overseas_companies_land_ownership_analysis.ipynb

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    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Load-and-process\" data-toc-modified-id=\"Load-and-process-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Load and process</a></div><div class=\"lev1 toc-item\"><a href=\"#Data-dictionary\" data-toc-modified-id=\"Data-dictionary-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Data dictionary</a></div><div class=\"lev1 toc-item\"><a href=\"#Analysis\" data-toc-modified-id=\"Analysis-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Analysis</a></div><div class=\"lev3 toc-item\"><a href=\"#How-many-UK-properties-are-owned-by-overseas-companies-compared-to-2015?-How-many-titles-are-held-by-companies-incorporated-in-secrecy-jurisdictions-in-2015-compared-to-2017?-Secrecy-jurisdictions-are-defined-as-those-jurisdictions-that-have-a-secrecy-score-of-60-or-above-in-the-Tax-Justice-Network-2015-Financial-Screcy-Index.\" data-toc-modified-id=\"How-many-UK-properties-are-owned-by-overseas-companies-compared-to-2015?-How-many-titles-are-held-by-companies-incorporated-in-secrecy-jurisdictions-in-2015-compared-to-2017?-Secrecy-jurisdictions-are-defined-as-those-jurisdictions-that-have-a-secrecy-score-of-60-or-above-in-the-Tax-Justice-Network-2015-Financial-Screcy-Index.-301\"><span class=\"toc-item-num\">3.0.1&nbsp;&nbsp;</span>How many UK properties are owned by overseas companies compared to 2015? How many titles are held by companies incorporated in secrecy jurisdictions in 2015 compared to 2017? Secrecy jurisdictions are defined as those jurisdictions that have a secrecy score of 60 or above in the Tax Justice Network 2015 Financial Screcy Index.</a></div><div class=\"lev3 toc-item\"><a href=\"#What’s-the-total-amount?-Have-these-figures-increased-since-July-2015?\" data-toc-modified-id=\"What’s-the-total-amount?-Have-these-figures-increased-since-July-2015?-302\"><span class=\"toc-item-num\">3.0.2&nbsp;&nbsp;</span>What’s the total amount? Have these figures increased since July 2015?</a></div><div class=\"lev3 toc-item\"><a href=\"#Number-of-properties-worth-over-£1,000,000\" data-toc-modified-id=\"Number-of-properties-worth-over-£1,000,000-303\"><span class=\"toc-item-num\">3.0.3&nbsp;&nbsp;</span>Number of properties worth over £1,000,000</a></div><div class=\"lev3 toc-item\"><a href=\"#How-many-properties-owned-by-companies-incorproated-in-secrecy-jurisdiction-are-in-London?-Has-this-increased-since-2015?\" data-toc-modified-id=\"How-many-properties-owned-by-companies-incorproated-in-secrecy-jurisdiction-are-in-London?-Has-this-increased-since-2015?-304\"><span class=\"toc-item-num\">3.0.4&nbsp;&nbsp;</span>How many properties owned by companies incorproated in secrecy jurisdiction are in London? Has this increased since 2015?</a></div><div class=\"lev3 toc-item\"><a href=\"#Which-areas-of-England-and-Wales-have-the-most-properties-owned-by-companies-incorporated-in-secrecy-jurisdictions?-Has-this-changed-since-2015?\" data-toc-modified-id=\"Which-areas-of-England-and-Wales-have-the-most-properties-owned-by-companies-incorporated-in-secrecy-jurisdictions?-Has-this-changed-since-2015?-305\"><span class=\"toc-item-num\">3.0.5&nbsp;&nbsp;</span>Which areas of England and Wales have the most properties owned by companies incorporated in secrecy jurisdictions? Has this changed since 2015?</a></div><div class=\"lev3 toc-item\"><a href=\"#How-many-properties-owned-by-companies-incoporated-in-secrecy-jurisdictions-are-in-Kensington-&amp;-Chelsea-/-City-of-Westminster?-Has-this-increased-since-2015?\" data-toc-modified-id=\"How-many-properties-owned-by-companies-incoporated-in-secrecy-jurisdictions-are-in-Kensington-&amp;-Chelsea-/-City-of-Westminster?-Has-this-increased-since-2015?-306\"><span class=\"toc-item-num\">3.0.6&nbsp;&nbsp;</span>How many properties owned by companies incoporated in secrecy jurisdictions are in Kensington &amp; Chelsea / City of Westminster? Has this increased since 2015?</a></div><div class=\"lev3 toc-item\"><a href=\"#How-many-properties-owned-by-companies-incorporated-in-secrecy-jurisdictions-by-London-borough\" data-toc-modified-id=\"How-many-properties-owned-by-companies-incorporated-in-secrecy-jurisdictions-by-London-borough-307\"><span class=\"toc-item-num\">3.0.7&nbsp;&nbsp;</span>How many properties owned by companies incorporated in secrecy jurisdictions by London borough</a></div><div class=\"lev3 toc-item\"><a href=\"#What-proportion-of-overseas-owned-properties-in-London-are-owned-by-companies-incorproated-in-secrecy-jurisdictions?\" data-toc-modified-id=\"What-proportion-of-overseas-owned-properties-in-London-are-owned-by-companies-incorproated-in-secrecy-jurisdictions?-308\"><span class=\"toc-item-num\">3.0.8&nbsp;&nbsp;</span>What proportion of overseas owned properties in London are owned by companies incorproated in secrecy jurisdictions?</a></div><div class=\"lev3 toc-item\"><a href=\"#What-is-the-breakdown-of-company-registration-by-jurisdiction?-How-does-this-compare-to-2015?\" data-toc-modified-id=\"What-is-the-breakdown-of-company-registration-by-jurisdiction?-How-does-this-compare-to-2015?-309\"><span class=\"toc-item-num\">3.0.9&nbsp;&nbsp;</span>What is the breakdown of company registration by jurisdiction? How does this compare to 2015?</a></div><div class=\"lev3 toc-item\"><a href=\"#How-many-are-owned-by-companies-registered-in-the-British-Crown-Dependencies-and-Overseas-Territories?-Has-this-changed-since-2015?\" data-toc-modified-id=\"How-many-are-owned-by-companies-registered-in-the-British-Crown-Dependencies-and-Overseas-Territories?-Has-this-changed-since-2015?-3010\"><span class=\"toc-item-num\">3.0.10&nbsp;&nbsp;</span>How many are owned by companies registered in the British Crown Dependencies and Overseas Territories? Has this changed since 2015?</a></div><div class=\"lev3 toc-item\"><a href=\"#Which-areas-saw-the-most-new-titles-in-the-period-2015-2017?\" data-toc-modified-id=\"Which-areas-saw-the-most-new-titles-in-the-period-2015-2017?-3011\"><span class=\"toc-item-num\">3.0.11&nbsp;&nbsp;</span>Which areas saw the most new titles in the period 2015-2017?</a></div><div class=\"lev3 toc-item\"><a href=\"#How-many-secrecy-jurisdiction-titles-have-been-bought-and-sold-between-2015-and-2017?\" data-toc-modified-id=\"How-many-secrecy-jurisdiction-titles-have-been-bought-and-sold-between-2015-and-2017?-3012\"><span class=\"toc-item-num\">3.0.12&nbsp;&nbsp;</span>How many secrecy jurisdiction titles have been bought and sold between 2015 and 2017?</a></div><div class=\"lev3 toc-item\"><a href=\"#Proportion-of-new-titles-not-sold-at-full-market-value\" data-toc-modified-id=\"Proportion-of-new-titles-not-sold-at-full-market-value-3013\"><span class=\"toc-item-num\">3.0.13&nbsp;&nbsp;</span>Proportion of new titles not sold at full market value</a></div>"
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   "source": [
    "# Load and process"
   ]
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    "##Load libraries\n",
    "import pandas as pd\n",
    "import time\n",
    "import missingno as mgno\n",
    "import re\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "from log_progress import log_progress"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "##Set styling for numbers (thousand commans, no scientific notaiton etc) and charts\n",
    "pd.options.display.float_format = '{:20,.2f}'.format\n",
    "matplotlib.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
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   "execution_count": 3,
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   "source": [
    "##Load October 2015 data\n",
    "data_2015 = pd.read_csv('data/oct_2015_ov_data.csv',low_memory=False)\n",
    "##Load October 2017 data\n",
    "data_2017 = pd.read_sql('data',con='sqlite:///data/oct_2017_ov_data_product.sqlite')\n",
    "##Add in snapshot date\n",
    "data_2015['date_snapshot'] = pd.to_datetime('2015-10-31')\n",
    "data_2017['date_snapshot'] = pd.to_datetime('2017-10-1')\n",
    "##Combine files into a single dataframe\n",
    "data = pd.concat([data_2015,data_2017])\n",
    "##Exclude last row which appears to be a summary\n",
    "data = data[:-1]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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    {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title_Number</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Property_Address</th>\n",
       "      <th>Price_Paid</th>\n",
       "      <th>District</th>\n",
       "      <th>County</th>\n",
       "      <th>Region</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Proprietor_Name_1</th>\n",
       "      <th>Company_Registration_No_1</th>\n",
       "      <th>...</th>\n",
       "      <th>Company_Registration_No_4</th>\n",
       "      <th>Proprietorship_4</th>\n",
       "      <th>Country_Incorporated_4</th>\n",
       "      <th>Proprietor_4_Address_1</th>\n",
       "      <th>Proprietor_4_Address_2</th>\n",
       "      <th>Proprietor_4_Address_3</th>\n",
       "      <th>Date_Proprietor_Added</th>\n",
       "      <th>Additional_Proprietor_Indicator</th>\n",
       "      <th>Multiple_Address_Indicator</th>\n",
       "      <th>date_snapshot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99214</th>\n",
       "      <td>AGL401155</td>\n",
       "      <td>Leasehold</td>\n",
       "      <td>Apartment 2101, Wiverton Tower, 4 New Drum Str...</td>\n",
       "      <td>nan</td>\n",
       "      <td>TOWER HAMLETS</td>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>E1 7AT</td>\n",
       "      <td>GS ALDGATE TRUSTEE 1 LIMITED</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>16-01-2017</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2017-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99215</th>\n",
       "      <td>AGL401163</td>\n",
       "      <td>Leasehold</td>\n",
       "      <td>Apartment 2207, Wiverton Tower, 4 New Drum Str...</td>\n",
       "      <td>nan</td>\n",
       "      <td>TOWER HAMLETS</td>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>E1 7AT</td>\n",
       "      <td>GS ALDGATE TRUSTEE 1 LIMITED</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>16-01-2017</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2017-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99216</th>\n",
       "      <td>AGL402005</td>\n",
       "      <td>Leasehold</td>\n",
       "      <td>Flat 41, Goldhawk House, 10 Beaufort Square, L...</td>\n",
       "      <td>nan</td>\n",
       "      <td>BARNET</td>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>NW9 5SQ</td>\n",
       "      <td>NOMURO NOMINEES LIMITED</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>24-01-2017</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2017-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99217</th>\n",
       "      <td>AGL402840</td>\n",
       "      <td>Leasehold</td>\n",
       "      <td>11, Birkdale House, 18 St Annes Street, London...</td>\n",
       "      <td>390,000.00</td>\n",
       "      <td>TOWER HAMLETS</td>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>E14 7QB</td>\n",
       "      <td>HAPPY GOAL DEVELOPMENT LTD</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>02-02-2017</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2017-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99218</th>\n",
       "      <td>AGL403429</td>\n",
       "      <td>Leasehold</td>\n",
       "      <td>Block A, Victoria Wharf, Palmers Road, London ...</td>\n",
       "      <td>nan</td>\n",
       "      <td>TOWER HAMLETS</td>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>GREATER LONDON</td>\n",
       "      <td>E2 0SF</td>\n",
       "      <td>PINEHILL CAPITAL S.A.</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>07-02-2017</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2017-10-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Title_Number     Tenure  \\\n",
       "99214    AGL401155  Leasehold   \n",
       "99215    AGL401163  Leasehold   \n",
       "99216    AGL402005  Leasehold   \n",
       "99217    AGL402840  Leasehold   \n",
       "99218    AGL403429  Leasehold   \n",
       "\n",
       "                                        Property_Address           Price_Paid  \\\n",
       "99214  Apartment 2101, Wiverton Tower, 4 New Drum Str...                  nan   \n",
       "99215  Apartment 2207, Wiverton Tower, 4 New Drum Str...                  nan   \n",
       "99216  Flat 41, Goldhawk House, 10 Beaufort Square, L...                  nan   \n",
       "99217  11, Birkdale House, 18 St Annes Street, London...           390,000.00   \n",
       "99218  Block A, Victoria Wharf, Palmers Road, London ...                  nan   \n",
       "\n",
       "            District          County          Region Postcode  \\\n",
       "99214  TOWER HAMLETS  GREATER LONDON  GREATER LONDON   E1 7AT   \n",
       "99215  TOWER HAMLETS  GREATER LONDON  GREATER LONDON   E1 7AT   \n",
       "99216         BARNET  GREATER LONDON  GREATER LONDON  NW9 5SQ   \n",
       "99217  TOWER HAMLETS  GREATER LONDON  GREATER LONDON  E14 7QB   \n",
       "99218  TOWER HAMLETS  GREATER LONDON  GREATER LONDON   E2 0SF   \n",
       "\n",
       "                  Proprietor_Name_1 Company_Registration_No_1      ...       \\\n",
       "99214  GS ALDGATE TRUSTEE 1 LIMITED                      None      ...        \n",
       "99215  GS ALDGATE TRUSTEE 1 LIMITED                      None      ...        \n",
       "99216       NOMURO NOMINEES LIMITED                      None      ...        \n",
       "99217    HAPPY GOAL DEVELOPMENT LTD                      None      ...        \n",
       "99218         PINEHILL CAPITAL S.A.                      None      ...        \n",
       "\n",
       "      Company_Registration_No_4 Proprietorship_4 Country_Incorporated_4  \\\n",
       "99214                       nan             None                   None   \n",
       "99215                       nan             None                   None   \n",
       "99216                       nan             None                   None   \n",
       "99217                       nan             None                   None   \n",
       "99218                       nan             None                   None   \n",
       "\n",
       "      Proprietor_4_Address_1 Proprietor_4_Address_2 Proprietor_4_Address_3  \\\n",
       "99214                   None                   None                    nan   \n",
       "99215                   None                   None                    nan   \n",
       "99216                   None                   None                    nan   \n",
       "99217                   None                   None                    nan   \n",
       "99218                   None                   None                    nan   \n",
       "\n",
       "      Date_Proprietor_Added Additional_Proprietor_Indicator  \\\n",
       "99214            16-01-2017                               N   \n",
       "99215            16-01-2017                               N   \n",
       "99216            24-01-2017                               N   \n",
       "99217            02-02-2017                               N   \n",
       "99218            07-02-2017                               N   \n",
       "\n",
       "      Multiple_Address_Indicator date_snapshot  \n",
       "99214                          N    2017-10-01  \n",
       "99215                          N    2017-10-01  \n",
       "99216                          N    2017-10-01  \n",
       "99217                          N    2017-10-01  \n",
       "99218                          N    2017-10-01  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Inspect first few rows of data\n",
    "data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "source": [
    "##Create stacked version of original table which is better for counts\n",
    "data.columns = ['Title_Number', 'Tenure', 'Property_Address', 'Price_Paid', 'District',\\\n",
    "       'County', 'Region', 'Postcode', '1-Proprietor_Name',\\\n",
    "       '1-Company_Registration', '1-Proprietorship',\\\n",
    "       '1-Country_Incorporated', '1-Proprietor_Address_1',\\\n",
    "       '1-Proprietor_Address_2', '1-Proprietor_Address_3', '2-Proprietor_Name',\\\n",
    "       '2-Company_Registration', '2-Proprietorship',\\\n",
    "       '2-Country_Incorporated', '2-Proprietor_Address_1',\\\n",
    "       '2-Proprietor_Address_2', '2-Proprietor_Address_3', '3-Proprietor_Name',\\\n",
    "       '3-Company_Registration', '3-Proprietorship',\\\n",
    "       '3-Country_Incorporated', '3-Proprietor_Address_1',\\\n",
    "       '3-Proprietor_Address_2', '3-Proprietor_Address_3', '4-Proprietor_Name',\\\n",
    "       '4-Company_Registration', '4-Proprietorship',\\\n",
    "       '4-Country_Incorporated', '4-Proprietor_Address_1',\\\n",
    "       '4-Proprietor_Address_2', '4-Proprietor_Address_3',\\\n",
    "       'Date_Proprietor_Added', 'Additional_Proprietor_Indicator',\\\n",
    "       'Multiple_Address_Indicator','date_snapshot']\n",
    "data.set_index(['Title_Number','Tenure', 'Property_Address', 'Price_Paid', 'District',\\\n",
    "       'County', 'Region', 'Postcode','Date_Proprietor_Added', 'Additional_Proprietor_Indicator',\\\n",
    "       'Multiple_Address_Indicator','date_snapshot'], inplace=True)\n",
    "tuples = tuple(data.columns.str.split(\"-\"))\n",
    "tuples = [x[::-1] for x in tuples]\n",
    "data.columns = pd.MultiIndex.from_tuples(tuples)\n",
    "data = data.stack(level = 1)\n",
    "##data = data.reset_index(level = 1, drop = True)\n",
    "data = data.reset_index()\n",
    "data.rename(columns={'level_12': 'Proprietor_Number'},inplace=True)\n",
    "data.set_index('date_snapshot',inplace=True)"
   ]
  },
  {
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   "execution_count": 6,
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   "source": [
    "##Create a pseudo unique column for companies which is a combination of first line of address and name. May\n",
    "##want to bottom this out a bit more\n",
    "data['Proprietor_Address_1'] = data['Proprietor_Address_1'].fillna('')\n",
    "data['Proprietor_Name'] = data['Proprietor_Name'].fillna('')\n",
    "data['unique_company'] = data['Proprietor_Name'] + data['Proprietor_Address_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(208353, 20)\n"
     ]
    }
   ],
   "source": [
    "##Size of dataset\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eeD5VJuNA3zsivl0e/y6wyuQ2rzrBEGUmyCqSLouI4RExa3nsceA/wNYRMUX5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\n15GRf4dKejWyDvKRZCOyJ7r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mGx++S9Zm3LAc/5CMGLqVnJhtUI0YanePllzoFL4OrEU2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gIeworSdnhuHoPfyenNMKpy7OU5n373xRk9t+KCv0WGSR/gG0m34XoJa+JMlFHAIRExrLTmXArM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\nFiamkfp1BwAAACiDnj3zi4P79OmTW+2IiD/84Q8xf/78eOCBB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m699dZ44YUX4s9//nOG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\nLkmSJEmSJElSN/8PqSzvzal1hWsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1035f0da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Look at how well filled out fields are\n",
    "mgno.matrix(data_2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iavn/PGrTtjktQXtHZi7o+/3JzL2pG+pPAY5uN9RHyU+BfwL+KyKeAwtr7M+B\nhYltry1mzxgxFo2xGAyT2YPxUmqZwUsyc+fMfCbV2VcGTomIJ2TmDdQmdjtGxPbtdQ8DXgesCMz4\nWnSNsegYiyaqFt3nqUTUS6kLpx0y81dtVuExVEJ7Q+DLEbFu30yJDamajldP9N4zkP2iYyw6xqJp\nyZifA5cAO2fmVb3n2mygz1GrNf4KHBARG2YrOdRmSsxhdC4gl9QvvhoRm2Yt7+19sEyqTt/bWlxG\nafaUY6RjLIpx6NMS06cCNwK7Utdanwa2i4g9gJOpyQR/YbTPnZPWL0bgHOoYASJiDepv42VU2cKL\ngQXApi2BPeb/c1Yd6P6E9repzygrTmvDp96l1Lh/GrBvRGwFCxPay/XFxWuLER8jjbHoGIsBsMzI\nAETEm4E9gE0y8/q+x59L1fZcH9iKWtp3JPBsavbDbdSOqM/NzPOmu91TwVh0jEWnfUj6FvD+zDy+\nPbYZsAW1gco5mfmNiHgp8E5qlvZ+1F3QxwPPAp6SmZdPf+snl/2iYyw6xqITEcdQHzbXzMyb2mNP\npM4FawPnZ+YpEbEjcCC1SdeR1AfTjajYPDlneC1LWOZ+8YLs24AqInYFDqYS/vtkWy490zlGOsai\nGIexIuLJ1OSA3TLzzPbYOsA3gWsyc+s2ueCVVP3s+zKC5077RcdYLJxQ839UEurfqdI7CyLi09QM\nym2AmydK0MbYUl73y8w/T1/Lp07v94qIjYAPAB+lrhkuB96dbZ+Sca/x2mJEx0iPsegYi8FwZvZg\n/JFKut0fxmyY8nXqDs7VwMeBa6ndwV8CnEB9CH/yiHV0Y9ExFp051O6+cwEi4sXAWdQHqj2AL0TE\n5zPzROB5VE3c/YF9gEcCzxuFRHZjv+gYi46x6JxIbabyZoCIeCG1bH4PYC/qfPHldmPs2VTN/Z2A\nl1GJ7WfM9GRMn6X1iz8AR0bEer0XZOYnqCXBHxmVD5uNY6RjLIpxGOs+1LXWX6FqAGfV+P0m8NiI\nmN/OCZ8EtqfKJ4ziudN+0ZnVsWhlMu4EDqA2aLu6L2n9U+CJwAN6M5HHv74lfJdr/x6JRDYs/L3m\nUNdaT6LKJTwbeDg1Q/uJEfGpiNi47zVeW4zgGBnHWHSMxQA4M3saRMTK1I7f84GrqM2mvg/8JTP/\nrR2zYrbdvyPiFdQs00My8yMDafQUMRYdYzGxNiNiVSoWZwJ7UrWzvwwcRW22EsD7gf/LzP9or3so\ndXF1V1tYz0WpAAAgAElEQVTGMyPZLzrGomMsFq8tB34rtXLjc9RyvROAY6mNYgN4H3BKZu7cXjOf\nWjq/XGbeMoh2T4Z/sF/sS214eXT/czOdY6RjLIpxWLKI+CeqRNMbM/PovsffBuxNbXZ33bjXrEd9\nGJ+x5077RcdYdKJq3n4AOC4zf9T3eG9W8mpUbH4C7NqbgT2KWixeAawBXJ9jN3/9AvDdzPxQS15/\ng24S0hMz8xyvLUZ2jBiLxlgMB2dmT7H2h+8HwNuBdwAfo2aMHU7NevgcQGbe3ncH5xhq5+SXD6LN\nU8VYdIzFWBFxn/bhicy8syWjDwJeC+xMLWP7Qmb+uS3dOYG6y/m0iNiivc0VmXndDE9k2y8aY9Ex\nFmP1ny8AMvNGaqn88sBrqJlDn8zM37fzxeeo2UFbRm0iC7Vs+LaZmoyBe9UvLgB26D03/S2ffI6R\njrEoxmFRE5w7L6BmWvdmZvc+F95GTRyY1/faldtrfj+Tz532i46xWMTTgddTdZ4373u8N/PvNmqS\nzVOpTVH7x8zIaP3iJ9TEgDcBb+z7rAXwa+A/WpL/F8B5wP3a19XAawtGcIwYi46xGB4jdwIeJhGx\nAnAStYndDsCjgFcB61Ed+VDgBRFxLCxy4v81MK/NUp3xjEXHWEzoscB7I2L/vse+Qs2s/DDwTNrF\nZETMzaqLm8A6wEOglsBNa4snmf2iYyw6xmJCi5wvMvNCaibhY4FbM/NPsPB8cSOV0H4wni+g+sVK\no9IvHCMdY1GMw2JNdK11cmYe2/7d+1y4NrWR1a0AEbEq8ImI+NS0tXQK2C86xmJC5wC/p/bb2Ceq\npnxvc8O5WaUy3g2sRSV5x5QVGQXtd/k41S+2BTYBtsjM77Xn5wBnAyu33/1YYFOqbMLawIci4hmD\naPtkc4x0jEXHWAwXAzm1HgI8FNi/zX4gIr4KvJjaMfxU6sLxXRGxJvAGqt7O6u35PzA6NxyMRcdY\nLOq29nX/iFgzM/fIzOsi4iPUzKDtgX+PiAuzq7u2CrXL+PUTvN9MZL/oGIuOsVhU//lijczcEyAz\nz4qIragl8LTH7mr/vD9wRf9zM5z9omMsOsaiGIeJTXStdVubYXl33/XVGlQy+86ocgOHUImtLQfQ\n5slkv+gYi0X9hVrZNY9KUL0tIg7IzB9m5l1RJQOujYi3AkdExM8y8xMz/eb4OPOovvGVzLwEICK2\njIjtqM9dvwaOA+6KiF9R11bbZOb3IuKXwGep1bSjwDHSMRYdYzFEDOTUWp46yS8HC2eI/Z3arO7R\n1IXix4BdqDufP6DuCn+Vqv2576gs08FY9DMWi1qB2t3388CrIuIIgKyadUdSmw7tDxwcEVtFxNOo\nWZj3AX4xmCZPOvtFx1h0jMWi+s8X/xkRh/c9d0Zmnh8RG0XEFhFxv4jYiJo1cTc162oU2C86xqJj\nLIpxmNj4a63DYeHs0rlt1iXU5tt3Usmrw4D/Bzw1M38ygDZPJvtFx1j0aTd07qTKAHyG2kD6sdTm\nhpu2w1ZvX79BjaF3RMSLpr2xU2sB9XuuBhC1ofZpwOOAfwHeRpUqexhwI7BtS2TPaeeHx2Xm5YNo\n+BRwjHSMRcdYDBGT2VPrOmrzqSdFxCp9M8Rupy4oV8rMmzLzJOAxwEeoejunAZv17vaMCGPRMRaL\n2gi4lNrU8cPAqyPiQwCZ2atJtT/wauAU4HhgM+B5mXnlQFo8+ewXHWPRMRaLGn++eG1EfBAWLgde\nmVr2egY1i+grVKmiF2fmHwfT5Elnv+gYi46xKMZhYhOdO3sJ7buAFdtxN1AbcX+K2rfkqZn5s+lv\n7qSzX3SMRZ++2dXnAztn5jeovXkeBbw9Is4Gjm+zs6+kEt4XMDoTanrupH6vJ0XEA4H/ojbFfB5V\nfuWFVD3t1YE3ZOZZUNde7fWjlKRzjHSMRcdYDBGT2VMoM6+hlhyck2M3SvkbNTNiRVh4N/hW4PDM\nfD11x+biaW/wFDIWHWMxoRWpDRx/BnyImgm0a98M7Ysy8wDqDufzgJcAz8jM8wbV4Mlmv+gYi46x\nmNBE54tX9yVlbgO+RZ0nDgLeg+eLke0XxqJjLIpxWKyJzp2v6Tt3/r0d931gXaqsyGaZee4gGjvZ\n7BcdY7FYFwNrRcSqmfkpYB/gWcDGwCnZZlRm5hnAdpn528E1dfJlzU4/DHgaVRf8LuC0zLy5Je1+\nBHyQKvG4yQSvXzD+sZnKMdIxFh1jMVysmT3Fspbc/AQgIpZvfyTuS9357NWuWyVqU5X7RcSzqIEw\ncoxFx1gs4ovA6QCZ+ceI+HB7fI+IWJCZvY1WLgEuGVAbp5z9omMsOsZiEUs6X8zJzDdl5vXAl9t/\nI8l+0TEWHWNRjMOElnTuJDP3aN9fTNXFPTBrc92RYb/oGIsJnUn9/k8Evk3NRIZK3m4VEedl2wyx\nJapGTmb+OCL2Bg4AVqI2hKRdX90eEd+jJkQ+aIDNnBaOkY6x6BiL4TFnwYKRuYE2Y0TE7sCB1HK/\na4EjgB2BZ2XmjwfZtulmLDqzPRbtImlB3/fzqeVtewBHZeZeA2vcAM32ftHPWHRmeyyWcr44OjPf\nPLDGDdBs7xf9jEXHWBTjsOznzjYz9a8Daua0sl90ZnMsomrGz6VKAnyUKmf4EuDpwD8DRwPfBHZp\nK8BGVkSsCuwJ7EuVd9wrM69ozz2IKt/20cz82PhzyqibzWNkPGPRMRaDYZmRwVlAba5wKLWxytNm\ncUc3Fp1ZG4vxF0KZeRVV0/EQYM+IOHAgDRsOs7ZfTMBYdGZtLJZyvtjD88Xs7BcTMBYdY1FmdRyW\n4dz5/vb4rEhk95nV/WKcWRmLzFzQZlj+H3AMsA3w75n568z8HPAa4O2jnsiGheP/cGp29jbApyLi\n9RHxUqrm/oOpOI1UaZF7YFaOkcUwFh1jMc1MZk+j6HYJv5Yq8fI/wKsYnY1Vlpmx6BiLxWsfsj5G\nbf547ICbM63sFx1j0TEWi+f5ArBfGIs+xqIYhyUbd+48ZrCtmT72i46xGONzVKJ2u8z8fu/BzDyp\nlTucFTLzZmqm6XbAetSGmAdRM0+flZmXDbB5084x0jEWHWMxWJYZGYCIeDTwS+DvwJMy8/wBN2lg\njEXHWCxeRMzNbrfgWcV+0TEWHWOxeJ4v7BdgLPoZi2Iclmy2njvtFx1jUSLiPqNaE/sfERFrAmtT\ndX+vz8wbB9ykgXGMdIxFx1gMhhtADsbF1OyHzMyLBt2YATMWHWOxGLPxw1Uf+0XHWHSMxWJ4vrBf\nNMaiYyyKcViCWXzutF90jAWju7njPyozbwBuGHQ7hoRjpGMsOsZiAJyZPSCzdfbDRIxFx1hoIvaL\njrHoGAtNxH7RMRYdY1GMgyZiv+gYC2nJHCMdY9ExFtPPZLYkSZIkSZIkaei5AaQkSZIkSZIkaehZ\nM1uSJEmSJEmSZqiImAvsDuwKrA9cARwNHJWZCyLi8cA5E7z00Mzcq73HPOD9wMuAVYBvAm/MzKv6\nfs5awOHAC6lJ0v8L7JmZN/Ud8xjgA8Bm7aHvA2/OzEv7jtkaeA/wGOAq4EPAhzNzqSVEnJktSZIk\nSZIkSTPXfsCBwHHAi4AEPgjs3Z5/HHALsPm4/z7U9x4fBXYC3gLs0l7ztZYo7/lf4BnAa4A3tZ91\nQu/JiLgfcCawNvAquuT6dyNizXbM5sCpwAXANsAngMPa+y2VM7MlSZIkSZIkaQZqyeY9gYMz84D2\n8OkRsS6wF3AQsAlwQWaevZj3eDiVyN4hM09qj50HXEQlnL8YEVsCWwKbZeaP2jFXAqdFxKaZeS6w\nM7AS8PzM/Es75mzgSmAHarb4HsAvgVe2mdinRcSjgddTs76XyJnZkiRJkiRJkjQzrQ4cC3xx3OMX\nAetGxCpUMvv8JbzHM9vXr/QeyMzfUEnnrdtDWwF/7iWymzOAm/qO+R1wSC+R3d7nj+2Yh7aH3gy8\nbFxJkduBeUto30LOzJYkSZIkSZKkGagljv9rgqdeCFyZmbdExMbA3yPi51Sd6t8B78nMz7RjNwL+\nmJm3jHuPy9pzvWMuGfez746Iy3vH9GZ194uIpwBrARe2Y37f99yaVKmSnYD3Lsvv68xsSZIkSZIk\nSRoREfGf1EzqgyJiPrAOsCFwAPA84DvAMRGxU3vJ6sDNE7zVze25ZT1mfDvWAD4G/B743LjnHgL8\nBfgMVT/7I8vyu43izOyl7nopSZIkSZIkSTPInGU5KCJ2pDZz/ALwYaqG9XOAX2Tm1e2w01qSe3+q\nRMkcFp9Tvbvv59+9lGP627Em8HWqvMgzM/PWcYfcRJU3eQDwHuCHEfEvExw3xigms7nqqqsG3QTm\nz58/FO0YBsaiYyw6xqJjLDrGomMsOsaiYyw6xqJjLIpx6BiLjrHoGIuOsegYi46x6BiLzrDEYv78\n+ct0XETsCRwCnALs2OpS3wZ8a4LDvwFsHRGrAjcCq01wzGrtOdrXBy7mmIvGtePBdInsF42rsw0s\nLI9yRjv+Aqqm90uo5PpiWWZEkiRJkiRJkmawiDgQOBT4LPCSzLy9Pb5RRLw2IsZvsLgylei+BfgN\n8ICIWHncMQ+jS1T/pn3f/zOXAzboO4aIeATwA2A+sFVmnj7uNdtGxL+O+zkXAHe01yyRyWxJkiRJ\nkiRJmqEiYnfgrcARwCsy886+px8EHE3Vyu4dPwd4MXBWm719OjCX2jSyd8yGwGPbc7SvD4yIJ/a9\n95ZUvezT22vWpGaBrwA8NTPPnqC5bwEOHvfYlu01v1ja7zqSZUYkSZIkSZIkadRFxAOBD1CJ4BOB\nJ0VE/yE/AL4HfDQi1gKuBnYDNgGeApCZl0bE54FPtE0b/wK8jyr98aX2Pt8GfgR8MSL2ppLPhwBf\nzcyftmPeRZUWeROwekRs1teOP2bm5dQmlKdExMeABDYC3g2cCXxtab+vM7MlSZIkSZIkaWZ6DjAP\n2Bj44QT/rQZsA5xMJY2/CNwP+Le+JDTALsBJVGL8k8B5wPMy8y6ANoP7RcD3gY8DhwGnAjv0vcc2\n7esHJ2jHXu19Tm3Hbdpevx9VGuX57WcskTOzJUmSJEmSJGkGysxjgGOW4dDXLOV9bqFmbO+2hGP+\nDGy/hOc3WIZ2kJmnUJtU3mPOzJYkSZIkSZIkDT2T2ZIkSZIkSZKkoWcyW5IkSZIkSZI09ExmS5Ik\nSZIkSZKGnslsSZIkSZIkSdLQM5ktSZIkSZIkSRp6JrMlSZIkSZIkSUPPZLYkSZIkSZIkaeiZzJYk\nSZIkSZIkDT2T2ZIkSZIkSZKkoWcyW5IkSZIkSZI09ExmS5IkSZIkSZKGnslsSZIkSZIkSdLQW366\nf2BEvAg4PjNXW8px/wQcATwJuB44CjgoMxdMfSslSZIkSZIkScNkWpPZEfFk4DhgzlKOux9wGnAB\nEMCmwAHAXcAhU9xMSZIkSZIkSdKQmZZkdkTMA3YH3gPcAqy4lJe8nmrbizLzVuBr7T3eGhFHZOYd\nU9pgSZIkSZIkSdJQma6a2c8F3grsDRy5DMdvBZzeEtk9XwLuC/zr5DdPkiRJkiRJkjTMpiuZ/RPg\noZn5IWBZal5vBFwy7rHL+p6TJEmSJEmSJM0i01JmJDP/cA9fsjpw87jHbu57TpIkSZIkSZI0i0zr\nBpD3wBwWP4P77qW9eP78+ffqh2+55Zb36vWT5Ywzzhh0E4xFH2Mx+e7tWB0G9ovOMMRiGOIAxmIq\njML5YrKMQiwcIx1j0TEWk2sUzhVgv+hnLCbXqIyRyTAqsXCMlGGIAxiLfsMQi1EyrMnsG4HVxj22\nWt9zS3TVVVdNeoMGYVR+j8lgLDqjEov58+ePzO8yDIxlMQ6dUYqF54uOsZg8xrFjLDqjEgvPFZPL\nWHZGJRaOkY6xmFzGsmMsOpMRi1G56TQZhjWZ/RvgYeMe631/0TS3RZIk6V7bcccdB90Ejj/++EE3\nQZIkSZL+YdO1AeQ9dTqwVUSs0vfYtsB1wM8H0yRJkiRJkiRJ0qAMxczsiHg4sG5mnt0eOhp4A/C1\niDgYeBzwVuAtmXn7gJopSZIkSZIkSRqQYZmZvR/ww943mXk1sBWVbP8CsBuwb2YeMpjmSZIkSZIk\nSZIGadpnZmfmO4F3jnvsFcArxj12DvCUaWqWJEmSJEmSJGmIDcvMbEmSJEmSJEmSFstktiRJkiRJ\nkiRp6JnMliRJkiRJkiQNPZPZkiRJkiRJkqShZzJbkiRJkiRJkjT0TGZLkiRJkiRJkoaeyWxJkiRJ\nkiRJ0tAzmS1JkiRJkiRJGnomsyVJkiRJkiRJQ89ktiRJkiRJkiRp6JnMliRJkiRJkiQNPZPZkiRJ\nkiRJkqShZzJbkiRJkiRJkjT0TGZLkiRJkiRJkoaeyWxJkiRJkiRJ0tAzmS1JkiRJkiRJGnomsyVJ\nkiRJkiRJQ89ktiRJkiRJkiRp6JnMliRJkiRJkiQNPZPZkiRJkiRJkqShZzJbkiRJkiRJkjT0TGZL\nkiRJkiRJkoaeyWxJkiRJkiRJ0tAzmS1JkiRJkiRJGnomsyVJkiRJkiRJQ89ktiRJkiRJkiRp6JnM\nliRJkiRJkiQNPZPZkiRJkiRJkqShZzJbkiRJkiRJkjT0TGZLkiRJkiRJkoaeyWxJkiRJkiRJ0tAz\nmS1JkiRJkiRJGnomsyVJkiRJkiRJQ89ktiRJkiRJkiRp6JnMliRJkiRJkiQNPZPZkiRJkiRJkqSh\nZzJbkiRJkiRJkjT0TGZLkiRJkiRJkoaeyWxJkiRJkiRJ0tAzmS1JkiRJkiRJGnomsyVJkiRJkiRJ\nQ89ktiRJkiRJkiRp6JnMliRJkiRJkiQNPZPZkiRJkiRJkqShZzJbkiRJkiRJkjT0TGZLkiRJkiRJ\nkoaeyWxJkiRJkiRJ0tAzmS1JkiRJkiRJGnomsyVJkiRJkiRJQ89ktiRJkiRJkiRp6JnMliRJkiRJ\nkiQNPZPZkiRJkiRJkqSht/ygGyBJkiRJkiRJ+sdExFxgd2BXYH3gCuBo4KjMXBARc4C3Aa8G1gG+\nD7whMy/se4+1gIOA5wMrAWcBe2TmZX3HPBI4FNgC+DtwIrBvZv6175gjgf+aoJkbZ+YF7ZitgfcA\njwGuAj4EfDgzFyztd3VmtiRJkiRJkiTNXPsBBwLHAS8CEvggsHd7/h3A24FDgJcCawCnR8Qafe/x\nufbatwA7Aeu1Y1aFhcnu06hk+A7Am4EAjh/Xlse1n7/5uP8ube+zOXAqcAGwDfAJ4DDgTcvyizoz\nW5IkSZIkSZJmoDYre0/g4Mw8oD18ekSsC+wVER8B9gLemZkfaq85i5q9/SrgsIi4H/Ac4FWZeWw7\n5mLgImBr4AvAy4AHAI/PzD/3/exjIuLBmXll+9kbAydk5tmLafIewC+BV7aZ2KdFxKOB1wOHL+33\ndWa2JEmSJEmSJM1MqwPHAl8c9/hFwLrAM4FVgVN6T2TmX4DvUIlqqLIiADf1vf669vW+7evngCf3\nEtnN7e3rPICIWB9YEzh/Ce19M/CycSVFbu+9x9I4M1uSJEmSJEmSZqCWmJ6oRvULgSuBB7fvLx33\n/GVUmQ8y83cR8RVg34i4ALiemiV9E/C1vp/zE4CIWBl4EvBe4LTM7L33Ju3rLhHxRWAtKmn+hsy8\nqL3P73sNiIg1qdImO7X3WipnZkuSJEmSJEnSiIiI/wS2ojZ0XB34e2bePu6wm9tzPbu3738N/Al4\nMfDivvIh/X4FnEHNwt677/FeMnsVqjb3/6OS6d9pZU/62/gQ4C/AZ6j62R9Zlt9tzoIFS90kcqYZ\nuV9IkiRJkiRJ0qw2Z1kOiogdqQTxydQGjW8F9svMlccd917gNZm5TkQ8CDgX+DPwTmpG9q5UGZJn\nj69/HRFbURU/9qQ2d9wiM8+LiA2ARwHf7JURaaVHLgYOyMz39L3HWsA/U3W43wPcBfxLZt66pN9v\nJMuMXHXVVffq9TvuuOMkteTeOf748ZuBTj9j0TEWk2v+/Pn3eqwOA/tFZxhiMQxxAGMx2TxfTJ5h\n6RfGomMsOsZi8ozKeRPsF/2MxeQZpTFyb41SLBwjZRjiAMai32TEYv78+ct0XETsCRxC1cfeMTMX\nRMSNwLyIWCEz7+g7fDXgxvbvXaiSIJtm5h/ae50G/ICa3f20/p+Tmae1Y84ELqFmdb8yMy8HLh93\n7O8i4tfA48Y9/hdqdjettMn5wEuo+t+LZZkRSZIkSZIkSZrBIuJA4FDgs8BL+sqK/Iaa1f3QcS95\nGLVJJMB6wO97iWyANrP6+8Bj2vtvFhEv6n+DzPwbcCEwvx3z/Ih4/gTNWxm4th2zbUT867jnLwDu\n6L3PkpjMliRJkiRJkqQZKiJ2p8qJHAG8IjPv7Hv6B8DfgG37jl8LeDpwenvoYmD9iHgwYz0J+G37\n97bAZyNijb73WRvYFPhF7yHgkxFxn75jNgY2pDaCBHgLcPC4n7MlsELf+yzWSJYZkSRJkiRJkqRR\nFxEPBD5AJYJPBJ4UEf2HnAMcCbwnIu6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Ps8cYh6qnVs+dcz51znlF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q6VAn87ARelW0+RdFBLjrelERG34z4j2ycV1fGYsH4Tq6dOAAY1EvgqvKNZJRq4FZfVGI+bST+e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\nrOdgtpmZmZmZmZmZmZmN9f4fFMdweci3az4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11565f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mgno.bar(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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36mo/dyx/+5PINh67AIsDPyfbe2wJ7BkRPSQNAI4u/21R9wO3hmi19eKgqZm1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GAgNAUAAAAAGAhNAQAAAAAGQlMAAAAAgIHQFAAAAABgIDQFAAAAABhc5kLTqlq31mO4rPLsAQAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1166ed0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Correlation matrix for filled values\n",
    "mgno.heatmap(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "##Format dates that proprietor was added\n",
    "data['Date_Proprietor_Added'] = pd.to_datetime(data['Date_Proprietor_Added'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1172d91d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mObSqqn2qqjogyTPSXawaAAAAyrYbPFU2XrthfdrbfjjV3kWtZaL5zFjaVX9J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\nREREpIz9f3uK1RK5pY+WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116aabe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Date_Proprietor_Added'].value_counts().plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "##Load in data on secrecy jurisdictions, normalize and take average of secrecy score from Financial Secrecy Index 2015\n",
    "secrecy_index = pd.read_csv('data/financial_secrecy_index_2015.csv')\n",
    "secrecy_index['normalized_jurisdiction'] = secrecy_index.Jurisdiction.str.upper()\n",
    "def derive_mean_or_leave_score(row):\n",
    "    pat = re.compile('.*(\\d\\d)-(\\d\\d).*')\n",
    "    if ')' in row['Secrecy Score']:\n",
    "        return int(re.search(pat,row['Secrecy Score']).group(1)) + int(re.search(pat,row['Secrecy Score']).group(2)) / 2\n",
    "    else:\n",
    "        return row['Secrecy Score']\n",
    "secrecy_index['secret_score_mean'] = secrecy_index.apply(derive_mean_or_leave_score,axis=1)\n",
    "secrecy_index.secret_score_mean = secrecy_index.secret_score_mean.astype(float)\n",
    "secret_jurisdictions = secrecy_index[secrecy_index['secret_score_mean'] >= 60]['normalized_jurisdiction'].tolist()\n",
    "##Add in all USA states\n",
    "data.Country_Incorporated = data.Country_Incorporated.fillna('')\n",
    "us_secret = data[data.Country_Incorporated.str.contains('U.S.A')].Country_Incorporated.unique().tolist()\n",
    "secret_jurisdictions.extend(us_secret)\n",
    "##Normalziation of Country Incorproated field\n",
    "data['incorporated_normalized'] = data.Country_Incorporated\n",
    "data.incorporated_normalized = data.incorporated_normalized.str.strip()\n",
    "data.incorporated_normalized = data.incorporated_normalized.str.replace('ST KITTS AND NEVIS','ST KITTS & NEVIS')\n",
    "data.incorporated_normalized = data.incorporated_normalized.str.replace('BELIZ$','BELIZE')\n",
    "data.incorporated_normalized = data.incorporated_normalized.str.replace('TURKS AND CAICOS ISLANDS','TURKS & CAICOS ISLANDS')\n",
    "data.incorporated_normalized = data.incorporated_normalized.str.replace('DUBAI','UNITED ARAB EMIRATES (DUBAI)')\n",
    "data.incorporated_normalized = data.incorporated_normalized.str.replace('ILSE OF MAN','ISLE OF MAN')\n",
    "data.incorporated_normalized = data.incorporated_normalized.str.replace('REPUBLIC OF SEYCHELLE','SEYCHELLES')\n",
    "data.incorporated_normalized = data.incorporated_normalized.str.replace('THE SYECHELLES','SEYCHELLES')\n",
    "data.incorporated_normalized = data.incorporated_normalized.str.replace('NORTH CYPRUS','CYPRUS')\n",
    "data.incorporated_normalized = data.incorporated_normalized.str.replace('PAMANA','PANAMA')\n",
    "##Add this on to data\n",
    "data['secrecy_jurisdiction'] = data.incorporated_normalized.apply(lambda x: True if x in secret_jurisdictions else False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CANADA', 'LUXEMBOURG', 'NETHERLANDS', 'DUTCH ANTILLES',\n",
       "       'ST VINCENT AND GRENADINES', 'IRELAND', 'UNITED ARAB EMIRATES',\n",
       "       'GERMANY', 'CHANNEL ISLANDS', 'DENMARK', 'CYPRUS', 'MALTA',\n",
       "       'PORTUGAL', 'LABUAN, MALAYSIA', 'KOREA', 'EGYPT', 'KUWAIT',\n",
       "       'FRANCE', 'SPAIN', 'BELGIUM', 'ITALY', 'NIGERIA',\n",
       "       'THE VIRGIN ISLANDS', 'MADEIRA', '387503', 'NEW ZEALAND', 'NIUE',\n",
       "       'SWEDEN', 'RAS AL KHAIMAH FREE TRADE ZONE', 'QATAR', 'JORDAN',\n",
       "       'AUSTRIA', 'GREECE', 'ALDERNEY', 'INDIA', 'AUSTRALIA', 'ISRAEL',\n",
       "       'JAPAN', '1762155', 'MALAYSIA', 'BRAZIL', 'REPUBLIC OF KOREA',\n",
       "       'INDONESIA', 'SOUTH KOREA', 'CZECH REPUBLIC', 'SOUTH AFRICA',\n",
       "       'CHINA', 'OMAN', 'HASHEMITE KINGDOM OF JORDAN', 'NORWAY',\n",
       "       'THAILAND', 'SRI LANKA', 'VIETNAM', 'RAS AL KHAIMAH', 'ABU DHABI',\n",
       "       'ETHIOPIA', 'SWAZILAND', 'CHONBURI', 'ALBERTA, CANADA',\n",
       "       'THE WEST INDIES', 'ONTARIO, CANADA', 'NIUE ISLAND',\n",
       "       'THE REPUBLIC OF KOREA',\n",
       "       \"THE SOCIALIST PEOPLE'S LIBYAN ARAB JAMAHIRIYA\", 'ROVEREDO GR',\n",
       "       'ANTIGUA AND BARBUDA', 'HEILIGKREUZ',\n",
       "       'THE HASHEMITE KINGDOM OF JORDAN', 'FINLAND', 'CHILE', 'BRUNEI',\n",
       "       'NEW BRUNSWICK, CANADA', 'KENYA', 'FALKLAND ISLANDS',\n",
       "       'BRITISH COLUMBIA, CANADA', '1005646', '101634', '2875892',\n",
       "       '113166C', '125159', '66528', 'NOVA SCOTIA, CANADA', '63605',\n",
       "       'TRINIDAD AND TOBAGO', '02366665', 'OC389182', 'OC373223',\n",
       "       'LITHUANIA', 'ESTONIA', 'AJMAN FREE ZONE', 'IRELANND', 'IRAQ',\n",
       "       'IRISH REPUBLIC', 'THE EUROPEAN UNION', 'ZIMBABWE', 'BANGLADESH',\n",
       "       'OC343252', '1370007', 'PART VII OF THE CHARITIES ACT 1993',\n",
       "       '89146', '229088', 'ICELAND', 'BULGARIA', 'BAHAMES', 'CROATIA',\n",
       "       '154738502', 'JARSEY', 'MEXICO', 'IP18077R',\n",
       "       'FUJAIRAH MEDIA FREE ZONE', 'THE ISLAND OF NIUE',\n",
       "       'IRELAND, CO. REGN. NO. 374321', 'ANGUILIA', '7550', '49716',\n",
       "       'POLAND', '109688', 'IRELEND', 'JEBEL ALI FREE ZONE', 'LIBYA',\n",
       "       '1654979', '55311', 'FAROE ISLANDS', '7366', '32203', '02127156',\n",
       "       'MACAU', 'COSTA RICA', 'SHARJAH', 'WEST-BRABANT', '56013',\n",
       "       '7604385', 'JAMAICA', '1737844', 'SLOVAKIA', 'UGANDA',\n",
       "       'WESTERN SAMOA', 'GEORGIA', 'KUALA LUMPUR', '7465508', 'THE NIUE',\n",
       "       '1012258', 'IRAN', 'SWISS CONFEDERATION', 'FEDERATION OF MALAYA',\n",
       "       'THE TURKISH REPUBLIC', 'THE REBUBLIC OF IRELAND',\n",
       "       'STATE OF GRENADA', 'SWITERLAND', 'THE REPUBLIC OF LIBE RIA',\n",
       "       'MONGOLIA', 'MOROCCO', 'ITLAY', 'ALGERIA', 'BELARUS',\n",
       "       'SIERRA LEONE', 'SERBIA', 'BREDA', 'HAMBURG', 'REIDEN',\n",
       "       'THE CHENNAL ISLES', 'RUPUBLIC OF IRELAND', 'QUEBEC, CANADA',\n",
       "       '58936', '14259', '00002065', '76028', 'OC323303', 'SAMAO',\n",
       "       'TENERIFE', 'BC0698768', 'THE HELLENIC REPUBLIC', 'THE GAMBIA',\n",
       "       'THE BRITISH ISLANDS', 'MALAYASIA', 'NORTH KOREA',\n",
       "       'REPUBLIC OF MARITIUS', 'AMERICA', 'NUIE', '25374', '38026',\n",
       "       '102718', 'IP18922R', 'TURKISH REPUBLIC OF NORTH',\n",
       "       'THE REUPLIC OF IRELAND COMPANY NO.250972', 'GREMANY', '085544C',\n",
       "       '118586', '200512967K', 'PAKISTAN', 'REPULBIC OF IRELAND',\n",
       "       '0003280', 'THE REPUBLI C OF IRELAND', 'HUNGARY', 'IP28410R',\n",
       "       '05986778', '43290', 'AJMAN', '', 'COLOMBIA', 'LATVIA', 'BERLIZE',\n",
       "       'FC029833', 'PENANG', 'TOGO', \"COTE D'IVOIRE\", 'CITY OF PANANMA',\n",
       "       '1811435',\n",
       "       'THE EUROPEAN UNION UNDER THE TREATY ON EUROPEAN UNION AND THE TREATY ON THE FUNCTIONING OF THE EUROPEAN UNION'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##List of countries to clean up...\n",
    "data[data.secrecy_jurisdiction == False].incorporated_normalized.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANGUILLA -> 585\n",
      "BERMUDA -> 1404\n",
      "BRITISH ANTARCTIC TERRITORY -> 0\n",
      "BRITISH INDIAN OCEAN TERRITORY -> 0\n",
      "BRITISH VIRGIN ISLANDS -> 45943\n",
      "CAYMAN ISLANDS -> 2401\n",
      "ISLE OF MAN -> 22530\n",
      "JERSEY -> 47675\n",
      "SOVEREIGN BASE AREAS OF AKROTIRI AND DHEKELIA ON CYPRUSFALKLAND ISLANDS -> 0\n",
      "GIBRALTAR -> 5114\n",
      "MONTSERRAT -> 0\n",
      "PITCAIRN ISLANDS -> 0\n",
      "SAINT HELENA AND ITS DEPENDENCIES (ASCENSION ISLAND AND TRISTAN DA CUNHA) -> 0\n",
      "SOUTH GEORGIA -> 0\n",
      "SOUTH SANDWICH ISLANDS -> 0\n",
      "TURKS AND CAICOS ISLANDS -> 490\n"
     ]
    }
   ],
   "source": [
    "##Add in extra field to mark if company incorproated in overseas terrirtory or crown dependency\n",
    "list_of_cd_ot = [\n",
    "'Anguilla',\n",
    "'Bermuda',\n",
    "'British Antarctic Territory',\n",
    "'British Indian Ocean Territory',\n",
    "'British Virgin Islands',\n",
    "'Cayman Islands',\n",
    "'Isle of Man',\n",
    "'Jersey',\n",
    "'Sovereign Base Areas of Akrotiri and Dhekelia on Cyprus'\n",
    "'Falkland Islands',\n",
    "'Gibraltar',\n",
    "'Montserrat',\n",
    "'Pitcairn Islands',\n",
    "'Saint Helena and its dependencies (Ascension Island and Tristan da Cunha)',\n",
    "'South Georgia',\n",
    "'South Sandwich Islands',\n",
    "'Turks and Caicos Islands',\n",
    "]\n",
    "list_of_cd_ot = [x.upper() for x in list_of_cd_ot]\n",
    "##Quick sense check of how well the different names of jurisdictions align\n",
    "for t in list_of_cd_ot:\n",
    "    print(t,'->',len(data[data.Country_Incorporated == t]))\n",
    "##Create column\n",
    "data['overseas_territory_crown_dependency'] = data['incorporated_normalized'].isin(list_of_cd_ot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where fields occur in original dataset, descriptions are taken from [gov.uk documnetation](https://www.gov.uk/government/publications/overseas-companies-chargeable-dataset-data-specification/overseas-companies-chargeable-dataset-data-specification#data-content-and-structure)\n",
    "\n",
    "Column name | Data type | Description\n",
    "--- | --- | ---\n",
    "Title_Number | String | Number which uniquely identifies a registered title to land or a caution against first registration.\n",
    "Tenure | String | Freehold or Leasehold.\n",
    "Property_Address | String | The unformatted A1 Address entry or an address from the Land Registry Property Gazetteer (LRPG)\n",
    "Price_Paid | Float | The sale price stated on the transfer deed. Highly incomplete, see below. Prices not included for properties part of a larger deal.\n",
    "District | String | \tDistrict name. Name of an administrative district created since local government reorganisation 1974. Administrative district also covers the London boroughs, unitary authorities and (for HM Land Registry’s purposes) the Isles Of Scilly parishes.\n",
    "County | String | County. Name of current County in England and Wales.\n",
    "Region | String | \tRegion name. Name of a geographic region which comprises one or more current counties, former counties or unitary authorities or any combination of these. On HQ instructions, the names and extents of the regions are the economic planning regions used by various bodies. N.B. region names allocated by the Office for National Statistics differ in some instances from names in this field.\n",
    "Postcode | String | Postcode. Code which is a combination of up to 7 letters and numbers (plus one embedded blank), which defines different levels of geographic units. It is part of a coding system created and used by the post office across the UK, to facilitate the mail service.\n",
    "Date_Proprietor_Added | Date | The date a proprietor was added to the register.\n",
    "Additional_Proprietor_Indicator | Boolean string (Y/N) | Indicator as to whether there is more than one proprietor for the propetry.\n",
    "Multiple_Address_Indicator | Indicates the title has more than one address.\n",
    "Proprietor_Number | Integer | Ordinal number representing the number of the propreitor in event that there are multiple\n",
    "Company_Registration | String | Company Registration Number. Number which is a unique identifier assigned to a company when it is registered at Companies’ House. Although the name of the company may change, the company registration number will remain the same.\n",
    "Country_Incorporated | String | The name of the country where the company/territory is incorporated.\n",
    "Proprietor_Address_1 | String | Register address string. Text which is the address (including the postcode) either of a property or a registered proprietor, in the form in which it appears as the property description on the register. \n",
    "Proprietor_Address_2 | String | Register address string. Text which is the address (including the postcode) either of a property or a registered proprietor, in the form in which it appears as the property description on the register.\n",
    "Proprietor_Address_3 | String | Register address string. Text which is the address (including the postcode) either of a property or a registered proprietor, in the form in which it appears as the property description on the register.\n",
    "Proprietor_Name | String | Non – Private Individual Name. Name of a company, corporate body, local authority or other organisation or establishment other than that of a private individual.\n",
    "Proprietorship | String | Name category description. Text which describes the category in which a name falls.\n",
    "unique_company | String | A pseudo-unique identifier for companies which is a combination. Acombination of proprietor name and address. Not in original data.\n",
    "incorporated_normalized | String | Cleaned version of Country_Incorporated to allow for easier analysis. Not in original data.\n",
    "secrecy_jurisdiction | Boolean | True if country of incorporation is in a secrecy jurisdiction. Secrecy jurisdictions are countries or territories which have secrey score of 60 or above in [Tax Justice Network's 2015 Financial Secrecy Index](http://www.financialsecrecyindex.com/). Not in original data.\n",
    "overseas_territory_crown_dependency | Boolean | True if country of incorporation is a British Overseas Territory or Crown Dependency."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": true,
    "hideOutput": true,
    "hidePrompt": false
   },
   "source": [
    "### How many UK properties are owned by overseas companies compared to 2015? How many titles are held by companies incorporated in secrecy jurisdictions in 2015 compared to 2017? Secrecy jurisdictions are defined as those jurisdictions that have a secrecy score of 60 or above in the Tax Justice Network 2015 Financial Screcy Index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caveats:\n",
    "- Here we count every unique Land Registry title as a property in the rest of the analysis we  count unique addresses rather than Land Registry titles as properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_properties</th>\n",
       "      <th>owner_secret_company</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_snapshot</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td>99344</td>\n",
       "      <td>86765</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>99219</td>\n",
       "      <td>86379</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               total_properties  owner_secret_company           percentage\n",
       "date_snapshot                                                             \n",
       "2015-10-31                99344                 86765                 0.87\n",
       "2017-10-01                99219                 86379                 0.87"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = data.pivot_table(index='date_snapshot',values=['Title_Number'],aggfunc=lambda x: len(x.unique()))\n",
    "s1 = data.pivot_table(index='date_snapshot',columns=['secrecy_jurisdiction'],values='Title_Number',aggfunc=lambda x: len(x.unique()))[True]\n",
    "df1['owner_secret_company'] = s1\n",
    "df1.columns = ['total_properties','owner_secret_company']\n",
    "df1['percentage'] = df1['owner_secret_company'] / df1['total_properties']\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### What’s the total amount? Have these figures increased since July 2015?\n",
    "\n",
    "Some caveats on the analysis below:\n",
    "\n",
    "- Price paid data is incomplete less than 1 in 10 values are filled across the whole dataset\n",
    "- The distribution is skewed towards the higher prices which which affect the mean as a measure of the center of the observations\n",
    "- Price paid data is more incomplete in 2015 than 2017 therefore comparisons between the two years in terms of sums should not be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.088141562431214168"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Note that 91% of price paid vaues for 2017 snapshot are missing\n",
    "data['2017'].Price_Paid.count() / len(data['2017'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>date_snapshot</th>\n",
       "      <th>2015-10-31 00:00:00</th>\n",
       "      <th>2017-10-01 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price_Paid</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date_snapshot           2015-10-31           2017-10-01\n",
       "Price_Paid                    0.07                 0.09"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Proportion of filled values across the two different two snapshots\n",
    "data.pivot_table(columns='date_snapshot',values='Price_Paid',aggfunc=lambda x: x.count() / len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fe72b70>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a5064a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Distribution of price paid values where they exist. Notice the heavy skew towards high values will affect the mean.\n",
    "sns.distplot(data['Price_Paid'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_snapshot</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td>6,038.00</td>\n",
       "      <td>1,550,546.99</td>\n",
       "      <td>4,486,116.33</td>\n",
       "      <td>1.00</td>\n",
       "      <td>190,000.00</td>\n",
       "      <td>440,000.00</td>\n",
       "      <td>1,183,750.00</td>\n",
       "      <td>86,031,326.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>7,995.00</td>\n",
       "      <td>2,251,413.41</td>\n",
       "      <td>8,061,926.03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>202,000.00</td>\n",
       "      <td>495,000.00</td>\n",
       "      <td>1,400,000.00</td>\n",
       "      <td>215,399,999.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count                 mean                  std  \\\n",
       "date_snapshot                                                                  \n",
       "2015-10-31                6,038.00         1,550,546.99         4,486,116.33   \n",
       "2017-10-01                7,995.00         2,251,413.41         8,061,926.03   \n",
       "\n",
       "                               min                  25%                  50%  \\\n",
       "date_snapshot                                                                  \n",
       "2015-10-31                    1.00           190,000.00           440,000.00   \n",
       "2017-10-01                    1.00           202,000.00           495,000.00   \n",
       "\n",
       "                               75%                  max  \n",
       "date_snapshot                                            \n",
       "2015-10-31            1,183,750.00        86,031,326.00  \n",
       "2017-10-01            1,400,000.00       215,399,999.00  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Interpret the following in the light of the above\n",
    "data[data.secrecy_jurisdiction == True].groupby('date_snapshot').describe()['Price_Paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_snapshot\n",
       "2017-10-01         215,399,999.00\n",
       "2017-10-01         197,999,000.00\n",
       "2017-10-01         197,999,000.00\n",
       "2017-10-01         180,364,651.00\n",
       "2017-10-01         180,364,651.00\n",
       "2017-10-01         180,364,651.00\n",
       "2017-10-01         161,523,809.00\n",
       "2017-10-01          99,000,000.00\n",
       "2017-10-01          90,200,000.00\n",
       "2015-10-31          86,031,326.00\n",
       "Name: Price_Paid, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Looks like other properties were brought at same time\n",
    "data[data.secrecy_jurisdiction == True]['Price_Paid'].sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1589247087.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Value of top 10 most expesnive properties\n",
    "data[data.secrecy_jurisdiction == True]['Price_Paid'].sort_values(ascending=False).head(10).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_snapshot\n",
       "2015-10-31       9,362,202,746.00\n",
       "2017-10-01      18,000,050,218.00\n",
       "Name: Price_Paid, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##What is the value of property owned by comapnies incorproated in secrecy jurisdictions?\n",
    "data[data.secrecy_jurisdiction == True].groupby('date_snapshot').sum()['Price_Paid']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of properties worth over £1,000,000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caveats:\n",
    "    \n",
    "* As above, incompleteness of price paid data means that this number is likely far too low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2935"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[(data.secrecy_jurisdiction == True) & (data.Price_Paid >= 1000000)].Property_Address.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### How many properties owned by companies incorproated in secrecy jurisdiction are in London? Has this increased since 2015?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_snapshot\n",
       "2015-10-31 00:00:00              37,926.00\n",
       "2017-10-01 00:00:00              37,066.00\n",
       "pct_change                           -0.02\n",
       "Name: GREATER LONDON, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data[data.secrecy_jurisdiction == True].pivot_table(index='County',columns='date_snapshot',values='Property_Address',aggfunc=lambda x: len(x.unique()))\\\n",
    ".sort_values(by='2017-10-01 00:00:00',ascending=False)\n",
    "df['pct_change'] = df.pct_change(axis=1)['2017-10-01']\n",
    "df.loc['GREATER LONDON',:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### Which areas of England and Wales have the most properties owned by companies incorporated in secrecy jurisdictions? Has this changed since 2015?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>date_snapshot</th>\n",
       "      <th>2015-10-31 00:00:00</th>\n",
       "      <th>2017-10-01 00:00:00</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GREATER LONDON</th>\n",
       "      <td>37926</td>\n",
       "      <td>37066</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOUTH EAST</th>\n",
       "      <td>13683</td>\n",
       "      <td>13886</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NORTH WEST</th>\n",
       "      <td>9382</td>\n",
       "      <td>9455</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YORKS AND HUMBER</th>\n",
       "      <td>4524</td>\n",
       "      <td>4843</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOUTH WEST</th>\n",
       "      <td>4653</td>\n",
       "      <td>4584</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEST MIDLANDS</th>\n",
       "      <td>4354</td>\n",
       "      <td>4394</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EAST MIDLANDS</th>\n",
       "      <td>3588</td>\n",
       "      <td>3726</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NORTH</th>\n",
       "      <td>2843</td>\n",
       "      <td>2769</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALES</th>\n",
       "      <td>2377</td>\n",
       "      <td>2297</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EAST ANGLIA</th>\n",
       "      <td>1992</td>\n",
       "      <td>2033</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date_snapshot     2015-10-31 00:00:00  2017-10-01 00:00:00  \\\n",
       "Region                                                       \n",
       "GREATER LONDON                  37926                37066   \n",
       "SOUTH EAST                      13683                13886   \n",
       "NORTH WEST                       9382                 9455   \n",
       "YORKS AND HUMBER                 4524                 4843   \n",
       "SOUTH WEST                       4653                 4584   \n",
       "WEST MIDLANDS                    4354                 4394   \n",
       "EAST MIDLANDS                    3588                 3726   \n",
       "NORTH                            2843                 2769   \n",
       "WALES                            2377                 2297   \n",
       "EAST ANGLIA                      1992                 2033   \n",
       "\n",
       "date_snapshot              pct_change  \n",
       "Region                                 \n",
       "GREATER LONDON                  -0.02  \n",
       "SOUTH EAST                       0.01  \n",
       "NORTH WEST                       0.01  \n",
       "YORKS AND HUMBER                 0.07  \n",
       "SOUTH WEST                      -0.01  \n",
       "WEST MIDLANDS                    0.01  \n",
       "EAST MIDLANDS                    0.04  \n",
       "NORTH                           -0.03  \n",
       "WALES                           -0.03  \n",
       "EAST ANGLIA                      0.02  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data[data.secrecy_jurisdiction == True].pivot_table(index='Region',columns='date_snapshot',values='Property_Address',aggfunc=lambda x: len(x.unique()))\\\n",
    ".sort_values(by='2017-10-01 00:00:00',ascending=False)\n",
    "df['pct_change'] = df.pct_change(axis=1)['2017-10-01']\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>date_snapshot</th>\n",
       "      <th>2015-10-31 00:00:00</th>\n",
       "      <th>2017-10-01 00:00:00</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Property_Address</th>\n",
       "      <td>47396</td>\n",
       "      <td>47987</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date_snapshot     2015-10-31 00:00:00  2017-10-01 00:00:00  \\\n",
       "Property_Address                47396                47987   \n",
       "\n",
       "date_snapshot              pct_change  \n",
       "Property_Address                 0.01  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data[(data.Region != 'GREATER LONDON') & (data.secrecy_jurisdiction == True)].groupby('date_snapshot').agg(lambda x: len(x.unique()))['Property_Address'])\n",
    "df = df.T\n",
    "df['pct_change'] = df.pct_change(axis=1)['2017-10-01']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### How many properties owned by companies incoporated in secrecy jurisdictions are in Kensington & Chelsea / City of Westminster? Has this increased since 2015?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>date_snapshot</th>\n",
       "      <th>2015-10-31 00:00:00</th>\n",
       "      <th>2017-10-01 00:00:00</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CITY OF WESTMINSTER</th>\n",
       "      <td>11835</td>\n",
       "      <td>10379</td>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KENSINGTON AND CHELSEA</th>\n",
       "      <td>6117</td>\n",
       "      <td>5710</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMDEN</th>\n",
       "      <td>2141</td>\n",
       "      <td>2222</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WANDSWORTH</th>\n",
       "      <td>1693</td>\n",
       "      <td>1845</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOWER HAMLETS</th>\n",
       "      <td>1433</td>\n",
       "      <td>1597</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HAMMERSMITH AND FULHAM</th>\n",
       "      <td>1402</td>\n",
       "      <td>1423</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANCHESTER</th>\n",
       "      <td>1237</td>\n",
       "      <td>1355</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BARNET</th>\n",
       "      <td>1259</td>\n",
       "      <td>1242</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUNSLOW</th>\n",
       "      <td>1166</td>\n",
       "      <td>1231</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEEDS</th>\n",
       "      <td>1119</td>\n",
       "      <td>1199</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date_snapshot           2015-10-31 00:00:00  2017-10-01 00:00:00  \\\n",
       "District                                                           \n",
       "CITY OF WESTMINSTER                   11835                10379   \n",
       "KENSINGTON AND CHELSEA                 6117                 5710   \n",
       "CAMDEN                                 2141                 2222   \n",
       "WANDSWORTH                             1693                 1845   \n",
       "TOWER HAMLETS                          1433                 1597   \n",
       "HAMMERSMITH AND FULHAM                 1402                 1423   \n",
       "MANCHESTER                             1237                 1355   \n",
       "BARNET                                 1259                 1242   \n",
       "HOUNSLOW                               1166                 1231   \n",
       "LEEDS                                  1119                 1199   \n",
       "\n",
       "date_snapshot                    pct_change  \n",
       "District                                     \n",
       "CITY OF WESTMINSTER                   -0.12  \n",
       "KENSINGTON AND CHELSEA                -0.07  \n",
       "CAMDEN                                 0.04  \n",
       "WANDSWORTH                             0.09  \n",
       "TOWER HAMLETS                          0.11  \n",
       "HAMMERSMITH AND FULHAM                 0.01  \n",
       "MANCHESTER                             0.10  \n",
       "BARNET                                -0.01  \n",
       "HOUNSLOW                               0.06  \n",
       "LEEDS                                  0.07  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data[data.secrecy_jurisdiction == True].pivot_table(index='District',columns='date_snapshot',values='Property_Address',aggfunc=lambda x: len(x.unique()))\\\n",
    ".sort_values(by='2017-10-01 00:00:00',ascending=False)\n",
    "df['pct_change'] = df.pct_change(axis=1)['2017-10-01']\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### How many properties owned by companies incorporated in secrecy jurisdictions by London borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>date_snapshot</th>\n",
       "      <th>2015-10-31 00:00:00</th>\n",
       "      <th>2017-10-01 00:00:00</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>City Of Westminster</th>\n",
       "      <td>11835</td>\n",
       "      <td>10379</td>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kensington And Chelsea</th>\n",
       "      <td>6117</td>\n",
       "      <td>5710</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Camden</th>\n",
       "      <td>2141</td>\n",
       "      <td>2222</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wandsworth</th>\n",
       "      <td>1693</td>\n",
       "      <td>1845</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tower Hamlets</th>\n",
       "      <td>1433</td>\n",
       "      <td>1597</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hammersmith And Fulham</th>\n",
       "      <td>1402</td>\n",
       "      <td>1423</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barnet</th>\n",
       "      <td>1259</td>\n",
       "      <td>1242</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hounslow</th>\n",
       "      <td>1166</td>\n",
       "      <td>1231</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lambeth</th>\n",
       "      <td>988</td>\n",
       "      <td>1043</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Islington</th>\n",
       "      <td>898</td>\n",
       "      <td>926</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date_snapshot           2015-10-31 00:00:00  2017-10-01 00:00:00  \\\n",
       "District                                                           \n",
       "City Of Westminster                   11835                10379   \n",
       "Kensington And Chelsea                 6117                 5710   \n",
       "Camden                                 2141                 2222   \n",
       "Wandsworth                             1693                 1845   \n",
       "Tower Hamlets                          1433                 1597   \n",
       "Hammersmith And Fulham                 1402                 1423   \n",
       "Barnet                                 1259                 1242   \n",
       "Hounslow                               1166                 1231   \n",
       "Lambeth                                 988                 1043   \n",
       "Islington                               898                  926   \n",
       "\n",
       "date_snapshot                    pct_change  \n",
       "District                                     \n",
       "City Of Westminster                   -0.12  \n",
       "Kensington And Chelsea                -0.07  \n",
       "Camden                                 0.04  \n",
       "Wandsworth                             0.09  \n",
       "Tower Hamlets                          0.11  \n",
       "Hammersmith And Fulham                 0.01  \n",
       "Barnet                                -0.01  \n",
       "Hounslow                               0.06  \n",
       "Lambeth                                0.06  \n",
       "Islington                              0.03  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london_df = data[(data.Region == 'GREATER LONDON') & data.secrecy_jurisdiction == True].copy()\n",
    "df = london_df.pivot_table(index='District',columns='date_snapshot',values='Property_Address',aggfunc=lambda x: len(x.unique()))\\\n",
    ".sort_values(by='2017-10-01 00:00:00',ascending=False)\n",
    "df['pct_change'] = df.pct_change(axis=1)['2017-10-01']\n",
    "df.index = df.index.str.title()\n",
    "df.to_csv('outputs/london_district_counts.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What proportion of overseas owned properties in London are owned by companies incorproated in secrecy jurisdictions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overseas_owned</th>\n",
       "      <th>secrecy_jurisdiction_owned</th>\n",
       "      <th>proportion_secret_ownership</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CITY OF WESTMINSTER</th>\n",
       "      <td>14,260.00</td>\n",
       "      <td>13,158.00</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KENSINGTON AND CHELSEA</th>\n",
       "      <td>7,314.00</td>\n",
       "      <td>6,732.00</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITY OF LONDON</th>\n",
       "      <td>1,380.00</td>\n",
       "      <td>878.00</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             overseas_owned  secrecy_jurisdiction_owned  \\\n",
       "District                                                                  \n",
       "CITY OF WESTMINSTER               14,260.00                   13,158.00   \n",
       "KENSINGTON AND CHELSEA             7,314.00                    6,732.00   \n",
       "CITY OF LONDON                     1,380.00                      878.00   \n",
       "\n",
       "                        proportion_secret_ownership  \n",
       "District                                             \n",
       "CITY OF WESTMINSTER                            0.92  \n",
       "KENSINGTON AND CHELSEA                         0.92  \n",
       "CITY OF LONDON                                 0.64  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = data.pivot_table(index='District',values='Property_Address',aggfunc=lambda x: len(x.unique()))\n",
    "s2 = data.pivot_table(index='District',columns='secrecy_jurisdiction',values='Property_Address',aggfunc=lambda x: len(x.unique()))[True]\n",
    "df = pd.concat([s1,s2],axis=1)\n",
    "df['proportion_secret_ownership'] = df.iloc[:,1] / df.iloc[:,0]\n",
    "df.columns = ['overseas_owned','secrecy_jurisdiction_owned','proportion_secret_ownership']\n",
    "df.overseas_owned = df.overseas_owned.astype(float)\n",
    "df.loc[['CITY OF LONDON','KENSINGTON AND CHELSEA','CITY OF WESTMINSTER']].sort_values(by='proportion_secret_ownership',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### What is the breakdown of company registration by jurisdiction? How does this compare to 2015?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>date_snapshot</th>\n",
       "      <th>2015-10-31 00:00:00</th>\n",
       "      <th>2017-10-01 00:00:00</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incorporated_normalized</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BRITISH VIRGIN ISLANDS</th>\n",
       "      <td>16,930.00</td>\n",
       "      <td>16,507.00</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JERSEY</th>\n",
       "      <td>9,139.00</td>\n",
       "      <td>8,676.00</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISLE OF MAN</th>\n",
       "      <td>6,594.00</td>\n",
       "      <td>6,281.00</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GUERNSEY</th>\n",
       "      <td>5,019.00</td>\n",
       "      <td>4,982.00</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PANAMA</th>\n",
       "      <td>1,677.00</td>\n",
       "      <td>1,477.00</td>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIBRALTAR</th>\n",
       "      <td>1,379.00</td>\n",
       "      <td>1,315.00</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUXEMBOURG</th>\n",
       "      <td>1,058.00</td>\n",
       "      <td>1,202.00</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEYCHELLES</th>\n",
       "      <td>1,032.00</td>\n",
       "      <td>1,099.00</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HONG KONG</th>\n",
       "      <td>772.00</td>\n",
       "      <td>926.00</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAHAMAS</th>\n",
       "      <td>943.00</td>\n",
       "      <td>885.00</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYPRUS</th>\n",
       "      <td>815.00</td>\n",
       "      <td>828.00</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IRELAND</th>\n",
       "      <td>882.00</td>\n",
       "      <td>800.00</td>\n",
       "      <td>-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAYMAN ISLANDS</th>\n",
       "      <td>646.00</td>\n",
       "      <td>605.00</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NETHERLANDS</th>\n",
       "      <td>466.00</td>\n",
       "      <td>476.00</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAURITIUS</th>\n",
       "      <td>408.00</td>\n",
       "      <td>414.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SINGAPORE</th>\n",
       "      <td>331.00</td>\n",
       "      <td>401.00</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELAWARE, U.S.A.</th>\n",
       "      <td>373.00</td>\n",
       "      <td>374.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BELIZE</th>\n",
       "      <td>368.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIBERIA</th>\n",
       "      <td>416.00</td>\n",
       "      <td>347.00</td>\n",
       "      <td>-0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUSTRALIA</th>\n",
       "      <td>318.00</td>\n",
       "      <td>345.00</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date_snapshot            2015-10-31 00:00:00  2017-10-01 00:00:00  \\\n",
       "incorporated_normalized                                             \n",
       "BRITISH VIRGIN ISLANDS             16,930.00            16,507.00   \n",
       "JERSEY                              9,139.00             8,676.00   \n",
       "ISLE OF MAN                         6,594.00             6,281.00   \n",
       "GUERNSEY                            5,019.00             4,982.00   \n",
       "PANAMA                              1,677.00             1,477.00   \n",
       "GIBRALTAR                           1,379.00             1,315.00   \n",
       "LUXEMBOURG                          1,058.00             1,202.00   \n",
       "SEYCHELLES                          1,032.00             1,099.00   \n",
       "HONG KONG                             772.00               926.00   \n",
       "BAHAMAS                               943.00               885.00   \n",
       "CYPRUS                                815.00               828.00   \n",
       "IRELAND                               882.00               800.00   \n",
       "CAYMAN ISLANDS                        646.00               605.00   \n",
       "NETHERLANDS                           466.00               476.00   \n",
       "MAURITIUS                             408.00               414.00   \n",
       "SINGAPORE                             331.00               401.00   \n",
       "DELAWARE, U.S.A.                      373.00               374.00   \n",
       "BELIZE                                368.00               360.00   \n",
       "LIBERIA                               416.00               347.00   \n",
       "AUSTRALIA                             318.00               345.00   \n",
       "\n",
       "date_snapshot                     pct_change  \n",
       "incorporated_normalized                       \n",
       "BRITISH VIRGIN ISLANDS                 -0.02  \n",
       "JERSEY                                 -0.05  \n",
       "ISLE OF MAN                            -0.05  \n",
       "GUERNSEY                               -0.01  \n",
       "PANAMA                                 -0.12  \n",
       "GIBRALTAR                              -0.05  \n",
       "LUXEMBOURG                              0.14  \n",
       "SEYCHELLES                              0.06  \n",
       "HONG KONG                               0.20  \n",
       "BAHAMAS                                -0.06  \n",
       "CYPRUS                                  0.02  \n",
       "IRELAND                                -0.09  \n",
       "CAYMAN ISLANDS                         -0.06  \n",
       "NETHERLANDS                             0.02  \n",
       "MAURITIUS                               0.01  \n",
       "SINGAPORE                               0.21  \n",
       "DELAWARE, U.S.A.                        0.00  \n",
       "BELIZE                                 -0.02  \n",
       "LIBERIA                                -0.17  \n",
       "AUSTRALIA                               0.08  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.pivot_table(index='incorporated_normalized',columns='date_snapshot',values='unique_company',aggfunc=lambda x: len(x.unique()))\\\n",
    ".sort_values(by='2017-10-01 00:00:00',ascending=False)\n",
    "df['pct_change'] = df.pct_change(axis=1)['2017-10-01']\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### How many are owned by companies registered in the British Crown Dependencies and Overseas Territories? Has this changed since 2015?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_snapshot\n",
       "2015-10-31 00:00:00              58,675.00\n",
       "2017-10-01 00:00:00              57,318.00\n",
       "pct_change                           -0.02\n",
       "Name: True, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.pivot_table(index='overseas_territory_crown_dependency',columns='date_snapshot',values='Property_Address',aggfunc=lambda x: len(x.unique()))\\\n",
    ".sort_values(by='2017-10-01 00:00:00',ascending=False)\n",
    "df['pct_change'] = df.pct_change(axis=1)['2017-10-01']\n",
    "df.loc[True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2015-10-31 00:00:00</th>\n",
       "      <th>2017-10-01 00:00:00</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incorporated_normalized</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ANGUILLA</th>\n",
       "      <td>282.00</td>\n",
       "      <td>293.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BERMUDA</th>\n",
       "      <td>678.00</td>\n",
       "      <td>682.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRITISH ANTARCTIC TERRITORY</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRITISH INDIAN OCEAN TERRITORY</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRITISH VIRGIN ISLANDS</th>\n",
       "      <td>22,597.00</td>\n",
       "      <td>22,579.00</td>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAYMAN ISLANDS</th>\n",
       "      <td>1,315.00</td>\n",
       "      <td>1,042.00</td>\n",
       "      <td>-0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIBRALTAR</th>\n",
       "      <td>2,597.00</td>\n",
       "      <td>2,453.00</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISLE OF MAN</th>\n",
       "      <td>10,933.00</td>\n",
       "      <td>10,607.00</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JERSEY</th>\n",
       "      <td>20,336.00</td>\n",
       "      <td>19,734.00</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTSERRAT</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PITCAIRN ISLANDS</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAINT HELENA AND ITS DEPENDENCIES (ASCENSION ISLAND AND TRISTAN DA CUNHA)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOUTH GEORGIA</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOUTH SANDWICH ISLANDS</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOVEREIGN BASE AREAS OF AKROTIRI AND DHEKELIA ON CYPRUSFALKLAND ISLANDS</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TURKS AND CAICOS ISLANDS</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    2015-10-31 00:00:00  \\\n",
       "incorporated_normalized                                                   \n",
       "ANGUILLA                                                         282.00   \n",
       "BERMUDA                                                          678.00   \n",
       "BRITISH ANTARCTIC TERRITORY                                        0.00   \n",
       "BRITISH INDIAN OCEAN TERRITORY                                     0.00   \n",
       "BRITISH VIRGIN ISLANDS                                        22,597.00   \n",
       "CAYMAN ISLANDS                                                 1,315.00   \n",
       "GIBRALTAR                                                      2,597.00   \n",
       "ISLE OF MAN                                                   10,933.00   \n",
       "JERSEY                                                        20,336.00   \n",
       "MONTSERRAT                                                         0.00   \n",
       "PITCAIRN ISLANDS                                                   0.00   \n",
       "SAINT HELENA AND ITS DEPENDENCIES (ASCENSION IS...                 0.00   \n",
       "SOUTH GEORGIA                                                      0.00   \n",
       "SOUTH SANDWICH ISLANDS                                             0.00   \n",
       "SOVEREIGN BASE AREAS OF AKROTIRI AND DHEKELIA O...                 0.00   \n",
       "TURKS AND CAICOS ISLANDS                                           0.00   \n",
       "\n",
       "                                                    2017-10-01 00:00:00  \\\n",
       "incorporated_normalized                                                   \n",
       "ANGUILLA                                                         293.00   \n",
       "BERMUDA                                                          682.00   \n",
       "BRITISH ANTARCTIC TERRITORY                                        0.00   \n",
       "BRITISH INDIAN OCEAN TERRITORY                                     0.00   \n",
       "BRITISH VIRGIN ISLANDS                                        22,579.00   \n",
       "CAYMAN ISLANDS                                                 1,042.00   \n",
       "GIBRALTAR                                                      2,453.00   \n",
       "ISLE OF MAN                                                   10,607.00   \n",
       "JERSEY                                                        19,734.00   \n",
       "MONTSERRAT                                                         0.00   \n",
       "PITCAIRN ISLANDS                                                   0.00   \n",
       "SAINT HELENA AND ITS DEPENDENCIES (ASCENSION IS...                 0.00   \n",
       "SOUTH GEORGIA                                                      0.00   \n",
       "SOUTH SANDWICH ISLANDS                                             0.00   \n",
       "SOVEREIGN BASE AREAS OF AKROTIRI AND DHEKELIA O...                 0.00   \n",
       "TURKS AND CAICOS ISLANDS                                           0.00   \n",
       "\n",
       "                                                             pct_change  \n",
       "incorporated_normalized                                                  \n",
       "ANGUILLA                                                           0.04  \n",
       "BERMUDA                                                            0.01  \n",
       "BRITISH ANTARCTIC TERRITORY                                        0.00  \n",
       "BRITISH INDIAN OCEAN TERRITORY                                     0.00  \n",
       "BRITISH VIRGIN ISLANDS                                            -0.00  \n",
       "CAYMAN ISLANDS                                                    -0.21  \n",
       "GIBRALTAR                                                         -0.06  \n",
       "ISLE OF MAN                                                       -0.03  \n",
       "JERSEY                                                            -0.03  \n",
       "MONTSERRAT                                                         0.00  \n",
       "PITCAIRN ISLANDS                                                   0.00  \n",
       "SAINT HELENA AND ITS DEPENDENCIES (ASCENSION IS...                 0.00  \n",
       "SOUTH GEORGIA                                                      0.00  \n",
       "SOUTH SANDWICH ISLANDS                                             0.00  \n",
       "SOVEREIGN BASE AREAS OF AKROTIRI AND DHEKELIA O...                 0.00  \n",
       "TURKS AND CAICOS ISLANDS                                           0.00  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data[data.overseas_territory_crown_dependency == True].pivot_table(index='incorporated_normalized',columns='date_snapshot',values='Property_Address',aggfunc=lambda x: len(x.unique()))\\\n",
    ".sort_values(by='2017-10-01 00:00:00',ascending=False)\n",
    "df = pd.merge(pd.DataFrame(list_of_cd_ot,columns=['incorporated_normalized']).set_index('incorporated_normalized'),df,left_index=True,right_index=True,how='outer')\n",
    "df['pct_change'] = df.pct_change(axis=1)['2017-10-01']\n",
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which areas saw the most new titles in the period 2015-2017?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true,
    "hideOutput": true
   },
   "outputs": [],
   "source": [
    "##Create DataFrame the new titles acquired since 2015\n",
    "new_titles = data[~ data.Title_Number.isin(data['2015'].Title_Number)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Property_Address</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>City Of Westminster</th>\n",
       "      <td>1122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manchester</th>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kensington And Chelsea</th>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liverpool</th>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tower Hamlets</th>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Camden</th>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wandsworth</th>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salford</th>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hammersmith And Fulham</th>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hackney</th>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Property_Address\n",
       "District                                \n",
       "City Of Westminster                 1122\n",
       "Manchester                           531\n",
       "Kensington And Chelsea               525\n",
       "Liverpool                            381\n",
       "Tower Hamlets                        347\n",
       "Camden                               344\n",
       "Wandsworth                           331\n",
       "Salford                              263\n",
       "Hammersmith And Fulham               239\n",
       "Hackney                              235"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_secret = new_titles[new_titles.secrecy_jurisdiction == True].copy()\n",
    "df = new_secret.pivot_table(index='District',values='Property_Address',aggfunc=lambda x: len(x.unique())).sort_values(by='Property_Address',ascending=False)\n",
    "df.index = df.index.str.title()\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many secrecy jurisdiction titles have been bought and sold between 2015 and 2017?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Title bought: 14473\n",
      "Title sold: 14927\n"
     ]
    }
   ],
   "source": [
    "titles_bought = len(new_secret.Title_Number.unique())\n",
    "titles_sold = len(data[( ~ data.Title_Number.isin(data['2017'].Title_Number)) & (data.secrecy_jurisdiction == True)].Title_Number.unique())\n",
    "print('Title bought: ' + str(titles_bought))\n",
    "print('Title sold: ' + str(titles_sold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proportion of new titles not sold at full market value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24915131615560132"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_titles.Price_Paid.count() / len(new_titles)"
   ]
  }
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