QM_Research_Assignment_20191229_v1.2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read me"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Overview`\n",
    "<br>\n",
    "\n",
    "`Process flow diagram`\n",
    "![title](3_Images/QM_Flow.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"700\"\n",
       "            src=\"https://www.draw.io/?lightbox=1&highlight=0000ff&edit=_blank&layers=1&nav=1&title=QM_Flow#R7V1bk5s4Fv41rpl9iAuJ%2B2Nfk1T1ZLPpVM3maQpj2WaDwQM43T2%2FfiUutpGELRuEjNuZmi4DAmN9R%2Bd%2Bjkb63fL1Y%2BKtFn%2FEUxSOoDZ9Hen3IwgdC%2BK%2F5MRbcQJoRnlmngTT8tz2xHPwD6oGlmfXwRSltYFZHIdZsKqf9OMoQn5WO%2BclSfxSHzaLw%2Fq3rrw5Yk48%2B17Inv0zmGaL8ndBe3v%2BEwrmi%2BqbgeUWV5ZeNbj8JenCm8YvO6f0h5F%2Bl8RxVnxavt6hkExeNS%2FFfY8NVzcvlqAoE7nh75tfN18%2F%2Flx8%2Bo%2F95%2BvHh6ckXIAPwDCK5%2FzywnX5k8vXzd6qOUjidTRF5DHaSL99WQQZel55Prn6glHH5xbZMsRHAH%2BcxVH26C2DkCD%2BPVhi8KD2Bb3gv9%2FipReVQ0qkASTHQRjexWGc5F%2Bnz0zyHz6fZkn8E%2B1csfJ%2F5RN2zhf%2F8Pnyt6AkQ6%2BN0wQ2k4%2BpFsVLlCVveEh5g26XeJUUC7Xy%2BGULv15R52IHetcqT3olyc03z96igj%2BUwBwFkn5%2BIM2Q5fs8kKa2O9G0bsAwtDoYQNNZNKDFQUMiGPAKRjHFjiAWui4LDPe6MJq5FIAcLDYDO8fCMc8PC6WSxKAkicERJDpPkMhCqHqfK0KvdURKgBzOIoIOj6FJky66wyCCplghLQ%2FjJFvE8zjywoft2ds6ZtsxT3G8KpH6H8qytxIHb53FdRzRa5D9l9w%2BNsujHztX7l%2FLJ%2BcHb9VBhH%2Fvzk3k8Mfute1t%2BVF1Xwc04%2FiIz2EnjmmYWw5LZm4%2FZeCJjteJj%2FYhUirImZfMUbZPMeCTWoJCLwt%2B1V%2Bke7rhqfFWiN%2F3doI%2FzMmH5%2B83375XZ%2FG3bC7sYQKgDyYwNZEzNXiAOnCi50ygg8WOxeEY2Ic4MldoGtKWu7sPth1IrL%2FXxE7MZ%2FJDmk%2FlDR4AjNVrPjvV9S2kxVPI%2BFYP%2Bv3mX%2Fjyw2uWeD550iqJfZSmO2RUfEX9a%2FdSF4Ywq5OQFwZzTCf3IZqRKwTmAJvgN%2BXpZTCdFowO4Tf2JvmDyDJfxUGU5ZiYtyPznjwJ87a0pDSK8AwxWu1erACebsbTk6E0MgNXwb8XISgo%2BC15nOAMdbNeLBl4siVjy8IC8rjye8CCWhbC8lGXxrl4jOsY%2BQgbxNrNWOPLKstbEvCiSbrakWeTpNW3fV6usFaeQ%2BH%2F9OYE%2Fp7JqSP9STcP8s1%2BFSigAWYu37fB1LH5U7LCw9aPUvOnrRpNFi6HR0BJPIL9qq%2Bf8TWs8oYEXExtWOVeJ3GaedkQWcWpckQimzgs0XtgGx2uyyoWemhduiqXJdBMpdx5hzdvOfVB7qxpsMafx4ahH%2BDR%2BdFXlOAFlqHkLBg3EKUQYLUkkfzWmyTx3nYGlKb59slfyYktizDrLEIvKeVRcLijU8RZfP%2BWVDc%2FpAX1Niuf6QrDJ8LttQP%2BGTHx1PQULKHGoEFGHSOTmp7%2FjAgE6xX%2BU0qoMEC5t6eLp%2B%2B8eDGfB72R3Qu8jgwnixJ4LkfgaTzDSZrAs4ZAu7pE2r2PX6Iw9qZ4zGwd%2BVkQR1e6PUC3m9wXZXRbpSMNyZyrKwsb264bc87oWCtwBLUCQ%2BMTjlytAIA6QVp1tUCSnHeGwCsNibzyzstnKWfImhcRnjndsM8rz9zLM02eD4zHM%2BUZt6z340usgo2e7GzohrVZogaPsxdfbWxZVhknPo7bbdlZNSSezVKUMQh3wLRgc8BfmGk1uau7crHDsfkBju0OGFfTd9zFke9lKML%2F58hPQpR29Ohz4FgdueMcSqiKannSOFazcdKW5LqwhhuJbYH8nyPC7ksHcPEXnyntjDD2vbz6wcu8IdIJ1JgIj3A0Vhqt2BxaeRee27ZuuXZ2mDU8O0xqWK1jOwyKUkFrO6xdJkQX8XeJbJp59NNFsGBt%2Bw%2FW2LHBKX%2Fplx1X7FcxP1ZraohaGk1g97R42eKYH5zUkx75evX5uIDcSKZ%2FrWPKqNbiuZMGLx4%2BBE%2FVt3W0o2h39NBZQMxG%2FZEVGIt4OVmnnQuLjjxRdH0RYIUDL4nVkeaIql5gSFqjVO6ijRRXr1R16oetDaMlPzrN36%2FXSdi2qRp2ajxsNdw1NIrGJUQTIGuwovVf09lfhY99l2UV3IeoqrPEWyJm7aQLb0U%2BYuDxzSiM53gYvmO1k2hSu7aTgSI%2FJRmBqYlsHiG7lq17HVXtUL4rm6cA9xuhVJvSdASPU2vhGsKaUFvO0w5OVhOaBnm2gJe%2FRTxjVyydAVr5na%2FLl7VnAeNSNJUnx8Brka1cI0i0ZLaQlcqWfnPcf2O2%2FH5Lyh%2B%2FJ16UzuJkybdsKFpi6xvPogzR5oV97D7XnWlf153MlF9DtFijtbLfbt1Jqda4HevSarous6BLoPtFv75l0x0ef5DuPuiQP5iiKrkJVfIHa4AlfZdJBkoqQ%2BgEO5fua0j7kJyW490evEIma2Z2JPOwFt2X1PuGSqm3joK%2F1yj%2FOUnAN34HIP%2FozgOOpVwAQoZKNnPu4wnNSocEFwBt8jYqc%2BAYNCpXxDT218t8eg5BMinwe5q0CHFQbV0kQOYqj4ebV1%2BCXGEl6kswlSbKWIq9wvBKB0oDWZBycjrGcUrIseNd7cB4mlFqfSg5Ak3oRubt5y%2BkVxdXZ9HuFnGKaoV6xaVmReO8veyz2Qw29O2xJpbZnZe9Tk08U75XF7vJpnhvNJmXBcoWiOihfol2TY3Bv5r8%2BASleanx5WoydGDE5Tloe1VmrKsyI1WIWcJCTGlgxNrLx9sYq10UfHMf%2FhiEWc5SSvtHm8VNDGZ4hqrBd3AorMlQ6qi9%2FCxSSzTB2G4oZeyJT3RRx8XlE12kkHIfnkdyV5gpNLpKhscPuJti9Kw4DDByMyjFQTiNU2nVmMXmPs5ywYxRhtp0NjDjrZ8UKaq1lcvZVaVf281mi77%2Bvc5W62Zj7D3DZ1CeGZOFr9ckG5uNIXyOhgdeP44Tk27moRo8tmTra9Vwf4B6CuO0VDy7rF37vJ582GxpoM3xpK56n2klrb4t2qhUjQ27e1TlMFTuAYQ7HkAff2POATvAwKbrU1RjcGrTwJfy7YjdFcXJkhTnMb0kqd1nBJuqnMsGNPkTyhcEHWBPrz%2FlkofXZ6Mr7B%2B%2B3J8p8gLVeF0jT6961cg7kAHhTG13tU45W7R1oqOkdSJtRwLNORAiPnCDnBhx2bh5ANQ2zNobcSpV2oDA3lt708Z1bEoPMZWhpSALhhlOAi698JX7jytDkXEcjngV03kqZP6LLtGlKMPSoNOBAHdPul69jO5gYognuP5PDjuq7BXhiPYkc80uFBxGITEoxdihdxUrXqy8iyK%2FLvpAV%2FR%2FJcnBkaTTdp%2BbBpKkNyek9%2BaWTJLV7%2BfIRUYoBtmlSsQOBCAn3e4MZKDTNtHK4Cmq1SPI4NOf8oTmiHSCv%2B7z2xzfoBpwcfKyeJ4caZtlOqz%2F9uuZxTaU7vpLR%2FtU%2B9udZp%2FrtgHFHWlA8VQ0mC62ZvCwdvFGMpgG1ItCp5YKUN6MwmVNThXan1pvkSPsLVK6j6AjxVt0NzauHSOOE3in7tUtzXHkDiZ4MUx3siPaUsZV6k6uXrN7BtFcXt%2BaJdwnmNyg9lvkRb9V707A5TWcu83ByhlIVeFQnkQh8slpQJYl8pKL4CuilfiWLL4CNLWlUEf4gobZhMQVdja2LYU6KZqqU6lrurV%2Ff1p6vNlHq1mgqU27vxLpZmDbOhwlRGrBPogUXIn0PIgUNG18d95UavfRuqCaxB0F8nFTufJINC80ZfUy4mynTcWr851V76j2BDon5a3n4DPP8a7YNdpL2jetaRuGoKa9MfUl7BqhdM%2FnM6wm7lpAmMICQqkR77IlAJ0Y8WZ%2FXr7HYA6K62U4dlX0OakSloZvmgszDHkuv7ZV503bAD2vl8ui6X%2BaYXJPs8BP%2BYRzVJBFkAHIwIrXKKLXIAsASvuiSU6p6ZJHixZ4AwCV8ui9Ics2PBr0yKNhcf2uTOHF6wMvj2rTyKKP5qW4Urnb0%2FfKrwFQupfsafrdWLMhpeMB%2FYCWlx%2FtmG%2FnwFZE47sA6ErZiqQArzluit7IYCt6cf0TVh1yU%2F4iYryW8hgv0K6bUUvlEkAT7UsHlLaX2bznvoQpsgMpHHFCqCwb2DQbHf5uXv20y6Dy53Tlm%2FGVHosaRTy8lpk2xBgf%2B%2BmvgUHZU9chHdZtxWpfQ4VYwr1YwiuWgliaUD2WbCObXSz1K5aCWFqaeizb2vwaVzkP1ylGLojmg%2FaxWbxUpX59bHAwO9IOVD0GwlY0bNv6oSUlDHCDRcrLcjGUoGTvLIPS0KvtsoVzLY4c7%2BgmRbUyMogqkXgla%2BVkrSvpbdM3Wbt9dMIB%2BgCbJl8qWStJMu6brIFm9ZLwKcmZb%2FWXx%2FGM8m0OSXmGX9gpoyo8eAEefV6H6X49%2BlBpytc7YH0VnAKKKjwL1tfHJqybWemaM9n9caZ7FE0vJ7hoK88mA1BtQ8IdRiScnQCGxYqE6xNae09OYkWmQdPk%2FvoEZnwvShVkA11M6HP%2F3op1XvQURPwy9PN2wqvZFsBWH%2BiEbHCMiIKkgKdKNA4LVMm4IF17YZCSspUGhIe%2BhSKDEq%2BnCA8licJkME0Nh6rXiu6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       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x111957210>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Interactive version of flow diagram. \n",
    "\n",
    "flowlink = \"https://www.draw.io/?lightbox=1&highlight=0000ff&edit=_blank&layers=1&nav=1&title=QM_Flow#R7V1bk5s4Fv41rpl9iAuJ%2B2Nfk1T1ZLPpVM3maQpj2WaDwQM43T2%2FfiUutpGELRuEjNuZmi4DAmN9R%2Bd%2Bjkb63fL1Y%2BKtFn%2FEUxSOoDZ9Hen3IwgdC%2BK%2F5MRbcQJoRnlmngTT8tz2xHPwD6oGlmfXwRSltYFZHIdZsKqf9OMoQn5WO%2BclSfxSHzaLw%2Fq3rrw5Yk48%2B17Inv0zmGaL8ndBe3v%2BEwrmi%2BqbgeUWV5ZeNbj8JenCm8YvO6f0h5F%2Bl8RxVnxavt6hkExeNS%2FFfY8NVzcvlqAoE7nh75tfN18%2F%2Flx8%2Bo%2F95%2BvHh6ckXIAPwDCK5%2FzywnX5k8vXzd6qOUjidTRF5DHaSL99WQQZel55Prn6glHH5xbZMsRHAH%2BcxVH26C2DkCD%2BPVhi8KD2Bb3gv9%2FipReVQ0qkASTHQRjexWGc5F%2Bnz0zyHz6fZkn8E%2B1csfJ%2F5RN2zhf%2F8Pnyt6AkQ6%2BN0wQ2k4%2BpFsVLlCVveEh5g26XeJUUC7Xy%2BGULv15R52IHetcqT3olyc03z96igj%2BUwBwFkn5%2BIM2Q5fs8kKa2O9G0bsAwtDoYQNNZNKDFQUMiGPAKRjHFjiAWui4LDPe6MJq5FIAcLDYDO8fCMc8PC6WSxKAkicERJDpPkMhCqHqfK0KvdURKgBzOIoIOj6FJky66wyCCplghLQ%2FjJFvE8zjywoft2ds6ZtsxT3G8KpH6H8qytxIHb53FdRzRa5D9l9w%2BNsujHztX7l%2FLJ%2BcHb9VBhH%2Fvzk3k8Mfute1t%2BVF1Xwc04%2FiIz2EnjmmYWw5LZm4%2FZeCJjteJj%2FYhUirImZfMUbZPMeCTWoJCLwt%2B1V%2Bke7rhqfFWiN%2F3doI%2FzMmH5%2B83375XZ%2FG3bC7sYQKgDyYwNZEzNXiAOnCi50ygg8WOxeEY2Ic4MldoGtKWu7sPth1IrL%2FXxE7MZ%2FJDmk%2FlDR4AjNVrPjvV9S2kxVPI%2BFYP%2Bv3mX%2Fjyw2uWeD550iqJfZSmO2RUfEX9a%2FdSF4Ywq5OQFwZzTCf3IZqRKwTmAJvgN%2BXpZTCdFowO4Tf2JvmDyDJfxUGU5ZiYtyPznjwJ87a0pDSK8AwxWu1erACebsbTk6E0MgNXwb8XISgo%2BC15nOAMdbNeLBl4siVjy8IC8rjye8CCWhbC8lGXxrl4jOsY%2BQgbxNrNWOPLKstbEvCiSbrakWeTpNW3fV6usFaeQ%2BH%2F9OYE%2Fp7JqSP9STcP8s1%2BFSigAWYu37fB1LH5U7LCw9aPUvOnrRpNFi6HR0BJPIL9qq%2Bf8TWs8oYEXExtWOVeJ3GaedkQWcWpckQimzgs0XtgGx2uyyoWemhduiqXJdBMpdx5hzdvOfVB7qxpsMafx4ahH%2BDR%2BdFXlOAFlqHkLBg3EKUQYLUkkfzWmyTx3nYGlKb59slfyYktizDrLEIvKeVRcLijU8RZfP%2BWVDc%2FpAX1Niuf6QrDJ8LttQP%2BGTHx1PQULKHGoEFGHSOTmp7%2FjAgE6xX%2BU0qoMEC5t6eLp%2B%2B8eDGfB72R3Qu8jgwnixJ4LkfgaTzDSZrAs4ZAu7pE2r2PX6Iw9qZ4zGwd%2BVkQR1e6PUC3m9wXZXRbpSMNyZyrKwsb264bc87oWCtwBLUCQ%2BMTjlytAIA6QVp1tUCSnHeGwCsNibzyzstnKWfImhcRnjndsM8rz9zLM02eD4zHM%2BUZt6z340usgo2e7GzohrVZogaPsxdfbWxZVhknPo7bbdlZNSSezVKUMQh3wLRgc8BfmGk1uau7crHDsfkBju0OGFfTd9zFke9lKML%2F58hPQpR29Ohz4FgdueMcSqiKannSOFazcdKW5LqwhhuJbYH8nyPC7ksHcPEXnyntjDD2vbz6wcu8IdIJ1JgIj3A0Vhqt2BxaeRee27ZuuXZ2mDU8O0xqWK1jOwyKUkFrO6xdJkQX8XeJbJp59NNFsGBt%2Bw%2FW2LHBKX%2Fplx1X7FcxP1ZraohaGk1g97R42eKYH5zUkx75evX5uIDcSKZ%2FrWPKqNbiuZMGLx4%2BBE%2FVt3W0o2h39NBZQMxG%2FZEVGIt4OVmnnQuLjjxRdH0RYIUDL4nVkeaIql5gSFqjVO6ijRRXr1R16oetDaMlPzrN36%2FXSdi2qRp2ajxsNdw1NIrGJUQTIGuwovVf09lfhY99l2UV3IeoqrPEWyJm7aQLb0U%2BYuDxzSiM53gYvmO1k2hSu7aTgSI%2FJRmBqYlsHiG7lq17HVXtUL4rm6cA9xuhVJvSdASPU2vhGsKaUFvO0w5OVhOaBnm2gJe%2FRTxjVyydAVr5na%2FLl7VnAeNSNJUnx8Brka1cI0i0ZLaQlcqWfnPcf2O2%2FH5Lyh%2B%2FJ16UzuJkybdsKFpi6xvPogzR5oV97D7XnWlf153MlF9DtFijtbLfbt1Jqda4HevSarous6BLoPtFv75l0x0ef5DuPuiQP5iiKrkJVfIHa4AlfZdJBkoqQ%2BgEO5fua0j7kJyW490evEIma2Z2JPOwFt2X1PuGSqm3joK%2F1yj%2FOUnAN34HIP%2FozgOOpVwAQoZKNnPu4wnNSocEFwBt8jYqc%2BAYNCpXxDT218t8eg5BMinwe5q0CHFQbV0kQOYqj4ebV1%2BCXGEl6kswlSbKWIq9wvBKB0oDWZBycjrGcUrIseNd7cB4mlFqfSg5Ak3oRubt5y%2BkVxdXZ9HuFnGKaoV6xaVmReO8veyz2Qw29O2xJpbZnZe9Tk08U75XF7vJpnhvNJmXBcoWiOihfol2TY3Bv5r8%2BASleanx5WoydGDE5Tloe1VmrKsyI1WIWcJCTGlgxNrLx9sYq10UfHMf%2FhiEWc5SSvtHm8VNDGZ4hqrBd3AorMlQ6qi9%2FCxSSzTB2G4oZeyJT3RRx8XlE12kkHIfnkdyV5gpNLpKhscPuJti9Kw4DDByMyjFQTiNU2nVmMXmPs5ywYxRhtp0NjDjrZ8UKaq1lcvZVaVf281mi77%2Bvc5W62Zj7D3DZ1CeGZOFr9ckG5uNIXyOhgdeP44Tk27moRo8tmTra9Vwf4B6CuO0VDy7rF37vJ582GxpoM3xpK56n2klrb4t2qhUjQ27e1TlMFTuAYQ7HkAff2POATvAwKbrU1RjcGrTwJfy7YjdFcXJkhTnMb0kqd1nBJuqnMsGNPkTyhcEHWBPrz%2FlkofXZ6Mr7B%2B%2B3J8p8gLVeF0jT6961cg7kAHhTG13tU45W7R1oqOkdSJtRwLNORAiPnCDnBhx2bh5ANQ2zNobcSpV2oDA3lt708Z1bEoPMZWhpSALhhlOAi698JX7jytDkXEcjngV03kqZP6LLtGlKMPSoNOBAHdPul69jO5gYognuP5PDjuq7BXhiPYkc80uFBxGITEoxdihdxUrXqy8iyK%2FLvpAV%2FR%2FJcnBkaTTdp%2BbBpKkNyek9%2BaWTJLV7%2BfIRUYoBtmlSsQOBCAn3e4MZKDTNtHK4Cmq1SPI4NOf8oTmiHSCv%2B7z2xzfoBpwcfKyeJ4caZtlOqz%2F9uuZxTaU7vpLR%2FtU%2B9udZp%2FrtgHFHWlA8VQ0mC62ZvCwdvFGMpgG1ItCp5YKUN6MwmVNThXan1pvkSPsLVK6j6AjxVt0NzauHSOOE3in7tUtzXHkDiZ4MUx3siPaUsZV6k6uXrN7BtFcXt%2BaJdwnmNyg9lvkRb9V707A5TWcu83ByhlIVeFQnkQh8slpQJYl8pKL4CuilfiWLL4CNLWlUEf4gobZhMQVdja2LYU6KZqqU6lrurV%2Ff1p6vNlHq1mgqU27vxLpZmDbOhwlRGrBPogUXIn0PIgUNG18d95UavfRuqCaxB0F8nFTufJINC80ZfUy4mynTcWr851V76j2BDon5a3n4DPP8a7YNdpL2jetaRuGoKa9MfUl7BqhdM%2FnM6wm7lpAmMICQqkR77IlAJ0Y8WZ%2FXr7HYA6K62U4dlX0OakSloZvmgszDHkuv7ZV503bAD2vl8ui6X%2BaYXJPs8BP%2BYRzVJBFkAHIwIrXKKLXIAsASvuiSU6p6ZJHixZ4AwCV8ui9Ics2PBr0yKNhcf2uTOHF6wMvj2rTyKKP5qW4Urnb0%2FfKrwFQupfsafrdWLMhpeMB%2FYCWlx%2FtmG%2FnwFZE47sA6ErZiqQArzluit7IYCt6cf0TVh1yU%2F4iYryW8hgv0K6bUUvlEkAT7UsHlLaX2bznvoQpsgMpHHFCqCwb2DQbHf5uXv20y6Dy53Tlm%2FGVHosaRTy8lpk2xBgf%2B%2BmvgUHZU9chHdZtxWpfQ4VYwr1YwiuWgliaUD2WbCObXSz1K5aCWFqaeizb2vwaVzkP1ylGLojmg%2FaxWbxUpX59bHAwO9IOVD0GwlY0bNv6oSUlDHCDRcrLcjGUoGTvLIPS0KvtsoVzLY4c7%2BgmRbUyMogqkXgla%2BVkrSvpbdM3Wbt9dMIB%2BgCbJl8qWStJMu6brIFm9ZLwKcmZb%2FWXx%2FGM8m0OSXmGX9gpoyo8eAEefV6H6X49%2BlBpytc7YH0VnAKKKjwL1tfHJqybWemaM9n9caZ7FE0vJ7hoK88mA1BtQ8IdRiScnQCGxYqE6xNae09OYkWmQdPk%2FvoEZnwvShVkA11M6HP%2F3op1XvQURPwy9PN2wqvZFsBWH%2BiEbHCMiIKkgKdKNA4LVMm4IF17YZCSspUGhIe%2BhSKDEq%2BnCA8licJkME0Nh6rXiu6MBqDSrdGAPryilg%2FaWKOSHg1M0INLejyCSHSlWY%2BbF%2B3aHnH6s0fuNu6RDKXZRRglDuCE3Ps1SvTBpEwrXuq6sJ9Db9u0tiWiklwP7liT16HmU4ASL%2FEXpEnjaOMKzVM2hr%2FMXdEmMxLVRdZTvpslZVyzpARV%2F02raHUGms5mvH3HEpFg58dJPpHv0CZzTMH0NYmylC0rLpUWGhMtzvc58r2s0GhWYXwO24%2F1gxSXHfaLVPXk89d6Bmo968JVprpa69kYYMWPdEpQ2on%2FCNoxFGvakkqUQZNN3T6md%2Ft8d%2FOFIPVaKHERK3YGqGADTX10T2c17KvwZ4U%2FH6qepT8vwkULgWh6kyTxCz6K4ogw%2FamXLjbbRZ6%2BBOTyVxaHnWk2ObNcnWu5fQmbAEbvPVDwfWb%2FEuZJ1sEnyd6cx9jbHYi3z2hzMcn5bTLawZq2qt4cFUTc%2FU06Yr%2F4MImJENtijCdu8Uc8RWTE%2FwE%3D\"\n",
    "from IPython.display import IFrame\n",
    "IFrame(flowlink, width='100%', height=700)\n",
    "\n",
    "#UNPACK INTERACTIVE VIEW IF NEEDED."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PART A - EXTRACT DATA FROM EUROSTAT (0-2)\n",
    "\n",
    "**Two approaches:**\n",
    "\n",
    "1. Local version where data referenced is that used in final assignment\n",
    "2. API-based version allowing for variable adjustements and refresh https://pypi.org/project/eurostatapiclient/\n",
    "    <br> guidance from eurostat on constructing the apis can be found here:\n",
    "https://ec.europa.eu/eurostat/web/json-and-unicode-web-services/data-scope-and-query-size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=== (0) EXTRACT PACKAGES\n",
    "\n",
    "#(0.1) install package\n",
    "#pip install eurostatapiclient\n",
    "\n",
    "#(0.2) import package\n",
    "from eurostatapiclient import EurostatAPIClient #eurostat api\n",
    "import pandas as pd #for dataframes\n",
    "import datetime #for time and file version control\n",
    "import matplotlib.cm as cm #for chart colouring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using local files.\n"
     ]
    }
   ],
   "source": [
    "#=== (1) LOCAL DATA\n",
    "\n",
    "#Ignore warning caused by numpy triggered by the 'index_col' parameter on lines 11 and 15\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "#try loading files (specifically those used for the remainder of the analysis)\n",
    "try:\n",
    "    #table 1\n",
    "    table1_path = \"0_Data_download/urb_cpop1_20191229_232126.csv\"\n",
    "    table1 = pd.read_csv(table1_path,index_col = \"Unnamed: 0\")\n",
    "    \n",
    "    #table 2\n",
    "    table2_path = \"0_Data_download/urb_cpopcb_20191229_232132.csv\"\n",
    "    table2 = pd.read_csv(table2_path,index_col = \"Unnamed: 0\")\n",
    "    \n",
    "    #concatenate tables\n",
    "    table_all ={\"urb_cpop1\":table1,\"urb_cpopcb\":table2}\n",
    "    \n",
    "    #Mark variable as True to give option of local processing\n",
    "    local_files = True\n",
    "    print(\"Using local files.\")\n",
    "    \n",
    "except:\n",
    "    local_files = False\n",
    "    print(\"Cannot find files, need to get them from EUROSTAT - get ready!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](3_Images/REST_REQUEST_2_1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=== (2) API DATA guide sourced from: https://pypi.org/project/eurostatapiclient/\n",
    "\n",
    "#===(2.1) VARIABLE FORMAT SETTINGS\n",
    "# Choose service version : only v2.1 is currently available\n",
    "api_VERSION = 'v2.1'\n",
    "# Only json is currently available\n",
    "api_FORMAT = 'json'\n",
    "# Specify language : en, fr, de\n",
    "api_LANGUAGE = 'en'\n",
    "\n",
    "#define client\n",
    "client = EurostatAPIClient(api_VERSION, api_FORMAT, api_LANGUAGE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define tables and variables of interest from Eurostat based on eurostat table of content found [here (pdf download)](http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&file=table_of_contents_en.pdf). Link found on source page, [here](https://ec.europa.eu/eurostat/web/json-and-unicode-web-services/data-scope-and-query-size).\n",
    "\n",
    "**Tables of interest include:** _table name (table id)_\n",
    "- Population on 1 January by age groups and sex - cities and greater cities (urb_cpop1)\n",
    "- Population by citizenship and country of birth - cities and greater cities (urb_cpopcb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#===(2.2) DEFINE LIST OF TABLES OF INTEREST\n",
    "\n",
    "euro_tables = [\"urb_cpop1\",\"urb_cpopcb\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#===(2.3) DATA DOWNLOAD FUNCTION\n",
    "def f_euro_api_data_extract(p_table_list):\n",
    "    \n",
    "    all_dataframes = {}\n",
    "    \n",
    "    #for each table, (i) download data (ii) convert to dataframe (iii) save as .csv\n",
    "    for table in p_table_list:\n",
    "    \n",
    "        #(i) extract dataset\n",
    "        print(\"\\n\")\n",
    "        print(table.upper(),\"downloading ...\")\n",
    "        print(\"========================================================\\n\")\n",
    "        dataset = client.get_dataset(table)\n",
    "        print(dataset.label)\n",
    "\n",
    "        #(ii) convert to pandas data frame\n",
    "        dataframe = dataset.to_dataframe()\n",
    "        print(dataframe.head())\n",
    "        \n",
    "        #no filtering of data on call\n",
    "        \n",
    "        #(iii) save as .csv\n",
    "        time_now = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')\n",
    "        dataframe.to_csv(f\"0_Data_download/{table}_{time_now}.csv\")\n",
    "        \n",
    "        all_dataframes[table] = dataframe\n",
    "        \n",
    "        print(table.upper(),\"csv file saved.\")\n",
    "    print(\"\\n========================================================\")\n",
    "    print(\"All tables downloaded.\")\n",
    "    \n",
    "    return all_dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API not run, will attempt to use local files\n"
     ]
    }
   ],
   "source": [
    "#===(2.4) CALL DATA DOWNLOAD FUNCTION (uncomment to run)\n",
    "\n",
    "run_locally = True\n",
    "\n",
    "if run_locally is True:\n",
    "    print(\"API not run, will attempt to use local files\")\n",
    "else:\n",
    "    eurostat_api_dataframes = f_euro_api_data_extract(euro_tables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local files from step 1 used\n"
     ]
    }
   ],
   "source": [
    "#===(2.5) SEPARATE DATAFRAMES (TEMP)\n",
    "\n",
    "try:\n",
    "    #call API generated dataframes\n",
    "    eu_df_urb_population = eurostat_api_dataframes[\"urb_cpop1\"]\n",
    "    eu_df_urb_citizenship = eurostat_api_dataframes[\"urb_cpopcb\"]\n",
    "    \n",
    "    #===(2.6) CONCATENATE ALL ITEMS IN DICTIONARY\n",
    "    eu_df_all = pd.concat(eurostat_api_dataframes)\n",
    "    api_use_working = True\n",
    "except:\n",
    "    #===(2.7) CONCATENATE ALL ITEMS IN LOCAL DICTIONARY\n",
    "    api_use_working = False\n",
    "    \n",
    "    if local_files is True:\n",
    "        #if we wish to use\n",
    "        eu_df_all = pd.concat(table_all)\n",
    "        eu_df_all.head()\n",
    "        print(\"Local files from step 1 used\")\n",
    "        local_use_working = True\n",
    "    else:\n",
    "        print(\"No local files available, return to step 1\")\n",
    "        local_use_working = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PART B - TRANSFORM DATA (3)\n",
    "\n",
    "- Merge based on cities id and time _creating uniqueID\n",
    "- separate countries and city data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=== (3) IMPORT PACKAGES\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using dictionary, cycle through each table and calculate number of variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using local files\n",
      "\n",
      "Unique variables in urb_cpop1: 36\n",
      "Unique variables in urb_cpopcb: 14\n",
      "Unique variables across dataframes: 50\n"
     ]
    }
   ],
   "source": [
    "#===(3.1) REPORT COUNT ALL UNIQUE VARIABLE\n",
    "\n",
    "#store unique variables\n",
    "unique_variable_list = []\n",
    "\n",
    "if api_use_working is True:\n",
    "    df_dictionary = eurostat_api_dataframes\n",
    "    print(\"Using API files\\n\")\n",
    "else:\n",
    "    if local_use_working is True:\n",
    "        df_dictionary = table_all\n",
    "        print(\"Using local files\\n\")\n",
    "    else:\n",
    "        print(\"No files to use\")\n",
    "\n",
    "#cycle through unique keys in dictionary\n",
    "for key in df_dictionary:\n",
    "    \n",
    "    for variable in df_dictionary[key].indic_ur.unique():\n",
    "        unique_variable_list.append(variable)\n",
    "    print(f\"Unique variables in {key}: {len(df_dictionary[key].indic_ur.unique())}\")\n",
    "\n",
    "print(f\"Unique variables across dataframes: {len(unique_variable_list)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===[ALL GOOD]=== Desired variable (DE1001V) present\n",
      "===[ALL GOOD]=== Desired variable (DE2003V) present\n",
      "===[ALL GOOD]=== Desired variable (DE2002V) present\n",
      "===[ALL GOOD]=== Desired variable (DE2001V) present\n",
      "===[ALL GOOD]=== Desired variable (DE2003I) present\n",
      "===[ALL GOOD]=== Desired variable (DE2002I) present\n",
      "===[ALL GOOD]=== Desired variable (DE2001I) present\n"
     ]
    }
   ],
   "source": [
    "#===(3.2) WRITE OUT DESIRED VARIABLES AND CHECK THEY EXIST\n",
    "\n",
    "euro_selected_variables = [\"DE1001V\",\"DE2003V\",\"DE2002V\",\"DE2001V\",\"DE2003I\",\"DE2002I\",\"DE2001I\"]\n",
    "euro_variable_present = 0\n",
    "euro_variable_absent = 0\n",
    "\n",
    "\n",
    "for variable in euro_selected_variables:\n",
    "    if variable in unique_variable_list:\n",
    "        print(f\"===[ALL GOOD]=== Desired variable ({variable}) present\")\n",
    "        euro_variable_present += 1\n",
    "    else:\n",
    "        print(f\"===[WARNING]==== Desired variable ({variable}) absent\")\n",
    "        euro_variable_absent += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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></th>\n",
       "      <th>values</th>\n",
       "      <th>indic_ur</th>\n",
       "      <th>cities</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">urb_cpop1</th>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>DE1001V</td>\n",
       "      <td>AT</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7795786.0</td>\n",
       "      <td>DE1001V</td>\n",
       "      <td>AT</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7867796.0</td>\n",
       "      <td>DE1001V</td>\n",
       "      <td>AT</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7962003.0</td>\n",
       "      <td>DE1001V</td>\n",
       "      <td>AT</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8015027.0</td>\n",
       "      <td>DE1001V</td>\n",
       "      <td>AT</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                values indic_ur cities  time\n",
       "urb_cpop1 0        NaN  DE1001V     AT  1990\n",
       "          1  7795786.0  DE1001V     AT  1991\n",
       "          2  7867796.0  DE1001V     AT  1992\n",
       "          3  7962003.0  DE1001V     AT  1993\n",
       "          4  8015027.0  DE1001V     AT  1994"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#===(3.3) FILTER TABLE FOR SELECT VARIABLES\n",
    "#filter based on values in euro_selected_variables from previous cell\n",
    "eu_df_filter_by_var = eu_df_all[eu_df_all.indic_ur.isin(euro_selected_variables)]\n",
    "eu_df_filter_by_var.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>cities</th>\n",
       "      <th>time</th>\n",
       "      <th>DE1001V</th>\n",
       "      <th>DE2001I</th>\n",
       "      <th>DE2001V</th>\n",
       "      <th>DE2002I</th>\n",
       "      <th>DE2002V</th>\n",
       "      <th>DE2003I</th>\n",
       "      <th>DE2003V</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AT</td>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AT</td>\n",
       "      <td>1991</td>\n",
       "      <td>7795786.0</td>\n",
       "      <td>93.4</td>\n",
       "      <td>7278096.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>79437.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>438253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AT</td>\n",
       "      <td>1992</td>\n",
       "      <td>7867796.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AT</td>\n",
       "      <td>1993</td>\n",
       "      <td>7962003.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AT</td>\n",
       "      <td>1994</td>\n",
       "      <td>8015027.0</td>\n",
       "      <td>91.2</td>\n",
       "      <td>7308692.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cities  time    DE1001V  DE2001I    DE2001V  DE2002I  DE2002V  DE2003I  \\\n",
       "0     AT  1990        NaN      NaN        NaN      NaN      NaN      NaN   \n",
       "1     AT  1991  7795786.0     93.4  7278096.0      1.0  79437.0      5.6   \n",
       "2     AT  1992  7867796.0      NaN        NaN      NaN      NaN      NaN   \n",
       "3     AT  1993  7962003.0      NaN        NaN      NaN      NaN      NaN   \n",
       "4     AT  1994  8015027.0     91.2  7308692.0      NaN      NaN      NaN   \n",
       "\n",
       "    DE2003V  \n",
       "0       NaN  \n",
       "1  438253.0  \n",
       "2       NaN  \n",
       "3       NaN  \n",
       "4       NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#===(3.4) TRANSPOSE\n",
    "\n",
    "#break multi index\n",
    "eu_df_filter_by_var_break = pd.DataFrame(eu_df_filter_by_var.to_records())\n",
    "\n",
    "#drop unwanted columns\n",
    "eu_df_filter_by_var_break.drop(columns = [\"level_0\",\"level_1\"],inplace = True)\n",
    "\n",
    "#set new index (leaving only variable column and values)\n",
    "eu_df_filter_by_var_index = eu_df_filter_by_var_break.set_index(keys = [\"cities\",\"time\",\"indic_ur\"], drop = True)\n",
    "\n",
    "#pivot table by unstacking one level\n",
    "eu_df_filter_by_var_index_unstack = eu_df_filter_by_var_index.unstack(level =-1)\n",
    "\n",
    "#flatten table and break multi-level index\n",
    "eu_df_filter_by_var_flat = pd.DataFrame(eu_df_filter_by_var_index_unstack.to_records())\n",
    "\n",
    "#rename columns\n",
    "#isolate variable columns (after our index columns - there should be two remaining)\n",
    "\n",
    "#start position defined by: number of successful columns - total columns\n",
    "eu_df_start = len(eu_df_filter_by_var_flat.columns) - euro_variable_present\n",
    "eu_df_columns_original = eu_df_filter_by_var_flat.columns[eu_df_start:]\n",
    "eu_df_columns_new_name = []\n",
    "\n",
    "for column in eu_df_columns_original:\n",
    "    new_column = column[-9:-2]\n",
    "    eu_df_columns_new_name.append(new_column)\n",
    "\n",
    "#create dictionary to rename columns\n",
    "eu_df_columns_rename_dic = {}\n",
    "for i in range(0,len(eu_df_columns_new_name)):\n",
    "    eu_df_columns_rename_dic[eu_df_columns_original[i]] = eu_df_columns_new_name[i]\n",
    "\n",
    "eu_df_columns_rename_dic\n",
    "\n",
    "#7 character IDs\n",
    "\n",
    "eu_df_filter_by_var_flat.rename(columns = eu_df_columns_rename_dic, inplace = True)\n",
    "\n",
    "eu_df_filter_by_var_flat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#===(3.5) SEPARATE COUNTRIES AND CITIES\n",
    "#function that creates a fork, return table 0 for cities and table 1 for country data\n",
    "def f_city_country_separate(p_df):\n",
    "    \n",
    "    df = p_df.copy()\n",
    "    \n",
    "    #create flag for countries\n",
    "    country_flag = []\n",
    "\n",
    "    for entry in df.cities:\n",
    "        if len(entry) == 2:\n",
    "            country_flag.append(\"country\")\n",
    "        else:\n",
    "            country_flag.append(\"city\")\n",
    "\n",
    "    df[\"country_flag\"] = country_flag\n",
    "    \n",
    "    #filter dataframe by flag value and then drop flag column\n",
    "    df_country = df[df[\"country_flag\"] == \"country\"].copy()\n",
    "    df_country.drop(columns = [\"country_flag\"], inplace = True)\n",
    "    \n",
    "    df_city = df[df[\"country_flag\"] == \"city\"].copy()\n",
    "    df_city.drop(columns = [\"country_flag\"], inplace = True)\n",
    "    \n",
    "    #return two dataframes\n",
    "    return df_city,df_country\n",
    "\n",
    "#run function\n",
    "eu_df_filter_cities = f_city_country_separate(eu_df_filter_by_var_flat)[0]\n",
    "eu_df_filter_countries = f_city_country_separate(eu_df_filter_by_var_flat)[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PART C - LOAD DATA (4-10)\n",
    "\n",
    "- summary statistics\n",
    "- final dropna\n",
    "- clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#===(4.0) IMPORT PACKAGES\n",
    "\n",
    "#hiearchical clustering\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "import scipy.cluster.hierarchy as shc\n",
    "#silluete scores\n",
    "import sklearn.metrics as sklm\n",
    "\n",
    "#dendrogram\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.metrics as sklm\n",
    "import scipy.cluster.hierarchy as spch\n",
    "\n",
    "#final plots\n",
    "import seaborn as sns\n",
    "import matplotlib as plt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#scaling \n",
    "from sklearn import preprocessing\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>DE1001V</th>\n",
       "      <th>DE2001I</th>\n",
       "      <th>DE2001V</th>\n",
       "      <th>DE2002I</th>\n",
       "      <th>DE2002V</th>\n",
       "      <th>DE2003I</th>\n",
       "      <th>DE2003V</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cities</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>UK109C1</th>\n",
       "      <td>338449.0</td>\n",
       "      <td>71.8</td>\n",
       "      <td>242855.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>53320.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>42275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK113C1</th>\n",
       "      <td>182493.0</td>\n",
       "      <td>71.6</td>\n",
       "      <td>130700.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>29970.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH010C1</th>\n",
       "      <td>51203.0</td>\n",
       "      <td>71.3</td>\n",
       "      <td>36507.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>7802.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>6894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES054C1</th>\n",
       "      <td>68045.0</td>\n",
       "      <td>69.8</td>\n",
       "      <td>47490.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>10624.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>9931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH001K1</th>\n",
       "      <td>590548.0</td>\n",
       "      <td>69.6</td>\n",
       "      <td>411183.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>110339.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>69026.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH001C1</th>\n",
       "      <td>372857.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>259141.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>72889.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>40827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK105C1</th>\n",
       "      <td>311215.0</td>\n",
       "      <td>68.9</td>\n",
       "      <td>214470.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>53790.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>42955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE001C1</th>\n",
       "      <td>1136778.0</td>\n",
       "      <td>67.4</td>\n",
       "      <td>766744.0</td>\n",
       "      <td>19.6</td>\n",
       "      <td>222908.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>147126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES060C1</th>\n",
       "      <td>72019.0</td>\n",
       "      <td>67.2</td>\n",
       "      <td>48379.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>14570.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK125C1</th>\n",
       "      <td>307984.0</td>\n",
       "      <td>66.4</td>\n",
       "      <td>204600.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>40600.0</td>\n",
       "      <td>20.4</td>\n",
       "      <td>62785.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH003C1</th>\n",
       "      <td>163216.0</td>\n",
       "      <td>65.9</td>\n",
       "      <td>107635.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>31270.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>24311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK133C1</th>\n",
       "      <td>219396.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>142220.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>39245.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>37935.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK120C1</th>\n",
       "      <td>158649.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>100430.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>33270.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>24950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH009K1</th>\n",
       "      <td>73266.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>46189.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>19515.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>7562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH009C2</th>\n",
       "      <td>54667.0</td>\n",
       "      <td>62.2</td>\n",
       "      <td>33999.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>15101.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>5567.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH005K1</th>\n",
       "      <td>215621.0</td>\n",
       "      <td>61.8</td>\n",
       "      <td>133331.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>53059.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>29231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH005C1</th>\n",
       "      <td>127821.0</td>\n",
       "      <td>59.6</td>\n",
       "      <td>76228.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>31712.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>19881.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH002K1</th>\n",
       "      <td>349052.0</td>\n",
       "      <td>58.9</td>\n",
       "      <td>205470.0</td>\n",
       "      <td>26.9</td>\n",
       "      <td>93753.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>49829.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH002C1</th>\n",
       "      <td>187470.0</td>\n",
       "      <td>54.3</td>\n",
       "      <td>101741.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>54453.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>31276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES035C1</th>\n",
       "      <td>90097.0</td>\n",
       "      <td>54.2</td>\n",
       "      <td>48863.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>22836.0</td>\n",
       "      <td>20.4</td>\n",
       "      <td>18397.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           DE1001V  DE2001I   DE2001V  DE2002I   DE2002V  DE2003I   DE2003V\n",
       "cities                                                                     \n",
       "UK109C1   338449.0     71.8  242855.0     15.8   53320.0     12.5   42275.0\n",
       "UK113C1   182493.0     71.6  130700.0     16.4   29970.0     12.0   21825.0\n",
       "CH010C1    51203.0     71.3   36507.0     15.2    7802.0     13.5    6894.0\n",
       "ES054C1    68045.0     69.8   47490.0     15.6   10624.0     14.6    9931.0\n",
       "CH001K1   590548.0     69.6  411183.0     18.7  110339.0     11.7   69026.0\n",
       "CH001C1   372857.0     69.5  259141.0     19.5   72889.0     10.9   40827.0\n",
       "UK105C1   311215.0     68.9  214470.0     17.3   53790.0     13.8   42955.0\n",
       "BE001C1  1136778.0     67.4  766744.0     19.6  222908.0     12.9  147126.0\n",
       "ES060C1    72019.0     67.2   48379.0     20.2   14570.0     12.6    9069.0\n",
       "UK125C1   307984.0     66.4  204600.0     13.2   40600.0     20.4   62785.0\n",
       "CH003C1   163216.0     65.9  107635.0     19.2   31270.0     14.9   24311.0\n",
       "UK133C1   219396.0     64.8  142220.0     17.9   39245.0     17.3   37935.0\n",
       "UK120C1   158649.0     63.3  100430.0     21.0   33270.0     15.7   24950.0\n",
       "CH009K1    73266.0     63.0   46189.0     26.6   19515.0     10.3    7562.0\n",
       "CH009C2    54667.0     62.2   33999.0     27.6   15101.0     10.2    5567.0\n",
       "CH005K1   215621.0     61.8  133331.0     24.6   53059.0     13.6   29231.0\n",
       "CH005C1   127821.0     59.6   76228.0     24.8   31712.0     15.6   19881.0\n",
       "CH002K1   349052.0     58.9  205470.0     26.9   93753.0     14.3   49829.0\n",
       "CH002C1   187470.0     54.3  101741.0     29.0   54453.0     16.7   31276.0\n",
       "ES035C1    90097.0     54.2   48863.0     25.3   22836.0     20.4   18397.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#===(4.1) DROPNA TABLES\n",
    "\n",
    "eu_df_filter_cities_no_nan = eu_df_filter_cities.dropna()\n",
    "\n",
    "#===(4.2) FUNCTION TO FILTER AND SORT TABLE\n",
    "\n",
    "def f_df_timefilter_sort(p_df,p_column_sort,p_year):\n",
    "    \"\"\"dataframe, column by which to sort as string, year (string or int)\"\"\"\n",
    "    \n",
    "    try:\n",
    "        df_time = p_df[p_df[\"time\"]==p_year]\n",
    "    except:\n",
    "        df_time = p_df[p_df[\"time\"]==str(p_year)]\n",
    "    \n",
    "    df_indexed = df_time.set_index(\"cities\")\n",
    "    #drop time as there is only one year now\n",
    "    df_indexed.drop(columns = [\"time\"],inplace = True)\n",
    "    \n",
    "    #sort by population column\n",
    "    df_indexed.sort_values(by = p_column_sort,ascending = False, inplace = True)\n",
    "    \n",
    "    return df_indexed\n",
    "    \n",
    "f_df_timefilter_sort(eu_df_filter_cities_no_nan,[\"DE2001I\"],2011).tail(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SUMMARY STATISTICS\n",
    "\n",
    "- Figure 1 - cities per country `eu_df_filter_cities`\n",
    "<br>No filtering of data\n",
    "\n",
    "\n",
    "- Figure 2 - \n",
    "<br>Use da with only data across all measures (step 4.1)\n",
    "\n",
    "- Figure 3 - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f6f0f90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===(5.0) FIGURE 1 and TABLE 4: CITIES PER COUNTRY\n",
    "\n",
    "fig_1_data = eu_df_filter_cities.cities.unique()\n",
    "\n",
    "#create new data frame with one column for our unique IDs\n",
    "fig_1_unique_city_df = pd.DataFrame.from_records(fig_1_data.reshape(len(fig_1_data),1), columns = [\"City_ID\"])\n",
    "\n",
    "#Country IDs in separate column\n",
    "fig_1_country_id = [] #two digit ISO code, e.g. AT for Austria\n",
    "fig_1_city_type_flag = [] #either a 'C' or 'K' to denote city or greater city respectively\n",
    "\n",
    "for cityid in fig_1_unique_city_df[\"City_ID\"]:\n",
    "    fig_1_country_id.append(cityid[:2])\n",
    "    fig_1_city_type_flag.append(cityid[5:6])\n",
    "    \n",
    "#create country id column (31 unique countries)\n",
    "fig_1_unique_city_df[\"Country_ID\"] = fig_1_country_id\n",
    "fig_1_unique_city_df[\"City_flag\"] = fig_1_city_type_flag\n",
    "    \n",
    "fig_1_group_country_df = fig_1_unique_city_df.groupby(by = [\"Country_ID\",\"City_flag\"]).count()\n",
    "fig_1_group_country_df.to_csv(\"1_Data_export/Country_city_count.csv\")\n",
    "\n",
    "\n",
    "fig1_df_unstack = pd.DataFrame(fig_1_group_country_df.to_records())\n",
    "fig1_df_pivot = fig1_df_unstack.pivot(index = \"Country_ID\",columns = \"City_flag\", values = \"City_ID\")\n",
    "fig1_df_pivot.sort_values(by = \"C\",ascending = False,inplace = True)\n",
    "\n",
    "fig1_df_pivot.plot.bar(stacked = True,title = \"3 | Figure 1 | Unique city count by country\", figsize = (10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13179cc50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===(5.1) FIGURE 2: CITIES WITH DATA BY YEAR\n",
    "fig_2_year_list = eu_df_filter_cities_no_nan.time.unique()\n",
    "fig_2_year_city_count = []\n",
    "\n",
    "#cycle through each year \n",
    "for year in fig_2_year_list:\n",
    "    \n",
    "    df = f_df_timefilter_sort(eu_df_filter_cities_no_nan,[\"DE1001V\"],year)\n",
    "    fig_2_year_city_count.append(len(df))\n",
    "\n",
    "fig_2_year_city_count\n",
    "\n",
    "fig_2_entry_by_year_df = pd.DataFrame.from_records(fig_2_year_list.reshape(len(fig_2_year_list),1), columns = [\"Year\"])\n",
    "fig_2_entry_by_year_df[\"COUNT\"] = fig_2_year_city_count\n",
    "fig_2_entry_by_year_df.sort_values(by = \"Year\", inplace = True)\n",
    "\n",
    "fig_2_entry_by_year_df.to_csv(\"1_Data_export/city_year_count.csv\")\n",
    "\n",
    "fig_2_entry_by_year_df.set_index(\"Year\").plot()\n",
    "\n",
    "#fig_2_entry_by_year_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 24 countries in the clean dataset.\n"
     ]
    },
    {
     "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>DE1001V</th>\n",
       "      <th>DE2001I</th>\n",
       "      <th>DE2001V</th>\n",
       "      <th>DE2002I</th>\n",
       "      <th>DE2002V</th>\n",
       "      <th>DE2003I</th>\n",
       "      <th>DE2003V</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>845.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>253,101.7</td>\n",
       "      <td>91.4</td>\n",
       "      <td>226,751.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>10,130.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>15,787.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>560,523.2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>477,632.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>37,680.2</td>\n",
       "      <td>4.3</td>\n",
       "      <td>53,706.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7,375.0</td>\n",
       "      <td>54.2</td>\n",
       "      <td>5,570.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>86,504.0</td>\n",
       "      <td>87.9</td>\n",
       "      <td>78,713.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1,135.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2,196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>134,844.0</td>\n",
       "      <td>93.2</td>\n",
       "      <td>124,396.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3,211.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5,403.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>224,897.0</td>\n",
       "      <td>96.5</td>\n",
       "      <td>201,932.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>8,147.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>13,080.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9,623,467.0</td>\n",
       "      <td>128.2</td>\n",
       "      <td>8,284,218.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>913,775.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>970,893.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DE1001V  DE2001I     DE2001V  DE2002I   DE2002V  DE2003I   DE2003V\n",
       "count       845.0    845.0       845.0    845.0     845.0    845.0     845.0\n",
       "mean    253,101.7     91.4   226,751.1      3.5  10,130.9      5.2  15,787.4\n",
       "std     560,523.2      7.6   477,632.6      4.1  37,680.2      4.3  53,706.5\n",
       "min       7,375.0     54.2     5,570.0      0.0       7.0      0.0      24.0\n",
       "25%      86,504.0     87.9    78,713.0      1.1   1,135.0      1.8   2,196.0\n",
       "50%     134,844.0     93.2   124,396.0      2.3   3,211.0      4.1   5,403.0\n",
       "75%     224,897.0     96.5   201,932.0      4.4   8,147.0      7.4  13,080.0\n",
       "max   9,623,467.0    128.2 8,284,218.0     29.0 913,775.0     25.6 970,893.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===(5.2) APPENDIX 3: MEASURE HISTOGRAMS\n",
    "fig_3_df = f_df_timefilter_sort(eu_df_filter_cities_no_nan,[\"DE1001V\"],2011)\n",
    "fig_3_df.hist(figsize = (14,14), bins = 20)\n",
    "\n",
    "fig_3_df.to_csv(\"1_Data_export/fig_3_hist_data.csv\")\n",
    "fig_3_df.to_excel(\"1_Data_export/fig_3_hist_data.xlsx\")\n",
    "\n",
    "#forgo scientific notation and include thousands separator\n",
    "\n",
    "#===(5.2b) unique country count\n",
    "country_list = []\n",
    "for city in fig_3_df.index:\n",
    "    country_code = city[:2]\n",
    "    country_list.append(country_code)\n",
    "\n",
    "fig_3_df[\"Country\"] = country_list\n",
    "\n",
    "print(\"There are\",len(fig_3_df[\"Country\"].unique()),\"countries in the clean dataset.\")\n",
    "\n",
    "#===(5.3) TABLE: SUMMARY STATISTICS\n",
    "pd.set_option('display.float_format', lambda x: f'{x:,.1f}')\n",
    "\n",
    "fig_3_df.describe().to_csv(\"1_Data_export/Table_4_SummaryStats.csv\")\n",
    "fig_3_df.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CLUSTERING\n",
    "\n",
    "Hints followed from tutorial [here](https://stackabuse.com/hierarchical-clustering-with-python-and-scikit-learn/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DE2002I</th>\n",
       "      <th>DE2003I</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cities</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FR001C1</th>\n",
       "      <td>3.8</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK001K2</th>\n",
       "      <td>11.2</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT002K1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE001C1</th>\n",
       "      <td>3.8</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES001C1</th>\n",
       "      <td>3.7</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DE2002I  DE2003I\n",
       "cities                   \n",
       "FR001C1      3.8     10.1\n",
       "UK001K2     11.2     10.4\n",
       "IT002K1      2.5      6.0\n",
       "DE001C1      3.8      9.2\n",
       "ES001C1      3.7     11.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#===(6.0) SET UP CLUSTERING DATA \n",
    "#set up cluster data\n",
    "\n",
    "cluster_data = f_df_timefilter_sort(eu_df_filter_cities_no_nan,[\"DE1001V\"],2011)\n",
    "\n",
    "#percentages\n",
    "cluster_data_process = cluster_data.loc[:,[\"DE2002I\",\"DE2003I\"]].copy()\n",
    "\n",
    "#absolute values\n",
    "cluster_data_process.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pM+N9Q6Z9e8ck7+y9/9LMeD+daVj9i5PcsaCuv1GuWdfPmri5tfadmV7Z8dokX1Nw7t45yTdnWuf/WNBoqz7fXpTkT5PcLcnr+syeV23qdfVbSb4kycd670+eGa/seF4ce5dneh3z3Xrv33qRP7lYvOq6oHrfVtcF1fEemuS/9N4/NifOUrx/n+SFmbbhA3rv/3ZmvOcmuSp158Zdln78xt7798yM94wkb0nyD5K8q/f+72bGq66by9pBK2i3PDfTeVa1rtXl+8Ikf5epQ8Qreu8vmhmv+jr+vCR/kmmUzBXFbfCKdtoLM5Wt6tz9ukzb7keT3LegfI/OdG78UaZ7wG+bGe9VmXrGP6/3/kdzYi3iVdd9pdeOc2I/vOCe9xVJ3pTkxXP37XlizyrfCtoFz0vhubuCuq86XnW74GeTvDV126/0WnRO7JJO7Evx7tN7/92ZMUrv8VdwLa9up1U+v6lugz8vtXVB9XWj+v60+h6wuq6qbmfsl69lep4xt3zV50b19qu+Jy+7hyl5JdsR+2dJPpjphJ49EqW19rOttae11p6eZO7kjLdL8qJMF/Y3FJTtBUtlm9UYX7hhpovR/juo5/qGTL1Xe6Z9MtdlvffH9d6/K8l1CuLdLNMw0Ccm+fsF8b44yf0Xjd1bF8S7c5K7Lir8z7jYLx/AW5O8qvf+05kuUHO9p/f+8Ezn3O3nBlvB8XyrJDfpvT89ybsK4v1Jkl/rvb8k81+1UVpP5ezx8YtJZiUUFirXtboeTc4ey89NzbH81Zkmkv7lJPcviPeiTMPMvyNTL+C57pDk65L8+8XyXH+yaBjcMdMrXef6zUxD7P9VpvWe64aZrh3vzNTRYq6TSX460/n2TQXxqs+3/5jklUleOPeGYeFjmc7hjyS5uiBe5fH81ky9t56Tmnq5ui6o3rfVdUF1vPsm+bHW2pNbaxXtlo8meVWS/5ZrzgNxWL+dqc1cdW48OdPrv++WaX7WuT6Q6dVl70zN+/er6+bKdlB1u+UtxetaXb77JXlopteWVUx4XX0df1Pv/YcWy181N9gK2mmnU3td++Ikv5DpVXJ3Koj395LcoPf+giRvL4j3K5nmcLp/a+2nCuJV132l147W2iuWjpeHzo2XaV2/Pcn1W2vPmRusuHzV7YLSczf1dV91vOp2wZuLt1/ptai19ujF12My/3VjWRzH+8fyU+bGS/E9fuqv5dXttMr1rW6DV9cF1deN6vvT6nvA6rqqup2xX74PpqZ81edG9farvicvu4fZxDm1ntJ7/5Mkaa3N7imVqbL5sUW8h82M9ZTe+/sWsX5kdsmSl/Te/9Mi3gML4r0vyX7PwVYQ7+GZKv6re+8V73W9rJ1972fF8Ny/7b1f1Vr7wSS/keSHZ8a7T5L912RVDLe8OskDW2tflKkn8Fyv6r3/3mK5Yvjr/msinpxptNZc1cfzVZkeOCVT77+5Xtp7348zdz686nrqpZkSCj+XZPbNYGrXNamtR5P6Y/kvM/UOvaq19v6CeE/pvb8sSVpr9yiI99dJXtB77621irrlDUnSe//x1trjCuJ9Y6aG0Zne+48WxPvPSU5lmkfxeQXxPj3JLTP19KlotJWeb733l7TWdpJ8wdxYC7+S5AGZHipWTFJdeTx/IMlfttYen5rOLtV1QXVdWl0XVMf7w977M1trN8/UZpv7wOTXMz3Y7Zk6CczSe/+51tpXZOplW+Epvff/nnxyZMVcv5+p7fisTPXMXNV1c2U7qLrdUr2u1eX7yiTv7r1/orVW8QrX6uv4q5Kk9/5LrbX/WRCvtJ22dF2rSOAlyQf79LqxZ2WqZ542M94rcrbTx+/PjJUkf9qnVwE9vSDWquq+ymvHs/vijRqttfsWxHtdn1719OupmUensnzV7YLqc7e67quOV9ouSP32q74W3S7JMxbLNy2Id1VfvHGhtfbIi/3yAVTf46/iWl7ZTqt+flNZj5Yey9XXjRVcx6vvAavrqup2xirq5spzo7p81ffkZfcwm/j6wbtm6pXdk+z23isq6xKttftlysJ/ItMrmn6zIOaNkqTPfN3TufEyvX5wdg/51tqNk9yh9/7/zo11TtyqOZyqt9+Np3Bl8T4vU1JwdsXfWrtlpnlWPpGpx8qsjHeb3vF85SLeb/WCuTMq90f1+p4Te9brBKrrqaV17UmeX7BvPz/J5/TeX1n96oQKKziWP/nO7tbaHfrMOd3a2XlWegrOjdbOvre7tXbz3vt7Z8YrPzcW5+79eu/PL4i1f34k0/vjK86Peyd5SZLLe++zRi+t4Hy7S6bEYFKwvufErnhVSdnxvIJtV10XVJevui6ojvf3eu8VPXXPF3v2taNN88M9OHX7Yz9e1fHy+Uk+t/f+O0Xru6rjubQdNOi6ruz+r9XMPVl9HR/2fvdcRcfL9fti/o22mJtxZrxVtcOrjufquq/02rGIeZdM97yvL4hVfjxXlW8F7YLqY2/oeOfErqgLRj93r+hnO7HPnmuvtXbd3ntF8u58sUe8lle308qOl1XUo0uxh2sznxO78lgZtXyjtzOGPTcW8Ya9h9nEkVpfn+R7F8tPzMyMfJvmcbpdpp39tj5vHqd79MXQzdba92R6ZdOcsn1/phFVfXGv9EMX+5uDxlt8zY33fyzipLV2ZUH5rvEe2yRz32O7iu23vzxcvCSPTvITmdb50Zn/epEfyNl3ZD85ydx3ZJfujxSvbztnTrxMdc1hldZTqd+3j0/y39o0KXKSzG0kVNajSf36fu1iXT+RaR6nWUmtTPP0lZ0bSb69tXbbLLZfplEBc1SfGw/NNB9Zb619Wu99d2b5qs+P+2Yq3z/MtH/nlq/6+HtQatst+z3LWqbXFMztpVx5PFdvu9HjVdcF1fH+YZvmF/1EktfOPXeLr5PJ9FrTyv1RHe/xSV7ZWrvb4ue5HUCGPZ5XsG+r13UV93+3XfxYcd2tvo6PfL+7iuPl4SNvv9Qfz9V1Vem1o01vnHnjYvlk7/17L/InF1N9PFeWb+i6avR4K6gLRj9379Za+2Qn9sx83pfkG9o0KmjUdtrodV/l8VJdjw7dZh79WNnCdsbI50Yy8D3MJia1bpDpwdX+8lyXLSWiHjMz1k0XD/+S5LNmxkqm0VRPS5LWWsV7Tqvjfbg43osyvS6iZep1P3eS29G3X3W8/VdK9qXlOfbfkZ1MvVbmGn19K18nUF1PVa/rW3vvz2utXZnkCZn/esnKejSpX98rlsr3HQXxqs+NtlS+Q0+SuRxv8X3U7Vd9flSXr3r7Va9v9atKKo/n6m03erzquqA6XvW5Uf3andH371t77/+58Fo58vqOvm9Xcf/3uKSu3VJ8HR/5fjdZwfFSXL7R2+GjX4s+1Hv/+SRprc1NaCX1+6OyfKPXVaPHq64LRj93SzuxJ/ls7bRZKo+X6np09H2xdeXbsnbG6OUri7eJrx+8QZIvX/z48v0hhDPifUemidh6knf03p85I1ZLctdMQ+H/YE65FvH+xSJekuztN96Ocbwr+9J7bHvvrxmsfKPHu1WmSaqTaQjnO2fGu1uShyx+/OXe++tmxht9fcteJ7Cop74iU71SUU9Vr+s/6L2/YbFc8fqysnp0Ea96fZ+Y5O8W5bt8PyEwI171ubFt26/6Ol5dvurtV72+pa8qqTyeV7DtRo9XXRdUx6s+N6pfuzP6/q2+Vg67vhuwb4e9/1tRvNHXt/p4GX37DXvuLuJVXzvun+k+Jkle1nv/rzPjVe+PsvJtQF1VfU9ZXb7qumD0c/fHkuw/57tL7/27Z8bTTpsXr+x4WUE9Ovq+2LbybVs7Y/TylcXbxJFa/zSL1yAluX5mvmao9/70Nr1vsvf57xR+VKaMcm+tva3PfwXX8zNNoNaTvHxmrE2I98rW2gMW8X6rIN7o61sd7wuT3GYR745JZlU0mYbB7/u7mbGS8de38nUCt8o04eG3LX5+0cyyVa/r57bW/lXOrussxfVoUry+vfcnt9ZusliumLi09NzovX9y4vHW2mVz42X87Vd9Ha8uX/X5Vrq+KX5VSWqP5+ptN3q86utkdd1SfW5Uv3Zn9P1beq3M2Os7+r4tv//bX6647q6gHTTy/W5SfLyMvv0y9rmb1F+L/jDJF2Qq34j7o7J8Q9dVqb+nrC5f9bVj9HP3u3O2E/svzIylnTbW+VZdj46+L7aqfFvYzhi9fGXxKh6eHbUreu/f2adXRtx8brA2vZP5zknu2lr74ZnhLlsq23Xmli3TnEbvT/LXmd7retzj/WCSDyT5YFG80de3Ot49lo6/u130ty9u/z2s3/v/t3e+oXtWZRz/XItNs5wRM7WZGBGSrqX0bxDKKmpWlCD1QolFJBS0nNleCOZcUGmWK/eif66yqMQiZL0IsiIqgoooVjoJDPrvRrFwrtAir16c88DDD2XR+d7uevZ8PzB+9wPjy3Wd61znXPd97vscxvewhfr+zvR2CPReC7wVuBZ42bBltX1Vj6Mg9jfaeW7bgG0Rcf2wdeLciIjbIuJzvdDaNqpH/fZTz+Nq+9T5JvWXvlVJz1+FnrI/TzVWVdVTz5PqsWWq3JDMHdSP7zL5W91X9bwhnXcnqIMq3+9C/TpSPe9Wzl3Qz0VXA98A7u7Xo6jjobSv9FiF/p5yqtxQzR3Vc3cbsBXYGu3M0iFcpw2j7C/qcXRRYrEU9i1hnVHdPpneIn6ptbYP/olm4ejI7C2LGN+TeVVE3Er/pHHYsvpnNVivlt7sTLdEc6abeh/W6v4q9bYAD2bmYxHx8LhppX0F7TgKevvU57mpc+NgZt4UEbuoGV91+83P44o6RG2fuv3U/qr1lP25+lhVfZ5U61XPDevV0atsG+jv/9TzrroOqny/C/XrSPU8WT0/qp+doY6H0r7qNZ/6nrJ6blS3T30+oeu0MZT9RT2OVm+7ZdOrXqdVr5vLxncRz9TaBNxPs334E91oezK/htaYo3syr51d0j5rPDJo20XAFd021VkN1jtx9V4AnNR/7s/B5I6Is4GX9J/fzcyjg3rz/t6Vmb8Y1FP7ewptKwtoDzsODmidnJmP9OszMvNQFdsm0lPvva+O7VbgQlrfU5znps6NKzPzqxERwJ7MfM+gXun265rPAFYDR3N8j2x1fKXt1zVl/qr1lP15AcYqtd4UZ10o9dS5UT0eZeuC6nqVbet6p9LGvMdoDxUPD+qp513Z/WTXU/urtm+KOlJmX9dUzpPVx6r5szO+J6hLn0P7UiERnJ3RNZXxkNnXx4DZmc/3peDMUnXNN6e7LjP/JtBRxqJ0+02Qa+pzedR1mjQe1fW6pqS/zNXg0Oah0Rq8dNtNoKeeJ9X2Va/TqtfNdftfZi7UP+AqYC/wduBkgd7LgQ39+hWDWjcC3wJ2AjvFfr/LetY7hsbVYpuWTe9j/e9a4DNVtBZE77L+71Lg1GqxXaF9ie077vZtA54F3Al8qqB96rFF6u8EejJ/F2CsUuvdDuwGPg7srqa3QluRG9XjUbYuqK5X2baus30W3wniPJwbK/SuLO6vwj5pfCewTz1PVh+rLgZuAz4BXCzWHrZ14rpvyD7a9oXvqOqr7Tu+/eNx9M6Zu94k1pbcw4jjUV1P1l9o59Wd1q/fWdDX6nryeVJsn+y5f9eQ1mkTtJ/avrL9b+G2H8zMvcDeiHgj8Gvg+YOSm2irtfcCLwZ+PGDbByJiew6+sfEErLGe9Y7B5RFx7uxH9k/jBXqzLw9H9Ta3BflGZu4Z1FP7+4WI2El7+2r0k2Sl1iLozb6aXUM7L03V94Dx2EbbFvYocDrwB+CHI3qIc6Pb9w9gndg+oGT7PQ3YDHyItq3KEBPGF5CMLVJ/J9BT9ufqY5Va717a1jG3C7TkehPkRvV4VK4LqutVtk3OBPPuPOuEWlOgsG/K+CrsU8+T1ceqzZm5HSAibgB+NKinRh0PJUGzT4XaV9s3hjrX3hdtS69zgMuBn4yITVCnqeNRXU/ZX34J3BTt7KWTjvWf/weqt51aTz1Pqu2TPfefiNJ1M4X738ItakXEJcBbgMO0rwJGeSGQEfGmfj1i22XAhq5FZn5zUO8eWvJ9HsG+ldY7sfVoh+V+H83e5wD3AIdo2zoMb/VJW4Te168VY4/a36toB/AeBK5nbGFGqVVeLzN/MLuOiAvGTAP0sT2UmbdExI2ZebNAT50bavuqt98qYEtmfi0iXiXQq95+an/Vesr+XHqsmkBvPa2G3AiQmb8qpqfOjerxqFwXVNerbBvAeQAR8QDwG2D0xShpbkTE7rmfz2PcPqm/E9gnje8E9qnnyepj1SNPcP1/ERHfBv4EHAE2UiweYvteCjy1b001vLUf+r5n+8ZQ59pO4BbgP8DQtrUddZ2mjkd1PWV/OQx8GLgZeOawZfXbTq2nnifV9sme+3e2dL3NtEXQUnUVevvK9r+FW9QCzgV2ZOajEXE148H5Dm3vyl3ARwe11gNf7tfnD2oBPAC8H3h9Zl5jPesdg4doezIH7S2E/YN6rwauA66h5ccoP83M3wOIclftL8DP+t9qWqX1VjyMGDo/rKOO7aURcSbw3IjYLXhLT50bavuqt98/BTbNU7391P6q9dT9uexYNYHeff3vRbS+MroIpdZT5wbUjkf1uqC6XmXbfkvb4v2vwGkCPXVuzB6YrqE9ZB9F7a/aPtDGV22fep6E2mPV3yPig13vd4Na0PpeZuaeiNgu0FPHQ2nffuBhdG+Nq321fWOoc20XbcHjdOBWxh88q+cidTyq6yn7y2mZ+S/g2v7FzCjV206tB9p5Um2f8rk/wF0CjZUo209tX9n+t4iLWvuB8/o2ZusFehcBZ9EOSb9hUGv17IuFiLhw1DBakB+hrWBuFLyxa70TWC8z7xi0ZyU/Bx5F5++f+9vngSB31f5m5ntn1xGxo4rWIujRYno+cAA4Y1Rsgr68j7all+KBLuhzQ2rfArTf/Ncoinm8evup/VXryfpz9bFqAr0vjmpMqYc+N6rH445RjRV61f1dmroFeDrwStq5ym8W6MlzIyLeALwOzU4iUn/V9k3RX8TtJ50nq49VwAW0h0JPAa6knc84wnz7PXtQa6Weug4ate/fmflpgIhQvGSq9tX2DTBBrv2F9vWs6sGu+h5LHY/qesr+cmTu+qFBLajfdlK9Ceo+tb/K5/5T3AOWvkelcP9bxEWt+cWiAwK9VbRP/DYApwxqrYuImYZiP/ADtOQ7QPN79MGp9U5sPTVq+9S5KyUi1vbL1QxuB6nUWgS9/jBiqvMEFai39FLnhto+NWr75r9GUYwF1dtP7a9aT9afq49Var0FQJobyxaP6v4uU91CG5/Ozsw/RtuSbxR1bnyddk7DZ9GcxSH1V23fBP1F3X7SebL6WDXBomD1ukWpd9bcS5dnDmqB3lfbV4spF2UU9zDqeFTXU/YX9bPc6m0n1ZtgnlT7q3zuL6d6nUHh/heZKbBncYmIt83/HlnR7J8Ov7v//GRmPjhimzHmySMibuyXjwL7MvP+CloLovci2puhX4F6iworxvnMzC8dN2MeB9s3RnX7lokFGKuketVR58ayxaO6v8tUt6iZIDdKz0MLMBZUb7/q/Xm2KHg31KvDK6N8FjQFtq8WEfER2v1uAFdk5nWDelPORcPxqK6nRP0st3rbTaA3ZV1Qzl81C1BnlI3H0i9qGWOMGaP6wwhjjDHGGGNORFyHG/PkUP3BuDHGLBte1DLGGGOMMcYYY4wxxhhjjDHlWXW8DTDGGGOMMcYYY4wxxhhjjDHmWHhRyxhjjDHGGGOMMcYYY4wxxpTHi1rGGGOMMcYYY4wxxhhjjDGmPF7UMsYYY4wxxhhjjDHGGGOMMeXxopYxxhhjjDHGGGOMMcYYY4wpz38B5tAV1eqrEtkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6/CqmAyvRWvuR1trTWmvfm6Ri4b/7Jnl+lkbAf14brLX2vCPlW9XY3rih9/6E3vvXJrldQbz3yjKU8klJ/qeCeHfJUpkfzuW71udl6Wnbk1RkvT8mySM2D6/7rIz14UkeuGlk3/F6v3wMr0ryy733Z2Z5KVjr9b33L0nymUk+eG2wLVzL/yLJLyX56bUvPhv3TnL33vv3JvmDgni/k+Qne+8vSM0w18/Mcg2/MUuiZK3Da+45SVZ9IL9Rur9bqJsP749npeb++PQsi+P+eJJHFMR7fpah6V+dpUfwGvdL8llJ/ulme623Zjm/b0nypoJ4lfuaJL+zaTQ9IMuUt2v9XJYpA/5+lnKudZcsx/C1WTpkrHUxyTOz1AVfVBCvui6oPr/V8R6W5Ltba09pra19jifLfPy/nOQ/5h3n5j+JV2XpBffPU/Mcqq73qq+Vp2SZPvtBWdZWXesNWab/em1q1kaorluq2xm/XVy+6nbGL2Z5H6pqp31KkkdnmTKpYgHp6mflK3rv37bZrlhAuvR8FLerLqe2Df4xSX4sy5RYH1oQ7wOT3Ln3/rwkrymI9xNZ1pl5RGvthwriVd8b1c/J6vJVv7NVPyur677q67m6fJXtlmTuuu+VxWUrfe621l50pF5+9OrSLfft45LcqbX2z9cGa609fvP1lVk/nWI2+3q4v09dGy/1n7dUt3Or22nV7ebK41f9HEq2V7f8adbXLdXnorqer34HLH2H2ULdV3r8ZlrD6hW99+9OktbaYwriPbX3fvMm3ncUxHtB7/1/38R7VEG8G9rVuR0rhlT+Re/9ja21b03yfyb59pXxbk5y2MOxrYyVJF+SpeJ8U++9Yg7uj01yOJ3T2mGVb0ryqNbaR2XpEbzWL/fef3WzXTG89XA4+VOyjLJaq/Ra7r2/oLW2l+RDVpds8cYsH3YlS8+ktX62934YZ/Vadlnqlt9Jktba6t6dSX42ywfyP5pkdYM29ftbXTdX3x//PUvvtTe21v64IN5Te+8/nySttQevjPUnSZ7Xe++ttYrh3z+R5JFZPqCqWHC3cl+TTeeQ3vv3tdaeUBDvC7I0eq703r+rIN6/THIpyxqSzy6I925J3i9LD6eKBm11XVB9fqvj/Ubv/RmttXtmaSOsfWH+qSwfZPYsCew13pDkv7fWnpiaTjbV9d42rpX/lLx9BMNaL87SDv3+LPfJWtV1S3U7o7p8pe2M3vuPttY+OUvv3QqfmuR1vfe3tdYqpqatflb+cpL03v91a+0vC+JVt/vK2lVH2uAVib4k+dO+TNn1/Vnq1KetjPeiXO1g8+KVsZLkv/Zlup/vLYi1jXuj9DlZXb4tXC/Vz8rqe636eq4uX2W7JZm77qsuW/Vz9wf7ZtaZ1trDCuK9rC9TUP5UatY4um+Sp2+271EQ7419M1NHa+0rrvfLx1D9+UN1O3cb7bTKdnPl8at+X0vmrluqz0V1PV/9Dlj9DlNd95UevymnBKzQWvuULNnft2WZSujnCmLeNUn6ymmJbiFu1bpEpeU7Mpz0Lb1mbsy7Jblf7/3/WRvrSLxesb+ttXtlSaatbmy31t4vyzozb8vSU2JV5rstc4o+fBPvF3rBWhWV18pmGO4XbH58Tu991UO2+vhdE3v1EPrW2gOzjKzoSfYL97cneW7B9fJBSf5m7/2XqqcMqLCF++Pt80a31u7XV67L1q6uvdKz8n5r7eq80a21e/be/3BN2a6JXTF9Q9m+buKV37ubuupTeu/PLYh1eO8mdXXVQ5O8IMmNvfdVo962UBdUn9/qeB/Ye6/ohXlLsVfVfVs4F9X1XnX53jfLtMhV5fugJO/be/8PRc/dbR2/WeNVtzMOz2/J9XJN7Ir1CkufldXH75rY07WrDhXda3fqmzUM2mYtv5XxttVmrrrXSu+NLTwnt3nvVtbNs5Zv6uv5mtgzvqOWxZu9LtjE/Igsny39ZkGs6v29qV/tjL96Lb/W2u177xWJh1uKPV3dssVnx3Ttvurn0C3En6qumvlcbOJN/c6xiTlt3TfNCKu2rOl03yw79uq+ck2nLGsHfc0m9tdnmVpoTfm+KctIo755r/q26/2b68R7h7lFk6yd47q6fG+Pt/laG+9/28RJa+3hReU73F61v5WxNh6f5J9lOX6Pz/rpPr4lV+e3fkqStfNbl14rWSrMb9hsPynre4WUHr92zXp2SdauZ/fZmXh/N//+P7ZlkdIkWdugqK6bq/f3Mzb7+rYsaxOtSlhlWQew6n57XGvtA7I5dllGG5xYa+2wZ2jLMvR9bS+Yyn1N6u/dR2dZm6y31u7Qe99fWb7qe/dhWcr3cVmuvbXlq743qs9vdbyPa8v6m29L8tK157e4rq8+F7PH+8rieE9M8kuttQdtfl77Af/sx686XnVdVXp+29V1eJOCZ1uKn5UpPn7V7cjKdtUW2rhfMvO5yPx1X/Vzsvrerb5eZn9nm/p6PgPvqJXxpq4L2jIj08s32xd7799wnX9yPdX7+6DW2ts742flZ5tJPq8to29mbIMn87fTqp8dlddL9XNo9rpq5nORTH4tz173TZOwyrKm02GC6SsL4t1j82FXkrxnQby39N6fliRtWbh9rednmSahZemhvXYxvOryVcd788Tlq97XwykU+5HtNQ7nt06W3hJrVe/vnbN8YHu4vVb18aseQj/7/r6q9/7s1trDk3xz1k8jWV03V+/vTUfK99UF8Srvt3akbBULYlZP31Bdt8x+bqvv3eryzf7sqI5Xffwq6/rqc7Fr8V7Ve/+Xhc+h2fe3Ot7s7Ywbeu9PSOraBcXPyurjV92OrGxXVZetFbf5Zr+WZ3/uzv5ONH35Jr+eZ39HrYw3e13wZ733f5UkrbW1H9gm9ftb2hk/yXtP3AZP5q/rZ273VT+HkrnrqpnPRTL/tTd13TfNlICbivJe2azp1Ht/xsp4LckDswxt+/WC8n3hJl6SHBye1BXxHt6PzC3ae/+1ycq3M/G2ULZ7Z1k8OlmGab52ZbwHJfn8zY8/3nt/2cp41ft75ySftPnxhYdTL6yIV338qofQ3znJJ2epq2bc3w/rvf/nzXbFtHHVdXP1/j4pyV9vynfjYUJnRbyy+20Lx650+oYt1C2zn9vquqq6fLM/O6rjVR+/srp+C+di1+JVP4dm39/qeLO3q6qfbdXxqo9fdTuybH9nLtsm3uzX8uzP3dnfiWYv3+zX8zbeUSvLV/bOewbqgkdk2dck+fne+79fGa96f787yeFnmh/Re/+6lfGmbYNvYsxe10/b7qt+Dm1iTltXzXwutlS+nar7phlh1Xv/3rbMd9h7zXzAj82SCe6ttVf39dNYPTfLAmc9yQvXFi7LVCmP3MT7hYJ41eXbpXjVZbt/kvffxHtAklWVSJah2of+emWspH5//24203YluVPWT4tVffyqh9DfO8sCh1+++fn5K+NV7+/7ttb+fq7u7ypbqJtL97f3/pTW2t032xULPpfdb733ty8K3lq7YU2sjdLpG1Jft8x+bkvrqi2Ub/ZnR2m8LRy/yrq++lzsWrzS51Dm39/qeFO3q6qfbVtoZ1Qfv9J2ZPH+zly2ZPJreQvxpm5Xpf6daOrynYHrufp8VJev8p139rrgN5J8yCbejNfK1+VqZ/wfWxlr9jZ4Mn9dP3O7r/o5lMxdV818LpL5r72p676KD9BKtGXuxA9P8sDW2rcXhLyh9/41fZmy4nYF8b4lyR8n+ZMsc4Gu9a1J3pDkT4viVZdvl+JVl+3BR669B133t6/vcB7Qb8j6+WKT+v296cj+3rMgXvXxO4z3tUXxPiXJo5N8TZKPLog39f5uoW4u3d+2rMn22CSPba3949WlK7zfWmvPaK09a/Oy/NiCsr33kXNbca9V1y2zn9vSumoL5Zv92VEab4vHr6Lu21a9vGvxqp67Z2V/q+JN3a6qfrZtoZ2xreM3Y7tq5rIlk1/LW4g3dbsqZ6du3rXruep8VJev8p139rrg8Ul+MsnzNttrVe/vY5N8YZIvbMv6r6tM3gY/Gm/Wun7mdl/1cyiZu66a+Vwk8197U9d904ywyjJ34o8lZXMn3tBa+55shoAXxJt9LQjx5oiVXF0/radm/bTqeVSr9/eum0ZPT02dUn38quN9apLX9d7f1lr784J4s+9vdd1cXb6Z12R7fe/921trT07Nvlbfa9V1y+zntvr4VZdv9mdHdbyZj9/s9bJ45zve0bqqotNddfmqn23V7YzZj1/l/s5ctmT33hFmb1ftWrxdu56ry1f5zjv7sateF6Z6f6vXlJ65Db6L8Sqvl+rnUDJ3O3fmsiXzX3vbrPtWH7+Z1rB6RJYPz3tq5k686+FmlqGzf7Yy3oOS/L1N+arWghBvgnhbKNv9k7zb5sdf7ytvstbavZI8ZPPjC3vvb1oZ7+j+/kTv/aVr4m1i3j3J7ZO8qa+f07b6+N0xyzDXZPmQ5fUr4/2N3vtfbLbv2Xv/w8nKVx2vel7b6vP7hUn2slzPFWuyld1vrbWLvff91lpL8n2998etKdsmZuW9Vl23TH1uNzErj1/1tVd9/KrX26uOV338yuq+M1AvV8eb/bm7a/HukqWeeluWD6neMFn5Sp9tW3gHnP34le3vzGU7ErPyuTt73Xd07YZfKGhXHa7JnSQv7yvXMd1CvOrzUV2+2a/n0v2tLt81cd+r9/7/rowx8+cF984yGqWnYF2YTczK/a1ej626DT57XTV1vE3MkuvlyPtastR7q97XNjGrj19ZO+0MtMGnvvaq677W2sckeWWWXNP66UZ771N8JXlokg/bbH98QbwnJf9/e+cWullVhvHfa8zUWDkg42EyxIgQGhsVEbxRRr2YqTBB6iJvJBIUCkfTC0Ed58JzOuZcdHIojSKqi5guhOwiVISKQEZtIjDwlAcExUPiKPJ2sfYHn38Undaz+L/ffM8Phv/+QB+edx3evfZaa6/NfcAOYIfY66XWOzT1FFrAZeL4qut9Fzga+DXwo4L+bp/+HgH8xHoHrXf+9G8b8Olq9btC+6yq/kTeqve16nUrLb8B/tT1cTewC7gT2FVNb0D5yXLfAuRltZ7vu7X0ts/qRFE3an8rtLv77gq9C5es/Lrirext0lCPW6rnvjOBu4AfAGcq/AHfVnkdoCevD7E/9fySfBwujresvxHeejVGag+I9/i56zPE8UqeYYrnqup6svZC+9bc+un6ElEbUccrG6cNuA+px5Cl295KbYHGxcAe4FvAJ3r1Kh0JeAZtVe9x4DTg4R6xbB8S3J6duw8+gLXWO2T1FFoXRMQJsx85vb4t0Ju9Ldirt6UtzDcyc3en3ieBLcCNtKMDelGX388jYgdtV5LiOIhl05vtjFhL++aZqj0D/fUb7ejXN4CjgKeBB3v0EPa3ydt/gQ0ib6P6miq3VK9bafkNbHuAJPc9Tjvy4+5OnSF6A8pPmfuq52W1nu+7tfTUSP0NuLfNs0GopWJk/fbGW9kb6Mct1XPflszcDhAR1wEPdeoFrQxVqPXU9aH2J51fQt+e1fFW9ld9vkCNOt4rox2zdTxwAfCXHrEBY/Dquaq6nrK9PALcHO0bfh//sP/4I6KOV0n1MXj1ticlM/cAeyLiPOAx4As9epUWrL4EZER8bbruIiLOB06a9MjMP3Tq3U8b9PwMwVmR1qujp/ZG+2Ddn9GcAQpwP/Ai7RXN/tcqW+LYO11LzlQGtmbmbyPiHIGeuvwupn0o9gXgGvoXXJZKLzMfmF1HxKY+a4C+fl/MzNsi4vrMvEWgp+xvam/qvqbOLdXrVl1+an/q8juONq7aDJCZjxbTU5efMveVzssD9HzfraV3IkBEPAH8C+jdWKT2J+27EbFr7ufn6Y+3dPmJ463sDfT33eq5760PuP5/OR1YNx1t13Uc2yA9dX2o/Unnl9C3Z3W8lf2Vni+IiD8CzwKvAZupl/t2ALcB7wLdR8yjH4NXz1XV9ZTt5WXgJuAW4MhuZw11vFtpuXkLbVGtzDhI7A2Ktz117ouIs4Bv0Nrhtl5/lRas/kQ7e3In8H2B3nHAL6frLwr0ngCuBb6SmZdb75DSU3t7lXYmcNB2de3r1DsXuBq4nNY/evlrZj4FEBGX0Z+E36Q/xnnU5Qfwt+mvyufS6K2YvOj6XteEun63RcSxwOciYpdgh52yv6m9qfuaOrdUr1t1+an9qcvvH9PfUye93gUmtZ66/ECbS8vm5QF6vu/W0vs37Vjzl4D1Aj3Q+lP33dmE41raw3Iv1ctPHW9lb+r7LtTOfa9ExA2T3pOdWtD8vI5uR7VaD7T1ofannl9St2d1vJX9VZ8vuI922sTuiNjeaw59vDtpiw9HAXfQPymvvo9Xz1XV9ZTtZX1mvg18b3rzSIE63t8INOZR3ofU3qq3PXXuOwG4KjMPKOaaKy1YnQpspH0A/jqB3prZmwERcYpA73XaTql1EbFZsLPYenX0pN4y856e//99+DtwAF3Z/WfaIR+0hd1e5nfdd+upyy8zr5hdR8RV1jtogrbovx84pldsQP/YSzumTHUUhLK/qb1J+xri3LIAdasuP6m/Abnv3sp66MtPlvuq5+UBevf0aqzQqx5vaT3gU8DZtDPgv94rNsCfvO9GxFeBLyPYjUnx8lPGW9nbhPoZoXTuAzbRJpA+BlxI+/ZjD+9k5o8BIkKxqVKqNyC3qONVzy+px5HqeCv7Kz1fwHv9fUasp6iL52hvDKsmqdXPWKVz1QLoKdvLa3PXr3ZqzVDfO2TPlAPGBern3UVqe4rctw84MdonaLpzX6UFq8Nor/GdBBwu0NsQETMdxRnc+2mDnv203Ry9iwbWq6On9qZG7W9+AXd/pxa8d9e9Qk9KRBwxXa5BcxzlUulNkxejvgeoQH1MmbK/qb2p+1r13Fe9/NT+lg1p+SlzX/W8rNZTUz3e6nq0/PTZzHwm2rF2XQzwp+67v6N9Y+anaL63ULr8lPFW9jYhve9Wz30DFvw2zm0yPLaa3oD6UMernl9SjyPV8Vb2V3q+AL0/td7ISWrFM0zpXLUAesr2op4HB328MqqPC1iutgfiuebIzF4NCRFx0fzv3pXN6RXX70w/f5iZz/foGWMWk4i4fro8AOzNzH9a76D0TqbtEv0V1JuUX3HvyMz8xaqZWUFlb4tA9fKr7q866vJT5r4FyMtSPTXV462up2ZAvOq+WzqXVi6/yt5GsAB9bbbg93uQLOaq50fUeiPbX7l41dif+ahExK20Z/EAvpmZV3fqjbyPl+u71fWUjJgHLx5v9XHB0rS9EZRZsDLGGFOP6pMXxhhjjDHGrDYeMxtjRrBsk9TGGANesDLGGGOMMcYYY4wxxhhjjDGrzGGrbcAYY4wxxhhjjDHGGGOMMcYsN16wMsYYY4wxxhhjjDHGGGOMMauKF6yMMcYYY4wxxhhjjDHGGGPMquIFK2OMMcYYY4wxxhhjjDHGGLOqeMHKGGOMMcYYY4wxxhhjjDHGrCr/A4UJRUujBDmMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===(7.0) DENDROGRAMS [FIGURE 4]\n",
    "\n",
    "#run dendrograms with different types of linkages\n",
    "\n",
    "def f_dendrogram(p_data,p_linkages):\n",
    "    \n",
    "    if type(p_linkages) is list:\n",
    "        \n",
    "        for link in p_linkages:\n",
    "            plt.figure(figsize=(30,15))\n",
    "            plt.title(f\"LIST DENDROGRAM (LINKAGE: {link.upper()})\")\n",
    "            dendro = shc.dendrogram(shc.linkage(p_data, method = link), labels = p_data.index)\n",
    "    \n",
    "    elif type(p_linkages) is str:\n",
    "        \n",
    "        plt.figure(figsize=(30,15))\n",
    "        plt.title(f\"ONE ENTRY DENDROGRAM (LINKAGE: {p_linkages.upper()})\")\n",
    "        dendro = shc.dendrogram(shc.linkage(p_data, method = p_linkages), labels = p_data.index)\n",
    "\n",
    "#Define linkages\n",
    "linkages = [\"single\",\"complete\",\"ward\",\"median\",\"centroid\",\"weighted\",\"average\"]\n",
    "\n",
    "#Run function on sample of top 84 cities\n",
    "f_dendrogram(cluster_data_process.iloc[:84,:],linkages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CLUSTER TESTING\n",
    "\n",
    "Test cluster quality with different techniques:\n",
    "\n",
    "Code from [here](https://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "silhouette_score processing\n",
      "calinski_harabasz_score processing\n",
      "davies_bouldin_score processing\n",
      "cluster test complete\n"
     ]
    }
   ],
   "source": [
    "#===(8.0) CLUSTER TESTING: SET UP\n",
    "\n",
    "cluster_range = list(range(2,15,1))\n",
    "cluster_test_list = [\"silhouette_score\",\"calinski_harabasz_score\",\"davies_bouldin_score\"]\n",
    "\n",
    "#===(8.1) CLUSTER TESTING: FUNCTION (LINKAGE SET TO WARD)\n",
    "\n",
    "def f_cluster_test(p_range,p_data,p_tests):\n",
    "    \n",
    "    cluster_test_df = pd.DataFrame(p_range, columns = [\"Clusters\"])\n",
    "    cluster_test_df.set_index(\"Clusters\", inplace= True)\n",
    "    #for every type of test do the following\n",
    "    for test in p_tests:\n",
    "        f_score_num = []\n",
    "        f_score_output = []\n",
    "        \n",
    "        print(f\"{test} processing\")\n",
    "        for number in cluster_range:\n",
    "            cluster_number = number\n",
    "            cluster_method_linkage = \"ward\"\n",
    "\n",
    "            #CLUSTERING\n",
    "            cluster = AgglomerativeClustering(n_clusters = cluster_number,affinity = \"euclidean\", linkage = cluster_method_linkage)\n",
    "            cluster.fit_predict(p_data)\n",
    "\n",
    "            #Silhuette score\n",
    "            model = cluster.fit_predict(p_data)\n",
    "            labels = cluster.labels_\n",
    "        \n",
    "            #run test\n",
    "            if test == \"silhouette_score\":\n",
    "                f_score = sklm.silhouette_score(p_data, labels)\n",
    "                #force scale the score to help it show up on 1 axis plot\n",
    "                f_score = f_score * 10000 \n",
    "            elif test == \"calinski_harabasz_score\":\n",
    "                f_score = sklm.calinski_harabasz_score(p_data, labels)\n",
    "            elif test == \"davies_bouldin_score\":\n",
    "                f_score = sklm.davies_bouldin_score(p_data, labels)\n",
    "                #force scale the score to help it show up on 1 axis plot\n",
    "                f_score = f_score * 10000\n",
    "            else:\n",
    "                f_score = f\"Test failed for cluster {number}\"\n",
    "            \n",
    "            #append score to score list\n",
    "            f_score_num.append(f_score)\n",
    "            #readible list\n",
    "            f_score_output.append(f\"Score for {number} clusters is: {f_score}\")\n",
    "        \n",
    "        cluster_test_df[test] = f_score_num\n",
    "    \n",
    "    #return dataframe with number of clusters and results from all tests\n",
    "    print(\"cluster test complete\")\n",
    "    return cluster_test_df\n",
    "\n",
    "#===(8.2) CLUSTER TESTING: CALL FUNCTION\n",
    "\n",
    "cluster_test_output = f_cluster_test(cluster_range,cluster_data,cluster_test_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>Silhouette score</th>\n",
       "      <th>Calinski Harabasz score</th>\n",
       "      <th>Davies Bouldin test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clusters</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9,692.3</td>\n",
       "      <td>1,031.3</td>\n",
       "      <td>1,287.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8,748.6</td>\n",
       "      <td>2,086.0</td>\n",
       "      <td>4,746.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8,438.7</td>\n",
       "      <td>3,395.6</td>\n",
       "      <td>3,552.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6,564.4</td>\n",
       "      <td>4,434.9</td>\n",
       "      <td>4,728.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6,572.9</td>\n",
       "      <td>4,881.9</td>\n",
       "      <td>4,571.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Silhouette score  Calinski Harabasz score  Davies Bouldin test\n",
       "Clusters                                                                \n",
       "2                  9,692.3                  1,031.3              1,287.9\n",
       "3                  8,748.6                  2,086.0              4,746.9\n",
       "4                  8,438.7                  3,395.6              3,552.1\n",
       "5                  6,564.4                  4,434.9              4,728.8\n",
       "6                  6,572.9                  4,881.9              4,571.0"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===(8.3) FIGURE 5\n",
    "\n",
    "#CLUSTER TESTING: CALINSKI HARABASZ SCORE - higher score is better\n",
    "#CLUSTER TESTING: SILHOUETTE SCORE        - the closer to positive 1 (10,000 in this version) the better\n",
    "#CLUSTER TESTING: DAVIES BOULDIN TEST     - lower score is better (also scaled up to go from 10k - 0)\n",
    "\n",
    "cluster_test_output.columns = [\"Silhouette score\",\"Calinski Harabasz score\",\"Davies Bouldin test\"]\n",
    "\n",
    "cluster_test_output.plot(figsize = (20,3))\n",
    "cluster_test_output.to_csv(\"1_Data_export/fig_4_cluster_test.csv\")\n",
    "cluster_test_output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#===(9.0) CLUSTER ANALYSIS: END TO END HIERARCHICAL CLUSTERING FUNCTION\n",
    "\n",
    "def f_cluster_end_to_end(p_data,p_cluster_number,p_linkage,p_data_master,p_colour = \"Pastel2\",p_title = \"auto\",p_axes = \"auto\"):\n",
    "    \n",
    "    df_return = p_data_master.copy()\n",
    "    \n",
    "    for link in p_linkage:\n",
    "    \n",
    "        #CLUSTERING\n",
    "        cluster = AgglomerativeClustering(n_clusters = p_cluster_number,affinity = \"euclidean\", linkage = link)\n",
    "        cluster.fit_predict(p_data)\n",
    "        \n",
    "        #SCATTER-PLOT TITLE\n",
    "        plt_title_string = \"\" \n",
    "        column_count = 1\n",
    "        for column in p_data.columns:\n",
    "            plt_title_string = plt_title_string + f\"{column.upper()}\"\n",
    "\n",
    "            if column_count < len(cluster_data_process.columns):\n",
    "                plt_title_string = plt_title_string + \"_\"\n",
    "                column_count += 1\n",
    "            else:\n",
    "                pass\n",
    "        \n",
    "        #Title setting conditional\n",
    "        if p_title == \"auto\":\n",
    "            plt_title_string_final = plt_title_string+\" CLUSTER PLOT \" + link.upper()\n",
    "        else:\n",
    "            plt_title_string_final = p_title\n",
    "            \n",
    "        ###Add cluster info to DF\n",
    "        column_name = plt_title_string+\"_\"+link\n",
    "        df_return[column_name] = cluster.labels_\n",
    "        \n",
    "        #create cluster group names\n",
    "        cluster_group_list = []\n",
    "        \n",
    "        for entry in df_return[column_name]:\n",
    "            cluster_tag = \"Cluster \" + str(entry+1)\n",
    "            cluster_group_list.append(cluster_tag)\n",
    "        \n",
    "        df_return[\"Cluster group\"] = cluster_group_list\n",
    "            \n",
    "        #Initiative axes, also required to show X axis (https://stackoverflow.com/questions/43578976/pandas-missing-x-tick-labels)\n",
    "        fig, ax = plt.subplots()\n",
    "                \n",
    "        #SCATTER-PLOT TITLE\n",
    "        f_plot = p_data.plot(\n",
    "            kind = \"scatter\",\n",
    "            title = plt_title_string_final,\n",
    "            figsize = (20,10),\n",
    "            \n",
    "            #column names that will be found in column axis\n",
    "            x = p_data.columns[0],\n",
    "            y = p_data.columns[-1],\n",
    "            \n",
    "            #xlabel = \"X LABEL\",\n",
    "            #ylabel = \"Y LAVEL\",\n",
    "\n",
    "            #xlim = (0,500000),\n",
    "            #ylim = (0,1200000),\n",
    "            c = cluster.labels_,\n",
    "            cmap = p_colour,\n",
    "            alpha = 0.45,\n",
    "            ax=ax\n",
    "        )\n",
    "        \n",
    "         #axes labels conditional\n",
    "        if p_axes == \"auto\":\n",
    "            pass\n",
    "        else:\n",
    "            f_plot.set_xlabel(p_axes[0])\n",
    "            f_plot.set_ylabel(p_axes[1])\n",
    "    \n",
    "        #adds major gridlines\n",
    "        ax.grid(color='grey', linestyle='-', linewidth=0.25, alpha=0.5)        \n",
    "        #see colour maps here: https://matplotlib.org/examples/color/colormaps_reference.html\n",
    "    \n",
    "    return df_return\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "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>DE1001V</th>\n",
       "      <th>DE2001I</th>\n",
       "      <th>DE2001V</th>\n",
       "      <th>DE2002I</th>\n",
       "      <th>DE2002V</th>\n",
       "      <th>DE2003I</th>\n",
       "      <th>DE2003V</th>\n",
       "      <th>DE2002I_DE2003I_ward</th>\n",
       "      <th>Cluster group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cities</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FR001C1</th>\n",
       "      <td>9,623,467.0</td>\n",
       "      <td>86.1</td>\n",
       "      <td>8,284,218.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>368,356.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>970,893.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK001K2</th>\n",
       "      <td>8,173,941.0</td>\n",
       "      <td>78.4</td>\n",
       "      <td>6,406,780.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>913,775.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>853,395.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT002K1</th>\n",
       "      <td>3,854,555.0</td>\n",
       "      <td>72.7</td>\n",
       "      <td>2,800,651.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>96,415.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>232,243.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE001C1</th>\n",
       "      <td>3,460,725.0</td>\n",
       "      <td>86.3</td>\n",
       "      <td>2,988,274.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>133,200.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>317,900.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES001C1</th>\n",
       "      <td>3,198,645.0</td>\n",
       "      <td>85.3</td>\n",
       "      <td>2,728,680.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>118,425.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>351,540.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DE1001V  DE2001I     DE2001V  DE2002I   DE2002V  DE2003I  \\\n",
       "cities                                                                 \n",
       "FR001C1 9,623,467.0     86.1 8,284,218.0      3.8 368,356.0     10.1   \n",
       "UK001K2 8,173,941.0     78.4 6,406,780.0     11.2 913,775.0     10.4   \n",
       "IT002K1 3,854,555.0     72.7 2,800,651.0      2.5  96,415.0      6.0   \n",
       "DE001C1 3,460,725.0     86.3 2,988,274.0      3.8 133,200.0      9.2   \n",
       "ES001C1 3,198,645.0     85.3 2,728,680.0      3.7 118,425.0     11.0   \n",
       "\n",
       "          DE2003V  DE2002I_DE2003I_ward Cluster group  \n",
       "cities                                                 \n",
       "FR001C1 970,893.0                     0     Cluster 1  \n",
       "UK001K2 853,395.0                     2     Cluster 3  \n",
       "IT002K1 232,243.0                     1     Cluster 2  \n",
       "DE001C1 317,900.0                     0     Cluster 1  \n",
       "ES001C1 351,540.0                     0     Cluster 1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===(9.1) CLUSTER ANALYSIS: USER INPUT AND EXECUTION\n",
    "\n",
    "#cluster data from step 6.0\n",
    "cluster_data_process\n",
    "cluster_number = 4\n",
    "cluster_colour = \"Dark2\"\n",
    "title = \"Non-EU vs EU foreigners (%) in 2011 coloured by cluster.\"\n",
    "\n",
    "#all valid linkages\n",
    "#cluster_method_linkage_list = [\"ward\",\"single\",\"complete\",\"average\"]\n",
    "cluster_method_linkage_list = [\"ward\"]\n",
    "\n",
    "cluster_data_labeled = f_cluster_end_to_end(cluster_data_process,cluster_number,cluster_method_linkage_list,cluster_data,cluster_colour,title,(\"EU foreigners\",\"Non-EU foreigners\"))\n",
    "\n",
    "cluster_data_labeled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistical description of each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualise each cluster individually (to check cluster number label vs points)\n",
    "#cluster_data_labeled[cluster_data_labeled[\"Cluster group\"] == \"Cluster 3\"].plot(x = \"DE2002I\",y=\"DE2003I\",kind = \"scatter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>DE1001V</th>\n",
       "      <th>DE2001I</th>\n",
       "      <th>DE2001V</th>\n",
       "      <th>DE2002I</th>\n",
       "      <th>DE2002V</th>\n",
       "      <th>DE2003I</th>\n",
       "      <th>DE2003V</th>\n",
       "      <th>DE2002I_DE2003I_ward</th>\n",
       "      <th>Total_EU_NonEU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>325.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>183,396.1</td>\n",
       "      <td>97.7</td>\n",
       "      <td>179,440.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1,877.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2,491.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>263,533.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>260,599.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3,346.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3,778.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17,157.0</td>\n",
       "      <td>91.7</td>\n",
       "      <td>16,364.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>79,572.0</td>\n",
       "      <td>95.9</td>\n",
       "      <td>76,707.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>416.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>115,254.0</td>\n",
       "      <td>97.6</td>\n",
       "      <td>112,675.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1,006.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1,500.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>190,655.0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>187,210.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2,055.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2,975.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3,055,218.0</td>\n",
       "      <td>128.2</td>\n",
       "      <td>3,049,993.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>36,004.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33,213.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DE1001V  DE2001I     DE2001V  DE2002I  DE2002V  DE2003I  DE2003V  \\\n",
       "count       325.0    325.0       325.0    325.0    325.0    325.0    325.0   \n",
       "mean    183,396.1     97.7   179,440.0      1.0  1,877.8      1.4  2,491.4   \n",
       "std     263,533.8      2.7   260,599.0      1.0  3,346.1      1.1  3,778.4   \n",
       "min      17,157.0     91.7    16,364.0      0.0      7.0      0.0     24.0   \n",
       "25%      79,572.0     95.9    76,707.0      0.1    196.0      0.3    416.0   \n",
       "50%     115,254.0     97.6   112,675.0      0.9  1,006.0      1.3  1,500.0   \n",
       "75%     190,655.0     99.5   187,210.0      1.7  2,055.0      2.2  2,975.0   \n",
       "max   3,055,218.0    128.2 3,049,993.0      4.1 36,004.0      4.0 33,213.0   \n",
       "\n",
       "       DE2002I_DE2003I_ward  Total_EU_NonEU  \n",
       "count                 325.0           325.0  \n",
       "mean                    3.0             2.5  \n",
       "std                     0.0             1.8  \n",
       "min                     3.0             0.0  \n",
       "25%                     3.0             0.6  \n",
       "50%                     3.0             2.5  \n",
       "75%                     3.0             4.0  \n",
       "max                     3.0             6.2  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AlSSpAANWkqQCDFhJkgowYCVJKqArARsR/ycibomImyPi6xExNSL2jYjrIuKOiLgwIrbuRm2SJLVDxwM2IvYETgHmZebBwCTgBOBjwJmZeQCwAnhTp2uTJKldujVEPBmYFhGTgW2BZcCLgW/U888Dju1SbZIkjVtkZuc3GnEq8GFgLfAD4FTg2sx8aj1/b+B79R7u4HVPBk4GmDVr1mELFiwoVueaNWuYPn16sfa7oVf6tHbt2paXXb9+PVOmTClYTXtNmzZtxGV65XkaDfvUG+zT6M2fP/+GzJw3ePrkYltsIiJ2BI4B9gVWAhcDLx9i0SGTPzPPBs4GmDdvXvb19ZUpFFi4cCEl2++GXulTf39/y8suXbqU2bNnF6ymvebOnTviMr3yPI2GfeoN9ql9ujFE/EfArzNzeWauB74FPA+YWQ8ZA+wFLO1CbZIktUU3AnYx8JyI2DYiAngJcCtwNXBcvcxJwCVdqE2SpLboeMBm5nVUH2a6EfhlXcPZwGnAOyLiTmBn4JxO1yZJUrt0/BgsQGaeDpw+aPIi4NldKEeSpLbzTE6SJBVgwEqSVIABK0lSAQasJEkFGLCSJBVgwEqSVIABK0lSAQasJEkFGLCSJBVgwEqSVIABK0lSAQasJEkFGLCSJBVgwEqSVIABK0lSAQasJEkFGLCSJBVgwEqSVIABK0lSAQasJEkFGLCSJBVgwEqSVIABK0lSAQasJEkFTO52AdJEt2Txyra2N2XSfU+6f+DBs9ravqSJwT1YSZIKMGAlSSrAgJUkqQADVpKkAgxYSZIKMGAlSSrAr+lIHbbo17c/6f76jfdutszatWvp7+8f97bmzp077jYkjY17sJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFTC52wVIv+uWLF652bSNmzYOOX20pky6D4ADD5417rYkjY57sJIkFWDASpJUQEsBGxEHly5EkqQtSat7sP8aET+LiL+JiJlFK5IkaQvQUsBm5guAE4G9gesj4msRcUTRyiRJ6mEtH4PNzDuADwCnAS8CzoqI2yPi1aWKkySpV7V6DPaZEXEmcBvwYuCozHxGffvMgvVJktSTWv0e7OeALwLvz8y1AxMzc2lEfKBIZZIk9bBWA/ZIYG1mbgSIiK2AqZn5aGaeX6w6SZJ6VKvHYH8ITGu4v209bUwiYmZEfKM+hntbRDw3InaKiCsj4o76945jbV+SpG5rNWCnZuaagTv17W3Hsd3PAv+emb8HzKU6tvte4KrMPAC4qr4vSVJPajVgH4mIQwfuRMRhwNphlm8qIrYHDgfOAcjMdZm5EjgGOK9e7Dzg2LG0L0nSRNDqMdi3AxdHxNL6/h7Aa8e4zf2A5cC/RcRc4AbgVGBWZi4DyMxlEbHbGNuXJKnrIjNbWzBiCvB0IIDbM3P9mDYYMQ+4Fnh+Zl4XEZ8FHgb+NjNnNiy3IjM3Ow4bEScDJwPMmjXrsAULFoyljJasWbOG6dOnF2u/G3qlT2vXtj5Asn79eqZMmVKslnXrNhZru7mNwKRxt7LNNlMBmDq1+xfO6pXX3mjYp95Quk/z58+/ITPnDZ4+moB9HjCHhr3ezPzKaAuJiN2BazNzTn3/hVTHW58K9NV7r3sACzPz6cO1NW/evLz++utHW0LLFi5cSF9fX7H2u6FX+tTf39/yskuXLmX27NnFamnHZeNGa+Om1Uzaasa429lv398DJsbl6nrltTca9qk3lO5TRAwZsC29rY2I84H9gZuo3loDJDDqgM3MeyNiSUQ8PTN/BbwEuLX+OQk4o/59yWjbliRpomh13GgecGC2urs7sr8FLoiIrYFFwBupPnB1UUS8CVgMHN+mbUmS1HGtBuzNwO7AsnZsNDNvogrtwV7SjvYlSeq2VgN2F+DWiPgZ8PjAxMw8ukhVkiT1uFYD9oMli5AkaUvTUsBm5jUR8RTggMz8YURsSzu+QyCN05LFK9m4aWNXPukrScNp9XJ1bwG+AXyhnrQn8J1SRUmS1OtaPVXiW4HnU50QYuDi655pSZKkJloN2Mczc93AnYiYTPU9WEmSNIRWA/aaiHg/MC0ijgAuBr5brixJknpbqwH7XqoT9P8S+EvgCuADpYqSJKnXtfop4k3AF+sfSZI0glbPRfxrhjjmmpn7tb0iSZK2AKM5F/GAqVTnCd6p/eVInbVo1fKObm+/HXbt6PYkdU9Lx2Az88GGn3sy8zPAiwvXJklSz2p1iPjQhrtbUe3Rjv9ilZIkbaFaHSL+VMPtDcBdwGvaXo0kSVuIVj9FPL90IZIkbUlaHSJ+x3DzM/PT7SlHkqQtw2g+Rfws4NL6/lHAj4AlJYqSJKnXjeaC64dm5mqAiPggcHFmvrlUYZIk9bJWT5W4D7Cu4f46YE7bq5EkaQvR6h7s+cDPIuLbVGd0ehXwlWJVSZLU41r9FPGHI+J7wAvrSW/MzJ+XK0uSpN7W6hAxwLbAw5n5WeDuiNi3UE2SJPW8lgI2Ik4HTgPeV0+aAny1VFGSJPW6VvdgXwUcDTwCkJlL8VSJkiQ11WrArsvMpL5kXURsV64kSZJ6X6sBe1FEfAGYGRFvAX6IF1+XJKmpVj9F/MmIOAJ4GHg68PeZeWXRyiRJ6mEjBmxETAK+n5l/BBiqkiS1YMQh4szcCDwaETt0oB5JkrYIrZ7J6THglxFxJfUniQEy85QiVUmS1ONaDdjL6x9JktSCYQM2IvbJzMWZeV6nCpIkaUsw0jHY7wzciIhvFq5FkqQtxkgBGw239ytZiCRJW5KRAjab3JYkScMY6UNOcyPiYao92Wn1ber7mZnbF61OkqQeNWzAZuakThUiSdKWZDTXg5UkSS0yYCVJKsCAlSSpAANWkqQCDFhJkgowYCVJKsCAlSSpAANWkqQCDFhJkgpo9Xqwktpg0arlLS23+7aTuHd1a8sO5+Hl23LIrvuMux1Jo+cerCRJBRiwkiQVYMBKklSAAStJUgEGrCRJBRiwkiQVYMBKklSAAStJUgEGrCRJBRiwkiQVYMBKklSAAStJUgEGrCRJBXg1HWkL9sA9v2HRmkdZv/Hejmxv7ty5HdmO1Avcg5UkqQADVpKkAgxYSZIK6FrARsSkiPh5RFxW3983Iq6LiDsi4sKI2LpbtUmSNF7d3IM9Fbit4f7HgDMz8wBgBfCmrlQlSVIbdCVgI2Iv4BXAl+r7AbwY+Ea9yHnAsd2oTZKkdujWHuxngPcAm+r7OwMrM3NDff9uYM9uFCZJUjtEZnZ2gxGvBI7MzL+JiD7gXcAbgZ9m5lPrZfYGrsjM3x9i/ZOBkwFmzZp12IIFC4rVumbNGqZPn16s/W7olT6tXbu2peXWrdsIbAQmjWk7j29cP6b1SpuyVbB+U3v+NreZNIWttx7b4zNa06ZNazqvV157o2GfekPpPs2fP/+GzJw3eHo3TjTxfODoiDgSmApsT7VHOzMiJtd7sXsBS4daOTPPBs4GmDdvXvb19RUrdOHChZRsvxt6pU/9/f0tLbdk8Uo2blrNpK1mjGk7965ePqb1Stt920nc++jGtrS13w47MXv2zLa0NZLhTjTRK6+90bBPvaFbfer4EHFmvi8z98rMOcAJwH9k5onA1cBx9WInAZd0ujZJktplIn0P9jTgHRFxJ9Ux2XO6XI8kSWPW1XMRZ+ZCYGF9exHw7G7WI0lSu0ykPVhJkrYYBqwkSQUYsJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUQFcvuC4NduXiWwFYunxJS8uvWvUYu287iXtXLy9ZliSNmnuwkiQVYMBKklSAAStJUgEGrCRJBRiwkiQVYMBKklSAAStJUgEGrCRJBRiwkiQVYMBKklSAAStJUgEGrCRJBRiwkiQVYMBKklSAl6uTtnCLVi3nweWrO7Kt+xdP4Yh9DuzItqSJzj1YSZIKMGAlSSrAgJUkqQADVpKkAgxYSZIKMGAlSSrAgJUkqQADVpKkAgxYSZIKMGAlSSrAgJUkqQADVpKkAgxYSZIK8Go60u+AVfc+VnwbO+w+tfg2pF7iHqwkSQUYsJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFeDl6qRCtt9w65jXnZQHsP2GO9pYTeXhyQe2vU1JQ3MPVpKkAgxYSZIK6HjARsTeEXF1RNwWEbdExKn19J0i4sqIuKP+vWOna5MkqV26sQe7AXhnZj4DeA7w1og4EHgvcFVmHgBcVd+XJKkndTxgM3NZZt5Y314N3AbsCRwDnFcvdh5wbKdrkySpXbp6DDYi5gB/AFwHzMrMZVCFMLBb9yqTJGl8IjO7s+GI6cA1wIcz81sRsTIzZzbMX5GZmx2HjYiTgZMBZs2addiCBQuK1bhmzRqmT59erP1umOh9Wr3uMQDWPf54S8tvXL+JKVsF6zd153U8nEn52NjXnbQNGze29hiMxsaY2vY2B0yashVbb7MNM7YeehsT/bU3FvapN5Tu0/z582/IzHmDp3fle7ARMQX4JnBBZn6rnnxfROyRmcsiYg/g/qHWzcyzgbMB5s2bl319fcXqXLhwISXb74aJ3qcrF1ffHX3ofxa1tPyqVevZfdtJ3PvoxpJljcl4vsc6Y8YBrF7dW9+D3WH3Kcx+2j707TP0Nib6a28s7FNv6FafuvEp4gDOAW7LzE83zLoUOKm+fRJwSadrkySpXbqxB/t84PXALyPipnra+4EzgIsi4k3AYuD4LtQmSVJbdDxgM/PHQDSZ/ZJO1iKp/QaG+Qd7bN1jTeeNxxFNhqSlbvNMTpIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUQFfO5CSpO7bf0P6vyQyY9sgUZqxY3HT+uo27MmPFws2mr96xr1hNUje5BytJUgEGrCRJBRiwkiQVYMBKklSAAStJUgEGrCRJBRiwkiQVYMBKklSAAStJUgEGrCRJBRiwkiQVYMBKklSAAStJUgEGrCRJBXi5Om0RSl6GTa15dMV6Vk19rOn8jes3serezec/+NAjLbW/6/7bjbk2qRvcg5UkqQADVpKkAgxYSZIKMGAlSSrAgJUkqQADVpKkAgxYSZIKMGAlSSrAgJUkqQADVpKkAgxYSZIKMGAlSSrAgJUkqQCvpiOpJyz/36GvunPrw/e1bRsHHjyrbW1J7sFKklSAAStJUgEGrCRJBXgMVsXcevPoj40tX14dZ1t172PtLkdbqJuWL25bW/csfnDEZY7Y58C2bU9bNvdgJUkqwICVJKkAh4gltc2yZWuazpsxYxMPPbT5/Icn/6ZkSaOyfuPUpvNmP22/DlaiLYF7sJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUgAErSVIBBqwkSQUYsJIkFWDASpJUgKdKlKRRuHLxrU/cfmzdY0+6X4pX8OlN7sFKklSAAStJUgEGrCRJBXgMVpIGmfbILzebNmPF4s2mrdu4KzNWLCxez7X1Nlbv2Dfissv/95FxbWvqmke59eb7hl3mwINnjWsbvyvcg5UkqQADVpKkAgxYSZIK8BisRuWxpd9tedkpq1eOuv2d1y0HYN2GNS2vMykPGPV2NHFsv6H890hblXdXvx8dYt7tKx7YbNqMGTty+883n17Kw5PLPFa77PmUIu3+rnMPVpKkAgxYSZIKmFBDxBHxMuCzwCTgS5l5Rqe2PdTQZ67fNKoh0XabOvuoYef39/ePus21a9eOab0B6x8a/uP7jR5Y3vow74B7Hx39OpLa66blm38lqdE9ix984nYnvqY0WoO/zjTUKS07cfrJCbMHGxGTgH8GXg4cCLwuIjwBpySpJ02YgAWeDdyZmYsycx2wADimyzVJkjQmEylg9wSWNNy/u54mSVLPmUjHYGOIabnZQhEnAyfXd9dExK8K1rQL0LnP4HeGfeoN9nsElRsAAAf0SURBVKk32KfeULpPQ37PaSIF7N3A3g339wKWDl4oM88Gzu5EQRFxfWbO68S2OsU+9Qb71BvsU2/oVp8m0hDxfwMHRMS+EbE1cAJwaZdrkiRpTCbMHmxmboiItwHfp/qazpcz85YulyVJ0phMmIAFyMwrgCu6XUeDjgxFd5h96g32qTfYp97QlT5F5mafI5IkSeM0kY7BSpK0xTBgm4iIUyPi5oi4JSLe3u16xiIivhwR90fEzQ3TdoqIKyPijvr3jt2scbSa9On4+nnaFBE99+nHJn36RETcHhG/iIhvR8TMbtY4Wk369I91f26KiB9ExOxu1jhaQ/WpYd67IiIjYpdu1DZWTZ6nD0bEPfXzdFNEHNnNGker2fMUEX8bEb+q/1d8vBO1GLBDiIiDgbdQnV1qLvDKiOjFa6KdC7xs0LT3Aldl5gHAVfX9XnIum/fpZuDVwI86Xk17nMvmfboSODgznwn8D/C+Thc1TueyeZ8+kZnPzMxDgMuAv+94VeNzLpv3iYjYGzgCGP4EvhPTuQzRJ+DMzDyk/plIn4tpxbkM6lNEzKc6M+AzM/Mg4JOdKMSAHdozgGsz89HM3ABcA7yqyzWNWmb+CHho0ORjgPPq2+cBx3a0qHEaqk+ZeVtmljzhSFFN+vSD+rUHcC3V98J7RpM+PdxwdzuGOJHMRNbk7wngTOA99Fh/YNg+9awmffpr4IzMfLxe5v5O1GLADu1m4PCI2DkitgWO5MknwehlszJzGUD9e7cu16OR/QXwvW4X0Q4R8eGIWAKcSO/twW4mIo4G7snMsV+iamJ6Wz2c/+VeO4zUxNOAF0bEdRFxTUQ8qxMbNWCHkJm3AR+jGqb7d6Af2DDsSlIBEfF3VK+9C7pdSztk5t9l5t5U/Xlbt+sZj/rN99+xBbxRGORfgP2BQ4BlwKe6W05bTAZ2BJ4DvBu4KCKGOj1vWxmwTWTmOZl5aGYeTjXccEe3a2qT+yJiD4D6d0eGSjR6EXES8ErgxNzyvk/3NeBPul3EOO0P7Av0R8RdVMP4N0bE7l2tapwy877M3JiZm4AvUn0WpdfdDXwrKz8DNlGdn7goA7aJiNit/r0P1Qdovt7ditrmUuCk+vZJwCVdrEVNRMTLgNOAozPz0W7X0w6DPih4NHB7t2pph8z8ZWbulplzMnMO1T/xQzPz3i6XNi4Db8Brr6I6ZNbrvgO8GCAingZsTQcuaDChzuQ0wXwzInYG1gNvzcwV3S5otCLi60AfsEtE3A2cDpxBNTzyJqpPPR7fvQpHr0mfHgL+CdgVuDwibsrMP+5elaPTpE/vA7YBrqxHsq7NzL/qWpGj1KRPR0bE06n2Hn4D9Ex/YOg+ZeY53a1qfJo8T30RcQjVh7buAv6yawWOQZM+fRn4cv3VnXXASZ0YFfJMTpIkFeAQsSRJBRiwkiQVYMBKklSAAStJUgEGrCRJBRiwUptExJoJUMOcoa720qFt90XEZSMsc0jj1Vki4uiI6LULTkgtMWAlddIhVOf2BiAzL83MM7pYj1SMASsVFBFPiYir6hOnX1WfGYyI2D8iro2I/46IfxjY+42IrSLi8/U1Ky+LiCsi4rh63mH1icpviIjvN5zy8rCI6I+InwJvbVJHX0T8qL627K0R8a8RsVU973UR8cuorn/8sYZ11kTEpyLixrr2XevpC6O+7m5E7FKfJnDw9p4dET+JiJ/Xv58eEVsD/wC8tr7O6Gsj4g0R8bkRHqtzI+Ksup1FA4+HNNEZsFJZnwO+Ul/X9QLgrHr6Z4HPZuazgKUNy78amAP8PvBm4LkAETGF6mxVx2XmYVRnpvlwvc6/Aadk5nNHqOXZwDvrtvcHXh3VRc8/RnUauUOAZ0XEwCUMtwNuzMxDqS7ZePoo+n07cHhm/gHVyfA/kpnr6tsX1tcZvXDQOs0eK4A9gBdQnZvZPV71BANWKuu5VCe2BzifKiQGpl9c3/5aw/IvAC7OzE31OW2vrqc/HTiY6tSJNwEfAPaKiB2AmZl5TcM2mvlZZi7KzI1U59Z+AfAsYGFmLq+vP3sBcHi9/CZgIAS/2lB7K3YALq6PB58JHNTCOs0eK4Dv1I/JrcCsUdQhdY3nIpY6a6Rzkza7hFYAtwzeS42ImS202WzbOcz2hlt/A799cz61ybL/CFydma+KiDnAwlFsZ/D2AB5vuF38MmNSO7gHK5X1E+CE+vaJwI/r29fy28u1ndCw/I+BP6mPxc6iOmk5wK+AXSPiiSHjiDgoM1cCqyJiYG/vxGFqeXZE7Fsfe31tva3rgBfVx1InAa+jGg6G6v/DwPHOP22o/S7gsPp2s+OhOwD31Lff0DB9NTCjyTrNHiupJxmwUvtsGxF3N/y8AzgFeGNE/AJ4PXBqvezbgXdExM+oji+uqqd/k+qyZzcDX6AKwFX18cvjgI9FRD9wE/C8ep03Av9cf8hp7TD1/ZTq+OXNwK+Bb2fmMqor91wN9FMdcx24hOEjwEERcQPVMdp/qKd/EvjriPgJza+p+XHgoxHxX8CkhulXAwcOfMhp0DrNHiupJ3k1HakLImJbYG1mZkScALwuM4+p503PzDVRXS7xZ8Dzx3uN0YjoA96Vma8cxTprMnP6eLYr/S7zGKzUHYcBn4vqYq8rgb9omHdZfWx1a+Afe/0C3tLvKvdgJUkqwGOwkiQVYMBKklSAAStJUgEGrCRJBRiwkiQVYMBKklTA/wfH7KToAoyo/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 540x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===(9.2) FIGURE 8 \n",
    "\n",
    "cluster_list = cluster_data_labeled[\"Cluster group\"].unique()\n",
    "cluster_list.sort()\n",
    "colour_list = [\"#1B9E77\",\"#7570B3\",\"#E6AB02\",\"#666666\"]\n",
    "colour_counter = 0\n",
    "\n",
    "list_of_descriptions = []\n",
    "\n",
    "for cluster in cluster_list:\n",
    "    subset = cluster_data_labeled[cluster_data_labeled[\"Cluster group\"] == cluster]\n",
    "    list_of_descriptions.append(subset.describe())\n",
    "    hist_ax = np.log(subset[\"DE1001V\"]).hist(\n",
    "    alpha = 0.3,\n",
    "    figsize = (7.5,7.5),\n",
    "    color = colour_list[colour_counter]\n",
    "    )\n",
    "    colour_counter += 1\n",
    "    \n",
    "    plt.title(\"Logged population distribution by cluster\")\n",
    "    \n",
    "hist_ax.set_xlabel(\"Logged population\")\n",
    "hist_ax.set_ylabel(\"Frequency\")\n",
    "#subset = flights[flights['name'] == airline]\n",
    "    \n",
    "list_of_descriptions[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>DE1001V</th>\n",
       "      <th>DE2001I</th>\n",
       "      <th>DE2001V</th>\n",
       "      <th>DE2002I</th>\n",
       "      <th>DE2002V</th>\n",
       "      <th>DE2003I</th>\n",
       "      <th>DE2003V</th>\n",
       "      <th>DE2002I_DE2003I_ward</th>\n",
       "      <th>Cluster group</th>\n",
       "      <th>Total_EU_NonEU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cities</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FR001C1</th>\n",
       "      <td>9,623,467.0</td>\n",
       "      <td>86.1</td>\n",
       "      <td>8,284,218.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>368,356.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>970,893.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK001K2</th>\n",
       "      <td>8,173,941.0</td>\n",
       "      <td>78.4</td>\n",
       "      <td>6,406,780.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>913,775.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>853,395.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT002K1</th>\n",
       "      <td>3,854,555.0</td>\n",
       "      <td>72.7</td>\n",
       "      <td>2,800,651.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>96,415.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>232,243.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE001C1</th>\n",
       "      <td>3,460,725.0</td>\n",
       "      <td>86.3</td>\n",
       "      <td>2,988,274.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>133,200.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>317,900.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES001C1</th>\n",
       "      <td>3,198,645.0</td>\n",
       "      <td>85.3</td>\n",
       "      <td>2,728,680.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>118,425.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>351,540.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>14.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DE1001V  DE2001I     DE2001V  DE2002I   DE2002V  DE2003I  \\\n",
       "cities                                                                 \n",
       "FR001C1 9,623,467.0     86.1 8,284,218.0      3.8 368,356.0     10.1   \n",
       "UK001K2 8,173,941.0     78.4 6,406,780.0     11.2 913,775.0     10.4   \n",
       "IT002K1 3,854,555.0     72.7 2,800,651.0      2.5  96,415.0      6.0   \n",
       "DE001C1 3,460,725.0     86.3 2,988,274.0      3.8 133,200.0      9.2   \n",
       "ES001C1 3,198,645.0     85.3 2,728,680.0      3.7 118,425.0     11.0   \n",
       "\n",
       "          DE2003V  DE2002I_DE2003I_ward Cluster group  Total_EU_NonEU  \n",
       "cities                                                                 \n",
       "FR001C1 970,893.0                     0     Cluster 1            13.9  \n",
       "UK001K2 853,395.0                     2     Cluster 3            21.6  \n",
       "IT002K1 232,243.0                     1     Cluster 2             8.5  \n",
       "DE001C1 317,900.0                     0     Cluster 1            13.0  \n",
       "ES001C1 351,540.0                     0     Cluster 1            14.7  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#===(9.2) FIGURE 7\n",
    "#Create new column\n",
    "cluster_data_labeled[\"Total_EU_NonEU\"] = cluster_data_labeled[\"DE2002I\"]+cluster_data_labeled[\"DE2003I\"]\n",
    "cluster_data_labeled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set up histogram for loop\n",
    "colour_counter = 0\n",
    "list_of_total = []\n",
    "\n",
    "#for loop\n",
    "for cluster in cluster_list:\n",
    "    subset = cluster_data_labeled[cluster_data_labeled[\"Cluster group\"] == cluster]\n",
    "    list_of_descriptions.append(subset.describe())\n",
    "    hist_ax = subset[\"Total_EU_NonEU\"].hist(\n",
    "    alpha = 0.3,\n",
    "    figsize = (7.5,7.5),\n",
    "    color = colour_list[colour_counter]\n",
    "    )\n",
    "    colour_counter += 1\n",
    "    \n",
    "    \n",
    "plt.title(\"Distribution of cities based on EU + non-EU migrant population %\")\n",
    "    \n",
    "hist_ax.set_xlabel(\"EU + non-EU population %\")\n",
    "hist_ax.set_ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": false
   },
   "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>DE1001V</th>\n",
       "      <th>DE2001I</th>\n",
       "      <th>DE2001V</th>\n",
       "      <th>DE2002I</th>\n",
       "      <th>DE2002V</th>\n",
       "      <th>DE2003I</th>\n",
       "      <th>DE2003V</th>\n",
       "      <th>DE2002I_DE2003I_ward</th>\n",
       "      <th>Cluster group</th>\n",
       "      <th>DE1001V_tweaked</th>\n",
       "      <th>Pop_log</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cities</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FR001C1</th>\n",
       "      <td>9,623,467.0</td>\n",
       "      <td>86.1</td>\n",
       "      <td>8,284,218.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>368,356.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>970,893.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>10,023,467.0</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK001K2</th>\n",
       "      <td>8,173,941.0</td>\n",
       "      <td>78.4</td>\n",
       "      <td>6,406,780.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>913,775.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>853,395.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>9,373,941.0</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT002K1</th>\n",
       "      <td>3,854,555.0</td>\n",
       "      <td>72.7</td>\n",
       "      <td>2,800,651.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>96,415.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>232,243.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>4,654,555.0</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE001C1</th>\n",
       "      <td>3,460,725.0</td>\n",
       "      <td>86.3</td>\n",
       "      <td>2,988,274.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>133,200.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>317,900.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>3,860,725.0</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES001C1</th>\n",
       "      <td>3,198,645.0</td>\n",
       "      <td>85.3</td>\n",
       "      <td>2,728,680.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>118,425.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>351,540.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>3,598,645.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DE1001V  DE2001I     DE2001V  DE2002I   DE2002V  DE2003I  \\\n",
       "cities                                                                 \n",
       "FR001C1 9,623,467.0     86.1 8,284,218.0      3.8 368,356.0     10.1   \n",
       "UK001K2 8,173,941.0     78.4 6,406,780.0     11.2 913,775.0     10.4   \n",
       "IT002K1 3,854,555.0     72.7 2,800,651.0      2.5  96,415.0      6.0   \n",
       "DE001C1 3,460,725.0     86.3 2,988,274.0      3.8 133,200.0      9.2   \n",
       "ES001C1 3,198,645.0     85.3 2,728,680.0      3.7 118,425.0     11.0   \n",
       "\n",
       "          DE2003V  DE2002I_DE2003I_ward Cluster group  DE1001V_tweaked  \\\n",
       "cities                                                                   \n",
       "FR001C1 970,893.0                     0     Cluster 1     10,023,467.0   \n",
       "UK001K2 853,395.0                     2     Cluster 3      9,373,941.0   \n",
       "IT002K1 232,243.0                     1     Cluster 2      4,654,555.0   \n",
       "DE001C1 317,900.0                     0     Cluster 1      3,860,725.0   \n",
       "ES001C1 351,540.0                     0     Cluster 1      3,598,645.0   \n",
       "\n",
       "         Pop_log  \n",
       "cities            \n",
       "FR001C1     16.1  \n",
       "UK001K2     15.9  \n",
       "IT002K1     15.2  \n",
       "DE001C1     15.1  \n",
       "ES001C1     15.0  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##===(9.2) NATIONALS VS NATIONALS PLOT\n",
    "\n",
    "#create custom df\n",
    "cluster_nat_plot = cluster_data_labeled.copy()\n",
    "\n",
    "#add 'jumping column' to differentiate clusters, number of clusters indicated by cluster_number &\n",
    "new_nat_values = []\n",
    "for index, value in enumerate(cluster_nat_plot.DE2002I_DE2003I_ward):\n",
    "    #print(index,value)\n",
    "    new_nat_values.append(cluster_nat_plot.iloc[index,0] + (value + 1) * 400000)\n",
    "\n",
    "cluster_nat_plot[\"DE1001V_tweaked\"] = new_nat_values\n",
    "cluster_nat_plot[\"Pop_log\"] = np.log(cluster_nat_plot[\"DE1001V\"])\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "pop_plot = cluster_nat_plot.plot(\n",
    "    kind = \"scatter\",\n",
    "    x = cluster_nat_plot.columns[-1],\n",
    "    y = cluster_nat_plot.columns[-2],\n",
    "    figsize = (20,10),\n",
    "    title = \"Logged population against stepped population to illustrate disribution of clusters\",\n",
    "    #xlim = (0,300000),\n",
    "    #ylim = (0,1250000),\n",
    "    c = cluster_nat_plot.iloc[:,-4],\n",
    "    cmap = cluster_colour,\n",
    "    alpha = 0.45,\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "pop_plot.set_xlabel(\"Population of city logged\")\n",
    "pop_plot.set_ylabel(\"\")\n",
    "\n",
    "\n",
    "#cluster_nat_plot.iloc[0,0]\n",
    "cluster_nat_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DBSCAN EXPERIMENT\n",
    "\n",
    "DBSCAN code followed by labelling of clusters and visulisations\n",
    "\n",
    "**Resources:**\n",
    "- <https://www.datacamp.com/community/tutorials/dbscan-macroscopic-investigation-python>\n",
    "- <https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py>\n",
    "- <https://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py>\n",
    "- <https://uwspace.uwaterloo.ca/bitstream/handle/10012/7742/Haghtalab_Nika.pdf>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x131cb0b10>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_nat_plot.plot(\n",
    "    kind = \"scatter\",\n",
    "    x = cluster_nat_plot.columns[0],\n",
    "    y = cluster_nat_plot.columns[-5],\n",
    "    figsize = (20,10),\n",
    "    #xlim = (0,300000),\n",
    "    #ylim = (0,1250000),\n",
    "    c = cluster_nat_plot.iloc[:,-4],\n",
    "    cmap = cluster_colour,\n",
    "    alpha = 0.35\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters:  67\n"
     ]
    },
    {
     "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>DE2002I</th>\n",
       "      <th>DE2003I</th>\n",
       "      <th>DBSCAN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cities</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FR001C1</th>\n",
       "      <td>3.8</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK001K2</th>\n",
       "      <td>11.2</td>\n",
       "      <td>10.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT002K1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE001C1</th>\n",
       "      <td>3.8</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES001C1</th>\n",
       "      <td>3.7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DE2002I  DE2003I  DBSCAN\n",
       "cities                           \n",
       "FR001C1      3.8     10.1       0\n",
       "UK001K2     11.2     10.4       1\n",
       "IT002K1      2.5      6.0       0\n",
       "DE001C1      3.8      9.2       0\n",
       "ES001C1      3.7     11.0       0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===(10.0) CLUSTER ANALYSIS: DBSCAN EXPERIMENT\n",
    "from sklearn.cluster import DBSCAN\n",
    "import sklearn.utils\n",
    "\n",
    "dbscan_data = cluster_data_process.copy()\n",
    "\n",
    "dbscan_clustering = DBSCAN(eps=0.75, min_samples=1).fit(cluster_data_process)\n",
    "dbscan_data[\"DBSCAN\"] = dbscan_clustering.labels_\n",
    "\n",
    "#SCATTER-PLOT\n",
    "\n",
    "dbscan_data.plot(\n",
    "    kind = \"scatter\",\n",
    "    title = \"\",\n",
    "    x = dbscan_data.columns[0],\n",
    "    y = dbscan_data.columns[1],\n",
    "    figsize = (20,10),\n",
    "    #xlim = (0,300000),\n",
    "    #ylim = (0,1250000),\n",
    "\n",
    "    c = dbscan_clustering.labels_,\n",
    "    cmap = cluster_colour,\n",
    "    alpha = 0.35\n",
    ")\n",
    "\n",
    "#plt.figure(figsize=(20, 10))\n",
    "#plt.title(\"EU and Non-EU resident plot\")\n",
    "#plt.scatter(dbscan_data.iloc[:,0], dbscan_data.iloc[:,1], c=dbscan_clustering.labels_, cmap=cluster_colour, alpha = 0.35)\n",
    "\n",
    "print(\"Number of clusters: \",len(dbscan_data[\"DBSCAN\"].unique()))\n",
    "dbscan_data.head()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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