diff --git a/notebooks/2_match_households_and_individuals.ipynb b/notebooks/2_match_households_and_individuals.ipynb
index 05e5b85..1b010dc 100644
--- a/notebooks/2_match_households_and_individuals.ipynb
+++ b/notebooks/2_match_households_and_individuals.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,364 +67,9 @@
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-      "text/plain": [
-       "   id  household  workplace  \\\n",
-       "0   0          0        NaN   \n",
-       "1   1          1        NaN   \n",
-       "2   2          1        NaN   \n",
-       "3   3          2    56126.0   \n",
-       "4   4          2        NaN   \n",
-       "\n",
-       "                                            location  \\\n",
-       "0  {'x': -1.7892179489135742, 'y': 53.91915130615...   \n",
-       "1  {'x': -1.8262380361557007, 'y': 53.92028045654...   \n",
-       "2  {'x': -1.8262380361557007, 'y': 53.92028045654...   \n",
-       "3  {'x': -1.8749940395355225, 'y': 53.94298934936...   \n",
-       "4  {'x': -1.8749940395355225, 'y': 53.94298934936...   \n",
-       "\n",
-       "                                              events  \\\n",
-       "0  {'concert_f': 1.2791347489984115e-31, 'concert...   \n",
-       "1  {'concert_f': 9.743248151956307e-21, 'concert_...   \n",
-       "2  {'concert_f': 8.46716103992468e-16, 'concert_f...   \n",
-       "3  {'concert_f': 1.8844366073608398, 'concert_fs'...   \n",
-       "4  {'concert_f': 4.877435207366943, 'concert_fs':...   \n",
-       "\n",
-       "                                     weekday_diaries  \\\n",
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-       "4                               [1289, 12528, 12870]   \n",
-       "\n",
-       "                                     weekend_diaries            orig_pid  \\\n",
-       "0                                      [1582, 13160]  E02002183_0001_001   \n",
-       "1  [2901, 4949, 4973, 7425, 10285, 10585, 12198, ...  E02002183_0002_001   \n",
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-       "4                               [1288, 12529, 12871]  E02002183_0003_002   \n",
-       "\n",
-       "   id_tus_hh  id_tus_p   pid_hs       msoa         oa members        bmi  \\\n",
-       "0   11291218         1  2905399  E02002183  E00053954     [0]  24.879356   \n",
-       "1   17291219         1  2905308  E02002183  E00053953  [1, 2]  27.491207   \n",
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-       "3   20310313         1  2902817  E02002183  E00053689  [3, 4]  20.852091   \n",
-       "4   13010909         3  2900884  E02002183  E00053689  [3, 4]  20.032526   \n",
-       "\n",
-       "   has_cardiovascular_disease  has_diabetes  has_high_blood_pressure  \\\n",
-       "0                       False         False                    False   \n",
-       "1                       False         False                     True   \n",
-       "2                       False          True                     True   \n",
-       "3                       False         False                    False   \n",
-       "4                       False         False                    False   \n",
-       "\n",
-       "   number_medications  self_assessed_health  life_satisfaction sic1d2007  \\\n",
-       "0                 NaN                   3.0                2.0         J   \n",
-       "1                 NaN                   3.0                NaN         C   \n",
-       "2                 NaN                   2.0                4.0         P   \n",
-       "3                 NaN                   2.0                1.0         C   \n",
-       "4                 1.0                   2.0                3.0         J   \n",
-       "\n",
-       "   sic2d2007  soc2010  pwkstat  salary_yearly  salary_hourly             hid  \\\n",
-       "0       58.0   1115.0        6            NaN            NaN  E02002183_0001   \n",
-       "1       25.0   1121.0        6            NaN            NaN  E02002183_0002   \n",
-       "2       85.0   2311.0        6            NaN            NaN  E02002183_0002   \n",
-       "3       31.0   3422.0        1   32857.859375      14.360952  E02002183_0003   \n",
-       "4       62.0   7214.0        1   18162.451172       9.439944  E02002183_0003   \n",
-       "\n",
-       "   accommodation_type  communal_type  num_rooms  central_heat  tenure  \\\n",
-       "0                 1.0            NaN        2.0          True     2.0   \n",
-       "1                 3.0            NaN        6.0          True     2.0   \n",
-       "2                 3.0            NaN        6.0          True     2.0   \n",
-       "3                 3.0            NaN        6.0          True     2.0   \n",
-       "4                 3.0            NaN        6.0          True     2.0   \n",
-       "\n",
-       "   num_cars  sex  age_years  ethnicity  nssec8  \n",
-       "0         2    1         86          1     1.0  \n",
-       "1         2    1         74          3     1.0  \n",
-       "2         2    2         68          1     2.0  \n",
-       "3         1    1         27          1     4.0  \n",
-       "4         1    2         26          1     6.0  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Read in the spc data (parquet format)\n",
     "spc = pd.read_parquet('../data/external/spc_output/' + region + '_people_hh.parquet')\n",
@@ -433,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
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    "source": [
@@ -447,7 +92,7 @@
   },
   {
    "cell_type": "code",
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    "metadata": {},
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    "source": [
@@ -470,7 +115,7 @@
   },
   {
    "cell_type": "code",
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+   "execution_count": null,
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    "outputs": [],
    "source": [
@@ -487,7 +132,7 @@
   },
   {
    "cell_type": "code",
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    "source": [
@@ -539,7 +184,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
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    "source": [
@@ -587,7 +232,7 @@
   },
   {
    "cell_type": "code",
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    "source": [
@@ -635,7 +280,7 @@
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   {
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@@ -658,7 +303,7 @@
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@@ -678,7 +323,7 @@
   },
   {
    "cell_type": "code",
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@@ -830,7 +475,7 @@
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@@ -851,34 +496,9 @@
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",
-      "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Individuals in SPC = 15000\n",
-      "Individuals without reported income = 9226\n",
-      "% of individuals with reported income = 38.5\n",
-      "Individuals with reported income: 0 = 0\n",
-      "Households in SPC = 6725\n",
-      "Households without reported income = 4605\n",
-      "% of households with reported income = 68.5\n",
-      "Households with reported income: 0 = 4605\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# histogram for individuals and households (include NAs as 0)\n",
     "fig, ax = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n",
@@ -914,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -948,30 +568,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# bar plot showing spc_edited.salary_yearly_hh_cat and nts_households.HHIncome2002_B02ID side by side\n",
     "fig, ax = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n",
@@ -998,25 +597,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "HHIncome2002_B02ID\n",
-       " 1.0    35.969773\n",
-       " 3.0    34.382872\n",
-       " 2.0    29.559194\n",
-       "-8.0     0.088161\n",
-       "Name: proportion, dtype: float64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# get the % of households in each income bracket for the nts\n",
     "nts_households['HHIncome2002_B02ID'].value_counts(normalize=True) * 100"
@@ -1031,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1051,43 +634,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'0': 'Not applicable (age < 16)',\n",
-       "  '1': 'Employee FT',\n",
-       "  '2': 'Employee PT',\n",
-       "  '3': 'Employee unspecified',\n",
-       "  '4': 'Self-employed',\n",
-       "  '5': 'Unemployed',\n",
-       "  '6': 'Retired',\n",
-       "  '7': 'Homemaker/Maternal leave',\n",
-       "  '8': 'Student',\n",
-       "  '9': 'Long term sickness/disability',\n",
-       "  '10': 'Other'},\n",
-       " {'1': 'None',\n",
-       "  '2': '0 FT, 1 PT',\n",
-       "  '3': '1 FT, 0 PT',\n",
-       "  '4': '0 FT, 2 PT',\n",
-       "  '5': '1 FT, 1 PT',\n",
-       "  '6': '2 FT, 0 PT',\n",
-       "  '7': '1 FT, 2+ PT',\n",
-       "  '8': '2 FT, 1+ PT',\n",
-       "  '9': '0 FT, 3+ PT',\n",
-       "  '10': '3+ FT, 0 PT',\n",
-       "  '11': '3+ FT, 1+ PT',\n",
-       "  '-8': 'NA',\n",
-       "  '-10': 'DEAD'})"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Employment status\n",
     "\n",
@@ -1106,114 +655,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pwkstat_FT_hh</th>\n",
-       "      <th>pwkstat_PT_hh</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>household</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           pwkstat_FT_hh  pwkstat_PT_hh\n",
-       "household                              \n",
-       "0                      0              0\n",
-       "1                      0              0\n",
-       "2                      2              0\n",
-       "3                      1              0\n",
-       "4                      0              0\n",
-       "5                      1              0\n",
-       "6                      0              0\n",
-       "7                      1              0\n",
-       "8                      1              0\n",
-       "9                      0              1"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We will only use '1' and '2' for the employment status\n",
     "\n",
@@ -1235,141 +679,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>household</th>\n",
-       "      <th>pwkstat</th>\n",
-       "      <th>pwkstat_FT_hh</th>\n",
-       "      <th>pwkstat_PT_hh</th>\n",
-       "      <th>pwkstat_NTS_match</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>4</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   household  pwkstat  pwkstat_FT_hh  pwkstat_PT_hh  pwkstat_NTS_match\n",
-       "0          0        6              0              0                  1\n",
-       "1          1        6              0              0                  1\n",
-       "2          1        6              0              0                  1\n",
-       "3          2        1              2              0                  6\n",
-       "4          2        1              2              0                  6\n",
-       "5          3        1              1              0                  3\n",
-       "6          4       10              0              0                  1\n",
-       "7          4        4              0              0                  1\n",
-       "8          4        0              0              0                  1\n",
-       "9          5        1              1              0                  3"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We want to match the SPC values to the NTS\n",
     "dict_nts['HHoldEmploy_B01ID']\n",
@@ -1427,30 +739,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# bar plot of counts_df['pwkstat_NTS_match'] and nts_households['HHoldEmploy_B01ID']\n",
     "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
@@ -1487,330 +778,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>household</th>\n",
-       "      <th>location</th>\n",
-       "      <th>pid_hs</th>\n",
-       "      <th>msoa</th>\n",
-       "      <th>oa</th>\n",
-       "      <th>members</th>\n",
-       "      <th>sic1d2007</th>\n",
-       "      <th>sic2d2007</th>\n",
-       "      <th>pwkstat</th>\n",
-       "      <th>salary_yearly</th>\n",
-       "      <th>salary_hourly</th>\n",
-       "      <th>hid</th>\n",
-       "      <th>accommodation_type</th>\n",
-       "      <th>communal_type</th>\n",
-       "      <th>num_rooms</th>\n",
-       "      <th>central_heat</th>\n",
-       "      <th>tenure</th>\n",
-       "      <th>num_cars</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age_years</th>\n",
-       "      <th>ethnicity</th>\n",
-       "      <th>nssec8</th>\n",
-       "      <th>salary_yearly_hh</th>\n",
-       "      <th>salary_yearly_hh_cat</th>\n",
-       "      <th>is_adult</th>\n",
-       "      <th>num_adults</th>\n",
-       "      <th>is_child</th>\n",
-       "      <th>num_children</th>\n",
-       "      <th>is_pension_age</th>\n",
-       "      <th>num_pension_age</th>\n",
-       "      <th>pwkstat_FT_hh</th>\n",
-       "      <th>pwkstat_PT_hh</th>\n",
-       "      <th>pwkstat_NTS_match</th>\n",
-       "      <th>OA11CD</th>\n",
-       "      <th>RUC11</th>\n",
-       "      <th>RUC11CD</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "      <td>{'x': -1.7892179489135742, 'y': 53.91915130615...</td>\n",
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-       "      <td>E00053954</td>\n",
-       "      <td>[0]</td>\n",
-       "      <td>J</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E02002183_0001</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>86</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.000000</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>E00053954</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>{'x': -1.8262380361557007, 'y': 53.92028045654...</td>\n",
-       "      <td>2905308</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>[1, 2]</td>\n",
-       "      <td>C</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E02002183_0002</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>74</td>\n",
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-       "      <td>0.000000</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>{'x': -1.8262380361557007, 'y': 53.92028045654...</td>\n",
-       "      <td>2907681</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>[1, 2]</td>\n",
-       "      <td>P</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E02002183_0002</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>68</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>2</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>{'x': -1.8749940395355225, 'y': 53.94298934936...</td>\n",
-       "      <td>2902817</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>[3, 4]</td>\n",
-       "      <td>C</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>32857.859375</td>\n",
-       "      <td>14.360952</td>\n",
-       "      <td>E02002183_0003</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>27</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>51020.310547</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>Rural town and fringe</td>\n",
-       "      <td>D1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>{'x': -1.8749940395355225, 'y': 53.94298934936...</td>\n",
-       "      <td>2900884</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>[3, 4]</td>\n",
-       "      <td>J</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18162.451172</td>\n",
-       "      <td>9.439944</td>\n",
-       "      <td>E02002183_0003</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>26</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>51020.310547</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>Rural town and fringe</td>\n",
-       "      <td>D1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id  household                                           location   pid_hs  \\\n",
-       "0   0          0  {'x': -1.7892179489135742, 'y': 53.91915130615...  2905399   \n",
-       "1   1          1  {'x': -1.8262380361557007, 'y': 53.92028045654...  2905308   \n",
-       "2   2          1  {'x': -1.8262380361557007, 'y': 53.92028045654...  2907681   \n",
-       "3   3          2  {'x': -1.8749940395355225, 'y': 53.94298934936...  2902817   \n",
-       "4   4          2  {'x': -1.8749940395355225, 'y': 53.94298934936...  2900884   \n",
-       "\n",
-       "        msoa         oa members sic1d2007  sic2d2007  pwkstat  salary_yearly  \\\n",
-       "0  E02002183  E00053954     [0]         J       58.0        6            NaN   \n",
-       "1  E02002183  E00053953  [1, 2]         C       25.0        6            NaN   \n",
-       "2  E02002183  E00053953  [1, 2]         P       85.0        6            NaN   \n",
-       "3  E02002183  E00053689  [3, 4]         C       31.0        1   32857.859375   \n",
-       "4  E02002183  E00053689  [3, 4]         J       62.0        1   18162.451172   \n",
-       "\n",
-       "   salary_hourly             hid  accommodation_type  communal_type  \\\n",
-       "0            NaN  E02002183_0001                 1.0            NaN   \n",
-       "1            NaN  E02002183_0002                 3.0            NaN   \n",
-       "2            NaN  E02002183_0002                 3.0            NaN   \n",
-       "3      14.360952  E02002183_0003                 3.0            NaN   \n",
-       "4       9.439944  E02002183_0003                 3.0            NaN   \n",
-       "\n",
-       "   num_rooms  central_heat  tenure  num_cars  sex  age_years  ethnicity  \\\n",
-       "0        2.0          True     2.0         2    1         86          1   \n",
-       "1        6.0          True     2.0         2    1         74          3   \n",
-       "2        6.0          True     2.0         2    2         68          1   \n",
-       "3        6.0          True     2.0         1    1         27          1   \n",
-       "4        6.0          True     2.0         1    2         26          1   \n",
-       "\n",
-       "   nssec8  salary_yearly_hh  salary_yearly_hh_cat  is_adult  num_adults  \\\n",
-       "0     1.0          0.000000                     1         1           1   \n",
-       "1     1.0          0.000000                     1         1           2   \n",
-       "2     2.0          0.000000                     1         1           2   \n",
-       "3     4.0      51020.310547                     3         1           2   \n",
-       "4     6.0      51020.310547                     3         1           2   \n",
-       "\n",
-       "   is_child  num_children  is_pension_age  num_pension_age  pwkstat_FT_hh  \\\n",
-       "0         0             0               1                1              0   \n",
-       "1         0             0               1                2              0   \n",
-       "2         0             0               1                2              0   \n",
-       "3         0             0               0                0              2   \n",
-       "4         0             0               0                0              2   \n",
-       "\n",
-       "   pwkstat_PT_hh  pwkstat_NTS_match     OA11CD                  RUC11 RUC11CD  \n",
-       "0              0                  1  E00053954    Urban city and town      C1  \n",
-       "1              0                  1  E00053953    Urban city and town      C1  \n",
-       "2              0                  1  E00053953    Urban city and town      C1  \n",
-       "3              0                  6  E00053689  Rural town and fringe      D1  \n",
-       "4              0                  6  E00053689  Rural town and fringe      D1  "
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# read the rural urban classification data\n",
     "rural_urban = pd.read_csv('../data/external/census_2011_rural_urban.csv', sep=',')\n",
@@ -1823,7 +793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1876,368 +846,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>household</th>\n",
-       "      <th>location</th>\n",
-       "      <th>pid_hs</th>\n",
-       "      <th>msoa</th>\n",
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-       "      <th>pwkstat</th>\n",
-       "      <th>salary_yearly</th>\n",
-       "      <th>salary_hourly</th>\n",
-       "      <th>hid</th>\n",
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-       "      <th>communal_type</th>\n",
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-       "      <th>central_heat</th>\n",
-       "      <th>tenure</th>\n",
-       "      <th>num_cars</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age_years</th>\n",
-       "      <th>ethnicity</th>\n",
-       "      <th>nssec8</th>\n",
-       "      <th>salary_yearly_hh</th>\n",
-       "      <th>salary_yearly_hh_cat</th>\n",
-       "      <th>is_adult</th>\n",
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-       "      <th>pwkstat_NTS_match</th>\n",
-       "      <th>OA11CD</th>\n",
-       "      <th>RUC11</th>\n",
-       "      <th>RUC11CD</th>\n",
-       "      <th>Settlement2011EW_B03ID_spc</th>\n",
-       "      <th>Settlement2011EW_B04ID_spc</th>\n",
-       "      <th>Settlement2011EW_B03ID_spc_CD</th>\n",
-       "      <th>Settlement2011EW_B04ID_spc_CD</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <th>0</th>\n",
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-       "      <td>1</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>E00053954</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
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-       "      <td>E02002183</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>[1, 2]</td>\n",
-       "      <td>C</td>\n",
-       "      <td>25.0</td>\n",
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-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
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-       "      <td>74</td>\n",
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-       "      <td>Urban city and town</td>\n",
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-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
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-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>{'x': -1.8749940395355225, 'y': 53.94298934936...</td>\n",
-       "      <td>2902817</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>[3, 4]</td>\n",
-       "      <td>C</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>32857.859375</td>\n",
-       "      <td>14.360952</td>\n",
-       "      <td>E02002183_0003</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>27</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>51020.310547</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>Rural town and fringe</td>\n",
-       "      <td>D1</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>{'x': -1.8749940395355225, 'y': 53.94298934936...</td>\n",
-       "      <td>2900884</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>[3, 4]</td>\n",
-       "      <td>J</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18162.451172</td>\n",
-       "      <td>9.439944</td>\n",
-       "      <td>E02002183_0003</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>26</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>51020.310547</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>Rural town and fringe</td>\n",
-       "      <td>D1</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id  household                                           location   pid_hs  \\\n",
-       "0   0          0  {'x': -1.7892179489135742, 'y': 53.91915130615...  2905399   \n",
-       "1   1          1  {'x': -1.8262380361557007, 'y': 53.92028045654...  2905308   \n",
-       "2   2          1  {'x': -1.8262380361557007, 'y': 53.92028045654...  2907681   \n",
-       "3   3          2  {'x': -1.8749940395355225, 'y': 53.94298934936...  2902817   \n",
-       "4   4          2  {'x': -1.8749940395355225, 'y': 53.94298934936...  2900884   \n",
-       "\n",
-       "        msoa         oa members sic1d2007  sic2d2007  pwkstat  salary_yearly  \\\n",
-       "0  E02002183  E00053954     [0]         J       58.0        6            NaN   \n",
-       "1  E02002183  E00053953  [1, 2]         C       25.0        6            NaN   \n",
-       "2  E02002183  E00053953  [1, 2]         P       85.0        6            NaN   \n",
-       "3  E02002183  E00053689  [3, 4]         C       31.0        1   32857.859375   \n",
-       "4  E02002183  E00053689  [3, 4]         J       62.0        1   18162.451172   \n",
-       "\n",
-       "   salary_hourly             hid  accommodation_type  communal_type  \\\n",
-       "0            NaN  E02002183_0001                 1.0            NaN   \n",
-       "1            NaN  E02002183_0002                 3.0            NaN   \n",
-       "2            NaN  E02002183_0002                 3.0            NaN   \n",
-       "3      14.360952  E02002183_0003                 3.0            NaN   \n",
-       "4       9.439944  E02002183_0003                 3.0            NaN   \n",
-       "\n",
-       "   num_rooms  central_heat  tenure  num_cars  sex  age_years  ethnicity  \\\n",
-       "0        2.0          True     2.0         2    1         86          1   \n",
-       "1        6.0          True     2.0         2    1         74          3   \n",
-       "2        6.0          True     2.0         2    2         68          1   \n",
-       "3        6.0          True     2.0         1    1         27          1   \n",
-       "4        6.0          True     2.0         1    2         26          1   \n",
-       "\n",
-       "   nssec8  salary_yearly_hh  salary_yearly_hh_cat  is_adult  num_adults  \\\n",
-       "0     1.0          0.000000                     1         1           1   \n",
-       "1     1.0          0.000000                     1         1           2   \n",
-       "2     2.0          0.000000                     1         1           2   \n",
-       "3     4.0      51020.310547                     3         1           2   \n",
-       "4     6.0      51020.310547                     3         1           2   \n",
-       "\n",
-       "   is_child  num_children  is_pension_age  num_pension_age  pwkstat_FT_hh  \\\n",
-       "0         0             0               1                1              0   \n",
-       "1         0             0               1                2              0   \n",
-       "2         0             0               1                2              0   \n",
-       "3         0             0               0                0              2   \n",
-       "4         0             0               0                0              2   \n",
-       "\n",
-       "   pwkstat_PT_hh  pwkstat_NTS_match     OA11CD                  RUC11 RUC11CD  \\\n",
-       "0              0                  1  E00053954    Urban city and town      C1   \n",
-       "1              0                  1  E00053953    Urban city and town      C1   \n",
-       "2              0                  1  E00053953    Urban city and town      C1   \n",
-       "3              0                  6  E00053689  Rural town and fringe      D1   \n",
-       "4              0                  6  E00053689  Rural town and fringe      D1   \n",
-       "\n",
-       "  Settlement2011EW_B03ID_spc Settlement2011EW_B04ID_spc  \\\n",
-       "0                      Urban        Urban City and Town   \n",
-       "1                      Urban        Urban City and Town   \n",
-       "2                      Urban        Urban City and Town   \n",
-       "3                      Rural      Rural Town and Fringe   \n",
-       "4                      Rural      Rural Town and Fringe   \n",
-       "\n",
-       "   Settlement2011EW_B03ID_spc_CD  Settlement2011EW_B04ID_spc_CD  \n",
-       "0                              1                              2  \n",
-       "1                              1                              2  \n",
-       "2                              1                              2  \n",
-       "3                              2                              3  \n",
-       "4                              2                              3  "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# add the nts Settlement2011EW_B03ID and Settlement2011EW_B04ID columns to the spc\n",
     "spc_edited['Settlement2011EW_B03ID_spc'] = spc_edited['RUC11'].map(census_2011_to_nts_B03ID)\n",
@@ -2272,7 +883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2300,168 +911,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>NumCar</th>\n",
-       "      <th>NumCar_SPC_match</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>142954</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "    <tr>\n",
-       "      <th>142955</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "      <th>142956</th>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>142957</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142958</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "    <tr>\n",
-       "      <th>142959</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142960</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142961</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142962</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142963</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142964</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142965</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142966</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142967</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "    <tr>\n",
-       "      <th>142968</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142969</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142970</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142971</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142972</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142973</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        NumCar  NumCar_SPC_match\n",
-       "142954     1.0               1.0\n",
-       "142955     1.0               1.0\n",
-       "142956     0.0               0.0\n",
-       "142957     2.0               2.0\n",
-       "142958     1.0               1.0\n",
-       "142959     1.0               1.0\n",
-       "142960     2.0               2.0\n",
-       "142961     1.0               1.0\n",
-       "142962     2.0               2.0\n",
-       "142963     1.0               1.0\n",
-       "142964     1.0               1.0\n",
-       "142965     1.0               1.0\n",
-       "142966     0.0               0.0\n",
-       "142967     1.0               1.0\n",
-       "142968     1.0               1.0\n",
-       "142969     0.0               0.0\n",
-       "142970     2.0               2.0\n",
-       "142971     0.0               0.0\n",
-       "142972     1.0               1.0\n",
-       "142973     3.0               2.0"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "# Create a new column in NTS\n",
@@ -2481,33 +933,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'1': 'Owns / buying',\n",
-       "  '2': 'Rents',\n",
-       "  '3': 'Other (including rent free)',\n",
-       "  '-8': 'NA',\n",
-       "  '-9': 'DNA',\n",
-       "  '-10': 'DEAD'},\n",
-       " {'1': 'Owned: Owned outright',\n",
-       "  '2': 'Owned: Owned with a mortgage or loan or shared ownership',\n",
-       "  '3': 'Rented or living rent free: Total',\n",
-       "  '4': 'Rented: Social rented',\n",
-       "  '5': 'Rented: Private rented or living rent free',\n",
-       "  '-8': 'NA',\n",
-       "  '-9': 'DNA',\n",
-       "  '-10': 'DEAD'})"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dict_nts['Ten1_B02ID'], dict_spc['tenure']"
    ]
@@ -2521,7 +949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2562,7 +990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2614,220 +1042,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>hid</th>\n",
-       "      <th>salary_yearly_hh_cat</th>\n",
-       "      <th>num_adults</th>\n",
-       "      <th>num_children</th>\n",
-       "      <th>num_pension_age</th>\n",
-       "      <th>pwkstat_NTS_match</th>\n",
-       "      <th>num_cars</th>\n",
-       "      <th>tenure_spc_for_matching</th>\n",
-       "      <th>Settlement2011EW_B03ID_spc_CD</th>\n",
-       "      <th>Settlement2011EW_B04ID_spc_CD</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>E02002183_0001</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>E02002183_0002</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>E02002183_0003</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>E02002183_0004</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>E02002183_0005</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>E02002183_0006</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>E02002183_0007</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>E02002183_0008</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>E02002183_0009</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>E02002183_0010</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               hid  salary_yearly_hh_cat  num_adults  num_children  \\\n",
-       "0   E02002183_0001                     1           1             0   \n",
-       "1   E02002183_0002                     1           2             0   \n",
-       "3   E02002183_0003                     3           2             0   \n",
-       "5   E02002183_0004                     2           1             0   \n",
-       "6   E02002183_0005                     1           2             1   \n",
-       "9   E02002183_0006                     3           1             0   \n",
-       "10  E02002183_0007                     1           2             1   \n",
-       "13  E02002183_0008                     2           1             0   \n",
-       "14  E02002183_0009                     1           2             0   \n",
-       "16  E02002183_0010                     1           2             1   \n",
-       "\n",
-       "    num_pension_age  pwkstat_NTS_match  num_cars  tenure_spc_for_matching  \\\n",
-       "0                 1                  1         2                      1.0   \n",
-       "1                 2                  1         2                      1.0   \n",
-       "3                 0                  6         1                      1.0   \n",
-       "5                 0                  3         1                      1.0   \n",
-       "6                 1                  1         2                      1.0   \n",
-       "9                 0                  3         1                      2.0   \n",
-       "10                2                  1         1                      1.0   \n",
-       "13                0                  3         2                      1.0   \n",
-       "14                0                  3         2                      1.0   \n",
-       "16                0                  2         2                      1.0   \n",
-       "\n",
-       "    Settlement2011EW_B03ID_spc_CD  Settlement2011EW_B04ID_spc_CD  \n",
-       "0                               1                              2  \n",
-       "1                               1                              2  \n",
-       "3                               2                              3  \n",
-       "5                               2                              3  \n",
-       "6                               2                              3  \n",
-       "9                               1                              2  \n",
-       "10                              1                              2  \n",
-       "13                              2                              4  \n",
-       "14                              2                              4  \n",
-       "16                              1                              2  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Select multiple columns\n",
     "spc_matching = spc_edited[[\n",
@@ -2852,232 +1069,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>HouseholdID</th>\n",
-       "      <th>HHIncome2002_B02ID</th>\n",
-       "      <th>HHoldNumAdults</th>\n",
-       "      <th>HHoldNumChildren</th>\n",
-       "      <th>num_pension_age_nts</th>\n",
-       "      <th>HHoldEmploy_B01ID</th>\n",
-       "      <th>NumCar_SPC_match</th>\n",
-       "      <th>tenure_nts_for_matching</th>\n",
-       "      <th>Settlement2011EW_B03ID</th>\n",
-       "      <th>Settlement2011EW_B04ID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>142954</th>\n",
-       "      <td>2019001895</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142955</th>\n",
-       "      <td>2019002676</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142956</th>\n",
-       "      <td>2019001891</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142957</th>\n",
-       "      <td>2019002687</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142958</th>\n",
-       "      <td>2019001913</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142959</th>\n",
-       "      <td>2019002273</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142960</th>\n",
-       "      <td>2019001906</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142961</th>\n",
-       "      <td>2019001910</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142962</th>\n",
-       "      <td>2019002688</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142963</th>\n",
-       "      <td>2019002686</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        HouseholdID  HHIncome2002_B02ID  HHoldNumAdults  HHoldNumChildren  \\\n",
-       "142954   2019001895                 1.0               3                 0   \n",
-       "142955   2019002676                 1.0               1                 0   \n",
-       "142956   2019001891                 1.0               2                 0   \n",
-       "142957   2019002687                 2.0               4                 0   \n",
-       "142958   2019001913                 1.0               1                 0   \n",
-       "142959   2019002273                 2.0               1                 0   \n",
-       "142960   2019001906                 3.0               3                 0   \n",
-       "142961   2019001910                 1.0               2                 0   \n",
-       "142962   2019002688                 3.0               2                 2   \n",
-       "142963   2019002686                 1.0               2                 0   \n",
-       "\n",
-       "        num_pension_age_nts  HHoldEmploy_B01ID  NumCar_SPC_match  \\\n",
-       "142954                    2                  1               1.0   \n",
-       "142955                    0                  3               1.0   \n",
-       "142956                    0                  1               0.0   \n",
-       "142957                    0                  4               2.0   \n",
-       "142958                    0                  3               1.0   \n",
-       "142959                    0                  3               1.0   \n",
-       "142960                    0                  5               2.0   \n",
-       "142961                    2                  1               1.0   \n",
-       "142962                    0                  6               2.0   \n",
-       "142963                    2                  2               1.0   \n",
-       "\n",
-       "        tenure_nts_for_matching  Settlement2011EW_B03ID  \\\n",
-       "142954                      1.0                       1   \n",
-       "142955                      1.0                       1   \n",
-       "142956                      2.0                       1   \n",
-       "142957                      2.0                       1   \n",
-       "142958                      1.0                       1   \n",
-       "142959                      2.0                       1   \n",
-       "142960                      1.0                       1   \n",
-       "142961                      1.0                       1   \n",
-       "142962                      1.0                       1   \n",
-       "142963                      1.0                       1   \n",
-       "\n",
-       "        Settlement2011EW_B04ID  \n",
-       "142954                       1  \n",
-       "142955                       1  \n",
-       "142956                       1  \n",
-       "142957                       2  \n",
-       "142958                       1  \n",
-       "142959                       1  \n",
-       "142960                       1  \n",
-       "142961                       1  \n",
-       "142962                       2  \n",
-       "142963                       2  "
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nts_matching = nts_households[[\n",
     "    'HouseholdID','HHIncome2002_B02ID',\n",
@@ -3098,31 +1092,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'household_id': ['hid', 'HouseholdID'],\n",
-       " 'yearly_income': ['salary_yearly_hh_cat', 'HHIncome2002_B02ID'],\n",
-       " 'number_adults': ['num_adults', 'HHoldNumAdults'],\n",
-       " 'number_children': ['num_children', 'HHoldNumChildren'],\n",
-       " 'num_pension_age': ['num_pension_age', 'num_pension_age_nts'],\n",
-       " 'employment_status': ['pwkstat_NTS_match', 'HHoldEmploy_B01ID'],\n",
-       " 'number_cars': ['num_cars', 'NumCar_SPC_match'],\n",
-       " 'tenure_status': ['tenure_spc_for_matching', 'tenure_nts_for_matching'],\n",
-       " 'rural_urban_2_categories': ['Settlement2011EW_B03ID_spc_CD',\n",
-       "  'Settlement2011EW_B03ID'],\n",
-       " 'rural_urban_4_categories': ['Settlement2011EW_B04ID_spc_CD',\n",
-       "  'Settlement2011EW_B04ID']}"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# column_names (keys) for the dictionary\n",
     "matching_ids = ['household_id', 'yearly_income', 'number_adults', 'number_children', 'num_pension_age',\n",
@@ -3144,7 +1116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3164,17 +1136,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "matching rows  0 to 50000  out of  6725\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "matches_hh_level = match_categorical(\n",
     "    df_pop = spc_matching,\n",
@@ -3196,20 +1160,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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YQl+gmiSIRREvuIzXpEkTjQZVjxs3Dhs3bsSRI0cwZ86cIo8jLCwM3333HS5cuIDc3Fyp/Nn+uXr1KpycnNT++L2KgmPSJonq3LkzrK2tsWnTJiQkJODdd9+Fu7s7rl+/XqhtwTin5cuXIzExUW3cRMElkhe5evUqgKfJ5MsUjKN6lp2dHR48ePDSdQGgatWqhdYFgAcPHkChUODff/+FkZFRofeppjMJk5KS1F7b2NioJe5Fefvtt+Hj41Oo/L///lN7/Sqft5fp3bs3fvrpJ3zyySeYOHEiOnTogF69euGDDz6QktzLly/j/PnzL/3eKPB8HwPqv6O7d+8iKyur2ONQqVS4efMmateuXai+oA9q1KihVl6hQgW15Op1XL58GUKIQvt4dl8FevfujUWLFmHbtm3o27cvMjMzsXPnTnz66afS5+3y5csAUOykhoK/BcSEyGApFAo4OTnhzJkzGrW/evUqOnToAA8PDyxYsADOzs4wNTXFzp07sXDhwmL/u3uWSqXCe++9h/HjxxdZ/84772h1DK/iZX8cNJWWloY2bdpAoVAgODgY1atXh5mZGY4fP44JEyZo1B/Pq1ixosZ/WF/k2rVr0pfg6dOnC9WvW7cOAwYMQI8ePTBu3Dg4ODjA2NgYISEhUnKgSw8ePICFhYVWfS+Xy9GrVy+EhYXh2rVrLxxMPGfOHEydOhWDBg3CrFmzYG9vDyMjI4wePfqVfg8vUjAmQ9frvygR1sbzkyPWrFmjl9tfFJf8Pj/43NzcHPv370dsbCx27NiB3bt3Y9OmTWjfvj3++usvGBsbQ6VSoW7duliwYEGR23x+3E5J93FJU6lUkMlk2LVrV5HH8uzNQZs1awZXV1ds3rwZffv2RWRkJB4/fozevXurbQ94Oo5IqVQW2l5ZmGFbVrAnDFiXLl2watUqxMXFwdvb+4VtIyMjkZ2djW3btqn9B1bU6dbivgyrV6+OzMzMIv8bfZaLiwtiY2ORlZWldpboypUrhdoBwMWLFwtt48KFC6hUqZJW/60+G6dKpcK5c+eKvV/N3r17cf/+fYSHh6vNCktMTNR6fwU8PDxea33g6ZffgAEDoFAoMHr0aOkeQb169ZLa/Pbbb6hWrRrCw8PVflfPXxqrXr06/vzzT6Smpr7wLNHLzvwkJiaiVq1aWh9L3759sXr1ahgZGaFPnz7Ftvvtt9/Qrl07/Pzzz2rlaWlpqFSp0kvjLLi8cubMmZe+N0uai4sLVCoVEhMT1c4QPP/eL05UVJTa66LOcrxObIBmnzc7Ozu1WYIFCs6wPMvIyAgdOnRAhw4dsGDBAsyZMweTJ09GbGysdPnr5MmT6NChg07uoly5cmVYWFgUexxGRkbFDo4u6IPLly+rnXHJzc1FYmKiNHHhdVSvXh1CCLi5uWn0T+JHH32ExYsXIyMjA5s2bYKrqyuaNWumtj0AcHBw0Pv7u6zjGCIDNn78eFhaWuKTTz5BcnJyofqrV69K044L/lN59r+s9PR0rFmzptB6lpaWRX4ZfvTRR4iLi8Off/5ZqC4tLQ15eXkAAF9fX+Tm5uLHH3+U6lUqFZYtW6a2TpUqVdCgQQOEhYWp7e/MmTP466+/0Llz5xccffF69OgBIyMjBAcHFzrDUHD8RfVHTk4Oli9f/kr7BABvb2+cOXPmtR51sWDBAhw8eBCrVq3CrFmz0Lx5cwwfPlxtFlFRsR8+fBhxcXFq2/L394cQAjNnziy0n2fXLe73XeD48eMaz2R8Vrt27TBr1ix8//33Rf5nW8DY2LjQf/9btmzBrVu31MoK/lg/H2ujRo3g5uaGRYsWFaor7bMKvr6+AFDofbR06VKN1vfx8VFbirqdxqvS5vNWvXp1pKen49SpU1LZnTt3sHXrVrVtpqamFtpPwT8hBZ+Djz76CLdu3VL7Pijw+PFjPHr0SKvjMDY2RseOHfHHH3+oXYJNTk7Ghg0b0LJly2IvI3l5eaFy5cpYuXIlcnJypPLQ0NAXfga00atXLxgbG2PmzJmF3n9CCNy/f1+trHfv3sjOzkZYWBh2795d6P5dvr6+UCgUmDNnjtrl8QJ3797VSdxvAp4hMmDVq1fHhg0b0Lt3b9SqVUvtTtUHDx7Eli1bpNPtHTt2hKmpKbp27YpPP/0UmZmZ+PHHH+Hg4IA7d+6obbdx48ZYsWIFvv76a7i7u8PBwQHt27fHuHHjsG3bNnTp0gUDBgxA48aN8ejRI5w+fRq//fYbrl+/jkqVKqFHjx5o0qQJvvjiC1y5cgUeHh7Ytm2b9OX57H+J33zzDTp16gRvb28MHjxYmgZsY2PzyvdscXd3x+TJkzFr1iy0atUKvXr1glwux9GjR+Hk5ISQkBA0b94cdnZ2CAwMxMiRIyGTyfDLL7+81h/Q7t27Y9asWdi3b580tf9Zu3btKnKwdvPmzVGtWjWcP38eU6dOxYABA9C1a1cAT7+oGzRogM8++wybN28G8PTMYHh4OHr27Ak/Pz8kJiZi5cqV8PT0VBuc2a5dO/Tr1w9LlizB5cuX8f7770OlUuHvv/9Gu3btpEc+NG7cGHv27MGCBQvg5OQENzc3adpyfHw8UlNT0b17d637w8jICFOmTHlpuy5duiA4OBgDBw5E8+bNcfr0aaxfv77QmI7q1avD1tYWK1euhLW1NSwtLdG0aVO4ublhxYoV6Nq1Kxo0aICBAweiSpUquHDhAs6ePVtkAl9SGjduDH9/fyxatAj379+Xpt1funQJgHbjsEqCpp+3Pn36YMKECejZsydGjhwpTfN+55131Aa6BwcHY//+/fDz84OLiwtSUlKwfPlyvP3229Kg4H79+mHz5s0YNmwYYmNj0aJFC+Tn5+PChQvYvHkz/vzzT61vWPr1119L9z/67LPPYGJigh9++AHZ2dmYP39+setVqFABX3/9NT799FO0b98evXv3RmJiItasWaPVGKIrV67g66+/LlTesGFD+Pn54euvv8akSZNw/fp19OjRA9bW1khMTMTWrVsxdOhQfPnll9I6jRo1kr6zsrOz1S6XAU+HR6xYsQL9+vVDo0aN0KdPH1SuXBk3btzAjh070KJFC3z//fcax/5GK/V5bVTmXLp0SQwZMkS4uroKU1NTYW1tLVq0aCGWLl2qNn1827Ztol69esLMzEy4urqKefPmidWrVwsAIjExUWqXlJQk/Pz8hLW1daGpqA8fPhSTJk0S7u7uwtTUVFSqVEk0b95cfPvttyInJ0dqd/fuXdG3b19hbW0tbGxsxIABA8Q///wjAIiNGzeqxb9nzx7RokULYW5uLhQKhejatas4d+6cWpuC6aRFTe8tbqrp6tWrRcOGDYVcLhd2dnaiTZs2IioqSqr/559/RLNmzYS5ublwcnIS48ePF3/++afaNF0hNJ92L4QQ9erVE4MHD1Yre9G0e/z/aeR5eXni3XffFW+//bZIS0tTW3/x4sUCgNi0aZMQ4ul03Tlz5ggXFxchl8tFw4YNxfbt24uMMy8vT3zzzTfCw8NDmJqaisqVK4tOnTqJ+Ph4qc2FCxdE69athbm5uQCgNgV/woQJomrVqmrT14vz7LT74hQ37f6LL74QVapUEebm5qJFixYiLi5OtGnTptA06D/++EN4enoKExOTQlPwDxw4IN577z1hbW0tLC0tRb169dSmahcXX1HvHxQz7f7591/B7/bZz8+jR49EUFCQsLe3F1ZWVqJHjx7i4sWLAoCYO3fuC/tHWwBEUFBQkXUFsT077V4IzT5vQgjx119/iTp16ghTU1NRs2ZNsW7dukJ9FR0dLbp37y6cnJyEqampcHJyEh9//HGhW3Pk5OSIefPmidq1a0ufx8aNG4uZM2eK9PT0lx6Pi4tLoVtDHD9+XPj6+gorKythYWEh2rVrJw4ePKjW5vlp9wWWL18u3NzchFwuF15eXmL//v1Fvt+K4uLiUuxn+dnP/u+//y5atmwpLC0thaWlpfDw8BBBQUHi4sWLhbY5efJkAUC4u7sXu9/Y2Fjh6+srbGxshJmZmahevboYMGCAOHbsmNTG0Kfdy4QoJyPNyOBFRESgZ8+eOHDgAFq0aKHvcErEL7/8gqCgINy4caPQFPDyJjs7G66urpg4caLazQ1JewkJCWjYsCHWrVuHgIAAfYdD9EbiGCIqk55/2nl+fj6WLl0KhUJR5N2K3xQBAQGoWrVqofFS5dGaNWtQoUIFje79RP/n+fc+8PQO8EZGRq/8WBciejmeIaIy6ZNPPsHjx4/h7e2N7OxshIeH4+DBg5gzZw4mTZqk7/CISszMmTMRHx+Pdu3awcTEBLt27cKuXbswdOhQ/PDDD/oOj+iNxYSIyqQNGzbgu+++w5UrV/DkyRO4u7tj+PDh0kBeojdVVFQUZs6ciXPnziEzMxNVq1ZFv379MHnyZN4zhqgEMSEiIiIig8cxRERERGTwmBARERGRweMFaQ2pVCrcvn0b1tbWer85GhEREWlGCIGHDx/CyclJemhwUZgQaej27dvFPt+GiIiIyrabN2/i7bffLraeCZGGrK2tATzt0OKec0NERERlS0ZGBpydnaW/48VhQqShgstkCoWCCREREVE587LhLhxUTURERAaPCREREREZPCZEREREZPCYEBEREZHBY0JEREREBo8JERERERk8JkRERERk8JgQERERkcFjQkREREQGjwkRERERGTwmRERERGTwmBARERGRwWNCRERERAaPCREREREZPCZEREREZPBM9B0AAa4Td+h1/9fn+ul1/0RERPrGM0RERERk8JgQERERkcFjQkREREQGjwkRERERGTwmRERERGTwmBARERGRwWNCRERERAaPCREREREZPCZEREREZPD0mhDt378fXbt2hZOTE2QyGSIiIgq1OX/+PLp16wYbGxtYWlri3XffxY0bN6T6J0+eICgoCBUrVoSVlRX8/f2RnJysto0bN27Az88PFhYWcHBwwLhx45CXl1fSh0dERETlhF4TokePHqF+/fpYtmxZkfVXr15Fy5Yt4eHhgb179+LUqVOYOnUqzMzMpDZjxoxBZGQktmzZgn379uH27dvo1auXVJ+fnw8/Pz/k5OTg4MGDCAsLQ2hoKKZNm1bix0dERETlg0wIIfQdBADIZDJs3boVPXr0kMr69OmDChUq4JdffilynfT0dFSuXBkbNmzABx98AAC4cOECatWqhbi4ODRr1gy7du1Cly5dcPv2bTg6OgIAVq5ciQkTJuDu3bswNTXVKL6MjAzY2NggPT0dCoXi9Q72OXyWGRERUcnQ9O93mR1DpFKpsGPHDrzzzjvw9fWFg4MDmjZtqnZZLT4+Hrm5ufDx8ZHKPDw8ULVqVcTFxQEA4uLiULduXSkZAgBfX19kZGTg7Nmzxe4/OzsbGRkZagsRERG9mcpsQpSSkoLMzEzMnTsX77//Pv766y/07NkTvXr1wr59+wAASUlJMDU1ha2trdq6jo6OSEpKkto8mwwV1BfUFSckJAQ2NjbS4uzsrMOjIyIiorKkzCZEKpUKANC9e3eMGTMGDRo0wMSJE9GlSxesXLmyxPc/adIkpKenS8vNmzdLfJ9ERESkH2U2IapUqRJMTEzg6empVl6rVi1plplSqUROTg7S0tLU2iQnJ0OpVEptnp91VvC6oE1R5HI5FAqF2kJERERvpjKbEJmamuLdd9/FxYsX1covXboEFxcXAEDjxo1RoUIFREdHS/UXL17EjRs34O3tDQDw9vbG6dOnkZKSIrWJioqCQqEolGwRERGRYTLR584zMzNx5coV6XViYiISEhJgb2+PqlWrYty4cejduzdat26Ndu3aYffu3YiMjMTevXsBADY2Nhg8eDDGjh0Le3t7KBQKfP755/D29kazZs0AAB07doSnpyf69euH+fPnIykpCVOmTEFQUBDkcrk+DpuIiIjKGL0mRMeOHUO7du2k12PHjgUABAYGIjQ0FD179sTKlSsREhKCkSNHombNmvj999/RsmVLaZ2FCxfCyMgI/v7+yM7Ohq+vL5YvXy7VGxsbY/v27Rg+fDi8vb1haWmJwMBABAcHl96BEhERUZlWZu5DVNbxPkRERETlT7m/DxERERFRaWFCRERERAaPCREREREZPCZEREREZPCYEBEREZHBY0JEREREBo8JERERERk8JkRERERk8JgQERERkcFjQkREREQGjwkRERERGTwmRERERGTwmBARERGRwWNCRERERAaPCREREREZPCZEREREZPCYEBEREZHBY0JEREREBo8JERERERk8JkRERERk8JgQERERkcFjQkREREQGjwkRERERGTwmRERERGTwmBARERGRwWNCRERERAbvtROivLw8ZGZm6iIWIiIiIr3QOCGKjIxEaGioWtns2bNhZWUFW1tbdOzYEQ8ePNB1fEREREQlTuOEaMGCBXj06JH0+uDBg5g2bRqmTp2KzZs34+bNm5g1a1aJBElERERUkjROiM6ePYvmzZtLr3/77Te89957mDx5Mnr16oXvvvsOkZGRJRIkERERUUnSOCF6+PAhKlasKL0+cOAAOnToIL2uXbs2bt++rdvoiIiIiEqBxgnRW2+9hfPnzwMAMjMzcfLkSbUzRvfv34eFhYVWO9+/fz+6du0KJycnyGQyREREFNt22LBhkMlkWLRokVp5amoqAgICoFAoYGtri8GDBxca5H3q1Cm0atUKZmZmcHZ2xvz587WKk4iIiN5sGidEH374IUaPHo1ffvkFQ4YMgVKpRLNmzaT6Y8eOoWbNmlrt/NGjR6hfvz6WLVv2wnZbt27FoUOH4OTkVKguICAAZ8+eRVRUFLZv3479+/dj6NChUn1GRgY6duwIFxcXxMfH45tvvsGMGTOwatUqrWIlIiKiN5eJpg2nTZuGW7duYeTIkVAqlVi3bh2MjY2l+l9//RVdu3bVauedOnVCp06dXtjm1q1b+Pzzz/Hnn3/Cz89Pre78+fPYvXs3jh49Ci8vLwDA0qVL0blzZ3z77bdwcnLC+vXrkZOTg9WrV8PU1BS1a9dGQkICFixYoJY4ERERkeHSOCG6e/cuQkNDYWRU9Eml2NhYnQVVQKVSoV+/fhg3bhxq165dqD4uLg62trZSMgQAPj4+MDIywuHDh9GzZ0/ExcWhdevWMDU1ldr4+vpi3rx5ePDgAezs7HQeNxEREZUvGl8yc3Nzw71790oylkLmzZsHExMTjBw5ssj6pKQkODg4qJWZmJjA3t4eSUlJUhtHR0e1NgWvC9oUJTs7GxkZGWoLERERvZk0ToiEECUZRyHx8fFYvHgxQkNDIZPJSnXfABASEgIbGxtpcXZ2LvUYiIiIqHRo9eiO0kxM/v77b6SkpKBq1aowMTGBiYkJ/v33X3zxxRdwdXUFACiVSqSkpKitl5eXh9TUVCiVSqlNcnKyWpuC1wVtijJp0iSkp6dLy82bN3V4dERERFSWaDyGCACmTp360qn1CxYseK2ACvTr1w8+Pj5qZb6+vujXrx8GDhwIAPD29kZaWhri4+PRuHFjAEBMTAxUKhWaNm0qtZk8eTJyc3NRoUIFAEBUVBRq1qz5wvFDcrkccrlcJ8dCREREZZtWCdHp06fVBic/T9szSJmZmbhy5Yr0OjExEQkJCbC3t0fVqlXVbgQJABUqVIBSqZSm99eqVQvvv/8+hgwZgpUrVyI3NxcjRoxAnz59pCn6ffv2xcyZMzF48GBMmDABZ86cweLFi7Fw4UKtYiUiIqI3l1YJ0datWwsNYn4dx44dQ7t27aTXY8eOBQAEBgYWepBscdavX48RI0agQ4cOMDIygr+/P5YsWSLV29jY4K+//kJQUBAaN26MSpUqYdq0aZxyT0RERBKNE6KSGD/Utm1brQZrX79+vVCZvb09NmzY8ML16tWrh7///lvb8IiIiMhAlNlZZkRERESlReOEaM2aNbCxsSnJWIiIiIj0QuNLZm5ubjh8+PBL27Vu3fq1AiIiIiIqbRonRG3btoVMJivy0lnB+CKZTIa8vDzdRUdERERUCjROiB48eFBkeVZWFhYvXowlS5agWrVqOguMiIiIqLRonBA9P35IpVJh9erVmDlzJoyMjLBs2TIEBgbqPEAiIiKikqbVfYgKhIeH46uvvsLdu3cxadIkfP7557yrMxEREZVbWj3LbN++fWjWrBn69euHXr164dq1a/jyyy+ZDBEREVG5pvEZos6dO2PPnj0YNGgQIiIiXvhgVCIiIqLyROOEaPfu3TAxMcGmTZuwefPmYtulpqbqJDAiIiKi0qJxQrRmzZqSjIOIiIhIbzROiDiDjIiIiN5UWg2qJiIiInoTMSEiIiIig8eEiIiIiAweEyIiIiIyeEyIiIiIyOBp/eiO/Px8hIaGIjo6GikpKVCpVGr1MTExOguOiIiIqDRonRCNGjUKoaGh8PPzQ506dSCTyUoiLiIiIqJSo3VCtHHjRmzevBmdO3cuiXiIiIiISp3WY4hMTU3h7u5eErEQERER6YXWCdEXX3yBxYsXQwhREvEQERERlTqtL5kdOHAAsbGx2LVrF2rXro0KFSqo1YeHh+ssOCIiIqLSoHVCZGtri549e5ZELERERER6oXVCxKfeExER0ZuGN2YkIiIig6fRGaJGjRohOjoadnZ2aNiw4QvvPXT8+HGdBUdERERUGjRKiLp37w65XA4A6NGjR0nGQ0RERFTqNEqIpk+fXuTPRERERG8CjiEiIiIig8eEiIiIiAyeXhOi/fv3o2vXrnBycoJMJkNERIRUl5ubiwkTJqBu3bqwtLSEk5MT+vfvj9u3b6ttIzU1FQEBAVAoFLC1tcXgwYORmZmp1ubUqVNo1aoVzMzM4OzsjPnz55fG4REREVE5odeE6NGjR6hfvz6WLVtWqC4rKwvHjx/H1KlTcfz4cYSHh+PixYvo1q2bWruAgACcPXsWUVFR2L59O/bv34+hQ4dK9RkZGejYsSNcXFwQHx+Pb775BjNmzMCqVatK/PiIiIiofJCJ13woWX5+Pk6fPg0XFxfY2dm9eiAyGbZu3frCWWxHjx5FkyZN8O+//6Jq1ao4f/48PD09cfToUXh5eQEAdu/ejc6dO+O///6Dk5MTVqxYgcmTJyMpKQmmpqYAgIkTJyIiIgIXLlzQOL6MjAzY2NggPT0dCoXilY+zKK4Td+h0e9q6PtdPr/snIiIqKZr+/db6DNHo0aPx888/A3iaDLVp0waNGjWCs7Mz9u7d+8oBayI9PR0ymQy2trYAgLi4ONja2krJEAD4+PjAyMgIhw8fltq0bt1aSoYAwNfXFxcvXsSDBw9KNF4iIiIqH7ROiH777TfUr18fABAZGYnExERcuHABY8aMweTJk3UeYIEnT55gwoQJ+Pjjj6UMLykpCQ4ODmrtTExMYG9vj6SkJKmNo6OjWpuC1wVtipKdnY2MjAy1hYiIiN5MWidE9+7dg1KpBADs3LkTH374Id555x0MGjQIp0+f1nmAwNMB1h999BGEEFixYkWJ7ON5ISEhsLGxkRZnZ+dS2S8RERGVPq0TIkdHR5w7dw75+fnYvXs33nvvPQBPB0EbGxvrPMCCZOjff/9FVFSU2vU/pVKJlJQUtfZ5eXlITU2VkjalUonk5GS1NgWvC9oUZdKkSUhPT5eWmzdv6uqQiIiIqIzROiEaOHAgPvroI9SpUwcymQw+Pj4AgMOHD8PDw0OnwRUkQ5cvX8aePXtQsWJFtXpvb2+kpaUhPj5eKouJiYFKpULTpk2lNvv370dubq7UJioqCjVr1nzhIHC5XA6FQqG2EBER0ZtJo0d3PGvGjBmoU6cObt68iQ8//FB6xpmxsTEmTpyo1bYyMzNx5coV6XViYiISEhJgb2+PKlWq4IMPPsDx48exfft25OfnS2N+7O3tYWpqilq1auH999/HkCFDsHLlSuTm5mLEiBHo06cPnJycAAB9+/bFzJkzMXjwYEyYMAFnzpzB4sWLsXDhQm0PnYiIiN5Qrz3t/nXs3bsX7dq1K1QeGBiIGTNmwM3Nrcj1YmNj0bZtWwBPb8w4YsQIREZGwsjICP7+/liyZAmsrKyk9qdOnUJQUBCOHj2KSpUq4fPPP8eECRO0ipXT7omIiMofTf9+a5QQLVmyROMdjxw5UuO25QkTIiIiovJH07/fGl0ye/7y0t27d5GVlSXdDygtLQ0WFhZwcHB4YxMiIiIienNpNKg6MTFRWmbPno0GDRrg/PnzSE1NRWpqKs6fP49GjRph1qxZJR0vERERkc5pPcts6tSpWLp0KWrWrCmV1axZEwsXLsSUKVN0GhwRERFRadA6Ibpz5w7y8vIKlefn5xe63w8RERFReaB1QtShQwd8+umnOH78uFQWHx+P4cOHS/ckIiIiIipPtE6IVq9eDaVSCS8vL8jlcsjlcjRp0gSOjo746aefSiJGIiIiohKl9Y0ZK1eujJ07d+LSpUu4cOECAMDDwwPvvPOOzoMjIiIiKg1aJ0QF3nnnHSZBRERE9EbQKCEaO3asxhtcsGDBKwdDREREpA8aJUQnTpzQaGMymey1giEiIiLSB40SotjY2JKOg4iIiEhvtJ5l9qz//vsP//33n65iISIiItILrRMilUqF4OBg2NjYwMXFBS4uLrC1tcWsWbOgUqlKIkYiIiKiEqX1LLPJkyfj559/xty5c9GiRQsAwIEDBzBjxgw8efIEs2fP1nmQRERERCVJ64QoLCwMP/30E7p16yaV1atXD2+99RY+++wzJkRERERU7mh9ySw1NRUeHh6Fyj08PJCamqqToIiIiIhKk9YJUf369fH9998XKv/+++9Rv359nQRFREREVJq0vmQ2f/58+Pn5Yc+ePfD29gYAxMXF4ebNm9i5c6fOAyQiIiIqaVqfIWrTpg0uXbqEnj17Ii0tDWlpaejVqxcuXryIVq1alUSMRERERCXqlZ5l5uTkxMHTRERE9MZ4pYQoLS0NR44cQUpKSqF7D/Xv318ngRERERGVFq0TosjISAQEBCAzMxMKhULt+WUymYwJEREREZU7Wo8h+uKLLzBo0CBkZmYiLS0NDx48kBZOuyciIqLySOuE6NatWxg5ciQsLCxKIh4iIiKiUqd1QuTr64tjx46VRCxEREREeqHRGKJt27ZJP/v5+WHcuHE4d+4c6tatiwoVKqi1ffaRHkRERETlgUYJUY8ePQqVBQcHFyqTyWTIz89/7aCIiIiISpNGCdHzU+uJiIiI3iRajyF61pMnT3QVBxEREZHeaJ0Q5efnY9asWXjrrbdgZWWFa9euAQCmTp2Kn3/+WecBEhEREZU0rROi2bNnIzQ0FPPnz4epqalUXqdOHfz00086DY6IiIioNGidEK1duxarVq1CQEAAjI2NpfL69evjwoULOg2OiIiIqDS80o0Z3d3dC5WrVCrk5uZqta39+/eja9eucHJygkwmQ0REhFq9EALTpk1DlSpVYG5uDh8fH1y+fFmtTWpqKgICAqBQKGBra4vBgwcjMzNTrc2pU6fQqlUrmJmZwdnZGfPnz9cqTiIiInqzaZ0QeXp64u+//y5U/ttvv6Fhw4ZabevRo0eoX78+li1bVmT9/PnzsWTJEqxcuRKHDx+GpaUlfH191QZzBwQE4OzZs4iKisL27duxf/9+DB06VKrPyMhAx44d4eLigvj4eHzzzTeYMWMGVq1apVWsRERE9ObS+uGu06ZNQ2BgIG7dugWVSoXw8HBcvHgRa9euxfbt27XaVqdOndCpU6ci64QQWLRoEaZMmYLu3bsDeHq5ztHREREREejTpw/Onz+P3bt34+jRo/Dy8gIALF26FJ07d8a3334LJycnrF+/Hjk5OVi9ejVMTU1Ru3ZtJCQkYMGCBWqJExERERkurc8Qde/eHZGRkdizZw8sLS0xbdo0nD9/HpGRkXjvvfd0FlhiYiKSkpLg4+MjldnY2KBp06aIi4sDAMTFxcHW1lZKhgDAx8cHRkZGOHz4sNSmdevWagPAfX19cfHiRTx48KDY/WdnZyMjI0NtISIiojeT1meIAKBVq1aIiorSdSxqkpKSAACOjo5q5Y6OjlJdUlISHBwc1OpNTExgb2+v1sbNza3QNgrq7Ozsitx/SEgIZs6c+foHQkRERGWe1meIbt68if/++096feTIEYwePfqNG5MzadIkpKenS8vNmzf1HRIRERGVEK3PEPXt2xdDhw5Fv379pEtaderUwfr165GUlIRp06bpJDClUgkASE5ORpUqVaTy5ORkNGjQQGqTkpKitl5eXh5SU1Ol9ZVKJZKTk9XaFLwuaFMUuVwOuVz+2sdBRERUHrhO3KHX/V+f66fX/Wt9hujMmTNo0qQJAGDz5s2oW7cuDh48iPXr1yM0NFRngbm5uUGpVCI6Oloqy8jIwOHDh+Ht7Q0A8Pb2RlpaGuLj46U2MTExUKlUaNq0qdRm//79arcEiIqKQs2aNYu9XEZERESGReuEKDc3VzpzsmfPHnTr1g0A4OHhgTt37mi1rczMTCQkJCAhIQHA04HUCQkJuHHjBmQyGUaPHo2vv/4a27Ztw+nTp9G/f384OTmhR48eAIBatWrh/fffx5AhQ3DkyBH8888/GDFiBPr06QMnJycAT89omZqaYvDgwTh79iw2bdqExYsXY+zYsdoeOhEREb2htL5kVrt2baxcuRJ+fn6IiorCrFmzAAC3b99GxYoVtdrWsWPH0K5dO+l1QZISGBiI0NBQjB8/Ho8ePcLQoUORlpaGli1bYvfu3TAzM5PWWb9+PUaMGIEOHTrAyMgI/v7+WLJkiVRvY2ODv/76C0FBQWjcuDEqVaqEadOmcco9ERERSWRCCKHNCnv37kXPnj2RkZGBwMBArF69GgDw1Vdf4cKFCwgPDy+RQPUtIyMDNjY2SE9Ph0Kh0Om2Df26LRER6d+b+rdI07/fWp8hatu2Le7du4eMjAy1MThDhw6FhYXFq0VLREREpEevdB8iY2PjQgOSXV1ddREPERERUanTOiFyc3ODTCYrtv7atWuvFRARERFRadM6IRo9erTa69zcXJw4cQK7d+/GuHHjdBUXERERUanROiEaNWpUkeXLli3DsWPHXjsgIiIiotKm9X2IitOpUyf8/vvvutocERERUanRWUL022+/wd7eXlebIyIiIio1Wl8ya9iwodqgaiEEkpKScPfuXSxfvlynwRERERGVBq0TooLHZhQwMjJC5cqV0bZtW3h4eOgqLiIiIqJSo3VCNH369JKIg4iIiEhvXunGjPn5+YiIiMD58+cBPH2+Wbdu3WBsbKzT4IiIiIhKg9YJ0ZUrV9C5c2fcunULNWvWBACEhITA2dkZO3bsQPXq1XUeJBEREVFJ0nqW2ciRI1G9enXcvHkTx48fx/Hjx3Hjxg24ublh5MiRJREjERERUYnS+gzRvn37cOjQIbUp9hUrVsTcuXPRokULnQZHREREVBq0PkMkl8vx8OHDQuWZmZkwNTXVSVBEREREpUnrhKhLly4YOnQoDh8+DCEEhBA4dOgQhg0bhm7dupVEjEREREQlSuuEaMmSJahevTq8vb1hZmYGMzMztGjRAu7u7li8eHFJxEhERERUorQeQ2Rra4s//vgDly9fxoULFwAAtWrVgru7u86DIyIiIioNr3QfIgCoUaMGatSooctYiIiIiPRC64QoPz8foaGhiI6ORkpKClQqlVp9TEyMzoIjIiIiKg1aJ0SjRo1CaGgo/Pz8UKdOHbUHvRIRERGVR1onRBs3bsTmzZvRuXPnkoiHiIiIqNRpPcvM1NSUA6iJiIjojaJ1QvTFF19g8eLFEEKURDxEREREpU6jS2a9evVSex0TE4Ndu3ahdu3aqFChglpdeHi47qIjIiIiKgUaJUQ2NjZqr3v27FkiwRARERHpg0YJ0Zo1a0o6DiIiIiK90XoMEREREdGbhgkRERERGTwmRERERGTwmBARERGRwSvTCVF+fj6mTp0KNzc3mJubo3r16pg1a5baPZCEEJg2bRqqVKkCc3Nz+Pj44PLly2rbSU1NRUBAABQKBWxtbTF48GBkZmaW9uEQERFRGaX1ozuWLFlSZLlMJoOZmRnc3d3RunVrGBsbv3Zw8+bNw4oVKxAWFobatWvj2LFjGDhwIGxsbDBy5EgAwPz587FkyRKEhYXBzc0NU6dOha+vL86dOwczMzMAQEBAAO7cuYOoqCjk5uZi4MCBGDp0KDZs2PDaMRIREVH5p3VCtHDhQty9exdZWVmws7MDADx48AAWFhawsrJCSkoKqlWrhtjYWDg7O79WcAcPHkT37t3h5+cHAHB1dcWvv/6KI0eOAHh6dmjRokWYMmUKunfvDgBYu3YtHB0dERERgT59+uD8+fPYvXs3jh49Ci8vLwDA0qVL0blzZ3z77bdwcnJ6rRiJiIio/NP6ktmcOXPw7rvv4vLly7h//z7u37+PS5cuoWnTpli8eDFu3LgBpVKJMWPGvHZwzZs3R3R0NC5dugQAOHnyJA4cOIBOnToBABITE5GUlAQfHx9pHRsbGzRt2hRxcXEAgLi4ONja2krJEAD4+PjAyMgIhw8fLnbf2dnZyMjIUFuIiIjozaT1GaIpU6bg999/R/Xq1aUyd3d3fPvtt/D398e1a9cwf/58+Pv7v3ZwEydOREZGBjw8PGBsbIz8/HzMnj0bAQEBAICkpCQAgKOjo9p6jo6OUl1SUhIcHBzU6k1MTGBvby+1KUpISAhmzpz52sdAREREZZ/WZ4ju3LmDvLy8QuV5eXlSguHk5ISHDx++dnCbN2/G+vXrsWHDBhw/fhxhYWH49ttvERYW9trbfplJkyYhPT1dWm7evFni+yQiIiL90DohateuHT799FOcOHFCKjtx4gSGDx+O9u3bAwBOnz4NNze31w5u3LhxmDhxIvr06YO6deuiX79+GDNmDEJCQgAASqUSAJCcnKy2XnJyslSnVCqRkpKiVp+Xl4fU1FSpTVHkcjkUCoXaQkRERG8mrROin3/+Gfb29mjcuDHkcjnkcjm8vLxgb2+Pn3/+GQBgZWWF77777rWDy8rKgpGReojGxsZQqVQAADc3NyiVSkRHR0v1GRkZOHz4MLy9vQEA3t7eSEtLQ3x8vNQmJiYGKpUKTZs2fe0YiYiIqPzTegyRUqlEVFQULly4IA12rlmzJmrWrCm1adeunU6C69q1K2bPno2qVauidu3aOHHiBBYsWIBBgwYBeDrVf/To0fj6669Ro0YNadq9k5MTevToAQCoVasW3n//fQwZMgQrV65Ebm4uRowYgT59+nCGGRERAQBcJ+7Qdwi4PtdP3yEYNK0TogIeHh7w8PDQZSyFLF26FFOnTsVnn32GlJQUODk54dNPP8W0adOkNuPHj8ejR48wdOhQpKWloWXLlti9e7d0DyIAWL9+PUaMGIEOHTrAyMgI/v7+xd5PiYiIiAyPTDx722cN5OfnIzQ0FNHR0UhJSZEuXxWIiYnRaYBlRUZGBmxsbJCenq7z8UT6/s+E/5UQkaHT9/cwoP/vYn33QUkdv6Z/v7U+QzRq1CiEhobCz88PderUgUwme61AiYiIiPRN64Ro48aN2Lx5Mzp37lwS8RARERGVOq1nmZmamsLd3b0kYiEiIiLSC60Toi+++AKLFy+GlkOPiIiIiMosrS+ZHThwALGxsdi1axdq166NChUqqNWHh4frLDgiIiKi0qB1QmRra4uePXuWRCxEREREeqF1QrRmzZqSiIOIiIhIb7QeQ0RERET0ptHoDFGjRo0QHR0NOzs7NGzY8IX3Hjp+/LjOgiMiIiIqDRolRN27d4dcLgcA6RlhRERERG8KjRKi6dOnF/kzERER0ZvglR/umpOTU+SzzKpWrfraQRERERGVJq0TokuXLmHw4ME4ePCgWrkQAjKZDPn5+ToLjoiIiKg0aJ0QDRw4ECYmJti+fTuqVKnCh7sSERFRuad1QpSQkID4+Hh4eHiURDxEREREpU7rhMjT0xP37t0riVhIT1wn7tDr/q/P9dPr/omIiDS6MWNGRoa0zJs3D+PHj8fevXtx//59tbqMjIySjpeIiIhI5zQ6Q2Rra6s2VkgIgQ4dOqi14aBqIiIiKq80SohiY2NLOg4iIiIivdEoIWrTpk1Jx0FERESkN1o/3HXNmjXYsmVLofItW7YgLCxMJ0ERERERlSatE6KQkBBUqlSpULmDgwPmzJmjk6CIiIiISpPWCdGNGzfg5uZWqNzFxQU3btzQSVBEREREpUnr+xA5ODjg1KlTcHV1VSs/efIkKlasqKu4iEoV78VERGTYtD5D9PHHH2PkyJGIjY1Ffn4+8vPzERMTg1GjRqFPnz4lESMRERFRidL6DNGsWbNw/fp1dOjQASYmT1dXqVTo378/Zs+erfMAiYiIiEqa1gmRqakpNm3ahK+//hoJCQkwNzdH3bp14eLiUhLxEREREZU4rS+ZBQcHIysrCzVq1MCHH36ILl26wMXFBY8fP0ZwcHBJxEhERERUorROiGbOnInMzMxC5VlZWZg5c6ZOgiIiIiIqTVonRAXPLHveyZMnYW9vr5OgiIiIiEqTxmOI7OzsIJPJIJPJ8M4776glRfn5+cjMzMSwYcNKJEgiIiKikqRxQrRo0SIIITBo0CDMnDkTNjY2Up2pqSlcXV3h7e2t8wBv3bqFCRMmYNeuXcjKyoK7uzvWrFkDLy8vAE/PWE2fPh0//vgj0tLS0KJFC6xYsQI1atSQtpGamorPP/8ckZGRMDIygr+/PxYvXgwrKyudx0tERETlj8YJUWBgIADAzc0NzZs3R4UKFUosqAIPHjxAixYt0K5dO+zatQuVK1fG5cuXYWdnJ7WZP38+lixZgrCwMLi5uWHq1Knw9fXFuXPnYGZmBgAICAjAnTt3EBUVhdzcXAwcOBBDhw7Fhg0bSvwYiIiIqOzTetr9s0++f/LkCXJyctTqFQrF60f1/82bNw/Ozs5Ys2aNVPbsY0OEEFi0aBGmTJmC7t27AwDWrl0LR0dHREREoE+fPjh//jx2796No0ePSmeVli5dis6dO+Pbb7+Fk5OTzuIlIiKi8knrQdVZWVkYMWIEHBwcYGlpCTs7O7VFl7Zt2wYvLy98+OGHcHBwQMOGDfHjjz9K9YmJiUhKSoKPj49UZmNjg6ZNmyIuLg4AEBcXB1tbWykZAgAfHx8YGRnh8OHDxe47OzsbGRkZagsRERG9mbROiMaNG4eYmBisWLECcrkcP/30E2bOnAknJyesXbtWp8Fdu3ZNGg/0559/Yvjw4Rg5ciTCwsIAAElJSQAAR0dHtfUcHR2luqSkJDg4OKjVm5iYwN7eXmpTlJCQENjY2EiLs7OzLg+NiIiIyhCtL5lFRkZi7dq1aNu2LQYOHIhWrVrB3d0dLi4uWL9+PQICAnQWnEqlgpeXF+bMmQMAaNiwIc6cOYOVK1dKY5pKyqRJkzB27FjpdUZGBpMiIiKiN5TWZ4hSU1NRrVo1AE/HC6WmpgIAWrZsif379+s0uCpVqsDT01OtrFatWrhx4wYAQKlUAgCSk5PV2iQnJ0t1SqUSKSkpavV5eXlITU2V2hRFLpdDoVCoLURERPRm0johqlatGhITEwEAHh4e2Lx5M4CnZ45sbW11GlyLFi1w8eJFtbJLly5Jz01zc3ODUqlEdHS0VJ+RkYHDhw9LtwDw9vZGWloa4uPjpTYxMTFQqVRo2rSpTuMlIiKi8knrhGjgwIE4efIkAGDixIlYtmwZzMzMMGbMGIwbN06nwY0ZMwaHDh3CnDlzcOXKFWzYsAGrVq1CUFAQAEAmk2H06NH4+uuvsW3bNpw+fRr9+/eHk5MTevToAeDpGaX3338fQ4YMwZEjR/DPP/9gxIgR6NOnD2eYEREREYBXGEM0ZswY6WcfHx9cuHAB8fHxcHd3R7169XQa3LvvvoutW7di0qRJCA4OhpubGxYtWqQ2Tmn8+PF49OgRhg4dirS0NLRs2RK7d++W7kEEAOvXr8eIESPQoUMH6caMS5Ys0WmsREREVH5pnRA9z8XFRbqEVRK6dOmCLl26FFsvk8kQHByM4ODgYtvY29vzJoxERERUrFdKiI4ePYrY2FikpKRApVKp1S1YsEAngRERERGVFq0Tojlz5mDKlCmoWbMmHB0d1R7y+uzPREREROWF1gnR4sWLsXr1agwYMKAEwiEiIiIqfVrPMjMyMkKLFi1KIhYiIiIivdA6IRozZgyWLVtWErEQERER6YXWl8y+/PJL+Pn5oXr16vD09ESFChXU6sPDw3UWHBEREVFp0DohGjlyJGJjY9GuXTtUrFiRA6mJiIio3NM6IQoLC8Pvv/8OPz+/koiHiIiIqNRpPYbI3t4e1atXL4lYiIiIiPRC64RoxowZmD59OrKyskoiHiIiIqJSp/UlsyVLluDq1atwdHSEq6troUHVx48f11lwRERERKVB64So4CnyRERERG8KrROi6dOnl0QcRERERHqj9RgiIiIiojcNEyIiIiIyeEyIiIiIyOBplBBlZGSUdBxEREREeqNRQmRnZ4eUlBQAQPv27ZGWllaSMRERERGVKo0SIisrK9y/fx8AsHfvXuTm5pZoUERERESlSaNp9z4+PmjXrh1q1aoFAOjZsydMTU2LbBsTE6O76IiIiIhKgUYJ0bp16xAWFoarV69i3759qF27NiwsLEo6NiIiIqJSoVFCZG5ujmHDhgEAjh07hnnz5sHW1rYk4yIiIgPiOnGHvkMgA6f1napjY2Oln4UQAACZTKa7iIiIiIhKmdYJEQCsXbsW33zzDS5fvgwAeOeddzBu3Dj069dPp8GRYeB/hkREpG9aJ0QLFizA1KlTMWLECLRo0QIAcODAAQwbNgz37t3DmDFjdB4k0ZtO30nh9bl+et0/EZG+aZ0QLV26FCtWrED//v2lsm7duqF27dqYMWMGEyIiIiIqd7R+dMedO3fQvHnzQuXNmzfHnTt3dBIUERERUWnSOiFyd3fH5s2bC5Vv2rQJNWrU0ElQRERERKVJ60tmM2fORO/evbF//35pDNE///yD6OjoIhMlIiIiorJO6zNE/v7+OHz4MCpVqoSIiAhERESgUqVKOHLkCHr27FkSMRIRERGVqFeadt+4cWOsW7dO17EQERER6YXWZ4j0ae7cuZDJZBg9erRU9uTJEwQFBaFixYqwsrKCv78/kpOT1da7ceMG/Pz8YGFhAQcHB4wbNw55eXmlHD0RERGVVeUmITp69Ch++OEH1KtXT618zJgxiIyMxJYtW7Bv3z7cvn0bvXr1kurz8/Ph5+eHnJwcHDx4EGFhYQgNDcW0adNK+xCIiIiojCoXCVFmZiYCAgLw448/ws7OTipPT0/Hzz//jAULFqB9+/Zo3Lgx1qxZg4MHD+LQoUMAgL/++gvnzp3DunXr0KBBA3Tq1AmzZs3CsmXLkJOTo69DIiIiojKkXCREQUFB8PPzg4+Pj1p5fHw8cnNz1co9PDxQtWpVxMXFAQDi4uJQt25dODo6Sm18fX2RkZGBs2fPFrvP7OxsZGRkqC1ERET0ZnqlQdWlaePGjTh+/DiOHj1aqC4pKQmmpqawtbVVK3d0dERSUpLU5tlkqKC+oK44ISEhmDlz5mtGT0REROWBzs4QLV++HMHBwbraHADg5s2bGDVqFNavXw8zMzOdbvtlJk2ahPT0dGm5efNmqe6fiIiISo/OEqLff/8doaGhutocgKeXxFJSUtCoUSOYmJjAxMQE+/btw5IlS2BiYgJHR0fk5OQgLS1Nbb3k5GQolUoAgFKpLDTrrOB1QZuiyOVyKBQKtYWIiIjeTDpLiKKjo3Ht2jVdbQ4A0KFDB5w+fRoJCQnS4uXlhYCAAOnnChUqIDo6Wlrn4sWLuHHjBry9vQEA3t7eOH36NFJSUqQ2UVFRUCgU8PT01Gm8REREVD691hgiIQQAQCaT6SSY51lbW6NOnTpqZZaWlqhYsaJUPnjwYIwdOxb29vZQKBT4/PPP4e3tjWbNmgEAOnbsCE9PT/Tr1w/z589HUlISpkyZgqCgIMjl8hKJm4iIiMqXVzpDtHbtWtStWxfm5uYwNzdHvXr18Msvv+g6No0sXLgQXbp0gb+/P1q3bg2lUonw8HCp3tjYGNu3b4exsTG8vb3xv//9D/3799f5eCciIiIqv7Q+Q7RgwQJMnToVI0aMkB7ueuDAAQwbNgz37t3DmDFjdB7ks/bu3av22szMDMuWLcOyZcuKXcfFxQU7d+4s0biIiIio/NI6IVq6dClWrFiB/v37S2XdunVD7dq1MWPGjBJPiIiIiIh0TetLZnfu3EHz5s0LlTdv3hx37tzRSVBEREREpUnrhMjd3R2bN28uVL5p0ybUqFFDJ0ERERERlSatL5nNnDkTvXv3xv79+6UxRP/88w+io6OLTJSIiIiIyjqtzxD5+/vj8OHDqFSpEiIiIhAREYFKlSrhyJEj6NmzZ0nESERERFSiXuk+RI0bN8a6det0HQsRERGRXpSLp90TERERlSSNzxAZGRm99I7UMpkMeXl5rx0UERERUWnSOCHaunVrsXVxcXFYsmQJVCqVToIiIiIiKk0aJ0Tdu3cvVHbx4kVMnDgRkZGRCAgI4OMwiIiIqFx6pTFEt2/fxpAhQ1C3bl3k5eUhISEBYWFhcHFx0XV8RERERCVOq4QoPT0dEyZMgLu7O86ePYvo6GhERkYWeiI9ERERUXmi8SWz+fPnY968eVAqlfj111+LvIRGROWT68Qdet3/9bl+et0/EZHGCdHEiRNhbm4Od3d3hIWFISwsrMh24eHhOguOiIiIqDRonBD179//pdPuiYiIiMojjROi0NDQEgyDiIiISH94p2oiIiIyeEyIiIiIyOAxISIiIiKDx4SIiIiIDB4TIiIiIjJ4TIiIiIjI4DEhIiIiIoPHhIiIiIgMHhMiIiIiMnhMiIiIiMjgMSEiIiIig6fxs8yIiEqK68Qd+g4B1+f66TsEItIjniEiIiIig8eEiIiIiAweEyIiIiIyeGU+IQoJCcG7774La2trODg4oEePHrh48aJamydPniAoKAgVK1aElZUV/P39kZycrNbmxo0b8PPzg4WFBRwcHDBu3Djk5eWV5qEQERFRGVXmE6J9+/YhKCgIhw4dQlRUFHJzc9GxY0c8evRIajNmzBhERkZiy5Yt2LdvH27fvo1evXpJ9fn5+fDz80NOTg4OHjyIsLAwhIaGYtq0afo4JCIiIipjyvwss927d6u9Dg0NhYODA+Lj49G6dWukp6fj559/xoYNG9C+fXsAwJo1a1CrVi0cOnQIzZo1w19//YVz585hz549cHR0RIMGDTBr1ixMmDABM2bMgKmpqT4OjYiIiMqIMn+G6Hnp6ekAAHt7ewBAfHw8cnNz4ePjI7Xx8PBA1apVERcXBwCIi4tD3bp14ejoKLXx9fVFRkYGzp49W4rRExERUVlU5s8QPUulUmH06NFo0aIF6tSpAwBISkqCqakpbG1t1do6OjoiKSlJavNsMlRQX1BXlOzsbGRnZ0uvMzIydHUYREREVMaUqzNEQUFBOHPmDDZu3Fji+woJCYGNjY20ODs7l/g+iYiISD/KTUI0YsQIbN++HbGxsXj77belcqVSiZycHKSlpam1T05OhlKplNo8P+us4HVBm+dNmjQJ6enp0nLz5k0dHg0RERGVJWU+IRJCYMSIEdi6dStiYmLg5uamVt+4cWNUqFAB0dHRUtnFixdx48YNeHt7AwC8vb1x+vRppKSkSG2ioqKgUCjg6elZ5H7lcjkUCoXaQkRERG+mMj+GKCgoCBs2bMAff/wBa2tracyPjY0NzM3NYWNjg8GDB2Ps2LGwt7eHQqHA559/Dm9vbzRr1gwA0LFjR3h6eqJfv36YP38+kpKSMGXKFAQFBUEul+vz8IiIiKgMKPMJ0YoVKwAAbdu2VStfs2YNBgwYAABYuHAhjIyM4O/vj+zsbPj6+mL58uVSW2NjY2zfvh3Dhw+Ht7c3LC0tERgYiODg4NI6DCIiIirDynxCJIR4aRszMzMsW7YMy5YtK7aNi4sLdu7cqcvQiIiI6A1R5scQEREREZU0JkRERERk8JgQERERkcFjQkREREQGjwkRERERGTwmRERERGTwmBARERGRwWNCRERERAaPCREREREZPCZEREREZPCYEBEREZHBY0JEREREBo8JERERERk8JkRERERk8JgQERERkcFjQkREREQGjwkRERERGTwmRERERGTwmBARERGRwWNCRERERAaPCREREREZPCZEREREZPCYEBEREZHBY0JEREREBo8JERERERk8JkRERERk8JgQERERkcFjQkREREQGjwkRERERGTwmRERERGTwmBARERGRwTOohGjZsmVwdXWFmZkZmjZtiiNHjug7JCIiIioDDCYh2rRpE8aOHYvp06fj+PHjqF+/Pnx9fZGSkqLv0IiIiEjPDCYhWrBgAYYMGYKBAwfC09MTK1euhIWFBVavXq3v0IiIiEjPDCIhysnJQXx8PHx8fKQyIyMj+Pj4IC4uTo+RERERUVlgou8ASsO9e/eQn58PR0dHtXJHR0dcuHChyHWys7ORnZ0tvU5PTwcAZGRk6Dw+VXaWzrdJRNopic82aY7fg/p/D+r7d1BSx1+wXSHEC9sZREL0KkJCQjBz5sxC5c7OznqIhohKms0ifUdAhs7Q34MlffwPHz6EjY1NsfUGkRBVqlQJxsbGSE5OVitPTk6GUqkscp1JkyZh7Nix0muVSoXU1FRUrFgRMplMZ7FlZGTA2dkZN2/ehEKh0Nl2DRX7U7fYn7rF/tQt9qfuvYl9KoTAw4cP4eTk9MJ2BpEQmZqaonHjxoiOjkaPHj0APE1woqOjMWLEiCLXkcvlkMvlamW2trYlFqNCoXhj3nxlAftTt9ifusX+1C32p+69aX36ojNDBQwiIQKAsWPHIjAwEF5eXmjSpAkWLVqER48eYeDAgfoOjYiIiPTMYBKi3r174+7du5g2bRqSkpLQoEED7N69u9BAayIiIjI8BpMQAcCIESOKvUSmL3K5HNOnTy90eY5eDftTt9ifusX+1C32p+4Zcp/KxMvmoRERERG94QzixoxEREREL8KEiIiIiAweEyIiIiIyeEyIiIiIyOAxIdKjZcuWwdXVFWZmZmjatCmOHDmi75DKpJCQELz77ruwtraGg4MDevTogYsXL6q1efLkCYKCglCxYkVYWVnB39+/0J3Jb9y4AT8/P1hYWMDBwQHjxo1DXl5eaR5KmTR37lzIZDKMHj1aKmN/aufWrVv43//+h4oVK8Lc3Bx169bFsWPHpHohBKZNm4YqVarA3NwcPj4+uHz5sto2UlNTERAQAIVCAVtbWwwePBiZmZmlfSh6l5+fj6lTp8LNzQ3m5uaoXr06Zs2apfYcKvbni+3fvx9du3aFk5MTZDIZIiIi1Op11X+nTp1Cq1atYGZmBmdnZ8yfP7+kD61kCdKLjRs3ClNTU7F69Wpx9uxZMWTIEGFrayuSk5P1HVqZ4+vrK9asWSPOnDkjEhISROfOnUXVqlVFZmam1GbYsGHC2dlZREdHi2PHjolmzZqJ5s2bS/V5eXmiTp06wsfHR5w4cULs3LlTVKpUSUyaNEkfh1RmHDlyRLi6uop69eqJUaNGSeXsT82lpqYKFxcXMWDAAHH48GFx7do18eeff4orV65IbebOnStsbGxERESEOHnypOjWrZtwc3MTjx8/ltq8//77on79+uLQoUPi77//Fu7u7uLjjz/WxyHp1ezZs0XFihXF9u3bRWJiotiyZYuwsrISixcvltqwP19s586dYvLkySI8PFwAEFu3blWr10X/paenC0dHRxEQECDOnDkjfv31V2Fubi5++OGH0jpMnWNCpCdNmjQRQUFB0uv8/Hzh5OQkQkJC9BhV+ZCSkiIAiH379gkhhEhLSxMVKlQQW7ZskdqcP39eABBxcXFCiKdfEEZGRiIpKUlqs2LFCqFQKER2dnbpHkAZ8fDhQ1GjRg0RFRUl2rRpIyVE7E/tTJgwQbRs2bLYepVKJZRKpfjmm2+ksrS0NCGXy8Wvv/4qhBDi3LlzAoA4evSo1GbXrl1CJpOJW7dulVzwZZCfn58YNGiQWlmvXr1EQECAEIL9qa3nEyJd9d/y5cuFnZ2d2ud9woQJombNmiV8RCWHl8z0ICcnB/Hx8fDx8ZHKjIyM4OPjg7i4OD1GVj6kp6cDAOzt7QEA8fHxyM3NVetPDw8PVK1aVerPuLg41K1bV+3O5L6+vsjIyMDZs2dLMfqyIygoCH5+fmr9BrA/tbVt2zZ4eXnhww8/hIODAxo2bIgff/xRqk9MTERSUpJaf9rY2KBp06Zq/WlrawsvLy+pjY+PD4yMjHD48OHSO5gyoHnz5oiOjsalS5cAACdPnsSBAwfQqVMnAOzP16Wr/ouLi0Pr1q1hamoqtfH19cXFixfx4MGDUjoa3TKoO1WXFffu3UN+fn6hx4Y4OjriwoULeoqqfFCpVBg9ejRatGiBOnXqAACSkpJgampa6OG7jo6OSEpKktoU1d8FdYZm48aNOH78OI4ePVqojv2pnWvXrmHFihUYO3YsvvrqKxw9ehQjR46EqakpAgMDpf4oqr+e7U8HBwe1ehMTE9jb2xtcf06cOBEZGRnw8PCAsbEx8vPzMXv2bAQEBAAA+/M16ar/kpKS4ObmVmgbBXV2dnYlEn9JYkJE5UpQUBDOnDmDAwcO6DuUcuvmzZsYNWoUoqKiYGZmpu9wyj2VSgUvLy/MmTMHANCwYUOcOXMGK1euRGBgoJ6jK382b96M9evXY8OGDahduzYSEhIwevRoODk5sT+pRPGSmR5UqlQJxsbGhWbtJCcnQ6lU6imqsm/EiBHYvn07YmNj8fbbb0vlSqUSOTk5SEtLU2v/bH8qlcoi+7ugzpDEx8cjJSUFjRo1gomJCUxMTLBv3z4sWbIEJiYmcHR0ZH9qoUqVKvD09FQrq1WrFm7cuAHg//rjRZ93pVKJlJQUtfq8vDykpqYaXH+OGzcOEydORJ8+fVC3bl3069cPY8aMQUhICAD25+vSVf+9id8BTIj0wNTUFI0bN0Z0dLRUplKpEB0dDW9vbz1GVjYJITBixAhs3boVMTExhU7TNm7cGBUqVFDrz4sXL+LGjRtSf3p7e+P06dNqH/KoqCgoFIpCf8zedB06dMDp06eRkJAgLV5eXggICJB+Zn9qrkWLFoVuA3Hp0iW4uLgAANzc3KBUKtX6MyMjA4cPH1brz7S0NMTHx0ttYmJioFKp0LRp01I4irIjKysLRkbqf5qMjY2hUqkAsD9fl676z9vbG/v370dubq7UJioqCjVr1iyXl8sAcNq9vmzcuFHI5XIRGhoqzp07J4YOHSpsbW3VZu3QU8OHDxc2NjZi79694s6dO9KSlZUltRk2bJioWrWqiImJEceOHRPe3t7C29tbqi+YJt6xY0eRkJAgdu/eLSpXrmyQ08SL8uwsMyHYn9o4cuSIMDExEbNnzxaXL18W69evFxYWFmLdunVSm7lz5wpbW1vxxx9/iFOnTonu3bsXOc25YcOG4vDhw+LAgQOiRo0aBjNN/FmBgYHirbfekqbdh4eHi0qVKonx48dLbdifL/bw4UNx4sQJceLECQFALFiwQJw4cUL8+++/Qgjd9F9aWppwdHQU/fr1E2fOnBEbN24UFhYWnHZPr2bp0qWiatWqwtTUVDRp0kQcOnRI3yGVSQCKXNasWSO1efz4sfjss8+EnZ2dsLCwED179hR37txR287169dFp06dhLm5uahUqZL44osvRG5ubikfTdn0fELE/tROZGSkqFOnjpDL5cLDw0OsWrVKrV6lUompU6cKR0dHIZfLRYcOHcTFixfV2ty/f198/PHHwsrKSigUCjFw4EDx8OHD0jyMMiEjI0OMGjVKVK1aVZiZmYlq1aqJyZMnq03vZn++WGxsbJHfmYGBgUII3fXfyZMnRcuWLYVcLhdvvfWWmDt3bmkdYomQCfHM7T+JiIiIDBDHEBEREZHBY0JEREREBo8JERERERk8JkRERERk8JgQERERkcFjQkREREQGjwkRERERGTwmRET0WrKysuDv7w+FQgGZTFboGWjlzd69e3VyHDKZDBEREcXWX79+HTKZDAkJCa+0/bZt22L06NGvtC4RFcaEiKicGTBgAGQyGebOnatWHhERAZlMVurxhIWF4e+//8bBgwdx584d2NjYFGoTGhoKW1vbItd/WeJARQsPD8esWbNeaxt3797F8OHDUbVqVcjlciiVSvj6+uKff/6R2ri6ukImk0Emk8HS0hKNGjXCli1b1LaTkZGByZMnw8PDA2ZmZlAqlfDx8UF4eDh4718qL5gQEZVDZmZmmDdvHh48eKDvUHD16lXUqlULderUgVKp1EtSZojs7e1hbW39Wtvw9/fHiRMnEBYWhkuXLmHbtm1o27Yt7t+/r9YuODgYd+7cwYkTJ/Duu++id+/eOHjwIAAgLS0NzZs3x9q1azFp0iQcP34c+/fvR+/evTF+/Hikp6e/VoxEpYUJEVE55OPjA6VSiZCQkBe2+/3331G7dm3I5XK4urriu+++03pfL9pG27Zt8d1332H//v2QyWRo27at1tt/3unTp9G+fXuYm5ujYsWKGDp0KDIzM9X2+fyloh49emDAgAHS6+XLl6NGjRowMzODo6MjPvjgA6lOpVIhJCQEbm5uMDc3R/369fHbb78ViiM+Ph5eXl6wsLBA8+bNCz3RfsWKFahevTpMTU1Rs2ZN/PLLLy88riNHjqBhw4YwMzODl5cXTpw4oVb/4MEDBAQEoHLlyjA3N0eNGjWwZs2aYrf3fD+4urpizpw5GDRoEKytrVG1alWsWrWq2PXT0tLw999/Y968eWjXrh1cXFzQpEkTTJo0Cd26dVNra21tDaVSiXfeeQfLli2Dubk5IiMjAQBfffUVrl+/jsOHDyMwMBCenp545513MGTIECQkJMDKyuqF/UJUVjAhIiqHjI2NMWfOHCxduhT//fdfkW3i4+Px0UcfoU+fPjh9+jRmzJiBqVOnIjQ0VOP9vGwb4eHhGDJkCLy9vXHnzh2Eh4e/1nE9evQIvr6+sLOzw9GjR7Flyxbs2bMHI0aM0Hgbx44dw8iRIxEcHIyLFy9i9+7daN26tVQfEhKCtWvXYuXKlTh79izGjBmD//3vf9i3b5/adiZPnozvvvsOx44dg4mJCQYNGiTVbd26FaNGjcIXX3yBM2fO4NNPP8XAgQMRGxtbZEyZmZno0qULPD09ER8fjxkzZuDLL79UazN16lScO3cOu3btwvnz57FixQpUqlRJ4+MGgO+++05Ktj777DMMHz68UCJXwMrKClZWVoiIiEB2drbG+zAxMUGFChWQk5MDlUqFjRs3IiAgAE5OTkXuw8TERKtjINIbPT9cloi0FBgYKLp37y6EEKJZs2Zi0KBBQgghtm7dKp79SPft21e89957auuOGzdOeHp6arwvTbYxatQo0aZNmxduZ82aNQKAsLS0LLQAEFu3bhVCCLFq1SphZ2cnMjMzpXV37NghjIyMRFJSkhBCiDZt2ohRo0apbb979+7Sk7x///13oVAoREZGRqE4njx5IiwsLMTBgwfVygcPHiw+/vhjIcT/PSl8z549ajEAEI8fPxZCCNG8eXMxZMgQtW18+OGHonPnztLrZ4/rhx9+EBUrVpTWF0KIFStWCADixIkTQgghunbtKgYOHPiiblTzfD+4uLiI//3vf9JrlUolHBwcxIoVK4rdxm+//Sbs7OyEmZmZaN68uZg0aZI4efKkWhsXFxexcOFCIYQQ2dnZYs6cOQKA2L59u0hOThYAxIIFCzSOm6is4hkionJs3rx5CAsLw/nz5wvVnT9/Hi1atFAra9GiBS5fvoz8/HyNtq+LbRSwtrZGQkJCoeX5/dWvXx+WlpZq+1OpVMWe6Xjee++9BxcXF1SrVg39+vXD+vXrkZWVBQC4cuUKsrKy8N5770lnSKysrLB27VpcvXpVbTv16tWTfq5SpQoAICUlRYqzqH4p6vdQ0L5evXowMzOTyry9vdXaDB8+HBs3bkSDBg0wfvx4aYyONp6NWSaTQalUSjEXxd/fH7dv38a2bdvw/vvvY+/evWjUqFGhs4gTJkyAlZUVLCwsMG/ePMydOxd+fn4cME1vFJ7LJCrHWrduDV9fX0yaNEltDE1ZZGRkBHd3d51s5/k/xLm5udLP1tbWOH78OPbu3Yu//voL06ZNw4wZM3D06FFpLNKOHTvw1ltvqW1DLperva5QoYL0c8FAcZVK9drxF6dTp074999/sXPnTkRFRaFDhw4ICgrCt99+q/E2no0ZeBr3y2I2MzPDe++9h/feew9Tp07FJ598gunTp6u9n8aNG4cBAwbAysoKjo6OUn9UrlwZtra2uHDhguYHSlRG8QwRUTk3d+5cREZGIi4uTq28Vq1aatOnAeCff/7BO++8A2NjY422rYttaKNWrVo4efIkHj16pLY/IyMj1KxZE8DTP8J37tyR6vPz83HmzBm17ZiYmMDHxwfz58/HqVOncP36dcTExMDT0xNyuRw3btyAu7u72uLs7KxVnEX1i6enZ7HtT506hSdPnkhlhw4dKtSucuXKCAwMxLp167Bo0aIXDoouKZ6enmr9DwCVKlWCu7t7oVmERkZG6NOnD9avX4/bt28X2lZmZiby8vJKPGYiXWBCRFTO1a1bFwEBAViyZIla+RdffIHo6GjMmjULly5dQlhYGL7//nu1wbwdOnTA999/X+y2NdmGLgUEBMDMzAyBgYE4c+YMYmNj8fnnn6Nfv35wdHQEALRv3x47duzAjh07cOHCBQwfPlztJorbt2/HkiVLkJCQgH///Rdr166FSqVCzZo1YW1tjS+//BJjxoxBWFgYrl69iuPHj2Pp0qUICwvTOM5x48YhNDQUK1aswOXLl7FgwQKEh4cX2y99+/aFTCbDkCFDcO7cOezcubPQmZ9p06bhjz/+wJUrV3D27Fls374dtWrV0r4TNXT//n20b98e69atw6lTp5CYmIgtW7Zg/vz56N69u8bbmT17NpydndG0aVOsXbsW586dw+XLl7F69Wo0bNhQbYYgUVnGS2ZEb4Dg4GBs2rRJraxRo0bYvHkzpk2bhlmzZqFKlSoIDg5WuxRy9epV3Lt3r9jtarINXbKwsMCff/6JUaNG4d1334WFhQX8/f2xYMECqc2gQYNw8uRJ9O/fHyYmJhgzZgzatWsn1dva2iI8PBwzZszAkydPUKNGDfz666+oXbs2AGDWrFmoXLkyQkJCcO3aNdja2qJRo0b46quvNI6zR48eWLx4Mb799luMGjUKbm5uWLNmTbG3HbCyskJkZCSGDRuGhg0bwtPTE/PmzYO/v7/UxtTUFJMmTcL169dhbm6OVq1aYePGjVr2oOasrKzQtGlTLFy4EFevXkVubi6cnZ0xZMgQrfrC3t4ehw4dwty5c/H111/j33//hZ2dHerWrYtvvvmmyBt1EpVFMsFRcURERGTgeMmMiIiIDB4TIiIiIjJ4TIiIiIjI4DEhIiIiIoPHhIiIiIgMHhMiIiIiMnhMiIiIiMjgMSEiIiIig8eEiIiIiAweEyIiIiIyeEyIiIiIyOAxISIiIiKD9/8Am/KqQGG4x0UAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get the counts of each key\n",
     "counts = [len(v) for v in matches_hh_level.values()]\n",
@@ -3233,18 +1186,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "266 households in the SPC had no match\n",
-      "4.0 % of households in the SPC had no match\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# no. of keys where value is na\n",
     "na_count = sum([1 for v in matches_hh_level.values() if pd.isna(v).all()])\n",
@@ -3257,17 +1201,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('E02002183_0091', [2019001902.0, 2019004101.0, 2019004092.0, 2019004108.0, 2019004125.0, 2019004121.0, 2019001719.0, 2019001714.0, 2019001119.0, 2019001130.0, 2019001148.0, 2019000880.0, 2019003240.0, 2019002767.0, 2019002775.0, 2019002769.0, 2019005597.0, 2019002770.0, 2019003252.0, 2019005438.0, 2019006462.0, 2019006475.0, 2019003208.0, 2019004196.0, 2019004191.0, 2019004200.0, 2019004197.0, 2019000839.0, 2019000350.0, 2019000855.0, 2019001091.0, 2019001721.0, 2019001716.0, 2019000813.0, 2019004159.0, 2019000823.0, 2019004177.0, 2019001167.0, 2019000633.0, 2019000536.0, 2019005323.0, 2019005356.0, 2019004637.0, 2019005612.0, 2019002547.0, 2019005638.0, 2019001816.0, 2019002329.0, 2019002894.0, 2019001865.0, 2019003329.0, 2019002919.0, 2019002929.0, 2019005553.0, 2019000307.0, 2019004714.0, 2019002980.0, 2019001202.0, 2019001205.0, 2019001210.0, 2019001782.0, 2019001788.0, 2019002721.0, 2019005579.0, 2019000863.0, 2019001896.0, 2019001909.0, 2019000589.0, 2019000701.0, 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2022002878.0, 2022002897.0, 2022002907.0, 2022002908.0, 2022002931.0, 2022002937.0, 2022002967.0, 2022002974.0, 2022002987.0, 2022003004.0, 2022003042.0, 2022003049.0, 2022003056.0, 2022003058.0, 2022003065.0, 2022003067.0, 2022003104.0, 2022003106.0, 2022003114.0, 2022003116.0, 2022004222.0, 2022004240.0, 2022004255.0, 2022004305.0, 2022004329.0, 2022004359.0])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# print the 6th key, value in the matches_hh_level dictionary\n",
     "print(list(matches_hh_level.items())[90])"
@@ -3275,387 +1211,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
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-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>household</th>\n",
-       "      <th>location</th>\n",
-       "      <th>pid_hs</th>\n",
-       "      <th>msoa</th>\n",
-       "      <th>oa</th>\n",
-       "      <th>members</th>\n",
-       "      <th>sic1d2007</th>\n",
-       "      <th>sic2d2007</th>\n",
-       "      <th>pwkstat</th>\n",
-       "      <th>salary_yearly</th>\n",
-       "      <th>salary_hourly</th>\n",
-       "      <th>hid</th>\n",
-       "      <th>accommodation_type</th>\n",
-       "      <th>communal_type</th>\n",
-       "      <th>num_rooms</th>\n",
-       "      <th>central_heat</th>\n",
-       "      <th>tenure</th>\n",
-       "      <th>num_cars</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age_years</th>\n",
-       "      <th>ethnicity</th>\n",
-       "      <th>nssec8</th>\n",
-       "      <th>salary_yearly_hh</th>\n",
-       "      <th>salary_yearly_hh_cat</th>\n",
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-       "      <th>is_pension_age</th>\n",
-       "      <th>num_pension_age</th>\n",
-       "      <th>pwkstat_FT_hh</th>\n",
-       "      <th>pwkstat_PT_hh</th>\n",
-       "      <th>pwkstat_NTS_match</th>\n",
-       "      <th>OA11CD</th>\n",
-       "      <th>RUC11</th>\n",
-       "      <th>RUC11CD</th>\n",
-       "      <th>Settlement2011EW_B03ID_spc</th>\n",
-       "      <th>Settlement2011EW_B04ID_spc</th>\n",
-       "      <th>Settlement2011EW_B03ID_spc_CD</th>\n",
-       "      <th>Settlement2011EW_B04ID_spc_CD</th>\n",
-       "      <th>tenure_spc_for_matching</th>\n",
-       "      <th>nts_hh_id</th>\n",
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-       "    <tr>\n",
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-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>E00053954</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[2019004064.0, 2019000229.0, 2019002914.0, 201...</td>\n",
-       "    </tr>\n",
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-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>E00053953</td>\n",
-       "      <td>[1, 2]</td>\n",
-       "      <td>C</td>\n",
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-       "      <td>E02002183_0002</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>2.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>74</td>\n",
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-       "      <td>1</td>\n",
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-       "      <td>E00053953</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
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-       "      <td>[2019004130.0, 2019004126.0, 2019004144.0, 201...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
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-       "      <td>1</td>\n",
-       "      <td>{'x': -1.8262380361557007, 'y': 53.92028045654...</td>\n",
-       "      <td>2907681</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>[1, 2]</td>\n",
-       "      <td>P</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E02002183_0002</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>68</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[2019004130.0, 2019004126.0, 2019004144.0, 201...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>{'x': -1.8749940395355225, 'y': 53.94298934936...</td>\n",
-       "      <td>2902817</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>[3, 4]</td>\n",
-       "      <td>C</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>32857.859375</td>\n",
-       "      <td>14.360952</td>\n",
-       "      <td>E02002183_0003</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>27</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>51020.310547</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>Rural town and fringe</td>\n",
-       "      <td>D1</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[2019001923.0, 2019003253.0, 2019001755.0, 201...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>{'x': -1.8749940395355225, 'y': 53.94298934936...</td>\n",
-       "      <td>2900884</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>[3, 4]</td>\n",
-       "      <td>J</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18162.451172</td>\n",
-       "      <td>9.439944</td>\n",
-       "      <td>E02002183_0003</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>26</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>51020.310547</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>Rural town and fringe</td>\n",
-       "      <td>D1</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[2019001923.0, 2019003253.0, 2019001755.0, 201...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id  household                                           location   pid_hs  \\\n",
-       "0   0          0  {'x': -1.7892179489135742, 'y': 53.91915130615...  2905399   \n",
-       "1   1          1  {'x': -1.8262380361557007, 'y': 53.92028045654...  2905308   \n",
-       "2   2          1  {'x': -1.8262380361557007, 'y': 53.92028045654...  2907681   \n",
-       "3   3          2  {'x': -1.8749940395355225, 'y': 53.94298934936...  2902817   \n",
-       "4   4          2  {'x': -1.8749940395355225, 'y': 53.94298934936...  2900884   \n",
-       "\n",
-       "        msoa         oa members sic1d2007  sic2d2007  pwkstat  salary_yearly  \\\n",
-       "0  E02002183  E00053954     [0]         J       58.0        6            NaN   \n",
-       "1  E02002183  E00053953  [1, 2]         C       25.0        6            NaN   \n",
-       "2  E02002183  E00053953  [1, 2]         P       85.0        6            NaN   \n",
-       "3  E02002183  E00053689  [3, 4]         C       31.0        1   32857.859375   \n",
-       "4  E02002183  E00053689  [3, 4]         J       62.0        1   18162.451172   \n",
-       "\n",
-       "   salary_hourly             hid  accommodation_type  communal_type  \\\n",
-       "0            NaN  E02002183_0001                 1.0            NaN   \n",
-       "1            NaN  E02002183_0002                 3.0            NaN   \n",
-       "2            NaN  E02002183_0002                 3.0            NaN   \n",
-       "3      14.360952  E02002183_0003                 3.0            NaN   \n",
-       "4       9.439944  E02002183_0003                 3.0            NaN   \n",
-       "\n",
-       "   num_rooms  central_heat  tenure  num_cars  sex  age_years  ethnicity  \\\n",
-       "0        2.0          True     2.0         2    1         86          1   \n",
-       "1        6.0          True     2.0         2    1         74          3   \n",
-       "2        6.0          True     2.0         2    2         68          1   \n",
-       "3        6.0          True     2.0         1    1         27          1   \n",
-       "4        6.0          True     2.0         1    2         26          1   \n",
-       "\n",
-       "   nssec8  salary_yearly_hh  salary_yearly_hh_cat  is_adult  num_adults  \\\n",
-       "0     1.0          0.000000                     1         1           1   \n",
-       "1     1.0          0.000000                     1         1           2   \n",
-       "2     2.0          0.000000                     1         1           2   \n",
-       "3     4.0      51020.310547                     3         1           2   \n",
-       "4     6.0      51020.310547                     3         1           2   \n",
-       "\n",
-       "   is_child  num_children  is_pension_age  num_pension_age  pwkstat_FT_hh  \\\n",
-       "0         0             0               1                1              0   \n",
-       "1         0             0               1                2              0   \n",
-       "2         0             0               1                2              0   \n",
-       "3         0             0               0                0              2   \n",
-       "4         0             0               0                0              2   \n",
-       "\n",
-       "   pwkstat_PT_hh  pwkstat_NTS_match     OA11CD                  RUC11 RUC11CD  \\\n",
-       "0              0                  1  E00053954    Urban city and town      C1   \n",
-       "1              0                  1  E00053953    Urban city and town      C1   \n",
-       "2              0                  1  E00053953    Urban city and town      C1   \n",
-       "3              0                  6  E00053689  Rural town and fringe      D1   \n",
-       "4              0                  6  E00053689  Rural town and fringe      D1   \n",
-       "\n",
-       "  Settlement2011EW_B03ID_spc Settlement2011EW_B04ID_spc  \\\n",
-       "0                      Urban        Urban City and Town   \n",
-       "1                      Urban        Urban City and Town   \n",
-       "2                      Urban        Urban City and Town   \n",
-       "3                      Rural      Rural Town and Fringe   \n",
-       "4                      Rural      Rural Town and Fringe   \n",
-       "\n",
-       "   Settlement2011EW_B03ID_spc_CD  Settlement2011EW_B04ID_spc_CD  \\\n",
-       "0                              1                              2   \n",
-       "1                              1                              2   \n",
-       "2                              1                              2   \n",
-       "3                              2                              3   \n",
-       "4                              2                              3   \n",
-       "\n",
-       "   tenure_spc_for_matching                                          nts_hh_id  \n",
-       "0                      1.0  [2019004064.0, 2019000229.0, 2019002914.0, 201...  \n",
-       "1                      1.0  [2019004130.0, 2019004126.0, 2019004144.0, 201...  \n",
-       "2                      1.0  [2019004130.0, 2019004126.0, 2019004144.0, 201...  \n",
-       "3                      1.0  [2019001923.0, 2019003253.0, 2019001755.0, 201...  \n",
-       "4                      1.0  [2019001923.0, 2019003253.0, 2019001755.0, 201...  "
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "## add matches_hh_level as a column in spc_edited\n",
     "spc_edited['nts_hh_id'] = spc_edited['hid'].map(matches_hh_level)\n",
@@ -3674,7 +1232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3697,17 +1255,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('E02002183_0595', 2019003190.0)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(list(matches_hh_level_sample.items())[568])"
    ]
@@ -3721,7 +1271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3789,288 +1339,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>IndividualID</th>\n",
-       "      <th>HouseholdID</th>\n",
-       "      <th>PSUID</th>\n",
-       "      <th>Age_B01ID</th>\n",
-       "      <th>Age_B04ID</th>\n",
-       "      <th>Sex_B01ID</th>\n",
-       "      <th>OfPenAge_B01ID</th>\n",
-       "      <th>HRPRelation_B01ID</th>\n",
-       "      <th>EdAttn1_B01ID</th>\n",
-       "      <th>EdAttn2_B01ID</th>\n",
-       "      <th>EdAttn3_B01ID</th>\n",
-       "      <th>OwnCycle_B01ID</th>\n",
-       "      <th>DrivLic_B02ID</th>\n",
-       "      <th>CarAccess_B01ID</th>\n",
-       "      <th>IndIncome2002_B02ID</th>\n",
-       "      <th>IndWkGOR_B02ID</th>\n",
-       "      <th>EcoStat_B02ID</th>\n",
-       "      <th>EcoStat_B03ID</th>\n",
-       "      <th>NSSec_B03ID</th>\n",
-       "      <th>SC_B01ID</th>\n",
-       "      <th>Stat_B01ID</th>\n",
-       "      <th>WkMode_B01ID</th>\n",
-       "      <th>WkHome_B01ID</th>\n",
-       "      <th>PossHom_B01ID</th>\n",
-       "      <th>OftHome_B01ID</th>\n",
-       "      <th>TravSh_B01ID</th>\n",
-       "      <th>SchDly_B01ID</th>\n",
-       "      <th>SchTrav_B01ID</th>\n",
-       "      <th>SchAcc_B01ID</th>\n",
-       "      <th>FdShp_B01ID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>340872</th>\n",
-       "      <td>2019005376</td>\n",
-       "      <td>2019002277</td>\n",
-       "      <td>2019000253</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>4</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9.0</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340873</th>\n",
-       "      <td>2019005377</td>\n",
-       "      <td>2019002278</td>\n",
-       "      <td>2019000253</td>\n",
-       "      <td>12</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>99</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>2</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340874</th>\n",
-       "      <td>2019005378</td>\n",
-       "      <td>2019002278</td>\n",
-       "      <td>2019000253</td>\n",
-       "      <td>13</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>7</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340875</th>\n",
-       "      <td>2019005379</td>\n",
-       "      <td>2019002279</td>\n",
-       "      <td>2019000253</td>\n",
-       "      <td>20</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>99</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-9.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340876</th>\n",
-       "      <td>2019005380</td>\n",
-       "      <td>2019002280</td>\n",
-       "      <td>2019000253</td>\n",
-       "      <td>13</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>99</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>-9.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>7</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        IndividualID  HouseholdID       PSUID  Age_B01ID  Age_B04ID  \\\n",
-       "340872    2019005376   2019002277  2019000253          4          2   \n",
-       "340873    2019005377   2019002278  2019000253         12          5   \n",
-       "340874    2019005378   2019002278  2019000253         13          6   \n",
-       "340875    2019005379   2019002279  2019000253         20          9   \n",
-       "340876    2019005380   2019002280  2019000253         13          6   \n",
-       "\n",
-       "        Sex_B01ID  OfPenAge_B01ID  HRPRelation_B01ID  EdAttn1_B01ID  \\\n",
-       "340872          1               2                  3             -9   \n",
-       "340873          2               2                 99              1   \n",
-       "340874          1               2                  2              1   \n",
-       "340875          2               1                 99              1   \n",
-       "340876          1               2                 99              1   \n",
-       "\n",
-       "        EdAttn2_B01ID  EdAttn3_B01ID  OwnCycle_B01ID  DrivLic_B02ID  \\\n",
-       "340872             -9             -9             -10             -9   \n",
-       "340873             -9             -9             -10              1   \n",
-       "340874             -9             -9             -10              1   \n",
-       "340875             -9             -9             -10              3   \n",
-       "340876             -9             -9             -10              1   \n",
-       "\n",
-       "        CarAccess_B01ID  IndIncome2002_B02ID  IndWkGOR_B02ID  EcoStat_B02ID  \\\n",
-       "340872                4                   -9            -9.0             -9   \n",
-       "340873                2                    3             8.0              1   \n",
-       "340874                2                    3             7.0              1   \n",
-       "340875                6                    1            -9.0              4   \n",
-       "340876                5                    2            -9.0              1   \n",
-       "\n",
-       "        EcoStat_B03ID  NSSec_B03ID  SC_B01ID  Stat_B01ID  WkMode_B01ID  \\\n",
-       "340872             -9           -9        -9          -9            -9   \n",
-       "340873              1            1         1           1             1   \n",
-       "340874              1            1         2           1             8   \n",
-       "340875              3            3         3           1            -9   \n",
-       "340876              1            3         4           1             1   \n",
-       "\n",
-       "        WkHome_B01ID  PossHom_B01ID  OftHome_B01ID  TravSh_B01ID  \\\n",
-       "340872           -10            -10             -9           -10   \n",
-       "340873           -10            -10              2           -10   \n",
-       "340874           -10            -10              7           -10   \n",
-       "340875           -10            -10             -9           -10   \n",
-       "340876           -10            -10              7           -10   \n",
-       "\n",
-       "        SchDly_B01ID  SchTrav_B01ID  SchAcc_B01ID  FdShp_B01ID  \n",
-       "340872             1              9             1          -10  \n",
-       "340873            -9             -9            -9          -10  \n",
-       "340874            -9             -9            -9          -10  \n",
-       "340875            -9             -9            -9          -10  \n",
-       "340876            -9             -9            -9          -10  "
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nts_individuals.head()"
    ]
@@ -4084,7 +1355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4116,7 +1387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4152,29 +1423,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 349184,\n",
-       " 1: 368506,\n",
-       " 2: 368505,\n",
-       " 3: 355607,\n",
-       " 4: 355606,\n",
-       " 5: 344330,\n",
-       " 9: 352777,\n",
-       " 10: 354879,\n",
-       " 11: 354878,\n",
-       " 12: 354880}"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import itertools\n",
     "\n",
@@ -4184,7 +1435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4197,406 +1448,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>household</th>\n",
-       "      <th>location</th>\n",
-       "      <th>pid_hs</th>\n",
-       "      <th>msoa</th>\n",
-       "      <th>oa</th>\n",
-       "      <th>members</th>\n",
-       "      <th>sic1d2007</th>\n",
-       "      <th>sic2d2007</th>\n",
-       "      <th>pwkstat</th>\n",
-       "      <th>salary_yearly</th>\n",
-       "      <th>salary_hourly</th>\n",
-       "      <th>hid</th>\n",
-       "      <th>accommodation_type</th>\n",
-       "      <th>communal_type</th>\n",
-       "      <th>num_rooms</th>\n",
-       "      <th>central_heat</th>\n",
-       "      <th>tenure</th>\n",
-       "      <th>num_cars</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age_years</th>\n",
-       "      <th>ethnicity</th>\n",
-       "      <th>nssec8</th>\n",
-       "      <th>salary_yearly_hh</th>\n",
-       "      <th>salary_yearly_hh_cat</th>\n",
-       "      <th>is_adult</th>\n",
-       "      <th>num_adults</th>\n",
-       "      <th>is_child</th>\n",
-       "      <th>num_children</th>\n",
-       "      <th>is_pension_age</th>\n",
-       "      <th>num_pension_age</th>\n",
-       "      <th>pwkstat_FT_hh</th>\n",
-       "      <th>pwkstat_PT_hh</th>\n",
-       "      <th>pwkstat_NTS_match</th>\n",
-       "      <th>OA11CD</th>\n",
-       "      <th>RUC11</th>\n",
-       "      <th>RUC11CD</th>\n",
-       "      <th>Settlement2011EW_B03ID_spc</th>\n",
-       "      <th>Settlement2011EW_B04ID_spc</th>\n",
-       "      <th>Settlement2011EW_B03ID_spc_CD</th>\n",
-       "      <th>Settlement2011EW_B04ID_spc_CD</th>\n",
-       "      <th>tenure_spc_for_matching</th>\n",
-       "      <th>nts_hh_id</th>\n",
-       "      <th>age_group</th>\n",
-       "      <th>nts_ind_id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>{'x': -1.7892179489135742, 'y': 53.91915130615...</td>\n",
-       "      <td>2905399</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053954</td>\n",
-       "      <td>[0]</td>\n",
-       "      <td>J</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E02002183_0001</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>86</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>E00053954</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[2019004064.0, 2019000229.0, 2019002914.0, 201...</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2.019009e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>{'x': -1.8262380361557007, 'y': 53.92028045654...</td>\n",
-       "      <td>2905308</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>[1, 2]</td>\n",
-       "      <td>C</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E02002183_0002</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>74</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[2019004130.0, 2019004126.0, 2019004144.0, 201...</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2.021011e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>{'x': -1.8262380361557007, 'y': 53.92028045654...</td>\n",
-       "      <td>2907681</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>[1, 2]</td>\n",
-       "      <td>P</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>6</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>68</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>E00053953</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[2019004130.0, 2019004126.0, 2019004144.0, 201...</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2.021011e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>{'x': -1.8749940395355225, 'y': 53.94298934936...</td>\n",
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-       "      <td>E02002183</td>\n",
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-       "      <td>[3, 4]</td>\n",
-       "      <td>C</td>\n",
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-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
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-       "      <td>1</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
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-       "      <td>3</td>\n",
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-       "      <td>6</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>Rural town and fringe</td>\n",
-       "      <td>D1</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[2019001923.0, 2019003253.0, 2019001755.0, 201...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.019010e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>{'x': -1.8749940395355225, 'y': 53.94298934936...</td>\n",
-       "      <td>2900884</td>\n",
-       "      <td>E02002183</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>[3, 4]</td>\n",
-       "      <td>J</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18162.451172</td>\n",
-       "      <td>9.439944</td>\n",
-       "      <td>E02002183_0003</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>26</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>51020.310547</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>E00053689</td>\n",
-       "      <td>Rural town and fringe</td>\n",
-       "      <td>D1</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[2019001923.0, 2019003253.0, 2019001755.0, 201...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.019010e+09</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id  household                                           location   pid_hs  \\\n",
-       "0   0          0  {'x': -1.7892179489135742, 'y': 53.91915130615...  2905399   \n",
-       "1   1          1  {'x': -1.8262380361557007, 'y': 53.92028045654...  2905308   \n",
-       "2   2          1  {'x': -1.8262380361557007, 'y': 53.92028045654...  2907681   \n",
-       "3   3          2  {'x': -1.8749940395355225, 'y': 53.94298934936...  2902817   \n",
-       "4   4          2  {'x': -1.8749940395355225, 'y': 53.94298934936...  2900884   \n",
-       "\n",
-       "        msoa         oa members sic1d2007  sic2d2007  pwkstat  salary_yearly  \\\n",
-       "0  E02002183  E00053954     [0]         J       58.0        6            NaN   \n",
-       "1  E02002183  E00053953  [1, 2]         C       25.0        6            NaN   \n",
-       "2  E02002183  E00053953  [1, 2]         P       85.0        6            NaN   \n",
-       "3  E02002183  E00053689  [3, 4]         C       31.0        1   32857.859375   \n",
-       "4  E02002183  E00053689  [3, 4]         J       62.0        1   18162.451172   \n",
-       "\n",
-       "   salary_hourly             hid  accommodation_type  communal_type  \\\n",
-       "0            NaN  E02002183_0001                 1.0            NaN   \n",
-       "1            NaN  E02002183_0002                 3.0            NaN   \n",
-       "2            NaN  E02002183_0002                 3.0            NaN   \n",
-       "3      14.360952  E02002183_0003                 3.0            NaN   \n",
-       "4       9.439944  E02002183_0003                 3.0            NaN   \n",
-       "\n",
-       "   num_rooms  central_heat  tenure  num_cars  sex  age_years  ethnicity  \\\n",
-       "0        2.0          True     2.0         2    1         86          1   \n",
-       "1        6.0          True     2.0         2    1         74          3   \n",
-       "2        6.0          True     2.0         2    2         68          1   \n",
-       "3        6.0          True     2.0         1    1         27          1   \n",
-       "4        6.0          True     2.0         1    2         26          1   \n",
-       "\n",
-       "   nssec8  salary_yearly_hh  salary_yearly_hh_cat  is_adult  num_adults  \\\n",
-       "0     1.0          0.000000                     1         1           1   \n",
-       "1     1.0          0.000000                     1         1           2   \n",
-       "2     2.0          0.000000                     1         1           2   \n",
-       "3     4.0      51020.310547                     3         1           2   \n",
-       "4     6.0      51020.310547                     3         1           2   \n",
-       "\n",
-       "   is_child  num_children  is_pension_age  num_pension_age  pwkstat_FT_hh  \\\n",
-       "0         0             0               1                1              0   \n",
-       "1         0             0               1                2              0   \n",
-       "2         0             0               1                2              0   \n",
-       "3         0             0               0                0              2   \n",
-       "4         0             0               0                0              2   \n",
-       "\n",
-       "   pwkstat_PT_hh  pwkstat_NTS_match     OA11CD                  RUC11 RUC11CD  \\\n",
-       "0              0                  1  E00053954    Urban city and town      C1   \n",
-       "1              0                  1  E00053953    Urban city and town      C1   \n",
-       "2              0                  1  E00053953    Urban city and town      C1   \n",
-       "3              0                  6  E00053689  Rural town and fringe      D1   \n",
-       "4              0                  6  E00053689  Rural town and fringe      D1   \n",
-       "\n",
-       "  Settlement2011EW_B03ID_spc Settlement2011EW_B04ID_spc  \\\n",
-       "0                      Urban        Urban City and Town   \n",
-       "1                      Urban        Urban City and Town   \n",
-       "2                      Urban        Urban City and Town   \n",
-       "3                      Rural      Rural Town and Fringe   \n",
-       "4                      Rural      Rural Town and Fringe   \n",
-       "\n",
-       "   Settlement2011EW_B03ID_spc_CD  Settlement2011EW_B04ID_spc_CD  \\\n",
-       "0                              1                              2   \n",
-       "1                              1                              2   \n",
-       "2                              1                              2   \n",
-       "3                              2                              3   \n",
-       "4                              2                              3   \n",
-       "\n",
-       "   tenure_spc_for_matching                                          nts_hh_id  \\\n",
-       "0                      1.0  [2019004064.0, 2019000229.0, 2019002914.0, 201...   \n",
-       "1                      1.0  [2019004130.0, 2019004126.0, 2019004144.0, 201...   \n",
-       "2                      1.0  [2019004130.0, 2019004126.0, 2019004144.0, 201...   \n",
-       "3                      1.0  [2019001923.0, 2019003253.0, 2019001755.0, 201...   \n",
-       "4                      1.0  [2019001923.0, 2019003253.0, 2019001755.0, 201...   \n",
-       "\n",
-       "   age_group    nts_ind_id  \n",
-       "0          9  2.019009e+09  \n",
-       "1          9  2.021011e+09  \n",
-       "2          9  2.021011e+09  \n",
-       "3          5  2.019010e+09  \n",
-       "4          5  2.019010e+09  "
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "spc_edited.head(5)"
    ]
@@ -4610,465 +1464,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>household</th>\n",
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-       "      <th>tenure</th>\n",
-       "      <th>num_cars</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age_years</th>\n",
-       "      <th>ethnicity</th>\n",
-       "      <th>nssec8</th>\n",
-       "      <th>salary_yearly_hh</th>\n",
-       "      <th>salary_yearly_hh_cat</th>\n",
-       "      <th>is_adult</th>\n",
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-       "      <th>num_pension_age</th>\n",
-       "      <th>pwkstat_FT_hh</th>\n",
-       "      <th>pwkstat_PT_hh</th>\n",
-       "      <th>pwkstat_NTS_match</th>\n",
-       "      <th>OA11CD</th>\n",
-       "      <th>RUC11</th>\n",
-       "      <th>RUC11CD</th>\n",
-       "      <th>Settlement2011EW_B03ID_spc</th>\n",
-       "      <th>Settlement2011EW_B04ID_spc</th>\n",
-       "      <th>Settlement2011EW_B03ID_spc_CD</th>\n",
-       "      <th>Settlement2011EW_B04ID_spc_CD</th>\n",
-       "      <th>tenure_spc_for_matching</th>\n",
-       "      <th>nts_hh_id</th>\n",
-       "      <th>age_group</th>\n",
-       "      <th>nts_ind_id</th>\n",
-       "    </tr>\n",
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-       "      <th>117</th>\n",
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-       "      <td>61</td>\n",
-       "      <td>{'x': -1.887002944946289, 'y': 53.944278717041...</td>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>5020.788086</td>\n",
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-       "      <td>2</td>\n",
-       "      <td>E00053688</td>\n",
-       "      <td>Rural town and fringe</td>\n",
-       "      <td>D1</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.0</td>\n",
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-       "      <td>9</td>\n",
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-       "      <th>118</th>\n",
-       "      <td>118</td>\n",
-       "      <td>62</td>\n",
-       "      <td>{'x': -1.8956769704818726, 'y': 53.94247055053...</td>\n",
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-       "      <td>E02002183</td>\n",
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-       "      <td>61.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18557.246094</td>\n",
-       "      <td>9.391318</td>\n",
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-       "      <td>E00053691</td>\n",
-       "      <td>Rural town and fringe</td>\n",
-       "      <td>D1</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
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-       "      <td>E00053954</td>\n",
-       "      <td>Urban city and town</td>\n",
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-       "      <td>Urban</td>\n",
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-       "    <tr>\n",
-       "      <th>121</th>\n",
-       "      <td>121</td>\n",
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-       "      <td>E00053954</td>\n",
-       "      <td>Urban city and town</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
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-       "      <th>122</th>\n",
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-       "      <td>M</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>True</td>\n",
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-       "      <td>3</td>\n",
-       "      <td>E00053696</td>\n",
-       "      <td>Rural town and fringe</td>\n",
-       "      <td>D1</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>[2019000933.0, 2019001918.0, 2019001705.0, 201...</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2022004957.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      id household                                           location  \\\n",
-       "117  117        61  {'x': -1.887002944946289, 'y': 53.944278717041...   \n",
-       "118  118        62  {'x': -1.8956769704818726, 'y': 53.94247055053...   \n",
-       "119  119        62  {'x': -1.8956769704818726, 'y': 53.94247055053...   \n",
-       "120  120        63  {'x': -1.7892179489135742, 'y': 53.91915130615...   \n",
-       "121  121        63  {'x': -1.7892179489135742, 'y': 53.91915130615...   \n",
-       "122  122        64  {'x': -1.8792779445648193, 'y': 53.94593048095...   \n",
-       "\n",
-       "      pid_hs       msoa         oa     members sic1d2007 sic2d2007 pwkstat  \\\n",
-       "117  2904126  E02002183  E00053688  [116, 117]         G      47.0      10   \n",
-       "118  2908490  E02002183  E00053691  [118, 119]         J      61.0       1   \n",
-       "119  2911873  E02002183  E00053691  [118, 119]         M      70.0       4   \n",
-       "120  2911879  E02002183  E00053954  [120, 121]         C      27.0       1   \n",
-       "121  2904728  E02002183  E00053954  [120, 121]         C      14.0       4   \n",
-       "122  2910111  E02002183  E00053696       [122]         M      71.0       1   \n",
-       "\n",
-       "    salary_yearly salary_hourly             hid accommodation_type  \\\n",
-       "117           NaN           NaN  E02002183_0062                3.0   \n",
-       "118  18557.246094      9.391318  E02002183_0063                1.0   \n",
-       "119           NaN           NaN  E02002183_0063                1.0   \n",
-       "120  53864.953125     25.896612  E02002183_0064                4.0   \n",
-       "121           NaN           NaN  E02002183_0064                4.0   \n",
-       "122  51522.851562     24.770601  E02002183_0065                2.0   \n",
-       "\n",
-       "    communal_type num_rooms central_heat tenure num_cars sex age_years  \\\n",
-       "117           NaN       4.0         True    5.0        2   1        71   \n",
-       "118           NaN       5.0         True    1.0        1   1        30   \n",
-       "119           NaN       5.0         True    1.0        1   2        29   \n",
-       "120           NaN       6.0         True    1.0        2   1        42   \n",
-       "121           NaN       6.0         True    1.0        2   2        42   \n",
-       "122           NaN       3.0         True    4.0        1   1        53   \n",
-       "\n",
-       "    ethnicity nssec8 salary_yearly_hh salary_yearly_hh_cat is_adult  \\\n",
-       "117         1    1.0      5020.788086                    1        1   \n",
-       "118         1    4.0     18557.246094                    1        1   \n",
-       "119         1    2.0     18557.246094                    1        1   \n",
-       "120         1    1.0     53864.953125                    3        1   \n",
-       "121         1    5.0     53864.953125                    3        1   \n",
-       "122         3    2.0     51522.851562                    3        1   \n",
-       "\n",
-       "    num_adults is_child num_children is_pension_age num_pension_age  \\\n",
-       "117          2        0            0              1               2   \n",
-       "118          2        0            0              0               0   \n",
-       "119          2        0            0              0               0   \n",
-       "120          2        0            0              0               0   \n",
-       "121          2        0            0              0               0   \n",
-       "122          1        0            0              0               0   \n",
-       "\n",
-       "    pwkstat_FT_hh pwkstat_PT_hh pwkstat_NTS_match     OA11CD  \\\n",
-       "117             0             1                 2  E00053688   \n",
-       "118             1             0                 3  E00053691   \n",
-       "119             1             0                 3  E00053691   \n",
-       "120             1             0                 3  E00053954   \n",
-       "121             1             0                 3  E00053954   \n",
-       "122             1             0                 3  E00053696   \n",
-       "\n",
-       "                     RUC11 RUC11CD Settlement2011EW_B03ID_spc  \\\n",
-       "117  Rural town and fringe      D1                      Rural   \n",
-       "118  Rural town and fringe      D1                      Rural   \n",
-       "119  Rural town and fringe      D1                      Rural   \n",
-       "120    Urban city and town      C1                      Urban   \n",
-       "121    Urban city and town      C1                      Urban   \n",
-       "122  Rural town and fringe      D1                      Rural   \n",
-       "\n",
-       "    Settlement2011EW_B04ID_spc Settlement2011EW_B03ID_spc_CD  \\\n",
-       "117      Rural Town and Fringe                             2   \n",
-       "118      Rural Town and Fringe                             2   \n",
-       "119      Rural Town and Fringe                             2   \n",
-       "120        Urban City and Town                             1   \n",
-       "121        Urban City and Town                             1   \n",
-       "122      Rural Town and Fringe                             2   \n",
-       "\n",
-       "    Settlement2011EW_B04ID_spc_CD tenure_spc_for_matching  \\\n",
-       "117                             3                     2.0   \n",
-       "118                             3                     1.0   \n",
-       "119                             3                     1.0   \n",
-       "120                             2                     1.0   \n",
-       "121                             2                     1.0   \n",
-       "122                             3                     2.0   \n",
-       "\n",
-       "                                             nts_hh_id age_group    nts_ind_id  \n",
-       "117  [2019000929.0, 2019003194.0, 2019003199.0, 201...         9  2022001198.0  \n",
-       "118  [2019001923.0, 2019003253.0, 2019001755.0, 201...         6  2019007422.0  \n",
-       "119  [2019001923.0, 2019003253.0, 2019001755.0, 201...         5  2019007423.0  \n",
-       "120  [2019001902.0, 2019004101.0, 2019004092.0, 201...         7  2022006066.0  \n",
-       "121  [2019001902.0, 2019004101.0, 2019004092.0, 201...         7  2022006067.0  \n",
-       "122  [2019000933.0, 2019001918.0, 2019001705.0, 201...         8  2022004957.0  "
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#ids = [99, 100, 101, 102]\n",
     "ids = [109, 110, 111, 112, 113, 114]\n",
@@ -5099,393 +1497,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>household</th>\n",
-       "      <th>pwkstat</th>\n",
-       "      <th>salary_yearly</th>\n",
-       "      <th>salary_hourly</th>\n",
-       "      <th>hid</th>\n",
-       "      <th>tenure</th>\n",
-       "      <th>num_cars</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age_years</th>\n",
-       "      <th>age_group</th>\n",
-       "      <th>nssec8</th>\n",
-       "      <th>salary_yearly_hh</th>\n",
-       "      <th>salary_yearly_hh_cat</th>\n",
-       "      <th>is_adult</th>\n",
-       "      <th>is_child</th>\n",
-       "      <th>is_pension_age</th>\n",
-       "      <th>pwkstat_FT_hh</th>\n",
-       "      <th>pwkstat_PT_hh</th>\n",
-       "      <th>pwkstat_NTS_match</th>\n",
-       "      <th>Settlement2011EW_B03ID_spc</th>\n",
-       "      <th>Settlement2011EW_B04ID_spc</th>\n",
-       "      <th>Settlement2011EW_B03ID_spc_CD</th>\n",
-       "      <th>Settlement2011EW_B04ID_spc_CD</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>117</th>\n",
-       "      <td>117</td>\n",
-       "      <td>61</td>\n",
-       "      <td>10</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>71</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5020.788086</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>118</td>\n",
-       "      <td>62</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18557.246094</td>\n",
-       "      <td>9.391318</td>\n",
-       "      <td>E02002183_0063</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>30</td>\n",
-       "      <td>6</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>119</th>\n",
-       "      <td>119</td>\n",
-       "      <td>62</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E02002183_0063</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>29</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>18557.246094</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>120</th>\n",
-       "      <td>120</td>\n",
-       "      <td>63</td>\n",
-       "      <td>1</td>\n",
-       "      <td>53864.953125</td>\n",
-       "      <td>25.896612</td>\n",
-       "      <td>E02002183_0064</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>42</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>53864.953125</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>121</th>\n",
-       "      <td>121</td>\n",
-       "      <td>63</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E02002183_0064</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>42</td>\n",
-       "      <td>7</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>53864.953125</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>Urban City and Town</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>122</td>\n",
-       "      <td>64</td>\n",
-       "      <td>1</td>\n",
-       "      <td>51522.851562</td>\n",
-       "      <td>24.770601</td>\n",
-       "      <td>E02002183_0065</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>53</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>51522.851562</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>Rural Town and Fringe</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      id household pwkstat salary_yearly salary_hourly             hid tenure  \\\n",
-       "117  117        61      10           NaN           NaN  E02002183_0062    5.0   \n",
-       "118  118        62       1  18557.246094      9.391318  E02002183_0063    1.0   \n",
-       "119  119        62       4           NaN           NaN  E02002183_0063    1.0   \n",
-       "120  120        63       1  53864.953125     25.896612  E02002183_0064    1.0   \n",
-       "121  121        63       4           NaN           NaN  E02002183_0064    1.0   \n",
-       "122  122        64       1  51522.851562     24.770601  E02002183_0065    4.0   \n",
-       "\n",
-       "    num_cars sex age_years age_group nssec8 salary_yearly_hh  \\\n",
-       "117        2   1        71         9    1.0      5020.788086   \n",
-       "118        1   1        30         6    4.0     18557.246094   \n",
-       "119        1   2        29         5    2.0     18557.246094   \n",
-       "120        2   1        42         7    1.0     53864.953125   \n",
-       "121        2   2        42         7    5.0     53864.953125   \n",
-       "122        1   1        53         8    2.0     51522.851562   \n",
-       "\n",
-       "    salary_yearly_hh_cat is_adult is_child is_pension_age pwkstat_FT_hh  \\\n",
-       "117                    1        1        0              1             0   \n",
-       "118                    1        1        0              0             1   \n",
-       "119                    1        1        0              0             1   \n",
-       "120                    3        1        0              0             1   \n",
-       "121                    3        1        0              0             1   \n",
-       "122                    3        1        0              0             1   \n",
-       "\n",
-       "    pwkstat_PT_hh pwkstat_NTS_match Settlement2011EW_B03ID_spc  \\\n",
-       "117             1                 2                      Rural   \n",
-       "118             0                 3                      Rural   \n",
-       "119             0                 3                      Rural   \n",
-       "120             0                 3                      Urban   \n",
-       "121             0                 3                      Urban   \n",
-       "122             0                 3                      Rural   \n",
-       "\n",
-       "    Settlement2011EW_B04ID_spc Settlement2011EW_B03ID_spc_CD  \\\n",
-       "117      Rural Town and Fringe                             2   \n",
-       "118      Rural Town and Fringe                             2   \n",
-       "119      Rural Town and Fringe                             2   \n",
-       "120        Urban City and Town                             1   \n",
-       "121        Urban City and Town                             1   \n",
-       "122      Rural Town and Fringe                             2   \n",
-       "\n",
-       "    Settlement2011EW_B04ID_spc_CD  \n",
-       "117                             3  \n",
-       "118                             3  \n",
-       "119                             3  \n",
-       "120                             2  \n",
-       "121                             2  \n",
-       "122                             3  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    .dataframe tbody tr th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>IndividualID</th>\n",
-       "      <th>HouseholdID</th>\n",
-       "      <th>Age_B01ID</th>\n",
-       "      <th>age_group</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>OfPenAge_B01ID</th>\n",
-       "      <th>IndIncome2002_B02ID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>375894</th>\n",
-       "      <td>2.022001e+09</td>\n",
-       "      <td>2.022001e+09</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>351919</th>\n",
-       "      <td>2.019007e+09</td>\n",
-       "      <td>2.019003e+09</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>351920</th>\n",
-       "      <td>2.019007e+09</td>\n",
-       "      <td>2.019003e+09</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>380675</th>\n",
-       "      <td>2.022006e+09</td>\n",
-       "      <td>2.022003e+09</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>380676</th>\n",
-       "      <td>2.022006e+09</td>\n",
-       "      <td>2.022003e+09</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>378401</th>\n",
-       "      <td>2.022005e+09</td>\n",
-       "      <td>2.022002e+09</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        IndividualID   HouseholdID  Age_B01ID  age_group  sex  OfPenAge_B01ID  \\\n",
-       "375894  2.022001e+09  2.022001e+09       18.0        9.0  1.0             1.0   \n",
-       "351919  2.019007e+09  2.019003e+09       16.0        9.0  1.0             2.0   \n",
-       "351920  2.019007e+09  2.019003e+09       16.0        9.0  1.0             2.0   \n",
-       "380675  2.022006e+09  2.022003e+09       14.0        7.0  1.0             2.0   \n",
-       "380676  2.022006e+09  2.022003e+09       14.0        7.0  2.0             2.0   \n",
-       "378401  2.022005e+09  2.022002e+09       17.0        9.0  2.0             2.0   \n",
-       "\n",
-       "        IndIncome2002_B02ID  \n",
-       "375894                  1.0  \n",
-       "351919                  1.0  \n",
-       "351920                  3.0  \n",
-       "380675                  1.0  \n",
-       "380676                  3.0  \n",
-       "378401                  1.0  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from IPython.display import display\n",
     "\n",
@@ -5572,1321 +1586,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>TripID</th>\n",
-       "      <th>DayID</th>\n",
-       "      <th>IndividualID</th>\n",
-       "      <th>HouseholdID</th>\n",
-       "      <th>PSUID</th>\n",
-       "      <th>PersNo</th>\n",
-       "      <th>TravDay</th>\n",
-       "      <th>JourSeq</th>\n",
-       "      <th>ShortWalkTrip_B01ID</th>\n",
-       "      <th>NumStages</th>\n",
-       "      <th>MainMode_B03ID</th>\n",
-       "      <th>MainMode_B04ID</th>\n",
-       "      <th>TripPurpFrom_B01ID</th>\n",
-       "      <th>TripPurpTo_B01ID</th>\n",
-       "      <th>TripPurpose_B04ID</th>\n",
-       "      <th>TripStart</th>\n",
-       "      <th>TripEnd</th>\n",
-       "      <th>TripDisIncSW</th>\n",
-       "      <th>TripDisExSW</th>\n",
-       "      <th>TripTotalTime</th>\n",
-       "      <th>TripTravTime</th>\n",
-       "      <th>TripOrigGOR_B02ID</th>\n",
-       "      <th>TripDestGOR_B02ID</th>\n",
-       "      <th>W5</th>\n",
-       "      <th>W5xHH</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4659583</th>\n",
-       "      <td>2019009707</td>\n",
-       "      <td>2019004508</td>\n",
-       "      <td>2019000702</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>810.0</td>\n",
-       "      <td>840.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.756184</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659584</th>\n",
-       "      <td>2019009709</td>\n",
-       "      <td>2019004508</td>\n",
-       "      <td>2019000702</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>23</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1050.0</td>\n",
-       "      <td>1060.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.004688</td>\n",
-       "      <td>1.328628</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659585</th>\n",
-       "      <td>2019009711</td>\n",
-       "      <td>2019004512</td>\n",
-       "      <td>2019000703</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18</td>\n",
-       "      <td>7</td>\n",
-       "      <td>10</td>\n",
-       "      <td>23</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1200.0</td>\n",
-       "      <td>1230.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.767622</td>\n",
-       "      <td>1.015126</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659586</th>\n",
-       "      <td>2019010362</td>\n",
-       "      <td>2019004797</td>\n",
-       "      <td>2019000744</td>\n",
-       "      <td>2019000306</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>12</td>\n",
-       "      <td>23</td>\n",
-       "      <td>7</td>\n",
-       "      <td>990.0</td>\n",
-       "      <td>995.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.048438</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659587</th>\n",
-       "      <td>2019010377</td>\n",
-       "      <td>2019004802</td>\n",
-       "      <td>2019000744</td>\n",
-       "      <td>2019000306</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>26</td>\n",
-       "      <td>12</td>\n",
-       "      <td>3</td>\n",
-       "      <td>12</td>\n",
-       "      <td>7</td>\n",
-       "      <td>925.0</td>\n",
-       "      <td>958.0</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>33</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.123451</td>\n",
-       "      <td>1.071548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659588</th>\n",
-       "      <td>2019010379</td>\n",
-       "      <td>2019004803</td>\n",
-       "      <td>2019000747</td>\n",
-       "      <td>2019000308</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>450.0</td>\n",
-       "      <td>470.0</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.817287</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659589</th>\n",
-       "      <td>2019010913</td>\n",
-       "      <td>2019005097</td>\n",
-       "      <td>2019000795</td>\n",
-       "      <td>2019000328</td>\n",
-       "      <td>2019000036</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>10</td>\n",
-       "      <td>15</td>\n",
-       "      <td>7</td>\n",
-       "      <td>800.0</td>\n",
-       "      <td>820.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.059917</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659590</th>\n",
-       "      <td>2019010628</td>\n",
-       "      <td>2019004945</td>\n",
-       "      <td>2019000771</td>\n",
-       "      <td>2019000319</td>\n",
-       "      <td>2019000035</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>23</td>\n",
-       "      <td>4</td>\n",
-       "      <td>795.0</td>\n",
-       "      <td>810.0</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.997967</td>\n",
-       "      <td>1.227954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659591</th>\n",
-       "      <td>2019010630</td>\n",
-       "      <td>2019004946</td>\n",
-       "      <td>2019000771</td>\n",
-       "      <td>2019000319</td>\n",
-       "      <td>2019000035</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>23</td>\n",
-       "      <td>8</td>\n",
-       "      <td>460.0</td>\n",
-       "      <td>480.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.913560</td>\n",
-       "      <td>1.124095</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659592</th>\n",
-       "      <td>2019010643</td>\n",
-       "      <td>2019004949</td>\n",
-       "      <td>2019000771</td>\n",
-       "      <td>2019000319</td>\n",
-       "      <td>2019000035</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23</td>\n",
-       "      <td>12</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1040.0</td>\n",
-       "      <td>1050.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.870854</td>\n",
-       "      <td>1.071548</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             TripID       DayID  IndividualID  HouseholdID       PSUID  \\\n",
-       "4659583  2019009707  2019004508    2019000702   2019000291  2019000032   \n",
-       "4659584  2019009709  2019004508    2019000702   2019000291  2019000032   \n",
-       "4659585  2019009711  2019004512    2019000703   2019000291  2019000032   \n",
-       "4659586  2019010362  2019004797    2019000744   2019000306  2019000033   \n",
-       "4659587  2019010377  2019004802    2019000744   2019000306  2019000033   \n",
-       "4659588  2019010379  2019004803    2019000747   2019000308  2019000033   \n",
-       "4659589  2019010913  2019005097    2019000795   2019000328  2019000036   \n",
-       "4659590  2019010628  2019004945    2019000771   2019000319  2019000035   \n",
-       "4659591  2019010630  2019004946    2019000771   2019000319  2019000035   \n",
-       "4659592  2019010643  2019004949    2019000771   2019000319  2019000035   \n",
-       "\n",
-       "         PersNo  TravDay  JourSeq  ShortWalkTrip_B01ID  NumStages  \\\n",
-       "4659583       2        7        1                    2          1   \n",
-       "4659584       2        7        3                    2          1   \n",
-       "4659585       3        4        2                    2          1   \n",
-       "4659586       2        2        3                    2          1   \n",
-       "4659587       2        7        2                    2          1   \n",
-       "4659588       1        1        1                    2          2   \n",
-       "4659589       3        1        2                    2          1   \n",
-       "4659590       1        3        3                    2          1   \n",
-       "4659591       1        4        2                    2          1   \n",
-       "4659592       1        7        3                    2          1   \n",
-       "\n",
-       "         MainMode_B03ID  MainMode_B04ID  TripPurpFrom_B01ID  TripPurpTo_B01ID  \\\n",
-       "4659583               5               3                  23                 2   \n",
-       "4659584               5               3                   4                23   \n",
-       "4659585              18               7                  10                23   \n",
-       "4659586               7               4                  12                23   \n",
-       "4659587              26              12                   3                12   \n",
-       "4659588               5               3                  23                 1   \n",
-       "4659589               8               4                  10                15   \n",
-       "4659590               5               3                   4                23   \n",
-       "4659591               2               1                  15                23   \n",
-       "4659592               5               3                  23                12   \n",
-       "\n",
-       "         TripPurpose_B04ID  TripStart  TripEnd  TripDisIncSW  TripDisExSW  \\\n",
-       "4659583                  2      810.0    840.0           4.0          4.0   \n",
-       "4659584                  4     1050.0   1060.0           1.0          1.0   \n",
-       "4659585                  7     1200.0   1230.0           3.0          3.0   \n",
-       "4659586                  7      990.0    995.0           2.0          2.0   \n",
-       "4659587                  7      925.0    958.0           6.9          6.9   \n",
-       "4659588                  1      450.0    470.0           2.2          2.0   \n",
-       "4659589                  7      800.0    820.0          10.0         10.0   \n",
-       "4659590                  4      795.0    810.0           6.1          6.1   \n",
-       "4659591                  8      460.0    480.0           1.0          1.0   \n",
-       "4659592                  7     1040.0   1050.0           3.0          3.0   \n",
-       "\n",
-       "         TripTotalTime  TripTravTime  TripOrigGOR_B02ID  TripDestGOR_B02ID  \\\n",
-       "4659583             30          30.0                  7                7.0   \n",
-       "4659584             10          10.0                  7                7.0   \n",
-       "4659585             30          30.0                  7                7.0   \n",
-       "4659586              5           5.0                  2                2.0   \n",
-       "4659587             33          33.0                  2                2.0   \n",
-       "4659588             20          20.0                  2                2.0   \n",
-       "4659589             20          20.0                  8                8.0   \n",
-       "4659590             15          15.0                  8                8.0   \n",
-       "4659591             20          20.0                  8                8.0   \n",
-       "4659592             10           7.0                  8                8.0   \n",
-       "\n",
-       "               W5     W5xHH  \n",
-       "4659583  0.756184  1.000000  \n",
-       "4659584  1.004688  1.328628  \n",
-       "4659585  0.767622  1.015126  \n",
-       "4659586  1.048438  1.000000  \n",
-       "4659587  1.123451  1.071548  \n",
-       "4659588  0.817287  1.000000  \n",
-       "4659589  1.059917  1.000000  \n",
-       "4659590  0.997967  1.227954  \n",
-       "4659591  0.913560  1.124095  \n",
-       "4659592  0.870854  1.071548  "
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nts_trips.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Rename columns and map actual modes and trip purposes to the trip table. \n",
-    "\n",
-    "Code taken from: https://github.com/arup-group/pam/blob/main/examples/07_travel_survey_to_matsim.ipynb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>TripID</th>\n",
-       "      <th>DayID</th>\n",
-       "      <th>IndividualID</th>\n",
-       "      <th>HouseholdID</th>\n",
-       "      <th>PSUID</th>\n",
-       "      <th>PersNo</th>\n",
-       "      <th>TravDay</th>\n",
-       "      <th>seq</th>\n",
-       "      <th>ShortWalkTrip_B01ID</th>\n",
-       "      <th>NumStages</th>\n",
-       "      <th>MainMode_B03ID</th>\n",
-       "      <th>mode</th>\n",
-       "      <th>oact</th>\n",
-       "      <th>dact</th>\n",
-       "      <th>TripPurpose_B04ID</th>\n",
-       "      <th>tst</th>\n",
-       "      <th>tet</th>\n",
-       "      <th>TripDisIncSW</th>\n",
-       "      <th>TripDisExSW</th>\n",
-       "      <th>TripTotalTime</th>\n",
-       "      <th>TripTravTime</th>\n",
-       "      <th>ozone</th>\n",
-       "      <th>dzone</th>\n",
-       "      <th>W5</th>\n",
-       "      <th>W5xHH</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4659583</th>\n",
-       "      <td>2019009707</td>\n",
-       "      <td>2019004508</td>\n",
-       "      <td>2019000702</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>810.0</td>\n",
-       "      <td>840.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.756184</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659584</th>\n",
-       "      <td>2019009709</td>\n",
-       "      <td>2019004508</td>\n",
-       "      <td>2019000702</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>23</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1050.0</td>\n",
-       "      <td>1060.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.004688</td>\n",
-       "      <td>1.328628</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659585</th>\n",
-       "      <td>2019009711</td>\n",
-       "      <td>2019004512</td>\n",
-       "      <td>2019000703</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18</td>\n",
-       "      <td>7</td>\n",
-       "      <td>10</td>\n",
-       "      <td>23</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1200.0</td>\n",
-       "      <td>1230.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.767622</td>\n",
-       "      <td>1.015126</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659586</th>\n",
-       "      <td>2019010362</td>\n",
-       "      <td>2019004797</td>\n",
-       "      <td>2019000744</td>\n",
-       "      <td>2019000306</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>12</td>\n",
-       "      <td>23</td>\n",
-       "      <td>7</td>\n",
-       "      <td>990.0</td>\n",
-       "      <td>995.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.048438</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659587</th>\n",
-       "      <td>2019010377</td>\n",
-       "      <td>2019004802</td>\n",
-       "      <td>2019000744</td>\n",
-       "      <td>2019000306</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>26</td>\n",
-       "      <td>12</td>\n",
-       "      <td>3</td>\n",
-       "      <td>12</td>\n",
-       "      <td>7</td>\n",
-       "      <td>925.0</td>\n",
-       "      <td>958.0</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>33</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.123451</td>\n",
-       "      <td>1.071548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659588</th>\n",
-       "      <td>2019010379</td>\n",
-       "      <td>2019004803</td>\n",
-       "      <td>2019000747</td>\n",
-       "      <td>2019000308</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>450.0</td>\n",
-       "      <td>470.0</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.817287</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659589</th>\n",
-       "      <td>2019010913</td>\n",
-       "      <td>2019005097</td>\n",
-       "      <td>2019000795</td>\n",
-       "      <td>2019000328</td>\n",
-       "      <td>2019000036</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>10</td>\n",
-       "      <td>15</td>\n",
-       "      <td>7</td>\n",
-       "      <td>800.0</td>\n",
-       "      <td>820.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.059917</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659590</th>\n",
-       "      <td>2019010628</td>\n",
-       "      <td>2019004945</td>\n",
-       "      <td>2019000771</td>\n",
-       "      <td>2019000319</td>\n",
-       "      <td>2019000035</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>23</td>\n",
-       "      <td>4</td>\n",
-       "      <td>795.0</td>\n",
-       "      <td>810.0</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.997967</td>\n",
-       "      <td>1.227954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659591</th>\n",
-       "      <td>2019010630</td>\n",
-       "      <td>2019004946</td>\n",
-       "      <td>2019000771</td>\n",
-       "      <td>2019000319</td>\n",
-       "      <td>2019000035</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>23</td>\n",
-       "      <td>8</td>\n",
-       "      <td>460.0</td>\n",
-       "      <td>480.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.913560</td>\n",
-       "      <td>1.124095</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659592</th>\n",
-       "      <td>2019010643</td>\n",
-       "      <td>2019004949</td>\n",
-       "      <td>2019000771</td>\n",
-       "      <td>2019000319</td>\n",
-       "      <td>2019000035</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>23</td>\n",
-       "      <td>12</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1040.0</td>\n",
-       "      <td>1050.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.870854</td>\n",
-       "      <td>1.071548</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             TripID       DayID  IndividualID  HouseholdID       PSUID  \\\n",
-       "4659583  2019009707  2019004508    2019000702   2019000291  2019000032   \n",
-       "4659584  2019009709  2019004508    2019000702   2019000291  2019000032   \n",
-       "4659585  2019009711  2019004512    2019000703   2019000291  2019000032   \n",
-       "4659586  2019010362  2019004797    2019000744   2019000306  2019000033   \n",
-       "4659587  2019010377  2019004802    2019000744   2019000306  2019000033   \n",
-       "4659588  2019010379  2019004803    2019000747   2019000308  2019000033   \n",
-       "4659589  2019010913  2019005097    2019000795   2019000328  2019000036   \n",
-       "4659590  2019010628  2019004945    2019000771   2019000319  2019000035   \n",
-       "4659591  2019010630  2019004946    2019000771   2019000319  2019000035   \n",
-       "4659592  2019010643  2019004949    2019000771   2019000319  2019000035   \n",
-       "\n",
-       "         PersNo  TravDay  seq  ShortWalkTrip_B01ID  NumStages  MainMode_B03ID  \\\n",
-       "4659583       2        7    1                    2          1               5   \n",
-       "4659584       2        7    3                    2          1               5   \n",
-       "4659585       3        4    2                    2          1              18   \n",
-       "4659586       2        2    3                    2          1               7   \n",
-       "4659587       2        7    2                    2          1              26   \n",
-       "4659588       1        1    1                    2          2               5   \n",
-       "4659589       3        1    2                    2          1               8   \n",
-       "4659590       1        3    3                    2          1               5   \n",
-       "4659591       1        4    2                    2          1               2   \n",
-       "4659592       1        7    3                    2          1               5   \n",
-       "\n",
-       "         mode  oact  dact  TripPurpose_B04ID     tst     tet  TripDisIncSW  \\\n",
-       "4659583     3    23     2                  2   810.0   840.0           4.0   \n",
-       "4659584     3     4    23                  4  1050.0  1060.0           1.0   \n",
-       "4659585     7    10    23                  7  1200.0  1230.0           3.0   \n",
-       "4659586     4    12    23                  7   990.0   995.0           2.0   \n",
-       "4659587    12     3    12                  7   925.0   958.0           6.9   \n",
-       "4659588     3    23     1                  1   450.0   470.0           2.2   \n",
-       "4659589     4    10    15                  7   800.0   820.0          10.0   \n",
-       "4659590     3     4    23                  4   795.0   810.0           6.1   \n",
-       "4659591     1    15    23                  8   460.0   480.0           1.0   \n",
-       "4659592     3    23    12                  7  1040.0  1050.0           3.0   \n",
-       "\n",
-       "         TripDisExSW  TripTotalTime  TripTravTime  ozone  dzone        W5  \\\n",
-       "4659583          4.0             30          30.0      7    7.0  0.756184   \n",
-       "4659584          1.0             10          10.0      7    7.0  1.004688   \n",
-       "4659585          3.0             30          30.0      7    7.0  0.767622   \n",
-       "4659586          2.0              5           5.0      2    2.0  1.048438   \n",
-       "4659587          6.9             33          33.0      2    2.0  1.123451   \n",
-       "4659588          2.0             20          20.0      2    2.0  0.817287   \n",
-       "4659589         10.0             20          20.0      8    8.0  1.059917   \n",
-       "4659590          6.1             15          15.0      8    8.0  0.997967   \n",
-       "4659591          1.0             20          20.0      8    8.0  0.913560   \n",
-       "4659592          3.0             10           7.0      8    8.0  0.870854   \n",
-       "\n",
-       "            W5xHH  \n",
-       "4659583  1.000000  \n",
-       "4659584  1.328628  \n",
-       "4659585  1.015126  \n",
-       "4659586  1.000000  \n",
-       "4659587  1.071548  \n",
-       "4659588  1.000000  \n",
-       "4659589  1.000000  \n",
-       "4659590  1.227954  \n",
-       "4659591  1.124095  \n",
-       "4659592  1.071548  "
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nts_trips = nts_trips.rename(\n",
-    "    columns={  # rename data\n",
-    "        \"JourSeq\": \"seq\",\n",
-    "        \"TripOrigGOR_B02ID\": \"ozone\",\n",
-    "        \"TripDestGOR_B02ID\": \"dzone\",\n",
-    "        \"TripPurpFrom_B01ID\": \"oact\",\n",
-    "        \"TripPurpTo_B01ID\": \"dact\",\n",
-    "        \"MainMode_B04ID\": \"mode\",\n",
-    "        \"TripStart\": \"tst\",\n",
-    "        \"TripEnd\": \"tet\",\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "nts_trips.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "mode_mapping = {\n",
-    "    1: \"walk\",\n",
-    "    2: \"bike\",\n",
-    "    3: \"car\",  #'Car/van driver'\n",
-    "    4: \"car\",  #'Car/van driver'\n",
-    "    5: \"motorcycle\",  #'Motorcycle',\n",
-    "    6: \"car\",  #'Other private transport',\n",
-    "    7: \"pt\",  # Bus in London',\n",
-    "    8: \"pt\",  #'Other local bus',\n",
-    "    9: \"pt\",  #'Non-local bus',\n",
-    "    10: \"pt\",  #'London Underground',\n",
-    "    11: \"pt\",  #'Surface Rail',\n",
-    "    12: \"car\",  #'Taxi/minicab',\n",
-    "    13: \"pt\",  #'Other public transport',\n",
-    "    -10: \"DEAD\",\n",
-    "    -8: \"NA\",\n",
-    "}\n",
-    "\n",
-    "purp_mapping = {\n",
-    "    1: \"work\",\n",
-    "    2: \"work\",  #'In course of work',\n",
-    "    3: \"education\",\n",
-    "    4: \"shop\",  #'Food shopping',\n",
-    "    5: \"shop\",  #'Non food shopping',\n",
-    "    6: \"medical\",  #'Personal business medical',\n",
-    "    7: \"other\",  #'Personal business eat/drink',\n",
-    "    8: \"other\",  #'Personal business other',\n",
-    "    9: \"other\",  #'Eat/drink with friends',\n",
-    "    10: \"visit\",  #'Visit friends',\n",
-    "    11: \"other\",  #'Other social',\n",
-    "    12: \"other\",  #'Entertain/ public activity',\n",
-    "    13: \"other\",  #'Sport: participate',\n",
-    "    14: \"home\",  #'Holiday: base',\n",
-    "    15: \"other\",  #'Day trip/just walk',\n",
-    "    16: \"other\",  #'Other non-escort',\n",
-    "    17: \"escort\",  #'Escort home',\n",
-    "    18: \"escort\",  #'Escort work',\n",
-    "    19: \"escort\",  #'Escort in course of work',\n",
-    "    20: \"escort\",  #'Escort education',\n",
-    "    21: \"escort\",  #'Escort shopping/personal business',\n",
-    "    22: \"escort\",  #'Other escort',\n",
-    "    23: \"home\",  #'Home',\n",
-    "    -10: \"DEAD\",\n",
-    "    -8: \"NA\",\n",
-    "}\n",
-    "\n",
-    "\n",
-    "nts_trips[\"mode\"] = nts_trips[\"mode\"].map(mode_mapping)\n",
-    "\n",
-    "nts_trips[\"oact\"] = nts_trips[\"oact\"].map(purp_mapping)\n",
-    "\n",
-    "nts_trips[\"dact\"] = nts_trips[\"dact\"].map(purp_mapping)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>TripID</th>\n",
-       "      <th>DayID</th>\n",
-       "      <th>IndividualID</th>\n",
-       "      <th>HouseholdID</th>\n",
-       "      <th>PSUID</th>\n",
-       "      <th>PersNo</th>\n",
-       "      <th>TravDay</th>\n",
-       "      <th>seq</th>\n",
-       "      <th>ShortWalkTrip_B01ID</th>\n",
-       "      <th>NumStages</th>\n",
-       "      <th>MainMode_B03ID</th>\n",
-       "      <th>mode</th>\n",
-       "      <th>oact</th>\n",
-       "      <th>dact</th>\n",
-       "      <th>TripPurpose_B04ID</th>\n",
-       "      <th>tst</th>\n",
-       "      <th>tet</th>\n",
-       "      <th>TripDisIncSW</th>\n",
-       "      <th>TripDisExSW</th>\n",
-       "      <th>TripTotalTime</th>\n",
-       "      <th>TripTravTime</th>\n",
-       "      <th>ozone</th>\n",
-       "      <th>dzone</th>\n",
-       "      <th>W5</th>\n",
-       "      <th>W5xHH</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4659583</th>\n",
-       "      <td>2019009707</td>\n",
-       "      <td>2019004508</td>\n",
-       "      <td>2019000702</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>car</td>\n",
-       "      <td>home</td>\n",
-       "      <td>work</td>\n",
-       "      <td>2</td>\n",
-       "      <td>810.0</td>\n",
-       "      <td>840.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.756184</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659584</th>\n",
-       "      <td>2019009709</td>\n",
-       "      <td>2019004508</td>\n",
-       "      <td>2019000702</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>car</td>\n",
-       "      <td>shop</td>\n",
-       "      <td>home</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1050.0</td>\n",
-       "      <td>1060.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.004688</td>\n",
-       "      <td>1.328628</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659585</th>\n",
-       "      <td>2019009711</td>\n",
-       "      <td>2019004512</td>\n",
-       "      <td>2019000703</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18</td>\n",
-       "      <td>pt</td>\n",
-       "      <td>visit</td>\n",
-       "      <td>home</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1200.0</td>\n",
-       "      <td>1230.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.767622</td>\n",
-       "      <td>1.015126</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659586</th>\n",
-       "      <td>2019010362</td>\n",
-       "      <td>2019004797</td>\n",
-       "      <td>2019000744</td>\n",
-       "      <td>2019000306</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>car</td>\n",
-       "      <td>other</td>\n",
-       "      <td>home</td>\n",
-       "      <td>7</td>\n",
-       "      <td>990.0</td>\n",
-       "      <td>995.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.048438</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659587</th>\n",
-       "      <td>2019010377</td>\n",
-       "      <td>2019004802</td>\n",
-       "      <td>2019000744</td>\n",
-       "      <td>2019000306</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>26</td>\n",
-       "      <td>car</td>\n",
-       "      <td>education</td>\n",
-       "      <td>other</td>\n",
-       "      <td>7</td>\n",
-       "      <td>925.0</td>\n",
-       "      <td>958.0</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>33</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.123451</td>\n",
-       "      <td>1.071548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659588</th>\n",
-       "      <td>2019010379</td>\n",
-       "      <td>2019004803</td>\n",
-       "      <td>2019000747</td>\n",
-       "      <td>2019000308</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>car</td>\n",
-       "      <td>home</td>\n",
-       "      <td>work</td>\n",
-       "      <td>1</td>\n",
-       "      <td>450.0</td>\n",
-       "      <td>470.0</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.817287</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659589</th>\n",
-       "      <td>2019010913</td>\n",
-       "      <td>2019005097</td>\n",
-       "      <td>2019000795</td>\n",
-       "      <td>2019000328</td>\n",
-       "      <td>2019000036</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
-       "      <td>car</td>\n",
-       "      <td>visit</td>\n",
-       "      <td>other</td>\n",
-       "      <td>7</td>\n",
-       "      <td>800.0</td>\n",
-       "      <td>820.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.059917</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659590</th>\n",
-       "      <td>2019010628</td>\n",
-       "      <td>2019004945</td>\n",
-       "      <td>2019000771</td>\n",
-       "      <td>2019000319</td>\n",
-       "      <td>2019000035</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>car</td>\n",
-       "      <td>shop</td>\n",
-       "      <td>home</td>\n",
-       "      <td>4</td>\n",
-       "      <td>795.0</td>\n",
-       "      <td>810.0</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.997967</td>\n",
-       "      <td>1.227954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659591</th>\n",
-       "      <td>2019010630</td>\n",
-       "      <td>2019004946</td>\n",
-       "      <td>2019000771</td>\n",
-       "      <td>2019000319</td>\n",
-       "      <td>2019000035</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>walk</td>\n",
-       "      <td>other</td>\n",
-       "      <td>home</td>\n",
-       "      <td>8</td>\n",
-       "      <td>460.0</td>\n",
-       "      <td>480.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.913560</td>\n",
-       "      <td>1.124095</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659592</th>\n",
-       "      <td>2019010643</td>\n",
-       "      <td>2019004949</td>\n",
-       "      <td>2019000771</td>\n",
-       "      <td>2019000319</td>\n",
-       "      <td>2019000035</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>car</td>\n",
-       "      <td>home</td>\n",
-       "      <td>other</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1040.0</td>\n",
-       "      <td>1050.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.870854</td>\n",
-       "      <td>1.071548</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             TripID       DayID  IndividualID  HouseholdID       PSUID  \\\n",
-       "4659583  2019009707  2019004508    2019000702   2019000291  2019000032   \n",
-       "4659584  2019009709  2019004508    2019000702   2019000291  2019000032   \n",
-       "4659585  2019009711  2019004512    2019000703   2019000291  2019000032   \n",
-       "4659586  2019010362  2019004797    2019000744   2019000306  2019000033   \n",
-       "4659587  2019010377  2019004802    2019000744   2019000306  2019000033   \n",
-       "4659588  2019010379  2019004803    2019000747   2019000308  2019000033   \n",
-       "4659589  2019010913  2019005097    2019000795   2019000328  2019000036   \n",
-       "4659590  2019010628  2019004945    2019000771   2019000319  2019000035   \n",
-       "4659591  2019010630  2019004946    2019000771   2019000319  2019000035   \n",
-       "4659592  2019010643  2019004949    2019000771   2019000319  2019000035   \n",
-       "\n",
-       "         PersNo  TravDay  seq  ShortWalkTrip_B01ID  NumStages  MainMode_B03ID  \\\n",
-       "4659583       2        7    1                    2          1               5   \n",
-       "4659584       2        7    3                    2          1               5   \n",
-       "4659585       3        4    2                    2          1              18   \n",
-       "4659586       2        2    3                    2          1               7   \n",
-       "4659587       2        7    2                    2          1              26   \n",
-       "4659588       1        1    1                    2          2               5   \n",
-       "4659589       3        1    2                    2          1               8   \n",
-       "4659590       1        3    3                    2          1               5   \n",
-       "4659591       1        4    2                    2          1               2   \n",
-       "4659592       1        7    3                    2          1               5   \n",
-       "\n",
-       "         mode       oact   dact  TripPurpose_B04ID     tst     tet  \\\n",
-       "4659583   car       home   work                  2   810.0   840.0   \n",
-       "4659584   car       shop   home                  4  1050.0  1060.0   \n",
-       "4659585    pt      visit   home                  7  1200.0  1230.0   \n",
-       "4659586   car      other   home                  7   990.0   995.0   \n",
-       "4659587   car  education  other                  7   925.0   958.0   \n",
-       "4659588   car       home   work                  1   450.0   470.0   \n",
-       "4659589   car      visit  other                  7   800.0   820.0   \n",
-       "4659590   car       shop   home                  4   795.0   810.0   \n",
-       "4659591  walk      other   home                  8   460.0   480.0   \n",
-       "4659592   car       home  other                  7  1040.0  1050.0   \n",
-       "\n",
-       "         TripDisIncSW  TripDisExSW  TripTotalTime  TripTravTime  ozone  dzone  \\\n",
-       "4659583           4.0          4.0             30          30.0      7    7.0   \n",
-       "4659584           1.0          1.0             10          10.0      7    7.0   \n",
-       "4659585           3.0          3.0             30          30.0      7    7.0   \n",
-       "4659586           2.0          2.0              5           5.0      2    2.0   \n",
-       "4659587           6.9          6.9             33          33.0      2    2.0   \n",
-       "4659588           2.2          2.0             20          20.0      2    2.0   \n",
-       "4659589          10.0         10.0             20          20.0      8    8.0   \n",
-       "4659590           6.1          6.1             15          15.0      8    8.0   \n",
-       "4659591           1.0          1.0             20          20.0      8    8.0   \n",
-       "4659592           3.0          3.0             10           7.0      8    8.0   \n",
-       "\n",
-       "               W5     W5xHH  \n",
-       "4659583  0.756184  1.000000  \n",
-       "4659584  1.004688  1.328628  \n",
-       "4659585  0.767622  1.015126  \n",
-       "4659586  1.048438  1.000000  \n",
-       "4659587  1.123451  1.071548  \n",
-       "4659588  0.817287  1.000000  \n",
-       "4659589  1.059917  1.000000  \n",
-       "4659590  0.997967  1.227954  \n",
-       "4659591  0.913560  1.124095  \n",
-       "4659592  0.870854  1.071548  "
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "nts_trips.head(10)"
    ]