GaussianMixture_RealEstatePrices

GaussianMixture.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import scipy\n",
    "from scipy.stats import gamma\n",
    "from scipy.stats import norm\n",
    "from scipy.stats import lognorm\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DOWNLOAD DATA FROM\n",
    "\n",
    "https://www.kaggle.com/hm-land-registry/uk-housing-prices-paid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selected columns\n",
    "columns = [\n",
    "       'Price', 'Date of Transfer',\n",
    "       'Property Type', 'Old/New', 'Duration', 'Town/City', 'District',\n",
    "       'County', 'PPDCategory Type'\n",
    "       ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min date: 2012-01-01 00:00\n",
      "Min max: 2014-12-30 00:00\n"
     ]
    }
   ],
   "source": [
    "# Read part of the dataset\n",
    "df = pd.read_csv(\"data/train_price_houses.csv\", usecols=columns)\n",
    "print(\"Min date: {}\".format(df['Date of Transfer'].min()))\n",
    "print(\"Min max: {}\".format(df['Date of Transfer'].max()))\n",
    "df['Date of Transfer'] = pd.DatetimeIndex(df['Date of Transfer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2461243, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adjust price for inflation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['count_col'] = 0\n",
    "df['Price_mean'] = df['Price'] \n",
    "df['Price_std'] = df['Price'] \n",
    "df_group = df.groupby(by=['Town/City', 'County']).agg({'count_col': 'count', \n",
    "                                                       'Price_mean' : 'mean', 'Price_std': 'std'})\n",
    "df_group = df_group.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group_date = df.groupby(by='Date of Transfer').agg({'Price_mean': 'mean', 'count_col': 'count'}).reset_index()\n",
    "df_group_date['Date of Transfer'] = pd.to_datetime(df_group_date['Date of Transfer'])\n",
    "\n",
    "# One week rolling window\n",
    "df_group_date['Price_x_count'] = df_group_date['Price_mean']*df_group_date['count_col']\n",
    "df_group_date['Price_sum'] = df_group_date['Price_x_count'].rolling(window=20, min_periods=7).sum()\n",
    "df_group_date['count_sum'] = df_group_date['count_col'].rolling(window=20, min_periods=7).sum()\n",
    "df_group_date['Price_mean'] = df_group_date['Price_sum']/df_group_date['count_sum']\n",
    "\n",
    "df_group_date = df_group_date.dropna().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.array(df_group_date.index, dtype=np.float32)\n",
    "y_train = np.array(df_group_date['Price_mean'])\n",
    "\n",
    "params, _ = scipy.optimize.curve_fit(lambda t,a,b: a*np.exp(b*t),  x_train,  y_train, p0=(2e5, 0.01))\n",
    "alpha, beta = params[0], params[1]\n",
    "y_pred = alpha*np.exp(beta*x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00022242820326361787"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "locator = mdates.YearLocator(1)\n",
    "locator_fmt = mdates.DateFormatter(\"%Y\")\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(df_group_date['Date of Transfer'], df_group_date['Price_mean'])\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(locator_fmt)\n",
    "plt.yticks([220e3, 240e3, 260e3, 280e3, 300e3], ['220k', '240k', '260k', '280k', '300k'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Mean price (£)')\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/prices.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "locator = mdates.YearLocator(1)\n",
    "locator_fmt = mdates.DateFormatter(\"%Y\")\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(df_group_date['Date of Transfer'], df_group_date['Price_mean'])\n",
    "ax.plot(df_group_date['Date of Transfer'], y_pred, color='r')\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(locator_fmt)\n",
    "plt.yticks([220e3, 240e3, 260e3, 280e3, 300e3], ['220k', '240k', '260k', '280k', '300k'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Mean price (£)')\n",
    "plt.grid()\n",
    "plt.legend(['Mean prices', 'Exponential fit'])\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/prices_fit.png', dpi=600)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correction factor\n",
    "ini_date = df_group_date['Date of Transfer'][0]\n",
    "df_group_date['factor'] = np.exp(-beta*(df_group_date['Date of Transfer'] - ini_date).dt.days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df = df.drop(columns=['factor'])\n",
    "except:\n",
    "    pass\n",
    "df = pd.merge(df, df_group_date[['Date of Transfer', 'factor']], on='Date of Transfer', how='left')\n",
    "df['Price_adj'] = df['Price']*df['factor']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Adjusted Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group_adj = df[df['Date of Transfer'] > ini_date].groupby(by='Date of Transfer').agg({'Price_adj': 'mean', 'count_col': 'count'}).reset_index()\n",
    "df_group_adj['Date of Transfer'] = pd.to_datetime(df_group_adj['Date of Transfer'])\n",
    "\n",
    "# One week rolling window\n",
    "df_group_adj['Price_x_count'] = df_group_adj['Price_adj']*df_group_adj['count_col']\n",
    "df_group_adj['Price_sum'] = df_group_adj['Price_x_count'].rolling(window=20, min_periods=7).sum()\n",
    "df_group_adj['count_sum'] = df_group_adj['count_col'].rolling(window=20, min_periods=7).sum()\n",
    "df_group_adj['Price_adj'] = df_group_adj['Price_sum']/df_group_adj['count_sum']\n",
    "df_group_adj = df_group_adj.dropna().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "locator = mdates.YearLocator(1)\n",
    "locator_fmt = mdates.DateFormatter(\"%Y\")\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(df_group_adj['Date of Transfer'], df_group_adj['Price_adj'])\n",
    "plt.hlines(alpha, df_group_adj['Date of Transfer'].min(), df_group_adj['Date of Transfer'].max(), color='r')\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(locator_fmt)\n",
    "plt.yticks([210e3, 220e3, 230e3, 240e3, 250e3, 260e3], ['210k', '220k', '230k', '240k', '250k', '260k'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Mean price (£)')\n",
    "plt.grid()\n",
    "plt.legend(['Adjusted prices', 'Mean price'])\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/prices_adjusted.png', dpi=600)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split between old and new\n",
    "col = 'County'\n",
    "set1 = ['GREATER LONDON']\n",
    "set2 = ['WEST MIDLANDS', 'GREATER MANCHESTER']\n",
    "df_set1 = df[df[col].isin(set1)]\n",
    "df_set2 = df[df[col].isin(set2)]\n",
    "df_subset = df[df[col].isin(set1+set2)]\n",
    "#df_old = df[df['Property Type'] == 'D']\n",
    "#df_new = df[df['Property Type'] != 'D']\n",
    "#PPDCategory Type\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(df_subset['Price_adj'], bins=np.arange(0, 1.2e6, 1e3), alpha=.5)\n",
    "plt.hist(df_set1['Price_adj'], bins=np.arange(0, 1.2e6, 1e3), alpha=.5)\n",
    "plt.hist(df_set2['Price_adj'], bins=np.arange(0, 1.2e6, 1e3), alpha=.5)\n",
    "#plt.hist(np.log(df['Price_adj']), bins=np.arange(8, 16, 0.01), alpha=.5)\n",
    "#plt.hist(np.log(df_old['Price_adj']), bins=np.arange(8, 16, 0.01), alpha=.5)\n",
    "#plt.hist(np.log(df_new['Price_adj']), bins=np.arange(8, 16, 0.01), alpha=.5)\n",
    "#plt.yscale('log')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.hist(df['Price_adj'], bins=np.arange(0, 1.2e6, 1e3), alpha=1)\n",
    "plt.xticks([0, 2e5, 4e5, 6e5, 8e5, 1e6, 1.2e6], ['0', '200k', '400k', '600k', '800k', '1M', '1.2M'])\n",
    "plt.yticks([0, 3e3, 6e3, 9e3, 12e3], ['0', '3k', '6k', '9k', '12k'])\n",
    "plt.xlabel('Price (£)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/frequencies_price.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure()\n",
    "# plt.hist(df['Price_adj'], bins=np.arange(0, 1e8, 5e5), alpha=1, log=True)\n",
    "# plt.xticks([0, 2e7, 4e7, 6e7, 8e7, 10e7], ['0', '20M', '40M', '60M', '80M', '100M'])\n",
    "# plt.yticks([1, 1e2, 1e4, 1e6], ['1', '100', '10k', '1M'])\n",
    "# plt.grid()\n",
    "# plt.xlabel('Price (£)')\n",
    "# plt.ylabel('Frequency')\n",
    "# plt.show()\n",
    "\n",
    "bins = np.arange(0, 1.2e6, 1e3)\n",
    "x = df['Price_adj']\n",
    "log_x = np.log(x)\n",
    "log_bins = np.arange(8, 19, 0.01)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_figwidth(10)\n",
    "\n",
    "# Plot 1\n",
    "ax1.hist(log_x, bins=log_bins, alpha=1, log=True)\n",
    "plt.setp(ax1, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "#plt.yticks([0, 4e3, 8e3, 12e3, 16e3], ['0', '4k', '8k', '12k', '16k'])\n",
    "ax1.grid()\n",
    "plt.setp(ax1, xlabel='Price (£)')\n",
    "plt.setp(ax1, ylabel='Frequency')\n",
    "\n",
    "# Plot 2\n",
    "ax2.hist(log_x, bins=log_bins, alpha=1)\n",
    "plt.setp(ax2, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "plt.setp(ax2, yticks=[0, 4e3, 8e3, 12e3, 16e3], yticklabels=['0', '4k', '8k', '12k', '16k'])\n",
    "ax2.grid()\n",
    "plt.setp(ax2, xlabel='Price (£)')\n",
    "plt.setp(ax2, ylabel='Frequency')\n",
    "\n",
    "plt.savefig('figures_mixture/frequencies_log_price.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "290114.4940007791\n",
      "114095.06862380102\n",
      "1142955.227998204\n"
     ]
    }
   ],
   "source": [
    "print(df_set1['Price_adj'].median())\n",
    "print(df_set2['Price_adj'].median())\n",
    "print(df['Price_adj'].quantile(.99))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['S' 'D' 'T' 'F' 'O']\n",
      "['N' 'Y']\n",
      "['F' 'L']\n",
      "1150\n",
      "349\n",
      "113\n",
      "['A' 'B']\n"
     ]
    }
   ],
   "source": [
    "print(df['Property Type'].unique())\n",
    "print(df['Old/New'].unique())\n",
    "print(df['Duration'].unique())\n",
    "print(len(df['Town/City'].unique()))\n",
    "print(len(df['District'].unique()))\n",
    "print(len(df['County'].unique()))\n",
    "print(df['PPDCategory Type'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>County</th>\n",
       "      <th>Price_adj</th>\n",
       "      <th>count_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BLAENAU GWENT</td>\n",
       "      <td>73269.808797</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MERTHYR TYDFIL</td>\n",
       "      <td>92139.130742</td>\n",
       "      <td>1817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CITY OF KINGSTON UPON HULL</td>\n",
       "      <td>93040.445431</td>\n",
       "      <td>8930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>STOKE-ON-TRENT</td>\n",
       "      <td>93357.951855</td>\n",
       "      <td>8884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHESHIRE</td>\n",
       "      <td>93582.763717</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RHONDDA CYNON TAFF</td>\n",
       "      <td>97942.358842</td>\n",
       "      <td>8668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NEATH PORT TALBOT</td>\n",
       "      <td>98825.860753</td>\n",
       "      <td>4795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BLACKPOOL</td>\n",
       "      <td>102804.562947</td>\n",
       "      <td>5346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BLACKBURN WITH DARWEN</td>\n",
       "      <td>104437.944282</td>\n",
       "      <td>4375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HARTLEPOOL</td>\n",
       "      <td>106497.267459</td>\n",
       "      <td>3278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NORTH EAST LINCOLNSHIRE</td>\n",
       "      <td>107329.134530</td>\n",
       "      <td>6363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>COUNTY DURHAM</td>\n",
       "      <td>109642.945978</td>\n",
       "      <td>19041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAERPHILLY</td>\n",
       "      <td>111230.417326</td>\n",
       "      <td>6052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>REDCAR AND CLEVELAND</td>\n",
       "      <td>113482.892914</td>\n",
       "      <td>5042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MIDDLESBROUGH</td>\n",
       "      <td>116489.333010</td>\n",
       "      <td>4511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NORTH LINCOLNSHIRE</td>\n",
       "      <td>117873.960134</td>\n",
       "      <td>6222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TORFAEN</td>\n",
       "      <td>119174.545248</td>\n",
       "      <td>2935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DARLINGTON</td>\n",
       "      <td>122229.516852</td>\n",
       "      <td>4389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CARMARTHENSHIRE</td>\n",
       "      <td>125318.178312</td>\n",
       "      <td>6601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BRIDGEND</td>\n",
       "      <td>126621.160547</td>\n",
       "      <td>5795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HALTON</td>\n",
       "      <td>127586.846821</td>\n",
       "      <td>4083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MERSEYSIDE</td>\n",
       "      <td>127900.100493</td>\n",
       "      <td>46197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DENBIGHSHIRE</td>\n",
       "      <td>128796.602233</td>\n",
       "      <td>3645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SOUTH YORKSHIRE</td>\n",
       "      <td>132634.798086</td>\n",
       "      <td>48310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TYNE AND WEAR</td>\n",
       "      <td>132836.084261</td>\n",
       "      <td>39882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>STOCKTON-ON-TEES</td>\n",
       "      <td>133128.442604</td>\n",
       "      <td>7856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LANCASHIRE</td>\n",
       "      <td>136263.712622</td>\n",
       "      <td>48494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LEICESTER</td>\n",
       "      <td>136328.622265</td>\n",
       "      <td>8937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CITY OF NOTTINGHAM</td>\n",
       "      <td>136861.714621</td>\n",
       "      <td>9726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WREXHAM</td>\n",
       "      <td>137288.756408</td>\n",
       "      <td>4211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>WREKIN</td>\n",
       "      <td>138486.625136</td>\n",
       "      <td>6979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NEWPORT</td>\n",
       "      <td>139437.992324</td>\n",
       "      <td>5377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SWANSEA</td>\n",
       "      <td>139771.051919</td>\n",
       "      <td>8619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>WEST YORKSHIRE</td>\n",
       "      <td>140539.665257</td>\n",
       "      <td>85211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CITY OF DERBY</td>\n",
       "      <td>141649.405825</td>\n",
       "      <td>10028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ISLE OF ANGLESEY</td>\n",
       "      <td>143291.146056</td>\n",
       "      <td>2690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>LINCOLNSHIRE</td>\n",
       "      <td>143491.347628</td>\n",
       "      <td>36561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NOTTINGHAMSHIRE</td>\n",
       "      <td>143506.978333</td>\n",
       "      <td>36573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>CITY OF PLYMOUTH</td>\n",
       "      <td>144657.571347</td>\n",
       "      <td>11370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>GREATER MANCHESTER</td>\n",
       "      <td>145231.369334</td>\n",
       "      <td>97432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CONWY</td>\n",
       "      <td>145332.987587</td>\n",
       "      <td>5180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>GWYNEDD</td>\n",
       "      <td>147036.995239</td>\n",
       "      <td>4208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>DERBYSHIRE</td>\n",
       "      <td>147294.649761</td>\n",
       "      <td>33630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>EAST RIDING OF YORKSHIRE</td>\n",
       "      <td>148328.992704</td>\n",
       "      <td>16176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>WEST MIDLANDS</td>\n",
       "      <td>149291.498659</td>\n",
       "      <td>88194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>LUTON</td>\n",
       "      <td>149538.754231</td>\n",
       "      <td>6908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>CUMBRIA</td>\n",
       "      <td>151739.284148</td>\n",
       "      <td>21670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>NORTHUMBERLAND</td>\n",
       "      <td>151911.398850</td>\n",
       "      <td>12656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>CITY OF PETERBOROUGH</td>\n",
       "      <td>152278.652859</td>\n",
       "      <td>8150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>PEMBROKESHIRE</td>\n",
       "      <td>152330.039110</td>\n",
       "      <td>4493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>POWYS</td>\n",
       "      <td>154791.276240</td>\n",
       "      <td>4342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>FLINTSHIRE</td>\n",
       "      <td>155422.313028</td>\n",
       "      <td>5682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>PORTSMOUTH</td>\n",
       "      <td>156280.267976</td>\n",
       "      <td>9020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>SWINDON</td>\n",
       "      <td>157336.880129</td>\n",
       "      <td>10587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>STAFFORDSHIRE</td>\n",
       "      <td>159243.168849</td>\n",
       "      <td>34041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>TORBAY</td>\n",
       "      <td>162658.155764</td>\n",
       "      <td>7395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>SOUTHAMPTON</td>\n",
       "      <td>164225.810983</td>\n",
       "      <td>9877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>MEDWAY</td>\n",
       "      <td>164857.459768</td>\n",
       "      <td>11829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>WARRINGTON</td>\n",
       "      <td>165903.758442</td>\n",
       "      <td>8687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>CEREDIGION</td>\n",
       "      <td>165913.966997</td>\n",
       "      <td>2371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>NORTHAMPTONSHIRE</td>\n",
       "      <td>167826.572749</td>\n",
       "      <td>35029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>THURROCK</td>\n",
       "      <td>169655.694670</td>\n",
       "      <td>6808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>NORFOLK</td>\n",
       "      <td>171960.048301</td>\n",
       "      <td>45387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>CARDIFF</td>\n",
       "      <td>175319.455507</td>\n",
       "      <td>14751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>ISLE OF WIGHT</td>\n",
       "      <td>177023.087517</td>\n",
       "      <td>8017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>LEICESTERSHIRE</td>\n",
       "      <td>178787.594166</td>\n",
       "      <td>32661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>SHROPSHIRE</td>\n",
       "      <td>180224.994285</td>\n",
       "      <td>12745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>SUFFOLK</td>\n",
       "      <td>186618.696740</td>\n",
       "      <td>37160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>THE VALE OF GLAMORGAN</td>\n",
       "      <td>186984.017697</td>\n",
       "      <td>5470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>CHESHIRE WEST AND CHESTER</td>\n",
       "      <td>187133.783921</td>\n",
       "      <td>14340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>SOMERSET</td>\n",
       "      <td>190042.943176</td>\n",
       "      <td>27498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>WORCESTERSHIRE</td>\n",
       "      <td>190473.349404</td>\n",
       "      <td>26388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>HEREFORDSHIRE</td>\n",
       "      <td>191937.252028</td>\n",
       "      <td>7669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>NORTH YORKSHIRE</td>\n",
       "      <td>192596.686729</td>\n",
       "      <td>28848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>SOUTH GLOUCESTERSHIRE</td>\n",
       "      <td>194889.125019</td>\n",
       "      <td>12794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>CORNWALL</td>\n",
       "      <td>199168.369268</td>\n",
       "      <td>27400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>BEDFORD</td>\n",
       "      <td>199342.258983</td>\n",
       "      <td>7971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>YORK</td>\n",
       "      <td>199457.010277</td>\n",
       "      <td>10186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>MONMOUTHSHIRE</td>\n",
       "      <td>200824.646281</td>\n",
       "      <td>3956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>NORTH SOMERSET</td>\n",
       "      <td>201204.299395</td>\n",
       "      <td>11683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>BOURNEMOUTH</td>\n",
       "      <td>202581.476247</td>\n",
       "      <td>10514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>SOUTHEND-ON-SEA</td>\n",
       "      <td>202829.761767</td>\n",
       "      <td>9059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>CHESHIRE EAST</td>\n",
       "      <td>203600.412792</td>\n",
       "      <td>17859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>CITY OF BRISTOL</td>\n",
       "      <td>204345.825712</td>\n",
       "      <td>21591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>WARWICKSHIRE</td>\n",
       "      <td>205537.558500</td>\n",
       "      <td>25625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>SLOUGH</td>\n",
       "      <td>207672.811222</td>\n",
       "      <td>4803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>CENTRAL BEDFORDSHIRE</td>\n",
       "      <td>208143.014559</td>\n",
       "      <td>14946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>MILTON KEYNES</td>\n",
       "      <td>210914.827990</td>\n",
       "      <td>12499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>DEVON</td>\n",
       "      <td>212786.196210</td>\n",
       "      <td>41792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>GLOUCESTERSHIRE</td>\n",
       "      <td>213038.633608</td>\n",
       "      <td>31422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>WILTSHIRE</td>\n",
       "      <td>217053.701071</td>\n",
       "      <td>23671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>KENT</td>\n",
       "      <td>222816.214665</td>\n",
       "      <td>73717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>CAMBRIDGESHIRE</td>\n",
       "      <td>226680.603347</td>\n",
       "      <td>32160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>EAST SUSSEX</td>\n",
       "      <td>229368.771959</td>\n",
       "      <td>31054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>ESSEX</td>\n",
       "      <td>234025.117029</td>\n",
       "      <td>70874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>RUTLAND</td>\n",
       "      <td>235240.240618</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>DORSET</td>\n",
       "      <td>239313.188820</td>\n",
       "      <td>22762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>READING</td>\n",
       "      <td>243440.568329</td>\n",
       "      <td>7985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>HAMPSHIRE</td>\n",
       "      <td>250628.699991</td>\n",
       "      <td>67579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>WEST SUSSEX</td>\n",
       "      <td>255318.828667</td>\n",
       "      <td>45916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>BRACKNELL FOREST</td>\n",
       "      <td>264293.307341</td>\n",
       "      <td>6143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>POOLE</td>\n",
       "      <td>265033.774489</td>\n",
       "      <td>8114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>BRIGHTON AND HOVE</td>\n",
       "      <td>276522.260494</td>\n",
       "      <td>14962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>BATH AND NORTH EAST SOMERSET</td>\n",
       "      <td>276606.562034</td>\n",
       "      <td>9021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>WEST BERKSHIRE</td>\n",
       "      <td>281778.408823</td>\n",
       "      <td>7781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>ISLES OF SCILLY</td>\n",
       "      <td>282605.156667</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>OXFORDSHIRE</td>\n",
       "      <td>295648.926991</td>\n",
       "      <td>30887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>WOKINGHAM</td>\n",
       "      <td>306010.842833</td>\n",
       "      <td>8156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>HERTFORDSHIRE</td>\n",
       "      <td>306911.661768</td>\n",
       "      <td>56536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>BUCKINGHAMSHIRE</td>\n",
       "      <td>333595.258420</td>\n",
       "      <td>26067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>SURREY</td>\n",
       "      <td>386774.070890</td>\n",
       "      <td>61605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>WINDSOR AND MAIDENHEAD</td>\n",
       "      <td>442982.051869</td>\n",
       "      <td>7118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>452225.698396</td>\n",
       "      <td>337924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           County      Price_adj  count_col\n",
       "0                   BLAENAU GWENT   73269.808797       2016\n",
       "1                  MERTHYR TYDFIL   92139.130742       1817\n",
       "2      CITY OF KINGSTON UPON HULL   93040.445431       8930\n",
       "3                  STOKE-ON-TRENT   93357.951855       8884\n",
       "4                        CHESHIRE   93582.763717          1\n",
       "5              RHONDDA CYNON TAFF   97942.358842       8668\n",
       "6               NEATH PORT TALBOT   98825.860753       4795\n",
       "7                       BLACKPOOL  102804.562947       5346\n",
       "8           BLACKBURN WITH DARWEN  104437.944282       4375\n",
       "9                      HARTLEPOOL  106497.267459       3278\n",
       "10        NORTH EAST LINCOLNSHIRE  107329.134530       6363\n",
       "11                  COUNTY DURHAM  109642.945978      19041\n",
       "12                     CAERPHILLY  111230.417326       6052\n",
       "13           REDCAR AND CLEVELAND  113482.892914       5042\n",
       "14                  MIDDLESBROUGH  116489.333010       4511\n",
       "15             NORTH LINCOLNSHIRE  117873.960134       6222\n",
       "16                        TORFAEN  119174.545248       2935\n",
       "17                     DARLINGTON  122229.516852       4389\n",
       "18                CARMARTHENSHIRE  125318.178312       6601\n",
       "19                       BRIDGEND  126621.160547       5795\n",
       "20                         HALTON  127586.846821       4083\n",
       "21                     MERSEYSIDE  127900.100493      46197\n",
       "22                   DENBIGHSHIRE  128796.602233       3645\n",
       "23                SOUTH YORKSHIRE  132634.798086      48310\n",
       "24                  TYNE AND WEAR  132836.084261      39882\n",
       "25               STOCKTON-ON-TEES  133128.442604       7856\n",
       "26                     LANCASHIRE  136263.712622      48494\n",
       "27                      LEICESTER  136328.622265       8937\n",
       "28             CITY OF NOTTINGHAM  136861.714621       9726\n",
       "29                        WREXHAM  137288.756408       4211\n",
       "30                         WREKIN  138486.625136       6979\n",
       "31                        NEWPORT  139437.992324       5377\n",
       "32                        SWANSEA  139771.051919       8619\n",
       "33                 WEST YORKSHIRE  140539.665257      85211\n",
       "34                  CITY OF DERBY  141649.405825      10028\n",
       "35               ISLE OF ANGLESEY  143291.146056       2690\n",
       "36                   LINCOLNSHIRE  143491.347628      36561\n",
       "37                NOTTINGHAMSHIRE  143506.978333      36573\n",
       "38               CITY OF PLYMOUTH  144657.571347      11370\n",
       "39             GREATER MANCHESTER  145231.369334      97432\n",
       "40                          CONWY  145332.987587       5180\n",
       "41                        GWYNEDD  147036.995239       4208\n",
       "42                     DERBYSHIRE  147294.649761      33630\n",
       "43       EAST RIDING OF YORKSHIRE  148328.992704      16176\n",
       "44                  WEST MIDLANDS  149291.498659      88194\n",
       "45                          LUTON  149538.754231       6908\n",
       "46                        CUMBRIA  151739.284148      21670\n",
       "47                 NORTHUMBERLAND  151911.398850      12656\n",
       "48           CITY OF PETERBOROUGH  152278.652859       8150\n",
       "49                  PEMBROKESHIRE  152330.039110       4493\n",
       "50                          POWYS  154791.276240       4342\n",
       "51                     FLINTSHIRE  155422.313028       5682\n",
       "52                     PORTSMOUTH  156280.267976       9020\n",
       "53                        SWINDON  157336.880129      10587\n",
       "54                  STAFFORDSHIRE  159243.168849      34041\n",
       "55                         TORBAY  162658.155764       7395\n",
       "56                    SOUTHAMPTON  164225.810983       9877\n",
       "57                         MEDWAY  164857.459768      11829\n",
       "58                     WARRINGTON  165903.758442       8687\n",
       "59                     CEREDIGION  165913.966997       2371\n",
       "60               NORTHAMPTONSHIRE  167826.572749      35029\n",
       "61                       THURROCK  169655.694670       6808\n",
       "62                        NORFOLK  171960.048301      45387\n",
       "63                        CARDIFF  175319.455507      14751\n",
       "64                  ISLE OF WIGHT  177023.087517       8017\n",
       "65                 LEICESTERSHIRE  178787.594166      32661\n",
       "66                     SHROPSHIRE  180224.994285      12745\n",
       "67                        SUFFOLK  186618.696740      37160\n",
       "68          THE VALE OF GLAMORGAN  186984.017697       5470\n",
       "69      CHESHIRE WEST AND CHESTER  187133.783921      14340\n",
       "70                       SOMERSET  190042.943176      27498\n",
       "71                 WORCESTERSHIRE  190473.349404      26388\n",
       "72                  HEREFORDSHIRE  191937.252028       7669\n",
       "73                NORTH YORKSHIRE  192596.686729      28848\n",
       "74          SOUTH GLOUCESTERSHIRE  194889.125019      12794\n",
       "75                       CORNWALL  199168.369268      27400\n",
       "76                        BEDFORD  199342.258983       7971\n",
       "77                           YORK  199457.010277      10186\n",
       "78                  MONMOUTHSHIRE  200824.646281       3956\n",
       "79                 NORTH SOMERSET  201204.299395      11683\n",
       "80                    BOURNEMOUTH  202581.476247      10514\n",
       "81                SOUTHEND-ON-SEA  202829.761767       9059\n",
       "82                  CHESHIRE EAST  203600.412792      17859\n",
       "83                CITY OF BRISTOL  204345.825712      21591\n",
       "84                   WARWICKSHIRE  205537.558500      25625\n",
       "85                         SLOUGH  207672.811222       4803\n",
       "86           CENTRAL BEDFORDSHIRE  208143.014559      14946\n",
       "87                  MILTON KEYNES  210914.827990      12499\n",
       "88                          DEVON  212786.196210      41792\n",
       "89                GLOUCESTERSHIRE  213038.633608      31422\n",
       "90                      WILTSHIRE  217053.701071      23671\n",
       "91                           KENT  222816.214665      73717\n",
       "92                 CAMBRIDGESHIRE  226680.603347      32160\n",
       "93                    EAST SUSSEX  229368.771959      31054\n",
       "94                          ESSEX  234025.117029      70874\n",
       "95                        RUTLAND  235240.240618       1957\n",
       "96                         DORSET  239313.188820      22762\n",
       "97                        READING  243440.568329       7985\n",
       "98                      HAMPSHIRE  250628.699991      67579\n",
       "99                    WEST SUSSEX  255318.828667      45916\n",
       "100              BRACKNELL FOREST  264293.307341       6143\n",
       "101                         POOLE  265033.774489       8114\n",
       "102             BRIGHTON AND HOVE  276522.260494      14962\n",
       "103  BATH AND NORTH EAST SOMERSET  276606.562034       9021\n",
       "104                WEST BERKSHIRE  281778.408823       7781\n",
       "105               ISLES OF SCILLY  282605.156667         45\n",
       "106                   OXFORDSHIRE  295648.926991      30887\n",
       "107                     WOKINGHAM  306010.842833       8156\n",
       "108                 HERTFORDSHIRE  306911.661768      56536\n",
       "109               BUCKINGHAMSHIRE  333595.258420      26067\n",
       "110                        SURREY  386774.070890      61605\n",
       "111        WINDSOR AND MAIDENHEAD  442982.051869       7118\n",
       "112                GREATER LONDON  452225.698396     337924"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum_county = df.groupby('County').agg({'Price_adj': 'mean', 'count_col': 'count'}).sort_values(by='Price_adj').reset_index()\n",
    "pd.set_option('display.max_rows', None)\n",
    "df_sum_county"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Greater London"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1dca6f31188>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(0, 1.2e6, 1e3)\n",
    "df_set1 = df_set1.dropna()\n",
    "df_set1_noout = df_set1[df_set1['Price_adj'] < df_set1['Price_adj'].quantile(.95)]\n",
    "y, x = np.histogram(df_set1_noout['Price_adj'], bins=bins, density=True)\n",
    "alpha, x0, inv_beta = gamma.fit(df_set1_noout['Price_adj'])\n",
    "\n",
    "# Plot of fit\n",
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.plot(bins[1:], y)\n",
    "ax.plot(bins, gamma.pdf(bins, alpha, loc=x0, scale=inv_beta), color='r')\n",
    "#plt.ylim([0, 3e-6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Greater Manchester & Midlands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 4.2e-06)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(0, 1.2e6, 1e3)\n",
    "df_subset = df_subset.dropna()\n",
    "df_subset_noout = df_subset[df_subset['Price_adj'] < df_subset['Price_adj'].quantile(.95)]\n",
    "y, x = np.histogram(df_subset_noout['Price_adj'], bins=bins, density=True)\n",
    "alpha, x0, inv_beta = gamma.fit(df_subset_noout['Price_adj'])\n",
    "\n",
    "# Plot of fit\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(bins[1:], y)\n",
    "ax.plot(bins, gamma.pdf(bins, alpha, loc=x0, scale=inv_beta), color='r')\n",
    "plt.ylim([0, 4.2e-6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(0, 1.2e6, 1e3)\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_figwidth(10)\n",
    "\n",
    "\n",
    "#=============================\n",
    "# Plot 1\n",
    "#=============================\n",
    "perc = df['Price_adj'].quantile(.95)\n",
    "df_noout = df[df['Price_adj'] < perc]\n",
    "#df_noout = df[df['Price_adj'] < 5e5]\n",
    "y, x = np.histogram(df_noout['Price_adj'], bins=bins, density=True)\n",
    "alpha, x0, inv_beta = gamma.fit(df_noout['Price_adj'])\n",
    "\n",
    "ax1.axis([0, 1.2e6, 0, 7e-6])\n",
    "ax1.plot(bins[1:], y)\n",
    "ax1.plot(bins, gamma.pdf(bins, alpha, loc=x0, scale=inv_beta), color='r')\n",
    "ax1.axvline(perc, color='g', linestyle='dashed')\n",
    "ax1.grid()\n",
    "\n",
    "plt.setp(ax1, xticks=[0, 2e5, 4e5, 6e5, 8e5, 1e6, 1.2e6], \n",
    "         xticklabels=['0', '200k', '400k', '600k', '800k', '1M', '1.2M'])\n",
    "plt.setp(ax1, xlabel='Price (£)')\n",
    "plt.setp(ax1, ylabel='pdf')\n",
    "ax1.legend(['Empirical distribution (p95)', 'Gamma fit', 'p95 threshold'])\n",
    "\n",
    "#=============================\n",
    "# Plot 2\n",
    "#=============================\n",
    "perc = df['Price_adj'].quantile(.99)\n",
    "df_noout = df[df['Price_adj'] < perc]\n",
    "y, x = np.histogram(df_noout['Price_adj'], bins=bins, density=True)\n",
    "alpha, x0, inv_beta = gamma.fit(df_noout['Price_adj'])\n",
    "\n",
    "\n",
    "ax2.axis([0, 1.2e6, 0, 7e-6])\n",
    "ax2.plot(bins[1:], y)\n",
    "ax2.plot(bins, gamma.pdf(bins, alpha, loc=x0, scale=inv_beta), color='r')\n",
    "ax2.axvline(perc, color='g', linestyle='dashed')\n",
    "ax2.grid()\n",
    "plt.setp(ax2, xticks=[0, 2e5, 4e5, 6e5, 8e5, 1e6, 1.2e6], \n",
    "         xticklabels=['0', '200k', '400k', '600k', '800k', '1M', '1.2M'])\n",
    "plt.setp(ax2, xlabel='Price(£)')\n",
    "plt.setp(ax2, ylabel='pdf')\n",
    "ax2.legend(['Empirical distribution (p99)', 'Gamma fit', 'p99 threshold'])\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('figures_mixture/gamma_fit.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df['Price_adj']\n",
    "log_x = np.log(x)\n",
    "log_bins = np.arange(8, 16, 0.01)\n",
    "plt.hist(log_x, bins=log_bins, alpha=1)\n",
    "plt.xticks(np.log(10**np.array([3, 4, 5, 6, 7])), ['1k', '10k', '100k', '1M', '10M'])\n",
    "plt.show()\n",
    "\n",
    "# Gamma fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.size'] = 12\n",
    "\n",
    "df = df.dropna()\n",
    "x = df['Price_adj']\n",
    "log_x = np.log(x)\n",
    "log_bins = np.arange(8, 19, 0.05)\n",
    "bins = np.exp(log_bins) #np.arange(1e3, 1e8, 1e5)\n",
    "y, _ = np.histogram(x, bins=bins, density=True)\n",
    "log_y, _ = np.histogram(log_x, bins=log_bins, density=True)\n",
    "\n",
    "# Normal fit - Baseline\n",
    "mu_singleFit, std_singleFit = norm.fit(log_x)\n",
    "pdf_single = norm.pdf(log_bins, loc=mu_singleFit, scale=std_singleFit)\n",
    "\n",
    "# Plot\n",
    "norm_y = sum(np.diff(bins)*y)\n",
    "norm_log_y = sum(np.diff(log_bins)*log_y)\n",
    "norm_single = sum(np.diff(log_bins)*pdf_single[1:])\n",
    "\n",
    "# Dif bins\n",
    "dif_bins = np.diff(bins)\n",
    "dif_log_bins = np.diff(log_bins)\n",
    "#=====================================================================\n",
    "# PLOTS\n",
    "#=====================================================================\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3)\n",
    "fig.set_figwidth(15)\n",
    "\n",
    "# Plot 1\n",
    "ax1.semilogy(log_bins[1:], log_y/norm_log_y/dif_bins*dif_log_bins)\n",
    "ax1.semilogy(log_bins[1:], pdf_single[1:]/norm_single/dif_bins*dif_log_bins)\n",
    "plt.setp(ax1, ylim=[1e-12, 1e-4])\n",
    "plt.setp(ax1, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "ax1.grid()\n",
    "ax1.legend(['Empirical distribution', 'Log-normal fit'])\n",
    "plt.setp(ax1, xlabel='Price (£)')\n",
    "plt.setp(ax1, ylabel='pdf')\n",
    "\n",
    "# Plot 2\n",
    "ax2.plot(log_bins[1:], log_y/norm_log_y/dif_bins*dif_log_bins)\n",
    "ax2.plot(log_bins[1:], pdf_single[1:]/norm_single/dif_bins*dif_log_bins)\n",
    "plt.setp(ax2, ylim=[1e-11, 6e-6])\n",
    "plt.setp(ax2, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "ax2.grid()\n",
    "ax2.legend(['Empirical distribution', 'Log-normal fit'])\n",
    "plt.setp(ax2, xlabel='Price (£)')\n",
    "plt.setp(ax2, ylabel='pdf')\n",
    "\n",
    "# Plot 3\n",
    "ax3.plot(bins[1:], y/norm_y)\n",
    "ax3.plot(bins, pdf_single/norm_single/np.exp(log_bins))\n",
    "plt.setp(ax3, xticks=[0, 2e5, 4e5, 6e5, 8e5, 1e6, 1.2e6], \n",
    "         xticklabels=['0', '200k', '400k', '600k', '800k', '1M', '1.2M'])\n",
    "ax3.axis([0, 1.2e6, 0, 6e-6])\n",
    "ax3.grid()\n",
    "ax3.legend(['Empirical distribution', 'Log-normal fit'])\n",
    "plt.setp(ax3, xlabel='Price (£)')\n",
    "plt.setp(ax3, ylabel='pdf')\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/lognormal_fit.png', dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EM Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['Price_adj']\n",
    "### Run the EM algorithm\n",
    "## Initialize the parameters\n",
    "KK_vec = np.arange(6, 7)\n",
    "\n",
    "# Parameters\n",
    "w_vec = []\n",
    "mu_vec = []\n",
    "std_vec = []\n",
    "w_ini_vec = []\n",
    "mu_ini_vec = []\n",
    "std_ini_vec = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========================================\n",
      "Mixture with 6 components\n",
      "[1, -4345968.634045105, inf]\n",
      "[2, -4561142.752665209, 0.04717548436614283]\n",
      "[3, -4677573.910375801, 0.02489135606223656]\n",
      "[4, -4761019.682703069, 0.01752686984900095]\n",
      "[5, -4828835.309894625, 0.014043889020732737]\n",
      "[6, -4887251.656693353, 0.011952801063297795]\n",
      "[7, -4938957.65514636, 0.01046901027773133]\n",
      "[8, -4985323.820098668, 0.009300532247349636]\n",
      "[9, -5027157.514330835, 0.008321540376030017]\n",
      "[10, -5064990.490991956, 0.007469505960259191]\n",
      "[11, -5099205.211596792, 0.006709814409316905]\n",
      "[12, -5130101.277128316, 0.006022505962849053]\n",
      "[13, -5157936.177906341, 0.005396519037450155]\n",
      "[14, -5182951.3166292375, 0.004826427491733671]\n",
      "[15, -5205386.634308244, 0.004310019457755051]\n",
      "[16, -5225485.672313755, 0.0038463483139954276]\n",
      "[17, -5243493.514417743, 0.003434321422248882]\n",
      "[18, -5259650.712175641, 0.0030719145894033168]\n",
      "[19, -5274186.172111279, 0.0027559626189341577]\n",
      "[20, -5287311.137114098, 0.0024823515511861456]\n",
      "[21, -5299215.302163264, 0.0022464014708566244]\n",
      "[22, -5310065.197849173, 0.0020432697681949433]\n",
      "[23, -5320004.439879939, 0.0018682770180150768]\n",
      "[24, -5329155.250590265, 0.0017171221854180886]\n",
      "[25, -5337620.687107154, 0.001585994399590318]\n",
      "[26, -5345487.138687189, 0.001471606118570984]\n",
      "[27, -5352826.801465474, 0.0013711750913136048]\n",
      "[28, -5359699.962458478, 0.0012823779392776903]\n",
      "[29, -5366157.014123997, 0.0012032916011445708]\n",
      "[30, -5372240.178371306, 0.0011323328900669297]\n",
      "[31, -5377984.952031568, 0.0010682018844422373]\n",
      "[32, -5383421.302217368, 0.001009831830096815]\n",
      "[33, -5388574.646027783, 0.0009563463715240004]\n",
      "[34, -5393466.649170534, 0.00090702389779367]\n",
      "[35, -5398115.875193564, 0.0008612682888849171]\n",
      "[36, -5402538.312908189, 0.0008185851646916964]\n",
      "[37, -5406747.805238593, 0.0007785627297662053]\n",
      "[38, -5410756.398655732, 0.0007408563834318437]\n",
      "[39, -5414574.628767919, 0.0007051763756104083]\n",
      "[40, -5418211.7546072975, 0.0006712779057197069]\n",
      "[41, -5421675.951648219, 0.0006389531709042741]\n",
      "[42, -5424974.4715629835, 0.0006080249652888372]\n",
      "[43, -5428113.775090402, 0.0005783415118940171]\n",
      "[44, -5431099.643094906, 0.0005497722746258005]\n",
      "[45, -5433937.26986467, 0.0005222045505569517]\n",
      "[46, -5436631.341883773, 0.0004955406849730401]\n",
      "[47, -5439186.104670285, 0.0004696957848746249]\n",
      "[48, -5441605.419764624, 0.00044459583297816127]\n",
      "[49, -5443892.813550451, 0.0004201761247270126]\n",
      "[50, -5446051.519271882, 0.0003963799669893727]\n",
      "[51, -5448084.513357526, 0.0003731575897288264]\n",
      "[52, -5449994.54695982, 0.0003504652318156886]\n",
      "[53, -5451784.1734563215, 0.00032826436989465043]\n",
      "[54, -5453455.772529614, 0.00030652106536059934]\n",
      "[55, -5455011.571337275, 0.000285205409248958]\n",
      "[56, -5456453.663198323, 0.00026429104873997846]\n",
      "[57, -5457784.024152769, 0.00024375478189654152]\n",
      "[58, -5459004.527694096, 0.000223576209753141]\n",
      "[59, -5460116.957927387, 0.00020373743673681656]\n",
      "[60, -5461123.021367006, 0.0001842228119898611]\n",
      "[61, -5462024.357555112, 0.00016501870535585062]\n",
      "[62, -5462822.548655455, 0.00014611331289528825]\n",
      "[63, -5463519.128153566, 0.0001274964874785172]\n",
      "[64, -5464115.588775476, 0.0001091595908284653]\n",
      "[65, -5464613.389720304, 9.109536381181689e-05]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "log_x = np.log(x)\n",
    "for KK in KK_vec:\n",
    "    # INITIALISATION\n",
    "    w = 1/KK*np.ones((KK, 1))  #Assign equal weight to each component to start with\n",
    "    mu = np.random.normal(loc=log_x.mean(), scale=log_x.std()/KK, size=KK)#\n",
    "    std = log_x.std()*np.ones(KK)/KK\n",
    "\n",
    "    # Initial parameters\n",
    "    w_ini = w.copy()\n",
    "    mu_ini = mu.copy()\n",
    "    std_ini = std.copy()\n",
    "    # Parameters\n",
    "    sw = False\n",
    "    QQ = -np.inf\n",
    "    epsilon = 1e-4\n",
    "    max_iter = 100\n",
    "    i = 0\n",
    "    # x = df_noout['Price_adj']\n",
    "    print(\"===========================================\")\n",
    "    print(\"Mixture with {} components\".format(KK))\n",
    "    while((~sw) & (i < max_iter)):\n",
    "        i+=1\n",
    "        ## E step\n",
    "        L = np.zeros([KK, len(x)])\n",
    "        v = np.zeros([KK, len(x)])\n",
    "        for k in range(KK):\n",
    "            L[k, :] = norm.logpdf(log_x, loc=mu[k], scale=std[k])\n",
    "        Lmax = np.amax(L, axis=0)\n",
    "        for k in range(KK):\n",
    "            L[k, :] -= Lmax\n",
    "        denom = (w*np.exp(L)).sum(axis=0)\n",
    "        for k in range(KK):\n",
    "            v[k, :] = w[k]*np.exp(L[k, :])/denom\n",
    "\n",
    "        ## M step\n",
    "        for k in range(KK):\n",
    "            w[k] = v[k,:].mean()\n",
    "            mu[k] = (v[k,:]*log_x).sum()/v[k, :].sum()\n",
    "            std[k] = np.sqrt((v[k,:]*(log_x-mu[k])**2).sum()/v[k,:].sum())\n",
    "\n",
    "        ##Check convergence\n",
    "        QQn = 0\n",
    "        for k in range(KK):\n",
    "            QQn += (v[k, :]*(np.log(w[k]) + norm.logpdf(log_x, loc=mu[k], scale=std[k]))).sum()\n",
    "        rel_error = abs(QQn-QQ)/abs(QQn)\n",
    "        if(rel_error < epsilon):\n",
    "            sw=True\n",
    "\n",
    "        QQ = QQn\n",
    "        print([i, QQ, rel_error])\n",
    "\n",
    "    ## ASSIGN Results\n",
    "    w_vec.append(w)\n",
    "    mu_vec.append(mu)\n",
    "    std_vec.append(std)\n",
    "    w_ini_vec.append(w_ini)\n",
    "    mu_ini_vec.append(mu_ini)\n",
    "    std_ini_vec.append(std_ini)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BIC for mixture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# n = len(x)\n",
    "# BIC_vec = []\n",
    "# for index, KK in enumerate(KK_vec):\n",
    "#     LL = np.zeros(n)\n",
    "#     for k in range(KK):\n",
    "#         LL += w_vec[index][k]*lognorm.pdf(x, loc=mu_vec[index][k], scale=std_vec[index][k])\n",
    "#     LL = np.log(LL).sum()\n",
    "#     BIC_vec.append(-2*LL + (3*KK-1)*np.log(n))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store and plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('EMfit_LogScale.pickle', 'wb') as f:\n",
    "    pickle.dump([KK_vec, w_vec, mu_vec, std_vec], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['Price_adj']\n",
    "### Run the EM algorithm\n",
    "## Initialize the parameters\n",
    "KK_vec = np.arange(6, 7)\n",
    "\n",
    "# Parameters\n",
    "w_vec = []\n",
    "mu_vec = []\n",
    "std_vec = []\n",
    "w_ini_vec = []\n",
    "mu_ini_vec = []\n",
    "std_ini_vec = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========================================\n",
      "Mixture with 6 components\n",
      "[1, -36051070.66555333, inf]\n",
      "[2, -35575849.8257936, 0.013357961709608407]\n",
      "[3, -35306239.79036588, 0.007636328224941378]\n",
      "[4, -35168907.97662805, 0.0039049211829124074]\n",
      "[5, -35100674.77568613, 0.0019439284679844588]\n",
      "[6, -35062128.22731208, 0.0010993784554134125]\n",
      "[7, -35035059.4184294, 0.0007726206072436282]\n",
      "[8, -35011893.844969004, 0.0006616486832437202]\n",
      "[9, -34989671.84047764, 0.0006351018264097872]\n",
      "[10, -34967525.91719405, 0.0006333282868232589]\n",
      "[11, -34945450.67152252, 0.0006317058514721757]\n",
      "[12, -34923684.17535287, 0.0006232588767083917]\n",
      "[13, -34902456.603622735, 0.0006081970667913826]\n",
      "[14, -34881917.07921315, 0.0005888301483815127]\n",
      "[15, -34862128.14693682, 0.0005676340868499061]\n",
      "[16, -34843079.18813082, 0.0005467071008032383]\n",
      "[17, -34824701.54171546, 0.0005277187054523998]\n",
      "[18, -34806880.9080733, 0.0005119859400566394]\n",
      "[19, -34789466.99835528, 0.0005005512076067454]\n",
      "[20, -34772281.60942785, 0.0004942266694047025]\n",
      "[21, -34755126.53253923, 0.0004935984587065763]\n",
      "[22, -34737792.65489288, 0.000498991914039055]\n",
      "[23, -34720071.383203976, 0.0005104042412042289]\n",
      "[24, -34701768.984793566, 0.0005274197525327848]\n",
      "[25, -34682723.4932734, 0.0005491348314632848]\n",
      "[26, -34662822.54579042, 0.0005741294569041849]\n",
      "[27, -34642019.2960363, 0.0006005207022240826]\n",
      "[28, -34620342.98380056, 0.000626114889904052]\n",
      "[29, -34597901.308159865, 0.0006486426861794059]\n",
      "[30, -34574873.40194717, 0.0006660300948896047]\n",
      "[31, -34551494.31964694, 0.0006766446071461768]\n",
      "[32, -34528033.708807595, 0.0006794655912699745]\n",
      "[33, -34504772.19738355, 0.0006741534559618444]\n",
      "[34, -34481978.9369646, 0.0006610194983477931]\n",
      "[35, -34459892.979228675, 0.0006409177692234454]\n",
      "[36, -34438710.1014483, 0.0006150891748842542]\n",
      "[37, -34418575.61802432, 0.0005849888632065897]\n",
      "[38, -34399582.78650502, 0.0005521238916522312]\n",
      "[39, -34381775.75027318, 0.0005179207834166821]\n",
      "[40, -34365155.627515234, 0.000483632983889113]\n",
      "[41, -34349688.34797278, 0.0004502887882348552]\n",
      "[42, -34335313.057657816, 0.00041867363465785627]\n",
      "[43, -34321950.226527154, 0.0003893377573962288]\n",
      "[44, -34309508.90458345, 0.00036262022806294713]\n",
      "[45, -34297892.82993694, 0.00033868187483433634]\n",
      "[46, -34287005.28767691, 0.0003175413591440578]\n",
      "[47, -34276752.75592885, 0.0002991103568374933]\n",
      "[48, -34267047.4632041, 0.00028322523950079047]\n",
      "[49, -34257809.02745552, 0.00026967386446614494]\n",
      "[50, -34248965.361238606, 0.0002582170329421451]\n",
      "[51, -34240453.018675745, 0.0002486048463850306]\n",
      "[52, -34232217.137733735, 0.00024058859257851235]\n",
      "[53, -34224211.10338515, 0.0002339289669643496]\n",
      "[54, -34216396.0291489, 0.0002284014432611127]\n",
      "[55, -34208740.129656255, 0.00022379951625306184]\n",
      "[56, -34201218.03668112, 0.00021993640598018038]\n",
      "[57, -34193810.09558194, 0.00021664567588327704]\n",
      "[58, -34186501.667691156, 0.0002137810988040364]\n",
      "[59, -34179282.45598682, 0.0002112160111503919]\n",
      "[60, -34172145.86556546, 0.00020884232583576502]\n",
      "[61, -34165088.40633261, 0.00020656932447850153]\n",
      "[62, -34158109.14242763, 0.0002043223140918068]\n",
      "[63, -34151209.1908302, 0.00020204120910841686]\n",
      "[64, -34144391.27009482, 0.0001996790829114292]\n",
      "[65, -34137659.29903924, 0.00019720072183659144]\n",
      "[66, -34131018.044351526, 0.0001945812070147343]\n",
      "[67, -34124472.81539794, 0.0001918045441754527]\n",
      "[68, -34118029.20396184, 0.00018886235771644661]\n",
      "[69, -34111692.86619716, 0.00018575266227726132]\n",
      "[70, -34105469.34372704, 0.0001824787223245983]\n",
      "[71, -34099363.92055333, 0.00017904800769701957]\n",
      "[72, -34093381.51226489, 0.00017547125052063293]\n",
      "[73, -34087526.58394172, 0.00017176160636806745]\n",
      "[74, -34081803.09314253, 0.0001679339201493191]\n",
      "[75, -34076214.4544325, 0.00016400409492382668]\n",
      "[76, -34070763.52204552, 0.00015998855979415985]\n",
      "[77, -34065452.58747229, 0.00015590383129640984]\n",
      "[78, -34060283.3890098, 0.0001517661612925454]\n",
      "[79, -34055257.13058643, 0.00014759126334297482]\n",
      "[80, -34050374.507477485, 0.0001433941088628426]\n",
      "[81, -34045635.73683703, 0.0001391887840510461]\n",
      "[82, -34041040.59128145, 0.0001349883985848269]\n",
      "[83, -34036588.43405976, 0.00013080503735899194]\n",
      "[84, -34032278.25462815, 0.0001266497470243167]\n",
      "[85, -34028108.70370457, 0.00012253254977772274]\n",
      "[86, -34024078.127110854, 0.00011846247762133498]\n",
      "[87, -34020184.59791211, 0.00011444762116259525]\n",
      "[88, -34016425.94653518, 0.00011049518790834949]\n",
      "[89, -34012799.78869053, 0.00010661156585684798]\n",
      "[90, -34009303.551036365, 0.00010280238902621964]\n",
      "[91, -34005934.49461243, 9.90726022972865e-05]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "for KK in KK_vec:\n",
    "    # INITIALISATION\n",
    "    w = 1/KK*np.ones((KK, 1))  #Assign equal weight to each component to start with\n",
    "    mu = np.random.normal(loc=x.mean(), scale=x.std()/KK, size=KK)#\n",
    "    std = x.std()*np.ones(KK)/KK\n",
    "\n",
    "    # Initial parameters\n",
    "    w_ini = w.copy()\n",
    "    mu_ini = mu.copy()\n",
    "    std_ini = std.copy()\n",
    "    # Parameters\n",
    "    sw = False\n",
    "    QQ = -np.inf\n",
    "    epsilon = 1e-4\n",
    "    max_iter = 100\n",
    "    i = 0\n",
    "    # x = df_noout['Price_adj']\n",
    "    print(\"===========================================\")\n",
    "    print(\"Mixture with {} components\".format(KK))\n",
    "    while((~sw) & (i < max_iter)):\n",
    "        i+=1\n",
    "        ## E step\n",
    "        L = np.zeros([KK, len(x)])\n",
    "        v = np.zeros([KK, len(x)])\n",
    "        for k in range(KK):\n",
    "            L[k, :] = norm.logpdf(x, loc=mu[k], scale=std[k])\n",
    "        Lmax = np.amax(L, axis=0)\n",
    "        for k in range(KK):\n",
    "            L[k, :] -= Lmax\n",
    "        denom = (w*np.exp(L)).sum(axis=0)\n",
    "        for k in range(KK):\n",
    "            v[k, :] = w[k]*np.exp(L[k, :])/denom\n",
    "\n",
    "        ## M step\n",
    "        for k in range(KK):\n",
    "            w[k] = v[k,:].mean()\n",
    "            mu[k] = (v[k,:]*x).sum()/v[k, :].sum()\n",
    "            std[k] = np.sqrt((v[k,:]*(x-mu[k])**2).sum()/v[k,:].sum())\n",
    "\n",
    "        ##Check convergence\n",
    "        QQn = 0\n",
    "        for k in range(KK):\n",
    "            QQn += (v[k, :]*(np.log(w[k]) + norm.logpdf(x, loc=mu[k], scale=std[k]))).sum()\n",
    "        rel_error = abs(QQn-QQ)/abs(QQn)\n",
    "        if(rel_error < epsilon):\n",
    "            sw=True\n",
    "\n",
    "        QQ = QQn\n",
    "        print([i, QQ, rel_error])\n",
    "\n",
    "    ## ASSIGN Results\n",
    "    w_vec.append(w)\n",
    "    mu_vec.append(mu)\n",
    "    std_vec.append(std)\n",
    "    w_ini_vec.append(w_ini)\n",
    "    mu_ini_vec.append(mu_ini)\n",
    "    std_ini_vec.append(std_ini)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('EMfit.pickle', 'wb') as f:\n",
    "    pickle.dump([KK_vec, w_vec, mu_vec, std_vec], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('EMfit_LogScale.pickle', 'rb') as f:\n",
    "    [KK_vec, w_vec, mu_vec, std_vec] = pickle.load(f)\n",
    "\n",
    "KK_log = KK_vec[0]\n",
    "w_log = w_vec[0]\n",
    "mu_log = mu_vec[0]\n",
    "std_log = std_vec[0]\n",
    "\n",
    "with open('EMfit.pickle', 'rb') as f:\n",
    "    [KK_vec, w_vec, mu_vec, std_vec] = pickle.load(f)\n",
    "\n",
    "KK_lin = KK_vec[0]\n",
    "w_lin = w_vec[0]\n",
    "mu_lin = mu_vec[0]\n",
    "std_lin = std_vec[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.size'] = 12\n",
    "df = df.dropna()\n",
    "x = df['Price_adj']\n",
    "log_x = np.log(x)\n",
    "log_bins = np.arange(8, 19, 0.05)\n",
    "bins = np.exp(log_bins) #np.arange(1e3, 1e8, 1e5)\n",
    "y, _ = np.histogram(x, bins=bins, density=True)\n",
    "log_y, _ = np.histogram(log_x, bins=log_bins, density=True)\n",
    "\n",
    "# Normal fit - Baseline\n",
    "mu_singleFit, std_singleFit = norm.fit(log_x)\n",
    "pdf_single = norm.pdf(log_bins, loc=mu_singleFit, scale=std_singleFit)\n",
    "\n",
    "\n",
    "# Dif bins\n",
    "dif_bins = np.diff(bins)\n",
    "dif_log_bins = np.diff(log_bins)\n",
    "\n",
    "# Initial pdf\n",
    "pdf_log = 0*log_bins\n",
    "pdf_lin = 0*bins\n",
    "for k in range(KK):\n",
    "    pdf_log += w_log[k]*norm.pdf(log_bins, loc=mu_log[k], scale=std_log[k])\n",
    "    pdf_lin += w_lin[k]*norm.pdf(bins, loc=mu_lin[k], scale=std_lin[k]) \n",
    "\n",
    "# Plot\n",
    "norm_y = sum(np.diff(bins)*y)\n",
    "norm_log_y = sum(np.diff(log_bins)*log_y)\n",
    "norm_single = sum(np.diff(log_bins)*pdf_single[1:])\n",
    "norm_log = sum(np.diff(log_bins)*pdf_log[1:])\n",
    "norm_lin = sum(np.diff(bins)*pdf_lin[1:])\n",
    "\n",
    "#=====================================================================\n",
    "# PLOTS\n",
    "#=====================================================================\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3)\n",
    "fig.set_figwidth(15)\n",
    "\n",
    "# Plot 1\n",
    "ax1.semilogy(log_bins[1:], log_y/norm_log_y/dif_bins*dif_log_bins)\n",
    "ax1.semilogy(log_bins[1:], pdf_single[1:]/norm_single/dif_bins*dif_log_bins)\n",
    "ax1.semilogy(log_bins[1:], pdf_log[1:]/norm_log/dif_bins*dif_log_bins)\n",
    "ax1.semilogy(log_bins[1:], pdf_lin[1:]/norm_lin)\n",
    "plt.setp(ax1, ylim=[1e-12, 6e-2])\n",
    "plt.setp(ax1, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "ax1.grid()\n",
    "ax1.legend(['data (pdf)', 'Log-normal fit', 'GM (log)', 'GM'])\n",
    "plt.setp(ax1, xlabel='Price (£)', ylabel='pdf')\n",
    "\n",
    "# Plot 2\n",
    "ax2.plot(log_bins[1:], log_y/norm_log_y/dif_bins*dif_log_bins)\n",
    "ax2.plot(log_bins[1:], pdf_single[1:]/norm_single/dif_bins*dif_log_bins)\n",
    "ax2.plot(log_bins[1:], pdf_log[1:]/norm_log/dif_bins*dif_log_bins)\n",
    "ax2.plot(log_bins[1:], pdf_lin[1:]/norm_lin)\n",
    "plt.setp(ax2, ylim=[1e-12, 6e-6])\n",
    "plt.setp(ax2, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "ax2.grid()\n",
    "ax2.legend(['data (pdf)', 'Log-normal fit', 'GM (log)', 'GM'])\n",
    "plt.setp(ax2, xlabel='Price (£)', ylabel='pdf')\n",
    "\n",
    "# Plot 3\n",
    "ax3.plot(bins[1:], y/norm_y)\n",
    "ax3.plot(bins, pdf_single/norm_single/np.exp(log_bins))\n",
    "ax3.plot(bins, pdf_log/norm_log/np.exp(log_bins))\n",
    "ax3.plot(bins, pdf_lin/norm_lin)\n",
    "plt.setp(ax3, xticks=[0, 2e5, 4e5, 6e5, 8e5, 1e6, 1.2e6], \n",
    "         xticklabels=['0', '200k', '400k', '600k', '800k', '1M', '1.2M'])\n",
    "ax3.axis([0, 1.2e6, 0, 6e-6])\n",
    "ax3.grid()\n",
    "ax3.legend(['data (pdf)', 'Log-normal fit', 'GM (log)', 'GM'])\n",
    "plt.setp(ax3, xlabel='Price (£)', ylabel='pdf')\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/gaussian_mixture_fit.png', dpi=600)\n",
    "\n",
    "#=====================================================================\n",
    "# PLOTS\n",
    "#=====================================================================\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3)\n",
    "fig.set_figwidth(15)\n",
    "for k in range(KK):\n",
    "    pdf_log = w_log[k]*norm.pdf(log_bins, loc=mu_log[k], scale=std_log[k])\n",
    "    ax1.semilogy(log_bins, pdf_log/bins)\n",
    "plt.setp(ax1, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "plt.setp(ax1, ylim=[1e-11, 3.5e-6])    \n",
    "plt.setp(ax1, xlabel='Price (£)', ylabel='pdf')\n",
    "ax1.grid()\n",
    "ax1.legend(['C1', 'C2', 'C3', 'C4', 'C5', 'C6'])\n",
    "\n",
    "for k in range(KK):\n",
    "    pdf_log = w_log[k]*norm.pdf(log_bins, loc=mu_log[k], scale=std_log[k])\n",
    "    ax2.plot(log_bins, pdf_log/bins)\n",
    "plt.setp(ax2, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "plt.setp(ax2, ylim=[1e-11, 3.5e-6])    \n",
    "plt.setp(ax2, xlabel='Price (£)', ylabel='pdf')\n",
    "ax2.grid()\n",
    "ax2.legend(['C1', 'C2', 'C3', 'C4', 'C5', 'C6'])\n",
    "\n",
    "for k in range(KK):\n",
    "    pdf_log = w_log[k]*norm.pdf(log_bins, loc=mu_log[k], scale=std_log[k])\n",
    "    ax3.plot(bins, pdf_log/norm_log/np.exp(log_bins))\n",
    "plt.setp(ax3, xticks=[0, 2e5, 4e5, 6e5, 8e5, 1e6, 1.2e6], \n",
    "         xticklabels=['0', '200k', '400k', '600k', '800k', '1M', '1.2M'])\n",
    "plt.setp(ax3, xlim=[1e-11, 1.2e6])\n",
    "plt.setp(ax3, ylim=[1e-11, 3.5e-6])\n",
    "plt.setp(ax3, xlabel='Price (£)', ylabel='pdf')\n",
    "ax3.grid()\n",
    "ax3.legend(['C1', 'C2', 'C3', 'C4', 'C5', 'C6'])\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/gaussian_mixture_log.png', dpi=600)\n",
    "\n",
    "#=====================================================================\n",
    "# PLOTS\n",
    "#=====================================================================\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3)\n",
    "fig.set_figwidth(15)\n",
    "\n",
    "for k in range(KK):\n",
    "    pdf_lin = w_lin[k]*norm.pdf(bins, loc=mu_lin[k], scale=std_lin[k])\n",
    "    ax1.semilogy(log_bins, pdf_lin)\n",
    "plt.setp(ax1, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "plt.setp(ax1, ylim=[1e-11, 3.5e-6])\n",
    "plt.setp(ax1, xlabel='Price (£)', ylabel='pdf')\n",
    "ax1.grid()\n",
    "ax1.legend(['C1', 'C2', 'C3', 'C4', 'C5', 'C6'])\n",
    "\n",
    "for k in range(KK):\n",
    "    pdf_lin = w_lin[k]*norm.pdf(bins, loc=mu_lin[k], scale=std_lin[k])\n",
    "    ax2.plot(log_bins, pdf_lin)\n",
    "plt.setp(ax2, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "plt.setp(ax2, ylim=[1e-11, 3.5e-6])\n",
    "plt.setp(ax2, xlabel='Price (£)', ylabel='pdf')\n",
    "ax2.grid()\n",
    "ax2.legend(['C1', 'C2', 'C3', 'C4', 'C5', 'C6'])\n",
    "\n",
    "for k in range(KK):\n",
    "    pdf_lin = w_lin[k]*norm.pdf(bins, loc=mu_lin[k], scale=std_lin[k])\n",
    "    ax3.plot(bins, pdf_lin)\n",
    "plt.setp(ax3, xticks=[0, 2e5, 4e5, 6e5, 8e5, 1e6, 1.2e6], \n",
    "         xticklabels=['0', '200k', '400k', '600k', '800k', '1M', '1.2M'])\n",
    "plt.setp(ax3, xlim=[0, 1.2e6])\n",
    "plt.setp(ax3, ylim=[1e-11, 3.5e-6])\n",
    "plt.setp(ax3, xlabel='Price (£)', ylabel='pdf')\n",
    "ax3.grid()\n",
    "ax3.legend(['C1', 'C2', 'C3', 'C4', 'C5', 'C6'])\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/gaussian_mixture_lin.png', dpi=600)\n"
   ]
  }
 ],
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gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import scipy\n",
    "from scipy.stats import gamma\n",
    "from scipy.stats import norm\n",
    "from scipy.stats import lognorm\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DOWNLOAD DATA FROM\n",
    "\n",
    "https://www.kaggle.com/hm-land-registry/uk-housing-prices-paid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selected columns\n",
    "columns = [\n",
    "       'Price', 'Date of Transfer',\n",
    "       'Property Type', 'Old/New', 'Duration', 'Town/City', 'District',\n",
    "       'County', 'PPDCategory Type'\n",
    "       ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min date: 2012-01-01 00:00\n",
      "Min max: 2014-12-30 00:00\n"
     ]
    }
   ],
   "source": [
    "# Read part of the dataset\n",
    "df = pd.read_csv(\"data/train_price_houses.csv\", usecols=columns)\n",
    "print(\"Min date: {}\".format(df['Date of Transfer'].min()))\n",
    "print(\"Min max: {}\".format(df['Date of Transfer'].max()))\n",
    "df['Date of Transfer'] = pd.DatetimeIndex(df['Date of Transfer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2461243, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adjust price for inflation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['count_col'] = 0\n",
    "df['Price_mean'] = df['Price'] \n",
    "df['Price_std'] = df['Price'] \n",
    "df_group = df.groupby(by=['Town/City', 'County']).agg({'count_col': 'count', \n",
    "                                                       'Price_mean' : 'mean', 'Price_std': 'std'})\n",
    "df_group = df_group.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group_date = df.groupby(by='Date of Transfer').agg({'Price_mean': 'mean', 'count_col': 'count'}).reset_index()\n",
    "df_group_date['Date of Transfer'] = pd.to_datetime(df_group_date['Date of Transfer'])\n",
    "\n",
    "# One week rolling window\n",
    "df_group_date['Price_x_count'] = df_group_date['Price_mean']*df_group_date['count_col']\n",
    "df_group_date['Price_sum'] = df_group_date['Price_x_count'].rolling(window=20, min_periods=7).sum()\n",
    "df_group_date['count_sum'] = df_group_date['count_col'].rolling(window=20, min_periods=7).sum()\n",
    "df_group_date['Price_mean'] = df_group_date['Price_sum']/df_group_date['count_sum']\n",
    "\n",
    "df_group_date = df_group_date.dropna().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.array(df_group_date.index, dtype=np.float32)\n",
    "y_train = np.array(df_group_date['Price_mean'])\n",
    "\n",
    "params, _ = scipy.optimize.curve_fit(lambda t,a,b: a*np.exp(b*t),  x_train,  y_train, p0=(2e5, 0.01))\n",
    "alpha, beta = params[0], params[1]\n",
    "y_pred = alpha*np.exp(beta*x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00022242820326361787"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "locator = mdates.YearLocator(1)\n",
    "locator_fmt = mdates.DateFormatter(\"%Y\")\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(df_group_date['Date of Transfer'], df_group_date['Price_mean'])\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(locator_fmt)\n",
    "plt.yticks([220e3, 240e3, 260e3, 280e3, 300e3], ['220k', '240k', '260k', '280k', '300k'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Mean price (£)')\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/prices.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "locator = mdates.YearLocator(1)\n",
    "locator_fmt = mdates.DateFormatter(\"%Y\")\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(df_group_date['Date of Transfer'], df_group_date['Price_mean'])\n",
    "ax.plot(df_group_date['Date of Transfer'], y_pred, color='r')\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(locator_fmt)\n",
    "plt.yticks([220e3, 240e3, 260e3, 280e3, 300e3], ['220k', '240k', '260k', '280k', '300k'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Mean price (£)')\n",
    "plt.grid()\n",
    "plt.legend(['Mean prices', 'Exponential fit'])\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/prices_fit.png', dpi=600)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correction factor\n",
    "ini_date = df_group_date['Date of Transfer'][0]\n",
    "df_group_date['factor'] = np.exp(-beta*(df_group_date['Date of Transfer'] - ini_date).dt.days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df = df.drop(columns=['factor'])\n",
    "except:\n",
    "    pass\n",
    "df = pd.merge(df, df_group_date[['Date of Transfer', 'factor']], on='Date of Transfer', how='left')\n",
    "df['Price_adj'] = df['Price']*df['factor']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Adjusted Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group_adj = df[df['Date of Transfer'] > ini_date].groupby(by='Date of Transfer').agg({'Price_adj': 'mean', 'count_col': 'count'}).reset_index()\n",
    "df_group_adj['Date of Transfer'] = pd.to_datetime(df_group_adj['Date of Transfer'])\n",
    "\n",
    "# One week rolling window\n",
    "df_group_adj['Price_x_count'] = df_group_adj['Price_adj']*df_group_adj['count_col']\n",
    "df_group_adj['Price_sum'] = df_group_adj['Price_x_count'].rolling(window=20, min_periods=7).sum()\n",
    "df_group_adj['count_sum'] = df_group_adj['count_col'].rolling(window=20, min_periods=7).sum()\n",
    "df_group_adj['Price_adj'] = df_group_adj['Price_sum']/df_group_adj['count_sum']\n",
    "df_group_adj = df_group_adj.dropna().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "locator = mdates.YearLocator(1)\n",
    "locator_fmt = mdates.DateFormatter(\"%Y\")\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(df_group_adj['Date of Transfer'], df_group_adj['Price_adj'])\n",
    "plt.hlines(alpha, df_group_adj['Date of Transfer'].min(), df_group_adj['Date of Transfer'].max(), color='r')\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(locator_fmt)\n",
    "plt.yticks([210e3, 220e3, 230e3, 240e3, 250e3, 260e3], ['210k', '220k', '230k', '240k', '250k', '260k'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Mean price (£)')\n",
    "plt.grid()\n",
    "plt.legend(['Adjusted prices', 'Mean price'])\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/prices_adjusted.png', dpi=600)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split between old and new\n",
    "col = 'County'\n",
    "set1 = ['GREATER LONDON']\n",
    "set2 = ['WEST MIDLANDS', 'GREATER MANCHESTER']\n",
    "df_set1 = df[df[col].isin(set1)]\n",
    "df_set2 = df[df[col].isin(set2)]\n",
    "df_subset = df[df[col].isin(set1+set2)]\n",
    "#df_old = df[df['Property Type'] == 'D']\n",
    "#df_new = df[df['Property Type'] != 'D']\n",
    "#PPDCategory Type\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(df_subset['Price_adj'], bins=np.arange(0, 1.2e6, 1e3), alpha=.5)\n",
    "plt.hist(df_set1['Price_adj'], bins=np.arange(0, 1.2e6, 1e3), alpha=.5)\n",
    "plt.hist(df_set2['Price_adj'], bins=np.arange(0, 1.2e6, 1e3), alpha=.5)\n",
    "#plt.hist(np.log(df['Price_adj']), bins=np.arange(8, 16, 0.01), alpha=.5)\n",
    "#plt.hist(np.log(df_old['Price_adj']), bins=np.arange(8, 16, 0.01), alpha=.5)\n",
    "#plt.hist(np.log(df_new['Price_adj']), bins=np.arange(8, 16, 0.01), alpha=.5)\n",
    "#plt.yscale('log')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.hist(df['Price_adj'], bins=np.arange(0, 1.2e6, 1e3), alpha=1)\n",
    "plt.xticks([0, 2e5, 4e5, 6e5, 8e5, 1e6, 1.2e6], ['0', '200k', '400k', '600k', '800k', '1M', '1.2M'])\n",
    "plt.yticks([0, 3e3, 6e3, 9e3, 12e3], ['0', '3k', '6k', '9k', '12k'])\n",
    "plt.xlabel('Price (£)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/frequencies_price.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure()\n",
    "# plt.hist(df['Price_adj'], bins=np.arange(0, 1e8, 5e5), alpha=1, log=True)\n",
    "# plt.xticks([0, 2e7, 4e7, 6e7, 8e7, 10e7], ['0', '20M', '40M', '60M', '80M', '100M'])\n",
    "# plt.yticks([1, 1e2, 1e4, 1e6], ['1', '100', '10k', '1M'])\n",
    "# plt.grid()\n",
    "# plt.xlabel('Price (£)')\n",
    "# plt.ylabel('Frequency')\n",
    "# plt.show()\n",
    "\n",
    "bins = np.arange(0, 1.2e6, 1e3)\n",
    "x = df['Price_adj']\n",
    "log_x = np.log(x)\n",
    "log_bins = np.arange(8, 19, 0.01)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_figwidth(10)\n",
    "\n",
    "# Plot 1\n",
    "ax1.hist(log_x, bins=log_bins, alpha=1, log=True)\n",
    "plt.setp(ax1, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "#plt.yticks([0, 4e3, 8e3, 12e3, 16e3], ['0', '4k', '8k', '12k', '16k'])\n",
    "ax1.grid()\n",
    "plt.setp(ax1, xlabel='Price (£)')\n",
    "plt.setp(ax1, ylabel='Frequency')\n",
    "\n",
    "# Plot 2\n",
    "ax2.hist(log_x, bins=log_bins, alpha=1)\n",
    "plt.setp(ax2, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "plt.setp(ax2, yticks=[0, 4e3, 8e3, 12e3, 16e3], yticklabels=['0', '4k', '8k', '12k', '16k'])\n",
    "ax2.grid()\n",
    "plt.setp(ax2, xlabel='Price (£)')\n",
    "plt.setp(ax2, ylabel='Frequency')\n",
    "\n",
    "plt.savefig('figures_mixture/frequencies_log_price.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "290114.4940007791\n",
      "114095.06862380102\n",
      "1142955.227998204\n"
     ]
    }
   ],
   "source": [
    "print(df_set1['Price_adj'].median())\n",
    "print(df_set2['Price_adj'].median())\n",
    "print(df['Price_adj'].quantile(.99))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['S' 'D' 'T' 'F' 'O']\n",
      "['N' 'Y']\n",
      "['F' 'L']\n",
      "1150\n",
      "349\n",
      "113\n",
      "['A' 'B']\n"
     ]
    }
   ],
   "source": [
    "print(df['Property Type'].unique())\n",
    "print(df['Old/New'].unique())\n",
    "print(df['Duration'].unique())\n",
    "print(len(df['Town/City'].unique()))\n",
    "print(len(df['District'].unique()))\n",
    "print(len(df['County'].unique()))\n",
    "print(df['PPDCategory Type'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>County</th>\n",
       "      <th>Price_adj</th>\n",
       "      <th>count_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BLAENAU GWENT</td>\n",
       "      <td>73269.808797</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MERTHYR TYDFIL</td>\n",
       "      <td>92139.130742</td>\n",
       "      <td>1817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CITY OF KINGSTON UPON HULL</td>\n",
       "      <td>93040.445431</td>\n",
       "      <td>8930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>STOKE-ON-TRENT</td>\n",
       "      <td>93357.951855</td>\n",
       "      <td>8884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHESHIRE</td>\n",
       "      <td>93582.763717</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RHONDDA CYNON TAFF</td>\n",
       "      <td>97942.358842</td>\n",
       "      <td>8668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NEATH PORT TALBOT</td>\n",
       "      <td>98825.860753</td>\n",
       "      <td>4795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BLACKPOOL</td>\n",
       "      <td>102804.562947</td>\n",
       "      <td>5346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BLACKBURN WITH DARWEN</td>\n",
       "      <td>104437.944282</td>\n",
       "      <td>4375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HARTLEPOOL</td>\n",
       "      <td>106497.267459</td>\n",
       "      <td>3278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NORTH EAST LINCOLNSHIRE</td>\n",
       "      <td>107329.134530</td>\n",
       "      <td>6363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>COUNTY DURHAM</td>\n",
       "      <td>109642.945978</td>\n",
       "      <td>19041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAERPHILLY</td>\n",
       "      <td>111230.417326</td>\n",
       "      <td>6052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>REDCAR AND CLEVELAND</td>\n",
       "      <td>113482.892914</td>\n",
       "      <td>5042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MIDDLESBROUGH</td>\n",
       "      <td>116489.333010</td>\n",
       "      <td>4511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NORTH LINCOLNSHIRE</td>\n",
       "      <td>117873.960134</td>\n",
       "      <td>6222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TORFAEN</td>\n",
       "      <td>119174.545248</td>\n",
       "      <td>2935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DARLINGTON</td>\n",
       "      <td>122229.516852</td>\n",
       "      <td>4389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CARMARTHENSHIRE</td>\n",
       "      <td>125318.178312</td>\n",
       "      <td>6601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BRIDGEND</td>\n",
       "      <td>126621.160547</td>\n",
       "      <td>5795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HALTON</td>\n",
       "      <td>127586.846821</td>\n",
       "      <td>4083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MERSEYSIDE</td>\n",
       "      <td>127900.100493</td>\n",
       "      <td>46197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DENBIGHSHIRE</td>\n",
       "      <td>128796.602233</td>\n",
       "      <td>3645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SOUTH YORKSHIRE</td>\n",
       "      <td>132634.798086</td>\n",
       "      <td>48310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TYNE AND WEAR</td>\n",
       "      <td>132836.084261</td>\n",
       "      <td>39882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>STOCKTON-ON-TEES</td>\n",
       "      <td>133128.442604</td>\n",
       "      <td>7856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LANCASHIRE</td>\n",
       "      <td>136263.712622</td>\n",
       "      <td>48494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LEICESTER</td>\n",
       "      <td>136328.622265</td>\n",
       "      <td>8937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CITY OF NOTTINGHAM</td>\n",
       "      <td>136861.714621</td>\n",
       "      <td>9726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WREXHAM</td>\n",
       "      <td>137288.756408</td>\n",
       "      <td>4211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>WREKIN</td>\n",
       "      <td>138486.625136</td>\n",
       "      <td>6979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NEWPORT</td>\n",
       "      <td>139437.992324</td>\n",
       "      <td>5377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SWANSEA</td>\n",
       "      <td>139771.051919</td>\n",
       "      <td>8619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>WEST YORKSHIRE</td>\n",
       "      <td>140539.665257</td>\n",
       "      <td>85211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CITY OF DERBY</td>\n",
       "      <td>141649.405825</td>\n",
       "      <td>10028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ISLE OF ANGLESEY</td>\n",
       "      <td>143291.146056</td>\n",
       "      <td>2690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>LINCOLNSHIRE</td>\n",
       "      <td>143491.347628</td>\n",
       "      <td>36561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NOTTINGHAMSHIRE</td>\n",
       "      <td>143506.978333</td>\n",
       "      <td>36573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>CITY OF PLYMOUTH</td>\n",
       "      <td>144657.571347</td>\n",
       "      <td>11370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>GREATER MANCHESTER</td>\n",
       "      <td>145231.369334</td>\n",
       "      <td>97432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CONWY</td>\n",
       "      <td>145332.987587</td>\n",
       "      <td>5180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>GWYNEDD</td>\n",
       "      <td>147036.995239</td>\n",
       "      <td>4208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>DERBYSHIRE</td>\n",
       "      <td>147294.649761</td>\n",
       "      <td>33630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>EAST RIDING OF YORKSHIRE</td>\n",
       "      <td>148328.992704</td>\n",
       "      <td>16176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>WEST MIDLANDS</td>\n",
       "      <td>149291.498659</td>\n",
       "      <td>88194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>LUTON</td>\n",
       "      <td>149538.754231</td>\n",
       "      <td>6908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>CUMBRIA</td>\n",
       "      <td>151739.284148</td>\n",
       "      <td>21670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>NORTHUMBERLAND</td>\n",
       "      <td>151911.398850</td>\n",
       "      <td>12656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>CITY OF PETERBOROUGH</td>\n",
       "      <td>152278.652859</td>\n",
       "      <td>8150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>PEMBROKESHIRE</td>\n",
       "      <td>152330.039110</td>\n",
       "      <td>4493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>POWYS</td>\n",
       "      <td>154791.276240</td>\n",
       "      <td>4342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>FLINTSHIRE</td>\n",
       "      <td>155422.313028</td>\n",
       "      <td>5682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>PORTSMOUTH</td>\n",
       "      <td>156280.267976</td>\n",
       "      <td>9020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>SWINDON</td>\n",
       "      <td>157336.880129</td>\n",
       "      <td>10587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>STAFFORDSHIRE</td>\n",
       "      <td>159243.168849</td>\n",
       "      <td>34041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>TORBAY</td>\n",
       "      <td>162658.155764</td>\n",
       "      <td>7395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>SOUTHAMPTON</td>\n",
       "      <td>164225.810983</td>\n",
       "      <td>9877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>MEDWAY</td>\n",
       "      <td>164857.459768</td>\n",
       "      <td>11829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>WARRINGTON</td>\n",
       "      <td>165903.758442</td>\n",
       "      <td>8687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>CEREDIGION</td>\n",
       "      <td>165913.966997</td>\n",
       "      <td>2371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>NORTHAMPTONSHIRE</td>\n",
       "      <td>167826.572749</td>\n",
       "      <td>35029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>THURROCK</td>\n",
       "      <td>169655.694670</td>\n",
       "      <td>6808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>NORFOLK</td>\n",
       "      <td>171960.048301</td>\n",
       "      <td>45387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>CARDIFF</td>\n",
       "      <td>175319.455507</td>\n",
       "      <td>14751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>ISLE OF WIGHT</td>\n",
       "      <td>177023.087517</td>\n",
       "      <td>8017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>LEICESTERSHIRE</td>\n",
       "      <td>178787.594166</td>\n",
       "      <td>32661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>SHROPSHIRE</td>\n",
       "      <td>180224.994285</td>\n",
       "      <td>12745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>SUFFOLK</td>\n",
       "      <td>186618.696740</td>\n",
       "      <td>37160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>THE VALE OF GLAMORGAN</td>\n",
       "      <td>186984.017697</td>\n",
       "      <td>5470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>CHESHIRE WEST AND CHESTER</td>\n",
       "      <td>187133.783921</td>\n",
       "      <td>14340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>SOMERSET</td>\n",
       "      <td>190042.943176</td>\n",
       "      <td>27498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>WORCESTERSHIRE</td>\n",
       "      <td>190473.349404</td>\n",
       "      <td>26388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>HEREFORDSHIRE</td>\n",
       "      <td>191937.252028</td>\n",
       "      <td>7669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>NORTH YORKSHIRE</td>\n",
       "      <td>192596.686729</td>\n",
       "      <td>28848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>SOUTH GLOUCESTERSHIRE</td>\n",
       "      <td>194889.125019</td>\n",
       "      <td>12794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>CORNWALL</td>\n",
       "      <td>199168.369268</td>\n",
       "      <td>27400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>BEDFORD</td>\n",
       "      <td>199342.258983</td>\n",
       "      <td>7971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>YORK</td>\n",
       "      <td>199457.010277</td>\n",
       "      <td>10186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>MONMOUTHSHIRE</td>\n",
       "      <td>200824.646281</td>\n",
       "      <td>3956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>NORTH SOMERSET</td>\n",
       "      <td>201204.299395</td>\n",
       "      <td>11683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>BOURNEMOUTH</td>\n",
       "      <td>202581.476247</td>\n",
       "      <td>10514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>SOUTHEND-ON-SEA</td>\n",
       "      <td>202829.761767</td>\n",
       "      <td>9059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>CHESHIRE EAST</td>\n",
       "      <td>203600.412792</td>\n",
       "      <td>17859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>CITY OF BRISTOL</td>\n",
       "      <td>204345.825712</td>\n",
       "      <td>21591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>WARWICKSHIRE</td>\n",
       "      <td>205537.558500</td>\n",
       "      <td>25625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>SLOUGH</td>\n",
       "      <td>207672.811222</td>\n",
       "      <td>4803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>CENTRAL BEDFORDSHIRE</td>\n",
       "      <td>208143.014559</td>\n",
       "      <td>14946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>MILTON KEYNES</td>\n",
       "      <td>210914.827990</td>\n",
       "      <td>12499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>DEVON</td>\n",
       "      <td>212786.196210</td>\n",
       "      <td>41792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>GLOUCESTERSHIRE</td>\n",
       "      <td>213038.633608</td>\n",
       "      <td>31422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>WILTSHIRE</td>\n",
       "      <td>217053.701071</td>\n",
       "      <td>23671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>KENT</td>\n",
       "      <td>222816.214665</td>\n",
       "      <td>73717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>CAMBRIDGESHIRE</td>\n",
       "      <td>226680.603347</td>\n",
       "      <td>32160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>EAST SUSSEX</td>\n",
       "      <td>229368.771959</td>\n",
       "      <td>31054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>ESSEX</td>\n",
       "      <td>234025.117029</td>\n",
       "      <td>70874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>RUTLAND</td>\n",
       "      <td>235240.240618</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>DORSET</td>\n",
       "      <td>239313.188820</td>\n",
       "      <td>22762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>READING</td>\n",
       "      <td>243440.568329</td>\n",
       "      <td>7985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>HAMPSHIRE</td>\n",
       "      <td>250628.699991</td>\n",
       "      <td>67579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>WEST SUSSEX</td>\n",
       "      <td>255318.828667</td>\n",
       "      <td>45916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>BRACKNELL FOREST</td>\n",
       "      <td>264293.307341</td>\n",
       "      <td>6143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>POOLE</td>\n",
       "      <td>265033.774489</td>\n",
       "      <td>8114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>BRIGHTON AND HOVE</td>\n",
       "      <td>276522.260494</td>\n",
       "      <td>14962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>BATH AND NORTH EAST SOMERSET</td>\n",
       "      <td>276606.562034</td>\n",
       "      <td>9021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>WEST BERKSHIRE</td>\n",
       "      <td>281778.408823</td>\n",
       "      <td>7781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>ISLES OF SCILLY</td>\n",
       "      <td>282605.156667</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>OXFORDSHIRE</td>\n",
       "      <td>295648.926991</td>\n",
       "      <td>30887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>WOKINGHAM</td>\n",
       "      <td>306010.842833</td>\n",
       "      <td>8156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>HERTFORDSHIRE</td>\n",
       "      <td>306911.661768</td>\n",
       "      <td>56536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>BUCKINGHAMSHIRE</td>\n",
       "      <td>333595.258420</td>\n",
       "      <td>26067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>SURREY</td>\n",
       "      <td>386774.070890</td>\n",
       "      <td>61605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>WINDSOR AND MAIDENHEAD</td>\n",
       "      <td>442982.051869</td>\n",
       "      <td>7118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>452225.698396</td>\n",
       "      <td>337924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           County      Price_adj  count_col\n",
       "0                   BLAENAU GWENT   73269.808797       2016\n",
       "1                  MERTHYR TYDFIL   92139.130742       1817\n",
       "2      CITY OF KINGSTON UPON HULL   93040.445431       8930\n",
       "3                  STOKE-ON-TRENT   93357.951855       8884\n",
       "4                        CHESHIRE   93582.763717          1\n",
       "5              RHONDDA CYNON TAFF   97942.358842       8668\n",
       "6               NEATH PORT TALBOT   98825.860753       4795\n",
       "7                       BLACKPOOL  102804.562947       5346\n",
       "8           BLACKBURN WITH DARWEN  104437.944282       4375\n",
       "9                      HARTLEPOOL  106497.267459       3278\n",
       "10        NORTH EAST LINCOLNSHIRE  107329.134530       6363\n",
       "11                  COUNTY DURHAM  109642.945978      19041\n",
       "12                     CAERPHILLY  111230.417326       6052\n",
       "13           REDCAR AND CLEVELAND  113482.892914       5042\n",
       "14                  MIDDLESBROUGH  116489.333010       4511\n",
       "15             NORTH LINCOLNSHIRE  117873.960134       6222\n",
       "16                        TORFAEN  119174.545248       2935\n",
       "17                     DARLINGTON  122229.516852       4389\n",
       "18                CARMARTHENSHIRE  125318.178312       6601\n",
       "19                       BRIDGEND  126621.160547       5795\n",
       "20                         HALTON  127586.846821       4083\n",
       "21                     MERSEYSIDE  127900.100493      46197\n",
       "22                   DENBIGHSHIRE  128796.602233       3645\n",
       "23                SOUTH YORKSHIRE  132634.798086      48310\n",
       "24                  TYNE AND WEAR  132836.084261      39882\n",
       "25               STOCKTON-ON-TEES  133128.442604       7856\n",
       "26                     LANCASHIRE  136263.712622      48494\n",
       "27                      LEICESTER  136328.622265       8937\n",
       "28             CITY OF NOTTINGHAM  136861.714621       9726\n",
       "29                        WREXHAM  137288.756408       4211\n",
       "30                         WREKIN  138486.625136       6979\n",
       "31                        NEWPORT  139437.992324       5377\n",
       "32                        SWANSEA  139771.051919       8619\n",
       "33                 WEST YORKSHIRE  140539.665257      85211\n",
       "34                  CITY OF DERBY  141649.405825      10028\n",
       "35               ISLE OF ANGLESEY  143291.146056       2690\n",
       "36                   LINCOLNSHIRE  143491.347628      36561\n",
       "37                NOTTINGHAMSHIRE  143506.978333      36573\n",
       "38               CITY OF PLYMOUTH  144657.571347      11370\n",
       "39             GREATER MANCHESTER  145231.369334      97432\n",
       "40                          CONWY  145332.987587       5180\n",
       "41                        GWYNEDD  147036.995239       4208\n",
       "42                     DERBYSHIRE  147294.649761      33630\n",
       "43       EAST RIDING OF YORKSHIRE  148328.992704      16176\n",
       "44                  WEST MIDLANDS  149291.498659      88194\n",
       "45                          LUTON  149538.754231       6908\n",
       "46                        CUMBRIA  151739.284148      21670\n",
       "47                 NORTHUMBERLAND  151911.398850      12656\n",
       "48           CITY OF PETERBOROUGH  152278.652859       8150\n",
       "49                  PEMBROKESHIRE  152330.039110       4493\n",
       "50                          POWYS  154791.276240       4342\n",
       "51                     FLINTSHIRE  155422.313028       5682\n",
       "52                     PORTSMOUTH  156280.267976       9020\n",
       "53                        SWINDON  157336.880129      10587\n",
       "54                  STAFFORDSHIRE  159243.168849      34041\n",
       "55                         TORBAY  162658.155764       7395\n",
       "56                    SOUTHAMPTON  164225.810983       9877\n",
       "57                         MEDWAY  164857.459768      11829\n",
       "58                     WARRINGTON  165903.758442       8687\n",
       "59                     CEREDIGION  165913.966997       2371\n",
       "60               NORTHAMPTONSHIRE  167826.572749      35029\n",
       "61                       THURROCK  169655.694670       6808\n",
       "62                        NORFOLK  171960.048301      45387\n",
       "63                        CARDIFF  175319.455507      14751\n",
       "64                  ISLE OF WIGHT  177023.087517       8017\n",
       "65                 LEICESTERSHIRE  178787.594166      32661\n",
       "66                     SHROPSHIRE  180224.994285      12745\n",
       "67                        SUFFOLK  186618.696740      37160\n",
       "68          THE VALE OF GLAMORGAN  186984.017697       5470\n",
       "69      CHESHIRE WEST AND CHESTER  187133.783921      14340\n",
       "70                       SOMERSET  190042.943176      27498\n",
       "71                 WORCESTERSHIRE  190473.349404      26388\n",
       "72                  HEREFORDSHIRE  191937.252028       7669\n",
       "73                NORTH YORKSHIRE  192596.686729      28848\n",
       "74          SOUTH GLOUCESTERSHIRE  194889.125019      12794\n",
       "75                       CORNWALL  199168.369268      27400\n",
       "76                        BEDFORD  199342.258983       7971\n",
       "77                           YORK  199457.010277      10186\n",
       "78                  MONMOUTHSHIRE  200824.646281       3956\n",
       "79                 NORTH SOMERSET  201204.299395      11683\n",
       "80                    BOURNEMOUTH  202581.476247      10514\n",
       "81                SOUTHEND-ON-SEA  202829.761767       9059\n",
       "82                  CHESHIRE EAST  203600.412792      17859\n",
       "83                CITY OF BRISTOL  204345.825712      21591\n",
       "84                   WARWICKSHIRE  205537.558500      25625\n",
       "85                         SLOUGH  207672.811222       4803\n",
       "86           CENTRAL BEDFORDSHIRE  208143.014559      14946\n",
       "87                  MILTON KEYNES  210914.827990      12499\n",
       "88                          DEVON  212786.196210      41792\n",
       "89                GLOUCESTERSHIRE  213038.633608      31422\n",
       "90                      WILTSHIRE  217053.701071      23671\n",
       "91                           KENT  222816.214665      73717\n",
       "92                 CAMBRIDGESHIRE  226680.603347      32160\n",
       "93                    EAST SUSSEX  229368.771959      31054\n",
       "94                          ESSEX  234025.117029      70874\n",
       "95                        RUTLAND  235240.240618       1957\n",
       "96                         DORSET  239313.188820      22762\n",
       "97                        READING  243440.568329       7985\n",
       "98                      HAMPSHIRE  250628.699991      67579\n",
       "99                    WEST SUSSEX  255318.828667      45916\n",
       "100              BRACKNELL FOREST  264293.307341       6143\n",
       "101                         POOLE  265033.774489       8114\n",
       "102             BRIGHTON AND HOVE  276522.260494      14962\n",
       "103  BATH AND NORTH EAST SOMERSET  276606.562034       9021\n",
       "104                WEST BERKSHIRE  281778.408823       7781\n",
       "105               ISLES OF SCILLY  282605.156667         45\n",
       "106                   OXFORDSHIRE  295648.926991      30887\n",
       "107                     WOKINGHAM  306010.842833       8156\n",
       "108                 HERTFORDSHIRE  306911.661768      56536\n",
       "109               BUCKINGHAMSHIRE  333595.258420      26067\n",
       "110                        SURREY  386774.070890      61605\n",
       "111        WINDSOR AND MAIDENHEAD  442982.051869       7118\n",
       "112                GREATER LONDON  452225.698396     337924"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum_county = df.groupby('County').agg({'Price_adj': 'mean', 'count_col': 'count'}).sort_values(by='Price_adj').reset_index()\n",
    "pd.set_option('display.max_rows', None)\n",
    "df_sum_county"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Greater London"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1dca6f31188>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(0, 1.2e6, 1e3)\n",
    "df_set1 = df_set1.dropna()\n",
    "df_set1_noout = df_set1[df_set1['Price_adj'] < df_set1['Price_adj'].quantile(.95)]\n",
    "y, x = np.histogram(df_set1_noout['Price_adj'], bins=bins, density=True)\n",
    "alpha, x0, inv_beta = gamma.fit(df_set1_noout['Price_adj'])\n",
    "\n",
    "# Plot of fit\n",
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.plot(bins[1:], y)\n",
    "ax.plot(bins, gamma.pdf(bins, alpha, loc=x0, scale=inv_beta), color='r')\n",
    "#plt.ylim([0, 3e-6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Greater Manchester & Midlands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 4.2e-06)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(0, 1.2e6, 1e3)\n",
    "df_subset = df_subset.dropna()\n",
    "df_subset_noout = df_subset[df_subset['Price_adj'] < df_subset['Price_adj'].quantile(.95)]\n",
    "y, x = np.histogram(df_subset_noout['Price_adj'], bins=bins, density=True)\n",
    "alpha, x0, inv_beta = gamma.fit(df_subset_noout['Price_adj'])\n",
    "\n",
    "# Plot of fit\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(bins[1:], y)\n",
    "ax.plot(bins, gamma.pdf(bins, alpha, loc=x0, scale=inv_beta), color='r')\n",
    "plt.ylim([0, 4.2e-6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(0, 1.2e6, 1e3)\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_figwidth(10)\n",
    "\n",
    "\n",
    "#=============================\n",
    "# Plot 1\n",
    "#=============================\n",
    "perc = df['Price_adj'].quantile(.95)\n",
    "df_noout = df[df['Price_adj'] < perc]\n",
    "#df_noout = df[df['Price_adj'] < 5e5]\n",
    "y, x = np.histogram(df_noout['Price_adj'], bins=bins, density=True)\n",
    "alpha, x0, inv_beta = gamma.fit(df_noout['Price_adj'])\n",
    "\n",
    "ax1.axis([0, 1.2e6, 0, 7e-6])\n",
    "ax1.plot(bins[1:], y)\n",
    "ax1.plot(bins, gamma.pdf(bins, alpha, loc=x0, scale=inv_beta), color='r')\n",
    "ax1.axvline(perc, color='g', linestyle='dashed')\n",
    "ax1.grid()\n",
    "\n",
    "plt.setp(ax1, xticks=[0, 2e5, 4e5, 6e5, 8e5, 1e6, 1.2e6], \n",
    "         xticklabels=['0', '200k', '400k', '600k', '800k', '1M', '1.2M'])\n",
    "plt.setp(ax1, xlabel='Price (£)')\n",
    "plt.setp(ax1, ylabel='pdf')\n",
    "ax1.legend(['Empirical distribution (p95)', 'Gamma fit', 'p95 threshold'])\n",
    "\n",
    "#=============================\n",
    "# Plot 2\n",
    "#=============================\n",
    "perc = df['Price_adj'].quantile(.99)\n",
    "df_noout = df[df['Price_adj'] < perc]\n",
    "y, x = np.histogram(df_noout['Price_adj'], bins=bins, density=True)\n",
    "alpha, x0, inv_beta = gamma.fit(df_noout['Price_adj'])\n",
    "\n",
    "\n",
    "ax2.axis([0, 1.2e6, 0, 7e-6])\n",
    "ax2.plot(bins[1:], y)\n",
    "ax2.plot(bins, gamma.pdf(bins, alpha, loc=x0, scale=inv_beta), color='r')\n",
    "ax2.axvline(perc, color='g', linestyle='dashed')\n",
    "ax2.grid()\n",
    "plt.setp(ax2, xticks=[0, 2e5, 4e5, 6e5, 8e5, 1e6, 1.2e6], \n",
    "         xticklabels=['0', '200k', '400k', '600k', '800k', '1M', '1.2M'])\n",
    "plt.setp(ax2, xlabel='Price(£)')\n",
    "plt.setp(ax2, ylabel='pdf')\n",
    "ax2.legend(['Empirical distribution (p99)', 'Gamma fit', 'p99 threshold'])\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('figures_mixture/gamma_fit.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df['Price_adj']\n",
    "log_x = np.log(x)\n",
    "log_bins = np.arange(8, 16, 0.01)\n",
    "plt.hist(log_x, bins=log_bins, alpha=1)\n",
    "plt.xticks(np.log(10**np.array([3, 4, 5, 6, 7])), ['1k', '10k', '100k', '1M', '10M'])\n",
    "plt.show()\n",
    "\n",
    "# Gamma fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.size'] = 12\n",
    "\n",
    "df = df.dropna()\n",
    "x = df['Price_adj']\n",
    "log_x = np.log(x)\n",
    "log_bins = np.arange(8, 19, 0.05)\n",
    "bins = np.exp(log_bins) #np.arange(1e3, 1e8, 1e5)\n",
    "y, _ = np.histogram(x, bins=bins, density=True)\n",
    "log_y, _ = np.histogram(log_x, bins=log_bins, density=True)\n",
    "\n",
    "# Normal fit - Baseline\n",
    "mu_singleFit, std_singleFit = norm.fit(log_x)\n",
    "pdf_single = norm.pdf(log_bins, loc=mu_singleFit, scale=std_singleFit)\n",
    "\n",
    "# Plot\n",
    "norm_y = sum(np.diff(bins)*y)\n",
    "norm_log_y = sum(np.diff(log_bins)*log_y)\n",
    "norm_single = sum(np.diff(log_bins)*pdf_single[1:])\n",
    "\n",
    "# Dif bins\n",
    "dif_bins = np.diff(bins)\n",
    "dif_log_bins = np.diff(log_bins)\n",
    "#=====================================================================\n",
    "# PLOTS\n",
    "#=====================================================================\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3)\n",
    "fig.set_figwidth(15)\n",
    "\n",
    "# Plot 1\n",
    "ax1.semilogy(log_bins[1:], log_y/norm_log_y/dif_bins*dif_log_bins)\n",
    "ax1.semilogy(log_bins[1:], pdf_single[1:]/norm_single/dif_bins*dif_log_bins)\n",
    "plt.setp(ax1, ylim=[1e-12, 1e-4])\n",
    "plt.setp(ax1, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "ax1.grid()\n",
    "ax1.legend(['Empirical distribution', 'Log-normal fit'])\n",
    "plt.setp(ax1, xlabel='Price (£)')\n",
    "plt.setp(ax1, ylabel='pdf')\n",
    "\n",
    "# Plot 2\n",
    "ax2.plot(log_bins[1:], log_y/norm_log_y/dif_bins*dif_log_bins)\n",
    "ax2.plot(log_bins[1:], pdf_single[1:]/norm_single/dif_bins*dif_log_bins)\n",
    "plt.setp(ax2, ylim=[1e-11, 6e-6])\n",
    "plt.setp(ax2, xticks=np.log(10**np.array([4, 5, 6, 7, 8])), xticklabels=['10k', '100k', '1M', '10M', '100M'])\n",
    "ax2.grid()\n",
    "ax2.legend(['Empirical distribution', 'Log-normal fit'])\n",
    "plt.setp(ax2, xlabel='Price (£)')\n",
    "plt.setp(ax2, ylabel='pdf')\n",
    "\n",
    "# Plot 3\n",
    "ax3.plot(bins[1:], y/norm_y)\n",
    "ax3.plot(bins, pdf_single/norm_single/np.exp(log_bins))\n",
    "plt.setp(ax3, xticks=[0, 2e5, 4e5, 6e5, 8e5, 1e6, 1.2e6], \n",
    "         xticklabels=['0', '200k', '400k', '600k', '800k', '1M', '1.2M'])\n",
    "ax3.axis([0, 1.2e6, 0, 6e-6])\n",
    "ax3.grid()\n",
    "ax3.legend(['Empirical distribution', 'Log-normal fit'])\n",
    "plt.setp(ax3, xlabel='Price (£)')\n",
    "plt.setp(ax3, ylabel='pdf')\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures_mixture/lognormal_fit.png', dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EM Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['Price_adj']\n",
    "### Run the EM algorithm\n",
    "## Initialize the parameters\n",
    "KK_vec = np.arange(6, 7)\n",
    "\n",
    "# Parameters\n",
    "w_vec = []\n",
    "mu_vec = []\n",
    "std_vec = []\n",
    "w_ini_vec = []\n",
    "mu_ini_vec = []\n",
    "std_ini_vec = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========================================\n",
      "Mixture with 6 components\n",
      "[1, -4345968.634045105, inf]\n",
      "[2, -4561142.752665209, 0.04717548436614283]\n",
      "[3, -4677573.910375801, 0.02489135606223656]\n",
      "[4, -4761019.682703069, 0.01752686984900095]\n",
      "[5, -4828835.309894625, 0.014043889020732737]\n",
      "[6, -4887251.656693353, 0.011952801063297795]\n",
      "[7, -4938957.65514636, 0.01046901027773133]\n",
      "[8, -4985323.820098668, 0.009300532247349636]\n",
      "[9, -5027157.514330835, 0.008321540376030017]\n",
      "[10, -5064990.490991956, 0.007469505960259191]\n",
      "[11, -5099205.211596792, 0.006709814409316905]\n",
      "[12, -5130101.277128316, 0.006022505962849053]\n",
      "[13, -5157936.177906341, 0.005396519037450155]\n",
      "[14, -5182951.3166292375, 0.004826427491733671]\n",
      "[15, -5205386.634308244, 0.004310019457755051]\n",
      "[16, -5225485.672313755, 0.0038463483139954276]\n",
      "[17, -5243493.514417743, 0.003434321422248882]\n",
      "[18, -5259650.712175641, 0.0030719145894033168]\n",
      "[19, -5274186.172111279, 0.0027559626189341577]\n",
      "[20, -5287311.137114098, 0.0024823515511861456]\n",
      "[21, -5299215.302163264, 0.0022464014708566244]\n",
      "[22, -5310065.197849173, 0.0020432697681949433]\n",
      "[23, -5320004.439879939, 0.0018682770180150768]\n",
      "[24, -5329155.250590265, 0.0017171221854180886]\n",
      "[25, -5337620.687107154, 0.001585994399590318]\n",
      "[26, -5345487.138687189, 0.001471606118570984]\n",
      "[27, -5352826.801465474, 0.0013711750913136048]\n",
      "[28, -5359699.962458478, 0.0012823779392776903]\n",
      "[29, -5366157.014123997, 0.0012032916011445708]\n",
      "[30, -5372240.178371306, 0.0011323328900669297]\n",
      "[31, -5377984.952031568, 0.0010682018844422373]\n",
      "[32, -5383421.302217368, 0.001009831830096815]\n",
      "[33, -5388574.646027783, 0.0009563463715240004]\n",
      "[34, -5393466.649170534, 0.00090702389779367]\n",
      "[35, -5398115.875193564, 0.0008612682888849171]\n",
      "[36, -5402538.312908189, 0.0008185851646916964]\n",
      "[37, -5406747.805238593, 0.0007785627297662053]\n",
      "[38, -5410756.398655732, 0.0007408563834318437]\n",
      "[39, -5414574.628767919, 0.0007051763756104083]\n",
      "[40, -5418211.7546072975, 0.0006712779057197069]\n",
      "[41, -5421675.951648219, 0.0006389531709042741]\n",
      "[42, -5424974.4715629835, 0.0006080249652888372]\n",
      "[43, -5428113.775090402, 0.0005783415118940171]\n",
      "[44, -5431099.643094906, 0.0005497722746258005]\n",
      "[45, -5433937.26986467, 0.0005222045505569517]\n",
      "[46, -5436631.341883773, 0.0004955406849730401]\n",
      "[47, -5439186.104670285, 0.0004696957848746249]\n",
      "[48, -5441605.419764624, 0.00044459583297816127]\n",
      "[49, -5443892.813550451, 0.0004201761247270126]\n",
      "[50, -5446051.519271882, 0.0003963799669893727]\n",
      "[51, -5448084.513357526, 0.0003731575897288264]\n",
      "[52, -5449994.54695982, 0.0003504652318156886]\n",
      "[53, -5451784.1734563215, 0.00032826436989465043]\n",
      "[54, -5453455.772529614, 0.00030652106536059934]\n",
      "[55, -5455011.571337275, 0.000285205409248958]\n",
      "[56, -5456453.663198323, 0.00026429104873997846]\n",
      "[57, -5457784.024152769, 0.00024375478189654152]\n",
      "[58, -5459004.527694096, 0.000223576209753141]\n",
      "[59, -5460116.957927387, 0.00020373743673681656]\n",
      "[60, -5461123.021367006, 0.0001842228119898611]\n",
      "[61, -5462024.357555112, 0.00016501870535585062]\n",
      "[62, -5462822.548655455, 0.00014611331289528825]\n",
      "[63, -5463519.128153566, 0.0001274964874785172]\n",
      "[64, -5464115.588775476, 0.0001091595908284653]\n",
      "[65, -5464613.389720304, 9.109536381181689e-05]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "log_x = np.log(x)\n",
    "for KK in KK_vec:\n",
    "    # INITIALISATION\n",
    "    w = 1/KK*np.ones((KK, 1))  #Assign equal weight to each component to start with\n",
    "    mu = np.random.normal(loc=log_x.mean(), scale=log_x.std()/KK, size=KK)#\n",
    "    std = log_x.std()*np.ones(KK)/KK\n",
    "\n",
    "    # Initial parameters\n",
    "    w_ini = w.copy()\n",
    "    mu_ini = mu.copy()\n",
    "    std_ini = std.copy()\n",
    "    # Parameters\n",
    "    sw = False\n",
    "    QQ = -np.inf\n",
    "    epsilon = 1e-4\n",
    "    max_iter = 100\n",
    "    i = 0\n",
    "    # x = df_noout['Price_adj']\n",
    "    print(\"===========================================\")\n",
    "    print(\"Mixture with {} components\".format(KK))\n",
    "    while((~sw) & (i < max_iter)):\n",
    "        i+=1\n",
    "        ## E step\n",
    "        L = np.zeros([KK, len(x)])\n",
    "        v = np.zeros([KK, len(x)])\n",
    "        for k in range(KK):\n",
    "            L[k, :] = norm.logpdf(log_x, loc=mu[k], scale=std[k])\n",
    "        Lmax = np.amax(L, axis=0)\n",
    "        for k in range(KK):\n",
    "            L[k, :] -= Lmax\n",
    "        denom = (w*np.exp(L)).sum(axis=0)\n",
    "        for k in range(KK):\n",
    "            v[k, :] = w[k]*np.exp(L[k, :])/denom\n",
    "\n",
    "        ## M step\n",
    "        for k in range(KK):\n",
    "            w[k] = v[k,:].mean()\n",
    "            mu[k] = (v[k,:]*log_x).sum()/v[k, :].sum()\n",
    "            std[k] = np.sqrt((v[k,:]*(log_x-mu[k])**2).sum()/v[k,:].sum())\n",
    "\n",
    "        ##Check convergence\n",
    "        QQn = 0\n",
    "        for k in range(KK):\n",
    "            QQn += (v[k, :]*(np.log(w[k]) + norm.logpdf(log_x, loc=mu[k], scale=std[k]))).sum()\n",
    "        rel_error = abs(QQn-QQ)/abs(QQn)\n",
    "        if(rel_error < epsilon):\n",
    "            sw=True\n",
    "\n",
    "        QQ = QQn\n",
    "        print([i, QQ, rel_error])\n",
    "\n",
    "    ## ASSIGN Results\n",
    "    w_vec.append(w)\n",
    "    mu_vec.append(mu)\n",
    "    std_vec.append(std)\n",
    "    w_ini_vec.append(w_ini)\n",
    "    mu_ini_vec.append(mu_ini)\n",
    "    std_ini_vec.append(std_ini)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BIC for mixture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# n = len(x)\n",
    "# BIC_vec = []\n",
    "# for index, KK in enumerate(KK_vec):\n",
    "#     LL = np.zeros(n)\n",
    "#     for k in range(KK):\n",
    "#         LL += w_vec[index][k]*lognorm.pdf(x, loc=mu_vec[index][k], scale=std_vec[index][k])\n",
    "#     LL = np.log(LL).sum()\n",
    "#     BIC_vec.append(-2*LL + (3*KK-1)*np.log(n))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store and plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('EMfit_LogScale.pickle', 'wb') as f:\n",
    "    pickle.dump([KK_vec, w_vec, mu_vec, std_vec], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['Price_adj']\n",
    "### Run the EM algorithm\n",
    "## Initialize the parameters\n",
    "KK_vec = np.arange(6, 7)\n",
    "\n",
    "# Parameters\n",
    "w_vec = []\n",
    "mu_vec = []\n",
    "std_vec = []\n",
    "w_ini_vec = []\n",
    "mu_ini_vec = []\n",
    "std_ini_vec = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========================================\n",
      "Mixture with 6 components\n",
      "[1, -36051070.66555333, inf]\n",
      "[2, -35575849.8257936, 0.013357961709608407]\n",
      "[3, -35306239.79036588, 0.007636328224941378]\n",
      "[4, -35168907.97662805, 0.0039049211829124074]\n",
      "[5, -35100674.77568613, 0.0019439284679844588]\n",
      "[6, -35062128.22731208, 0.0010993784554134125]\n",
      "[7, -35035059.4184294, 0.0007726206072436282]\n",
      "[8, -35011893.844969004, 0.0006616486832437202]\n",
      "[9, -34989671.84047764, 0.0006351018264097872]\n",
      "[10, -34967525.91719405, 0.0006333282868232589]\n",
      "[11, -34945450.67152252, 0.0006317058514721757]\n",
      "[12, -34923684.17535287, 0.0006232588767083917]\n",
      "[13, -34902456.603622735, 0.0006081970667913826]\n",
      "[14, -34881917.07921315, 0.0005888301483815127]\n",
      "[15, -34862128.14693682, 0.0005676340868499061]\n",
      "[16, -34843079.18813082, 0.0005467071008032383]\n",
      "[17, -34824701.54171546, 0.0005277187054523998]\n",
      "[18, -34806880.9080733, 0.0005119859400566394]\n",
      "[19, -34789466.99835528, 0.0005005512076067454]\n",
      "[20, -34772281.60942785, 0.0004942266694047025]\n",
      "[21, -34755126.53253923, 0.0004935984587065763]\n",
      "[22, -34737792.65489288, 0.000498991914039055]\n",
      "[23, -34720071.383203976, 0.0005104042412042289]\n",
      "[24, -34701768.984793566, 0.0005274197525327848]\n",
      "[25, -34682723.4932734, 0.0005491348314632848]\n",
      "[26, -34662822.54579042, 0.0005741294569041849]\n",
      "[27, -34642019.2960363, 0.0006005207022240826]\n",
      "[28, -34620342.98380056, 0.000626114889904052]\n",
      "[29, -34597901.308159865, 0.0006486426861794059]\n",
      "[30, -34574873.40194717, 0.0006660300948896047]\n",
      "[31, -34551494.31964694, 0.0006766446071461768]\n",
      "[32, -34528033.708807595, 0.0006794655912699745]\n",
      "[33, -34504772.19738355, 0.0006741534559618444]\n",
      "[34, -34481978.9369646, 0.0006610194983477931]\n",
      "[35, -34459892.979228675, 0.0006409177692234454]\n",
      "[36, -34438710.1014483, 0.0006150891748842542]\n",
      "[37, -34418575.61802432, 0.0005849888632065897]\n",
      "[38, -34399582.78650502, 0.0005521238916522312]\n",
      "[39, -34381775.75027318, 0.0005179207834166821]\n",
      "[40, -34365155.627515234, 0.000483632983889113]\n",
      "[41, -34349688.34797278, 0.0004502887882348552]\n",
      "[42, -34335313.057657816, 0.00041867363465785627]\n",
      "[43, -34321950.226527154, 0.0003893377573962288]\n",
      "[44, -34309508.90458345, 0.00036262022806294713]\n",
      "[45, -34297892.82993694, 0.00033868187483433634]\n",
      "[46, -34287005.28767691, 0.0003175413591440578]\n",
      "[47, -34276752.75592885, 0.0002991103568374933]\n",
      "[48, -34267047.4632041, 0.00028322523950079047]\n",
      "[49, -34257809.02745552, 0.00026967386446614494]\n",
      "[50, -34248965.361238606, 0.0002582170329421451]\n",
      "[51, -34240453.018675745, 0.0002486048463850306]\n",
      "[52, -34232217.137733735, 0.00024058859257851235]\n",
      "[53, -34224211.10338515, 0.0002339289669643496]\n",
      "[54, -34216396.0291489, 0.0002284014432611127]\n",
      "[55, -34208740.129656255, 0.00022379951625306184]\n",
      "[56, -34201218.03668112, 0.00021993640598018038]\n",
      "[57, -34193810.09558194, 0.00021664567588327704]\n",
      "[58, -34186501.667691156, 0.0002137810988040364]\n",
      "[59, -34179282.45598682, 0.0002112160111503919]\n",
      "[60, -34172145.86556546, 0.00020884232583576502]\n",
      "[61, -34165088.40633261, 0.00020656932447850153]\n",
      "[62, -34158109.14242763, 0.0002043223140918068]\n",
      "[63, -34151209.1908302, 0.00020204120910841686]\n",
      "[64, -34144391.27009482, 0.0001996790829114292]\n",
      "[65, -34137659.29903924, 0.00019720072183659144]\n",
      "[66, -34131018.044351526, 0.0001945812070147343]\n",
      "[67, -34124472.81539794, 0.0001918045441754527]\n",
      "[68, -34118029.20396184, 0.00018886235771644661]\n",
      "[69, -34111692.86619716, 0.00018575266227726132]\n",
      "[70, -34105469.34372704, 0.0001824787223245983]\n",
      "[71, -34099363.92055333, 0.00017904800769701957]\n",
      "[72, -34093381.51226489, 0.00017547125052063293]\n",
      "[73, -34087526.58394172, 0.00017176160636806745]\n",
      "[74, -34081803.09314253, 0.0001679339201493191]\n",
      "[75, -34076214.4544325, 0.00016400409492382668]\n",
      "[76, -34070763.52204552, 0.00015998855979415985]\n",
      "[77, -34065452.58747229, 0.00015590383129640984]\n",
      "[78, -34060283.3890098, 0.0001517661612925454]\n",
      "[79, -34055257.13058643, 0.00014759126334297482]\n",
      "[80, -34050374.507477485, 0.0001433941088628426]\n",
      "[81, -34045635.73683703, 0.0001391887840510461]\n",
      "[82, -34041040.59128145, 0.0001349883985848269]\n",
      "[83, -34036588.43405976, 0.00013080503735899194]\n",
      "[84, -34032278.25462815, 0.0001266497470243167]\n",
      "[85, -34028108.70370457, 0.00012253254977772274]\n",
      "[86, -34024078.127110854, 0.00011846247762133498]\n",
      "[87, -34020184.59791211, 0.00011444762116259525]\n",
      "[88, -34016425.94653518, 0.00011049518790834949]\n",
      "[89, -34012799.78869053, 0.00010661156585684798]\n",
      "[90, -34009303.551036365, 0.00010280238902621964]\n",
      "[91, -34005934.49461243, 9.90726022972865e-05]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "for KK in KK_vec:\n",
    "    # INITIALISATION\n",
    "    w = 1/KK*np.ones((KK, 1))  #Assign equal weight to each component to start with\n",
    "    mu = np.random.normal(loc=x.mean(), scale=x.std()/KK, size=KK)#\n",
    "    std = x.std()*np.ones(KK)/KK\n",
    "\n",
    "    # Initial parameters\n",
    "    w_ini = w.copy()\n",
    "    mu_ini = mu.copy()\n",
    "    std_ini = std.copy()\n",
    "    # Parameters\n",
    "    sw = False\n",
    "    QQ = -np.inf\n",
    "    epsilon = 1e-4\n",
    "    max_iter = 100\n",
    "    i = 0\n",
    "    # x = df_noout['Price_adj']\n",
    "    print(\"===========================================\")\n",
    "    print(\"Mixture with {} components\".format(KK))\n",
    "    while((~sw) & (i < max_iter)):\n",
    "        i+=1\n",
    "        ## E step\n",
    "        L = np.zeros([KK, len(x)])\n",
    "        v = np.zeros([KK, len(x)])\n",
    "        for k in range(KK):\n",
    "            L[k, :] = norm.logpdf(x, loc=mu[k], scale=std[k])\n",
    "        Lmax = np.amax(L, axis=0)\n",
    "        for k in range(KK):\n",
    "            L[k, :] -= Lmax\n",
    "        denom = (w*np.exp(L)).sum(axis=0)\n",
    "        for k in range(KK):\n",
    "            v[k, :] = w[k]*np.exp(L[k, :])/denom\n",
    "\n",
    "        ## M step\n",
    "        for k in range(KK):\n",
    "            w[k] = v[k,:].mean()\n",
    "            mu[k] = (v[k,:]*x).sum()/v[k, :].sum()\n",
    "            std[k] = np.sqrt((v[k,:]*(x-mu[k])**2).sum()/v[k,:].sum())\n",
    "\n",
    "        ##Check convergence\n",
    "        QQn = 0\n",
    "        for k in range(KK):\n",
    "            QQn += (v[k, :]*(np.log(w[k]) + norm.logpdf(x, loc=mu[k], scale=std[k]))).sum()\n",
    "        rel_error = abs(QQn-QQ)/abs(QQn)\n",
    "        if(rel_error < epsilon):\n",
    "            sw=True\n",
    "\n",
    "        QQ = QQn\n",
    "        print([i, QQ, rel_error])\n",
    "\n",
    "    ## ASSIGN Results\n",
    "    w_vec.append(w)\n",
    "    mu_vec.append(mu)\n",
    "    std_vec.append(std)\n",
    "    w_ini_vec.append(w_ini)\n",
    "    mu_ini_vec.append(mu_ini)\n",
    "    std_ini_vec.append(std_ini)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('EMfit.pickle', 'wb') as f:\n",
    "    pickle.dump([KK_vec, w_vec, mu_vec, std_vec], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('EMfit_LogScale.pickle', 'rb') as f:\n",
    "    [KK_vec, w_vec, mu_vec, std_vec] = pickle.load(f)\n",
    "\n",
    "KK_log = KK_vec[0]\n",
    "w_log = w_vec[0]\n",
    "mu_log = mu_vec[0]\n",
    "std_log = std_vec[0]\n",
    "\n",
    "with open('EMfit.pickle', 'rb') as f:\n",
    "    [KK_vec, w_vec, mu_vec, std_vec] = pickle.load(f)\n",
    "\n",
    "KK_lin = KK_vec[0]\n",
    "w_lin = w_vec[0]\n",
    "mu_lin = mu_vec[0]\n",
    "std_lin = std_vec[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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