt264sCBA4q19741a9YIAwMD0aZNGzFv3jwxd+5c0aZNG2FoaCh+//13xepITU0VEyZMECVKlBD6+vran8fo0aPFr7/+Knv7nTt3Fg0bNhR37tzJ8nzw8PCQvf0Mq1evFq9fv85yPSUlRaxevVqRGqKjo0WxYsWEm5ubMDAw0PlZdO7cWfb2g4KChKWlpfDx8RH9+/cXgwcP1rkpRe3XqLziq6++ElOmTMlyffr06eLrr7+Wrd18EywePXokAgIChEajEXp6etpfmB49eoghQ4bI3v67d+/EkydPdK7dvHlT3L9/X/a2M1SuXFmYmZkJIyMj4e7uLry9vXVuSmnatKnYvHmzYu29r3fv3qJly5bizZs3wszMTNy4cUPcvn1beHt7i4EDBypWx8aNG4WHh4dYuXKlOHPmjIiOjta5ya1cuXJi9uzZWa7PmjVLlCtXTvb2M4wfP164urqKtWvXCmNjY+3v5oYNG0SNGjVkb9/Ozk5ERUUJIXSD5o0bN4Spqans7WfQ09PL9vXg0aNHQk9PT5EavvjiCzFs2DAhhO734vjx48LJyUn29v38/HK8+fv7y95+Bn9/f/HHH38o1l5Onj59KmbOnCl69uwpevXqJWbPni2ePXumWPtFixYVMTExWa7HxMQIW1tb2drNN0MhgwcPhqGhIeLj41G+fHnt9bZt22Lw4MGydncC6eNiZ8+eRVxcHDp06ABzc3MUKlRI0SVdeWX/hmbNmmHw4ME4f/68KpPUZs6cicaNG8PW1havXr2Cr68vEhISULNmTUyaNEnWtjNr27YtgPShoQxKzjW5ceMGmjVrluV68+bNFe2KDg4OxrJly/DFF1/oLGvz8vLClStXZG8/OTk529/DR48eKbr3jMgDW/+rPUyYV7r/g4KCMHToUCQkJGT7GiX3BHMAOHPmDBo2bAhjY2NUr14dQgjMnj0bkyZNwv79+1GlShXZa3jx4kW2GzgaGhoiKSlJtnbzTbDYv38/9u3bp91LIkOZMmVkWcKW2e3bt9GoUSPEx8cjJSUF9evXh7m5OaZPn47Xr19jyZIlsrafQe4Z3R8r481jwoQJWe5T4g3VwsICx44dw+HDhxEZGYm0tDRUqVJF8eWvcsznyQ0HBwccOnQoywZphw4dgoODg2J13L17N9tN2tLS0vD27VvZ2/fx8UFwcLB27pNGo0FaWhpmzJjx0cu1/4uMrf81Gg2++OKLHLf+V0LhwoWzfcO4evVqll0oP2VfffUVAPVCP5D+Ybh58+ZYvny59jnx7t079OrVC4MGDcKRI0dkr6FixYrYsGFDli0R1q9fDw8PD9nazTfBQs1PJQMHDkTVqlURHR2tM1nzyy+/RK9evWRtOy9Se8JacHAw2rZti4CAAJ09Ct68eYP169crdviWnCscPsbQoUMxYMAAREVFoVatWtBoNDh27BhWrVqFefPmKVZHhQoVcPTo0Szfj02bNsHb21v29mfMmAE/Pz+cOXMGb968wQ8//ICLFy/iyZMnOH78uOztZ/QkRkVFoWHDhjlu/a+EFi1aYMKECdrzizQaDeLj4zFixAjFajh9+jQ2bdqU7UmzW7duVaQGtUM/kN5jkTlUAOmT8H/44QdUrVpVkRrGjBmDr776CnFxcdrXykOHDmHdunXYtGmTfA3LNsgiscaNG4vRo0cLIYR2XD01NVW0bt1afPXVV7K2XaRIEXHlyhVt2xnjljdv3hTGxsaytp3Zu3fvxIwZM0S1atWEnZ2dsLa21rkVFHlhLDuzixcvij/++EPs2LFD56aErVu3itq1awsbGxthY2MjateuLbZv365I2xl27twpLC0txdSpU4WJiYmYMWOG6NWrlyhUqJDYv3+/IjXcu3dPjB07VjRp0kQEBgaKUaNGib///luRtjOsWrVKvHr1StE235eYmChq164trKyshL6+vnBwcBCGhobCx8dHvHjxQvb2161bJwwNDUWTJk1EoUKFRNOmTUXZsmWFpaWl6Natm+zt5yW2trZi3759Wa6HhITIOr/hfbt37xa1atUSJiYmokiRIsLf31+EhYXJ2ma+CRYXL14UxYoVE40aNRKFChUSX3/9tShfvryws7MT169fl7Vta2trcfHiRSGEbrA4evSook+QMWPGCHt7ezFjxgxRuHBh8fPPP4uePXuKIkWKiHnz5ilWhxBCvHjxQuzZs0csXrxYzJs3T+cmN41GIx48eJDlelRUlKIBKy4uTnh5eWknFGs0Gu2f1Qg4agoJCRE+Pj7C1NRUGBsbi9q1a2f7ovqpe/r0qVi+fLkYMWKEdvXW2bNnxV9//aVoHYcOHRIzZswQ06ZNU3Tlkqenp1i4cKEQ4v9eK9PS0kTv3r3F2LFjFatDCCGCg4NFrVq1hL29vbh165YQQog5c+YoFryDgoJEqVKlxPr160V8fLy4c+eOWLdunShVqpSik8zVkG+ChRDqfSpp06aN6N27txDi/3pLnj9/LgICAhRN4a6urmL37t3aOjIC1bx580T79u0VqyMyMlIUL15cWFhYCH19fVGsWDGh0WiEqampcHFxka3dypUrC29vb6Gnpyc8PT11VsR4eXkJc3Nz0bp1a9naf1/Tpk1FixYtxIMHD4SZmZm4dOmSOHr0qKhevbo4cuSIYnUUdH/88Yc4evSo9uuFCxeKSpUqifbt22dZySUntZd65gUmJibi5s2bQoj0nt6MFQmXLl0SxYsXV6yORYsWiaJFi4qJEyfqrFRauXKl8PPzU6SGlJQUMWDAAFGoUCHthw0jIyMxaNCgbJclf0ryzc6barp79y4CAgKgr6+Pa9euoWrVqrh27RqKFi2KI0eOwNbWVpE6TE1NcfnyZTg6OsLe3h579uxBlSpVcOPGDXh7eyMxMVGROvz8/ODu7o7FixfDysoK0dHRMDQ0RKdOnTBw4EC0atVKlnbz2jH2ahzXnZttqpU4pjsv8PT0xLRp09C4cWOcP38eVatWxdChQ3H48GGUL18eK1euVKSOL774Ap999pniO8JmPhvin8h9CJiDgwP27t0LT09PVKpUCSNGjED79u1x8uRJNGrUSLHXKA8PD0yePBktW7bU+VlcuHABfn5+ePTokSJ1AOkHs2Wc3+Lm5ib7SkIbGxvExsaiaNGi//h6IddrRL6ZvAmk7yh36tSpbPd+l3PCXsmSJREVFYX169fj7NmzSEtLQ8+ePdGxY0cYGxvL1u77SpUqhXv37sHR0RFubm7aJUunT59WdFldVFQUli5dCn19fejr6yMlJQWurq6YPn06unbtKluwyGvH2KtxXPfcuXO1f378+DEmTpyIhg0barfTPnnyJPbt24cxY8bI0n52cnrx0mg0KFy4MNzc3NCtWzd0795dlvZv3rypneG+ZcsWNGvWDJMnT0ZkZCQaN24sS5vZOXPmDJYtW5blutxLPefMmaPz9cOHD/Hy5UtYWVkBSH/dNDExga2trezBom7dujhw4AA8PT3Rpk0bDBw4EIcPH8aBAwfwxRdfyNp2Zjdv3sx24rCRkRGSk5MVqaFHjx6YN28ezM3N4enpqb2enJyMoKAgrFixQpZ258yZoz0/a86cOarsOppvhkJ27twpzM3NhZ6enrC0tBRWVlbam5zj6m/evBEuLi7aORZqGj58uJg0aZIQQohNmzYJAwMD4ebmJgoVKiSGDx+uWB1FixYVV69eFUII4e7uLkJCQoQQQly+fFmRyawZ45UZ/vzzTzFw4ECxdOlS2dvOrE6dOmLbtm1CCCHat28vGjVqJI4dOya6dOkiKlSoIHv7rVq1EgsWLMhyfcGCBaJFixayt59h9uzZokiRIqJTp05i/vz5Yt68eaJTp06iaNGiYtKkSaJXr17CyMhILFu2TJb2M8+Bql27tvZ5oPTk6rywI+zvv/8uateurZ1sLoQQV65cEXXr1hVr166Vvf3Hjx+Lu3fvCiHSd2SdNm2aaNasmRg8eLCiw1Lly5fXzqXI/LOYN2+eqFKliiI15DTJ/OHDh0JfX1+RGtSSb4JFmTJlxMCBA0VycrLibZcoUUJcunRJ8Xb/SUREhJg1a5ZiKxAy1K9fX7tl9LfffiuqV68u1q5dKxo2bCiqV68ue/t16tQRwcHBQoj0eTfm5uaiZs2aokiRImL8+PGyt58hJCREbNmyRQiRPpGzfPnyQqPRiKJFi4pDhw7J3r6pqWm22xbHxsYquuNkq1atxOLFi7NcX7JkiWjVqpUQQoj58+eLihUrytJ+s2bNRMOGDcWECROEoaGhdqLkvn37RJkyZWRpMzt5YUdYV1dXbbjJ7MyZM8LZ2VmRGvKCFStWiJIlS4r169cLU1NTsW7dOjFx4kTtn+WUmJgonj17JjQajbh+/bpITEzU3p48eSJWr14t7O3tZa0hQ4cOHcSyZcu0HwSVkm+ChYmJiTZ1Km3KlCmia9eu4u3bt6q0n9ecPn1aHD58WAghxIMHD0RgYKAwNzcX3t7e2q2V5WRlZaX9RDZv3jxRq1YtIUT6G4mck0c/xuPHjxU5v0YIIRwdHcX06dOzXJ8+fbpwdHRUpAYhcg44165d0wac69evCxMTE1nav337tmjSpInw8vLSOZtk0KBBIigoSJY2s6P2Uk8hhDA2NhZ//vlnlut//vmnYr03qamp4urVq+Lo0aMiPDxc56akZcuWCUdHR+1qrVKlSilydk3mlWHZ3fT19cXEiRNlr0MIIb755htRtmxZodFohL29vWjXrp1YvHixuHz5sqzt5pvJm61atUK7du3Qpk0bxdv+8ssvcejQIZiZmcHT0xOmpqY69yu16UuJEiXg5+cHPz8/+Pr6qnqqpprywjH2ecGqVavQs2dPNGrUSDvHIiIiAiEhIfj111/RrVs3RepwdHTE4MGDMXjwYJ3rc+bMwZw5cxAfH4+YmBg0aNBA0dNn1aLmjrDNmjVDfHw8fvvtN3z22WfQaDQ4c+YMevfuDQcHB+zcuVPW9vPCceXv3r3D77//joYNG6J48eJ49OgR0tLSFJtkHx4eDiEEAgICsGXLFtjY2GjvK1SoEJycnFCiRAlFasmQkJCAsLAwhIWFITw8HLGxsbC1tcW9e/dkaS/fTN5s0qQJhg0bhkuXLil+PoWVlZViu9Z9yKxZsxAeHo7Zs2ejT58+sLOzg6+vrzZoZD5D5VOWF46xB9InYU2dOhWHDh3KdkLxjRs3ZG2/W7duKF++PObPn4+tW7dCCAEPDw8cP34cn3/+uaxtZzZmzBj07dsXoaGhqF69OjQaDU6dOoW9e/dqt7s/cOAAfH19JWszN+ccWFhYSNbux3h/R1glrVixAl27dkX16tW1r5Hv3r1Dw4YN8euvv8refl44rtzAwAB9+/bF5cuXAaRPrFZSxvP85s2bcHR0VGfy5HvMzc1hbW0Na2trWFlZwcDAAMWLF5etvXzTY6Gnp5fjfUol4bzk/v37CA0Nxe7du7FhwwakpaUp9j24f/8+vv/+e+0b6vtPIbnrCAsLw5dffomkpCR07dpVO7v6xx9/xJUrVxTrQWrfvj3Cw8PRuXPnbF9EBw4cqEgdecHx48excOFCXL16FUIIlCtXDkFBQahVq5Ys7enp6f3jC7ZQ8FyIDIcOHcKcOXNw+fJlaDQalCtXDoMGDVL8HJvY2FhcuXIFQgiUL18e7u7uirRramqK6OjobM+OUZK/vz8GDhyo6sGNMTEx2V7PWC3l6Ogo+8q24cOHIzw8HNHR0ahYsSJ8fHzg6+sLHx8f7aohOeSbYJEXPHz4EFevXoVGo4G7u7sqh/q8ePECx44dQ3h4OMLCwnDu3Dl4eHjA19c3y7IzuQQGBiI+Ph79+/fP9g21RYsWsteQmpqKpKQkWFtba6/dunVLu6xOCVZWVtizZw9q166tSHvZSUtLw/Xr17PtMfHx8VGpKvmFh4d/9GOl7Cn5kIULF2Lw4MH4+uuvdYamNm/ejNmzZ6N///6K1KGmgIAA/PDDD4odupaTTZs2YcSIERg8eDA+++yzLMPXSpxu+k/h19DQEG3btsXSpUtRuHBh2WooVqwYBg8ejBYtWijWq51vgsWdO3dyPLExIiICNWrUkK3tjHXHwcHB2hdvfX19dOnSBQsWLFDs6PTPP/8cMTExqFixIvz8/ODj44O6devKmjyzY25ujqNHj6Jy5cqKtpvXuLi4YO/evaoNQeWF8ewMBTXgZFayZEmMHDkyS4D45ZdfMGnSJPz999+y1yCEwObNmxEaGprtz0KO3rzMn8zj4uIwevRoDBs2TLXjyoG80cO9Y8cODB8+HMOGDdMem3769GnMmjUL48aNw7t37zBixAi0bdsWM2fOlKWG6Oho7YfQo0ePQl9fXzt87ufnJ99rl6xTQyVUtmxZ8ejRoyzXjx07JiwtLWVt+5tvvhGurq5i79692mVDe/bsEaVLlxZ9+vSRte3MrK2thY2NjWjTpo1YtGiRaktgy5cvn+2SNqUkJCSITp06CXt7e6Gvr59l1rVS1qxZI77++mtVlkALIUSlSpVE69atxaVLl8TTp0/Fs2fPdG5KOXnypHBxcdE5LyXzuSlKOHLkiOjYsaOoWbOmdrlpcHCwzlbfcjMzM1N9+W9QUJAwMjISjRo1El27dhXdunXTucnh/bNysnsOKPlcEEKIW7duffCmhGrVqmn3+MksJCREVKtWTQghxLZt24Srq6si9QiRfp5St27dhIGBgaw/j3wzebNu3bpo0KABwsLCtLuKHTlyBM2aNcNPP/0ka9tbtmzB5s2b4efnp73WuHFjGBsbo02bNli8eLGs7Wd48uQJYmJiEBYWhoMHD2LcuHHQ09ODr68v/P390adPH0XqmDt3LkaMGIGlS5fC2dlZkTYz69atG+Lj4zFmzBjFJ4h5e3vrtHf9+nXY2dnB2dk5y6ezyMhIWWu5du0aNm/erPp4ttoT9rZs2YLOnTujY8eOiIyMREpKCgDg+fPnmDx5Mvbu3atIHc2bN8e2bdswbNgwnes7duxAs2bNFKlh7dq12Lp1q6I7juaFI8rfZ2Zmpp3IfefOHSxfvhyvXr1C8+bNUbduXUVqOH/+PJycnLJcd3Jywvnz5wEAlStXlm1lRoZz585pV4QcPXoUSUlJqFy5Mvz9/WVrM98MhQgh0Lp1azx48AD79+/HyZMn0bx5c0ycOFH2SXImJiY4e/Zslm6jixcvonr16optEfu+s2fPYuHChVi7dq3skzff37Y5OTkZ7969g4mJSZY3VLnPqFBzKCbjvJKPkbEFuVzyyni22hP2vL29MXjwYHTp0kXnXIioqCg0atRI1iWumc/pSEpKwsyZM1G7dm2dORbHjx/H0KFDMXr0aNnqyODi4oI//vgD5cqVk72tvOj8+fNo1qwZ7ty5gzJlymD9+vVo1KgRkpOToaenh+TkZGzevFmRSZ3e3t6oVKkSli1bpj2/6O3bt+jduzeio6Nx7tw5HD9+HJ06dZItnFlbW+PFixeoVKmSdvjDx8dH9pVS+SZYAOk/lCZNmiA5ORkxMTGYMmWKIhOivvjiCxQpUgTBwcHaSTavXr1C165d8eTJExw8eFD2GoCsyfP58+faJ4y/vz+aNGkiW9urV6/+6Md27dpVtjqA9AOGfv/992zPAihItm3blifGs9UOOCYmJrh06RKcnZ11gsWNGzfg4eGB169fy9a2i4vLRz1Oo9HIvvwYSP89DQkJwYoVKxQ9xyiz2NhYhIWFZTvHY+zYsbK2HRgYCAMDAwwfPhxr167F7t270aBBA+1S26CgIJw9exYRERGy1gEAJ06cQPPmzaGnpwcvLy9oNBrExMQgNTUVu3fvRo0aNbBmzRokJCRk6eWSyu7duxUJEu/L08Eiu+U6z58/R/v27dGkSRP07dtXe13OF9ELFy6gUaNGeP36NSpVqgSNRoOoqCgULlwY+/btQ4UKFWRrOzMDAwN4e3trJ9+o8YTJC/bv349Zs2apNhSTYdSoUfDz80Pt2rUVm8CbWXYT1DQajeLLLNUOOKVLl8bSpUtRr149nWARHByMqVOn4tKlS7K2n5e8fPkSrVq1wvHjx1UZnlu+fDn69u2LokWLonjx4jq9nBqNRvb2M584/OLFC1hYWODUqVOoWrUqAODKlSuoUaMGnj17JmsdGV68eIG1a9ciNjZWuwy7Q4cO2uH8T1WeDhYZy3Uyl5j5ayVfRF+9eoW1a9dq14Z7eHgofrppUlJSnggSkZGRMDQ01J7Yt2PHDqxcuRIeHh746aefZD+23NraGi9fvlRtKCZDo0aNcOLECaSkpKBKlSrajcrq1Kmjc6S7XG7fvv3B+7Mb35WD2gFn+vTpWL16NVasWIH69etj7969uH37NgYPHoyxY8cWiGWeGdq0aYPQ0FB8/fXXsLOzyzLfRe7hOScnJ/Tr1w/Dhw+XtZ2c6OnpISEhQbvkPHPQBNL34ClRokSB2fdIrU388vTkTbUnBR0+fBg+Pj4wMDCAsbExevfurWo9GaHizZs32T5JHB0dFanj22+/xYgRI+Dp6YkbN26gbdu2aNWqFTZt2oSXL1/qHO0tB7n//Y8VEhKC1NRUnDp1Sruka9GiRXj16hWqVKkie3erUsHhn6j9e/rDDz8gMTER/v7+eP36NXx8fGBkZITvv/9e0VAhVFjq+b49e/Zg3759qFOnjuxtZefp06do3bq1Km1neD9MqbnzpZrDQgDQq1evD27iJ5c83WOhNn19fdy7d0+bfmvUqIEtW7agZMmSqtQTGxuLnj174sSJEzrXle76trS0RGRkJEqXLo1p06bh8OHD2LdvH44fP4527drhzp07itSRl1y9elW7Wmf79u2wsrLCw4cPZW83Li4Oc+fO1e70WL58eQwcOBClS5eWve285uXLl7h06RLS0tLg4eGhSK9RZgMGDMCyZcvg7++fbW/BypUrZa+hXLly2Lhxo2Lza97Xs2dPVKtWTbEVau/T09NDYGCgdkfLXbt2ISAgQLtBVkpKivYDgdzUHhYC1NvEL0/3WGQ2ZcoU2NnZoUePHjrXV6xYgYcPH8rS9fZ+5rp48aJ2KZsaunfvDgMDA+zevVu1ffiB9O9LRvo+ePAgmjZtCgBwcHDAo0ePZGkz8zDQP50TodRw0eLFixEeHo7w8HCkpqaibt268PX1xZgxYxR5Yd+3bx+aN2+OypUro3bt2hBC4MSJE6hQoQJ27dqF+vXry9b2zp07ERgYCENDw3882ErOc3wyMzEx0Y6lq0GNpZ7vmzVrFn744QcsWbJElflHbm5uGDNmDCIiIrKdbzNgwABZ239/4ninTp2yPKZLly6y1pBh4sSJmDRpkmrDQkD6sHHmQ9CUkm96LJydnfG///0vy9kDf/75J9q1aydLd+w/jdcpzdTUFGfPnlV9KVlAQAAcHBxQr1499OzZE5cuXYKbmxvCw8PRtWtX3Lp1S/I2M/ce5bRVrtI9Nxnb5Q4dOhR9+vRRfP6Lt7c3GjZsiKlTp+pcHzFiBPbv3y/rJ6LMvxtq73Ko9mFwGfLCUk+15x99aJWMUitj8goLCwtERUWp9n4BpIfdHTt2YPXq1YpOMM83PRYJCQmwt7fPcr1YsWKybTCi0WiydF+pOV7n4eEhW49AbsydOxcdO3bE9u3bMWrUKO3+BZs3b5bt0KnDhw9rk3doaKgsbeTW1q1bceTIEaxfvx5jx47VWStet25d2bviL1++jI0bN2a53qNHD9nnoWR+837/jVxpao0jv++nn37C+PHjVV3qqfb8I7Xn2+QlrVu3xv79+1UbFgLSe7Di4uIU38Qv3wQLBwcHHD9+PEsiPn78uGxn2wsh8MUXX8DAIP3b9PLlSzRr1izLqgc5Pxlm7vafNm0afvjhB0yePDnbbkalPjF7eXlpd47LbMaMGdDX15elzcwHSSl1qNQ/admypXajncTERBw9ehSbN29GixYtoNFoZB82K1asGKKiolCmTBmd61FRUYodxPb27Vs0aNAAS5cuVewEzff98ccfqh8GB6S/kaxbtw62traqLPUE5N9D5mO9efMGN2/eROnSpbWvnwWN2sNCAFQ73TXf/MR79eqFQYMG4e3btwgICACQfkTxDz/8gKFDh8rS5vtLs5Q4tfN9VlZWOp/AMsJOZkoPAeRErhP6svP69WvExMRk2/Wt1Jg+kN61nLEiJCwsDBcuXECRIkUUCT+9e/fGN998gxs3bqBWrVrQaDQ4duwYpk2bJtvvxPsMDQ1x4cIFVXvy1BpHfl+3bt1w9uxZdOrUKdvJm0p79eoV3r59q3NN7g8fL1++RFBQkHZDvdjYWLi6umLAgAEoUaIERowYIWv7ecmyZctgZmamnYeVmUajUSRYyL28OCf5Zo6FEAIjRozA/Pnz8ebNGwDpb2TDhw9XZNmOWvLK8dA2NjaIjY1F0aJFs2zv/T65x3FDQkLQpUuXbIeFlAxYXl5euHTpEmxsbODj46MdBqlYsaIi7QshMHfuXMyaNUt7cmaJEiUwbNgw2be5z2zo0KEwNDTMMtdDKWqNI7/P1NRU1aWeQPp8k+HDh2Pjxo14/Phxlvvl/t0YOHAgjh8/jrlz56JRo0aIiYmBq6srdu7ciXHjxuHcuXOytk9ZPXv2DJs3b0ZcXByGDRsGGxsbREZGws7OTrYVjvkmWGR48eIFLl++DGNjY5QpU0a7rKggiI+Ph4ODQ5Y3dSEE7ty5I+s+FqtXr0a7du1gZGSEVatWfTBYyN0d6+bmhoYNG2Ls2LGws7OTta0PWbhwoaJB4kOeP38OIH2CcXJyMs6ePavYceVBQUEIDg6Gm5sbqlatql3al2H27NmSt5ndYXBCCNWGIAD1l3oCwHfffYfQ0FBMmDABXbp0wS+//IK7d+9i6dKlmDp1Kjp27Chr+05OTtiwYQNq1KihM9n9+vXrqFKlyj+u6CJpxcTEoF69erC0tMStW7dw9epVuLq6YsyYMbh9+zaCg4NlaTffBYuC7P19NTI8fvwYtra2qg+FKMXCwgLnzp3LM3s15LXx5OjoaFSpUkWx58OHTknUaDQ4fPiw5G3mpcPgMuzZswcLFixQbaknkL5JXnBwMPz8/GBhYYHIyEi4ublhzZo1WLdunewnvZqYmODChQtwdXXVCRbR0dHw8fFBYmKirO2rbciQIfj5559hamqKIUOGfPCxcgTu99WrVw9VqlTB9OnTdX4eJ06cQIcOHWRZwQfkozkWAHD69Gls2rQJ8fHx2uGQDErsaqe2jLkU73vx4oWi8xs6duyo3b5ajQl7X3/9NcLCwlQPFq9evUL//v0L/HiyGqt01Bo7/pBOnTrh5cuXKF26tGpbzT958kQ7wd3CwkLbZp06dXTOVpJLtWrVsGfPHgQFBQH4v10vly9frj3x9VN27tw57byWDw37KDX/5vTp01i6dGmW6yVLlpT11N98EyzWr1+PLl26oEGDBjhw4AAaNGiAa9euISEhAV9++aXa5ckqI/lqNBqMGTNGZxw5NTUVf/75p6JHiJuZmWHWrFn49ttvUbx4cfj6+moPRlNiDf/ChQvRunVrHD16VLXZ1kD6fhHR0dEICwvTOdmzXr16GDduXIEJFmp5+vQp1q5di65du2aZlJiYmIjg4OBs75OL2ks9AcDV1RW3bt2Ck5MTPDw8sHHjRlSvXh27du2ClZWV7O1PmTIFjRo1wqVLl/Du3TvMmzcPFy9exMmTJ3M1Xyy/yhyy88Ky+MKFC2c7/HT16lUUK1ZMtnbzzVCIl5cXvv32W3z33XfaLh0XFxd8++23sLe3z1XXaH6T0dUcHh6OmjVr6ix3LVSoEJydnfH9999nWXYot4SEBO1qiPDwcMTGxsLW1la2fUUy/Prrr+jTpw+MjY1RpEiRLHuNKLUJT14dT1Z6KARQpzfx559/RkxMDDZt2pTt/W3atEGlSpUwatQoWdrPi+bMmQN9fX0MGDAAoaGhaNKkCVJTU/H27VvMmTNHkUm958+fx8yZM3H27FmkpaWhSpUqGD58uPbQQlLON998g4cPH2Ljxo2wsbFBTEwM9PX10bJlS/j4+MgWhvNNsDA1NcXFixfh7OyMokWLIjQ0FJ6enrh8+TICAgJkfzM7depUjofJKDFWBqRv6T1v3rw8ccIpkD4D/dixY9pwERkZCQ8PD9lnfhcvXhwDBgzAiBEjPrjro9zUGk/+py20b968iSFDhigWLP6pN1GuMzIqV66MWbNmZVl+neHQoUP4/vvvVVmJoMZSz+zEx8fjzJkzcHNzU3VSaUHRqlWrj36sEsP3SUlJaNy4MS5evIjnz5+jRIkSSEhIQM2aNbF3794sE62lkm+GQmxsbLQz30uWLIkLFy7A09MTz549w8uXL2Vte/LkyRg9ejTKli2bZX26kmvVlTjE6GMMHz4c4eHhiI6ORsWKFeHj44ORI0fCx8dHke7WN2/eoG3btqqGCkC98eSP2fRGyefl5MmTMWfOHG1v4rx583R6E+USFxf3wV66MmXKIC4uTrb236fmUs/Dhw+jf//+iIiI0Akwjo6OsLS0RK1atbBkyRLUrVtXthqAnM/x0Wg0MDIyyrK54KfG0tJS7RJ0WFhY4NixYzh8+DAiIyO1PUj16tWTt2GRT7Rv317MmjVLCCHExIkTRbFixUSvXr2Ek5OT+PLLL2Vt29bWVqxcuVLWNj6Gv7//B29K0Wg0wtbWVkyZMkVcunRJsXYzDBo0SEyaNEnxdt93/PhxYW5uLvr06SMKFy4sBg4cKOrVqydMTU3FmTNn1C5PMSYmJuLmzZtCCCGKFCkiYmJihBBCXLp0SRQvXly2di0tLcXJkydzvP/kyZPC0tJStvbf169fP1G+fHmxadMmYWxsLFasWCF+/vlnUapUKbF27VpZ227WrJmYPXt2jvfPmzdPtGzZUtYahEh/bdDT08vx5ujoKMaOHStSU1Nlr4V0vXr1SqSlpSnSVr7psVi4cCFev34NABg5ciQMDQ1x7NgxtGrVCmPGjJG1bT09PdW3CwaASpUq6Xz99u1bREVF4cKFC4pu5Xvu3DntbpOzZs2Cvr6+dvKmn58fypcvL2v7qampmD59Ovbt2wcvL68skzeVGpqqVasWjh8/jpkzZ6J06dLYv38/qlSpgpMnTxao8WS1ehO9vb2xfft21KhRI9v7t23bBm9vb9naf9+uXbu0Sz179OiBunXrws3NDU5OTvj9999l3UMiOjoa06ZNy/H+Bg0aYObMmbK1n2HVqlUYNWoUunXrhurVq0MIgdOnT2P16tUYPXo0Hj58iJkzZ8LIyAg//vij7PWo6dWrVxBCaCfb3759G9u2bYOHhwcaNGigSA1paWmYNGkSlixZgvv372tXro0ZMwbOzs7o2bOnPA0rEl/+g8TExI+6yWnatGli4MCBsrbxX4wbN04MHTpUtfajoqJEt27dhIGBgdDT05O9PT8/vw/eSFlq9SZu3rxZGBgYiAULFoh3795pr797907Mnz9fGBoaik2bNsnW/vtMTU3FrVu3hBBClCxZUvz5559CCCFu3LghTE1NZW3byMhIXLt2Lcf7r127JgoXLixrDUIIERAQIDZs2JDl+oYNG0RAQIAQQojg4GBRtmxZ2WtRW/369cXixYuFEEI8ffpU2NrailKlSonChQuLRYsWKVLD+PHjhaurq1i7dq0wNjYWcXFxQoj0n0eNGjVkazfPB4t/6lrLuMkpNTVVNGrUSLi6uoqmTZuKL7/8UuemtmvXrglra2tF24yMjBSzZ88WzZs3F9bW1kJfX1989tln4vvvv1e0DjV8zHNSX19f7TIV8/jxY3H37l0hRPrvyrRp00SzZs3E4MGDxZMnT2Rt+8cffxQajUZYWFiIypUrC29vb2FhYSH09PTE8OHDZW37fZ6eniIsLEwIkf6mkhH2582bJ0qUKCFr266urmLr1q053r9lyxbh4uIiaw1CCGFsbCxiY2OzXI+NjRXGxsZCiPSglfHnT1mRIkXEhQsXhBBCLF++XHh5eYnU1FSxceNGUa5cOUVqKF26tDh48KAQQggzMzNtsLh8+bKwsrKSrd08PxSSeS2wEAKNGzfGr7/+Ktse59kJCgpCaGgo/P39syxvzAtOnjyp6AZZ1tbWePHihfaY8N69e8PHx0exWe89evTAvHnzYG5urnM9OTkZQUFBWLFihaztb9u2Lcf7Tpw4gQULFkDkj8VW/0nGRD0DAwOYmZlpv+7Tp49iR0VPmjQJLVq0wO+//67d1tvHxwcdOnRA9erVFakhQ/fu3REdHQ1fX1+MHDkSTZo0wYIFC/Du3TvZh+caN26MsWPHIjAwMMtrwatXrzBu3Dg0bdpU1hoAoFSpUvjtt9+ynBvz22+/wcHBAUD6TsHW1tay16K2ly9fal+j9u/fj1atWkFPTw81atTA7du3Fanh7t27cHNzy3I9LS0ty6olSckWWWSSOXUp2ebu3bsVbTM77/eUtGzZUnz++edCX19f/PTTT4rVsWvXLtmHnz5ET09P3L9/P8v1hw8fqtZTcPnyZdGyZUuhr68vunTpIm7fvi1LO/PmzROvXr0SQghx+/ZtxSZjZScv9CbmZbdv3xZbtmwRR44cEd27d5e1rYSEBFGiRAnh4OAgpk2bJrZv3y527Nghpk6dKhwcHESJEiVEQkKCrDUIIcSOHTtEoUKFhJeXl+jZs6fo1auXqFSpkjAyMhK7du0SQgixaNEiMXjwYNlrUZunp6eYN2+eiI+PFxYWFuLEiRNCCCHOnDkj7OzsFKnhs88+E2vWrBFC6L53/vTTT6JOnTqytZvneyzyAhsbG9W3jwayLmXS09ND2bJlMWHCBMUmAwHQfvK5fv064uLi4OPjA2Nj4xy3HJdKUlISRPrwHZ4/f67zySw1NRV79+7Nco6K3P7++2+MGzcOq1evRsOGDREVFSXroWRDhgxBu3btULhwYbi4uGR7doxS8kJvYl7m6OgIR0dHREdHY/Xq1bL2pNnZ2eHEiRPo27cvRo4cqe0x02g0aNiwIRYtWqTIgX3NmzdHbGwslixZgqtXr0IIgcDAQGzfvl17fooSW4vnBWPHjkWHDh0wePBgBAQEaJeg79+/X7FJxePGjUPnzp1x9+5dpKWlYevWrbh69SqCg4Oxe/du+RqWLbLIRI0eixUrVog2bdqI5ORkRdvNqx49eiQCAgK0n1gzfh49evQQQ4YMka3df/qErK+vLyZOnChb+5k9e/ZM/PDDD8LY2FjUrFlTHDlyRJF2HRwcxKJFi8StW7eERqMRZ8+eFbdv3872pjQ1fjfzg6ioKEV7bp48eSJOnTol/vzzT9nnuNCH3bt3T0RGRur0LP7555/iypUritUQEhIifHx8hKmpqTA2Nha1a9cW+/btk7XNfNljofQch/nz5yMuLg52dnaqHsuc4ezZs7h8+TI0Gg08PDwUXVIHAIMHD4ahoSHi4+N1lpa2bdsWgwcPxqxZs2RpNzQ0FEIIBAQEYMuWLbCxsdHeV6hQITg5OaFEiRKytJ3Z9OnTMW3aNBQvXhzr1q1DixYtZG8zw+jRoxEUFIT+/ftDo9GgWrVqWR4j/n/PUUE57ZZ0WVtbZ/u8UNLLly+z3d69IOz++bG7b8q98+a7d+8wadIk9OjRQ/FzWvJ8sHj/h/T69Wv06dMny1akcv6QPmanQyU8ePAA7dq1Q1hYGKysrCCEQGJiIvz9/bF+/XpZD5XJbP/+/di3bx9KlSqlc71MmTKyTkry9fUFkL5ltaOjo2qTaEeMGAFjY2O4ublh9erV2tNN3yfHc/Kbb75B+/btcfv2bXh5eeHgwYMoUqSI5O0Q/RsPHz5E9+7d8ccff2R7f0EIu3ll900DAwPMmDFD0T2OtG0r3mIuvf9D6tSpk+I15JUjmoOCgpCUlISLFy9qewouXbqErl27YsCAAVi3bp0idSQnJ+ucsJrh0aNHMDIykr39y5cv486dO6hTpw4A4JdffsHy5cvh4eGBX375RfYZ5126dFF1ZZC5uTkqVqyIlStXonbt2op8zz+Wmt+Xc+fOwdPTEwYG//eytm3bNtlPP/6nT6jPnj2Ttf28ZNCgQXj69CkiIiLg7++Pbdu24f79+5g4caJsPZl5TV45egFIP2k5LCwM3bp1U7TdfHMIGaWHrIMHD2bp5jx16hQaNGig2AtYkyZNUKVKFfz8888wNzdHTEwMnJyc0K5dO6SmpmLLli2ytu/p6Ylp06ahcePGOH/+PKpWrYqhQ4fi8OHDKF++fJ76xVZC5qGx8uXLo0qVKoq0+/4b6q5duxAQEKBob2Jmenp6sLCwQKdOnTBmzBgEBwfjxx9/lHdZHdKXmX6MgvC8tLe3x44dO1C9enVYWFjgzJkzcHd3x86dOzF9+nQcO3ZM7RILlKVLl+Knn35Cx44d8dlnn2X53WzevLks7eb5Hou8IDU1FXPmzMHGjRuzHTd88uSJInWkpaVlmd8BAIaGhllOXJXTjBkz4OfnhzNnzuDNmzf44YcfcPHiRTx58gTHjx+Xvf2bN2/Cw8MDALBlyxY0a9YMkydPRmRkJBo3bix7+3mF2kNjeaE3MbNHjx4hJiYGS5cuhYuLC4D0LablVhACw8dKTk7WrlKysbHBw4cP4e7uDk9PT8XnotH/rcDJbh8VOedhMVh8hPHjx+PXX3/FkCFDMGbMGIwaNQq3bt3C9u3bMXbsWMXqCAgIwMCBA7Fu3TrtJMW7d+9i8ODBOR4dLQcPDw/ExMRg8eLF0NfXR3JyMlq1aoVvv/0W48aNk32DqkKFCmnPoDh48CC6dOkCIP2FLKfTFT9Fag+Nqf2GGhERAQMDA1StWhVA+s/fz88P69evR+HChfH27dtsNwci+ZQtWxZXr16Fs7MzKleujKVLl8LZ2RlLliyR9aRbyp6SHzh1yLrm5BPh6uqq3SDLzMxMXL9+XQiRvllR+/btFasjPj5eeHt7C0NDQ+Hq6ipKly4tDAwMRJUqVcSdO3cUqyMnSi2ra9asmWjYsKGYMGGCMDQ0FH/99ZcQQoh9+/aJMmXKyN5+XmFhYSFOnTqV5fqff/6p6Kmeavn888+zbFw3atQoUbx4cREdHS0mTpwoGjdurFJ1BdPatWu1J0FHRkaKYsWKCT09PVG4cGGxfv16dYsrQF6+fKndkEwIIUaMGCEGDx6svQ0bNky70Z4c2GPxERISErSnVZqZmSExMRFA+kZRcp+smpmDgwMiIyNx4MABXLlyBUIIVKhQQdHeirxg4cKF6NevHzZv3ozFixdrN2T6448/0KhRI5WrU05eGRpTy8WLF1GhQgXt1/PmzcOaNWtw5MgRlClTBgYGBpg+fbqKFRY8mU9w9fb2xq1bt3DlyhU4OjqiaNGiKlZWsGRsgJWxmeHChQtRoUIFGBsbAwCuXLmC4sWLY8iQIfIUIFtk+YS4u7uLiIgIIYQQderUEVOmTBFCCLF+/XpRrFgx2duPiIgQe/fu1bm2atUq4eTkJIoVKyZ69+4tXr9+LXsd/0TpjYAKuubNmwsfHx/tAWBCCPHXX38JX19f0bJlSxUrU0axYsXEgQMHhBDpm9iVLVtWZ2OwyMhIUbRoUbXKI1JN3bp1dQ6le3/zujVr1sh6uqmePHHl0/Lll1/i0KFDAICBAwdizJgxKFOmDLp06YIePXrI3v5PP/2EmJgY7dfnz59H7969Ub9+fYwYMQK7du3ClClTZK8jL4mLi8Po0aPRvn17PHjwAAAQEhKCixcvqlyZchYuXIjnz5/D2dkZpUuXhpubG1xcXPD8+XMsWLBA7fJk17x5c7Rp0wZ16tRB79698fXXX8PR0RFA+iZhU6dOVX2jqIIkOTkZY8eORcWKFWFmZgZzc3N4eXlhwoQJ2jlRpIzY2Fi4u7trvy5cuDD09P7v7b569eq4dOmSbO1zuem/EBERgRMnTsDNzU225TqZ2dvbY9euXdpJaqNGjUJ4eLh26damTZswbtw4WZ8owMet1w8PD5d9E5zw8HAEBgaidu3aOHLkCC5fvgxXV1dMnz4dp06dwubNm2VtP6/JPDTm4eGBevXqqV2SIl6/fo3p06dDX18fTZs2RWBgIBwdHeHt7Y0TJ07g+vXrOHHiBCpVqqR2qZ+8N2/eoFatWrhw4QICAwNRrlw5CCFw+fJlhISEoEqVKjhy5Ei2Q3ckPWNjY0RFRaFs2bLZ3n/lyhVUrlwZr1+/lqV9zrH4F2rUqIEaNWoo1t7Tp091DhAKDw/XmUtQrVo13LlzR/Y6/mlHOUtLS+0KDTmNGDECEydOxJAhQ3SOTvf398e8efNkbz+vqV+/PurXr692GYorXLiwzqqsqKgozJw5E+fPn0e1atWwZs2aArGFdF6wePFi/PXXX4iOjs7yZnblyhX4+flhyZIlCAoKUqnCgqVUqVK4cOFCjsEiJiYmy87JUmKPxUeKjY1FWFgYHjx4kGVinNxLTp2cnLBmzRr4+PjgzZs3sLKywq5du7STNs+fPw9fX1/F9tNQm5mZGc6fPw8XFxeYm5sjOjoarq6uuHXrFsqVKydbCiei7Pn6+qJNmzb47rvvsr1/wYIF2Lx5s+JnVhRUAwcOxMGDB3H27FmdU6AB4NWrV6hatSrq1asn2wcx9lh8hOXLl6Nv374oWrQoihcvrrNtsUajkT1YNGrUCCNGjMC0adOwfft2mJiYoG7dutr7Y2Ji8sSx7kqxsrLCvXv3tJsgZTh37hyP7CZSwaVLl+Dn55fj/f7+/pgwYYJyBRVwP/74IzZu3IiyZcuif//+cHd3h0ajwZUrV7Bw4UK8e/cOP/74o3wFyDYt9BPi6Ogopk6dqlr7Dx48EHXq1BEajUaYm5vrzPYVQoiAgADx448/qlSd8oYNGybq1Kkj7t27J8zNzcW1a9fEsWPHhKurq/jpp5/ULo+owDEwMBD37t3L8f6///5bGBoaKlgR3bhxQzRs2FDo6ekJjUYjNBqN0NPTEw0bNtRZISIHDoV8BAsLC0RFRcHV1VXVOhITE2FmZgZ9fX2d60+ePIGZmRkKFSqkUmXKuH79Otzc3PD27Vt0794d69atgxACBgYGSE1NRYcOHbBq1aos3x8ikpe+vj4SEhJy3Eb+/v37KFGiRIE43TSvefLkCa5fvw4AcHNzg42NjextMlh8hJ49e6JatWro06eP2qUUaHp6eihZsiT8/f3h7+8PX19fREZGIi0tDd7e3ihTpozaJSrKz88PPXr0QOvWrbUb3xCpQU9PDxUrVtQ5WTazd+/e4eLFiwwWBQTnWORg/vz52j+7ublhzJgxiIiIgKenZ5YlUwMGDFC6vAIpPDwc4eHhCAsLQ//+/fH69Ws4OjoiICAAb968gYmJSYGaY/HZZ5/hhx9+QFBQENq0aYOePXsqulqJKMO4ceP+8TFfffWVApVQXsAeixy8PzEwJxqNBjdu3JC5Gnrf27dvcfLkSYSFhSEsLAwRERFISUmBm5sbrl69qnZ5iklNTcXu3buxcuVK7N27F25ubujRowc6d+6ss0T5U5acnIypU6fi0KFD2a7a4u8nkbIYLChfe/XqFY4dO4Z9+/Zh+fLlePHiRYHtbn348CGWLl2KSZMmITU1FY0bN8aAAQMQEBCgdmmyat++PcLDw9G5c2fY29vrrNoC0pfeEZFyGCw+woQJE/D999/DxMRE5/qrV68wY8YMRY9OL+hev36NEydOIDQ0FGFhYTh9+jRcXFzg6+sLHx8f+Pr6FqjhkAynTp3CypUrsW7dOlhaWqJbt264d+8efv/9d/Tt2xczZ85Uu0TZWFlZYc+ePahdu7bapRARGCw+ir6+Pu7duwdbW1ud648fP4atrW2B/YSsNF9fX5w+fRqlS5fWhghfX98C0+X/vgcPHmDNmjVYuXIlrl27hmbNmqFXr15o2LCh9lP7wYMH0bJlS7x48ULlauXj4uKCvXv3onz58mqXQkTg5M2PIoTI0r0KANHR0Yos3aF0J06cgL29Pfz9/eHn5wcfH58CfRRzqVKlULp0afTo0QPdunXLdqlf9erVP/mDuH7++WeMHTsWq1evztKrSETKY4/FB1hbW0Oj0SAxMREWFhY64SI1NRUvXrxAnz598Msvv6hYZcGRnJyMo0ePIiwsDKGhoYiKioK7uzt8fX3h5+cHX1/fHNfRf4qOHj2qswNrQeXt7Y24uDgIIeDs7Jxl1VZkZKRKlVFmT5484QexAoLB4gNWr14NIQR69OiBuXPn6hzCVahQITg7O6NmzZoqVliwPX/+HMeOHdPOt4iOjkaZMmVw4cIFtUtTREBAALZu3QorKyud60lJSWjZsiUOHz6sTmEKGz9+/Afv/5ilkCQtLy8v1K1bFz169MBnn32G2NhYNG3aFLGxsWqXRgrgUMgHdO3aFUD6GG6tWrV45G8eY2pqChsbG9jY2MDa2hoGBga4fPmy2mUpJjw8HG/evMly/fXr1zh69KgKFamDwSHv6dq1Ky5cuAB/f3988cUXOHr06Cc/JEf/hz0WOUhKSvrox1pYWMhYCWVIS0vDmTNntEMhx48fR3Jyss5unP7+/nByclK7VFnFxMQAACpXrozDhw/rdC+npqYiJCQES5cuxa1bt1SqUB1nz57F5cuXodFo4OHhAW9vb7VLKjAy9g7R09PTub5hwwa0b98eZmZmuHXrFodCCggGixzo6ellO2Ezs4xJnVwVogwLCwskJyfD3t4efn5+8PPzg7+/f4E62RXQfW5m9+trbGyMBQsWoEePHkqXpooHDx6gXbt2CAsLg5WVFYQQSExMhL+/P9avX1+g5t2opU2bNvD390ffvn21106dOoV69eph2LBhOHnyJMqXL49Zs2apWCUphcEiB+Hh4R/9WF9fXxkroQxLly6Fv78/3N3d1S5FVbdv34YQAq6urjh16pTOG2ehQoVga2tboA5ia9u2LeLi4rBmzRrtktNLly6ha9eucHNzw7p161Su8NNnZ2eHsLAw7ff/8uXL8PHxwejRozFw4EAcPXoUnTp1wu3bt1WulJTAYEFE+ZqlpSUOHjyYZQz/1KlTaNCgAZ49e6ZOYQWImZkZIiMj4e7ujtu3b8PX1xdjx47V9ppdv34dlSpVQnJyssqVkhI4eTMXXr58ifj4+CwT5ry8vFSqiAqanTt3IjAwEIaGhti5c+cHH9u8eXOFqlJXWlpathOrDQ0Ns5wbQvKoXLkyBg0ahFatWmHixIno16+fzlBcSEgI3NzcVKyQlMQei4/w8OFDdO/eHX/88Ue293OOBSlFT08PCQkJsLW1zTJRLrOCNPenRYsWePbsGdatW4cSJUoAAO7evYuOHTvC2toa27ZtU7nCT9+ZM2fQtm1b6Ovro3nz5li9ejUmTpyIypUr48iRIxg/fjxmz56Nb775Ru1SSQHssfgIgwYNwtOnTxEREQF/f39s27YN9+/fx8SJEzkZiRSV+RM4P42nW7hwIVq0aAFnZ2c4ODhAo9EgPj4enp6eWLt2rdrlFQhVq1ZFXFyc9mtPT0+MHDkSCQkJMDY2xsCBAxkqChD2WHwEe3t77NixA9WrV4eFhQXOnDkDd3d37Ny5E9OnT8exY8fULpGowDtw4ACuXLkCIQQ8PDxQr149tUsq0IQQePDgAaytrVGoUCG1yyEF5dyXSlrJycnaA8hsbGzw8OFDAOmpnNsFk9IOHz4MDw+PbPdaSUxMRIUKFXDkyBEVKlNHcHAwUlJSUL9+fQQFBWHAgAGoV68e3rx5g+DgYLXLK7A0Gg3s7OwYKgogBouPULZsWVy9ehVA+iSlpUuX4u7du1iyZAns7e1Vro4Kmrlz56J3797ZbsxmaWmJb7/9FnPmzFGhMnV0794diYmJWa4/f/4c3bt3V6EiooKNweIjDBo0CPfu3QOQvn1wSEgIHB0dMX/+fEyePFnl6qigiY6ORqNGjXK8v0GDBjh79qyCFakrp9OH//rrL53zfYhIGZy8+QHXr1+Hm5sbOnbsqL3m7e2NW7du4cqVK3B0dCzQx3aTOu7fv//Bc2sMDAy0w3WfMm9vb2g0Gmg0GnzxxRcwMPi/l7PU1FTcvHnzgwGMiOTBYPEB7u7u2nMoAgIC4OfnB2dnZ5iYmKBKlSpql0cFVMmSJXH+/Pkc9wWIiYkpEEN0LVu2BABERUWhYcOGMDMz096XcfrwV199pVJ1RAUXV4V8wNGjRxEeHo6wsDCcPHkSr1+/hqOjIwICArQHXpUsWVLtMqmACQoKQlhYGE6fPo3ChQvr3Pfq1StUr14d/v7+mD9/vkoVKmv16tVo27Ztlu8FKS+nTds0Gg0KFy4MNzc3uLi4KFwVKY3B4iO9ffsWJ0+eRFhYGMLCwhAREYGUlBS4ublpJ3YSKeH+/fuoUqUK9PX10b9/f5QtWxYajQaXL1/GL7/8gtTUVERGRsLOzk7tUqmAyTgg7/23lYxrGo0GderUwfbt22Ftba1SlSQ3BotcevXqFY4dO4Z9+/Zh+fLlePHiRYHZ4ZDyjtu3b6Nv377Yt2+f9kVco9GgYcOGWLRoEZydndUtUEHW1tbZTt7M/Cm5W7duXCGigEOHDmHUqFGYNGkSqlevDiD9zJbRo0djzJgx2lVLn3/+OX777TeVqyW5MFj8g9evX+PEiRMIDQ3Vdj+7uLjA19cXPj4+8PX15XAIqebp06e4fv06hBAoU6ZMgfwUOGfOHEyaNAmBgYGoXr06hBA4ffo0QkJCMHjwYNy8eRNr1qzBggUL0Lt3b7XL/aRVrFgRy5YtQ61atXSuHz9+HN988w0uXryIgwcPokePHoiPj1epSpIbg8UH+Pr64vTp0yhdurQ2RPj6+rKLmSgP+eqrr1C/fn306dNH5/rSpUuxf/9+bNmyBQsWLMCyZctw/vx5laosGIyNjXH69GlUrFhR5/r58+dRvXp1vHr1Crdv30b58uXx8uVLlaokuTFYfIChoSHs7e3RsmVL+Pn5wcfHh8tLifIYMzMzREVFZVklc/36dVSuXBkvXrxAXFwcvLy8eGy3zOrUqQNzc3MEBwejWLFiANIPcezSpQuSk5Nx5MgRHDx4EP369UNsbKzK1ZJcuEHWBzx79gzLli2DiYkJpk2bhpIlS8LT0xP9+/fH5s2bC8ReAUR5nY2NDXbt2pXl+q5du2BjYwMgfVt+c3NzpUsrcH777TfcvHkTpUqVgpubG8qUKYNSpUrh1q1b+PXXXwEAL168wJgxY1SulOTEHotceP78OY4dO6adbxEdHY0yZcrgwoULapdGVGAtX74cffv2RePGjVG9enVoNBqcOnUKe/fuxZIlS9CzZ0/MmjULp06dwoYNG9Qu95MnhMC+ffsQGxsLIQTKlSuH+vXrQ0+Pn2MLCgaLXEhLS8Pp06cRGhqK0NBQHDt2DK9fv+aqECKVHT9+HAsXLsTVq1e1b2ZBQUFZJhESkfwYLD4gLS0NZ86cQVhYGEJDQ3H8+HEkJydrd+PMuDk5OaldKhFRnnDo0CEcOnQIDx48QFpams59K1asUKkqUhK39P4AKysrJCcnw97eHn5+fpg9ezb8/f1RunRptUsjokzS0tJw/fr1bN/MfHx8VKqq4Bk/fjwmTJiAqlWrwt7ePtv9RejTxx6LD1i6dCn8/f3h7u6udilElIOIiAh06NABt2/fznbHRw5VKsfe3h7Tp09H586d1S6FVMRgQUT5WuXKleHu7o7x48dn+ymZR6crp0iRIjh16hR7dQs4BgsiytdMTU0RHR2d42mvpJzhw4fDzMyMy0kLOM6xIKJ87fPPP8f169cZLPKA169fY9myZTh48CC8vLxgaGioc//s2bNVqoyUxGBBRPlaUFAQhg4dioSEBHh6emZ5M/Py8lKpsoInJiYGlStXBoAs+/twImfBwaEQIsrXstt4KfMx3Zy8SaQs9lgQUb528+ZNtUsgokzYY0FERP9aq1atsGrVKlhYWKBVq1YffOzWrVsVqorUxB4LIsp3du7cicDAQBgaGmLnzp0ffGzz5s0VqqpgsrS01M6f4NJeAthjQUT5kJ6eHhISEmBra/vBw604x4JIeTxujojynbS0NNja2mr/nNONoUJZa9euzfG+YcOGKVgJqYnBgog+Oc+ePVO7hAKpf//+2L17d5brgwcP/mDooE8LgwUR5WvTpk3Dhg0btF+3bt0aNjY2KFmyJKKjo1WsrOBZv349OnXqhCNHjmivBQUFYePGjQgNDVWxMlISgwUR5WtLly6Fg4MDAODAgQM4ePAgQkJCEBgYyO53hTVq1AhLlixBy5YtcebMGfTr1w9bt25FaGgoypUrp3Z5pBCuCiGifO3evXvaYLF79260adMGDRo0gLOzMz7//HOVqyt42rVrh6dPn6JOnTooVqwYwsPDud16AcNgQUT5mrW1Ne7cuQMHBweEhIRg4sSJAAAhBCdvKmDIkCHZXre1tYW3tzcWLVqkvcazQgoGBgsiytdatWqFDh06oEyZMnj8+DECAwMBAFFRUfykrIBz585le7106dJISkrS3s+zQgoOBgsiytfmzJkDZ2dn3LlzB9OnT4eZmRmA9CGSfv36qVzdp4+TMul93CCLiIj+s3fv3qFw4cKIiopCxYoV1S6HVMQeCyLK92JjYxEWFoYHDx4gLS1N576xY8eqVFXBYmBgACcnJ85rIfZYEFH+tnz5cvTt2xdFixZF8eLFdcbyNRoNIiMjVayuYFm5ciU2bdqEtWvXwsbGRu1ySCUMFkSUrzk5OaFfv34YPny42qUUeN7e3rh+/Trevn0LJycnmJqa6tzPkFcwcCiEiPK1p0+fonXr1mqXQQBatmypdgmUB7DHgojytZ49e6JatWro06eP2qUQEdhjQUT5nJubG8aMGYOIiAh4enrC0NBQ5/4BAwaoVBlRwcQeCyLK11xcXHK8T6PR4MaNGwpWU7ClpqZizpw52LhxI+Lj4/HmzRud+588eaJSZaQk9lgQUb528+ZNtUug/2/8+PH49ddfMWTIEIwZMwajRo3CrVu3sH37di77LUDYY0FEn4Q3b97g5s2bKF26NAwM+JlJDaVLl8b8+fPRpEkTmJubIyoqSnstIiIC//vf/9QukRTAY9OJKF97+fIlevbsCRMTE1SoUAHx8fEA0udWTJ06VeXqCpaEhAR4enoCAMzMzJCYmAgAaNq0Kfbs2aNmaaQgBgsiytdGjhyJ6OhohIWFoXDhwtrr9erVw4YNG1SsrOApVaoU7t27ByB9Uu3+/fsBAKdPn4aRkZGapZGCGCyIKF/bvn07Fi5ciDp16ujsuunh4YG4uDgVKyt4vvzySxw6dAgAMHDgQIwZMwZlypRBly5d0KNHD5WrI6VwIJKI8rWHDx/C1tY2y/Xk5GQe1a2wzENPX3/9NUqVKoUTJ07Azc0NzZs3V7EyUhKDBRHla9WqVcOePXsQFBQEANowsXz5ctSsWVPN0gq8GjVqoEaNGmqXQQpjsCCifG3KlClo1KgRLl26hHfv3mHevHm4ePEiTp48ifDwcLXLK1AeP36MIkWKAADu3LmD5cuX49WrV2jevDnq1q2rcnWkFM6xIKJ8rVatWjh+/DhevnyJ0qVLY//+/bCzs8PJkydRpkwZtcsrEM6fPw9nZ2fY2tqiXLlyiIqKQrVq1TBnzhwsW7YM/v7+2L59u9plkkK4jwUR5UszZ87E999/n+P9SUlJaNCgASIiIhSsqmAKDAyEgYEBhg8fjrVr12L37t1o0KABfv31VwBAUFAQzp49y59FAcFgQUT5krGxMRYtWoTu3btnue/FixeoX78+EhMTcenSJRWqK1iKFi2Kw4cPw8vLCy9evICFhQVOnTqFqlWrAgCuXLmCGjVq4NmzZ+oWSorgUAgR5Utr1qxBv379snSxv3jxAg0aNMCTJ08QGhqqTnEFzJMnT1C8eHEA6RtjmZqawsbGRnu/tbU1nj9/rlZ5pDBO3iSifOnrr7/Gs2fP0KFDB+zZswf+/v548eIFGjVqhEePHiE8PBx2dnZql1lgvL+0l0t9Cy4GCyLKt3r16oUnT56gZcuW2LFjB8aMGYOEhASEh4fD3t5e7fIKlG7duml313z9+jX69OkDU1NTAEBKSoqapZHCOMeCiPK9kSNHYvr06XB2dkZ4eDhKlSqldkkFSnbzXLKzcuVKmSuhvIDBgojypVatWul8vXfvXlSqVAklS5bUub5161YlyyIq8DgUQkT5kqWlpc7X7du3V6kSIsqMPRZEREQkGS43JSIiIskwWBAREZFkGCyIiIhIMgwWREREJBkGCyIiIpIMgwURERFJhsGCiIiIJMNgQURERJL5f4LBAJ4nQSZdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=top15[['Borough','2018']].plot(kind='bar')\n",
    "ax.set_xticklabels(top15.Borough)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cdf8cedf",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (1548609034.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Input \u001b[0;32mIn [40]\u001b[0;36m\u001b[0m\n\u001b[0;31m    Hounslow of London have seen the greatest increase in housing prices, on average, over the last two decades.\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "# Summary: \n",
    "#Hounslow of London have seen the greatest increase in housing prices, on average, over the last two decades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7357d160",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}