From 1c69de27b95f00022b10f09ae370d4eab7e8e29b Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Tue, 15 Oct 2024 10:15:41 +0100
Subject: [PATCH 01/27] iterative matching, see #13

---
 .../2_match_households_and_individuals.ipynb  | 122306 ++++++++++++++-
 scripts/2_match_households_and_individuals.py |     39 +-
 src/acbm/matching.py                          |    224 +-
 3 files changed, 122095 insertions(+), 474 deletions(-)

diff --git a/notebooks/2_match_households_and_individuals.ipynb b/notebooks/2_match_households_and_individuals.ipynb
index 0fb53b9..20d5d6a 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": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
     "from tqdm import trange\n",
     "\n",
     "# from tqdm.notebook import trange\n",
-    "from acbm.matching import match_categorical, match_individuals\n",
+    "from acbm.matching import MatcherExact, match_individuals\n",
     "from acbm.preprocessing import (\n",
     "    count_per_group,\n",
     "    #nts_filter_by_region,\n",
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -420,7 +420,7 @@
        "4         1    2         26          1               6.0  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -433,7 +433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -447,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -470,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -487,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -539,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -587,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -635,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -658,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -678,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -830,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -851,9 +851,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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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"
+     ]
+    }
+   ],
    "source": [
     "# histogram for individuals and households (include NAs as 0)\n",
     "fig, ax = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n",
@@ -889,7 +914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -923,9 +948,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9Zs+erQULFmjx4sXas2ePKlSooLCwMF2+fNnWZ9CgQTp48KBiYmK0bt06ffPNN3riiSdsy9PS0tSjRw8FBwcrLi5Ob7zxhqZPn6733nuvSPcXAAAAAABnK+vKF3/99dcVFBSkpUuX2trq1q1r+2/DMDRv3jxNmTJFffv2lSR99NFH8vf315o1a/Tggw/q8OHD2rhxo7777jvddtttkqS33npLvXv31pw5cxQYGKhPP/1UGRkZ+vDDD+Xu7q6mTZsqPj5eb775pt0HAAAAAAAAmI1Lz9h/+eWXuu2223TffffJz89PrVq10vvvv29bnpSUpOTkZHXr1s3W5uPjo7Zt2yo2NlaSFBsbK19fX1uol6Ru3brJzc1Ne/bssfW566675O7ubusTFhamxMREnT17Nkdd6enpSktLs3sAAICShfkeAFBSuDTY//bbb1q0aJFuvfVWRUdHa9SoUXrmmWe0bNkySVJycrIkyd/f3249f39/27Lk5GT5+fnZLS9btqyqVKli1ye3bVz7GteaOXOmfHx8bI+goCAn7C0AAChOmO8BACWFS4N9VlaWWrdurddee02tWrXSE088oREjRmjx4sWuLEuTJ09Wamqq7XHs2DGX1gMAAJyP+R4AUFK4NNjXqFFDTZo0sWtr3Lixjh49KkkKCAiQJKWkpNj1SUlJsS0LCAjQqVOn7JZfvXpVZ86cseuT2zaufY1reXh4yNvb2+4BAABKFuZ7AEBJ4dJg36FDByUmJtq1/fzzzwoODpb09430AgICtGXLFtvytLQ07dmzR1arVZJktVp17tw5xcXF2fps3bpVWVlZatu2ra3PN998oytXrtj6xMTEqGHDhnZ34AcAAAAAwGxcGuzHjh2r3bt367XXXtOvv/6q5cuX67333lNERIQkyWKxaMyYMXrllVf05ZdfKiEhQUOGDFFgYKD69esn6e8z/D179tSIESO0d+9e7dy5U6NHj9aDDz6owMBASdLDDz8sd3d3DR8+XAcPHtSKFSs0f/58jRs3zlW7DgAAAACAU7j05+7atGmj1atXa/LkyZoxY4bq1q2refPmadCgQbY+EyZM0MWLF/XEE0/o3LlzuuOOO7Rx40Z5enra+nz66acaPXq0unbtKjc3Nw0cOFALFiywLffx8dGmTZsUERGh0NBQVatWTVOnTuWn7gAAAAAApmcxDMNwdRHFXVpamnx8fJSamuq079/VmbTeKdtB0fl9VrirSwAAm8KYm0o7xhQAUJw4Mi+59FJ8AAAAAABwcwj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiZV1dAAAAAACgcNWZtL5IXuf3WeFF8jqwxxl7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyvr6gIAAAAAAChsdSatL5LX+X1WeJG8zrU4Yw8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIm5NNhPnz5dFovF7tGoUSPb8suXLysiIkJVq1ZVxYoVNXDgQKWkpNht4+jRowoPD1f58uXl5+en8ePH6+rVq3Z9tm3bptatW8vDw0P169dXVFRUUeweAAAAAACFzuVn7Js2baqTJ0/aHjt27LAtGzt2rNauXauVK1dq+/btOnHihAYMGGBbnpmZqfDwcGVkZGjXrl1atmyZoqKiNHXqVFufpKQkhYeHq3PnzoqPj9eYMWP0+OOPKzo6ukj3EwAAAACAwlDW5QWULauAgIAc7ampqVqyZImWL1+uLl26SJKWLl2qxo0ba/fu3WrXrp02bdqkQ4cOafPmzfL391dISIhefvllTZw4UdOnT5e7u7sWL16sunXrau7cuZKkxo0ba8eOHYqMjFRYWFiR7isAAAAAAM7m8jP2v/zyiwIDA1WvXj0NGjRIR48elSTFxcXpypUr6tatm61vo0aNVLt2bcXGxkqSYmNj1bx5c/n7+9v6hIWFKS0tTQcPHrT1uXYb2X2yt5Gb9PR0paWl2T0AAEDJwnwPACgpXBrs27Ztq6ioKG3cuFGLFi1SUlKS7rzzTp0/f17Jyclyd3eXr6+v3Tr+/v5KTk6WJCUnJ9uF+uzl2cuu1yctLU2XLl3Kta6ZM2fKx8fH9ggKCnLG7gIAgGKE+R4AUFK4NNj36tVL9913n1q0aKGwsDBt2LBB586d0+eff+7KsjR58mSlpqbaHseOHXNpPQAAwPmY7wEAJYXLv2N/LV9fXzVo0EC//vqrunfvroyMDJ07d87urH1KSortO/kBAQHau3ev3Tay75p/bZ9/3kk/JSVF3t7e8vLyyrUODw8PeXh4OGu3AABAMcR8DwAoKVz+HftrXbhwQUeOHFGNGjUUGhqqcuXKacuWLbbliYmJOnr0qKxWqyTJarUqISFBp06dsvWJiYmRt7e3mjRpYutz7Tay+2RvAwAAAAAAM3NpsH/++ee1fft2/f7779q1a5f69++vMmXK6KGHHpKPj4+GDx+ucePG6euvv1ZcXJyGDRsmq9Wqdu3aSZJ69OihJk2aaPDgwfrhhx8UHR2tKVOmKCIiwvYJ/MiRI/Xbb79pwoQJ+umnn/TOO+/o888/19ixY1256wAAAAAAOIVLL8U/fvy4HnroIf2///f/VL16dd1xxx3avXu3qlevLkmKjIyUm5ubBg4cqPT0dIWFhemdd96xrV+mTBmtW7dOo0aNktVqVYUKFTR06FDNmDHD1qdu3bpav369xo4dq/nz56tWrVr64IMP+Kk7AAAAAECJ4NJg/9lnn113uaenpxYuXKiFCxfm2Sc4OFgbNmy47nY6deqk/fv3F6hGAAAAAACKs2L1HXsAAAAAAOAYgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYi79uTsAQMHVmbTe1SWgAH6fFe7qEgAAQAnDGXsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxLgrPgAAAACg0BXVL/qUxl+g4Yw9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIOB/uNGzdqx44dtucLFy5USEiIHn74YZ09e9apxQEAAAAAgOtzONiPHz9eaWlpkqSEhAQ999xz6t27t5KSkjRu3DinFwgAAAAAAPJW1tEVkpKS1KRJE0nSqlWrdPfdd+u1117Tvn371Lt3b6cXCAAAAAAA8ubwGXt3d3f99ddfkqTNmzerR48ekqQqVarYzuQDAAAAAICi4fAZ+zvuuEPjxo1Thw4dtHfvXq1YsUKS9PPPP6tWrVpOLxAAAAAAAOTN4TP2b7/9tsqWLav//Oc/WrRokWrWrClJ+uqrr9SzZ0+nFwgAAAAAAPLm8Bn72rVra926dTnaIyMjnVIQAAAAAADIv3wFe0e+O+/t7V3gYgAAAAAAgGPyFex9fX1lsVjytcHMzMybKggAAAAAAORfvoL9119/bfvv33//XZMmTdKjjz4qq9UqSYqNjdWyZcs0c+bMwqkSAAAAAADkKl/BvmPHjrb/njFjht5880099NBDtrZ77rlHzZs313vvvaehQ4c6v0oAAAAAAJArh++KHxsbq9tuuy1H+2233aa9e/c6pSgAAAAAAJA/Dgf7oKAgvf/++znaP/jgAwUFBTmlKAAAAAAAkD8O/9xdZGSkBg4cqK+++kpt27aVJO3du1e//PKLVq1a5fQCAQAAAABA3hw+Y9+7d2/9/PPP6tOnj86cOaMzZ86oT58++vnnn9W7d+/CqBEAAAAAAOTB4TP20t+X47/22mvOrgUAAAAAADgoX8H+wIED+d5gixYtClwMAAAAAABwTL6CfUhIiCwWiwzDuG4/i8WizMxMpxQGAAAAAABuLF/BPikpqbDrAAAAAAAABZCvYB8cHFzYdQAAAAAAgAIo0M3zjhw5onnz5unw4cOSpCZNmujZZ5/VLbfc4tTiAAAAAADA9Tn8c3fR0dFq0qSJ9u7dqxYtWqhFixbas2ePmjZtqpiYmMKoEQAAAAAA5MHhM/aTJk3S2LFjNWvWrBztEydOVPfu3Z1WHAAAAAAAuD6Hz9gfPnxYw4cPz9H+2GOP6dChQ04pCgAAAAAA5I/Dwb569eqKj4/P0R4fHy8/Pz9n1AQAAAAAAPLJ4UvxR4wYoSeeeEK//fab2rdvL0nauXOnXn/9dY0bN87pBQIAAAAAgLw5HOxffPFFVapUSXPnztXkyZMlSYGBgZo+fbqeeeYZpxcIAAAAAADy5vCl+BaLRWPHjtXx48eVmpqq1NRUHT9+XM8++6wsFkuBC5k1a5YsFovGjBlja7t8+bIiIiJUtWpVVaxYUQMHDlRKSordekePHlV4eLjKly8vPz8/jR8/XlevXrXrs23bNrVu3VoeHh6qX7++oqKiClwnAAAAAADFicPB/lqVKlVSpUqVbrqI7777Tu+++65atGhh1z527FitXbtWK1eu1Pbt23XixAkNGDDAtjwzM1Ph4eHKyMjQrl27tGzZMkVFRWnq1Km2PklJSQoPD1fnzp0VHx+vMWPG6PHHH1d0dPRN1w0AAAAAgKs5HOxTUlI0ePBgBQYGqmzZsipTpozdw1EXLlzQoEGD9P7776ty5cq29tTUVC1ZskRvvvmmunTpotDQUC1dulS7du3S7t27JUmbNm3SoUOH9MknnygkJES9evXSyy+/rIULFyojI0OStHjxYtWtW1dz585V48aNNXr0aN17772KjIx0uFYAAAAAAIobh79j/+ijj+ro0aN68cUXVaNGjZu6/F6SIiIiFB4erm7duumVV16xtcfFxenKlSvq1q2bra1Ro0aqXbu2YmNj1a5dO8XGxqp58+by9/e39QkLC9OoUaN08OBBtWrVSrGxsXbbyO5z7SX//5Senq709HTb87S0tJvaRwAAUPww3wMASgqHg/2OHTv07bffKiQk5KZf/LPPPtO+ffv03Xff5ViWnJwsd3d3+fr62rX7+/srOTnZ1ufaUJ+9PHvZ9fqkpaXp0qVL8vLyyvHaM2fO1EsvvVTg/QIAAMUf8z0AoKRw+FL8oKAgGYZx0y987NgxPfvss/r000/l6el509tzpsmTJ9tuDJiamqpjx465uiQAAOBkzPcAgJLC4WA/b948TZo0Sb///vtNvXBcXJxOnTql1q1bq2zZsipbtqy2b9+uBQsWqGzZsvL391dGRobOnTtnt15KSooCAgIkSQEBATnukp/9/EZ9vL29cz1bL0keHh7y9va2ewAAgJKF+R4AUFLk61L8ypUr232X/uLFi7rllltUvnx5lStXzq7vmTNn8vXCXbt2VUJCgl3bsGHD1KhRI02cOFFBQUEqV66ctmzZooEDB0qSEhMTdfToUVmtVkmS1WrVq6++qlOnTsnPz0+SFBMTI29vbzVp0sTWZ8OGDXavExMTY9sGAAAAAABmlq9gP2/ePKe/cKVKldSsWTO7tgoVKqhq1aq29uHDh2vcuHGqUqWKvL299fTTT8tqtapdu3aSpB49eqhJkyYaPHiwZs+ereTkZE2ZMkURERHy8PCQJI0cOVJvv/22JkyYoMcee0xbt27V559/rvXr1zt9nwAAAAAAKGr5CvZDhw4t7DpyFRkZKTc3Nw0cOFDp6ekKCwvTO++8Y1tepkwZrVu3TqNGjZLValWFChU0dOhQzZgxw9anbt26Wr9+vcaOHav58+erVq1a+uCDDxQWFuaKXQIAAAAAwKkcviu+JB05ckRLly7VkSNHNH/+fPn5+emrr75S7dq11bRp0wIXs23bNrvnnp6eWrhwoRYuXJjnOsHBwTkutf+nTp06af/+/QWuCwAAAACA4srhm+dt375dzZs31549e/TFF1/owoULkqQffvhB06ZNc3qBAAAAAAAgbw4H+0mTJumVV15RTEyM3N3dbe1dunTR7t27nVocAAAAAAC4PoeDfUJCgvr375+j3c/PT6dPn3ZKUQAAAAAAIH8cDva+vr46efJkjvb9+/erZs2aTikKAAAAAADkj8PB/sEHH9TEiROVnJwsi8WirKws7dy5U88//7yGDBlSGDUCAAAAAIA8OBzsX3vtNTVq1EhBQUG6cOGCmjRporvuukvt27fXlClTCqNGAAAAAACQB4d/7s7d3V3vv/++pk6dqoSEBF24cEGtWrXSrbfeWhj1AQAAAACA6yjQ79hLUlBQkIKCgpSZmamEhASdPXtWlStXdmZtAAAAAADgBhy+FH/MmDFasmSJJCkzM1MdO3ZU69atFRQUpG3btjm7PgAAAAAAcB0OB/v//Oc/atmypSRp7dq1+u233/TTTz9p7NixeuGFF5xeIAAAAAAAyJvDwf706dMKCAiQJG3YsEH333+/GjRooMcee0wJCQlOLxAAAAAAAOTN4WDv7++vQ4cOKTMzUxs3blT37t0lSX/99ZfKlCnj9AIBAAAAAEDeHL553rBhw3T//ferRo0aslgs6tatmyRpz549atSokdMLBAAAAAAAeXM42E+fPl3NmjXTsWPHdN9998nDw0OSVKZMGU2aNMnpBQIAAAAAgLwV6Ofu7r333hxtQ4cOveliAAAAAACAYxwO9jNmzLju8qlTpxa4GAAAAAAA4BiHg/3q1avtnl+5ckVJSUkqW7asbrnlFoI9AAAAAABFyOFgv3///hxtaWlpevTRR9W/f3+nFAUAAAAAAPLH4Z+7y423t7deeuklvfjii87YHAAAAAAAyCenBHtJSk1NVWpqqrM2BwAAAAAA8sHhS/EXLFhg99wwDJ08eVIff/yxevXq5bTCAAAAAADAjTkc7CMjI+2eu7m5qXr16ho6dKgmT57stMIAAAAAAMCNORzsk5KSCqMOAAAAAABQADf1Hfvjx4/r+PHjzqoFAAAAAAA4yOFgn5WVpRkzZsjHx0fBwcEKDg6Wr6+vXn75ZWVlZRVGjQAAAAAAIA8OX4r/wgsvaMmSJZo1a5Y6dOggSdqxY4emT5+uy5cv69VXX3V6kQAAAAAAIHcOB/tly5bpgw8+0D333GNra9GihWrWrKmnnnqKYA8AAAAAQBFy+FL8M2fOqFGjRjnaGzVqpDNnzjilKAAAAAAAkD8OB/uWLVvq7bffztH+9ttvq2XLlk4pCgAAAAAA5I/Dl+LPnj1b4eHh2rx5s6xWqyQpNjZWx44d04YNG5xeIAAAAAAAyJvDZ+w7duyon3/+Wf3799e5c+d07tw5DRgwQImJibrzzjsLo0YAAAAAAJAHh8/YS1JgYCA3yQMAAAAAoBjId7A/cOBAvvq1aNGiwMUA+D91Jq13dQkogN9nhbu6BAAAAJQy+Q72ISEhslgsMgxDkmSxWCTJ9jy7LTMz08klAgAAAACAvOQ72CclJdn+2zAMNWvWTBs2bFBwcHChFAYAAAAAAG4s38H+nwHeYrGoVq1aBHsAAAAAAFzI4bviAwAAAACA4oNgDwAAAACAid1UsM++gR4AAAAAAHCNfH/HvlWrVnZB/tKlS+rTp4/c3d3t+u3bt8951QEAAAAAgOvKd7Dv16+f3fO+ffs6uxYAAAAAAOCgfAf7adOmFWYdAAAAAACgALh5HgAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATy1ewr1Klik6fPi1Jeuyxx3T+/PlCLQoAAAAAAORPvoJ9RkaG0tLSJEnLli3T5cuXC7UoAAAAAACQP/n6uTur1ap+/fopNDRUhmHomWeekZeXV659P/zwQ6cWCAAAAAAA8pavYP/JJ58oMjJSR44ckcViUWpqKmftAQAAAAAoBvIV7P39/TVr1ixJUt26dfXxxx+ratWqhVoYAAAAAAC4sXwF+2slJSUVRh0AAAAAAKAACvRzd9u3b1efPn1Uv3591a9fX/fcc4++/fZbZ9cGAAAAAABuwOFg/8knn6hbt24qX768nnnmGduN9Lp27arly5cXRo0AAAAAACAPDgf7V199VbNnz9aKFStswX7FihWaNWuWXn75ZYe2tWjRIrVo0ULe3t7y9vaW1WrVV199ZVt++fJlRUREqGrVqqpYsaIGDhyolJQUu20cPXpU4eHhKl++vPz8/DR+/HhdvXrVrs+2bdvUunVreXh4qH79+oqKinJ0twEAAAAAKJYcDva//fab+vTpk6P9nnvucfj797Vq1dKsWbMUFxen77//Xl26dFHfvn118OBBSdLYsWO1du1arVy5Utu3b9eJEyc0YMAA2/qZmZkKDw9XRkaGdu3apWXLlikqKkpTp0619UlKSlJ4eLg6d+6s+Ph4jRkzRo8//riio6Md3XUAAAAAAIodh2+eFxQUpC1btqh+/fp27Zs3b1ZQUJBD2/rnBwSvvvqqFi1apN27d6tWrVpasmSJli9fri5dukiSli5dqsaNG2v37t1q166dNm3apEOHDmnz5s3y9/dXSEiIXn75ZU2cOFHTp0+Xu7u7Fi9erLp162ru3LmSpMaNG2vHjh2KjIxUWFiYo7sPAAAAAECx4nCwf+655/TMM88oPj5e7du3lyTt3LlTUVFRmj9/foELyczM1MqVK3Xx4kVZrVbFxcXpypUr6tatm61Po0aNVLt2bcXGxqpdu3aKjY1V8+bN5e/vb+sTFhamUaNG6eDBg2rVqpViY2PttpHdZ8yYMXnWkp6ervT0dNvztLS0Au8XAAAonpjvAQAlhcPBftSoUQoICNDcuXP1+eefS/r7LPiKFSvUt29fhwtISEiQ1WrV5cuXVbFiRa1evVpNmjRRfHy83N3d5evra9ff399fycnJkqTk5GS7UJ+9PHvZ9fqkpaXp0qVL8vLyylHTzJkz9dJLLzm8LwAAwDyY7wEAJYXDwV6S+vfvr/79+zulgIYNGyo+Pl6pqan6z3/+o6FDh2r79u1O2XZBTZ48WePGjbM9T0tLc/hrBgAAoHhjvgcAlBQFCvbO5O7ubvu+fmhoqL777jvNnz9fDzzwgDIyMnTu3Dm7s/YpKSkKCAiQJAUEBGjv3r1228u+a/61ff55J/2UlBR5e3vnerZekjw8POTh4eGU/QMAAMUT8z0AoKRw+K74hS0rK0vp6ekKDQ1VuXLltGXLFtuyxMREHT16VFarVZJktVqVkJCgU6dO2frExMTI29tbTZo0sfW5dhvZfbK3AQAAAACAmbn0jP3kyZPVq1cv1a5dW+fPn9fy5cu1bds2RUdHy8fHR8OHD9e4ceNUpUoVeXt76+mnn5bValW7du0kST169FCTJk00ePBgzZ49W8nJyZoyZYoiIiJsn8CPHDlSb7/9tiZMmKDHHntMW7du1eeff67169e7ctcBAAAAAHAKlwb7U6dOaciQITp58qR8fHzUokULRUdHq3v37pKkyMhIubm5aeDAgUpPT1dYWJjeeecd2/plypTRunXrNGrUKFmtVlWoUEFDhw7VjBkzbH3q1q2r9evXa+zYsZo/f75q1aqlDz74gJ+6AwAAAACUCDcV7A3DkCRZLJYCrb9kyZLrLvf09NTChQu1cOHCPPsEBwdrw4YN191Op06dtH///gLVCAAAAABAcVag79h/9NFHat68uby8vOTl5aUWLVro448/dnZtAAAAAADgBhw+Y//mm2/qxRdf1OjRo9WhQwdJ0o4dOzRy5EidPn1aY8eOdXqRAAAAAAAgdw4H+7feekuLFi3SkCFDbG333HOPmjZtqunTpxPsAQAAAAAoQg5fin/y5Em1b98+R3v79u118uRJpxQFAAAAAADyx+FgX79+fX3++ec52lesWKFbb73VKUUBAAAAAID8cfhS/JdeekkPPPCAvvnmG9t37Hfu3KktW7bkGvgBAAAAAEDhcfiM/cCBA7Vnzx5Vq1ZNa9as0Zo1a1StWjXt3btX/fv3L4waAQAAAABAHgr0O/ahoaH65JNPnF0LAAAAAABwUIF+xx4AAAAAABQP+T5j7+bmJovFct0+FotFV69evemiAAAAAABA/uQ72K9evTrPZbGxsVqwYIGysrKcUhQAAAAAAMiffAf7vn375mhLTEzUpEmTtHbtWg0aNEgzZsxwanEAAAAAAOD6CvQd+xMnTmjEiBFq3ry5rl69qvj4eC1btkzBwcHOrg8AAAAAAFyHQ8E+NTVVEydOVP369XXw4EFt2bJFa9euVbNmzQqrPgAAAAAAcB35vhR/9uzZev311xUQEKB///vfuV6aDwAAAAAAila+g/2kSZPk5eWl+vXra9myZVq2bFmu/b744gunFQcAAAAAAK4v38F+yJAhN/y5OwAAAAAAULTyHeyjoqIKsQwAAAAAAFAQBborPgAAAAAAKB4I9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYi4N9jNnzlSbNm1UqVIl+fn5qV+/fkpMTLTrc/nyZUVERKhq1aqqWLGiBg4cqJSUFLs+R48eVXh4uMqXLy8/Pz+NHz9eV69eteuzbds2tW7dWh4eHqpfv76ioqIKe/cAAAAAACh0Lg3227dvV0REhHbv3q2YmBhduXJFPXr00MWLF219xo4dq7Vr12rlypXavn27Tpw4oQEDBtiWZ2ZmKjw8XBkZGdq1a5eWLVumqKgoTZ061dYnKSlJ4eHh6ty5s+Lj4zVmzBg9/vjjio6OLtL9BQAAAADA2cq68sU3btxo9zwqKkp+fn6Ki4vTXXfdpdTUVC1ZskTLly9Xly5dJElLly5V48aNtXv3brVr106bNm3SoUOHtHnzZvn7+yskJEQvv/yyJk6cqOnTp8vd3V2LFy9W3bp1NXfuXElS48aNtWPHDkVGRiosLCxHXenp6UpPT7c9T0tLK8RRAAAArsB8DwAoKYrVd+xTU1MlSVWqVJEkxcXF6cqVK+rWrZutT6NGjVS7dm3FxsZKkmJjY9W8eXP5+/vb+oSFhSktLU0HDx609bl2G9l9srfxTzNnzpSPj4/tERQU5LydBAAAxQLzPQCgpCg2wT4rK0tjxoxRhw4d1KxZM0lScnKy3N3d5evra9fX399fycnJtj7Xhvrs5dnLrtcnLS1Nly5dylHL5MmTlZqaanscO3bMKfsIAACKD+Z7AEBJ4dJL8a8VERGhH3/8UTt27HB1KfLw8JCHh4erywAAAIWI+R4AUFIUizP2o0eP1rp16/T111+rVq1atvaAgABlZGTo3Llzdv1TUlIUEBBg6/PPu+RnP79RH29vb3l5eTl7dwAAAAAAKDIuDfaGYWj06NFavXq1tm7dqrp169otDw0NVbly5bRlyxZbW2Jioo4ePSqr1SpJslqtSkhI0KlTp2x9YmJi5O3trSZNmtj6XLuN7D7Z2wAAAAAAwKxceil+RESEli9frv/+97+qVKmS7TvxPj4+8vLyko+Pj4YPH65x48apSpUq8vb21tNPPy2r1ap27dpJknr06KEmTZpo8ODBmj17tpKTkzVlyhRFRETYLq8bOXKk3n77bU2YMEGPPfaYtm7dqs8//1zr16932b4DAAAAAOAMLj1jv2jRIqWmpqpTp06qUaOG7bFixQpbn8jISN19990aOHCg7rrrLgUEBOiLL76wLS9TpozWrVunMmXKyGq16pFHHtGQIUM0Y8YMW5+6detq/fr1iomJUcuWLTV37lx98MEHuf7UHQAAAAAAZuLSM/aGYdywj6enpxYuXKiFCxfm2Sc4OFgbNmy47nY6deqk/fv3O1wjAAAAAADFWbG4eR4AAAAAACgYgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAE3NpsP/mm2/Up08fBQYGymKxaM2aNXbLDcPQ1KlTVaNGDXl5ealbt2765Zdf7PqcOXNGgwYNkre3t3x9fTV8+HBduHDBrs+BAwd05513ytPTU0FBQZo9e3Zh7xoAAAAAAEXCpcH+4sWLatmypRYuXJjr8tmzZ2vBggVavHix9uzZowoVKigsLEyXL1+29Rk0aJAOHjyomJgYrVu3Tt98842eeOIJ2/K0tDT16NFDwcHBiouL0xtvvKHp06frvffeK/T9AwAAAACgsJV15Yv36tVLvXr1ynWZYRiaN2+epkyZor59+0qSPvroI/n7+2vNmjV68MEHdfjwYW3cuFHfffedbrvtNknSW2+9pd69e2vOnDkKDAzUp59+qoyMDH344Ydyd3dX06ZNFR8frzfffNPuAwAAAAAAAMyo2H7HPikpScnJyerWrZutzcfHR23btlVsbKwkKTY2Vr6+vrZQL0ndunWTm5ub9uzZY+tz1113yd3d3dYnLCxMiYmJOnv2bK6vnZ6errS0NLsHAAAoWZjvAQAlRbEN9snJyZIkf39/u3Z/f3/bsuTkZPn5+dktL1u2rKpUqWLXJ7dtXPsa/zRz5kz5+PjYHkFBQTe/QwAAoFhhvgcAlBTFNti70uTJk5Wammp7HDt2zNUlAQAAJ2O+BwCUFC79jv31BAQESJJSUlJUo0YNW3tKSopCQkJsfU6dOmW33tWrV3XmzBnb+gEBAUpJSbHrk/08u88/eXh4yMPDwyn7AQAAiifmewBASVFsz9jXrVtXAQEB2rJli60tLS1Ne/bskdVqlSRZrVadO3dOcXFxtj5bt25VVlaW2rZta+vzzTff6MqVK7Y+MTExatiwoSpXrlxEewMAAAAAQOFwabC/cOGC4uPjFR8fL+nvG+bFx8fr6NGjslgsGjNmjF555RV9+eWXSkhI0JAhQxQYGKh+/fpJkho3bqyePXtqxIgR2rt3r3bu3KnRo0frwQcfVGBgoCTp4Ycflru7u4YPH66DBw9qxYoVmj9/vsaNG+eivQYAAAAAwHlcein+999/r86dO9ueZ4ftoUOHKioqShMmTNDFixf1xBNP6Ny5c7rjjju0ceNGeXp62tb59NNPNXr0aHXt2lVubm4aOHCgFixYYFvu4+OjTZs2KSIiQqGhoapWrZqmTp3KT90BAAAAAEoElwb7Tp06yTCMPJdbLBbNmDFDM2bMyLNPlSpVtHz58uu+TosWLfTtt98WuE4AAAAAAIqrYvsdewAAAAAAcGMEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDESlWwX7hwoerUqSNPT0+1bdtWe/fudXVJAAAAAADclFIT7FesWKFx48Zp2rRp2rdvn1q2bKmwsDCdOnXK1aUBAAAAAFBgpSbYv/nmmxoxYoSGDRumJk2aaPHixSpfvrw+/PBDV5cGAAAAAECBlXV1AUUhIyNDcXFxmjx5sq3Nzc1N3bp1U2xsbI7+6enpSk9Ptz1PTU2VJKWlpTmtpqz0v5y2LRQNZ/7/zw+OEXMqyuOEY8ScnHWMZG/HMAynbK80Kuz5vtm0aKds50Z+fCmsSF4HgLkV1fuG6/0NdXUNrn79gm4nP3N9qQj2p0+fVmZmpvz9/e3a/f399dNPP+XoP3PmTL300ks52oOCggqtRhR/PvNcXQHMgOMEN+LsY+T8+fPy8fFx7kZLiZIy3/N3B0BxUhz+Jrm6BlfM9RajFHzUf+LECdWsWVO7du2S1Wq1tU+YMEHbt2/Xnj177Pr/8xP8rKwsnTlzRlWrVpXFYrG1p6WlKSgoSMeOHZO3t3fh74gJMCb2GI+cGBN7jEdOjElOuY2JYRg6f/68AgMD5eZWar5Z51T5ne+LCsc+YyAxBhJjIDEG2Ur7ODgy15eKM/bVqlVTmTJllJKSYteekpKigICAHP09PDzk4eFh1+br65vn9r29vUvlgXY9jIk9xiMnxsQe45ETY5LTP8eEM/U3x9H5vqhw7DMGEmMgMQYSY5CtNI9Dfuf6UvERv7u7u0JDQ7VlyxZbW1ZWlrZs2WJ3Bh8AAAAAALMpFWfsJWncuHEaOnSobrvtNt1+++2aN2+eLl68qGHDhrm6NAAAAAAACqzUBPsHHnhA//vf/zR16lQlJycrJCREGzduzHFDPUd4eHho2rRpOS7jK80YE3uMR06MiT3GIyfGJCfGpHTg/zNjIDEGEmMgMQbZGIf8KxU3zwMAAAAAoKQqFd+xBwAAAACgpCLYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEexvYOHChapTp448PT3Vtm1b7d27N8++UVFRslgsdg9PT88irLZwffPNN+rTp48CAwNlsVi0Zs2aG66zbds2tW7dWh4eHqpfv76ioqIKvc6i5OiYbNu2LccxYrFYlJycXDQFF7KZM2eqTZs2qlSpkvz8/NSvXz8lJibecL2VK1eqUaNG8vT0VPPmzbVhw4YiqLbwFWQ8SvrfkUWLFqlFixby9vaWt7e3rFarvvrqq+uuU1KPj2yOjklJP0bwt59//ll9+/ZVtWrV5O3trTvuuENff/21q8sqcuvXr1fbtm3l5eWlypUrq1+/fq4uySXS09MVEhIii8Wi+Ph4V5dTpH7//XcNHz5cdevWlZeXl2655RZNmzZNGRkZri6tUDmSQUqagr6fLO0I9texYsUKjRs3TtOmTdO+ffvUsmVLhYWF6dSpU3mu4+3trZMnT9oef/zxRxFWXLguXryoli1bauHChfnqn5SUpPDwcHXu3Fnx8fEaM2aMHn/8cUVHRxdypUXH0THJlpiYaHec+Pn5FVKFRWv79u2KiIjQ7t27FRMToytXrqhHjx66ePFinuvs2rVLDz30kIYPH679+/erX79+6tevn3788ccirLxwFGQ8pJL9d6RWrVqaNWuW4uLi9P3336tLly7q27evDh48mGv/knx8ZHN0TKSSfYzgb3fffbeuXr2qrVu3Ki4uTi1bttTdd99dYj4Izo9Vq1Zp8ODBGjZsmH744Qft3LlTDz/8sKvLcokJEyYoMDDQ1WW4xE8//aSsrCy9++67OnjwoCIjI7V48WL961//cnVphaYgGaQkKej7p1LPQJ5uv/12IyIiwvY8MzPTCAwMNGbOnJlr/6VLlxo+Pj5FVJ1rSTJWr1593T4TJkwwmjZtatf2wAMPGGFhYYVYmevkZ0y+/vprQ5Jx9uzZIqnJ1U6dOmVIMrZv355nn/vvv98IDw+3a2vbtq3x5JNPFnZ5RS4/41Ga/o5kq1y5svHBBx/kuqw0HR/Xut6YlMZjpLT53//+Z0gyvvnmG1tbWlqaIcmIiYlxYWVF58qVK0bNmjXz/HdQmmzYsMFo1KiRcfDgQUOSsX//fleX5HKzZ8826tat6+oyCo2jGaSky8/7JxgGZ+zzkJGRobi4OHXr1s3W5ubmpm7duik2NjbP9S5cuKDg4GAFBQXd8IxLSRcbG2s3fpIUFhZ23fErLUJCQlSjRg11795dO3fudHU5hSY1NVWSVKVKlTz7lKbjJD/jIZWevyOZmZn67LPPdPHiRVmt1lz7lKbjQ8rfmEil5xgprapWraqGDRvqo48+0sWLF3X16lW9++678vPzU2hoqKvLKxL79u3Tn3/+KTc3N7Vq1Uo1atRQr169StTVOvmRkpKiESNG6OOPP1b58uVdXU6xkZqaesO51KwKmkFKsvy+fyrtCPZ5OH36tDIzM+Xv72/X7u/vn+dlcA0bNtSHH36o//73v/rkk0+UlZWl9u3b6/jx40VRcrGTnJyc6/ilpaXp0qVLLqrKtWrUqKHFixdr1apVWrVqlYKCgtSpUyft27fP1aU5XVZWlsaMGaMOHTqoWbNmefbL6zgpaZeb5nc8SsPfkYSEBFWsWFEeHh4aOXKkVq9erSZNmuTat7QcH46MSWk4Rko7i8WizZs3a//+/apUqZI8PT315ptvauPGjapcubKryysSv/32myRp+vTpmjJlitatW6fKlSurU6dOOnPmjIurKxqGYejRRx/VyJEjddttt7m6nGLj119/1VtvvaUnn3zS1aUUioJkkJIsv++fQLB3KqvVqiFDhigkJEQdO3bUF198oerVq+vdd991dWkoJho2bKgnn3xSoaGhat++vT788EO1b99ekZGRri7N6SIiIvTjjz/qs88+c3UpxUJ+x6M0/B1p2LCh4uPjtWfPHo0aNUpDhw7VoUOHXF2WSzkyJqXhGCmpJk2alOsNVK99/PTTTzIMQxEREfLz89O3336rvXv3ql+/furTp49Onjzp6t24Kfkdg6ysLEnSCy+8oIEDByo0NFRLly6VxWLRypUrXbwXNye/Y/DWW2/p/Pnzmjx5sqtLLhT5HYdr/fnnn+rZs6fuu+8+jRgxwkWVoyjxfjL/yrq6gOKqWrVqKlOmjFJSUuzaU1JSFBAQkK9tlCtXTq1atdKvv/5aGCUWewEBAbmOn7e3t7y8vFxUVfFz++23a8eOHa4uw6lGjx6tdevW6ZtvvlGtWrWu2zev4yS//87MwJHx+KeS+HfE3d1d9evXlySFhobqu+++0/z583MNpqXh+JAcG5N/KonHSEn13HPP6dFHH71un3r16mnr1q1at26dzp49K29vb0nSO++8o5iYGC1btkyTJk0qgmoLR37HIPsDjGuvXPHw8FC9evV09OjRwiyx0DlyHMTGxsrDw8Nu2W233aZBgwZp2bJlhVhl4cvvOGQ7ceKEOnfurPbt2+u9994r5OpcxxkZpKS4mfdPpRHBPg/u7u4KDQ3Vli1bbD+tkpWVpS1btmj06NH52kZmZqYSEhLUu3fvQqy0+LJarTl+liomJua63xstjeLj41WjRg1Xl+EUhmHo6aef1urVq7Vt2zbVrVv3hutYrVZt2bJFY8aMsbWVlOOkIOPxT6Xh70hWVpbS09NzXVaSj4/rud6Y/FNpOEZKiurVq6t69eo37PfXX39J+vt7tddyc3Oznck2q/yOQWhoqDw8PJSYmKg77rhDknTlyhX9/vvvCg4OLuwyC1V+x2DBggV65ZVXbM9PnDihsLAwrVixQm3bti3MEotEfsdB+vtMfefOnW1Xbvzz30ZJ4owMYnbOeP9UKrn01n3F3GeffWZ4eHgYUVFRxqFDh4wnnnjC8PX1NZKTkw3DMIzBgwcbkyZNsvV/6aWXjOjoaOPIkSNGXFyc8eCDDxqenp7GwYMHXbULTnX+/Hlj//79xv79+w1Jxptvvmns37/f+OOPPwzDMIxJkyYZgwcPtvX/7bffjPLlyxvjx483Dh8+bCxcuNAoU6aMsXHjRlftgtM5OiaRkZHGmjVrjF9++cVISEgwnn32WcPNzc3YvHmzq3bBqUaNGmX4+PgY27ZtM06ePGl7/PXXX7Y+//x3s3PnTqNs2bLGnDlzjMOHDxvTpk0zypUrZyQkJLhiF5yqIONR0v+OTJo0ydi+fbuRlJRkHDhwwJg0aZJhsViMTZs2GYZRuo6PbI6OSUk/RvD3XfGrVq1qDBgwwIiPjzcSExON559/3ihXrpwRHx/v6vKKzLPPPmvUrFnTiI6ONn766Sdj+PDhhp+fn3HmzBlXl+YSSUlJpfKu+MePHzfq169vdO3a1Th+/LjdfFpS3SiDlHT5ef+EnAj2N/DWW28ZtWvXNtzd3Y3bb7/d2L17t21Zx44djaFDh9qejxkzxtbX39/f6N27t7Fv3z4XVF04sn+q7Z+P7DEYOnSo0bFjxxzrhISEGO7u7ka9evWMpUuXFnndhcnRMXn99deNW265xfD09DSqVKlidOrUydi6datrii8EuY2FJLv/7//8d2MYhvH5558bDRo0MNzd3Y2mTZsa69evL9rCC0lBxqOk/x157LHHjODgYMPd3d2oXr260bVrV1uANYzSdXxkc3RMSvoxgr999913Ro8ePYwqVaoYlSpVMtq1a2ds2LDB1WUVqYyMDOO5554z/Pz8jEqVKhndunUzfvzxR1eX5TKlNdgvXbo0z/m0JLteBinp8vP+CTlZDMMwCvOKAAAAAAAAUHhK7hdUAAAAAAAoBQj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawB0q5OnXqaN68ea4uw3SioqLk6+t7U9v4/fffZbFYFB8fn2efbdu2yWKx6Ny5c3n2sVgsWrNmzU3V4iiOGwAwF/5uFwzzPceNWRDsYQqPPvqo+vXr5+oyCkVUVJQsFovtUbFiRYWGhuqLL75wdWn50qlTJ40ZM8Yp25o+fbpCQkJytOdnQgQAmB/zffHFfA8UbwR7oBjw9vbWyZMndfLkSe3fv19hYWG6//77lZiYmOc6GRkZRVghAAC4Wcz3AAoLwR6m1KlTJz3zzDOaMGGCqlSpooCAAE2fPt2uz7lz5/Tkk0/K399fnp6eatasmdatW2dbvmrVKjVt2lQeHh6qU6eO5s6da7d+nTp19Morr2jIkCGqWLGigoOD9eWXX+p///uf+vbtq4oVK6pFixb6/vvv7dbbsWOH7rzzTnl5eSkoKEjPPPOMLl68eN39sVgsCggIUEBAgG699Va98sorcnNz04EDB+zqefnllzVkyBB5e3vriSeekCRNnDhRDRo0UPny5VWvXj29+OKLunLlit32165dqzZt2sjT01PVqlVT//7986zlgw8+kK+vr7Zs2SJJ+vHHH9WrVy9VrFhR/v7+Gjx4sE6fPi3p7zMr27dv1/z5821nIH7//ffr7quzbN++Xbfffrs8PDxUo0YNTZo0SVevXrUtz+3SsZCQENtxYhiGpk+frtq1a8vDw0OBgYF65plnbH3T09P1/PPPq2bNmqpQoYLatm2rbdu25agjOjpajRs3VsWKFdWzZ0+dPHnStiwrK0szZsxQrVq15OHhoZCQEG3cuPG6+7VhwwY1aNBAXl5e6ty5c4HG89ixY7r//vvl6+urKlWqqG/fvrbtbNq0SZ6enjku9Xv22WfVpUsX2/OCHMcA4GzM98z3zPd5Y77HtQj2MK1ly5apQoUK2rNnj2bPnq0ZM2YoJiZG0t9/YHv16qWdO3fqk08+0aFDhzRr1iyVKVNGkhQXF6f7779fDz74oBISEjR9+nS9+OKLioqKsnuNyMhIdejQQfv371d4eLgGDx6sIUOG6JFHHtG+fft0yy23aMiQITIMQ5J05MgR9ezZUwMHDtSBAwe0YsUK7dixQ6NHj873fmVmZmrZsmWSpNatW9stmzNnjlq2bKn9+/frxRdflCRVqlRJUVFROnTokObPn6/3339fkZGRtnXWr1+v/v37q3fv3tq/f7+2bNmi22+/PdfXnj17tiZNmqRNmzapa9euOnfunLp06aJWrVrp+++/18aNG5WSkqL7779fkjR//nxZrVaNGDHCdgYiKCgo3/taUH/++ad69+6tNm3a6IcfftCiRYu0ZMkSvfLKK/nexqpVqxQZGal3331Xv/zyi9asWaPmzZvblo8ePVqxsbH67LPPdODAAd13333q2bOnfvnlF1ufv/76S3PmzNHHH3+sb775RkePHtXzzz9vWz5//nzNnTtXc+bM0YEDBxQWFqZ77rnHbhvXOnbsmAYMGKA+ffooPj5ejz/+uCZNmuTQ2Fy5ckVhYWGqVKmSvv32W+3cudP2JiQjI0Ndu3aVr6+vVq1aZVsnMzNTK1as0KBBgyQ55zgGAGdhvme+Z77PifkeORiACQwdOtTo27ev7XnHjh2NO+64w65PmzZtjIkTJxqGYRjR0dGGm5ubkZiYmOv2Hn74YaN79+52bePHjzeaNGliex4cHGw88sgjtucnT540JBkvvviirS02NtaQZJw8edIwDMMYPny48cQTT9ht99tvvzXc3NyMS5cu5VrL0qVLDUlGhQoVjAoVKhhubm6Gh4eHsXTpUrt+wcHBRr9+/XLdxrXeeOMNIzQ01PbcarUagwYNyrN/cHCwERkZaUyYMMGoUaOG8eOPP9qWvfzyy0aPHj3s+h87dsyQZBvbjh07Gs8+++wN68qPadOmGW5ubraxyH6UL1/ekGTs37/fMAzD+Ne//mU0bNjQyMrKsq27cOFCo2LFikZmZqbdfl2rZcuWxrRp0wzDMIy5c+caDRo0MDIyMnLU8ccffxhlypQx/vzzT7v2rl27GpMnTzYM4//+v/366692Nfj7+9ueBwYGGq+++qrdNtq0aWM89dRThmEYRlJSkt1+TZ482e4YNAzDmDhxoiHJOHv2bJ7jJslYvXq1YRiG8fHHH+cYm/T0dMPLy8uIjo42DMMwnn32WaNLly625dHR0YaHh4ftNfJzHOc2vgBws5jvme+Z78/mOW7M97ieskX9QQLgLC1atLB7XqNGDZ06dUqSFB8fr1q1aqlBgwa5rnv48GH17dvXrq1Dhw6aN2+eMjMzbZ/0X/sa/v7+kmT3KW9226lTpxQQEKAffvhBBw4c0KeffmrrYxiGsrKylJSUpMaNG+daT6VKlbRv3z5Jf38qvHnzZo0cOVJVq1ZVnz59bP1uu+22HOuuWLFCCxYs0JEjR3ThwgVdvXpV3t7etuXx8fEaMWJErq+bbe7cubp48aK+//571atXz9b+ww8/6Ouvv1bFihVzrHPkyJE8x/efXnvtNb322mu254cOHVLt2rVz7duwYUN9+eWXdm1//vmnOnXqZHt++PBhWa1WWSwWW1uHDh104cIFHT9+PM9tX+u+++7TvHnzVK9ePfXs2VO9e/dWnz59VLZsWSUkJCgzMzPH/qWnp6tq1aq25+XLl9ctt9xie37tMZiWlqYTJ06oQ4cOdtvo0KGDfvjhh1xrOnz4sNq2bWvXZrVab7gv1/rhhx/066+/qlKlSnbtly9f1pEjRyRJgwYNUrt27XTixAkFBgbq008/VXh4uO2uvwU9jgGgMDDf/435/m/M939jvsc/EexhWuXKlbN7brFYlJWVJUny8vJy+mtkTyq5tWW/7oULF/Tkk0/afXcr2/UmHzc3N9WvX9/2vEWLFtq0aZNef/11u4m+QoUKduvFxsZq0KBBeumllxQWFiYfHx999tlndt8fzM9Y3HnnnVq/fr0+//xzu0vBLly4oD59+uj111/PsU6NGjVuuN1sI0eOtF3OJ0mBgYF59nV3d7cbC0kqW9bxP1Vubm62SyazXftdxKCgICUmJmrz5s2KiYnRU089pTfeeEPbt2/XhQsXVKZMGcXFxdne9GW79k1PbsfgP1+zqF24cEGhoaF2k3S26tWrS5LatGmjW265RZ999plGjRql1atX212WWtDjGAAKA/M98/31MN8z3+NvBHuUSC1atNDx48f1888/5/opc+PGjbVz5067tp07d6pBgwY5/rA7onXr1jp06FCOiaogypQpo0uXLl23z65duxQcHKwXXnjB1vbHH3/Y9WnRooW2bNmiYcOG5bmd22+/XaNHj1bPnj1VtmxZ2/fGWrdurVWrVqlOnTp5Trbu7u7KzMy8bp1VqlRRlSpVrtvHEY0bN9aqVatkGIbtzdbOnTtVqVIl1apVS9Lfk9q1N7ZJS0tTUlKS3Xa8vLzUp08f9enTRxEREWrUqJESEhLUqlUrZWZm6tSpU7rzzjsLVKO3t7cCAwO1c+dOdezY0da+c+fOPL/z2Lhx4xxnL3bv3u3Q67Zu3VorVqyQn5+f3Zmcfxo0aJA+/fRT1apVS25ubgoPD7fbhrOOYwAoTMz3/4f5/m/M9/aY70sPbp6HEqljx4666667NHDgQMXExCgpKUlfffWV7Q6lzz33nLZs2aKXX35ZP//8s5YtW6a3337b7kYoBTFx4kTt2rVLo0ePVnx8vH755Rf997//veFNSAzDUHJyspKTk5WUlKT33ntP0dHROS4f/Kdbb71VR48e1WeffaYjR45owYIFWr16tV2fadOm6d///remTZumw4cPKyEhIddP5Nu3b68NGzbopZdest1dNiIiQmfOnNFDDz2k7777TkeOHFF0dLSGDRtmm9zr1KmjPXv26Pfff9fp06dtZzMK01NPPaVjx47p6aef1k8//aT//ve/mjZtmsaNGyc3t7//rHXp0kUff/yxvv32WyUkJGjo0KF2b+KioqK0ZMkS/fjjj/rtt9/0ySefyMvLS8HBwWrQoIEGDRqkIUOG6IsvvlBSUpL27t2rmTNnav369fmuc/z48Xr99de1YsUKJSYmatKkSYqPj9ezzz6ba/+RI0fql19+0fjx45WYmKjly5fnuMHTjQwaNEjVqlVT37599e233yopKUnbtm3TM888o+PHj9v127dvn1599VXde++98vDwsC0r6HEMAEWN+f7/MN8z3zPfl3Ku+Wo/4Jjcbqbzzxu49O3b1xg6dKjt+f/7f//PGDZsmFG1alXD09PTaNasmbFu3Trb8v/85z9GkyZNjHLlyhm1a9c23njjDbvt5XazEF1z0xLDyHkzFMMwjL179xrdu3c3KlasaFSoUMFo0aJFjhuqXCv7pizZDw8PD6NBgwbGq6++aly9evW69RjG3zcBqlq1qlGxYkXjgQceMCIjIw0fHx+7PqtWrTJCQkIMd3d3o1q1asaAAQPy3O727duNChUqGAsWLDAMwzB+/vlno3///oavr6/h5eVlNGrUyBgzZoztZi2JiYlGu3btDC8vL0OSkZSUlOe+3si0adOMli1b5mjPbZy3bdtmtGnTxnB3dzcCAgKMiRMnGleuXLEtT01NNR544AHD29vbCAoKMqKiouxuprN69Wqjbdu2hre3t1GhQgWjXbt2xubNm23rZ2RkGFOnTjXq1KljlCtXzqhRo4bRv39/48CBA4Zh/P3/7Z/jvHr1auPaP6uZmZnG9OnTjZo1axrlypUzWrZsaXz11VfX3a+1a9ca9evXNzw8PIw777zT+PDDDx26mY5h/H3jpyFDhhjVqlUzPDw8jHr16hkjRowwUlNT7da7/fbbDUnG1q1bc2zzRscxN9MBUBiY75nvme/P5jluzPe4HothuPgLIgAAAAAAoMC4FB8AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATOz/AwzaZZveD4f3AAAAAElFTkSuQmCC",
+      "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"
+    }
+   ],
    "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",
@@ -952,9 +998,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "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": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# get the % of households in each income bracket for the nts\n",
     "nts_households['HHIncome2002_B02ID'].value_counts(normalize=True) * 100"
@@ -969,7 +1031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -989,9 +1051,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "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": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Employment status\n",
     "\n",
@@ -1010,9 +1106,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "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": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# We will only use '1' and '2' for the employment status\n",
     "\n",
@@ -1034,452 +1235,121392 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We want to match the SPC values to the NTS\n",
-    "dict_nts['HHoldEmploy_B01ID']\n",
-    "'''\n",
-    "{\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'}\n",
-    " '''\n",
-    "\n",
-    "# 1) Match each row to the NTS\n",
-    "\n",
-    "# Define the conditions and outputs.\n",
-    "# We are using the keys in dict_nts['HHoldEmploy_B01ID'] as reference\n",
-    "conditions = [\n",
-    "    (counts_df['pwkstat_FT_hh'] == 0) & (counts_df['pwkstat_PT_hh'] == 0),\n",
-    "    (counts_df['pwkstat_FT_hh'] == 0) & (counts_df['pwkstat_PT_hh'] == 1),\n",
-    "    (counts_df['pwkstat_FT_hh'] == 1) & (counts_df['pwkstat_PT_hh'] == 0),\n",
-    "    (counts_df['pwkstat_FT_hh'] == 0) & (counts_df['pwkstat_PT_hh'] == 2),\n",
-    "    (counts_df['pwkstat_FT_hh'] == 1) & (counts_df['pwkstat_PT_hh'] == 1),\n",
-    "    (counts_df['pwkstat_FT_hh'] == 2) & (counts_df['pwkstat_PT_hh'] == 0),\n",
-    "    (counts_df['pwkstat_FT_hh'] == 1) & (counts_df['pwkstat_PT_hh'] >= 2),\n",
-    "    (counts_df['pwkstat_FT_hh'] == 2) & (counts_df['pwkstat_PT_hh'] >= 1),\n",
-    "    (counts_df['pwkstat_FT_hh'] == 0) & (counts_df['pwkstat_PT_hh'] >= 3),\n",
-    "    (counts_df['pwkstat_FT_hh'] >= 3) & (counts_df['pwkstat_PT_hh'] == 0),\n",
-    "    (counts_df['pwkstat_FT_hh'] >= 3) & (counts_df['pwkstat_PT_hh'] >= 1)\n",
-    "]\n",
-    "\n",
-    "# Define the corresponding outputs based on dict_nts['HHoldEmploy_B01ID]\n",
-    "outputs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
-    "\n",
-    "# Create a new column using np.select\n",
-    "counts_df['pwkstat_NTS_match'] = np.select(conditions,\n",
-    "                                           outputs,\n",
-    "                                           default= -8)\n",
-    "\n",
-    "\n",
-    "\n",
-    "# 2) merge back onto the spc\n",
-    "spc_edited = spc_edited.merge(counts_df, left_on='household', right_index=True)\n",
-    "\n",
-    "# check the output\n",
-    "spc_edited[['household', 'pwkstat', 'pwkstat_FT_hh', 'pwkstat_PT_hh', 'pwkstat_NTS_match']].head(10)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "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",
-    "ax[0].bar(counts_df['pwkstat_NTS_match'].value_counts().index, counts_df['pwkstat_NTS_match'].value_counts().values)\n",
-    "ax[0].set_title('SPC')\n",
-    "ax[0].set_xlabel('Employment status - Household level')\n",
-    "ax[0].set_ylabel('Frequency')\n",
-    "ax[1].bar(nts_households['HHoldEmploy_B01ID'].value_counts().index, nts_households['HHoldEmploy_B01ID'].value_counts().values)\n",
-    "ax[1].set_title('NTS')\n",
-    "ax[1].set_xlabel('Employment status - Household level')\n",
-    "plt.show()\n",
-    "\n",
-    "# same as above but percentages\n",
-    "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
-    "ax[0].bar(counts_df['pwkstat_NTS_match'].value_counts().index, counts_df['pwkstat_NTS_match'].value_counts(normalize=True).values)\n",
-    "ax[0].set_title('SPC')\n",
-    "ax[0].set_xlabel('Employment status - Household level')\n",
-    "ax[0].set_ylabel('Frequency (normalized)')\n",
-    "ax[1].bar(nts_households['HHoldEmploy_B01ID'].value_counts().index, nts_households['HHoldEmploy_B01ID'].value_counts(normalize=True).values)\n",
-    "ax[1].set_title('NTS')\n",
-    "ax[1].set_xlabel('Employment status - Household level')\n",
-    "plt.show()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 22,
    "metadata": {},
-   "source": [
-    "#### Urban Rural Classification\n",
-    "\n",
-    "We use the 2011 rural urban classification to match the SPC to the NTS. The NTS has 2 columns that we can use to match to the SPC: `Settlement2011EW_B03ID` and `Settlement2011EW_B04ID`. The `Settlement2011EW_B03ID` column is more general (urban / rural only), while the `Settlement2011EW_B04ID` column is more specific. We stick to the more general column for now."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "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": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We want to match the SPC values to the NTS\n",
+    "dict_nts['HHoldEmploy_B01ID']\n",
+    "'''\n",
+    "{\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'}\n",
+    " '''\n",
+    "\n",
+    "# 1) Match each row to the NTS\n",
+    "\n",
+    "# Define the conditions and outputs.\n",
+    "# We are using the keys in dict_nts['HHoldEmploy_B01ID'] as reference\n",
+    "conditions = [\n",
+    "    (counts_df['pwkstat_FT_hh'] == 0) & (counts_df['pwkstat_PT_hh'] == 0),\n",
+    "    (counts_df['pwkstat_FT_hh'] == 0) & (counts_df['pwkstat_PT_hh'] == 1),\n",
+    "    (counts_df['pwkstat_FT_hh'] == 1) & (counts_df['pwkstat_PT_hh'] == 0),\n",
+    "    (counts_df['pwkstat_FT_hh'] == 0) & (counts_df['pwkstat_PT_hh'] == 2),\n",
+    "    (counts_df['pwkstat_FT_hh'] == 1) & (counts_df['pwkstat_PT_hh'] == 1),\n",
+    "    (counts_df['pwkstat_FT_hh'] == 2) & (counts_df['pwkstat_PT_hh'] == 0),\n",
+    "    (counts_df['pwkstat_FT_hh'] == 1) & (counts_df['pwkstat_PT_hh'] >= 2),\n",
+    "    (counts_df['pwkstat_FT_hh'] == 2) & (counts_df['pwkstat_PT_hh'] >= 1),\n",
+    "    (counts_df['pwkstat_FT_hh'] == 0) & (counts_df['pwkstat_PT_hh'] >= 3),\n",
+    "    (counts_df['pwkstat_FT_hh'] >= 3) & (counts_df['pwkstat_PT_hh'] == 0),\n",
+    "    (counts_df['pwkstat_FT_hh'] >= 3) & (counts_df['pwkstat_PT_hh'] >= 1)\n",
+    "]\n",
+    "\n",
+    "# Define the corresponding outputs based on dict_nts['HHoldEmploy_B01ID]\n",
+    "outputs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
+    "\n",
+    "# Create a new column using np.select\n",
+    "counts_df['pwkstat_NTS_match'] = np.select(conditions,\n",
+    "                                           outputs,\n",
+    "                                           default= -8)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# 2) merge back onto the spc\n",
+    "spc_edited = spc_edited.merge(counts_df, left_on='household', right_index=True)\n",
+    "\n",
+    "# check the output\n",
+    "spc_edited[['household', 'pwkstat', 'pwkstat_FT_hh', 'pwkstat_PT_hh', 'pwkstat_NTS_match']].head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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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ECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMpE0H+rbfeUkhIiDw8PBQeHq5169YV2nbRokUKCwtT5cqV5eXlpdDQUL333ns2bQzDUHx8vAIDA+Xp6amIiAjt2rWrpA8DAAAAAIAS5/Qgv2DBAsXGxiohIUGbNm1SixYtFBkZqaNHjxbYvkqVKnr++eeVlpambdu2KTo6WtHR0fryyy+tbSZOnKjXX39dycnJWrt2rby8vBQZGalz586V1mEBAAAAAFAinB7kp0yZokGDBik6OlpNmjRRcnKyKlSooJkzZxbYvmPHjurRo4caN26sunXraujQoWrevLnWrFkj6eLV+KSkJL3wwgu699571bx5c82dO1eHDx/WkiVLSvHIAAAAAABwPKcG+fPnz2vjxo2KiIiwrnNxcVFERITS0tKu+H7DMJSamqqdO3fq9ttvlyTt3btX6enpNn36+PgoPDy80D5zcnKUmZlpswAAAMfhNjoAABzHqUH++PHjys3Nlb+/v816f39/paenF/q+06dPq2LFinJzc1O3bt30xhtv6I477pAk6/vs6TMxMVE+Pj7WJTg4+FoOCwAAXIbb6AAAcCynT62/GpUqVdKWLVu0fv16vfzyy4qNjdWqVauuur+4uDidPn3auhw4cMBxxQIAcIPjNjoAABzLqUG+WrVqcnV1VUZGhs36jIwMBQQEFPo+FxcX1atXT6GhoXrmmWfUq1cvJSYmSpL1ffb06e7uLm9vb5sFAABcO26jAwDA8Zwa5N3c3NS6dWulpqZa1+Xl5Sk1NVVt27Ytdj95eXnKycmRJNWuXVsBAQE2fWZmZmrt2rV29QkAAK4dt9EBAOB45ZxdQGxsrAYMGKCwsDC1adNGSUlJys7OVnR0tCSpf//+qlGjhvWKe2JiosLCwlS3bl3l5ORo2bJleu+99zR16lRJksVi0bBhwzRu3DjVr19ftWvX1osvvqigoCB1797dWYcJAADscOk2uqysLKWmpio2NlZ16tRRx44dr6q/uLg4xcbGWl9nZmYS5gEApuX0IN+nTx8dO3ZM8fHxSk9PV2hoqFJSUqxn2ffv3y8Xl78nDmRnZ+uJJ57QwYMH5enpqUaNGun9999Xnz59rG1GjBih7OxsPfbYYzp16pTat2+vlJQUeXh4lPrxAQBwI7vW2+gkKTQ0VNu3b1diYqI6duxocxtdYGCgTZ+hoaEF9ufu7i53d/drPBoAAMoGpwd5SYqJiVFMTEyB2/75ELtx48Zp3LhxRfZnsVj00ksv6aWXXnJUiQAA4CpcfhvdpZlxl26jK2zsL0hht9FdCu6XbqMbPHiwow8BAIAyp0wEeQAAcP3iNjoAAByLIA8AAEoUt9EBAOBYFsMwDGcXUdZkZmbKx8dHp0+fdshX0YWMXOqAquyzb3y3Ut/n1eCzAYDicfTYdKMric+ztMc0xjMAuL7YMzY59evnAAAAAACAfQjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCEEeAAAAAAATIcgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJnJVQf7ChQs6cOCAdu7cqRMnTlxzEW+99ZZCQkLk4eGh8PBwrVu3rtC2M2bM0G233SZfX1/5+voqIiIiX/tHHnlEFovFZomKirrmOgEAAAAAcLZiB/kzZ85o6tSp6tChg7y9vRUSEqLGjRurevXqqlWrlgYNGqT169fbXcCCBQsUGxurhIQEbdq0SS1atFBkZKSOHj1aYPtVq1bpwQcf1MqVK5WWlqbg4GB16dJFhw4dsmkXFRWlI0eOWJcPP/zQ7toAAAAAAChrihXkp0yZopCQEM2aNUsRERFasmSJtmzZol9//VVpaWlKSEjQX3/9pS5duigqKkq7du0qdgFTpkzRoEGDFB0drSZNmig5OVkVKlTQzJkzC2w/b948PfHEEwoNDVWjRo30zjvvKC8vT6mpqTbt3N3dFRAQYF18fX2LXRMAAHAsZt8BAOA45YrTaP369fr222918803F7i9TZs2evTRR5WcnKxZs2Zp9erVql+//hX7PX/+vDZu3Ki4uDjrOhcXF0VERCgtLa1YB3D27FlduHBBVapUsVm/atUq+fn5ydfXV//61780btw4Va1atcA+cnJylJOTY32dmZlZrH0DAIAruzT7Ljk5WeHh4UpKSlJkZKR27twpPz+/fO0vzb5r166dPDw8NGHCBHXp0kU///yzatSoYW0XFRWlWbNmWV+7u7uXyvEAAOBsxQryxZ2W7u7urscff7zYOz9+/Lhyc3Pl7+9vs97f3187duwoVh/PPfecgoKCFBERYV0XFRWlnj17qnbt2tqzZ49GjRqlO++8U2lpaXJ1dc3XR2JiosaMGVPsugEAQPFdPvtOkpKTk7V06VLNnDlTI0eOzNd+3rx5Nq/feecdffzxx0pNTVX//v2t6y/NvgMA4EZj6qfWjx8/XvPnz9fixYvl4eFhXf/AAw/onnvuUbNmzdS9e3d9/vnnWr9+vVatWlVgP3FxcTp9+rR1OXDgQCkdAQAA17dLs+8uP+Hu6Nl3DRs21ODBg/XHH38U2kdOTo4yMzNtFgAAzKpYV+R79uxZ7A4XLVpU7LbVqlWTq6urMjIybNZnZGRc8Qz7pEmTNH78eK1YsULNmzcvsm2dOnVUrVo17d69W507d8633d3dnel4AACUAGbfAQDgeMW6Iu/j42NdvL29lZqaqg0bNli3b9y4UampqfLx8bFr525ubmrdurXNg+ouPbiubdu2hb5v4sSJGjt2rFJSUhQWFnbF/Rw8eFB//PGHAgMD7aoPAAA4F7PvAADIr1hX5C9/kMxzzz2n+++/X8nJydYz3rm5uXriiSfk7e1tdwGxsbEaMGCAwsLC1KZNGyUlJSk7O9t6H13//v1Vo0YNJSYmSpImTJig+Ph4ffDBBwoJCVF6erokqWLFiqpYsaKysrI0ZswY3XfffQoICNCePXs0YsQI1atXT5GRkXbXBwAArh6z7wAAcDy775GfOXOmnn32WZtpa66uroqNjS30K+OK0qdPH02aNEnx8fEKDQ3Vli1blJKSYp2Ct3//fh05csTafurUqTp//rx69eqlwMBA6zJp0iRrLdu2bdM999yjBg0aaODAgWrdurVWr17NAA4AQClj9h0AAI5XrCvyl/vrr7+0Y8cONWzY0Gb9jh07lJeXd1VFxMTEKCYmpsBt/5wit2/fviL78vT01JdffnlVdQAAAMdj9h0AAI5ld5CPjo7WwIEDtWfPHrVp00aStHbtWo0fP946IAMAAFzSp08fHTt2TPHx8UpPT1doaGi+2XcuLn9PErx89t3lEhISNHr0aOvsuzlz5ujUqVMKCgpSly5dNHbsWGbfAQBuCHYH+UmTJikgIECTJ0+2TnkPDAzU8OHD9cwzzzi8QAAAYH7MvgMAwHHsDvIuLi4aMWKERowYYf0O1qt5yB0AAAAAALCf3Q+7ky7eJ79ixQp9+OGHslgskqTDhw8rKyvLocUBAAAAAABbdl+R//333xUVFaX9+/crJydHd9xxhypVqqQJEyYoJydHycnJJVEnAAAAAADQVVyRHzp0qMLCwnTy5El5enpa1/fo0cPmq2UAAAAAAIDj2X1FfvXq1fr+++/l5uZmsz4kJESHDh1yWGEAAAAAACA/u6/I5+XlKTc3N9/6gwcPqlKlSg4pCgAAAAAAFMzuIN+lSxclJSVZX1ssFmVlZSkhIUFdu3Z1ZG0AAAAAAOAf7J5aP3nyZEVGRqpJkyY6d+6cHnroIe3atUvVqlXThx9+WBI1AgAAAACA/7E7yN90003aunWrFixYoK1btyorK0sDBw5U3759bR5+BwAAAAAAHM/uIC9J5cqVU9++fdW3b19H1wMAAAAAAIpg9z3yrq6u6tSpk06cOGGzPiMjQ66urg4rDAAAAAAA5Gd3kDcMQzk5OQoLC9PPP/+cbxsAAAAAACg5dgd5i8Wijz/+WHfffbfatm2rTz75xGYbAAAAAAAoOVd1Rd7V1VWvvfaaJk2apD59+mjcuHFcjQcAAAAAoBRc1cPuLnnsscdUv3599e7dW99++62jagIAAAAAAIWw+4p8rVq1bB5q16lTJ/3www86cOCAQwsDAAAAAAD52X1Ffu/evfnW1atXT5s3b1ZGRoZDigIAAAAAAAWz+4p8YTw8PFSrVi1HdQcAAAAAAApQrCvyVapU0a+//qpq1arJ19e3yKfT//P75QEAAAAAgOMUK8i/+uqrqlSpkiQpKSmpJOsBAAAAAABFKFaQHzBgQIF/BgAAAAAApatYQT4zM7PYHXp7e191MQAAAAAAoGjFCvKVK1cu8r54STIMQxaLRbm5uQ4pDAAAAAAA5FesIL9y5cqSrgMAAAAAABRDsYJ8hw4dSroOAAAAAABQDMUK8gU5e/as9u/fr/Pnz9usb968+TUXBQAAAAAACmZ3kD927Jiio6P1xRdfFLide+QBAAAAACg5Lva+YdiwYTp16pTWrl0rT09PpaSkaM6cOapfv74+/fTTkqgRAAAAAAD8j91X5L/++mt98sknCgsLk4uLi2rVqqU77rhD3t7eSkxMVLdu3UqiTgAAAAAAoKu4Ip+dnS0/Pz9Jkq+vr44dOyZJatasmTZt2uTY6gAAAAAAgA27g3zDhg21c+dOSVKLFi00bdo0HTp0SMnJyQoMDHR4gQAAAAAA4G92T60fOnSojhw5IklKSEhQVFSU5s2bJzc3N82ePdvR9QEAAAAAgMvYHeT79etn/XPr1q31+++/a8eOHapZs6aqVavm0OIAAAAAAICtq/4e+UsqVKigVq1aOaIWAAAAAABwBXYHecMw9H//939auXKljh49qry8PJvtixYtclhxAAAAAADAlt1BftiwYZo2bZo6deokf39/WSyWkqgLAAAAAAAUwO4g/95772nRokXq2rVrSdQDAAAAAACKYPfXz/n4+KhOnTolUQsAAAAAALgCu4P86NGjNWbMGP35558lUQ8AAAAAACiC3VPr77//fn344Yfy8/NTSEiIypcvb7N906ZNDisOAAAAAADYsjvIDxgwQBs3blS/fv142B0AAAAAAKXM7iC/dOlSffnll2rfvn1J1AMAAAAAAIpg9z3ywcHB8vb2LolaAAAAAADAFdgd5CdPnqwRI0Zo3759JVAOAAAAAAAoit1T6/v166ezZ8+qbt26qlChQr6H3Z04ccJhxQEAAAAAAFt2B/mkpKQSKAMAAAAAABSHXUH+woUL+uabb/Tiiy+qdu3aJVUTAAAAAAAohF33yJcvX14ff/xxSdUCAAAAAACuwO6H3XXv3l1LlixxaBFvvfWWQkJC5OHhofDwcK1bt67QtjNmzNBtt90mX19f+fr6KiIiIl97wzAUHx+vwMBAeXp6KiIiQrt27XJozQAAAAAAOIPd98jXr19fL730kr777ju1bt1aXl5eNtuHDBliV38LFixQbGyskpOTFR4erqSkJEVGRmrnzp3y8/PL137VqlV68MEH1a5dO3l4eGjChAnq0qWLfv75Z9WoUUOSNHHiRL3++uuaM2eOateurRdffFGRkZH65Zdf5OHhYe8hAwAAAABQZth9Rf7dd99V5cqVtXHjRk2fPl2vvvqqdbmaB+FNmTJFgwYNUnR0tJo0aaLk5GRVqFBBM2fOLLD9vHnz9MQTTyg0NFSNGjXSO++8o7y8PKWmpkq6eDU+KSlJL7zwgu699141b95cc+fO1eHDhx0+kwAAABQPs+8AAHAcu4P83r17C11+++03u/o6f/68Nm7cqIiIiL8LcnFRRESE0tLSitXH2bNndeHCBVWpUsVaX3p6uk2fPj4+Cg8PL7TPnJwcZWZm2iwAAMAxLs2+S0hI0KZNm9SiRQtFRkbq6NGjBba/NPtu5cqVSktLU3BwsLp06aJDhw5Z21yafZecnKy1a9fKy8tLkZGROnfuXGkdFgAATmN3kL+cYRgyDOOq33/8+HHl5ubK39/fZr2/v7/S09OL1cdzzz2noKAga3C/9D57+kxMTJSPj491CQ4OtvdQAABAIZh9BwCAY11VkJ87d66aNWsmT09PeXp6qnnz5nrvvfccXdsVjR8/XvPnz9fixYuv6d73uLg4nT592rocOHDAgVUCAHDjYvYdAACOZ/fD7qZMmaIXX3xRMTExuvXWWyVJa9as0eOPP67jx4/r6aefLnZf1apVk6urqzIyMmzWZ2RkKCAgoMj3Tpo0SePHj9eKFSvUvHlz6/pL78vIyFBgYKBNn6GhoQX25e7uLnd392LXDQAAiqeo2Xc7duwoVh+Omn03ZswYe8uHg4SMXFqq+9s3vlup7g8ASpvdV+TfeOMNTZ06VRMmTNA999yje+65RxMnTtTbb7+t119/3a6+3Nzc1Lp1a+tUOUnWqXNt27Yt9H0TJ07U2LFjlZKSorCwMJtttWvXVkBAgE2fmZmZWrt2bZF9AgCAsofZdwAA5Gf3FfkjR46oXbt2+da3a9dOR44csbuA2NhYDRgwQGFhYWrTpo2SkpKUnZ2t6OhoSVL//v1Vo0YNJSYmSpImTJig+Ph4ffDBBwoJCbGeea9YsaIqVqwoi8WiYcOGady4capfv7716+eCgoLUvXt3u+sDAABXj9l3AAA4nt1X5OvVq6ePPvoo3/oFCxaofv36dhfQp08fTZo0SfHx8QoNDdWWLVuUkpJinS63f/9+mxMEU6dO1fnz59WrVy8FBgZal0mTJlnbjBgxQk899ZQee+wx3XLLLcrKylJKSgrfIQ8AQClj9h0AAI5n9xX5MWPGqE+fPvr222+t98h/9913Sk1NLTDgF0dMTIxiYmIK3LZq1Sqb1/v27btifxaLRS+99JJeeumlq6oHAAA4DrPvAABwLLuD/H333ae1a9fq1VdftX7FS+PGjbVu3Tq1bNnS0fUBAACT69Onj44dO6b4+Hilp6crNDQ03+w7F5e/JwlePvvucgkJCRo9erSki7PvsrOz9dhjj+nUqVNq3749s+8AADcMu4O8JLVu3Vrvv/++o2sBAADXKWbfAQDgOFcV5PPy8rR7924dPXpUeXl5Nttuv/12hxQGAAAAAADyszvI//DDD3rooYf0+++/yzAMm20Wi0W5ubkOKw4AAAAAANiyO8g//vjjCgsL09KlSxUYGCiLxVISdQEAAAAAgALYHeR37dql//u//1O9evVKoh4AAAAAAFAEu79HPjw8XLt37y6JWgAAAAAAwBXYfUX+qaee0jPPPKP09HQ1a9ZM5cuXt9nevHlzhxUHAAAAAABsXdX3yEvSo48+al1nsVhkGAYPuwMAAAAAoITZHeT37t1bEnUAAAAAAIBisDvI16pVqyTqAAAAAOBEISOXlvo+943vVur7BK4HxXrY3Q8//FDsDs+ePauff/75qgsCAAAAAACFK1aQf/jhhxUZGamFCxcqOzu7wDa//PKLRo0apbp162rjxo0OLRIAAAAAAFxUrKn1v/zyi6ZOnaoXXnhBDz30kBo0aKCgoCB5eHjo5MmT2rFjh7KystSjRw8tX75czZo1K+m6AQAAAAC4IRUryJcvX15DhgzRkCFDtGHDBq1Zs0a///67/vzzT7Vo0UJPP/20OnXqpCpVqpR0vQAAAAAA3NDsfthdWFiYwsLCSqIWAAAAAABwBcW6Rx4AAAAAAJQNBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBG7g/xvv/1WEnUAAAAAAIBisDvI16tXT506ddL777+vc+fOlURNAAAAAACgEHYH+U2bNql58+aKjY1VQECA/vOf/2jdunUlURsAAAAAAPgHu4N8aGioXnvtNR0+fFgzZ87UkSNH1L59ezVt2lRTpkzRsWPHSqJOAAAAAACga3jYXbly5dSzZ08tXLhQEyZM0O7du/Xss88qODhY/fv315EjRxxZJwAAAAAA0DUE+Q0bNuiJJ55QYGCgpkyZomeffVZ79uzRV199pcOHD+vee+91ZJ0AAAAAAEBSOXvfMGXKFM2aNUs7d+5U165dNXfuXHXt2lUuLhfPCdSuXVuzZ89WSEiIo2sFAAAAAOCGZ3eQnzp1qh599FE98sgjCgwMLLCNn5+f3n333WsuDgAAAAAA2LI7yO/ateuKbdzc3DRgwICrKggAAAAAABTO7nvkZ82apYULF+Zbv3DhQs2ZM8chRQEAAAAAgILZHeQTExNVrVq1fOv9/Pz0yiuvOKQoAAAAAABQMLuD/P79+1W7du1862vVqqX9+/c7pCgAAAAAAFAwu4O8n5+ftm3blm/91q1bVbVqVYcUBQAAAAAACmZ3kH/wwQc1ZMgQrVy5Urm5ucrNzdXXX3+toUOH6oEHHiiJGgEAAAAAwP/Y/dT6sWPHat++fercubPKlbv49ry8PPXv35975AEAAAAAKGF2B3k3NzctWLBAY8eO1datW+Xp6almzZqpVq1aJVEfAAAAAAC4jN1B/pIGDRqoQYMGjqwFAAAAAABcgd1BPjc3V7Nnz1ZqaqqOHj2qvLw8m+1ff/21w4oDAAAAAAC27A7yQ4cO1ezZs9WtWzc1bdpUFoulJOoCAAAAAAAFsDvIz58/Xx999JG6du1aEvUAAAAAAIAi2P31c25ubqpXr15J1AIAAAAAAK7A7iD/zDPP6LXXXpNhGCVRDwAAAAAAKILdU+vXrFmjlStX6osvvtDNN9+s8uXL22xftGiRw4oDAAAAAAC27A7ylStXVo8ePUqiFgAAAAAAcAV2B/lZs2aVRB0AAAAAAKAY7L5HXpL++usvrVixQtOmTdOZM2ckSYcPH1ZWVpZDiwMAAAAAALbsviL/+++/KyoqSvv371dOTo7uuOMOVapUSRMmTFBOTo6Sk5NLok4AAAAAAKCruCI/dOhQhYWF6eTJk/L09LSu79Gjh1JTUx1aHAAAAAAAsGX3FfnVq1fr+++/l5ubm836kJAQHTp0yGGFAQAAAACA/Oy+Ip+Xl6fc3Nx86w8ePKhKlSo5pCgAAAAAAFAwu4N8ly5dlJSUZH1tsViUlZWlhIQEde3a1ZG1AQAAAACAf7A7yE+ePFnfffedmjRponPnzumhhx6yTqufMGGC3QW89dZbCgkJkYeHh8LDw7Vu3bpC2/7888+67777FBISIovFYnNC4ZLRo0fLYrHYLI0aNbK7LgAAAAAAyiK7g/xNN92krVu3atSoUXr66afVsmVLjR8/Xps3b5afn59dfS1YsECxsbFKSEjQpk2b1KJFC0VGRuro0aMFtj979qzq1Kmj8ePHKyAgoNB+b775Zh05csS6rFmzxq66AACAY3HiHgAAx7H7YXeSVK5cOfXr1++adz5lyhQNGjRI0dHRkqTk5GQtXbpUM2fO1MiRI/O1v+WWW3TLLbdIUoHbL6+vqKD/Tzk5OcrJybG+zszMLPZ7AQBA0S6duE9OTlZ4eLiSkpIUGRmpnTt3FngR4NKJ+969e+vpp58utN+bb75ZK1assL4uV+6q/lkDAIDp2D3izZ07t8jt/fv3L1Y/58+f18aNGxUXF2dd5+LiooiICKWlpdlblo1du3YpKChIHh4eatu2rRITE1WzZs1C2ycmJmrMmDHXtE8AAFCwsnLiHgCA64XdQX7o0KE2ry9cuKCzZ8/Kzc1NFSpUKHaQP378uHJzc+Xv72+z3t/fXzt27LC3LKvw8HDNnj1bDRs21JEjRzRmzBjddttt+umnnwp9qn5cXJxiY2OtrzMzMxUcHHzVNQAAgIvKyol7Zt8BAK4ndgf5kydP5lu3a9cuDR48WMOHD3dIUdfizjvvtP65efPmCg8PV61atfTRRx9p4MCBBb7H3d1d7u7upVUiAAA3jLJy4p7ZdwCA64ndD7srSP369TV+/Ph8V+uLUq1aNbm6uiojI8NmfUZGhkOnyVWuXFkNGjTQ7t27HdYnAABwrjvvvFO9e/dW8+bNFRkZqWXLlunUqVP66KOPCmwfFxen06dPW5cDBw6UcsUAADiOQ4K8dPE+tcOHDxe7vZubm1q3bq3U1FTrury8PKWmpqpt27aOKktZWVnas2ePAgMDHdYnAAAonrJy4t7d3V3e3t42CwAAZmX31PpPP/3U5rVhGDpy5IjefPNN3XrrrXb1FRsbqwEDBigsLExt2rRRUlKSsrOzrQ/D6d+/v2rUqKHExERJF++z++WXX6x/PnTokLZs2aKKFSuqXr16kqRnn31Wd999t2rVqqXDhw8rISFBrq6uevDBB+09VAAAcI0uP3HfvXt3SX+fuI+JiXHYfi6duH/44Ycd1icAAGWV3UH+0iB8icViUfXq1fWvf/1LkydPtquvPn366NixY4qPj1d6erpCQ0OVkpJivY9u//79cnH5e9LA4cOH1bJlS+vrSZMmadKkSerQoYNWrVolSTp48KAefPBB/fHHH6pevbrat2+vH374QdWrV7f3UAEAgANw4h4AAMeyO8jn5eU5tICYmJhCz8hfCueXhISEyDCMIvubP3++o0oDAAAOwIl7AAAcy+4gDwAAYC9O3AMA4Dh2B/nLv2/9SqZMmWJv9wAAAAAAoAh2B/nNmzdr8+bNunDhgho2bChJ+vXXX+Xq6qpWrVpZ21ksFsdVCQAAAAAAJF1FkL/77rtVqVIlzZkzR76+vpKkkydPKjo6WrfddpueeeYZhxcJAAAAAAAusvt75CdPnqzExERriJckX19fjRs3zu6n1gMAAAAAAPvYHeQzMzN17NixfOuPHTumM2fOOKQoAAAAAABQMLuDfI8ePRQdHa1Fixbp4MGDOnjwoD7++GMNHDhQPXv2LIkaAQAAAADA/9h9j3xycrKeffZZPfTQQ7pw4cLFTsqV08CBA/Xf//7X4QUCAAAAAIC/2R3kK1SooLffflv//e9/tWfPHklS3bp15eXl5fDiAAAAAACALbun1l9y5MgRHTlyRPXr15eXl5cMw3BkXQAAAAAAoAB2B/k//vhDnTt3VoMGDdS1a1cdOXJEkjRw4EC+eg4AAAAAgBJmd5B/+umnVb58ee3fv18VKlSwru/Tp49SUlIcWhwAAAAAALBl9z3yy5cv15dffqmbbrrJZn39+vX1+++/O6wwAGVLyMilpb7PfeO7lfo+AQAAgLLO7ivy2dnZNlfiLzlx4oTc3d0dUhQAAAAAACiY3UH+tttu09y5c62vLRaL8vLyNHHiRHXq1MmhxQEAAAAAAFt2T62fOHGiOnfurA0bNuj8+fMaMWKEfv75Z504cULfffddSdQIAAAAAAD+x+4r8k2bNtWvv/6q9u3b695771V2drZ69uypzZs3q27duiVRIwAAAAAA+B+7rshfuHBBUVFRSk5O1vPPP19SNQEAAAAAgELYdUW+fPny2rZtW0nVAgAAAAAArsDuqfX9+vXTu+++WxK1AAAAAACAK7D7YXd//fWXZs6cqRUrVqh169by8vKy2T5lyhSHFQcAAAAAAGzZHeR/+ukntWrVSpL066+/2myzWCyOqQoAAAAAABSo2EH+t99+U+3atbVy5cqSrAcAAAAAABSh2PfI169fX8eOHbO+7tOnjzIyMkqkKAAAAAAAULBiB3nDMGxeL1u2TNnZ2Q4vCAAAAAAAFM7up9YDAAAAAADnKXaQt1gs+R5mx8PtAAAAAAAoXcV+2J1hGHrkkUfk7u4uSTp37pwef/zxfF8/t2jRIsdWCAAAAAAArIod5AcMGGDzul+/fg4vBgAAAAAAFK3YQX7WrFklWQcAAAAAACgGHnYHAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEScHuTfeusthYSEyMPDQ+Hh4Vq3bl2hbX/++Wfdd999CgkJkcViUVJS0jX3CQAAAACAmTg1yC9YsECxsbFKSEjQpk2b1KJFC0VGRuro0aMFtj979qzq1Kmj8ePHKyAgwCF9AgCAkseJewAAHMepQX7KlCkaNGiQoqOj1aRJEyUnJ6tChQqaOXNmge1vueUW/fe//9UDDzwgd3d3h/QJAABKFifuAQBwLKcF+fPnz2vjxo2KiIj4uxgXF0VERCgtLa1U+8zJyVFmZqbNAgAAHIMT9wAAOJbTgvzx48eVm5srf39/m/X+/v5KT08v1T4TExPl4+NjXYKDg69q/wAAwFZZOXHPSXsAwPWknLMLKAvi4uIUGxtrfZ2ZmUmYBwDAAYo6yb5jx45S6zMxMVFjxoy5qv0BJSlk5NJS3d++8d1KdX8ASobTrshXq1ZNrq6uysjIsFmfkZFR6P1wJdWnu7u7vL29bRYAAHD9iIuL0+nTp63LgQMHnF0SAABXzWlB3s3NTa1bt1Zqaqp1XV5enlJTU9W2bdsy0ycAALh6ZeXEPSftAQDXE6c+tT42NlYzZszQnDlztH37dg0ePFjZ2dmKjo6WJPXv319xcXHW9ufPn9eWLVu0ZcsWnT9/XocOHdKWLVu0e/fuYvcJAABKDyfuAQBwPKfeI9+nTx8dO3ZM8fHxSk9PV2hoqFJSUqz3vO3fv18uLn+fazh8+LBatmxpfT1p0iRNmjRJHTp00KpVq4rVJwAAKF2xsbEaMGCAwsLC1KZNGyUlJeU7cV+jRg0lJiZKunji/pdffrH++dKJ+4oVK6pevXrF6hMAgOuZ0x92FxMTo5iYmAK3XQrnl4SEhMgwjGvqEwAAlC5O3AMA4FhOD/IAAOD6x4l7AAAcx6n3yAMAAAAAAPsQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARHjY3Q0oZOTSUt3fvvHdSnV/AAAAAHA944o8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCLlnF0AAAAAHCtk5NJS3+e+8d1KfZ8AcKPiijwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAE+Fhd0AZVtoPK+JBRQAAAEDZxxV5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMhCAPAAAAAICJEOQBAAAAADARgjwAAAAAACZCkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIAwAAAABgIgR5AAAAAABMpEwE+bfeekshISHy8PBQeHi41q1bV2T7hQsXqlGjRvLw8FCzZs20bNkym+2PPPKILBaLzRIVFVWShwAAAAAAQKlwepBfsGCBYmNjlZCQoE2bNqlFixaKjIzU0aNHC2z//fff68EHH9TAgQO1efNmde/eXd27d9dPP/1k0y4qKkpHjhyxLh9++GFpHA4AACgAJ+0BAHAcpwf5KVOmaNCgQYqOjlaTJk2UnJysChUqaObMmQW2f+211xQVFaXhw4ercePGGjt2rFq1aqU333zTpp27u7sCAgKsi6+vb2kcDgAA+AdO2gMA4FhODfLnz5/Xxo0bFRERYV3n4uKiiIgIpaWlFfietLQ0m/aSFBkZma/9qlWr5Ofnp4YNG2rw4MH6448/Cq0jJydHmZmZNgsAAHAMTtoDAOBYTg3yx48fV25urvz9/W3W+/v7Kz09vcD3pKenX7F9VFSU5s6dq9TUVE2YMEHffPON7rzzTuXm5hbYZ2Jionx8fKxLcHDwNR4ZAACQOGkPAEBJKOfsAkrCAw88YP1zs2bN1Lx5c9WtW1erVq1S586d87WPi4tTbGys9XVmZiZhHgAAByjqpP2OHTsKfE9xT9r37NlTtWvX1p49ezRq1CjdeeedSktLk6ura74+ExMTNWbMGAccEQAAzufUIF+tWjW5uroqIyPDZn1GRoYCAgIKfE9AQIBd7SWpTp06qlatmnbv3l1gkHd3d5e7u/tVHAEAAHAGTtoDAG5kTp1a7+bmptatWys1NdW6Li8vT6mpqWrbtm2B72nbtq1Ne0n66quvCm0vSQcPHtQff/yhwMBAxxQOAACKxRkn7Qvi7u4ub29vmwUAALNy+lPrY2NjNWPGDM2ZM0fbt2/X4MGDlZ2drejoaElS//79FRcXZ20/dOhQpaSkaPLkydqxY4dGjx6tDRs2KCYmRpKUlZWl4cOH64cfftC+ffuUmpqqe++9V/Xq1VNkZKRTjhEAgBsVJ+0BAHA8p98j36dPHx07dkzx8fFKT09XaGioUlJSrPfG7d+/Xy4uf59vaNeunT744AO98MILGjVqlOrXr68lS5aoadOmkiRXV1dt27ZNc+bM0alTpxQUFKQuXbpo7NixTJ8HAMAJYmNjNWDAAIWFhalNmzZKSkrKd9K+Ro0aSkxMlHTxpH2HDh00efJkdevWTfPnz9eGDRs0ffp0SRdP2o8ZM0b33XefAgICtGfPHo0YMYKT9gCAG4bTg7wkxcTEWK+o/9OqVavyrevdu7d69+5dYHtPT099+eWXjiwPAABcA07aAwDgWGUiyAMAgOsbJ+0BAHAcp98jDwAAAAAAio8gDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiZRzdgEAAAAAAPMKGbm0VPe3b3y3Ut1fWcQVeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiZRzdgEAAAAAUNaFjFxaqvvbN75bqe4P5sIVeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIXz8HAACAG0Zpf4WYxNeIAXA8rsgDAAAAAGAiBHkAAAAAAEyEIA8AAAAAgIkQ5AEAAAAAMBGCPAAAAAAAJkKQBwAAAADARAjyAAAAAACYCN8jDwAAAKDMCRm5tFT3t298t1LdH3AtuCIPAAAAAICJEOQBAAAAADARptYDAAAAAK4LpX1LhuSc2zII8sBluBcLAAAAQFnH1HoAAAAAAEykTAT5t956SyEhIfLw8FB4eLjWrVtXZPuFCxeqUaNG8vDwULNmzbRs2TKb7YZhKD4+XoGBgfL09FRERIR27dpVkocAAACKwFgPAIDjOD3IL1iwQLGxsUpISNCmTZvUokULRUZG6ujRowW2//777/Xggw9q4MCB2rx5s7p3767u3bvrp59+sraZOHGiXn/9dSUnJ2vt2rXy8vJSZGSkzp07V1qHBQAA/oexHgAAx3L6PfJTpkzRoEGDFB0dLUlKTk7W0qVLNXPmTI0cOTJf+9dee01RUVEaPny4JGns2LH66quv9Oabbyo5OVmGYSgpKUkvvPCC7r33XknS3Llz5e/vryVLluiBBx4ovYMDcEO4UR6qcj3gZ+UcjPUAADiWU4P8+fPntXHjRsXFxVnXubi4KCIiQmlpaQW+Jy0tTbGxsTbrIiMjtWTJEknS3r17lZ6eroiICOt2Hx8fhYeHKy0trcDBPScnRzk5OdbXp0+fliRlZmZe9bFdLi/nrEP6sUdRtZd2PWWpFqls1XOl37GyVE9Z+1k1TfiyFCuRfhoTWei2svbZlCWl/XOSrt+f1aV+DMNwSH+l5UYZ6yX+n12W6ilLtUhlq56yVItUtuox07/LbvTxtSz93kjOGeudGuSPHz+u3Nxc+fv726z39/fXjh07CnxPenp6ge3T09Ot2y+tK6zNPyUmJmrMmDH51gcHBxfvQMognyRnV/C3slSLVLbqKUu1SNRTlLJUi1T26ilLytpn4+h6zpw5Ix8fH8d2WoIY60vO9f67fi3KUi1S2aqnLNUila16ylItEvUUpSzVIjlnrHf61PqyIC4uzubMf15enk6cOKGqVavKYrE4sbKyIzMzU8HBwTpw4IC8vb2dXU6ZwmdTOD6bovH5FI7PJj/DMHTmzBkFBQU5uxRTYqy/Mv7eFY7Ppmh8PoXjsykcn01+9oz1Tg3y1apVk6urqzIyMmzWZ2RkKCAgoMD3BAQEFNn+0n8zMjIUGBho0yY0NLTAPt3d3eXu7m6zrnLlyvYcyg3D29ubv2iF4LMpHJ9N0fh8CsdnY8tMV+IvYaw3H/7eFY7Ppmh8PoXjsykcn42t4o71Tn1qvZubm1q3bq3U1FTrury8PKWmpqpt27YFvqdt27Y27SXpq6++sravXbu2AgICbNpkZmZq7dq1hfYJAABKBmM9AACO5/Sp9bGxsRowYIDCwsLUpk0bJSUlKTs72/pk2/79+6tGjRpKTEyUJA0dOlQdOnTQ5MmT1a1bN82fP18bNmzQ9OnTJUkWi0XDhg3TuHHjVL9+fdWuXVsvvviigoKC1L17d2cdJgAANyzGegAAHMvpQb5Pnz46duyY4uPjlZ6ertDQUKWkpFgfYLN//365uPw9caBdu3b64IMP9MILL2jUqFGqX7++lixZoqZNm1rbjBgxQtnZ2Xrsscd06tQptW/fXikpKfLw8Cj147teuLu7KyEhId+0RPDZFIXPpmh8PoXjs7m+MNabA3/vCsdnUzQ+n8Lx2RSOz+baWAyzfY8NAAAAAAA3MKfeIw8AAAAAAOxDkAcAAAAAwEQI8gAAAAAAmAhBHgAAAAAAEyHIo0iJiYm65ZZbVKlSJfn5+al79+7auXOns8sqk8aPH2/9SiRIhw4dUr9+/VS1alV5enqqWbNm2rBhg7PLcrrc3Fy9+OKLql27tjw9PVW3bl2NHTtWN+JzR7/99lvdfffdCgoKksVi0ZIlS2y2G4ah+Ph4BQYGytPTUxEREdq1a5dzigWuY4z1xcdYb4uxvmCM9bYY70sGQR5F+uabb/Tkk0/qhx9+0FdffaULFy6oS5cuys7OdnZpZcr69es1bdo0NW/e3NmllAknT57UrbfeqvLly+uLL77QL7/8osmTJ8vX19fZpTndhAkTNHXqVL355pvavn27JkyYoIkTJ+qNN95wdmmlLjs7Wy1atNBbb71V4PaJEyfq9ddfV3JystauXSsvLy9FRkbq3LlzpVwpcH1jrC8exnpbjPWFY6y3xXhfMvj6Odjl2LFj8vPz0zfffKPbb7/d2eWUCVlZWWrVqpXefvttjRs3TqGhoUpKSnJ2WU41cuRIfffdd1q9erWzSylz7rrrLvn7++vdd9+1rrvvvvvk6emp999/34mVOZfFYtHixYvVvXt3SRfPzgcFBemZZ57Rs88+K0k6ffq0/P39NXv2bD3wwANOrBa4vjHW58dYnx9jfeEY6wvHeO84XJGHXU6fPi1JqlKlipMrKTuefPJJdevWTREREc4upcz49NNPFRYWpt69e8vPz08tW7bUjBkznF1WmdCuXTulpqbq119/lSRt3bpVa9as0Z133unkysqWvXv3Kj093ebvlY+Pj8LDw5WWlubEyoDrH2N9foz1+THWF46xvvgY769eOWcXAPPIy8vTsGHDdOutt6pp06bOLqdMmD9/vjZt2qT169c7u5Qy5bffftPUqVMVGxurUaNGaf369RoyZIjc3Nw0YMAAZ5fnVCNHjlRmZqYaNWokV1dX5ebm6uWXX1bfvn2dXVqZkp6eLkny9/e3We/v72/dBsDxGOvzY6wvGGN94Rjri4/x/uoR5FFsTz75pH766SetWbPG2aWUCQcOHNDQoUP11VdfycPDw9nllCl5eXkKCwvTK6+8Iklq2bKlfvrpJyUnJ9/wg/tHH32kefPm6YMPPtDNN9+sLVu2aNiwYQoKCrrhPxsAzsdYb4uxvnCM9YVjrEdpYGo9iiUmJkaff/65Vq5cqZtuusnZ5ZQJGzdu1NGjR9WqVSuVK1dO5cqV0zfffKPXX39d5cqVU25urrNLdJrAwEA1adLEZl3jxo21f/9+J1VUdgwfPlwjR47UAw88oGbNmunhhx/W008/rcTERGeXVqYEBARIkjIyMmzWZ2RkWLcBcCzG+vwY6wvHWF84xvriY7y/egR5FMkwDMXExGjx4sX6+uuvVbt2bWeXVGZ07txZP/74o7Zs2WJdwsLC1LdvX23ZskWurq7OLtFpbr311nxfXfTrr7+qVq1aTqqo7Dh79qxcXGz/1+vq6qq8vDwnVVQ21a5dWwEBAUpNTbWuy8zM1Nq1a9W2bVsnVgZcfxjrC8dYXzjG+sIx1hcf4/3VY2o9ivTkk0/qgw8+0CeffKJKlSpZ71Xx8fGRp6enk6tzrkqVKuW7f9DLy0tVq1a94e8rfPrpp9WuXTu98soruv/++7Vu3TpNnz5d06dPd3ZpTnf33Xfr5ZdfVs2aNXXzzTdr8+bNmjJlih599FFnl1bqsrKytHv3buvrvXv3asuWLapSpYpq1qypYcOGady4capfv75q166tF198UUFBQdYn3QJwDMb6wjHWF46xvnCM9bYY70uIARRBUoHLrFmznF1amdShQwdj6NChzi6jTPjss8+Mpk2bGu7u7kajRo2M6dOnO7ukMiEzM9MYOnSoUbNmTcPDw8OoU6eO8fzzzxs5OTnOLq3UrVy5ssD/vwwYMMAwDMPIy8szXnzxRcPf399wd3c3OnfubOzcudO5RQPXIcZ6+zDW/42xvmCM9bYY70sG3yMPAAAAAICJcI88AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwEYI8rmuPPPKIunfv7uwycAMLCQlRUlLSNfVRnN/jjh07atiwYYVuHz16tEJDQ6+pDnvNnj1blStXLtV9ArjxMNbD2RjrK5fqPnERQR4l7pFHHpHFYsm3REVFObs0U1m1apUsFotOnTpV6n3s27dPFotFW7Zsuep9O5LFYtGSJUvyrecfcwDgHIz1jsFY/zfGeqBo5ZxdAG4MUVFRmjVrls06d3d3J1UDAAAcjbEeAEoPV+RRKtzd3RUQEGCz+Pr6WrdbLBZNmzZNd911lypUqKDGjRsrLS1Nu3fvVseOHeXl5aV27dppz5491vdcmj40bdo0BQcHq0KFCrr//vt1+vTpQuvIycnRkCFD5OfnJw8PD7Vv317r16+XJBmGoXr16mnSpEk279myZYssFot279591bVK0ieffKJWrVrJw8NDderU0ZgxY/TXX3/ZfAbvvPOOevTooQoVKqh+/fr69NNPJV08S96pUydJkq+vrywWix555JECj/H333/X3XffLV9fX3l5eenmm2/WsmXLiuwjJSVF7du3V+XKlVW1alXdddddNvXXrl1bktSyZUtZLBZ17NhRUsFTvLp3725T29tvv6369evLw8ND/v7+6tWrV6E/H0cr6uctFTwdbMmSJbJYLNbXW7duVadOnVSpUiV5e3urdevW2rBhg3X7mjVrdNttt8nT01PBwcEaMmSIsrOzbfo8e/asHn30UVWqVEk1a9bU9OnTbbb/+OOP+te//iVPT09VrVpVjz32mLKysgo9ruzsbPXv318VK1ZUYGCgJk+efDUfj9555x01btxYHh4eatSokd5++23rtnbt2um5556zaX/s2DGVL19e3377raSLn++zzz6rGjVqyMvLS+Hh4Vq1atVV1QLA/BjrGesZ6xnrUXoI8igzxo4dq/79+2vLli1q1KiRHnroIf3nP/9RXFycNmzYIMMwFBMTY/Oe3bt366OPPtJnn32mlJQUbd68WU888USh+xgxYoQ+/vhjzZkzR5s2bVK9evUUGRmpEydOyGKx6NFHH813NWHWrFm6/fbbVa9evauudfXq1erfv7+GDh2qX375RdOmTdPs2bP18ssv2+xrzJgxuv/++7Vt2zZ17dpVffv21YkTJxQcHKyPP/5YkrRz504dOXJEr732WoHH+OSTTyonJ0fffvutfvzxR02YMEEVK1Ysso/s7GzFxsZqw4YNSk1NlYuLi3r06KG8vDxJ0rp16yRJK1as0JEjR7Ro0aLCf5CX2bBhg4YMGaKXXnpJO3fuVEpKim6//fZivdcRivp5F1ffvn110003af369dq4caNGjhyp8uXLS5L27NmjqKgo3Xfffdq2bZsWLFigNWvW5Ps9nTx5ssLCwqy/n4MHD9bOnTslXfzsIyMj5evrq/Xr12vhwoVasWJFvj4uN3z4cH3zzTf65JNPtHz5cq1atUqbNm2y67OZN2+e4uPj9fLLL2v79u165ZVX9OKLL2rOnDnW454/f74Mw7C+Z8GCBQoKCtJtt90mSYqJiVFaWprmz5+vbdu2qXfv3oqKitKuXbvsqgXAjYOxnrHe0RjrC8dYf50zgBI2YMAAw9XV1fDy8rJZXn75ZWsbScYLL7xgfZ2WlmZIMt59913rug8//NDw8PCwvk5ISDBcXV2NgwcPWtd98cUXhouLi3HkyBHrvu+9917DMAwjKyvLKF++vDFv3jxr+/PnzxtBQUHGxIkTDcMwjEOHDhmurq7G2rVrrdurVatmzJ49+5pq7dy5s/HKK6/YfC7vvfeeERgYWGi/WVlZhiTjiy++MAzDMFauXGlIMk6ePJn/Q75Ms2bNjNGjRxe4rbh9HDt2zJBk/Pjjj4ZhGMbevXsNScbmzZtt2nXo0MEYOnSozbp7773XGDBggGEYhvHxxx8b3t7eRmZmZpH7s5ckw8PDI9/vVLly5ez6ec+aNcvw8fGx6Xvx4sXG5f9rrFSpks3P/3IDBw40HnvsMZt1q1evNlxcXIw///zTMAzDqFWrltGvXz/r9ry8PMPPz8+YOnWqYRiGMX36dMPX19fIysqytlm6dKnh4uJipKenG4Zh+3t85swZw83Nzfjoo4+s7f/44w/D09Mz38/icgkJCUaLFi2sr+vWrWt88MEHNm3Gjh1rtG3b1jAMwzh69KhRrlw549tvv7Vub9u2rfHcc88ZhmEYv//+u+Hq6mocOnTIpo/OnTsbcXFxhmEU/PkCuD4x1jPWM9Yz1qN0cY88SkWnTp00depUm3VVqlSxed28eXPrn/39/SVJzZo1s1l37tw5ZWZmytvbW5JUs2ZN1ahRw9qmbdu2ysvL086dOxUQEGDT/549e3ThwgXdeuut1nXly5dXmzZttH37dklSUFCQunXrppkzZ6pNmzb67LPPlJOTo969e19TrVu3btV3331nc1Y+NzdX586d09mzZ1WhQoV8/Xp5ecnb21tHjx6VPYYMGaLBgwdr+fLlioiI0H333WfTb0F27dql+Ph4rV27VsePH7eend+/f7+aNm1q1/4vd8cdd6hWrVqqU6eOoqKiFBUVZZ1OWJCKFSta/9yvXz8lJycX2verr76qiIgIm3XPPfeccnNzJRXv510csbGx+ve//6333ntPERER6t27t+rWrSvp4lS8bdu2ad68edb2hmEoLy9Pe/fuVePGjSXZ/lwtFosCAgKsP9ft27erRYsW8vLysra59dZbrb/Hl36/LtmzZ4/Onz+v8PBw67oqVaqoYcOGxT6m7Oxs7dmzRwMHDtSgQYOs6//66y/5+PhIkqpXr64uXbpo3rx5uu2227R3716lpaVp2rRpki5OEczNzVWDBg1s+s7JyVHVqlWLXQuA6wdjPWM9Y/1FjPUoDQR5lAovLy+b6WoFuTSFSZL1vqWC1l0aeErKv//9bz388MN69dVXNWvWLPXp0yffYGRvrVlZWRozZox69uyZb38eHh4F9nupH3uP99///rciIyO1dOlSLV++XImJiZo8ebKeeuqpQt9z9913q1atWpoxY4aCgoKUl5enpk2b6vz580Xuy8XFxWY6liRduHDB+udKlSpp06ZNWrVqlZYvX674+HiNHj1a69evL/CrSi5/Uu6lf8AVJiAgIN/vVKVKlex6Su+V6pcu3p/50EMPaenSpfriiy+UkJCg+fPnq0ePHsrKytJ//vMfDRkyJF/fNWvWtP7ZET9XR7p0T96MGTNs/pEgSa6urtY/9+3bV0OGDNEbb7yhDz74QM2aNbP+IzYrK0uurq7auHGjzXsk23+kAbhxMNYz1jPW/42xHiWNe+Rhavv379fhw4etr3/44Qe5uLgUeMaybt26cnNz03fffWddd+HCBa1fv15NmjSxruvatau8vLw0depUpaSk6NFHH73mOlu1aqWdO3eqXr16+RYXl+L9NXRzc5Mk61noogQHB+vxxx/XokWL9Mwzz2jGjBmF9vHHH39o586deuGFF9S5c2c1btxYJ0+eLNa+q1evriNHjlhf5+bm6qeffrJpU65cOUVERGjixInatm2b9u3bp6+//rrAui//XPz8/K54nEUpzs+7evXqOnPmjM0Dawr62p0GDRro6aef1vLly9WzZ0/rvZWtWrXSL7/8UuDP9dJndiWNGzfW1q1bbWr47rvvivw9Ll++vNauXWtdd/LkSf3666/F2p908SpSUFCQfvvtt3x1X3rYkSTde++9OnfunFJSUvTBBx+ob9++1m0tW7ZUbm6ujh49mq+Pf14hA4BrwVhfMMZ6xvqiMNZf/7gij1KRk5Oj9PR0m3XlypVTtWrVrqlfDw8PDRgwQJMmTVJmZqaGDBmi+++/v8D/uXh5eWnw4MEaPny4qlSpopo1a2rixIk6e/asBg4caG3n6uqqRx55RHFxcapfv77atm17TTVKUnx8vO666y7VrFlTvXr1kouLi7Zu3aqffvpJ48aNK1YftWrVksVi0eeff66uXbvK09OzwLOhw4YN05133qkGDRro5MmTWrlypXXaV0F9+Pr6qmrVqpo+fboCAwO1f/9+jRw50qZPPz8/eXp6KiUlRTfddJM8PDzk4+Ojf/3rX4qNjdXSpUtVt25dTZkyxeYs+eeff67ffvtNt99+u3x9fbVs2TLl5eXZNTXsahXn5x0eHq4KFSpo1KhRGjJkiNauXavZs2db+/jzzz81fPhw9erVS7Vr19bBgwe1fv163XfffZIuTu/7f//v/ykmJkb//ve/5eXlpV9++UVfffWV3nzzzWLV2bdvXyUkJGjAgAEaPXq0jh07pqeeekoPP/xwvql20sUz4AMHDtTw4cNVtWpV+fn56fnnny/2PxIvGTNmjIYMGSIfHx9FRUUpJydHGzZs0MmTJxUbG2v9DLt3764XX3xR27dv14MPPmh9f4MGDdS3b1/1799fkydPVsuWLXXs2DGlpqaqefPm6tatm131ADA/xnrGesb6gjHWo0Q48wZ93BgGDBhgSMq3NGzY0NpGkrF48WLr64IeuPLPh7dceqDH22+/bQQFBRkeHh5Gr169jBMnTtjs+9KDQwzDMP7880/jqaeeMqpVq2a4u7sbt956q7Fu3bp8Ne/Zs8eQZH1QyuWuplbDMIyUlBSjXbt2hqenp+Ht7W20adPGmD59eqH9GoZh+Pj4GLNmzbK+fumll4yAgADDYrFYHzLzTzExMUbdunUNd3d3o3r16sbDDz9sHD9+vMg+vvrqK6Nx48aGu7u70bx5c2PVqlX56pkxY4YRHBxsuLi4GB06dDAM4+IDZQYPHmxUqVLF8PPzMxITE20egLN69WqjQ4cOhq+vr+Hp6Wk0b97cWLBgQYF126Ogz8owru7nvXjxYqNevXqGp6encddddxnTp0+3PgAnJyfHeOCBB4zg4GDDzc3NCAoKMmJiYqwPtzEMw1i3bp1xxx13GBUrVjS8vLyM5s2b2zzcqVatWsarr75qs88WLVoYCQkJ1tfbtm0zOnXqZHh4eBhVqlQxBg0aZJw5c6bQ4zpz5ozRr18/o0KFCoa/v78xceLEAh9GdLl/PgDHMAxj3rx5RmhoqOHm5mb4+voat99+u7Fo0SKbNsuWLTMkGbfffnu+Ps+fP2/Ex8cbISEhRvny5Y3AwECjR48exrZt2wzD4AE4wI2Esf4ixnrG+ksY61HSLIbxj5tGAJMYPXq0lixZUuD0qGu1evVqde7cWQcOHCjwTCkAACh5jPUAUDCm1gOXycnJ0bFjxzR69Gj17t2bgR0AgOsMYz2A6wEPuwMu8+GHH6pWrVo6deqUJk6c6OxyAACAgzHWA7geMLUeAAAAAAAT4Yo8AAAAAAAmQpAHAAAAAMBECPIAAAAAAJgIQR4AAAAAABMhyAMAAAAAYCIEeQAAAAAATIQgDwAAAACAiRDkAQAAAAAwkf8PRIs121Kjdu0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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",
+    "ax[0].bar(counts_df['pwkstat_NTS_match'].value_counts().index, counts_df['pwkstat_NTS_match'].value_counts().values)\n",
+    "ax[0].set_title('SPC')\n",
+    "ax[0].set_xlabel('Employment status - Household level')\n",
+    "ax[0].set_ylabel('Frequency')\n",
+    "ax[1].bar(nts_households['HHoldEmploy_B01ID'].value_counts().index, nts_households['HHoldEmploy_B01ID'].value_counts().values)\n",
+    "ax[1].set_title('NTS')\n",
+    "ax[1].set_xlabel('Employment status - Household level')\n",
+    "plt.show()\n",
+    "\n",
+    "# same as above but percentages\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "ax[0].bar(counts_df['pwkstat_NTS_match'].value_counts().index, counts_df['pwkstat_NTS_match'].value_counts(normalize=True).values)\n",
+    "ax[0].set_title('SPC')\n",
+    "ax[0].set_xlabel('Employment status - Household level')\n",
+    "ax[0].set_ylabel('Frequency (normalized)')\n",
+    "ax[1].bar(nts_households['HHoldEmploy_B01ID'].value_counts().index, nts_households['HHoldEmploy_B01ID'].value_counts(normalize=True).values)\n",
+    "ax[1].set_title('NTS')\n",
+    "ax[1].set_xlabel('Employment status - Household level')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Urban Rural Classification\n",
+    "\n",
+    "We use the 2011 rural urban classification to match the SPC to the NTS. The NTS has 2 columns that we can use to match to the SPC: `Settlement2011EW_B03ID` and `Settlement2011EW_B04ID`. The `Settlement2011EW_B03ID` column is more general (urban / rural only), while the `Settlement2011EW_B04ID` column is more specific. We stick to the more general column for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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>pid_hs</th>\n",
+       "      <th>msoa11cd</th>\n",
+       "      <th>oa11cd</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",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</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",
+       "      <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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</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>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",
+       "      <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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</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>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>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",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  household   pid_hs   msoa11cd     oa11cd members sic1d2007  sic2d2007  \\\n",
+       "0   0          0  2905399  E02002183  E00053954     [0]         J       58.0   \n",
+       "1   1          1  2905308  E02002183  E00053953  [1, 2]         C       25.0   \n",
+       "2   2          1  2907681  E02002183  E00053953  [1, 2]         P       85.0   \n",
+       "3   3          2  2902817  E02002183  E00053689  [3, 4]         C       31.0   \n",
+       "4   4          2  2900884  E02002183  E00053689  [3, 4]         J       62.0   \n",
+       "\n",
+       "   pwkstat  salary_yearly  salary_hourly             hid  accommodation_type  \\\n",
+       "0        6            NaN            NaN  E02002183_0001                 1.0   \n",
+       "1        6            NaN            NaN  E02002183_0002                 3.0   \n",
+       "2        6            NaN            NaN  E02002183_0002                 3.0   \n",
+       "3        1   32857.859375      14.360952  E02002183_0003                 3.0   \n",
+       "4        1   18162.451172       9.439944  E02002183_0003                 3.0   \n",
+       "\n",
+       "   communal_type  num_rooms  central_heat  tenure  num_cars  sex  age_years  \\\n",
+       "0            NaN        2.0          True     2.0         2    1         86   \n",
+       "1            NaN        6.0          True     2.0         2    1         74   \n",
+       "2            NaN        6.0          True     2.0         2    2         68   \n",
+       "3            NaN        6.0          True     2.0         1    1         27   \n",
+       "4            NaN        6.0          True     2.0         1    2         26   \n",
+       "\n",
+       "   ethnicity  nssec8  salary_yearly_hh  salary_yearly_hh_cat  is_adult  \\\n",
+       "0          1     1.0          0.000000                     1         1   \n",
+       "1          3     1.0          0.000000                     1         1   \n",
+       "2          1     1.0          0.000000                     1         1   \n",
+       "3          1     4.0      51020.310547                     3         1   \n",
+       "4          1     4.0      51020.310547                     3         1   \n",
+       "\n",
+       "   num_adults  is_child  num_children  is_pension_age  num_pension_age  \\\n",
+       "0           1         0             0               1                1   \n",
+       "1           2         0             0               1                2   \n",
+       "2           2         0             0               1                2   \n",
+       "3           2         0             0               0                0   \n",
+       "4           2         0             0               0                0   \n",
+       "\n",
+       "   pwkstat_FT_hh  pwkstat_PT_hh  pwkstat_NTS_match     OA11CD  \\\n",
+       "0              0              0                  1  E00053954   \n",
+       "1              0              0                  1  E00053953   \n",
+       "2              0              0                  1  E00053953   \n",
+       "3              2              0                  6  E00053689   \n",
+       "4              2              0                  6  E00053689   \n",
+       "\n",
+       "                   RUC11 RUC11CD  \n",
+       "0    Urban city and town      C1  \n",
+       "1    Urban city and town      C1  \n",
+       "2    Urban city and town      C1  \n",
+       "3  Rural town and fringe      D1  \n",
+       "4  Rural town and fringe      D1  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read the rural urban classification data\n",
+    "rural_urban = pd.read_csv('../data/external/census_2011_rural_urban.csv', sep=',')\n",
+    "\n",
+    "# merge the rural_urban data with the spc\n",
+    "spc_edited = spc_edited.merge(rural_urban[['OA11CD', 'RUC11', 'RUC11CD']], left_on='oa11cd', right_on='OA11CD')\n",
+    "spc_edited.head(5)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create dictionary from the NTS `Settlement2011EW_B03ID` column\n",
+    "Settlement2011EW_B03ID_nts_hh = {\n",
+    "    '1': 'Urban',\n",
+    "    '2': 'Rural',\n",
+    "    '3': 'Scotland',\n",
+    "    '-8': 'NA',\n",
+    "    '-10': 'DEAD'\n",
+    "}\n",
+    "\n",
+    "Settlement2011EW_B04ID_nts_hh = {\n",
+    "    '1': 'Urban Conurbation',\n",
+    "    '2': 'Urban City and Town',\n",
+    "    '3': 'Rural Town and Fringe',\n",
+    "    '4': 'Rural Village, Hamlet and Isolated Dwellings',\n",
+    "    '5': 'Scotland',\n",
+    "    '-8': 'NA',\n",
+    "    '-10': 'DEAD'\n",
+    "}\n",
+    "\n",
+    "\n",
+    "census_2011_to_nts_B03ID = {\n",
+    "    'Urban major conurbation': 'Urban',\n",
+    "    'Urban minor conurbation': 'Urban',\n",
+    "    'Urban city and town': 'Urban',\n",
+    "    'Urban city and town in a sparse setting': 'Urban',\n",
+    "    'Rural town and fringe': 'Rural',\n",
+    "    'Rural town and fringe in a sparse setting': 'Rural',\n",
+    "    'Rural village': 'Rural',\n",
+    "    'Rural village in a sparse setting': 'Rural',\n",
+    "    'Rural hamlets and isolated dwellings': 'Rural',\n",
+    "    'Rural hamlets and isolated dwellings in a sparse setting': 'Rural'\n",
+    "}\n",
+    "\n",
+    "census_2011_to_nts_B04ID = {\n",
+    "    'Urban major conurbation': 'Urban Conurbation',\n",
+    "    'Urban minor conurbation': 'Urban Conurbation',\n",
+    "    'Urban city and town': 'Urban City and Town',\n",
+    "    'Urban city and town in a sparse setting': 'Urban City and Town',\n",
+    "    'Rural town and fringe': 'Rural Town and Fringe',\n",
+    "    'Rural town and fringe in a sparse setting': 'Rural Town and Fringe',\n",
+    "    'Rural village': 'Rural Village, Hamlet and Isolated Dwellings',\n",
+    "    'Rural village in a sparse setting': 'Rural Village, Hamlet and Isolated Dwellings',\n",
+    "    'Rural hamlets and isolated dwellings': 'Rural Village, Hamlet and Isolated Dwellings',\n",
+    "    'Rural hamlets and isolated dwellings in a sparse setting': 'Rural Village, Hamlet and Isolated Dwellings'\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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",
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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>pid_hs</th>\n",
+       "      <th>msoa11cd</th>\n",
+       "      <th>oa11cd</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</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",
+       "      <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>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</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>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",
+       "      <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>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>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>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",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  household   pid_hs   msoa11cd     oa11cd members sic1d2007  sic2d2007  \\\n",
+       "0   0          0  2905399  E02002183  E00053954     [0]         J       58.0   \n",
+       "1   1          1  2905308  E02002183  E00053953  [1, 2]         C       25.0   \n",
+       "2   2          1  2907681  E02002183  E00053953  [1, 2]         P       85.0   \n",
+       "3   3          2  2902817  E02002183  E00053689  [3, 4]         C       31.0   \n",
+       "4   4          2  2900884  E02002183  E00053689  [3, 4]         J       62.0   \n",
+       "\n",
+       "   pwkstat  salary_yearly  salary_hourly             hid  accommodation_type  \\\n",
+       "0        6            NaN            NaN  E02002183_0001                 1.0   \n",
+       "1        6            NaN            NaN  E02002183_0002                 3.0   \n",
+       "2        6            NaN            NaN  E02002183_0002                 3.0   \n",
+       "3        1   32857.859375      14.360952  E02002183_0003                 3.0   \n",
+       "4        1   18162.451172       9.439944  E02002183_0003                 3.0   \n",
+       "\n",
+       "   communal_type  num_rooms  central_heat  tenure  num_cars  sex  age_years  \\\n",
+       "0            NaN        2.0          True     2.0         2    1         86   \n",
+       "1            NaN        6.0          True     2.0         2    1         74   \n",
+       "2            NaN        6.0          True     2.0         2    2         68   \n",
+       "3            NaN        6.0          True     2.0         1    1         27   \n",
+       "4            NaN        6.0          True     2.0         1    2         26   \n",
+       "\n",
+       "   ethnicity  nssec8  salary_yearly_hh  salary_yearly_hh_cat  is_adult  \\\n",
+       "0          1     1.0          0.000000                     1         1   \n",
+       "1          3     1.0          0.000000                     1         1   \n",
+       "2          1     1.0          0.000000                     1         1   \n",
+       "3          1     4.0      51020.310547                     3         1   \n",
+       "4          1     4.0      51020.310547                     3         1   \n",
+       "\n",
+       "   num_adults  is_child  num_children  is_pension_age  num_pension_age  \\\n",
+       "0           1         0             0               1                1   \n",
+       "1           2         0             0               1                2   \n",
+       "2           2         0             0               1                2   \n",
+       "3           2         0             0               0                0   \n",
+       "4           2         0             0               0                0   \n",
+       "\n",
+       "   pwkstat_FT_hh  pwkstat_PT_hh  pwkstat_NTS_match     OA11CD  \\\n",
+       "0              0              0                  1  E00053954   \n",
+       "1              0              0                  1  E00053953   \n",
+       "2              0              0                  1  E00053953   \n",
+       "3              2              0                  6  E00053689   \n",
+       "4              2              0                  6  E00053689   \n",
+       "\n",
+       "                   RUC11 RUC11CD Settlement2011EW_B03ID_spc  \\\n",
+       "0    Urban city and town      C1                      Urban   \n",
+       "1    Urban city and town      C1                      Urban   \n",
+       "2    Urban city and town      C1                      Urban   \n",
+       "3  Rural town and fringe      D1                      Rural   \n",
+       "4  Rural town and fringe      D1                      Rural   \n",
+       "\n",
+       "  Settlement2011EW_B04ID_spc  Settlement2011EW_B03ID_spc_CD  \\\n",
+       "0        Urban City and Town                              1   \n",
+       "1        Urban City and Town                              1   \n",
+       "2        Urban City and Town                              1   \n",
+       "3      Rural Town and Fringe                              2   \n",
+       "4      Rural Town and Fringe                              2   \n",
+       "\n",
+       "   Settlement2011EW_B04ID_spc_CD  \n",
+       "0                              2  \n",
+       "1                              2  \n",
+       "2                              2  \n",
+       "3                              3  \n",
+       "4                              3  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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",
+    "spc_edited['Settlement2011EW_B04ID_spc'] = spc_edited['RUC11'].map(census_2011_to_nts_B04ID)\n",
+    "spc_edited.head()\n",
+    "\n",
+    "# add the keys from nts_Settlement2011EW_B03ID and nts_Settlement2011EW_B04ID to the spc based on above mappings\n",
+    "\n",
+    "# reverse the dictionaries\n",
+    "Settlement2011EW_B03ID_nts_rev = {v: k for k, v in Settlement2011EW_B03ID_nts_hh.items()}\n",
+    "# map the values\n",
+    "spc_edited['Settlement2011EW_B03ID_spc_CD'] = spc_edited['Settlement2011EW_B03ID_spc'].map(Settlement2011EW_B03ID_nts_rev).astype('int')\n",
+    "\n",
+    "Settlement2011EW_B04ID_nts_rev = {v: k for k, v in Settlement2011EW_B04ID_nts_hh.items()}\n",
+    "spc_edited['Settlement2011EW_B04ID_spc_CD'] = spc_edited['Settlement2011EW_B04ID_spc'].map(Settlement2011EW_B04ID_nts_rev).astype('int')\n",
+    "spc_edited.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 Edit NTS columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Number of people of pension age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nts_pensioners = count_per_group(df = nts_individuals,\n",
+    "                                 group_col='HouseholdID',\n",
+    "                                 count_col='OfPenAge_B01ID',\n",
+    "                                 values=[1],\n",
+    "                                 value_names=['num_pension_age_nts'])\n",
+    "\n",
+    "nts_pensioners.head()\n",
+    "\n",
+    "# join onto the nts household df\n",
+    "nts_households = nts_households.merge(nts_pensioners, left_on='HouseholdID', right_index=True, how='left')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Number of cars\n",
+    "\n",
+    "- `SPC.num_cars` only has values [0, 1, 2]. 2 is for all households with 2 or more cars\n",
+    "- `NTS.NumCar` is more detailed. It has the actual value of the number of cars. We will cap this at 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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>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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142955</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142956</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <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",
+       "    </tr>\n",
+       "    <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",
+       "    </tr>\n",
+       "    <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": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create a new column in NTS\n",
+    "nts_households.loc[:, 'NumCar_SPC_match'] = nts_households['NumCar'].apply(truncate_values, upper = 2)\n",
+    "\n",
+    "nts_households[['NumCar', 'NumCar_SPC_match']].head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# read the rural urban classification data\n",
-    "rural_urban = pd.read_csv('../data/external/census_2011_rural_urban.csv', sep=',')\n",
+    "#### Type of tenancy\n",
     "\n",
-    "# merge the rural_urban data with the spc\n",
-    "spc_edited = spc_edited.merge(rural_urban[['OA11CD', 'RUC11', 'RUC11CD']], left_on='oa11cd', right_on='OA11CD')\n",
-    "spc_edited.head(5)\n",
-    "\n"
+    "Breakdown between NTS and SPC is different. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
+   "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": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict_nts['Ten1_B02ID'], dict_spc['tenure']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create dictionaries to map tenure onto the spc and nts dfs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# create dictionary from the NTS `Settlement2011EW_B03ID` column\n",
-    "Settlement2011EW_B03ID_nts_hh = {\n",
-    "    '1': 'Urban',\n",
-    "    '2': 'Rural',\n",
-    "    '3': 'Scotland',\n",
-    "    '-8': 'NA',\n",
-    "    '-10': 'DEAD'\n",
-    "}\n",
-    "\n",
-    "Settlement2011EW_B04ID_nts_hh = {\n",
-    "    '1': 'Urban Conurbation',\n",
-    "    '2': 'Urban City and Town',\n",
-    "    '3': 'Rural Town and Fringe',\n",
-    "    '4': 'Rural Village, Hamlet and Isolated Dwellings',\n",
-    "    '5': 'Scotland',\n",
-    "    '-8': 'NA',\n",
-    "    '-10': 'DEAD'\n",
+    "# Dictionary showing how we want the final columns to look like\n",
+    "tenure_dict_nts_spc = {\n",
+    "    1: 'Owned',\n",
+    "    2: 'Rented or rent free',\n",
+    "    -8: 'NA',\n",
+    "    -9: 'DNA',\n",
+    "    -10: 'DEAD'\n",
     "}\n",
     "\n",
+    "# Matching NTS to tenure_dict_nts_spc\n",
     "\n",
-    "census_2011_to_nts_B03ID = {\n",
-    "    'Urban major conurbation': 'Urban',\n",
-    "    'Urban minor conurbation': 'Urban',\n",
-    "    'Urban city and town': 'Urban',\n",
-    "    'Urban city and town in a sparse setting': 'Urban',\n",
-    "    'Rural town and fringe': 'Rural',\n",
-    "    'Rural town and fringe in a sparse setting': 'Rural',\n",
-    "    'Rural village': 'Rural',\n",
-    "    'Rural village in a sparse setting': 'Rural',\n",
-    "    'Rural hamlets and isolated dwellings': 'Rural',\n",
-    "    'Rural hamlets and isolated dwellings in a sparse setting': 'Rural'\n",
+    "# Create a new dictionary for matching\n",
+    "matching_dict_nts_tenure = {\n",
+    "    1: 1,\n",
+    "    2: 2,\n",
+    "    3: 2\n",
     "}\n",
     "\n",
-    "census_2011_to_nts_B04ID = {\n",
-    "    'Urban major conurbation': 'Urban Conurbation',\n",
-    "    'Urban minor conurbation': 'Urban Conurbation',\n",
-    "    'Urban city and town': 'Urban City and Town',\n",
-    "    'Urban city and town in a sparse setting': 'Urban City and Town',\n",
-    "    'Rural town and fringe': 'Rural Town and Fringe',\n",
-    "    'Rural town and fringe in a sparse setting': 'Rural Town and Fringe',\n",
-    "    'Rural village': 'Rural Village, Hamlet and Isolated Dwellings',\n",
-    "    'Rural village in a sparse setting': 'Rural Village, Hamlet and Isolated Dwellings',\n",
-    "    'Rural hamlets and isolated dwellings': 'Rural Village, Hamlet and Isolated Dwellings',\n",
-    "    'Rural hamlets and isolated dwellings in a sparse setting': 'Rural Village, Hamlet and Isolated Dwellings'\n",
-    "}\n"
+    "matching_dict_spc_tenure = {\n",
+    "    1: 1, #'Owned: Owned outright' : 'Owned'\n",
+    "    2: 1, #'Owned: Owned with a mortgage or loan or shared ownership', : 'Owned'\n",
+    "    3: 2, #'Rented or living rent free: Total', : 'Rented or rent free'\n",
+    "    4: 2, #'Rented: Social rented', : 'Rented or rent free'\n",
+    "    5: 2, #'Rented: Private rented or living rent free', : 'Rented or rent free'\n",
+    "}\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "map dictionaries to create comparable columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "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",
-    "spc_edited['Settlement2011EW_B04ID_spc'] = spc_edited['RUC11'].map(census_2011_to_nts_B04ID)\n",
-    "spc_edited.head()\n",
-    "\n",
-    "# add the keys from nts_Settlement2011EW_B03ID and nts_Settlement2011EW_B04ID to the spc based on above mappings\n",
+    "# Create a new column in nts_households\n",
+    "nts_households['tenure_nts_for_matching'] = (nts_households['Ten1_B02ID']\n",
+    "                                                    .map(matching_dict_nts_tenure) # map the values to the new dictionary\n",
+    "                                                    .fillna(nts_households['Ten1_B02ID'])) # fill the NaNs with the original values\n",
     "\n",
-    "# reverse the dictionaries\n",
-    "Settlement2011EW_B03ID_nts_rev = {v: k for k, v in Settlement2011EW_B03ID_nts_hh.items()}\n",
-    "# map the values\n",
-    "spc_edited['Settlement2011EW_B03ID_spc_CD'] = spc_edited['Settlement2011EW_B03ID_spc'].map(Settlement2011EW_B03ID_nts_rev).astype('int')\n",
+    "# Create a new column in spc\n",
+    "spc_edited['tenure_spc_for_matching'] = (spc_edited['tenure']\n",
+    "                                        .map(matching_dict_spc_tenure) # map the values to the new dictionary\n",
+    "                                        .fillna(spc_edited['tenure'])) # fill the NaNs with the original values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Matching at Household Level\n",
     "\n",
-    "Settlement2011EW_B04ID_nts_rev = {v: k for k, v in Settlement2011EW_B04ID_nts_hh.items()}\n",
-    "spc_edited['Settlement2011EW_B04ID_spc_CD'] = spc_edited['Settlement2011EW_B04ID_spc'].map(Settlement2011EW_B04ID_nts_rev).astype('int')\n",
-    "spc_edited.head()"
+    "Now that we've prepared all the columns, we can start matching."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2.2 Edit NTS columns"
+    "### 3.1 Categorical matching\n",
+    "\n",
+    "We will match on (a subset of) the following columns:\n",
+    "\n",
+    "| Matching variable | NTS column | SPC column |\n",
+    "| ------------------| ---------- | ---------- |\n",
+    "| Household income  | `HHIncome2002_BO2ID` | `salary_yearly_hh_cat` |\n",
+    "| Number of adults  | `HHoldNumAdults` | `num_adults` |\n",
+    "| Number of children | `HHoldNumChildren` | `num_children` |\n",
+    "| Employment status | `HHoldEmploy_B01ID` | `pwkstat_NTS_match` |\n",
+    "| Car ownership | `NumCar_SPC_match` | `num_cars` |\n",
+    "| Type of tenancy | `tenure_nts_for_matching` | `tenure_spc_for_matching` |\n",
+    "| Rural/Urban Classification | `Settlement2011EW_B03ID` | `Settlement2011EW_B03ID_spc_CD` |"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Number of people of pension age"
+    "Prepare SPC df for matching"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "\n",
+       "    .dataframe thead 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>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",
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+       "      <td>2</td>\n",
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+       "    <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",
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+       "    <tr>\n",
+       "      <th>5</th>\n",
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+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
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+       "      <td>1</td>\n",
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+       "    </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",
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+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
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+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>E02002183_0008</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "      <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": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "nts_pensioners = count_per_group(df = nts_individuals,\n",
-    "                                 group_col='HouseholdID',\n",
-    "                                 count_col='OfPenAge_B01ID',\n",
-    "                                 values=[1],\n",
-    "                                 value_names=['num_pension_age_nts'])\n",
+    "# Select multiple columns\n",
+    "spc_matching = spc_edited[[\n",
+    "    'hid',\n",
+    "    'salary_yearly_hh_cat', 'num_adults',\n",
+    "    'num_children', 'num_pension_age', 'pwkstat_NTS_match',\n",
+    "    'num_cars', 'tenure_spc_for_matching',\n",
+    "    'Settlement2011EW_B03ID_spc_CD', 'Settlement2011EW_B04ID_spc_CD']]\n",
     "\n",
-    "nts_pensioners.head()\n",
+    "# edit the df so that we have one row per hid\n",
+    "spc_matching = spc_matching.drop_duplicates(subset='hid')\n",
     "\n",
-    "# join onto the nts household df\n",
-    "nts_households = nts_households.merge(nts_pensioners, left_on='HouseholdID', right_index=True, how='left')"
+    "spc_matching.head(10)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Number of cars\n",
-    "\n",
-    "- `SPC.num_cars` only has values [0, 1, 2]. 2 is for all households with 2 or more cars\n",
-    "- `NTS.NumCar` is more detailed. It has the actual value of the number of cars. We will cap this at 2."
+    "Prepare NTS df for matching"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
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+       "      <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",
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+       "      <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": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
+    "nts_matching = nts_households[[\n",
+    "    'HouseholdID','HHIncome2002_B02ID',\n",
+    "    'HHoldNumAdults', 'HHoldNumChildren', 'num_pension_age_nts',\n",
+    "    'HHoldEmploy_B01ID', 'NumCar_SPC_match',\n",
+    "    'tenure_nts_for_matching',\n",
+    "    'Settlement2011EW_B03ID', 'Settlement2011EW_B04ID']]\n",
     "\n",
-    "# Create a new column in NTS\n",
-    "nts_households.loc[:, 'NumCar_SPC_match'] = nts_households['NumCar'].apply(truncate_values, upper = 2)\n",
-    "\n",
-    "nts_households[['NumCar', 'NumCar_SPC_match']].head(20)"
+    "nts_matching.head(10)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Type of tenancy\n",
-    "\n",
-    "Breakdown between NTS and SPC is different. "
+    "Dictionary of matching columns. We extract column names from this dictioary when matching on a subset of the columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "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": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "dict_nts['Ten1_B02ID'], dict_spc['tenure']"
+    "# column_names (keys) for the dictionary\n",
+    "matching_ids = ['household_id', 'yearly_income', 'number_adults', 'number_children', 'num_pension_age',\n",
+    "                'employment_status', 'number_cars', 'tenure_status', 'rural_urban_2_categories', 'rural_urban_4_categories']\n",
+    "\n",
+    "# i want the value to be a list with spc_matching and nts_matching\n",
+    "matching_dfs_dict = {column_name: [spc_value, nts_value] for column_name, spc_value, nts_value in zip(matching_ids, spc_matching, nts_matching)}\n",
+    "matching_dfs_dict"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Create dictionaries to map tenure onto the spc and nts dfs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dictionary showing how we want the final columns to look like\n",
-    "tenure_dict_nts_spc = {\n",
-    "    1: 'Owned',\n",
-    "    2: 'Rented or rent free',\n",
-    "    -8: 'NA',\n",
-    "    -9: 'DNA',\n",
-    "    -10: 'DEAD'\n",
-    "}\n",
-    "\n",
-    "# Matching NTS to tenure_dict_nts_spc\n",
-    "\n",
-    "# Create a new dictionary for matching\n",
-    "matching_dict_nts_tenure = {\n",
-    "    1: 1,\n",
-    "    2: 2,\n",
-    "    3: 2\n",
-    "}\n",
+    "#### Match on a subset of columns (exclude salary, tenure, and employment status)\n",
     "\n",
-    "matching_dict_spc_tenure = {\n",
-    "    1: 1, #'Owned: Owned outright' : 'Owned'\n",
-    "    2: 1, #'Owned: Owned with a mortgage or loan or shared ownership', : 'Owned'\n",
-    "    3: 2, #'Rented or living rent free: Total', : 'Rented or rent free'\n",
-    "    4: 2, #'Rented: Social rented', : 'Rented or rent free'\n",
-    "    5: 2, #'Rented: Private rented or living rent free', : 'Rented or rent free'\n",
-    "}\n",
-    "\n"
+    "To decide on the subset of columns to match on, we explore the results from different combinations. This is shown in a separate notebook: `2.1_sandbox-match_households.ipynb`."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "map dictionaries to create comparable columns"
+    "#### Create dictionary to access columns from each dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "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": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Create a new column in nts_households\n",
-    "nts_households['tenure_nts_for_matching'] = (nts_households['Ten1_B02ID']\n",
-    "                                                    .map(matching_dict_nts_tenure) # map the values to the new dictionary\n",
-    "                                                    .fillna(nts_households['Ten1_B02ID'])) # fill the NaNs with the original values\n",
+    "# column_names (keys) for the dictionary\n",
+    "matching_ids = [\n",
+    "    \"household_id\",\n",
+    "    \"yearly_income\",\n",
+    "    \"number_adults\",\n",
+    "    \"number_children\",\n",
+    "    \"num_pension_age\",\n",
+    "    \"employment_status\",\n",
+    "    \"number_cars\",\n",
+    "    \"tenure_status\",\n",
+    "    \"rural_urban_2_categories\",\n",
+    "    \"rural_urban_4_categories\",\n",
+    "]\n",
     "\n",
-    "# Create a new column in spc\n",
-    "spc_edited['tenure_spc_for_matching'] = (spc_edited['tenure']\n",
-    "                                        .map(matching_dict_spc_tenure) # map the values to the new dictionary\n",
-    "                                        .fillna(spc_edited['tenure'])) # fill the NaNs with the original values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3: Matching at Household Level\n",
+    "# i want the value to be a list with spc_matching and nts_matching\n",
+    "matching_dfs_dict = {\n",
+    "    column_name: [spc_value, nts_value]\n",
+    "    for column_name, spc_value, nts_value in zip(\n",
+    "        matching_ids, spc_matching, nts_matching\n",
+    "    )\n",
+    "}\n",
     "\n",
-    "Now that we've prepared all the columns, we can start matching."
+    "matching_dfs_dict\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.1 Categorical matching\n",
+    "#### Categorical matching \n",
     "\n",
-    "We will match on (a subset of) the following columns:\n",
+    "We match iteratively on a subset of columns. We start with all columns, and then remove one of the optionals columns at a time (relaxing the condition). Once a household has over n matches, we stop matching it to more matches. We continue until all optional columns are removed\n",
     "\n",
-    "| Matching variable | NTS column | SPC column |\n",
-    "| ------------------| ---------- | ---------- |\n",
-    "| Household income  | `HHIncome2002_BO2ID` | `salary_yearly_hh_cat` |\n",
-    "| Number of adults  | `HHoldNumAdults` | `num_adults` |\n",
-    "| Number of children | `HHoldNumChildren` | `num_children` |\n",
-    "| Employment status | `HHoldEmploy_B01ID` | `pwkstat_NTS_match` |\n",
-    "| Car ownership | `NumCar_SPC_match` | `num_cars` |\n",
-    "| Type of tenancy | `tenure_nts_for_matching` | `tenure_spc_for_matching` |\n",
-    "| Rural/Urban Classification | `Settlement2011EW_B03ID` | `Settlement2011EW_B03ID_spc_CD` |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Prepare SPC df for matching"
+    "See the MatcherExact class for more details"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Categorical matching level 0: ['num_adults', 'num_children', 'num_cars', 'num_pension_age', 'Settlement2011EW_B03ID_spc_CD', 'pwkstat_NTS_match', 'tenure_spc_for_matching']\n",
+      "matching rows  0 to 6725  out of  6725\n",
+      "Categorical matching level 1: ['num_adults', 'num_children', 'num_cars', 'num_pension_age', 'Settlement2011EW_B03ID_spc_CD', 'pwkstat_NTS_match']\n",
+      "matching rows  0 to 1695  out of  1695\n",
+      "Categorical matching level 2: ['num_adults', 'num_children', 'num_cars', 'num_pension_age', 'Settlement2011EW_B03ID_spc_CD']\n",
+      "matching rows  0 to 1357  out of  1357\n",
+      "Categorical matching level 3: ['num_adults', 'num_children', 'num_cars', 'num_pension_age']\n",
+      "matching rows  0 to 806  out of  806\n",
+      "Categorical matching level 4: ['num_adults', 'num_children', 'num_cars']\n",
+      "matching rows  0 to 729  out of  729\n",
+      "Categorical matching level 5: ['num_adults', 'num_children']\n",
+      "matching rows  0 to 191  out of  191\n"
+     ]
+    }
+   ],
    "source": [
-    "# Select multiple columns\n",
-    "spc_matching = spc_edited[[\n",
-    "    'hid',\n",
-    "    'salary_yearly_hh_cat', 'num_adults',\n",
-    "    'num_children', 'num_pension_age', 'pwkstat_NTS_match',\n",
-    "    'num_cars', 'tenure_spc_for_matching',\n",
-    "    'Settlement2011EW_B03ID_spc_CD', 'Settlement2011EW_B04ID_spc_CD']]\n",
-    "\n",
-    "# edit the df so that we have one row per hid\n",
-    "spc_matching = spc_matching.drop_duplicates(subset='hid')\n",
+    "# Define required columns for matching\n",
+    "required_columns = [\n",
+    "    \"number_adults\",\n",
+    "    \"number_children\",\n",
+    "]\n",
     "\n",
-    "spc_matching.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Prepare NTS df for matching"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nts_matching = nts_households[[\n",
-    "    'HouseholdID','HHIncome2002_B02ID',\n",
-    "    'HHoldNumAdults', 'HHoldNumChildren', 'num_pension_age_nts',\n",
-    "    'HHoldEmploy_B01ID', 'NumCar_SPC_match',\n",
-    "    'tenure_nts_for_matching',\n",
-    "    'Settlement2011EW_B03ID', 'Settlement2011EW_B04ID']]\n",
+    "# Define optional columns in order of importance (most to least important)\n",
+    "optional_columns = [\n",
+    "    \"number_cars\",\n",
+    "    \"num_pension_age\",\n",
+    "    \"rural_urban_2_categories\",\n",
+    "    \"employment_status\",\n",
+    "    \"tenure_status\",\n",
+    "]\n",
     "\n",
-    "nts_matching.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Dictionary of matching columns. We extract column names from this dictioary when matching on a subset of the columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# column_names (keys) for the dictionary\n",
-    "matching_ids = ['household_id', 'yearly_income', 'number_adults', 'number_children', 'num_pension_age',\n",
-    "                'employment_status', 'number_cars', 'tenure_status', 'rural_urban_2_categories', 'rural_urban_4_categories']\n",
     "\n",
-    "# i want the value to be a list with spc_matching and nts_matching\n",
-    "matching_dfs_dict = {column_name: [spc_value, nts_value] for column_name, spc_value, nts_value in zip(matching_ids, spc_matching, nts_matching)}\n",
-    "matching_dfs_dict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Match on a subset of columns (exclude salary, tenure, and employment status)\n",
+    "# # Match using iterative matching\n",
+    "matcher = MatcherExact(\n",
+    "    df_pop=spc_matching,\n",
+    "    df_pop_id=\"hid\",\n",
+    "    df_sample=nts_matching,\n",
+    "    df_sample_id=\"HouseholdID\",\n",
+    "    matching_dict = matching_dfs_dict,\n",
+    "    fixed_cols=required_columns,\n",
+    "    optional_cols=optional_columns,\n",
+    "    n_matches=10,  \n",
+    "    chunk_size=50000,\n",
+    "    show_progress=True,\n",
+    ")\n",
     "\n",
-    "To decide on the subset of columns to match on, we explore the results from different combinations. This is shown in a separate notebook: `2.1_sandbox-match_households.ipynb`."
+    "matches_hh_level = matcher.iterative_match_categorical()\n",
+    "#print(matches_hh_level)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'E02002183_0001': [2019004064.0,\n",
+       "  2019002914.0,\n",
+       "  2019002027.0,\n",
+       "  2019005210.0,\n",
+       "  2019006500.0,\n",
+       "  2019003666.0,\n",
+       "  2019003784.0,\n",
+       "  2021002641.0,\n",
+       "  2021002918.0,\n",
+       "  2021002795.0,\n",
+       "  2022003312.0,\n",
+       "  2022000721.0,\n",
+       "  2022003828.0,\n",
+       "  2022003896.0,\n",
+       "  2022003901.0,\n",
+       "  2022004050.0],\n",
+       " 'E02002183_0002': [2019004130.0,\n",
+       "  2019004126.0,\n",
+       "  2019004144.0,\n",
+       "  2019001121.0,\n",
+       "  2019000603.0,\n",
+       "  2019000875.0,\n",
+       "  2019003241.0,\n",
+       "  2019005426.0,\n",
+       "  2019006453.0,\n",
+       "  2019004199.0,\n",
+       "  2019004183.0,\n",
+       "  2019001718.0,\n",
+       "  2019004160.0,\n",
+       "  2019000797.0,\n",
+       "  2019001159.0,\n",
+       "  2019004653.0,\n",
+       "  2019002798.0,\n",
+       "  2019002802.0,\n",
+       "  2019001813.0,\n",
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+       "  2022002313.0,\n",
+       "  2022003079.0,\n",
+       "  2022004262.0,\n",
+       "  2022004264.0],\n",
+       " 'E02002183_1000': [2019004079.0,\n",
+       "  2019005344.0,\n",
+       "  2019000585.0,\n",
+       "  2019001060.0,\n",
+       "  2019001061.0,\n",
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+       "  2019005300.0,\n",
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+       "  2019005072.0,\n",
+       "  2019005856.0,\n",
+       "  2019001664.0,\n",
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+       "  2019002480.0,\n",
+       "  2019005831.0,\n",
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+       "  2019000298.0,\n",
+       "  2019002175.0,\n",
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+       "  2019000218.0,\n",
+       "  2019004915.0,\n",
+       "  2019004919.0,\n",
+       "  2019004862.0,\n",
+       "  2021002642.0,\n",
+       "  2021002355.0,\n",
+       "  2021003163.0,\n",
+       "  2021002979.0,\n",
+       "  2021001184.0,\n",
+       "  2021000579.0,\n",
+       "  2021000650.0,\n",
+       "  2021002173.0,\n",
+       "  2021001913.0,\n",
+       "  2021002453.0,\n",
+       "  2021000118.0,\n",
+       "  2021004127.0,\n",
+       "  2021002258.0,\n",
+       "  2021003315.0,\n",
+       "  2022001049.0,\n",
+       "  2022001063.0,\n",
+       "  2022000139.0,\n",
+       "  2022002648.0,\n",
+       "  2022003132.0,\n",
+       "  2022003202.0,\n",
+       "  2022003493.0,\n",
+       "  2022001688.0,\n",
+       "  2022001964.0,\n",
+       "  2022002102.0,\n",
+       "  2022000277.0,\n",
+       "  2022000448.0,\n",
+       "  2022002877.0,\n",
+       "  2022002904.0],\n",
+       " ...}"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# columns for matching\n",
-    "keys = ['number_adults', 'number_children', 'num_pension_age', 'number_cars', 'rural_urban_2_categories']\n",
-    "# extract equivalent column names from dictionary\n",
-    "spc_cols = [matching_dfs_dict[key][0] for key in keys]\n",
-    "nts_cols = [matching_dfs_dict[key][1] for key in keys]\n"
+    "matches_hh_level"
    ]
   },
   {
@@ -1489,23 +122630,6 @@
     "Match"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "matches_hh_level = match_categorical(\n",
-    "    df_pop = spc_matching,\n",
-    "    df_pop_cols = spc_cols,\n",
-    "    df_pop_id = 'hid',\n",
-    "    df_sample = nts_matching,\n",
-    "    df_sample_cols = nts_cols,\n",
-    "    df_sample_id = 'HouseholdID',\n",
-    "    chunk_size = 50000,\n",
-    "    show_progress = True)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1515,9 +122639,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Get the counts of each key\n",
     "counts = [len(v) for v in matches_hh_level.values()]\n",
@@ -1541,9 +122676,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "27 households in the SPC had no match\n",
+      "0.4 % of households in the SPC had no match\n"
+     ]
+    }
+   ],
    "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",
@@ -1556,9 +122700,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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2019003968.0, 2019003944.0, 2019003962.0, 2019003754.0, 2019000038.0, 2019004385.0, 2019003706.0, 2019004819.0, 2019006822.0, 2019006239.0, 2019003571.0, 2019006642.0, 2019006229.0, 2019006248.0, 2019006677.0, 2019003852.0, 2019004508.0, 2019004515.0, 2019003864.0, 2021003917.0, 2021003873.0, 2021003008.0, 2021003874.0, 2021002635.0, 2021002664.0, 2021002082.0, 2021002959.0, 2021004022.0, 2021003745.0, 2021004057.0, 2021004102.0, 2021004100.0, 2021002538.0, 2021003180.0, 2021001152.0, 2021000529.0, 2021004303.0, 2021004567.0, 2021001893.0, 2021000557.0, 2021000523.0, 2021000563.0, 2021004572.0, 2021001203.0, 2021004792.0, 2021004791.0, 2021000165.0, 2021000159.0, 2021001661.0, 2021001954.0, 2021001721.0, 2021004234.0, 2021000514.0, 2021000987.0, 2021000976.0, 2021000801.0, 2021003028.0, 2021003746.0, 2021002703.0, 2021002159.0, 2021002379.0, 2021001450.0, 2021001242.0, 2021001429.0, 2021001224.0, 2021001499.0, 2021000582.0, 2021004375.0, 2021004379.0, 2021001036.0, 2021001305.0, 2021004397.0, 2021001100.0, 2021001335.0, 2021003792.0, 2021002511.0, 2021003694.0, 2021003978.0, 2021003500.0, 2021003529.0, 2021001979.0, 2021001983.0, 2021001978.0, 2021002003.0, 2021000491.0, 2021000296.0, 2021000064.0, 2021003362.0, 2021004673.0, 2021003360.0, 2021003377.0, 2021002406.0, 2021000222.0, 2021000418.0, 2021001680.0, 2021000025.0, 2021002482.0, 2021004642.0, 2021000745.0, 2021004233.0, 2021001820.0, 2021000945.0, 2021000972.0, 2021000371.0, 2021000826.0, 2021003855.0, 2021002073.0, 2021000814.0, 2021000850.0, 2021004137.0, 2021004533.0, 2021004144.0, 2021004526.0, 2021001400.0, 2021002286.0, 2021000599.0, 2021003603.0, 2021000501.0, 2021000889.0, 2021001562.0, 2021001612.0, 2021001594.0, 2021003282.0, 2021002294.0, 2021002293.0, 2021003307.0, 2021003467.0, 2021004445.0, 2021001132.0, 2021004436.0, 2021001128.0, 2021003097.0, 2021004781.0, 2021004776.0, 2022001047.0, 2022001110.0, 2022001123.0, 2022001223.0, 2022001300.0, 2022001301.0, 2022001375.0, 2022001376.0, 2022001402.0, 2022001449.0, 2022001549.0, 2022000006.0, 2022000014.0, 2022000064.0, 2022000110.0, 2022000218.0, 2022002676.0, 2022002698.0, 2022002707.0, 2022002722.0, 2022002757.0, 2022002777.0, 2022003226.0, 2022003247.0, 2022003274.0, 2022003279.0, 2022003295.0, 2022003315.0, 2022003324.0, 2022003394.0, 2022003414.0, 2022003423.0, 2022003427.0, 2022003515.0, 2022003539.0, 2022003579.0, 2022003627.0, 2022000552.0, 2022000624.0, 2022000633.0, 2022000653.0, 2022000662.0, 2022000668.0, 2022000718.0, 2022000720.0, 2022000735.0, 2022000816.0, 2022000867.0, 2022000887.0, 2022001652.0, 2022001663.0, 2022001684.0, 2022001708.0, 2022001745.0, 2022001765.0, 2022001767.0, 2022001828.0, 2022001934.0, 2022001988.0, 2022001998.0, 2022002009.0, 2022002136.0, 2022002174.0, 2022002346.0, 2022002356.0, 2022002358.0, 2022002363.0, 2022002364.0, 2022002380.0, 2022002435.0, 2022002522.0, 2022002531.0, 2022002571.0, 2022003669.0, 2022003720.0, 2022003727.0, 2022003759.0, 2022003808.0, 2022003844.0, 2022003872.0, 2022003890.0, 2022003892.0, 2022003904.0, 2022003935.0, 2022003945.0, 2022003976.0, 2022003978.0, 2022004015.0, 2022004048.0, 2022004075.0, 2022004078.0, 2022004096.0, 2022000268.0, 2022000269.0, 2022000357.0, 2022000439.0, 2022000485.0, 2022002872.0, 2022002897.0, 2022002907.0, 2022002908.0, 2022002937.0, 2022003042.0, 2022003067.0, 2022003114.0, 2022003116.0, 2022004222.0, 2022004240.0, 2022004305.0, 2022004329.0])\n"
+     ]
+    }
+   ],
    "source": [
     "# print the 6th key, value in the matches_hh_level dictionary\n",
     "print(list(matches_hh_level.items())[90])"
@@ -1566,9 +122718,381 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "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>pid_hs</th>\n",
+       "      <th>msoa11cd</th>\n",
+       "      <th>oa11cd</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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, 2019002914.0, 2019002027.0, 201...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</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",
+       "      <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>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</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>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",
+       "      <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>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>[2019001219.0, 2019001010.0, 2019000952.0, 201...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</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>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>[2019001219.0, 2019001010.0, 2019000952.0, 201...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  household   pid_hs   msoa11cd     oa11cd members sic1d2007  sic2d2007  \\\n",
+       "0   0          0  2905399  E02002183  E00053954     [0]         J       58.0   \n",
+       "1   1          1  2905308  E02002183  E00053953  [1, 2]         C       25.0   \n",
+       "2   2          1  2907681  E02002183  E00053953  [1, 2]         P       85.0   \n",
+       "3   3          2  2902817  E02002183  E00053689  [3, 4]         C       31.0   \n",
+       "4   4          2  2900884  E02002183  E00053689  [3, 4]         J       62.0   \n",
+       "\n",
+       "   pwkstat  salary_yearly  salary_hourly             hid  accommodation_type  \\\n",
+       "0        6            NaN            NaN  E02002183_0001                 1.0   \n",
+       "1        6            NaN            NaN  E02002183_0002                 3.0   \n",
+       "2        6            NaN            NaN  E02002183_0002                 3.0   \n",
+       "3        1   32857.859375      14.360952  E02002183_0003                 3.0   \n",
+       "4        1   18162.451172       9.439944  E02002183_0003                 3.0   \n",
+       "\n",
+       "   communal_type  num_rooms  central_heat  tenure  num_cars  sex  age_years  \\\n",
+       "0            NaN        2.0          True     2.0         2    1         86   \n",
+       "1            NaN        6.0          True     2.0         2    1         74   \n",
+       "2            NaN        6.0          True     2.0         2    2         68   \n",
+       "3            NaN        6.0          True     2.0         1    1         27   \n",
+       "4            NaN        6.0          True     2.0         1    2         26   \n",
+       "\n",
+       "   ethnicity  nssec8  salary_yearly_hh  salary_yearly_hh_cat  is_adult  \\\n",
+       "0          1     1.0          0.000000                     1         1   \n",
+       "1          3     1.0          0.000000                     1         1   \n",
+       "2          1     1.0          0.000000                     1         1   \n",
+       "3          1     4.0      51020.310547                     3         1   \n",
+       "4          1     4.0      51020.310547                     3         1   \n",
+       "\n",
+       "   num_adults  is_child  num_children  is_pension_age  num_pension_age  \\\n",
+       "0           1         0             0               1                1   \n",
+       "1           2         0             0               1                2   \n",
+       "2           2         0             0               1                2   \n",
+       "3           2         0             0               0                0   \n",
+       "4           2         0             0               0                0   \n",
+       "\n",
+       "   pwkstat_FT_hh  pwkstat_PT_hh  pwkstat_NTS_match     OA11CD  \\\n",
+       "0              0              0                  1  E00053954   \n",
+       "1              0              0                  1  E00053953   \n",
+       "2              0              0                  1  E00053953   \n",
+       "3              2              0                  6  E00053689   \n",
+       "4              2              0                  6  E00053689   \n",
+       "\n",
+       "                   RUC11 RUC11CD Settlement2011EW_B03ID_spc  \\\n",
+       "0    Urban city and town      C1                      Urban   \n",
+       "1    Urban city and town      C1                      Urban   \n",
+       "2    Urban city and town      C1                      Urban   \n",
+       "3  Rural town and fringe      D1                      Rural   \n",
+       "4  Rural town and fringe      D1                      Rural   \n",
+       "\n",
+       "  Settlement2011EW_B04ID_spc  Settlement2011EW_B03ID_spc_CD  \\\n",
+       "0        Urban City and Town                              1   \n",
+       "1        Urban City and Town                              1   \n",
+       "2        Urban City and Town                              1   \n",
+       "3      Rural Town and Fringe                              2   \n",
+       "4      Rural Town and Fringe                              2   \n",
+       "\n",
+       "   Settlement2011EW_B04ID_spc_CD  tenure_spc_for_matching  \\\n",
+       "0                              2                      1.0   \n",
+       "1                              2                      1.0   \n",
+       "2                              2                      1.0   \n",
+       "3                              3                      1.0   \n",
+       "4                              3                      1.0   \n",
+       "\n",
+       "                                           nts_hh_id  \n",
+       "0  [2019004064.0, 2019002914.0, 2019002027.0, 201...  \n",
+       "1  [2019004130.0, 2019004126.0, 2019004144.0, 201...  \n",
+       "2  [2019004130.0, 2019004126.0, 2019004144.0, 201...  \n",
+       "3  [2019001219.0, 2019001010.0, 2019000952.0, 201...  \n",
+       "4  [2019001219.0, 2019001010.0, 2019000952.0, 201...  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "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",
@@ -1587,7 +123111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1610,9 +123134,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('E02002183_0571', 2019004347.0)\n"
+     ]
+    }
+   ],
    "source": [
     "print(list(matches_hh_level_sample.items())[568])"
    ]
@@ -1626,7 +123158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index bc2c249..8233ad5 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -11,7 +11,7 @@
 from acbm.cli import acbm_cli
 from acbm.config import load_config
 from acbm.logger_config import matching_logger as logger
-from acbm.matching import match_categorical, match_individuals
+from acbm.matching import MatcherExact, match_individuals
 from acbm.preprocessing import (
     count_per_group,
     # nts_filter_by_region,
@@ -763,7 +763,7 @@ def get_interim_path(
         "rural_urban_4_categories",
     ]
 
-    # i want the value to be a list with spc_matching and nts_matching
+    # Dict with value qual to a list with spc_matching and nts_matching column names
     matching_dfs_dict = {
         column_name: [spc_value, nts_value]
         for column_name, spc_value, nts_value in zip(
@@ -771,35 +771,42 @@ def get_interim_path(
         )
     }
 
-    # #### Match on a subset of columns (exclude salary, tenure, and employment status)
-    #
-    # To decide on the subset of columns to match on, we explore the results from different combinations. This is shown in a separate notebook: `2.1_sandbox-match_households.ipynb`.
+    # We match iteratively on a subset of columns. We start with all columns, and then remove
+    # one of the optionals columns at a time (relaxing the condition). Once a household has over n
+    # matches, we stop matching it to more matches. We continue until all optional columns are removed
 
-    # columns for matching
-    keys = [
+    # Define required columns for matching
+    required_columns = [
         "number_adults",
         "number_children",
-        "num_pension_age",
+    ]
+
+    # Define optional columns in order of importance (most to least important)
+    optional_columns = [
         "number_cars",
+        "num_pension_age",
         "rural_urban_2_categories",
+        "employment_status",
+        "tenure_status",
     ]
-    # extract equivalent column names from dictionary
-    spc_cols = [matching_dfs_dict[key][0] for key in keys]
-    nts_cols = [matching_dfs_dict[key][1] for key in keys]
-
-    # Match
 
-    matches_hh_level = match_categorical(
+    matcher_exact = MatcherExact(
         df_pop=spc_matching,
-        df_pop_cols=spc_cols,
         df_pop_id="hid",
         df_sample=nts_matching,
-        df_sample_cols=nts_cols,
         df_sample_id="HouseholdID",
+        matching_dict=matching_dfs_dict,
+        fixed_cols=required_columns,
+        optional_cols=optional_columns,
+        n_matches=10,
         chunk_size=50000,
         show_progress=True,
     )
 
+    # Match
+
+    matches_hh_level = matcher_exact.iterative_match_categorical()
+
     # Number of unmatched households
 
     # no. of keys where value is na
diff --git a/src/acbm/matching.py b/src/acbm/matching.py
index eb233d9..518deaf 100644
--- a/src/acbm/matching.py
+++ b/src/acbm/matching.py
@@ -1,82 +1,164 @@
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Dict, List
+
 import numpy as np
 import pandas as pd
 from sklearn.neighbors import NearestNeighbors
 
-# categorical (exact) matching
-
+# categorical (exact) matching - (for household level)
 
-def match_categorical(
-    df_pop: pd.DataFrame,
-    df_pop_cols: list,
-    df_pop_id: str,
-    df_sample: pd.DataFrame,
-    df_sample_cols: list,
-    df_sample_id: str,
-    chunk_size: int,
-    show_progress=True,
-) -> dict:
-    """
-    Match the rows in two DataFrames based on specified columns.
-    The function matches the rows in df_pop to the rows in df_sample based
-    on the columns in df_pop_cols and df_sample_cols. The matching is done
-    in chunks to avoid memory issues.
 
-    Parameters
-    ----------
-    df_pop: pandas DataFrame
-        The DataFrame to be matched on
-    df_pop_cols: list
-        The columns to be used for matching in df_pop
+@dataclass
+class MatcherExact:
+    df_pop: pd.DataFrame
     df_pop_id: str
-        The column name that contains the unique identifier in df_pop
-        It is the key in the final dictionary
-    df_sample: pandas DataFrame
-        The DataFrame to be matched with
-    df_sample_cols: list
-        The columns to be used for matching in df_sample
+    df_sample: pd.DataFrame
     df_sample_id: str
-        The column name that contains the unique identifier in df_sample
-        It is the value in the final dictionary
-    chunk_size: int
-        The number of rows to process at a time
-    show_progress: bool
-        Whether to print the progress of the matching to the console
-
-    Returns
-    -------
-    results: dict
-        A dictionary with the matched rows {df_pop_id: [df_sample_id]}
-
-    """
-
-    # dictionary to store results
-    results = {}
-
-    # loop over the df_pop DataFrame in chunks
-    for i in range(0, df_pop.shape[0], chunk_size):
-        # filter the df_pop DataFrame to the current chunk
-        j = i + chunk_size
-        if show_progress:
-            print("matching rows ", i, "to", j, " out of ", df_pop.shape[0])
-
-        df_pop_chunk = df_pop.iloc[i:j]
-
-        # merge the df_pop_chunk with the df_sample DataFrame
-        df_matched_chunk = df_pop_chunk.merge(
-            df_sample, left_on=df_pop_cols, right_on=df_sample_cols, how="left"
-        )
-
-        # convert the matched df to a dictionary:
-        df_matched_dict_i = (
-            df_matched_chunk.groupby(df_pop_id)[df_sample_id].apply(list).to_dict()
-        )
-
-        # add the dictionary to results{}
-        results.update(df_matched_dict_i)
-    return results
-
-
-# propensity score matching
+    matching_dict: Dict[str, List[str]]
+    fixed_cols: List[str]
+    optional_cols: List[str]
+    n_matches: int = 5
+    chunk_size: int = 50000
+    show_progress: bool = True
+    matched_dict: Dict[str, List[str]] = field(
+        default_factory=lambda: defaultdict(list)
+    )
+    match_count: Dict[str, int] = field(default_factory=lambda: defaultdict(int))
+
+    def __post_init__(self):
+        # Extract equivalent column names from dictionary
+        self.fixed_pop_cols = [self.matching_dict[col][0] for col in self.fixed_cols]
+        self.fixed_sample_cols = [self.matching_dict[col][1] for col in self.fixed_cols]
+        self.optional_pop_cols = [
+            self.matching_dict[col][0] for col in self.optional_cols
+        ]
+        self.optional_sample_cols = [
+            self.matching_dict[col][1] for col in self.optional_cols
+        ]
+        self.remaining_df_pop = self.df_pop.copy()
+        self.remaining_df_sample = self.df_sample.copy()
+
+    def _match_categorical(
+        self,
+        df_pop: pd.DataFrame,
+        df_pop_cols: List[str],
+        df_pop_id: str,
+        df_sample: pd.DataFrame,
+        df_sample_cols: List[str],
+        df_sample_id: str,
+        chunk_size: int,
+        show_progress: bool = True,
+    ) -> Dict[str, List[str]]:
+        """
+        Match the rows in two DataFrames based on specified columns.
+        The function matches the rows in df_pop to the rows in df_sample based
+        on the columns in df_pop_cols and df_sample_cols. The matching is done
+        in chunks to avoid memory issues.
+
+        Parameters
+        ----------
+        df_pop: pandas DataFrame
+            The DataFrame to be matched on
+        df_pop_cols: list
+            The columns to be used for matching in df_pop
+        df_pop_id: str
+            The column name that contains the unique identifier in df_pop
+            It is the key in the final dictionary
+        df_sample: pandas DataFrame
+            The DataFrame to be matched with
+        df_sample_cols: list
+            The columns to be used for matching in df_sample
+        df_sample_id: str
+            The column name that contains the unique identifier in df_sample
+            It is the value in the final dictionary
+        chunk_size: int
+            The number of rows to process at a time
+        show_progress: bool
+            Whether to print the progress of the matching to the console
+
+        Returns
+        -------
+        results: dict
+            A dictionary with the matched rows {df_pop_id: [df_sample_id_1, df_sample_id_2, ...]}
+        """
+        results = {}
+
+        # loop over the df_pop DataFrame in chunks
+        for i in range(0, df_pop.shape[0], chunk_size):
+            # Adjust chunk size if remaining rows are less than chunk size
+            j = min(i + chunk_size, df_pop.shape[0])
+            if show_progress:
+                print("matching rows ", i, "to", j, " out of ", df_pop.shape[0])
+
+            df_pop_chunk = df_pop.iloc[i:j]
+            # exact match through a join on the specified columns
+            df_matched_chunk = df_pop_chunk.merge(
+                df_sample, left_on=df_pop_cols, right_on=df_sample_cols, how="left"
+            )
+            # convert matched df to dictionary
+            df_matched_dict_i = (
+                df_matched_chunk.groupby(df_pop_id)[df_sample_id].apply(list).to_dict()
+            )
+            results.update(df_matched_dict_i)
+        return results
+
+    def iterative_match_categorical(self) -> Dict[str, List[str]]:
+        """
+        Perform categorical matching iteratively, relaxing constraints in each round by
+        removing one optional column at a time (optional columns are ordered by importance).
+        For each household in df_pop, we stop matching when if matches have exceeded
+        n_matches.
+
+        Returns
+        -------
+        dict
+            Dictionary with matched households.
+        """
+        for i in range(len(self.optional_pop_cols) + 1):
+            if i > 0:
+                self.optional_pop_cols.pop()
+                self.optional_sample_cols.pop()
+
+            current_pop_cols = self.fixed_pop_cols + self.optional_pop_cols
+            current_sample_cols = self.fixed_sample_cols + self.optional_sample_cols
+
+            print(f"Categorical matching level {i}: {current_pop_cols}")
+
+            current_matches = self._match_categorical(
+                df_pop=self.remaining_df_pop,
+                df_pop_cols=current_pop_cols,
+                df_pop_id=self.df_pop_id,
+                df_sample=self.remaining_df_sample,
+                df_sample_cols=current_sample_cols,
+                df_sample_id=self.df_sample_id,
+                chunk_size=self.chunk_size,
+                show_progress=self.show_progress,
+            )
+
+            for pop_id, sample_ids in current_matches.items():
+                unique_sample_ids = [
+                    sid for sid in sample_ids if sid not in self.matched_dict[pop_id]
+                ]
+                self.matched_dict[pop_id].extend(unique_sample_ids)
+                self.match_count[pop_id] += len(unique_sample_ids)
+
+            matched_ids = [
+                pop_id
+                for pop_id, count in self.match_count.items()
+                if count >= self.n_matches
+            ]
+            self.remaining_df_pop = self.remaining_df_pop[
+                ~self.remaining_df_pop[self.df_pop_id].isin(matched_ids)
+            ]
+
+            if self.remaining_df_pop.empty:
+                break
+
+        return dict(self.matched_dict)
+
+
+# propensity score matching - (for individual level)
 
 
 def match_psm(df1: pd.DataFrame, df2: pd.DataFrame, matching_columns: list) -> dict:

From 50219492550df81e384b06662c503d43ce05695d Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Tue, 15 Oct 2024 10:45:22 +0100
Subject: [PATCH 02/27] remove NAs from dict

---
 src/acbm/matching.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/acbm/matching.py b/src/acbm/matching.py
index 518deaf..9508fa7 100644
--- a/src/acbm/matching.py
+++ b/src/acbm/matching.py
@@ -100,6 +100,10 @@ def _match_categorical(
             df_matched_dict_i = (
                 df_matched_chunk.groupby(df_pop_id)[df_sample_id].apply(list).to_dict()
             )
+            # Filter out NaN values from the lists
+            df_matched_dict_i = {
+                k: [x for x in v if pd.notna(x)] for k, v in df_matched_dict_i.items()
+            }
             results.update(df_matched_dict_i)
         return results
 

From dc2a41e4c1b57e0edc68affd01e487d1314ae032 Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Tue, 15 Oct 2024 11:19:32 +0100
Subject: [PATCH 03/27] iterative_matching function test

---
 tests/test_matching.py | 93 +++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 92 insertions(+), 1 deletion(-)

diff --git a/tests/test_matching.py b/tests/test_matching.py
index c0e0da6..fcd17f1 100644
--- a/tests/test_matching.py
+++ b/tests/test_matching.py
@@ -1,6 +1,79 @@
+import pandas as pd
 import pytest
 
-from acbm.matching import match_categorical, match_individuals, match_psm  # noqa: F401
+from acbm.matching import MatcherExact, match_psm  # noqa: F401
+
+
+@pytest.fixture()
+def setup_data():
+    df_pop = pd.DataFrame(
+        {
+            "hid_pop": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "num_adults_pop": [2, 2, 1, 3, 1, 2, 2, 1, 2, 3],
+            "num_children_pop": [1, 0, 2, 1, 0, 1, 0, 2, 1, 1],
+            "num_cars_pop": [1, 2, 1, 0, 1, 2, 1, 0, 1, 2],
+            "urban_rural_pop": [
+                "urban",
+                "rural",
+                "urban",
+                "urban",
+                "rural",
+                "urban",
+                "urban",
+                "rural",
+                "urban",
+                "urban",
+            ],
+        }
+    )
+
+    df_sample = pd.DataFrame(
+        {
+            "hid_sample": [101, 102, 103, 104, 105],
+            "num_adults_sample": [2, 1, 2, 3, 2],
+            "num_children_sample": [1, 0, 0, 1, 1],
+            "num_cars_sample": [1, 2, 1, 0, 1],
+            "urban_rural_sample": ["urban", "rural", "urban", "urban", "rural"],
+        }
+    )
+
+    matching_dict = {
+        "num_adults": ["num_adults_pop", "num_adults_sample"],
+        "num_children": ["num_children_pop", "num_children_sample"],
+        "num_cars": ["num_cars_pop", "num_cars_sample"],
+        "urban_rural": ["urban_rural_pop", "urban_rural_sample"],
+    }
+    fixed_cols = [
+        "num_adults",
+        "num_children",
+    ]
+    optional_cols = [
+        "num_cars",
+        "urban_rural",
+    ]
+
+    return MatcherExact(
+        df_pop=df_pop,
+        df_pop_id="hid_pop",
+        df_sample=df_sample,
+        df_sample_id="hid_sample",
+        matching_dict=matching_dict,
+        fixed_cols=fixed_cols,
+        optional_cols=optional_cols,
+        n_matches=2,
+        chunk_size=2,
+        show_progress=False,
+    )
+
+
+def test_post_init(setup_data):
+    matcher = setup_data
+    assert matcher.fixed_pop_cols == ["num_adults_pop", "num_children_pop"]
+    assert matcher.fixed_sample_cols == ["num_adults_sample", "num_children_sample"]
+    assert matcher.optional_pop_cols == ["num_cars_pop", "urban_rural_pop"]
+    assert matcher.optional_sample_cols == ["num_cars_sample", "urban_rural_sample"]
+    assert not matcher.remaining_df_pop.empty
+    assert not matcher.remaining_df_sample.empty
 
 
 @pytest.mark.skip(reason="todo")
@@ -8,6 +81,24 @@ def test_match_categorical():
     pass
 
 
+def test_iterative_match_categorical(setup_data):
+    matcher = setup_data
+    result = matcher.iterative_match_categorical()
+    expected_result = {
+        1: [101.0, 105.0],
+        2: [103.0],
+        3: [],
+        4: [104.0],
+        5: [102],
+        6: [101, 105],
+        7: [103.0],
+        8: [],
+        9: [101.0, 105.0],
+        10: [104.0],
+    }
+    assert result == expected_result
+
+
 @pytest.mark.skip(reason="todo")
 def test_match_individuals():
     pass

From d500ee8286b555705f05c162271c3b2b0b51515e Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Fri, 18 Oct 2024 09:30:26 +0100
Subject: [PATCH 04/27] fix dictionary error

---
 .../2_match_households_and_individuals.ipynb  | 1982 +++++++++++------
 scripts/2_match_households_and_individuals.py |   25 +-
 2 files changed, 1368 insertions(+), 639 deletions(-)

diff --git a/notebooks/2_match_households_and_individuals.ipynb b/notebooks/2_match_households_and_individuals.ipynb
index 20d5d6a..925f49b 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": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -420,7 +420,7 @@
        "4         1    2         26          1               6.0  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -433,7 +433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -447,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -470,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -487,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -539,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -587,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -635,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -658,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -678,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -830,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -851,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -914,7 +914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -948,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -998,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1012,7 +1012,7 @@
        "Name: proportion, dtype: float64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1031,7 +1031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1051,7 +1051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1083,7 +1083,7 @@
        "  '-10': 'DEAD'})"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1106,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1209,7 +1209,7 @@
        "9                      0              1"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1235,7 +1235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1365,7 +1365,7 @@
        "9          5        1              1              0                  3"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1427,7 +1427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1487,7 +1487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1800,7 +1800,7 @@
        "4  Rural town and fringe      D1  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1817,7 +1817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1870,7 +1870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2221,7 +2221,7 @@
        "4                              3  "
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2260,7 +2260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2288,7 +2288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -2445,7 +2445,7 @@
        "142973     3.0               2.0"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2469,7 +2469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -2491,7 +2491,7 @@
        "  '-10': 'DEAD'})"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2509,7 +2509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2550,7 +2550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2600,248 +2600,248 @@
     "Prepare SPC df for matching"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\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>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",
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+       "      <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"
+    }
+   ],
+   "source": [
+    "# Select multiple columns\n",
+    "spc_matching = spc_edited[[\n",
+    "    'hid',\n",
+    "    'salary_yearly_hh_cat', 'num_adults',\n",
+    "    'num_children', 'num_pension_age', 'pwkstat_NTS_match',\n",
+    "    'num_cars', 'tenure_spc_for_matching',\n",
+    "    'Settlement2011EW_B03ID_spc_CD', 'Settlement2011EW_B04ID_spc_CD']]\n",
+    "\n",
+    "# edit the df so that we have one row per hid\n",
+    "spc_matching = spc_matching.drop_duplicates(subset='hid')\n",
+    "\n",
+    "spc_matching.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prepare NTS df for matching"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {},
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-       "    <tr>\n",
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-       "      <td>1</td>\n",
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-       "    </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": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Select multiple columns\n",
-    "spc_matching = spc_edited[[\n",
-    "    'hid',\n",
-    "    'salary_yearly_hh_cat', 'num_adults',\n",
-    "    'num_children', 'num_pension_age', 'pwkstat_NTS_match',\n",
-    "    'num_cars', 'tenure_spc_for_matching',\n",
-    "    'Settlement2011EW_B03ID_spc_CD', 'Settlement2011EW_B04ID_spc_CD']]\n",
-    "\n",
-    "# edit the df so that we have one row per hid\n",
-    "spc_matching = spc_matching.drop_duplicates(subset='hid')\n",
-    "\n",
-    "spc_matching.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Prepare NTS df for matching"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3061,7 +3061,7 @@
        "142963                       2  "
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3086,7 +3086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -3106,7 +3106,7 @@
        "  'Settlement2011EW_B04ID']}"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3139,7 +3139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -3159,7 +3159,7 @@
        "  'Settlement2011EW_B04ID']}"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3203,7 +3203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -3217,11 +3217,11 @@
       "Categorical matching level 2: ['num_adults', 'num_children', 'num_cars', 'num_pension_age', 'Settlement2011EW_B03ID_spc_CD']\n",
       "matching rows  0 to 1357  out of  1357\n",
       "Categorical matching level 3: ['num_adults', 'num_children', 'num_cars', 'num_pension_age']\n",
-      "matching rows  0 to 806  out of  806\n",
+      "matching rows  0 to 809  out of  809\n",
       "Categorical matching level 4: ['num_adults', 'num_children', 'num_cars']\n",
-      "matching rows  0 to 729  out of  729\n",
+      "matching rows  0 to 733  out of  733\n",
       "Categorical matching level 5: ['num_adults', 'num_children']\n",
-      "matching rows  0 to 191  out of  191\n"
+      "matching rows  0 to 192  out of  192\n"
      ]
     }
    ],
@@ -3262,7 +3262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
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@@ -116194,9 +115990,7 @@
        "  2022002496.0,\n",
        "  2022000475.0,\n",
        "  2022004266.0],\n",
-       " 'E02002183_0947': [nan,\n",
-       "  nan,\n",
-       "  2022002873.0,\n",
+       " 'E02002183_0947': [2022002873.0,\n",
        "  2019000349.0,\n",
        "  2019002794.0,\n",
        "  2019003197.0,\n",
@@ -116703,10 +116497,7 @@
        "  2022002072.0,\n",
        "  2022004049.0,\n",
        "  2022004101.0],\n",
-       " 'E02002183_0951': [nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  2021000738.0,\n",
+       " 'E02002183_0951': [2021000738.0,\n",
        "  2022002765.0,\n",
        "  2022003258.0,\n",
        "  2019004119.0,\n",
@@ -119136,8 +118927,7 @@
        "  2022002935.0,\n",
        "  2022003097.0,\n",
        "  2022003101.0],\n",
-       " 'E02002183_0963': [nan,\n",
-       "  2022000180.0,\n",
+       " 'E02002183_0963': [2022000180.0,\n",
        "  2019004119.0,\n",
        "  2019001710.0,\n",
        "  2019004122.0,\n",
@@ -119571,8 +119361,7 @@
        "  2022000463.0,\n",
        "  2022000489.0,\n",
        "  2022002977.0],\n",
-       " 'E02002183_0965': [nan,\n",
-       "  2019004293.0,\n",
+       " 'E02002183_0965': [2019004293.0,\n",
        "  2021000473.0,\n",
        "  2022002712.0,\n",
        "  2022001975.0,\n",
@@ -120357,8 +120146,7 @@
        "  2022003896.0,\n",
        "  2022003901.0,\n",
        "  2022004050.0],\n",
-       " 'E02002183_0974': [nan,\n",
-       "  2022002873.0,\n",
+       " 'E02002183_0974': [2022002873.0,\n",
        "  2019000349.0,\n",
        "  2019002794.0,\n",
        "  2019003197.0,\n",
@@ -120651,9 +120439,7 @@
        "  2022004316.0,\n",
        "  2022004318.0,\n",
        "  2022004364.0],\n",
-       " 'E02002183_0975': [nan,\n",
-       "  nan,\n",
-       "  2019003248.0,\n",
+       " 'E02002183_0975': [2019003248.0,\n",
        "  2019002439.0,\n",
        "  2019004629.0,\n",
        "  2019004633.0,\n",
@@ -120661,7 +120447,97 @@
        "  2019004970.0,\n",
        "  2019005229.0,\n",
        "  2021000653.0,\n",
-       "  2022001813.0],\n",
+       "  2022001813.0,\n",
+       "  2019002663.0,\n",
+       "  2019005569.0,\n",
+       "  2019004176.0,\n",
+       "  2019004228.0,\n",
+       "  2019005372.0,\n",
+       "  2019002792.0,\n",
+       "  2019001007.0,\n",
+       "  2019002928.0,\n",
+       "  2019000558.0,\n",
+       "  2019002323.0,\n",
+       "  2019001023.0,\n",
+       "  2019001454.0,\n",
+       "  2019002635.0,\n",
+       "  2019005406.0,\n",
+       "  2019006609.0,\n",
+       "  2019001031.0,\n",
+       "  2019005322.0,\n",
+       "  2019003044.0,\n",
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+       "  2019006494.0,\n",
+       "  2019002109.0,\n",
+       "  2019001191.0,\n",
+       "  2019000268.0,\n",
+       "  2019006726.0,\n",
+       "  2019005958.0,\n",
+       "  2019003173.0,\n",
+       "  2019002594.0,\n",
+       "  2019002592.0,\n",
+       "  2019003230.0,\n",
+       "  2019003350.0,\n",
+       "  2019005801.0,\n",
+       "  2019006019.0,\n",
+       "  2019003873.0,\n",
+       "  2019003874.0,\n",
+       "  2021003932.0,\n",
+       "  2021003791.0,\n",
+       "  2021002614.0,\n",
+       "  2021003985.0,\n",
+       "  2021003740.0,\n",
+       "  2021003736.0,\n",
+       "  2021001443.0,\n",
+       "  2021001141.0,\n",
+       "  2021003936.0,\n",
+       "  2021002368.0,\n",
+       "  2021002165.0,\n",
+       "  2021001442.0,\n",
+       "  2021001454.0,\n",
+       "  2021004586.0,\n",
+       "  2021000571.0,\n",
+       "  2021004585.0,\n",
+       "  2021002211.0,\n",
+       "  2021002166.0,\n",
+       "  2021002182.0,\n",
+       "  2021001941.0,\n",
+       "  2021001679.0,\n",
+       "  2021002056.0,\n",
+       "  2021001633.0,\n",
+       "  2021000379.0,\n",
+       "  2021001867.0,\n",
+       "  2021003611.0,\n",
+       "  2021003507.0,\n",
+       "  2021003462.0,\n",
+       "  2021003303.0,\n",
+       "  2022001473.0,\n",
+       "  2022000093.0,\n",
+       "  2022000165.0,\n",
+       "  2022002843.0,\n",
+       "  2022003206.0,\n",
+       "  2022003545.0,\n",
+       "  2022003547.0,\n",
+       "  2022000809.0,\n",
+       "  2022000911.0,\n",
+       "  2022001569.0,\n",
+       "  2022001572.0,\n",
+       "  2022001655.0,\n",
+       "  2022001871.0,\n",
+       "  2022002033.0,\n",
+       "  2022002070.0,\n",
+       "  2022002300.0,\n",
+       "  2022003680.0,\n",
+       "  2022003740.0,\n",
+       "  2022004034.0,\n",
+       "  2022004044.0,\n",
+       "  2022004094.0,\n",
+       "  2022004168.0,\n",
+       "  2022000415.0,\n",
+       "  2022000443.0,\n",
+       "  2022004218.0,\n",
+       "  2022004333.0],\n",
        " 'E02002183_0976': [2019005583.0,\n",
        "  2019005588.0,\n",
        "  2019002814.0,\n",
@@ -120802,9 +120678,7 @@
        "  2022003996.0,\n",
        "  2022000490.0,\n",
        "  2022004267.0],\n",
-       " 'E02002183_0979': [nan,\n",
-       "  nan,\n",
-       "  2019000349.0,\n",
+       " 'E02002183_0979': [2019000349.0,\n",
        "  2022002873.0,\n",
        "  2019002794.0,\n",
        "  2019003197.0,\n",
@@ -122614,7 +122488,7 @@
        " ...}"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122639,12 +122513,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -122676,7 +122550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -122700,7 +122574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -122718,7 +122592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -123088,7 +122962,7 @@
        "4  [2019001219.0, 2019001010.0, 2019000952.0, 201...  "
       ]
      },
-     "execution_count": 41,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123111,7 +122985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123126,10 +123000,11 @@
     "original list of values.\n",
     "\n",
     "'''\n",
-    "matches_hh_level_sample = {key: np.random.choice(value) for key, value in matches_hh_level.items()}\n",
-    "\n",
-    "# remove items in list where value is nan\n",
-    "matches_hh_level_sample = {key: value for key, value in matches_hh_level_sample.items() if not pd.isna(value)}\n"
+    "matches_hh_level_sample = {\n",
+    "    key: np.random.choice(value)  # Randomly select one match from the list of matches\n",
+    "    for key, value in matches_hh_level.items()  # Iterate over each key-value pair in matches_hh_level\n",
+    "    if value and not pd.isna(np.random.choice(value))  # Ensure the value list is not empty and the selected match is not NaN\n",
+    "}\n"
    ]
   },
   {
@@ -123158,13 +123033,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# same logic as cell above, but repeat it multiple times and store each result as a separate dictionary in a list\n",
-    "matches_hh_level_sample_list = [{key: np.random.choice(value) for key, value in matches_hh_level.items()} for i in range(100)]\n",
-    "\n",
+    "# Same logic as above, but repeat it multiple times and store each result as a separate dictionary in a list\n",
+    "matches_hh_level_sample_list = [\n",
+    "    {\n",
+    "        key: np.random.choice(value)  # Randomly select one match from the list of matches\n",
+    "        for key, value in matches_hh_level.items()  # Iterate over each key-value pair in matches_hh_level\n",
+    "        if value and not pd.isna(np.random.choice(value))  # Ensure the value list is not empty and the selected match is not NaN\n",
+    "    }\n",
+    "    for i in range(25)  # Repeat the process 25 times\n",
+    "]\n",
     "#matches_hh_level_sample_list"
    ]
   },
@@ -123242,7 +123123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123274,7 +123155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123292,7 +123173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123310,9 +123191,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 366174,\n",
+       " 1: 372226,\n",
+       " 2: 372227,\n",
+       " 3: 374715,\n",
+       " 4: 374714,\n",
+       " 5: 365881,\n",
+       " 8: 367254,\n",
+       " 7: 367252,\n",
+       " 6: 367253,\n",
+       " 9: 350173}"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import itertools\n",
     "\n",
@@ -123322,7 +123223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123351,9 +123252,450 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [],
+   "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>pid_hs</th>\n",
+       "      <th>msoa11cd</th>\n",
+       "      <th>oa11cd</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>109</th>\n",
+       "      <td>109</td>\n",
+       "      <td>58</td>\n",
+       "      <td>2903991</td>\n",
+       "      <td>E02002183</td>\n",
+       "      <td>E00053936</td>\n",
+       "      <td>[109, 110, 111]</td>\n",
+       "      <td>S</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3719.050049</td>\n",
+       "      <td>14.304039</td>\n",
+       "      <td>E02002183_0059</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>86</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3719.050049</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>E00053936</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>2.0</td>\n",
+       "      <td>[2022003362.0, 2019001152.0, 2019002761.0, 201...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2021009021.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>111</td>\n",
+       "      <td>58</td>\n",
+       "      <td>2911731</td>\n",
+       "      <td>E02002183</td>\n",
+       "      <td>E00053936</td>\n",
+       "      <td>[109, 110, 111]</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0059</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3719.050049</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>E00053936</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>2.0</td>\n",
+       "      <td>[2022003362.0, 2019001152.0, 2019002761.0, 201...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2021009023.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>110</td>\n",
+       "      <td>58</td>\n",
+       "      <td>2912327</td>\n",
+       "      <td>E02002183</td>\n",
+       "      <td>E00053936</td>\n",
+       "      <td>[109, 110, 111]</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0059</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3719.050049</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>E00053936</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>2.0</td>\n",
+       "      <td>[2022003362.0, 2019001152.0, 2019002761.0, 201...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2021009022.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>112</td>\n",
+       "      <td>59</td>\n",
+       "      <td>2905420</td>\n",
+       "      <td>E02002183</td>\n",
+       "      <td>E00053953</td>\n",
+       "      <td>[112, 113]</td>\n",
+       "      <td>M</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0060</td>\n",
+       "      <td>4.0</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>72</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</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>2022000807.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>113</td>\n",
+       "      <td>59</td>\n",
+       "      <td>2901528</td>\n",
+       "      <td>E02002183</td>\n",
+       "      <td>E00053953</td>\n",
+       "      <td>[112, 113]</td>\n",
+       "      <td>R</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0060</td>\n",
+       "      <td>4.0</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>0.0</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>2022000808.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>114</td>\n",
+       "      <td>60</td>\n",
+       "      <td>2900316</td>\n",
+       "      <td>E02002183</td>\n",
+       "      <td>E00053699</td>\n",
+       "      <td>[114, 115]</td>\n",
+       "      <td>J</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0061</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>78</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</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>E00053699</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>[2019000929.0, 2019003199.0, 2019003198.0, 201...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2021003253.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      id household   pid_hs   msoa11cd     oa11cd          members sic1d2007  \\\n",
+       "109  109        58  2903991  E02002183  E00053936  [109, 110, 111]         S   \n",
+       "111  111        58  2911731  E02002183  E00053936  [109, 110, 111]      None   \n",
+       "110  110        58  2912327  E02002183  E00053936  [109, 110, 111]      None   \n",
+       "112  112        59  2905420  E02002183  E00053953       [112, 113]         M   \n",
+       "113  113        59  2901528  E02002183  E00053953       [112, 113]         R   \n",
+       "114  114        60  2900316  E02002183  E00053699       [114, 115]         J   \n",
+       "\n",
+       "    sic2d2007 pwkstat salary_yearly salary_hourly             hid  \\\n",
+       "109      94.0       2   3719.050049     14.304039  E02002183_0059   \n",
+       "111       NaN       0           NaN           NaN  E02002183_0059   \n",
+       "110       NaN       6           NaN           NaN  E02002183_0059   \n",
+       "112      71.0       6           NaN           NaN  E02002183_0060   \n",
+       "113      93.0       6           NaN           NaN  E02002183_0060   \n",
+       "114      58.0       4           NaN           NaN  E02002183_0061   \n",
+       "\n",
+       "    accommodation_type communal_type num_rooms central_heat tenure num_cars  \\\n",
+       "109                3.0           NaN       6.0         True    5.0        0   \n",
+       "111                3.0           NaN       6.0         True    5.0        0   \n",
+       "110                3.0           NaN       6.0         True    5.0        0   \n",
+       "112                4.0           NaN       6.0         True    2.0        2   \n",
+       "113                4.0           NaN       6.0         True    2.0        2   \n",
+       "114                3.0           NaN       6.0         True    1.0        2   \n",
+       "\n",
+       "    sex age_years ethnicity nssec8 salary_yearly_hh salary_yearly_hh_cat  \\\n",
+       "109   1        86         1    2.0      3719.050049                    1   \n",
+       "111   2        13         1    2.0      3719.050049                    1   \n",
+       "110   2        76         1    2.0      3719.050049                    1   \n",
+       "112   1        72         1    2.0              0.0                    1   \n",
+       "113   2        68         1    2.0              0.0                    1   \n",
+       "114   1        78         1    3.0              0.0                    1   \n",
+       "\n",
+       "    is_adult num_adults is_child num_children is_pension_age num_pension_age  \\\n",
+       "109        1          2        0            1              1               2   \n",
+       "111        0          2        1            1              0               2   \n",
+       "110        1          2        0            1              1               2   \n",
+       "112        1          2        0            0              1               2   \n",
+       "113        1          2        0            0              1               2   \n",
+       "114        1          2        0            0              1               2   \n",
+       "\n",
+       "    pwkstat_FT_hh pwkstat_PT_hh pwkstat_NTS_match     OA11CD  \\\n",
+       "109             0             1                 2  E00053936   \n",
+       "111             0             1                 2  E00053936   \n",
+       "110             0             1                 2  E00053936   \n",
+       "112             0             0                 1  E00053953   \n",
+       "113             0             0                 1  E00053953   \n",
+       "114             0             0                 1  E00053699   \n",
+       "\n",
+       "                     RUC11 RUC11CD Settlement2011EW_B03ID_spc  \\\n",
+       "109    Urban city and town      C1                      Urban   \n",
+       "111    Urban city and town      C1                      Urban   \n",
+       "110    Urban city and town      C1                      Urban   \n",
+       "112    Urban city and town      C1                      Urban   \n",
+       "113    Urban city and town      C1                      Urban   \n",
+       "114  Rural town and fringe      D1                      Rural   \n",
+       "\n",
+       "    Settlement2011EW_B04ID_spc Settlement2011EW_B03ID_spc_CD  \\\n",
+       "109        Urban City and Town                             1   \n",
+       "111        Urban City and Town                             1   \n",
+       "110        Urban City and Town                             1   \n",
+       "112        Urban City and Town                             1   \n",
+       "113        Urban City and Town                             1   \n",
+       "114      Rural Town and Fringe                             2   \n",
+       "\n",
+       "    Settlement2011EW_B04ID_spc_CD tenure_spc_for_matching  \\\n",
+       "109                             2                     2.0   \n",
+       "111                             2                     2.0   \n",
+       "110                             2                     2.0   \n",
+       "112                             2                     1.0   \n",
+       "113                             2                     1.0   \n",
+       "114                             3                     1.0   \n",
+       "\n",
+       "                                             nts_hh_id age_group    nts_ind_id  \n",
+       "109  [2022003362.0, 2019001152.0, 2019002761.0, 201...         9  2021009021.0  \n",
+       "111  [2022003362.0, 2019001152.0, 2019002761.0, 201...         3  2021009023.0  \n",
+       "110  [2022003362.0, 2019001152.0, 2019002761.0, 201...         9  2021009022.0  \n",
+       "112  [2019004130.0, 2019004126.0, 2019004144.0, 201...         9  2022000807.0  \n",
+       "113  [2019004130.0, 2019004126.0, 2019004144.0, 201...         9  2022000808.0  \n",
+       "114  [2019000929.0, 2019003199.0, 2019003198.0, 201...         9  2021003253.0  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#ids = [99, 100, 101, 102]\n",
     "ids = [109, 110, 111, 112, 113, 114]\n",
@@ -123384,9 +123726,393 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {},
-   "outputs": [],
+   "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>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>109</th>\n",
+       "      <td>109</td>\n",
+       "      <td>58</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3719.050049</td>\n",
+       "      <td>14.304039</td>\n",
+       "      <td>E02002183_0059</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>86</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3719.050049</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>Urban</td>\n",
+       "      <td>Urban City and Town</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>111</td>\n",
+       "      <td>58</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0059</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3719.050049</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</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>110</th>\n",
+       "      <td>110</td>\n",
+       "      <td>58</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0059</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>76</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3719.050049</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>Urban</td>\n",
+       "      <td>Urban City and Town</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>112</td>\n",
+       "      <td>59</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0060</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>72</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>113</th>\n",
+       "      <td>113</td>\n",
+       "      <td>59</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0060</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>68</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>114</th>\n",
+       "      <td>114</td>\n",
+       "      <td>60</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E02002183_0061</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>78</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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",
+       "109  109        58       2   3719.050049     14.304039  E02002183_0059    5.0   \n",
+       "111  111        58       0           NaN           NaN  E02002183_0059    5.0   \n",
+       "110  110        58       6           NaN           NaN  E02002183_0059    5.0   \n",
+       "112  112        59       6           NaN           NaN  E02002183_0060    2.0   \n",
+       "113  113        59       6           NaN           NaN  E02002183_0060    2.0   \n",
+       "114  114        60       4           NaN           NaN  E02002183_0061    1.0   \n",
+       "\n",
+       "    num_cars sex age_years age_group nssec8 salary_yearly_hh  \\\n",
+       "109        0   1        86         9    2.0      3719.050049   \n",
+       "111        0   2        13         3    2.0      3719.050049   \n",
+       "110        0   2        76         9    2.0      3719.050049   \n",
+       "112        2   1        72         9    2.0              0.0   \n",
+       "113        2   2        68         9    2.0              0.0   \n",
+       "114        2   1        78         9    3.0              0.0   \n",
+       "\n",
+       "    salary_yearly_hh_cat is_adult is_child is_pension_age pwkstat_FT_hh  \\\n",
+       "109                    1        1        0              1             0   \n",
+       "111                    1        0        1              0             0   \n",
+       "110                    1        1        0              1             0   \n",
+       "112                    1        1        0              1             0   \n",
+       "113                    1        1        0              1             0   \n",
+       "114                    1        1        0              1             0   \n",
+       "\n",
+       "    pwkstat_PT_hh pwkstat_NTS_match Settlement2011EW_B03ID_spc  \\\n",
+       "109             1                 2                      Urban   \n",
+       "111             1                 2                      Urban   \n",
+       "110             1                 2                      Urban   \n",
+       "112             0                 1                      Urban   \n",
+       "113             0                 1                      Urban   \n",
+       "114             0                 1                      Rural   \n",
+       "\n",
+       "    Settlement2011EW_B04ID_spc Settlement2011EW_B03ID_spc_CD  \\\n",
+       "109        Urban City and Town                             1   \n",
+       "111        Urban City and Town                             1   \n",
+       "110        Urban City and Town                             1   \n",
+       "112        Urban City and Town                             1   \n",
+       "113        Urban City and Town                             1   \n",
+       "114      Rural Town and Fringe                             2   \n",
+       "\n",
+       "    Settlement2011EW_B04ID_spc_CD  \n",
+       "109                             2  \n",
+       "111                             2  \n",
+       "110                             2  \n",
+       "112                             2  \n",
+       "113                             2  \n",
+       "114                             3  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>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>367718</th>\n",
+       "      <td>2.021009e+09</td>\n",
+       "      <td>2.021004e+09</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367720</th>\n",
+       "      <td>2.021009e+09</td>\n",
+       "      <td>2.021004e+09</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367719</th>\n",
+       "      <td>2.021009e+09</td>\n",
+       "      <td>2.021004e+09</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377807</th>\n",
+       "      <td>2.022001e+09</td>\n",
+       "      <td>2.022000e+09</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377808</th>\n",
+       "      <td>2.022001e+09</td>\n",
+       "      <td>2.022000e+09</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364252</th>\n",
+       "      <td>2.021003e+09</td>\n",
+       "      <td>2.021001e+09</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        IndividualID   HouseholdID  Age_B01ID  age_group  sex  OfPenAge_B01ID  \\\n",
+       "367718  2.021009e+09  2.021004e+09       14.0        7.0  1.0             2.0   \n",
+       "367720  2.021009e+09  2.021004e+09        3.0        1.0  2.0             2.0   \n",
+       "367719  2.021009e+09  2.021004e+09       13.0        6.0  2.0             2.0   \n",
+       "377807  2.022001e+09  2.022000e+09       19.0        9.0  1.0             1.0   \n",
+       "377808  2.022001e+09  2.022000e+09       17.0        9.0  2.0             1.0   \n",
+       "364252  2.021003e+09  2.021001e+09       20.0        9.0  1.0             1.0   \n",
+       "\n",
+       "        IndIncome2002_B02ID  \n",
+       "367718                  3.0  \n",
+       "367720                 -9.0  \n",
+       "367719                  2.0  \n",
+       "377807                  3.0  \n",
+       "377808                  1.0  \n",
+       "364252                  1.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from IPython.display import display\n",
     "\n",
diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index 8233ad5..aaf766b 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -837,23 +837,26 @@ def get_interim_path(
     original list of values.
 
     """
+    # Randomly sample one match per household if it has one match or more
     matches_hh_level_sample = {
-        key: np.random.choice(value) for key, value in matches_hh_level.items()
-    }
-
-    # remove items in list where value is nan
-    matches_hh_level_sample = {
-        key: value
-        for key, value in matches_hh_level_sample.items()
-        if not pd.isna(value)
+        key: np.random.choice(value)
+        for key, value in matches_hh_level.items()
+        if value
+        and not pd.isna(
+            np.random.choice(value)
+        )  # Ensure the value list is not empty and the selected match is not NaN
     }
 
     # Multiple matches in case we want to try stochastic runs
 
-    # same logic as cell above, but repeat it multiple times and store each result as a separate dictionary in a list
+    # Same logic as above, but repeat it multiple times and store each result as a separate dictionary in a list
     matches_hh_level_sample_list = [
-        {key: np.random.choice(value) for key, value in matches_hh_level.items()}
-        for i in range(25)
+        {
+            key: np.random.choice(value)
+            for key, value in matches_hh_level.items()
+            if value and not pd.isna(np.random.choice(value))
+        }
+        for i in range(25)  # Repeat the process 25 times
     ]
 
     logger.info("Categorical matching: Random sampling complete")

From 3004f28e15ab4324729193d975219ba0ec7e165a Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Fri, 18 Oct 2024 13:58:16 +0100
Subject: [PATCH 05/27] example exact matching

---
 notebooks/2.1_sandbox-match_households.ipynb  |     36 +-
 .../2_match_households_and_individuals.ipynb  | 119326 +--------------
 2 files changed, 115 insertions(+), 119247 deletions(-)

diff --git a/notebooks/2.1_sandbox-match_households.ipynb b/notebooks/2.1_sandbox-match_households.ipynb
index 2635a6d..ed27282 100644
--- a/notebooks/2.1_sandbox-match_households.ipynb
+++ b/notebooks/2.1_sandbox-match_households.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
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@@ -299,7 +299,7 @@
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@@ -319,7 +319,7 @@
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@@ -471,7 +471,7 @@
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@@ -530,7 +530,7 @@
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    "source": [
@@ -610,7 +610,7 @@
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    "source": [
@@ -789,7 +789,7 @@
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@@ -879,7 +879,7 @@
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    "source": [
@@ -945,7 +945,7 @@
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    "source": [
@@ -986,7 +986,7 @@
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diff --git a/notebooks/2_match_households_and_individuals.ipynb b/notebooks/2_match_households_and_individuals.ipynb
index 925f49b..388ccd9 100644
--- a/notebooks/2_match_households_and_individuals.ipynb
+++ b/notebooks/2_match_households_and_individuals.ipynb
@@ -3262,119239 +3262,107 @@
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   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 60,
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+     ]
+    }
+   ],
+   "source": [
+    "# show some results\n",
+    "for key, value in list(matches_hh_level.items())[0:10]:\n",
+    "    print(f\"{key}: {value}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we want to do an exact match, we need to \n",
+    "- add all columns to required_columns\n",
+    "- keep optional_cols empty\n",
+    "- make n_matches = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Categorical matching level 0: ['num_adults', 'num_children', 'num_cars', 'num_pension_age', 'Settlement2011EW_B03ID_spc_CD']\n",
+      "matching rows  0 to 6725  out of  6725\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define required columns for matching\n",
+    "required_columns_fixed = [\n",
+    "    \"number_adults\",\n",
+    "    \"number_children\",\n",
+    "    \"number_cars\",\n",
+    "    \"num_pension_age\",\n",
+    "    \"rural_urban_2_categories\",\n",
+    "]\n",
+    "\n",
+    "# Define optional columns in order of importance (most to least important)\n",
+    "optional_columns_fixed = []\n",
+    "# # Match using iterative matching\n",
+    "matcher_fixed = MatcherExact(\n",
+    "    df_pop=spc_matching,\n",
+    "    df_pop_id=\"hid\",\n",
+    "    df_sample=nts_matching,\n",
+    "    df_sample_id=\"HouseholdID\",\n",
+    "    matching_dict = matching_dfs_dict,\n",
+    "    fixed_cols=required_columns_fixed,\n",
+    "    optional_cols=optional_columns_fixed,\n",
+    "    n_matches=0,  \n",
+    "    chunk_size=50000,\n",
+    "    show_progress=True,\n",
+    ")\n",
+    "\n",
+    "matches_hh_level_fixed = matcher_fixed.iterative_match_categorical()\n",
+    "#print(matches_hh_level)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
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+      "E02002183_0010: [2019004119.0, 2019004122.0, 2019002766.0, 2019005433.0, 2019006455.0, 2019000352.0, 2019001093.0, 2019000815.0, 2019001170.0, 2019005351.0, 2019005364.0, 2019002325.0, 2019005745.0, 2019004541.0, 2019005554.0, 2019005551.0, 2019004712.0, 2019001179.0, 2019002738.0, 2019005522.0, 2019004542.0, 2019005536.0, 2019005301.0, 2019002367.0, 2019000568.0, 2019001296.0, 2019002298.0, 2019001948.0, 2019002505.0, 2019003363.0, 2019003359.0, 2019000238.0, 2019004234.0, 2019005388.0, 2019002955.0, 2019002959.0, 2019005751.0, 2019005762.0, 2019005897.0, 2019005175.0, 2019005919.0, 2019002120.0, 2019005915.0, 2019003421.0, 2019000205.0, 2019005014.0, 2019006204.0, 2019006616.0, 2019000085.0, 2019005297.0, 2019005315.0, 2019001260.0, 2019003071.0, 2019001301.0, 2019003734.0, 2019006732.0, 2019003718.0, 2019003488.0, 2019004640.0, 2019000541.0, 2019005782.0, 2019001627.0, 2019005084.0, 2019001668.0, 2019002645.0, 2019003232.0, 2019001965.0, 2019002004.0, 2019002013.0, 2019001953.0, 2019001548.0, 2019006507.0, 2019001357.0, 2019001345.0, 2019001446.0, 2019003465.0, 2019004595.0, 2019001673.0, 2019002117.0, 2019006155.0, 2019006625.0, 2019006214.0, 2019005838.0, 2019002043.0, 2019002056.0, 2019002055.0, 2019002054.0, 2019002875.0, 2019005674.0, 2019000921.0, 2019003920.0, 2019003808.0, 2019005060.0, 2019002154.0, 2019004571.0, 2019003457.0, 2019004388.0, 2019003728.0, 2019006768.0, 2019006789.0, 2019002185.0, 2019004898.0, 2019006757.0, 2019000445.0, 2019003111.0, 2019003151.0, 2019000465.0, 2019000469.0, 2019003146.0, 2019003144.0, 2019006403.0, 2019006408.0, 2019003221.0, 2019003219.0, 2019002604.0, 2019004338.0, 2019003769.0, 2019004803.0, 2019004798.0, 2019004370.0, 2019004840.0, 2019006816.0, 2019003367.0, 2019003368.0, 2019000220.0, 2019006030.0, 2019003834.0, 2019004918.0, 2019003835.0, 2019004494.0, 2019003877.0, 2021003777.0, 2021003763.0, 2021002337.0, 2021002334.0, 2021002833.0, 2021004027.0, 2021004050.0, 2021004058.0, 2021002542.0, 2021002557.0, 2021000537.0, 2021001901.0, 2021004333.0, 2021004339.0, 2021001197.0, 2021000198.0, 2021000261.0, 2021000992.0, 2021003968.0, 2021003761.0, 2021002702.0, 2021002671.0, 2021002705.0, 2021001205.0, 2021001451.0, 2021001210.0, 2021000581.0, 2021001235.0, 2021004362.0, 2021000611.0, 2021001281.0, 2021000613.0, 2021002519.0, 2021002492.0, 2021004696.0, 2021000328.0, 2021003522.0, 2021000368.0, 2021002748.0, 2021002952.0, 2021001550.0, 2021002006.0, 2021001549.0, 2021003324.0, 2021000229.0, 2021001709.0, 2021000008.0, 2021000002.0, 2021000023.0, 2021002187.0, 2021000734.0, 2021004661.0, 2021000365.0, 2021004732.0, 2021000100.0, 2021002068.0, 2021004156.0, 2021001151.0, 2021000865.0, 2021001398.0, 2021003747.0, 2021003246.0, 2021003424.0, 2021000672.0, 2021001606.0, 2021000509.0, 2021002052.0, 2021001615.0, 2021002291.0, 2021004418.0, 2021004398.0, 2021001123.0, 2021001121.0, 2021003095.0, 2022001117.0, 2022001191.0, 2022001225.0, 2022001296.0, 2022001351.0, 2022001426.0, 2022001522.0, 2022001559.0, 2022000004.0, 2022000052.0, 2022000056.0, 2022000057.0, 2022000097.0, 2022000109.0, 2022000182.0, 2022000227.0, 2022002687.0, 2022002697.0, 2022002779.0, 2022002821.0, 2022002844.0, 2022003131.0, 2022003215.0, 2022003249.0, 2022003268.0, 2022003319.0, 2022003345.0, 2022003411.0, 2022003498.0, 2022003523.0, 2022000548.0, 2022000756.0, 2022000759.0, 2022000856.0, 2022001014.0, 2022001589.0, 2022001611.0, 2022001630.0, 2022001639.0, 2022001764.0, 2022001939.0, 2022001948.0, 2022002017.0, 2022002031.0, 2022002131.0, 2022002181.0, 2022002188.0, 2022002330.0, 2022002563.0, 2022003704.0, 2022003819.0, 2022003831.0, 2022003939.0, 2022004038.0, 2022004116.0, 2022004131.0, 2022004146.0, 2022000328.0, 2022000330.0, 2022000482.0, 2022002891.0, 2022002909.0, 2022002916.0, 2022002997.0, 2022003108.0, 2022004189.0, 2022004324.0, 2022004361.0, 2022004367.0]\n"
+     ]
     }
    ],
    "source": [
-    "matches_hh_level"
+    "# show some results\n",
+    "for key, value in list(matches_hh_level_fixed.items())[0:10]:\n",
+    "    print(f\"{key}: {value}\")"
    ]
   },
   {
@@ -122513,7 +3381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -122550,7 +3418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {

From 919673c140ca7036d0385d298849dd521e1a1421 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Mon, 21 Oct 2024 11:58:53 +0100
Subject: [PATCH 06/27] Remove obsolete time use data

---
 scripts/1_prep_synthpop.py | 29 -----------------------------
 1 file changed, 29 deletions(-)

diff --git a/scripts/1_prep_synthpop.py b/scripts/1_prep_synthpop.py
index 4d3d0e1..3df2dd8 100644
--- a/scripts/1_prep_synthpop.py
+++ b/scripts/1_prep_synthpop.py
@@ -27,35 +27,6 @@ def main(config_file):
         acbm.root_path / f"data/external/spc_output/{region}_people_hh.parquet"
     )
 
-    # People and time-use data
-    # Subset of (non-time-use) features to include and unnest
-    # The features can be found here: https://github.com/alan-turing-institute/uatk-spc/blob/main/synthpop.proto
-    features = {
-        "health": [
-            "bmi",
-            "has_cardiovascular_disease",
-            "has_diabetes",
-            "has_high_blood_pressure",
-            "self_assessed_health",
-            "life_satisfaction",
-        ],
-        "demographics": ["age_years", "ethnicity", "sex", "nssec8"],
-        "employment": ["sic1d2007", "sic2d2007", "pwkstat", "salary_yearly"],
-    }
-
-    # build the table
-    spc_people_tu = (
-        Builder(path, region, backend="polars", input_type="parquet")
-        .add_households()
-        .add_time_use_diaries(features, diary_type="weekday_diaries")
-        .build()
-    )
-
-    # save the output
-    spc_people_tu.write_parquet(
-        acbm.root_path / f"data/external/spc_output/{region}_people_tu.parquet"
-    )
-
 
 if __name__ == "__main__":
     main()

From db525102b42df962b2a116e733f5fb3f65950d0d Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Mon, 21 Oct 2024 12:00:02 +0100
Subject: [PATCH 07/27] Fix hard coded region

---
 scripts/2_match_households_and_individuals.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index aaf766b..7595cf60 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -39,14 +39,12 @@ def get_interim_path(
 
     # ### SPC
 
-    # useful variables
-    region = "leeds"
-
     logger.info("Loading SPC data")
 
     # Read in the spc data (parquet format)
     spc = pd.read_parquet(
-        acbm.root_path / "data/external/spc_output/" f"{region}_people_hh.parquet"
+        acbm.root_path / "data/external/spc_output/"
+        f"{config.region}_people_hh.parquet"
     )
 
     logger.info("Filtering SPC data to specific columns")

From 91e9f07b51c7e34b2228d30b3f6caa2219b21717 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 10:11:11 +0100
Subject: [PATCH 08/27] Refactor loop logic to improve performance

---
 src/acbm/matching.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/src/acbm/matching.py b/src/acbm/matching.py
index 9508fa7..88cafc2 100644
--- a/src/acbm/matching.py
+++ b/src/acbm/matching.py
@@ -264,13 +264,16 @@ def match_individuals(
     # Remove all unmateched households
     matches_hh = {key: value for key, value in matches_hh.items() if not pd.isna(value)}
 
-    # loop over all rows in the matches_hh dictionary
-    for i, (key, value) in enumerate(matches_hh.items(), 1):
-        # Get the rows in df1 and df2 that correspond to the matched hids
-        rows_df1 = df1[df1[df1_id] == key]
+    # loop over all groups of df1_id
+    for i, (key, rows_df1) in df1.groupby(df1_id):
+        try:
+            value = matches_hh[key]
+        except Exception:
+            # Continue if key not in matches_hh
+            continue
         rows_df2 = df2[df2[df2_id] == int(value)]
 
-        if show_progress:
+        if show_progress and i % 100 == 0:
             # Print the iteration number and the number of keys in the dict
             print(f"Matching for household {i} out of: {len(matches_hh)}")
 

From 69535ef9cbe3c0c3d2622881f684bdccad89a319 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 10:26:35 +0100
Subject: [PATCH 09/27] fix: add enumerate

---
 src/acbm/matching.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/acbm/matching.py b/src/acbm/matching.py
index 88cafc2..4b54376 100644
--- a/src/acbm/matching.py
+++ b/src/acbm/matching.py
@@ -265,7 +265,7 @@ def match_individuals(
     matches_hh = {key: value for key, value in matches_hh.items() if not pd.isna(value)}
 
     # loop over all groups of df1_id
-    for i, (key, rows_df1) in df1.groupby(df1_id):
+    for i, (key, rows_df1) in enumerate(df1.groupby(df1_id), 1):
         try:
             value = matches_hh[key]
         except Exception:

From 0d291d61c9f518fa4f849df7f7e2e6b8cfd3aad0 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 10:36:09 +0100
Subject: [PATCH 10/27] Add matching config and option to load household
 matches

---
 scripts/2_match_households_and_individuals.py | 333 +++++++++---------
 src/acbm/config.py                            |   6 +
 2 files changed, 180 insertions(+), 159 deletions(-)

diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index 7595cf60..c3be6a5 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -685,194 +685,209 @@ def get_interim_path(
     )  # fill the NaNs with the original values
 
     # ## Step 3: Matching at Household Level
+    if not config.matching.load_hh:
+        logger.info("Categorical matching: MATCHING HOUSEHOLDS")
+
+        #
+        # Now that we've prepared all the columns, we can start matching.
+
+        # ### 3.1 Categorical matching
+        #
+        # We will match on (a subset of) the following columns:
+        #
+        # | Matching variable | NTS column | SPC column |
+        # | ------------------| ---------- | ---------- |
+        # | Household income  | `HHIncome2002_BO2ID` | `salary_yearly_hh_cat` |
+        # | Number of adults  | `HHoldNumAdults` | `num_adults` |
+        # | Number of children | `HHoldNumChildren` | `num_children` |
+        # | Employment status | `HHoldEmploy_B01ID` | `pwkstat_NTS_match` |
+        # | Car ownership | `NumCar_SPC_match` | `num_cars` |
+        # | Type of tenancy | `tenure_nts_for_matching` | `tenure_spc_for_matching` |
+        # | Rural/Urban Classification | `Settlement2011EW_B03ID` | `Settlement2011EW_B03ID_spc_CD` |
+
+        # Prepare SPC df for matching
+
+        # Select multiple columns
+        spc_matching = spc_edited[
+            [
+                "hid",
+                "salary_yearly_hh_cat",
+                "num_adults",
+                "num_children",
+                "num_pension_age",
+                "pwkstat_NTS_match",
+                "num_cars",
+                "tenure_spc_for_matching",
+                "Settlement2011EW_B03ID_spc_CD",
+                "Settlement2011EW_B04ID_spc_CD",
+            ]
+        ]
 
-    logger.info("Categorical matching: MATCHING HOUSEHOLDS")
+        # edit the df so that we have one row per hid
+        spc_matching = spc_matching.drop_duplicates(subset="hid")
+
+        spc_matching.head(10)
+
+        # Prepare NTS df for matching
+
+        nts_matching = nts_households[
+            [
+                "HouseholdID",
+                "HHIncome2002_B02ID",
+                "HHoldNumAdults",
+                "HHoldNumChildren",
+                "num_pension_age_nts",
+                "HHoldEmploy_B01ID",
+                "NumCar_SPC_match",
+                "tenure_nts_for_matching",
+                "Settlement2011EW_B03ID",
+                "Settlement2011EW_B04ID",
+            ]
+        ]
 
-    #
-    # Now that we've prepared all the columns, we can start matching.
+        # Dictionary of matching columns. We extract column names from this dictioary when matching on a subset of the columns
 
-    # ### 3.1 Categorical matching
-    #
-    # We will match on (a subset of) the following columns:
-    #
-    # | Matching variable | NTS column | SPC column |
-    # | ------------------| ---------- | ---------- |
-    # | Household income  | `HHIncome2002_BO2ID` | `salary_yearly_hh_cat` |
-    # | Number of adults  | `HHoldNumAdults` | `num_adults` |
-    # | Number of children | `HHoldNumChildren` | `num_children` |
-    # | Employment status | `HHoldEmploy_B01ID` | `pwkstat_NTS_match` |
-    # | Car ownership | `NumCar_SPC_match` | `num_cars` |
-    # | Type of tenancy | `tenure_nts_for_matching` | `tenure_spc_for_matching` |
-    # | Rural/Urban Classification | `Settlement2011EW_B03ID` | `Settlement2011EW_B03ID_spc_CD` |
-
-    # Prepare SPC df for matching
-
-    # Select multiple columns
-    spc_matching = spc_edited[
-        [
-            "hid",
-            "salary_yearly_hh_cat",
-            "num_adults",
-            "num_children",
+        # column_names (keys) for the dictionary
+        matching_ids = [
+            "household_id",
+            "yearly_income",
+            "number_adults",
+            "number_children",
             "num_pension_age",
-            "pwkstat_NTS_match",
-            "num_cars",
-            "tenure_spc_for_matching",
-            "Settlement2011EW_B03ID_spc_CD",
-            "Settlement2011EW_B04ID_spc_CD",
+            "employment_status",
+            "number_cars",
+            "tenure_status",
+            "rural_urban_2_categories",
+            "rural_urban_4_categories",
         ]
-    ]
 
-    # edit the df so that we have one row per hid
-    spc_matching = spc_matching.drop_duplicates(subset="hid")
-
-    spc_matching.head(10)
+        # Dict with value qual to a list with spc_matching and nts_matching column names
+        matching_dfs_dict = {
+            column_name: [spc_value, nts_value]
+            for column_name, spc_value, nts_value in zip(
+                matching_ids, spc_matching, nts_matching
+            )
+        }
 
-    # Prepare NTS df for matching
+        # We match iteratively on a subset of columns. We start with all columns, and then remove
+        # one of the optionals columns at a time (relaxing the condition). Once a household has over n
+        # matches, we stop matching it to more matches. We continue until all optional columns are removed
 
-    nts_matching = nts_households[
-        [
-            "HouseholdID",
-            "HHIncome2002_B02ID",
-            "HHoldNumAdults",
-            "HHoldNumChildren",
-            "num_pension_age_nts",
-            "HHoldEmploy_B01ID",
-            "NumCar_SPC_match",
-            "tenure_nts_for_matching",
-            "Settlement2011EW_B03ID",
-            "Settlement2011EW_B04ID",
+        # Define required columns for matching
+        required_columns = [
+            "number_adults",
+            "number_children",
         ]
-    ]
 
-    # Dictionary of matching columns. We extract column names from this dictioary when matching on a subset of the columns
-
-    # column_names (keys) for the dictionary
-    matching_ids = [
-        "household_id",
-        "yearly_income",
-        "number_adults",
-        "number_children",
-        "num_pension_age",
-        "employment_status",
-        "number_cars",
-        "tenure_status",
-        "rural_urban_2_categories",
-        "rural_urban_4_categories",
-    ]
+        # Define optional columns in order of importance (most to least important)
+        optional_columns = [
+            "number_cars",
+            "num_pension_age",
+            "rural_urban_2_categories",
+            "employment_status",
+            "tenure_status",
+        ]
 
-    # Dict with value qual to a list with spc_matching and nts_matching column names
-    matching_dfs_dict = {
-        column_name: [spc_value, nts_value]
-        for column_name, spc_value, nts_value in zip(
-            matching_ids, spc_matching, nts_matching
+        matcher_exact = MatcherExact(
+            df_pop=spc_matching,
+            df_pop_id="hid",
+            df_sample=nts_matching,
+            df_sample_id="HouseholdID",
+            matching_dict=matching_dfs_dict,
+            fixed_cols=required_columns,
+            optional_cols=optional_columns,
+            n_matches=10,
+            chunk_size=50000,
+            show_progress=True,
         )
-    }
 
-    # We match iteratively on a subset of columns. We start with all columns, and then remove
-    # one of the optionals columns at a time (relaxing the condition). Once a household has over n
-    # matches, we stop matching it to more matches. We continue until all optional columns are removed
+        # Match
 
-    # Define required columns for matching
-    required_columns = [
-        "number_adults",
-        "number_children",
-    ]
+        matches_hh_level = matcher_exact.iterative_match_categorical()
 
-    # Define optional columns in order of importance (most to least important)
-    optional_columns = [
-        "number_cars",
-        "num_pension_age",
-        "rural_urban_2_categories",
-        "employment_status",
-        "tenure_status",
-    ]
+        # Number of unmatched households
 
-    matcher_exact = MatcherExact(
-        df_pop=spc_matching,
-        df_pop_id="hid",
-        df_sample=nts_matching,
-        df_sample_id="HouseholdID",
-        matching_dict=matching_dfs_dict,
-        fixed_cols=required_columns,
-        optional_cols=optional_columns,
-        n_matches=10,
-        chunk_size=50000,
-        show_progress=True,
-    )
-
-    # Match
-
-    matches_hh_level = matcher_exact.iterative_match_categorical()
+        # no. of keys where value is na
+        na_count = sum([1 for v in matches_hh_level.values() if pd.isna(v).all()])
 
-    # Number of unmatched households
-
-    # no. of keys where value is na
-    na_count = sum([1 for v in matches_hh_level.values() if pd.isna(v).all()])
-
-    logger.info(f"Categorical matching: {na_count} households in the SPC had no match")
-    logger.info(
-        f"{round((na_count / len(matches_hh_level)) * 100, 1)}% of households in the SPC had no match"
-    )
+        logger.info(
+            f"Categorical matching: {na_count} households in the SPC had no match"
+        )
+        logger.info(
+            f"{round((na_count / len(matches_hh_level)) * 100, 1)}% of households in the SPC had no match"
+        )
 
-    ## add matches_hh_level as a column in spc_edited
-    spc_edited["nts_hh_id"] = spc_edited["hid"].map(matches_hh_level)
+        ## add matches_hh_level as a column in spc_edited
+        spc_edited["nts_hh_id"] = spc_edited["hid"].map(matches_hh_level)
 
-    # ### Random Sampling from matched households
+        # ### Random Sampling from matched households
 
-    logger.info("Categorical matching: Randomly choosing one match per household")
-    #
-    # In categorical matching, many households in the SPC are matched to more than 1 household in the NTS. Which household to choose? We do random sampling
+        logger.info("Categorical matching: Randomly choosing one match per household")
+        #
+        # In categorical matching, many households in the SPC are matched to more than 1 household in the NTS. Which household to choose? We do random sampling
 
-    # for each key in the dictionary, sample 1 of the values associated with it and store it in a new dictionary
+        # for each key in the dictionary, sample 1 of the values associated with it and store it in a new dictionary
 
-    """
-    - iterate over each key-value pair in the matches_hh_result dictionary.
-    - For each key-value pair, use np.random.choice(value) to randomly select
-    one item from the list of values associated with the current key.
-    - create a new dictionary hid_to_HouseholdID_sample where each key from the
-    original dictionary is associated with one randomly selected value from the
-    original list of values.
+        """
+        - iterate over each key-value pair in the matches_hh_result dictionary.
+        - For each key-value pair, use np.random.choice(value) to randomly select
+        one item from the list of values associated with the current key.
+        - create a new dictionary hid_to_HouseholdID_sample where each key from the
+        original dictionary is associated with one randomly selected value from the
+        original list of values.
 
-    """
-    # Randomly sample one match per household if it has one match or more
-    matches_hh_level_sample = {
-        key: np.random.choice(value)
-        for key, value in matches_hh_level.items()
-        if value
-        and not pd.isna(
-            np.random.choice(value)
-        )  # Ensure the value list is not empty and the selected match is not NaN
-    }
-
-    # Multiple matches in case we want to try stochastic runs
-
-    # Same logic as above, but repeat it multiple times and store each result as a separate dictionary in a list
-    matches_hh_level_sample_list = [
-        {
+        """
+        # Randomly sample one match per household if it has one match or more
+        matches_hh_level_sample = {
             key: np.random.choice(value)
             for key, value in matches_hh_level.items()
-            if value and not pd.isna(np.random.choice(value))
+            if value
+            and not pd.isna(
+                np.random.choice(value)
+            )  # Ensure the value list is not empty and the selected match is not NaN
         }
-        for i in range(25)  # Repeat the process 25 times
-    ]
 
-    logger.info("Categorical matching: Random sampling complete")
+        # Multiple matches in case we want to try stochastic runs
 
-    # Save results
-    logger.info("Categorical matching: Saving results")
-    # random sample
-    with open(
-        get_interim_path("matches_hh_level_categorical_random_sample.pkl"), "wb"
-    ) as f:
-        pkl.dump(matches_hh_level_sample, f)
+        # Same logic as above, but repeat it multiple times and store each result as a separate dictionary in a list
+        matches_hh_level_sample_list = [
+            {
+                key: np.random.choice(value)
+                for key, value in matches_hh_level.items()
+                if value and not pd.isna(np.random.choice(value))
+            }
+            for i in range(25)  # Repeat the process 25 times
+        ]
 
-    # multiple random samples
-    with open(
-        get_interim_path("matches_hh_level_categorical_random_sample_multiple.pkl"),
-        "wb",
-    ) as f:
-        pkl.dump(matches_hh_level_sample_list, f)
+        logger.info("Categorical matching: Random sampling complete")
+
+        # Save results
+        logger.info("Categorical matching: Saving results")
+        # random sample
+        with open(
+            get_interim_path("matches_hh_level_categorical_random_sample.pkl"), "wb"
+        ) as f:
+            pkl.dump(matches_hh_level_sample, f)
+
+        # multiple random samples
+        with open(
+            get_interim_path("matches_hh_level_categorical_random_sample_multiple.pkl"),
+            "wb",
+        ) as f:
+            pkl.dump(matches_hh_level_sample_list, f)
+    else:
+        # Load matching result
+        with open(
+            get_interim_path("matches_hh_level_categorical_random_sample.pkl"), "rb"
+        ) as f:
+            matches_hh_level_sample = pkl.load(f)
+
+        # multiple random samples
+        with open(
+            get_interim_path("matches_hh_level_categorical_random_sample_multiple.pkl"),
+            "rb",
+        ) as f:
+            matches_hh_level_sample_list = pkl.load(f)
 
     # Do the same at the df level. Add nts_hh_id_sample column to the spc df
 
diff --git a/src/acbm/config.py b/src/acbm/config.py
index 1ae03fd..98e0e58 100644
--- a/src/acbm/config.py
+++ b/src/acbm/config.py
@@ -17,6 +17,11 @@ class Parameters(BaseModel):
     boundary_geography: str
 
 
+@dataclass(frozen=True)
+class MatchingParams(BaseModel):
+    load_hh: bool
+
+
 @dataclass(frozen=True)
 class WorkAssignmentParams(BaseModel):
     use_percentages: bool
@@ -30,6 +35,7 @@ class Config(BaseModel):
     work_assignment: WorkAssignmentParams = Field(
         description="Config: parameters for work assignment."
     )
+    matching: MatchingParams = Field(description="Config: parameters for matching.")
 
     @property
     def seed(self) -> int:

From ea5d7f0ac9fe1c12ede9f8918b922fd02c6151c4 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 17:19:25 +0100
Subject: [PATCH 11/27] Subset columns for SPC with NTS output dataframe

---
 scripts/2_match_households_and_individuals.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index c3be6a5..2652428 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -8,6 +8,7 @@
 # from joblib import Parallel, delayed
 # from tqdm import trange
 import acbm
+from acbm.assigning.utils import cols_for_assignment_all
 from acbm.cli import acbm_cli
 from acbm.config import load_config
 from acbm.logger_config import matching_logger as logger
@@ -1114,9 +1115,20 @@ def get_interim_path(
     # convert the nts_ind_id column to int for merging
     spc_edited_copy["nts_ind_id"] = spc_edited_copy["nts_ind_id"].astype(int)
 
+    # Add output columns required for assignment scripts
+    spc_output_cols = [
+        col for col in spc_edited_copy.columns if col in cols_for_assignment_all()
+    ]
+    nts_output_cols = [
+        col for col in nts_trips.columns if col in cols_for_assignment_all()
+    ]
+
     # merge the copy with nts_trips using IndividualID
-    spc_edited_copy = spc_edited_copy.merge(
-        nts_trips, left_on="nts_ind_id", right_on="IndividualID", how="left"
+    spc_edited_copy = spc_edited_copy[spc_output_cols].merge(
+        nts_trips[nts_output_cols],
+        left_on="nts_ind_id",
+        right_on="IndividualID",
+        how="left",
     )
 
     # save the file as a parquet file

From 2dea50dc7eb9720d64c0f55851da9537d2b3c971 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 17:24:22 +0100
Subject: [PATCH 12/27] Add option to load individual matches

---
 scripts/2_match_households_and_individuals.py | 116 +++++++++---------
 src/acbm/config.py                            |   3 +-
 2 files changed, 63 insertions(+), 56 deletions(-)

diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index 2652428..9fb01d1 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -908,69 +908,75 @@ def get_interim_path(
     #
     #
 
-    logger.info("Statistical matching: MATCHING INDIVIDUALS")
-
-    # Create an 'age' column in the SPC that matches the NTS categories
-
-    # create a dictionary for reference on how the labels for "Age_B04ID" match the actual age brackets
-
-    # dict_nts_ind_age = {-10: 'DEAD',
-    #                     -8: 'NA',
-    #                     1: '0-4',
-    #                     2: '5-10',
-    #                     3: '11-16',
-    #                     4: '17-20',
-    #                     5: '21-29',
-    #                     6: '30-39',
-    #                     7: '40-49',
-    #                     8: '50-59',
-    #                     9: '60+'
-    #                     }
-
-    # Define the bins and labels based on dict_nts_ind_age
-    bins = [0, 4, 10, 16, 20, 29, 39, 49, 59, np.inf]
-    labels = [1, 2, 3, 4, 5, 6, 7, 8, 9]
-
-    # Create a new column in spc_edited that maps the age_years to the keys of dict_nts_ind_age
-    spc_edited["age_group"] = (
-        pd.cut(spc_edited["age_years"], bins=bins, labels=labels)
-        .astype("int")
-        .fillna(-8)
-    )
+    if not config.matching.load_ind:
+        logger.info("Statistical matching: MATCHING INDIVIDUALS")
+
+        # Create an 'age' column in the SPC that matches the NTS categories
+
+        # create a dictionary for reference on how the labels for "Age_B04ID" match the actual age brackets
+
+        # dict_nts_ind_age = {-10: 'DEAD',
+        #                     -8: 'NA',
+        #                     1: '0-4',
+        #                     2: '5-10',
+        #                     3: '11-16',
+        #                     4: '17-20',
+        #                     5: '21-29',
+        #                     6: '30-39',
+        #                     7: '40-49',
+        #                     8: '50-59',
+        #                     9: '60+'
+        #                     }
+
+        # Define the bins and labels based on dict_nts_ind_age
+        bins = [0, 4, 10, 16, 20, 29, 39, 49, 59, np.inf]
+        labels = [1, 2, 3, 4, 5, 6, 7, 8, 9]
+
+        # Create a new column in spc_edited that maps the age_years to the keys of dict_nts_ind_age
+        spc_edited["age_group"] = (
+            pd.cut(spc_edited["age_years"], bins=bins, labels=labels)
+            .astype("int")
+            .fillna(-8)
+        )
 
-    # rename nts columns in preparation for matching
+        # rename nts columns in preparation for matching
 
-    nts_individuals.rename(
-        columns={"Age_B04ID": "age_group", "Sex_B01ID": "sex"}, inplace=True
-    )
+        nts_individuals.rename(
+            columns={"Age_B04ID": "age_group", "Sex_B01ID": "sex"}, inplace=True
+        )
 
-    # PSM matching using internal match_individuals function
+        # PSM matching using internal match_individuals function
 
-    matches_ind = match_individuals(
-        df1=spc_edited,
-        df2=nts_individuals,
-        matching_columns=["age_group", "sex"],
-        df1_id="hid",
-        df2_id="HouseholdID",
-        matches_hh=matches_hh_level_sample,
-        show_progress=True,
-    )
+        matches_ind = match_individuals(
+            df1=spc_edited,
+            df2=nts_individuals,
+            matching_columns=["age_group", "sex"],
+            df1_id="hid",
+            df2_id="HouseholdID",
+            matches_hh=matches_hh_level_sample,
+            show_progress=True,
+        )
 
-    # Add matches_ind values to spc_edited using map
-    spc_edited["nts_ind_id"] = spc_edited.index.map(matches_ind)
+        # Add matches_ind values to spc_edited using map
+        spc_edited["nts_ind_id"] = spc_edited.index.map(matches_ind)
 
-    # add the nts_individuals.IndividualID to spc_edit. The current nts_ind_id is the row index of nts_individuals
-    spc_edited["nts_ind_id"] = spc_edited["nts_ind_id"].map(
-        nts_individuals["IndividualID"]
-    )
+        # add the nts_individuals.IndividualID to spc_edit. The current nts_ind_id is the row index of nts_individuals
+        spc_edited["nts_ind_id"] = spc_edited["nts_ind_id"].map(
+            nts_individuals["IndividualID"]
+        )
 
-    logger.info("Statistical matching: Matching complete")
+        logger.info("Statistical matching: Matching complete")
 
-    # save random sample
-    with open(
-        get_interim_path("matches_ind_level_categorical_random_sample.pkl"), "wb"
-    ) as f:
-        pkl.dump(matches_ind, f)
+        # save random sample
+        with open(
+            get_interim_path("matches_ind_level_categorical_random_sample.pkl"), "wb"
+        ) as f:
+            pkl.dump(matches_ind, f)
+    else:
+        with open(
+            get_interim_path("matches_ind_level_categorical_random_sample.pkl"), "rb"
+        ) as f:
+            matches_ind = pkl.load(f)
 
     # ### Match on multiple samples
 
diff --git a/src/acbm/config.py b/src/acbm/config.py
index 98e0e58..cf48101 100644
--- a/src/acbm/config.py
+++ b/src/acbm/config.py
@@ -19,7 +19,8 @@ class Parameters(BaseModel):
 
 @dataclass(frozen=True)
 class MatchingParams(BaseModel):
-    load_hh: bool
+    load_hh: bool | None = False
+    load_ind: bool | None = False
 
 
 @dataclass(frozen=True)

From 3dc8831d5927d33612f6a338221645cf921cfc23 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 17:27:14 +0100
Subject: [PATCH 13/27] Fix missing column required for merge

---
 scripts/2_match_households_and_individuals.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index 9fb01d1..056777d 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -1127,7 +1127,7 @@ def get_interim_path(
     ]
     nts_output_cols = [
         col for col in nts_trips.columns if col in cols_for_assignment_all()
-    ]
+    ] + ["IndividualID"]
 
     # merge the copy with nts_trips using IndividualID
     spc_edited_copy = spc_edited_copy[spc_output_cols].merge(

From 9cbbe8a770cc332b45159385276cdd5a78a17a38 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 17:31:36 +0100
Subject: [PATCH 14/27] Add logging for loading case

---
 scripts/2_match_households_and_individuals.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index 056777d..9493acf 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -877,6 +877,7 @@ def get_interim_path(
         ) as f:
             pkl.dump(matches_hh_level_sample_list, f)
     else:
+        logger.info("Categorical matching: loading matched households")
         # Load matching result
         with open(
             get_interim_path("matches_hh_level_categorical_random_sample.pkl"), "rb"
@@ -973,6 +974,7 @@ def get_interim_path(
         ) as f:
             pkl.dump(matches_ind, f)
     else:
+        logger.info("Statistical matching: loading matched individuals")
         with open(
             get_interim_path("matches_ind_level_categorical_random_sample.pkl"), "rb"
         ) as f:

From 6e5c396ef287c646fc0e9de4af06f68eb699a6e6 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 17:50:48 +0100
Subject: [PATCH 15/27] Reorganise code for matching results to be loaded

---
 scripts/2_match_households_and_individuals.py | 99 ++++++++++---------
 1 file changed, 52 insertions(+), 47 deletions(-)

diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index 9493acf..367c696 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -818,9 +818,6 @@ def get_interim_path(
             f"{round((na_count / len(matches_hh_level)) * 100, 1)}% of households in the SPC had no match"
         )
 
-        ## add matches_hh_level as a column in spc_edited
-        spc_edited["nts_hh_id"] = spc_edited["hid"].map(matches_hh_level)
-
         # ### Random Sampling from matched households
 
         logger.info("Categorical matching: Randomly choosing one match per household")
@@ -864,6 +861,11 @@ def get_interim_path(
 
         # Save results
         logger.info("Categorical matching: Saving results")
+
+        # matching results
+        with open(get_interim_path("matches_hh_level_categorical.pkl"), "wb") as f:
+            pkl.dump(matches_hh_level, f)
+
         # random sample
         with open(
             get_interim_path("matches_hh_level_categorical_random_sample.pkl"), "wb"
@@ -891,6 +893,10 @@ def get_interim_path(
         ) as f:
             matches_hh_level_sample_list = pkl.load(f)
 
+    ## add matches_hh_level as a column in spc_edited
+    # TODO: update other scripts to only add this in-memory
+    # spc_edited["nts_hh_id"] = spc_edited["hid"].map(matches_hh_level)
+
     # Do the same at the df level. Add nts_hh_id_sample column to the spc df
 
     # # for each hid in spc_edited, sample a value from the nts_hh_id col.
@@ -909,45 +915,44 @@ def get_interim_path(
     #
     #
 
-    if not config.matching.load_ind:
-        logger.info("Statistical matching: MATCHING INDIVIDUALS")
+    # Create an 'age' column in the SPC that matches the NTS categories
+
+    # create a dictionary for reference on how the labels for "Age_B04ID" match the actual age brackets
+
+    # dict_nts_ind_age = {-10: 'DEAD',
+    #                     -8: 'NA',
+    #                     1: '0-4',
+    #                     2: '5-10',
+    #                     3: '11-16',
+    #                     4: '17-20',
+    #                     5: '21-29',
+    #                     6: '30-39',
+    #                     7: '40-49',
+    #                     8: '50-59',
+    #                     9: '60+'
+    #                     }
+
+    # Define the bins and labels based on dict_nts_ind_age
+    bins = [0, 4, 10, 16, 20, 29, 39, 49, 59, np.inf]
+    labels = [1, 2, 3, 4, 5, 6, 7, 8, 9]
+
+    # Create a new column in spc_edited that maps the age_years to the keys of dict_nts_ind_age
+    spc_edited["age_group"] = (
+        pd.cut(spc_edited["age_years"], bins=bins, labels=labels)
+        .astype("int")
+        .fillna(-8)
+    )
 
-        # Create an 'age' column in the SPC that matches the NTS categories
-
-        # create a dictionary for reference on how the labels for "Age_B04ID" match the actual age brackets
-
-        # dict_nts_ind_age = {-10: 'DEAD',
-        #                     -8: 'NA',
-        #                     1: '0-4',
-        #                     2: '5-10',
-        #                     3: '11-16',
-        #                     4: '17-20',
-        #                     5: '21-29',
-        #                     6: '30-39',
-        #                     7: '40-49',
-        #                     8: '50-59',
-        #                     9: '60+'
-        #                     }
-
-        # Define the bins and labels based on dict_nts_ind_age
-        bins = [0, 4, 10, 16, 20, 29, 39, 49, 59, np.inf]
-        labels = [1, 2, 3, 4, 5, 6, 7, 8, 9]
-
-        # Create a new column in spc_edited that maps the age_years to the keys of dict_nts_ind_age
-        spc_edited["age_group"] = (
-            pd.cut(spc_edited["age_years"], bins=bins, labels=labels)
-            .astype("int")
-            .fillna(-8)
-        )
+    # rename nts columns in preparation for matching
 
-        # rename nts columns in preparation for matching
+    nts_individuals.rename(
+        columns={"Age_B04ID": "age_group", "Sex_B01ID": "sex"}, inplace=True
+    )
 
-        nts_individuals.rename(
-            columns={"Age_B04ID": "age_group", "Sex_B01ID": "sex"}, inplace=True
-        )
+    if not config.matching.load_ind:
+        logger.info("Statistical matching: MATCHING INDIVIDUALS")
 
         # PSM matching using internal match_individuals function
-
         matches_ind = match_individuals(
             df1=spc_edited,
             df2=nts_individuals,
@@ -958,16 +963,6 @@ def get_interim_path(
             show_progress=True,
         )
 
-        # Add matches_ind values to spc_edited using map
-        spc_edited["nts_ind_id"] = spc_edited.index.map(matches_ind)
-
-        # add the nts_individuals.IndividualID to spc_edit. The current nts_ind_id is the row index of nts_individuals
-        spc_edited["nts_ind_id"] = spc_edited["nts_ind_id"].map(
-            nts_individuals["IndividualID"]
-        )
-
-        logger.info("Statistical matching: Matching complete")
-
         # save random sample
         with open(
             get_interim_path("matches_ind_level_categorical_random_sample.pkl"), "wb"
@@ -980,6 +975,16 @@ def get_interim_path(
         ) as f:
             matches_ind = pkl.load(f)
 
+    # Add matches_ind values to spc_edited using map
+    spc_edited["nts_ind_id"] = spc_edited.index.map(matches_ind)
+
+    # add the nts_individuals.IndividualID to spc_edit. The current nts_ind_id is the row index of nts_individuals
+    spc_edited["nts_ind_id"] = spc_edited["nts_ind_id"].map(
+        nts_individuals["IndividualID"]
+    )
+
+    logger.info("Statistical matching: Matching complete")
+
     # ### Match on multiple samples
 
     # logger.info("Statistical matching: Matching on multiple samples")

From 773d58950a2eb8da2378f6ed7ec46074e032caa1 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 18:03:34 +0100
Subject: [PATCH 16/27] Update nts_hh_id to be sample instead of full list

---
 scripts/2_match_households_and_individuals.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index 367c696..04e7a93 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -893,9 +893,12 @@ def get_interim_path(
         ) as f:
             matches_hh_level_sample_list = pkl.load(f)
 
+    # TODO: check if this:
+    #   - column is required and possibly update other scripts to add this column in-memory since it is large
+    #   - or can use the single sample hh for the new column
+    # For now, updated to use the sample dictionary
     ## add matches_hh_level as a column in spc_edited
-    # TODO: update other scripts to only add this in-memory
-    # spc_edited["nts_hh_id"] = spc_edited["hid"].map(matches_hh_level)
+    spc_edited["nts_hh_id"] = spc_edited["hid"].map(matches_hh_level_sample)
 
     # Do the same at the df level. Add nts_hh_id_sample column to the spc df
 

From b0fbb56f060c558256cc8fea48affe8110566be5 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 18:04:41 +0100
Subject: [PATCH 17/27] Add todo for "nts_hh_id"

---
 scripts/3.2.3_assign_secondary_zone.py | 1 +
 src/acbm/assigning/utils.py            | 1 +
 2 files changed, 2 insertions(+)

diff --git a/scripts/3.2.3_assign_secondary_zone.py b/scripts/3.2.3_assign_secondary_zone.py
index d649a70..dfc7418 100644
--- a/scripts/3.2.3_assign_secondary_zone.py
+++ b/scripts/3.2.3_assign_secondary_zone.py
@@ -224,6 +224,7 @@ def merge_columns_from_other(df: pd.DataFrame, other: pd.DataFrame) -> pd.DataFr
             "id",
             "household",
             "nts_ind_id",
+            # TODO: check if this column is required
             "nts_hh_id",
             "age_years",
             "oact",
diff --git a/src/acbm/assigning/utils.py b/src/acbm/assigning/utils.py
index 24e3561..1fb3ddc 100644
--- a/src/acbm/assigning/utils.py
+++ b/src/acbm/assigning/utils.py
@@ -14,6 +14,7 @@ def cols_for_assignment_all() -> list[str]:
         "household",
         "oact",
         "nts_ind_id",
+        # TODO: check if this column is required
         "nts_hh_id",
         "age_years",
         "TripDisIncSW",

From de4de69dd8524f83379252d3fa581eec177b001b Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 18:20:56 +0100
Subject: [PATCH 18/27] Add comment for individual matching

---
 src/acbm/matching.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/acbm/matching.py b/src/acbm/matching.py
index 4b54376..87fea06 100644
--- a/src/acbm/matching.py
+++ b/src/acbm/matching.py
@@ -265,6 +265,9 @@ def match_individuals(
     matches_hh = {key: value for key, value in matches_hh.items() if not pd.isna(value)}
 
     # loop over all groups of df1_id
+    # note: for large populations looping through the groups (keys) of the
+    # large dataframe (assumed to be df1) is more efficient than looping
+    # over keys and subsetting on a key in each iteration.
     for i, (key, rows_df1) in enumerate(df1.groupby(df1_id), 1):
         try:
             value = matches_hh[key]

From 38a43e224351736b63c34b3662b3d53b95625980 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Tue, 22 Oct 2024 18:30:00 +0100
Subject: [PATCH 19/27] Update config tomls

---
 config/base_500.toml | 7 +++++--
 config/base_all.toml | 7 +++++--
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/config/base_500.toml b/config/base_500.toml
index d9164a4..9c7ee5e 100644
--- a/config/base_500.toml
+++ b/config/base_500.toml
@@ -1,11 +1,14 @@
 [parameters]
 seed = 0
-region = "leeds"
+region = "greater-london"
 number_of_households = 500
 zone_id = "OA21CD"
-travel_times = true # Only set to true if you have travel time matrix at the level specified in boundary_geography
+travel_times = true        # Only set to true if you have travel time matrix at the level specified in boundary_geography
 boundary_geography = "OA"
 
+[matching]
+load_hh = false
+load_ind = false
 
 [work_assignment]
 use_percentages = true
diff --git a/config/base_all.toml b/config/base_all.toml
index bb1cc1e..ebe0431 100644
--- a/config/base_all.toml
+++ b/config/base_all.toml
@@ -1,10 +1,13 @@
 [parameters]
 seed = 0
-region = "leeds"
+region = "greater-london"
 zone_id = "OA21CD"
-travel_times = true # Only set to true if you have travel time matrix at the level specified in boundary_geography
+travel_times = true       # Only set to true if you have travel time matrix at the level specified in boundary_geography
 boundary_geography = "OA"
 
+[matching]
+load_hh = false
+load_ind = false
 
 [work_assignment]
 use_percentages = false

From c8f63962a7dfb4841f8b07b3004de89da607e221 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Wed, 23 Oct 2024 21:08:45 +0100
Subject: [PATCH 20/27] Add required and optional columns to config

---
 scripts/2_match_households_and_individuals.py | 20 ++-----------------
 src/acbm/config.py                            | 13 ++++++++++++
 2 files changed, 15 insertions(+), 18 deletions(-)

diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index 04e7a93..bc6de99 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -773,30 +773,14 @@ def get_interim_path(
         # We match iteratively on a subset of columns. We start with all columns, and then remove
         # one of the optionals columns at a time (relaxing the condition). Once a household has over n
         # matches, we stop matching it to more matches. We continue until all optional columns are removed
-
-        # Define required columns for matching
-        required_columns = [
-            "number_adults",
-            "number_children",
-        ]
-
-        # Define optional columns in order of importance (most to least important)
-        optional_columns = [
-            "number_cars",
-            "num_pension_age",
-            "rural_urban_2_categories",
-            "employment_status",
-            "tenure_status",
-        ]
-
         matcher_exact = MatcherExact(
             df_pop=spc_matching,
             df_pop_id="hid",
             df_sample=nts_matching,
             df_sample_id="HouseholdID",
             matching_dict=matching_dfs_dict,
-            fixed_cols=required_columns,
-            optional_cols=optional_columns,
+            fixed_cols=list(config.matching.required_columns),
+            optional_cols=list(config.matching.optional_columns),
             n_matches=10,
             chunk_size=50000,
             show_progress=True,
diff --git a/src/acbm/config.py b/src/acbm/config.py
index cf48101..61f594b 100644
--- a/src/acbm/config.py
+++ b/src/acbm/config.py
@@ -21,6 +21,19 @@ class Parameters(BaseModel):
 class MatchingParams(BaseModel):
     load_hh: bool | None = False
     load_ind: bool | None = False
+    # Define required columns for matching
+    required_columns: list[str] | tuple[str] = (
+        "number_adults",
+        "number_children",
+    )
+    # Define optional columns in order of importance (most to least important)
+    optional_columns: list[str] | tuple[str] = (
+        "number_cars",
+        "num_pension_age",
+        "rural_urban_2_categories",
+        "employment_status",
+        "tenure_status",
+    )
 
 
 @dataclass(frozen=True)

From 073fd460b33fcbcd9ef66a8389be2cffb2d9a5b1 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Wed, 23 Oct 2024 21:12:13 +0100
Subject: [PATCH 21/27] Extend config for matching and update tomls

---
 config/base_500.toml                          |  1 +
 config/base_5000.toml                         |  7 ++++++-
 config/base_all.toml                          |  1 +
 scripts/2_match_households_and_individuals.py |  4 ++--
 src/acbm/config.py                            |  2 ++
 src/acbm/matching.py                          | 16 ++++++++++------
 6 files changed, 22 insertions(+), 9 deletions(-)

diff --git a/config/base_500.toml b/config/base_500.toml
index 9c7ee5e..1356bd9 100644
--- a/config/base_500.toml
+++ b/config/base_500.toml
@@ -9,6 +9,7 @@ boundary_geography = "OA"
 [matching]
 load_hh = false
 load_ind = false
+n_matches = 10
 
 [work_assignment]
 use_percentages = true
diff --git a/config/base_5000.toml b/config/base_5000.toml
index d4f0134..3e857c4 100644
--- a/config/base_5000.toml
+++ b/config/base_5000.toml
@@ -3,9 +3,14 @@ seed = 0
 region = "leeds"
 number_of_households = 5000
 zone_id = "OA21CD"
-travel_times = true # Only set to true if you have travel time matrix at the level specified in boundary_geography
+travel_times = true         # Only set to true if you have travel time matrix at the level specified in boundary_geography
 boundary_geography = "OA"
 
+[matching]
+load_hh = false
+load_ind = false
+n_matches = 10
+
 [work_assignment]
 use_percentages = true
 weight_max_dev = 0.2
diff --git a/config/base_all.toml b/config/base_all.toml
index ebe0431..5bef78a 100644
--- a/config/base_all.toml
+++ b/config/base_all.toml
@@ -8,6 +8,7 @@ boundary_geography = "OA"
 [matching]
 load_hh = false
 load_ind = false
+n_matches = 10
 
 [work_assignment]
 use_percentages = false
diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index bc6de99..0e4fb69 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -781,8 +781,8 @@ def get_interim_path(
             matching_dict=matching_dfs_dict,
             fixed_cols=list(config.matching.required_columns),
             optional_cols=list(config.matching.optional_columns),
-            n_matches=10,
-            chunk_size=50000,
+            n_matches=config.matching.n_matches,
+            chunk_size=config.matching.chunk_size,
             show_progress=True,
         )
 
diff --git a/src/acbm/config.py b/src/acbm/config.py
index 61f594b..b0cf15b 100644
--- a/src/acbm/config.py
+++ b/src/acbm/config.py
@@ -34,6 +34,8 @@ class MatchingParams(BaseModel):
         "employment_status",
         "tenure_status",
     )
+    n_matches: int | None = None
+    chunk_size: int = 50_000
 
 
 @dataclass(frozen=True)
diff --git a/src/acbm/matching.py b/src/acbm/matching.py
index 87fea06..0be0dfd 100644
--- a/src/acbm/matching.py
+++ b/src/acbm/matching.py
@@ -18,7 +18,7 @@ class MatcherExact:
     matching_dict: Dict[str, List[str]]
     fixed_cols: List[str]
     optional_cols: List[str]
-    n_matches: int = 5
+    n_matches: int | None = 10
     chunk_size: int = 50000
     show_progress: bool = True
     matched_dict: Dict[str, List[str]] = field(
@@ -147,11 +147,15 @@ def iterative_match_categorical(self) -> Dict[str, List[str]]:
                 self.matched_dict[pop_id].extend(unique_sample_ids)
                 self.match_count[pop_id] += len(unique_sample_ids)
 
-            matched_ids = [
-                pop_id
-                for pop_id, count in self.match_count.items()
-                if count >= self.n_matches
-            ]
+            matched_ids = (
+                [
+                    pop_id
+                    for pop_id, count in self.match_count.items()
+                    if count >= self.n_matches
+                ]
+                if self.n_matches is not None
+                else []
+            )
             self.remaining_df_pop = self.remaining_df_pop[
                 ~self.remaining_df_pop[self.df_pop_id].isin(matched_ids)
             ]

From 37216aa445ef255557fda34868f2ea5ffaf831ef Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Thu, 24 Oct 2024 07:33:00 +0100
Subject: [PATCH 22/27] Add commute_level config

---
 config/base_all.toml                      | 2 +-
 scripts/3.2.2_assign_primary_zone_work.py | 8 ++++++--
 src/acbm/config.py                        | 1 +
 3 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/config/base_all.toml b/config/base_all.toml
index 5bef78a..e2d99c4 100644
--- a/config/base_all.toml
+++ b/config/base_all.toml
@@ -2,7 +2,7 @@
 seed = 0
 region = "greater-london"
 zone_id = "OA21CD"
-travel_times = true       # Only set to true if you have travel time matrix at the level specified in boundary_geography
+travel_times = false      # Only set to true if you have travel time matrix at the level specified in boundary_geography
 boundary_geography = "OA"
 
 [matching]
diff --git a/scripts/3.2.2_assign_primary_zone_work.py b/scripts/3.2.2_assign_primary_zone_work.py
index 82ee9ee..6dc6bb2 100644
--- a/scripts/3.2.2_assign_primary_zone_work.py
+++ b/scripts/3.2.2_assign_primary_zone_work.py
@@ -62,8 +62,12 @@ def main(config_file):
 
     # Commuting matrices (from 2021 census)
 
-    # TODO: consider making this configurable
-    commute_level = config.boundary_geography  # "OA" or "MSOA" data
+    # "OA" or "MSOA" data: set as config.boundary_geography if not passed
+    commute_level = (
+        config.boundary_geography
+        if config.work_assignment.commute_level is None
+        else config.work_assignment.commute_level
+    )
 
     logger.info(f"Loading commuting matrices at {commute_level} level")
 
diff --git a/src/acbm/config.py b/src/acbm/config.py
index b0cf15b..0d3eb0f 100644
--- a/src/acbm/config.py
+++ b/src/acbm/config.py
@@ -44,6 +44,7 @@ class WorkAssignmentParams(BaseModel):
     weight_max_dev: float
     weight_total_dev: float
     max_zones: int
+    commute_level: str | None
 
 
 class Config(BaseModel):

From 6722b1a725effb8817757cfc3fb52b1f7d4e00b5 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Thu, 24 Oct 2024 10:20:53 +0100
Subject: [PATCH 23/27] Update pre-commit, format, add raise from error

---
 .pre-commit-config.yaml                   |  2 +-
 scripts/3.2.2_assign_primary_zone_work.py | 16 ++++++++--------
 src/acbm/__init__.py                      |  1 +
 src/acbm/config.py                        |  2 +-
 tests/test_matching.py                    |  2 +-
 5 files changed, 12 insertions(+), 11 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 227e436..c1a3dc4 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -23,7 +23,7 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.2.0"
+    rev: "v0.7.0"
     hooks:
       # first, lint + autofix
       - id: ruff
diff --git a/scripts/3.2.2_assign_primary_zone_work.py b/scripts/3.2.2_assign_primary_zone_work.py
index 6dc6bb2..1c1da11 100644
--- a/scripts/3.2.2_assign_primary_zone_work.py
+++ b/scripts/3.2.2_assign_primary_zone_work.py
@@ -260,20 +260,20 @@ def main(config_file):
     workzone_assignment_opt["pct_of_o_total_actual"] = workzone_assignment_opt.groupby(
         "origin_zone"
     )["demand_actual"].transform(lambda x: (x / x.sum()) * 100)
-    workzone_assignment_opt[
-        "pct_of_o_total_assigned"
-    ] = workzone_assignment_opt.groupby("origin_zone")["demand_assigned"].transform(
-        lambda x: (x / x.sum()) * 100
+    workzone_assignment_opt["pct_of_o_total_assigned"] = (
+        workzone_assignment_opt.groupby(
+            "origin_zone"
+        )["demand_assigned"].transform(lambda x: (x / x.sum()) * 100)
     )
 
     # (3) For each OD pair, demand as % of total demand to each destination
     workzone_assignment_opt["pct_of_d_total_actual"] = workzone_assignment_opt.groupby(
         "assigned_zone"
     )["demand_actual"].transform(lambda x: (x / x.sum()) * 100)
-    workzone_assignment_opt[
-        "pct_of_d_total_assigned"
-    ] = workzone_assignment_opt.groupby("assigned_zone")["demand_assigned"].transform(
-        lambda x: (x / x.sum()) * 100
+    workzone_assignment_opt["pct_of_d_total_assigned"] = (
+        workzone_assignment_opt.groupby(
+            "assigned_zone"
+        )["demand_assigned"].transform(lambda x: (x / x.sum()) * 100)
     )
 
     # Define the output file path
diff --git a/src/acbm/__init__.py b/src/acbm/__init__.py
index 0171ca8..d630574 100644
--- a/src/acbm/__init__.py
+++ b/src/acbm/__init__.py
@@ -1,6 +1,7 @@
 """
 acbm: A package to create activity-based models (for transport demand modelling)
 """
+
 from __future__ import annotations
 
 import os
diff --git a/src/acbm/config.py b/src/acbm/config.py
index 0d3eb0f..08788e3 100644
--- a/src/acbm/config.py
+++ b/src/acbm/config.py
@@ -85,7 +85,7 @@ def init_rng(self):
             random.seed(self.seed)
         except Exception as err:
             msg = f"config does not provide a rng seed with err: {err}"
-            ValueError(msg)
+            raise ValueError(msg) from err
 
 
 def load_config(filepath: str | Path) -> Config:
diff --git a/tests/test_matching.py b/tests/test_matching.py
index fcd17f1..6aa7727 100644
--- a/tests/test_matching.py
+++ b/tests/test_matching.py
@@ -4,7 +4,7 @@
 from acbm.matching import MatcherExact, match_psm  # noqa: F401
 
 
-@pytest.fixture()
+@pytest.fixture
 def setup_data():
     df_pop = pd.DataFrame(
         {

From 44a87d5c022eda4f10a23bac009d05a5bb8bddb5 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Fri, 1 Nov 2024 14:16:55 +0000
Subject: [PATCH 24/27] Only retain single base.toml

---
 config/base_500.toml  | 18 ------------------
 config/base_5000.toml | 18 ------------------
 config/base_all.toml  | 17 -----------------
 3 files changed, 53 deletions(-)
 delete mode 100644 config/base_500.toml
 delete mode 100644 config/base_5000.toml
 delete mode 100644 config/base_all.toml

diff --git a/config/base_500.toml b/config/base_500.toml
deleted file mode 100644
index 1356bd9..0000000
--- a/config/base_500.toml
+++ /dev/null
@@ -1,18 +0,0 @@
-[parameters]
-seed = 0
-region = "greater-london"
-number_of_households = 500
-zone_id = "OA21CD"
-travel_times = true        # Only set to true if you have travel time matrix at the level specified in boundary_geography
-boundary_geography = "OA"
-
-[matching]
-load_hh = false
-load_ind = false
-n_matches = 10
-
-[work_assignment]
-use_percentages = true
-weight_max_dev = 0.2
-weight_total_dev = 0.8
-max_zones = 8
diff --git a/config/base_5000.toml b/config/base_5000.toml
deleted file mode 100644
index 3e857c4..0000000
--- a/config/base_5000.toml
+++ /dev/null
@@ -1,18 +0,0 @@
-[parameters]
-seed = 0
-region = "leeds"
-number_of_households = 5000
-zone_id = "OA21CD"
-travel_times = true         # Only set to true if you have travel time matrix at the level specified in boundary_geography
-boundary_geography = "OA"
-
-[matching]
-load_hh = false
-load_ind = false
-n_matches = 10
-
-[work_assignment]
-use_percentages = true
-weight_max_dev = 0.2
-weight_total_dev = 0.8
-max_zones = 8
diff --git a/config/base_all.toml b/config/base_all.toml
deleted file mode 100644
index e2d99c4..0000000
--- a/config/base_all.toml
+++ /dev/null
@@ -1,17 +0,0 @@
-[parameters]
-seed = 0
-region = "greater-london"
-zone_id = "OA21CD"
-travel_times = false      # Only set to true if you have travel time matrix at the level specified in boundary_geography
-boundary_geography = "OA"
-
-[matching]
-load_hh = false
-load_ind = false
-n_matches = 10
-
-[work_assignment]
-use_percentages = false
-weight_max_dev = 0.0
-weight_total_dev = 1.0
-max_zones = 4

From 0ca21949a104bd04643f5fc610a42bf86241fbe0 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Fri, 1 Nov 2024 14:17:29 +0000
Subject: [PATCH 25/27] Update gitignore

---
 .gitignore | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/.gitignore b/.gitignore
index 13c4a8c..f036118 100644
--- a/.gitignore
+++ b/.gitignore
@@ -167,3 +167,9 @@ logs/
 
 # pyright config
 pyrightconfig.json
+
+# scratch
+notebooks/scratch*
+
+# AcBM config
+config/

From 72c7c218dc273d786dbdff68473e55afe2a1f067 Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Fri, 1 Nov 2024 14:23:33 +0000
Subject: [PATCH 26/27] Remove load_hh and load_ind from config

---
 config/base.toml                              | 14 ++++++++++++--
 scripts/2_match_households_and_individuals.py |  8 ++++++--
 src/acbm/config.py                            | 17 ++---------------
 3 files changed, 20 insertions(+), 19 deletions(-)

diff --git a/config/base.toml b/config/base.toml
index 38a4f2f..9829be3 100644
--- a/config/base.toml
+++ b/config/base.toml
@@ -1,11 +1,21 @@
 [parameters]
 seed = 0
 region = "leeds"
-number_of_households = 10000
+number_of_households = 5000
 zone_id = "OA21CD"
-travel_times = true # Only set to true if you have travel time matrix at the level specified in boundary_geography
+travel_times = true         # Only set to true if you have travel time matrix at the level specified in boundary_geography
 boundary_geography = "OA"
 
+[matching]
+required_columns = ["number_adults", "number_children"]
+optional_columns = [
+    "number_cars",
+    "num_pension_age",
+    "rural_urban_2_categories",
+    "employment_status",
+    "tenure_status",
+]
+n_matches = 10
 
 [work_assignment]
 use_percentages = true
diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index 0e4fb69..982e97c 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -686,7 +686,9 @@ def get_interim_path(
     )  # fill the NaNs with the original values
 
     # ## Step 3: Matching at Household Level
-    if not config.matching.load_hh:
+    # TODO: remove once refactored into two scripts
+    load_households = False
+    if not load_households:
         logger.info("Categorical matching: MATCHING HOUSEHOLDS")
 
         #
@@ -936,7 +938,9 @@ def get_interim_path(
         columns={"Age_B04ID": "age_group", "Sex_B01ID": "sex"}, inplace=True
     )
 
-    if not config.matching.load_ind:
+    # TODO: remove once refactored into two scripts
+    load_individuals = False
+    if not load_individuals:
         logger.info("Statistical matching: MATCHING INDIVIDUALS")
 
         # PSM matching using internal match_individuals function
diff --git a/src/acbm/config.py b/src/acbm/config.py
index 08788e3..9ea592b 100644
--- a/src/acbm/config.py
+++ b/src/acbm/config.py
@@ -19,21 +19,8 @@ class Parameters(BaseModel):
 
 @dataclass(frozen=True)
 class MatchingParams(BaseModel):
-    load_hh: bool | None = False
-    load_ind: bool | None = False
-    # Define required columns for matching
-    required_columns: list[str] | tuple[str] = (
-        "number_adults",
-        "number_children",
-    )
-    # Define optional columns in order of importance (most to least important)
-    optional_columns: list[str] | tuple[str] = (
-        "number_cars",
-        "num_pension_age",
-        "rural_urban_2_categories",
-        "employment_status",
-        "tenure_status",
-    )
+    required_columns: list[str]
+    optional_columns: list[str]
     n_matches: int | None = None
     chunk_size: int = 50_000
 

From f5b159cbd53a1df76e2c2ec612803f002f63114d Mon Sep 17 00:00:00 2001
From: Sam Greenbury <sgreenbury@turing.ac.uk>
Date: Fri, 1 Nov 2024 17:13:27 +0000
Subject: [PATCH 27/27] Remove resolved todo

---
 scripts/2_match_households_and_individuals.py | 6 +-----
 scripts/3.2.3_assign_secondary_zone.py        | 1 -
 src/acbm/assigning/utils.py                   | 1 -
 3 files changed, 1 insertion(+), 7 deletions(-)

diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index 982e97c..d57d04c 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -879,11 +879,7 @@ def get_interim_path(
         ) as f:
             matches_hh_level_sample_list = pkl.load(f)
 
-    # TODO: check if this:
-    #   - column is required and possibly update other scripts to add this column in-memory since it is large
-    #   - or can use the single sample hh for the new column
-    # For now, updated to use the sample dictionary
-    ## add matches_hh_level as a column in spc_edited
+    ## add matches_hh_level_sample as a column in spc_edited
     spc_edited["nts_hh_id"] = spc_edited["hid"].map(matches_hh_level_sample)
 
     # Do the same at the df level. Add nts_hh_id_sample column to the spc df
diff --git a/scripts/3.2.3_assign_secondary_zone.py b/scripts/3.2.3_assign_secondary_zone.py
index dfc7418..d649a70 100644
--- a/scripts/3.2.3_assign_secondary_zone.py
+++ b/scripts/3.2.3_assign_secondary_zone.py
@@ -224,7 +224,6 @@ def merge_columns_from_other(df: pd.DataFrame, other: pd.DataFrame) -> pd.DataFr
             "id",
             "household",
             "nts_ind_id",
-            # TODO: check if this column is required
             "nts_hh_id",
             "age_years",
             "oact",
diff --git a/src/acbm/assigning/utils.py b/src/acbm/assigning/utils.py
index 1fb3ddc..24e3561 100644
--- a/src/acbm/assigning/utils.py
+++ b/src/acbm/assigning/utils.py
@@ -14,7 +14,6 @@ def cols_for_assignment_all() -> list[str]:
         "household",
         "oact",
         "nts_ind_id",
-        # TODO: check if this column is required
         "nts_hh_id",
         "age_years",
         "TripDisIncSW",