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@oztalha
Last active May 14, 2017 06:13
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Tourist Visa Refusals by Country
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{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# Nonimmigrant B Visa Adjusted Refusal Rates by Nationality\nDepartment of State provides some [Nonimmigrant Visa Statistics](https://travel.state.gov/content/visas/en/law-and-policy/statistics/non-immigrant-visas.html) in PDF or MS Excel formats. One of them is [Multi-Year B Visa Adjusted Refusal Rates by Nationality (FY2006-FY2016)](https://travel.state.gov/content/dam/visas/Statistics/Non-Immigrant-Statistics/refusalratelanguage.pdf), which is only available in PDFs. In this notebook, I combine the tables in multiple PDF files into a single dataframe (and save it as a [CSV file](https://www.dropbox.com/s/h6hvatxma33qjhd/ADJUSTED_REFUSAL_RATES.csv?dl=0)), and plot the B (Tourist) visa refusal rates over years for Turkey along with some other countries."
},
{
"metadata": {
"run_control": {
"read_only": false,
"frozen": false
},
"ExecuteTime": {
"start_time": "2017-04-30T14:25:42.243217Z",
"end_time": "2017-04-30T14:25:42.729063Z"
},
"trusted": true
},
"cell_type": "code",
"source": "import pandas as pd\nimport tabula\nimport os\nfrom glob import glob",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-04-30T12:02:01.064914Z",
"end_time": "2017-04-30T12:02:03.705964Z"
},
"run_control": {
"read_only": true,
"frozen": true
},
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "#download the PDFs\npath = 'https://travel.state.gov/content/dam/visas/Statistics/Non-Immigrant-Statistics/RefusalRates/'\nfor i in range(6,17):\n fname = 'FY{:02d}.pdf'.format(i)\n os.system('wget '+path+fname)",
"execution_count": 21,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-04-30T14:25:42.731023Z",
"end_time": "2017-04-30T14:26:09.477965Z"
},
"trusted": true
},
"cell_type": "code",
"source": "#PDF to dataframe using tabula\ntop = 100; left = 127; width = 356; height = 630\narea = (top,left,top+height,left+width)\npages = list(range(1,6))\nvr = {f[:4]:tabula.read_pdf(f,spreadsheet=True, area=area, pages=pages) for f in glob('*.pdf')} ",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-04-30T14:26:09.482013Z",
"end_time": "2017-04-30T14:26:09.639101Z"
},
"trusted": true
},
"cell_type": "code",
"source": "#preprocessing\nvr['FY14'] = vr['FY14'].reset_index()\nfor k,v in vr.items():\n v.columns = ['COUNTRY',k] # ADJUSTED REFUSAL RATE\n v['COUNTRY'] = v.COUNTRY.str.strip().str.upper()\n v = v.drop_duplicates(subset=['COUNTRY'], keep=False)\n vr[k] = v.set_index('COUNTRY')\n\n#create a dataframe\ndf = vr['FY06'].join([vr[v] for v in vr if v != 'FY06'], how='inner')\ndf.loc['LUXEMBOURG','FY06'] = '0.9%' #org value: 1\\r-0.8%\ndf.loc['KUWAIT','FY09'] = '-2.2%' #org value: -2.2%1\ndf = df.apply(lambda x: x.str.strip('%').astype(float))",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-04-30T14:26:09.642819Z",
"end_time": "2017-04-30T14:26:09.656988Z"
},
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "#save the percentage values\ndf.to_csv('ADJUSTED_REFUSAL_RATES.csv')",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-04-30T14:26:09.661104Z",
"end_time": "2017-04-30T14:26:10.306892Z"
},
"trusted": true
},
"cell_type": "code",
"source": "%matplotlib inline\nax = df.T[['TURKEY','ALBANIA','GREECE']].plot()\nax.set_xticks(range(len(df.columns)))\nax.set_xticklabels(df.columns.values)\nax.set(xlabel='Fiscal Years',ylabel='Percentage',\n title='Nonimmigrant B Visa Adjusted Refusal Rates by Nationality');",
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x1123bbb70>",
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ffJGdO3eyc+dOvL290x8/+eSTADz99NPs3LmT5cuXM3r0aJKSknjooYcICwtj\nzZo1ALzyyivcf//9pr3BMIwC51CyEJHewE7gJ+t5sIiscGZgzrZ27Vo8PDx4+OGH06cFBwdz8OBB\nbrzxRv7v//4PgNKlSzNjxgymTJni0HLd3d0ZNWoU77333jXFVa9ePUqXLk10dDQiwieffMKYMWMI\nCQnh119/Zdy4cde0XMMwjOvhaMliEtAOOA+glNoJBDonpIKxe/duWre++qppe/bsuWp6UFAQsbGx\nxMTkcvF4y2OPPcZ3333HhQsX8hzX9u3bqVevHlWrVgWgefPm9OzZk5tvvpnp06fj6Zl9n2rDMAxn\ncTRZJCul8r7nK4KUUtk2AotIjq+l8fHxYfjw4UyfPt3h9b733ns0aNCA9u3bM2nSpAyvPfbYYwQE\nBNC9e3eHl2cYhpGfHE0Wu0VkKOAmIvVE5ENgoxPjcromTZqwbdu2LKdnPlv86NGjlC1blnLlylGp\nUiWio6MzvH7x4kUqVKiQYdqYMWP44osviIuLcyiep59+mgMHDjB//nyGDx+e4WRDm82GzQy/bRiG\nCzm6B3oCaAJcBuYCMcAYZwVVEHr06MHly5f5/PPP06dt3bqVevXqsWHDhvRG5YSEBJ588knGjx8P\nQJcuXVixYgUXL14EYMmSJbRo0QI3t4zDZPv6+nLXXXfxxRdf5CmuO++8kzZt2jBr1qzr2TzDMIx8\n5VCyUErFK6VeVEq1VUq1sR4X6XE2RISlS5eyevVqgoKCaNKkCZMmTcLf35/ly5czefJkGjRoQLNm\nzWjbti2PP/44oNsQHn/8cTp37kxwcDCffvopM2fOzHIdzz777FW9ohzxyiuv8O6775Kamnpd22gY\nhpFfHBpIUERWcvWgJBeAEOAzZyeOrAYS3LdvH40aNXLmaost89kZRsmQnwMJOloNdRSIBT63bjHA\naaC+9dwwDMMoxhwd7qOlUqqL3fOVIrJeKdVFRPY4IzDDMAyj8HC0ZFFFRGqlPbEeV7ae5v/whoZh\nGEah4mjJ4llgg4gcAQSoAzwqImUA023HMAyjmHMoWSilfhCRekBDdLLYb9eo/b6zgjMMwzAKh7wM\nUV4PaAB4Ac1FBKXUN84JyzAMwyhMHB1IcCLwoXXrDrwF9HFiXIVeZGRk+jDj1atXTx9ivEKFCjRu\n3DjDvPbDmo8cOZI6deoQHBxMixYt+PXXX9Pn69atW/rZ46GhodSrV4+ff/6ZdevWUb58+fT1BQcH\ns3r1ajp3CjaoAAAgAElEQVR37syPP/6Y/v4FCxbQq1evAth6wzBKGkdLFgOBFsAOpdR9IlINyPpM\nNIuIeAHrgVLWehYppSaKSB1gHuALbAeGKaWKXCN5pUqV2LlzJ6CTQdmyZRk7diyhoaHccccdOb53\n2rRpDBw4kLVr1zJq1CgOHTqU4fWwsDB69uzJO++8Q8+ePVm3bh033XQTq1atyjCfn58fgwYNonv3\n7qSkpPDiiy/y008/5e+GGoZh4HiySFBKpYpIsoj4AGeAurm85zLQQykVKyIe6AbyH4FngPeUUvNE\n5FPgAeCTa92Aoqxjx46Eh4dnmHbq1CmGDx/O5MmT6dMn58Jb06ZN6d27N1OnTiUuLo7hw4cTFBTk\nzJANwyihHE0WISJSAX0C3jb0CXpbcnqD0qeGx1pPPaybAnoAQ63ps9DDn19Xsnh15R72Rjg2fLij\nGvv7MLF3/l2/Nis//fQT/fr1yzAtLVEMGjQow/Q//viD4ODg9OeLFy8mKCiIiRMn0qpVKzw9Pa8a\nANEwDCO/ONob6lHr4aci8hPgo5T6O7f3iYgbOrncAHwEHAHOK6WSrVnCgIA8R12IOTKE+bhx4xg/\nfjxnzpzhr7/+yjDfLbfcwrfffsvIkSMpXbp0+vSsqqEAypQpw+DBgylbtiylSpXKp60wDMPIyKFk\nISK/KqVuBlBKhWaelh2lVAoQbJVKlgJZDUiU5eBUIjIKGAVQq1atrGZJ5+wSQF5kNYR5VFRUhkuh\nTps2jTvvvJPp06czYsSIDEOljx8/ntmzZzNo0CCWL1+Ou3vuX5EZwtwwDGfLcQ8jIl4i4gtUFpGK\nIuJr3QIBf0dXopQ6D6wDOgAVRCRtD1gDiMjmPf+zRrhtU6VKFUdX5XJly5bFz88vvZdTVFQUP/30\nE507d84wn81m46mnniI1NZWff/45w2vvvfcePj4+PPDAAzgy0KNhGIaz5XY4OhpdjdTQuk+7LUdX\nK2VLRKpYJQpExBu4BdgHrEX3rgIYYS2rWPnmm2+YPHkywcHB9OjRg4kTJ2bZ8CwivPTSS7z11ltX\nTZ81axYnT55Mv45GWptF2m3RokUFsi2GYRjg+BDlTyilPszTgkWaoxuw3dBJaYFS6jURqcuVrrM7\ngHuVUpdzWpYZojx/mc/OMEqG/Byi3NEG7g9FpBMQaP+enM7gthrAW2Yx/SjQLs+RGoZhGC7jaAP3\nt0AQsBNIsSYrwAz3YRiGUQI4ep5FG6CxMq2thmEYJZKj/S13A9WdGYhhGIZReDlasqgM7BWRLehh\nPABQSpXowQQNwzBKCkeTxSRnBmEYhnE9LiZeJCI2gsiESFpUbUEZjzKuDqnYcagaSin1OxAKeFiP\nt6JHjC3xli5dioiwf/9+QA8t3rRp06vmsx+avGHDhrz66qsZXj979iweHh589tlnGaYHBgYyYMCA\n9OeLFi1i5MiRAHz99dc8/vjjGeZv0aIFQ4YMyY9NM4xCQSlF9KVo9kTuYfXx1czaM4spW6bwxG9P\nMHDFQDrN7USnuZ0YuHIgo9eMpvfS3qw4soJUlerq0IsVR3tDPYQeesMX3SsqAPgUyHG4j5Jg7ty5\ndO7cmXnz5jFp0qQc500bmvzSpUs0btyY4cOHpw8DsnDhQjp06MDcuXMZPXp0hveFhISwZ88emjTJ\neViTffv2kZqayvr164mLi6NMGXN0ZRR+qSqVyIRIIuIiOBl7kvDYcE7GWfexJ4mIiyAhOSHDe0q7\nl8a/rD/+Zf1pWbVl+mMvNy8++/szXtzwIvP3z+e5ds/RvEpzF21Z8eJoNdRj6HMjNgMopQ6JSFWn\nRVVExMbG8ueff7J27Vr69OmTa7JIc+mSviKt/c587ty5vPPOOwwdOpTw8HACAq6Mrzh27FjeeOMN\nvvvuuxyXO2fOHIYNG8a+fftYsWKFKWEYhUJKagpn4s8QERdBRKy+pSeDuJOcjD1JYmrGS9qUL1Ue\n/zL+1PapTUf/junJwL+Mvvfx9Ml20M6batzEqqOreG/be9zzwz30CerDmFZjqFK66AwbVBg5miwu\nK6US074ca2ynwtON9scJcOqf/F1m9WZw65QcZ1m2bBm9evWifv36+Pr6sn37dnx9fbOdf9y4cUye\nPJnDhw/z5JNPUrWqzrf//vsvp06dol27dtx1113Mnz+fZ555Jv19d911Fx9//DGHDx/OMZ758+ez\nevVqDhw4wIwZM0yyKAFSUlP4Zu83RMRG4GZzwyY23OTKvYhkeJ7tdJs1HclyOTaxpd+ym3455bJO\nBpmSwum40ySnDzSt+Xr5ElA2gAYVG9CjZg/8yvqlJwL/sv7X1eZgExt9gvpwc62b+fzvz/lm7zes\nOb6GUc1HMazxMDzdPK/3Yy+RHE0Wv4vIC4C3iPwHeBRY6bywioa5c+cyZswYAO6++27mzp3LY489\nlu38adVQsbGx3HzzzWzcuJFOnToxb9487rrrrvTlPPDAAxmShZubG+PGjePNN9/k1ltvzXLZW7du\npUqVKtSuXZsaNWpw//33Ex0dTcWKFfNxi43CZsbOGcz8ZyY+nj4opUhRKaSqVFJUSvpzVcDHdYJQ\npXQVAsoG0KJKCwLqBGRIBn5l/PBy93J6HGU8yjCm9RjurHcnb4e8zfvb32fxocWMazOObjW7ZVsy\nMbLmaLKYgL6i3T/owQV/IJfLqhaoXEoAzhAZGclvv/3G7t27ERFSUlIQER599NFc31u2bFm6devG\nhg0b6NSpE3PnzuX06dPp1UwREREcOnSIevXqpb9n2LBhvPnmm9m2W8ydO5f9+/cTGBgIQExMDIsX\nL+bBBx+8/o01CqU1x9cw85+ZDKg3gEmdJmU7X3ZJJO15qkolJVUnlRSVQmpq6lWvp92ymp722NPm\niV9ZP6qXro6Hm0fBfRC5qOVTi+k9prMxYiNTt0zlybVP0sm/E+PbjieogrmypKMcTRbewJdKqc8h\n/aJG3kC8swIr7BYtWsTw4cMz9F7q2rUrYWFhub43OTmZzZs388QTT3DgwAHi4uIyXF514sSJzJs3\nj5dffjl9moeHB08//TRTpkyhR48eGZaXmprKwoUL+fvvv9PbOtauXcvkyZNNsiimjpw/wosbXqR5\n5ea80P6FHOcVEdzF0b968dXJvxOL+ixiwYEFfLTzIwasGMCQhkN4uMXDlC9V3tXhFXqOnsH9Kzo5\npPEG1uR/OEXH3Llz6d+/f4ZpAwYM4I033uDAgQPUqFEj/bZw4UJAt1kEBwfTvHlzmjVrxp133pnt\ncubOnXvVOh944AGSk5Ovmr5+/XoCAgIyNIp36dKFvXv3cvLkyfzYXKMQuZh4kafWPoW3uzfvdnvX\n1MHngYfNg3sa3cOq/qsYUG8Ac/bP4Y6ld7DgwAJSUlNyX0AJ5ugQ5TuVUsG5TXMWM0R5/jKfXdGV\nqlJ56ren2BC+gZk9Z9K6WmtXh1Sk7Y/az5QtU9h2ehsNKjbguXbP0bZ6W1eHlW/yc4hyR0sWcSLS\nyi6A1kBCDvMbhuEEn/39GevC1jG27ViTKPJBQ9+GfNXzK97u+jYxiTHc//P9PLvuWSJis7yAZ4nm\naEXmU8BCEUn7BP2Awc4JyTCMrPz+7+98vPNj+gT1YWjDoa4Op9gQEXoG9qRLjS58vedrvvznS34P\n+537mt7H/U3vx9vdO/eFlAC5lixExAZ4oi+t+gi622wjpdQ2J8dmGIYl9EIoE/6YQCPfRrzc4WXT\n7dMJvN29eaTFI6zot4LuNbvz6a5P6bOsDz8d+wlzdQYHkoVSKhV4RymVpJTarZT6RymVVACxGYYB\nxCXFMWbtGNxt7rzf/f0COUehJPMr68e0rtP4utfXVChVgXHrxzHyp5Hsi9zn6tBcytE2i19EZICY\nwxnDKFBKKV7+82WOxRxjWtdp+Jf1d3VIJUbraq2Zd/s8JnacyLELxxi8ajCTNk4i6lKUq0NzCUeT\nxTPAQiBRRGJE5KKIxDgxLsMwgC93f8nq46t5utXTdPDr4OpwShw3mxsD6w9k1Z2ruKfRPSw/vJw7\nltzBN3u+ISm1ZFWwODpEeTmllE0p5aGU8rGe+zg7uMLu9OnTDB06lLp169K6dWs6duzI0qVLWbdu\nHeXLl6dly5Y0bNiQsWPHpr/n66+/pkqVKgQHB6ff9u7dS2hoKN7e3hmmf/ONvsR5bGwso0ePJigo\niCZNmtClSxc2b94M6KFA7N8zZUrBn81uOMfG8I1M3zGdXoG9GNFkhKvDKdF8PH14rt1zLO6zmOZV\nmjMtZBoDVgxgQ/gGV4dWYBwdolyAe4A6Sqn/ikhNwE8ptcWp0RViSin69evHiBEjmDNnDgDHjx9n\nxYoVVKxYkZtuuolVq1aRkJBAy5Yt6d+/PzfeeCMAgwcPZsaMGRmWFxoaSlBQEDt37rxqXQ8++CB1\n6tTh0KFD2Gw2jh49yr59uv7U29s7y/cYRVvYxTDGrR9HUIUgXu30qmnQLiTqVqjLJ7d8wvqw9by1\n9S0eWfMIXWt0ZVzbcdT2qe3q8JzK0Wqoj4GOQFp/vVjgI6dEVET89ttveHp68vDDD6dPq127Nk88\n8USG+dJKC/bDeeTFkSNH2Lx5M5MnT8Zm019X3bp1uf322689eKNQS0hOYMzaMSgUH3T7gNIepV0d\nkmFHROhasytL+y7lmdbPEHI6hH7L+/FuyLvEJsa6OjyncfQ8i/ZKqVYisgNAKRUtIoVmjIGpW6ay\nP2p/vi6zoW9Dnmv3XLav79mzh1atWmX7epro6GgOHTpEly5d0qfNnz+fDRuuFF83bdoE6MQQHHzl\npPgPP/yQ6OhogoODcXNzy3L5CQkJGd7z/PPPM3iwOQWmqFJKMWnjJA5GH+Sjmz+ipk9NV4dkZMPT\nzZP7mt5H76DefLD9A77a8xUrjqzgqVZP0feGvtjE0WPxosHRZJFkDR6oAESkCmCuWWjnscceY8OG\nDXh6ejJt2jT++OMPmjdvzoEDB5gwYQLVq1dPnzeraiggy2qoFStW5LheUw1VvMzeN5sfjv3AEy2f\n4KYaN7k6HMMBlb0r898b/8vgBoN5c8ubvLLxFRYcWMDLHV+mcaXGrg4v3ziaLKYDS4GqIvI6MBB4\nyWlR5VFOJQBnadKkCYsXL05//tFHH3Hu3DnatNHDsKS1WRw8eJDOnTvTv3//DCWAvKxn165dpKam\npldDGcXT1lNbeSfkHXrU7MGDzcxowUVN08pN+fbWb/n+6Pe8E/IOQ74fwpCGQ3g8+HHKepZ1dXjX\nzdHeUN8B44E3gZNAP6XUQmcGVtj16NGDS5cu8cknn6RPi4+/esT2+vXr8/zzzzN16tRrWk9QUBBt\n2rRh4sSJ6WeRHjp0iOXLl19b4EahdCruFGN/H0stn1q83vn1YleFUVLYxEbvoN6s6L+CQfUHMWff\nnGJzFniOv0gR8RKRMSIyA+gKfKaUmqGUKtmnMqIbuZYtW8bvv/9OnTp1aNeuHSNGjMgyKTz88MOs\nX7+eY8eOAbrNwr6768aNG4ErbRZpt+nTpwMwc+ZMTp06xQ033ECzZs146KGH8PfXJ2eltVmk3SZM\nmFBAn4CRXy6nXGbM2jFcTrnMB90/KBZHoSWdj6cPL3V4iTm3z6Gyd2XGrR/Hw2se5kTMCVeHds1y\nHKJcROYDScAfwK1AqFJqTAHFls4MUZ6/zGdXeCileGXjKyw7vIwPun9Aj1o9cn+TUaSkpKYw78A8\nPtzxIUkpSTzY/EEeaPpAgVyHpCCHKG+slLpXKfUZup2iSy7zpxORmiKyVkT2icgeEXnKmu4rIqtF\n5JB1by4SbZRYCw4sYNnhZYxuPtokimLKzebGPY3uYUW/FfSo1YOPd37MnSvuZFPEJleHlie5JYv0\n89mVUldfoi1nycCzSqlGQAfgMRFpjL6e969KqXroK/CZehOjRNpxZgdTtk7hpoCbeDQ492u3G0Vb\n1dJVmdZ1Gp/d8hmpKpVRq0cxfv14ziWcc3VoDsktWbSwxoKKEZGLQHNHx4ZSSp1USm23Hl8E9gEB\nQF9gljXbLKDf9W2CYRQ9Z+LP8My6Z/Av48+ULlNMg3YJ0imgE0v7LuWRFo+w5vgaei/tzdz9cwv9\nZV1z/IUqpdyssaDSxoNyv5axoUQkEGgJbAaqKaVOWss/CVTN5j2jRCRERELOnj2bXXyOhmBYzGfm\nekkpSTy77lnikuJ4v/v7+HiW+GHWSpxSbqV4NPhRlvRZQtPKTXlj8xvc88M97Inc4+rQsuX0wxkR\nKQssBsYopRweqVYp9T+lVBulVJsqVapc9bqXlxeRkZFm55cHSikiIyPx8jLXQ3ClqVunsvPsTl67\n8TXqVazn6nAMFwosH8j//vM/3uryFqfjTzP0+6G8sfkNLiZedHVoV3H0pLxrIiIe6ETxnVJqiTX5\ntIj4KaVOiogfcOZall2jRg3CwsLIrtRhZM3Ly4saNWq4OowSa+mhpcw/MJ/7mt5Hr8Berg7HKARE\nhFvr3ErngM58uOND5u2fx+rjqxnfdjy9AnsVmkEkc+w6e10L1ls4C4iy724rItOASKXUFBGZAPgq\npcbntKysus4aRlGz+9xuhv84nNbVWvPJLZ/gbnPqsZpRRO05t4fX/nqNvZF76eDXgZc6vHTNI9rm\nZ9dZZyaLzujzM/7hyjhSL6DbLRYAtYATwCClVI6XnjLJwijqIhMiGbxqMO42d+bdPo8KXhVcHZJR\niKWkprDg4AKmb59OYkoiDzR7gAeaPUApt1J5Wk6RSBb5ySQLoyhLSk1i1C+j+OfcP3x767c0qmRO\niDQcczb+LNO2TuPH0B+pVa4WL7Z/kU4BnRx+f0GelGcYxnV6N+RdQk6HMLHjRJMojDypUroKb3V9\ni8/+8xkAo9eMZvzv4zkbX/BttSZZGIYTrTyyktn7ZnNvo3vpHdTb1eEYRVQn/04s6buER1s8yq8n\nfqXPsj7M2TenQM/NMMnCMJxkf9R+Xtv0Gm2qteGZNs+4OhyjiCvlVopHgh9hSd8lNKvcjDe3vMnQ\nH4ay51zBnJthkoVhOMH5S+cZs3YMPqV8mNZ1Gh42D1eHZBQTtX1q89l/PmNal2mciT/DkO+H8Ppf\nrxOT6PBpbNfEJAvDyGcpqSmMXz+eM/FneL/b+1T2ruzqkIxiRkToVacXK/qtYEjDISw4uIA+S/vw\nw9EfnHaiskkWhpHPpu+YzqaTm3ipw0s0q9LM1eEYxVg5z3I83/555tw+h+plqvPcH8/x0OqHCL0Q\nmu/rMsnCMPLRz6E/8+XuL7mr/l3cWe9OV4djlBBNKjXhu9u+48X2L7Ln3B7uXHEnH+38KF/XYZKF\nYeSTQ9GHePnPl2lRpQUT2pmR942C5WZz4+6Gd7Oy/0r+U/s/fLrr03xdvkkWhpEPYhJjGLN2DGU8\nyvBut3fxcDMN2oZrVPauzNQuU/my55f5ulwzOI1hXKdUlcrzfzxPRGwEX/b6kqqlsxx13zAKVNvq\nbfN1eUUiWfwbFc+ExX9T2tOd0p5ulC7lRmkPN/28lJuelvZahsfueLqbwpPhXJ/s+oT1Yet5sf2L\ntKza0tXhGIZTFIlkEZ+Ywm/7z5CQmEJcYjKpeegZ5m4TvD3dKGMlkLTH3p5ulCnlhreHfQJy19Oy\nSDpVy5Wipm9p522kUSStPbGWT3d9St+gvgxuMNjV4RiG0xS5gQSVUlxOTiU+MYX4xGQrgejH8ZdT\niE9KISExmbjLKSQkpRB3OZn4xJT0RGN/H59+SyYuMYXE5NQc4+hQ15eRnQK5pVE13N1MiaWkO3bh\nGEO+H0KgTyCzbp2V5xFBDcPZ8nMgwSJRsrAnInh5uOHl4YZvGc98XXZySqqVbHQSibucnJ5w9p6M\n4bu/TvDw7O34lffi3g61ubttTSqVNTuIkuhS8iXG/j4WT5sn73V7zyQKo9grciULV0pJVfy67zSz\nNoXy5+FIPN1s3NHCjxEdA2lR01yfoCSZ/Ndk5h+Yz8c3f8xNNW5ydTiGkaUSXbJwJTeb8H9NqvN/\nTapz+MxFvtl0nMXbwliyPZzgmhUY0ak2tzXzo5S7m6tDNZxo9fHVzD8wn5FNRppEYWRJKVVoLoea\nX0zJ4jpdvJTE4m1hfLPpOEfPxVG5rCdD2tViaPta+JX3dnV4Rj4Ljw1n0IpBBJYPZFavWeZ8CiOD\n6LhExi3axZp9Z/B0s1HK3YandSuVfu+mp7nZKOWRdu+W8XlW82cxLX35bja8PGx4urnZLdNGmVIe\npmRRWJTz8mDkjXUY3jGQDYfP8c2mUGasPczH647Qq0l1hnesTbs6vsXuKKMkSkpN4rn1z6FQTO0y\n1SQKI4Ntx6N4fM4OImMTue/GQEq5u5GYnMrl5BTrPpXE5FQSU65Mi4tLzvDa5UzzFyYmWeQTm03o\nUr8KXepX4URkPLM3H2f+1n/5/p+TNKxejhGdAukb7E9pT/ORF1Uf7fiIXWd3Ma3LNGqWq+nqcIxC\nQinFzD+OMfWn/fhX8GbxI51oVqN8viw3KUXp5JKUYt3rZJOWhC7bJ6EMSUcnnIen5sMGWkw1lBMl\nJKawfGc4szYdZ9/JGHy83LmrTU2GdaxN7UplXB2ekQcbwzcyes1oBtQbwKROk1wdjlFIXIhP4tmF\nu1iz7zQ9m1TjrYEtKO9deEqc+dnAbZJFAVBKEXI8mq83hvLT7lOkKkX3BlUZ3rE2XepVwWYzVVSF\n2bmEcwxYMQBfL1/m3D4Hb3fTFmXArn/P89ic7ZyOucTztzbivhsDC111s+kNVcSICG0DfWkb6Mup\nC5eYs/k4c7acYORXZ6hTuQzDOtRmYJsa+HgVniMSQ0sb9yk+KZ4v/u8LkygMlFJ8vTGUN37YR9Vy\nXix8uBPBJaDrvClZuMjl5BR+2n2KrzeGsuPEeUp7unFnqwCGdwykfrVyrg7PsMz8ZyYfbP+ASR0n\nMaD+AFeHY7hYzKUknlv0Nz/uPsUtjary9qAWVCidvycH5ydTsigGSrm70Tc4gL7BAfwddp5vNh1n\nQUgYs/86QaegSgzvGMgtjaqaYUVcaOeZnczYMYNegb3MhYwMdodf4LE52wmLTuCF2xry0E11C121\nkzOZkkUhEhl7mfkh/zJ703EiLlwioII393Soxd1ta+X70CZGzi5cvsCglYNwEzcW9F5AOU9T2iup\nlFJ8t/kEr63ci28ZT2YMbUmbQF9Xh+UQ08BdzCWnpLJm3xm+2RTKxiOReLrb6N3cn5GdAvOlS56R\nM6UUz6x7hnX/ruPb276laeWmrg7JcJHYy8k8v+QfVu6KoEv9Krx3V4siNR6cqYYq5tzdbPRqWp1e\nTatz8PRFvtkUypLt4SzeHkYt39K0r+NL+7qVaF/HlxoVvUtUUbggzD8wnzUn1jC2zViTKEqwfSdj\neOy77YRGxjGuZwMe6RpUonsumpJFERFzKYnlO8JZf+gcW45FcSEhCQD/8l7piaNdHV/qVC5jksd1\nOBB1gKHfD6WdXzs+uvkjbGLajEoapRQLQv7lleV78PH2YPrdLekYVMnVYV2TIlENJSJfAncAZ5RS\nTa1pvsB8IBAIBe5SSkXntiyTLDJKTVUcPHORzUej2Hwsks1Ho4iMSwSgarlStLMredSrWtYkDwfF\nJ8UzeNVg4pLiWNRnEb5eRaNe2sg/8YnJvLR0N0t2hHPjDZV4f3BLqpQrOtVOmRWVZNEFiAW+sUsW\nbwFRSqkpIjIBqKiUei63ZZlkkTOlFEfOxrL5WFR6AjkdcxkA3zKetAv0pX1dXfJoVN2nRBelc/LS\nhpdYcWQFM/9vJu382rk6nCIhPjGZ7cfPs/lYJHGXU7i1WXVa16pYJH9jh05f5NHvtnP4bCxP3VyP\nJ3rUw60Iboe9IpEsAEQkEFhllywOAN2UUidFxA9Yp5RqkNtyTLLIG6UUJ6Li2Xw0ir+skkf4+QQA\nfLzcaWdVWbWvU4km/j6mey6w8shKXtjwAqObj+bxlo+7OpxC6+KlJEKOR6cflPwTdoHkVIVNwMPN\nxuXkVGpU9KZvsD/9ggOoV0TOGVq8LYyXlu2mTCk33h/cks71Krs6pHxRlJPFeaVUBbvXo5VSFbN5\n7yhgFECtWrVaHz9+3GlxlgRh0fFssUoeW0KjOHYuDoCypdxpXbsi7er40qGuL80CKuDpXrKSx/GY\n49y18i4a+jbki55f4G4z/T7SnI9PZGtoNJuPRrL5WBR7Ii6QqsDDTWheo4J10OGb3pX0lz2nWLYz\ngg2HzpKqoLGfD/1a+tOnRQDVy3u5eGuudikphYnL9zA/5F/a1/Fl+pCWVPMpfHFeqxKRLOyZkkX+\nOx1zyaq2imTLsSgOnYkFwMvDppNHYCXa1/UluGYFvDyK78WcElMSufeHe4mIi2BR70VUL1Pd1SG5\n1LnYy2w9FsXmY1H8dTSSA6cvohR4uttoWbNCeltYq1oV8fbM/ndx9uJlVv0dwbId4ewKu4AIdKxb\niX7BAfRqVr1QDG1z5Gwsj323nf2nLvJY9yCevqV+sStlF+VkYaqhCqnI2Mu65GHd9p+KSd9JBNes\noLvr1qlEq9oV8mWYdaUUKamKVAWp1uMUpUhNzWJ6qkp/nKquvFbLt/R1xzJ1y1Rm75vNhz0+pFvN\nbte9XUWN/UHD5mNRHLYOGrw93Ghdu2J6L7sW13HQcPRsLMt3RrB8ZzihkfF4utu4pVFV+gYH0K1B\nFZdcWXLFrgieX/w3nu423hscTLcGVQs8hoJQlJPFNCDSroHbVyk1PrflmGRR8C7EJ7ElNIotx/RO\nZHe4rn5wtwl1q5RBkCs7d2tHrhQZd/o5TM+Pn52bTWjq70Pr2r60DaxI68CKVC3neBXC2hNreXLt\nk9zb6F6ea5drP4tiwb46cvOxSEIj4wFdHdkmsCLt6+gSZVP/8vleHamUYue/51m+M4KVuyKIjEuk\nvLcHtzXzo1+wP20DfZ3eMH4pKYXJ3+9l9l8naF27Ih8OaYl/heI7OGSRSBYiMhfoBlQGTgMTgWXA\nAoUAPLAAABHZSURBVKAWcAIYpJSKym1ZJlm43sVLSWw7Hp1+9GkTsIlgswluIrjZBJsIbjbSH9sy\nTc88b3bT3Wx6uTYBt8zrsB4rFPtPXmRraBQ7/z2fflWxwEqlaROok0ebQF/qZnPeyam4UwxcORD/\nMv7Mvm02nm7FbzgVpRTHI3VyyNzRoby3B20DdTtV+zqVaORXrkCrYJJSUtlw+BzLd4Tz857TJCSl\nEFDBm94t/OnX0p+G1X3yfZ3HI+N49Lvt7ImIYXSXuozt2QCPYlbtlFmRSBb5ySQLIyeJyansibhA\nSGg0W0OjCDkeTZR13olvGU9a166Ynjya+pfHZkvlgZ8fYH/Ufhb0XkBtn9ou3oL8kdaF+q+jUVaV\n4pUu1JXKeKY3RrevW4kG1coVmu6t8YnJrN57mqU7wvnj0DlSUhUNq5ejX8sA+rTwz5cj/x//Ocn4\nRX9jswnvDGrBLY2r5UPkhZ9JFoaRA6UUR8/FERIaxdbQaEJCo9KrW0q52/Cv8zvn3L9n+A3PM7r1\noELR2JoXSiliEpIJOx9PeHQCJ6Li2X4imi3HojgXq5NkNZ9StK9TKb2XW1CVonFy5rnYy3z/90mW\n7Qxnx4nziEC7QF/6twzg1mZ+eb4KXWJyKm/8sI+vN4bSomYFPhrakhoVSzsp+sLHJAvDyKMzFy+x\nLTSaHw7/wboLk0mOaUVCxCBEoEG1crQN9KVNYEXaBvq6vA5bKUVkXCLh0QmERScQbiUF/TiB8OgE\nLl5OzvCegAretK/rm94RoXal0kUiOeQk9FxcesP40XNxeLrZ6N6wCv2CA+jesGquDe7/RsXz+Jzt\n7Aq7wP031mHCrQ1LXLdwkywM4xpEXYpi4IqBlPEow9c953Ag4rIueRyPYvvxaOISUwC9421jVVu1\nDaxI/ar5W2WTmqo4c/Ey4efjCbNLAmHRCYRHxxN+PoFLSakZ3lOulDsBFb2pUdGbgAre1KhYOsPz\nojQSal4ppfgn/AJLd4SzctdJzsVeppyXO7c19aNvS3861Kl01fezeu9pnl2wE6Vg2qDm9Grq56Lo\nXcskC8PIo1SVymO/PsaWk1uYc/scGvhm7LGdnJLK/lMX09s8th6L4sxFXd9fzsvdavfwpU3tiv/f\n3r0HSVWeeRz//pgbzTQzeI8MBm+oyZISjVHQrCGrSTSuMbtbKYlZtrJo9lKa3WjFNYaiSiomK2hM\nllU3ItlgNq6Wokk0WcFkEyPiBVBEwAuiYBwERYVhZpih5/LsH+ft2Z4b3TPT06e7eT5VU3P69Olz\nfnN6+jx9bu+b9TLSjq5udja199oTaAxFYPueNt7e00ZHV+/P3aG11TRMSBeCBA19isJQD7+Uq86u\nbp56/X1+8cJ2VmzcSWuqiw/VjeWSaRO5ZFoDU45KsnD5K9y1citTG+q4/bLTmXxYbdyxY+PFwrkh\nunvT3dyy9hbmnjWXWafMyjq9mdG4u401Gec90jcuVlWIjzXU84ljD+XDh41jx572XkVh5952uvt8\nrI4cX9OvAEwKhWHihAS1NX7X+FC1pbr4zcvv8Mt12/nD5l10dhv1iSqa2jqYPX0ycy/6SFnfUJoL\nLxbODcHG9zYy+39mM/OYmdw689ZhH8vf3ZriuTd3s+bND1i7bTcbGptIdXUzRnB0fVQIBjpUdHT9\n2IN+ozXaPmhN8esX32bla+9x8akTufjUiXFHKgpeLJzLUXOqmS898qWoj4KL76e+Jn89DbZ3dPF+\na4ojx9eU/fX6rjR5T3nO5cDMmP/0fHa27mTpBUvzWigAxlZV0FDGd/86l8m/Drmy9dBrD7Fi2wqu\nOu0qph05Le44zpU0LxauLG3ZvYWbVt/EjKNnMGfqnLjjOFfyvFi4stPW2ca1T1xLbVUt3/vT73k/\n2s7lgZ+zcGVn4ZqFbNmzhTs/cyeHJ8qjxzPn4uZfuVxZWb5tOcs2L+PyqZdz9sSz447jXNnwYuHK\nxlvNbzH/qfmcesSpXHnalXHHca6seLFwZaGjq4PrnrgOSSw4dwFVY7x5DOfyyc9ZuLKwaN0iNry3\ngR/M/AENyYa44zhXdnzPwpW8lY0rWbppKZeefCnnTz4/7jjOlSUvFq6kvbvvXeY+OZeTDjmJaz9x\nbdxxnCtbXixcyerq7uL6ldfT3tXOzZ+6mZqK8u3Twbm4ebFwJWvJhiWs3rmab5/1bY6vPz7uOM6V\nNS8WriQ9985z3LH+Di46/iIuOeGSuOM4V/b8aigXKzOjtaOVvam90c/+vTSlmti7f2+vcX2H39n3\nDpOSk5g3fV7J9zXtXCkoiWKxu303y7ctp666jvrqeuqq66irqSNZlaRijHcqEzczY1/nvpw29H2f\nb04102Vdg867QhXR+14Tve/1Y+s5pu4Yzqk5h8tOuYzaqoO3y0znCqkkOj9KHJewE284sd94IZJV\nSepq6noKSF314MP11fU9w+Orx3sDc0Qb+rbONlo6WmjpaKE11UpzRzOtHa20pFpo7QiPU63R8x2t\nNKeaexWA5lQzndY56DLSG/xe70nm+xIKwUDv27jKcb7n4NwwHXSdH510yEks+8Ky/t9U+zxu2t/E\nu/ve7RnX0d0x6DyFSFYnD1xcMjZiyaokY8aMoUIVjNHAvyX1Hp8xffon/Xik0hv51o5oI96SaunZ\nmGd73Jxq7hnf2tFKt3VnXV6iMkGyKkltVW1PgZ6UnJS9QNfU+wbfuTJQEsWiakwVUw6ZMqTXmBnt\nXe2DHhJp2t/Ub3yuhSYf+habvsVksGKzv3N/z0b+QIdv0hKViZ4NfG1VLcnqJJPrJveMS1YnexWB\nXo8zhivHlMS/inNulJTtFkASicoEicoER9UeNaTXZhaa9DH29Dfwbuumy7r6/Taz/uO7M57Hej3O\n/D3ofLvD66yL7u5o/NjKsf025umNfN8N/riqcd5GknMuL8q2WIzESAqNc86Vo1jO8Eq6QNKrkrZI\n+lYcGZxzzuWu4MVCUgVwO3Ah8FHgy5I+WugczjnnchfHnsWZwBYze8PMUsB9gN+C65xzRSyOYtEA\nvJXxuDGMc845V6TiKBYDXXDf785ASX8naa2ktbt27SpALOecc4OJo1g0AsdkPJ4EvN13IjNbbGZn\nmNkZRxxxRMHCOeec6y+OYrEGmCLpOEnVwCzg4RhyOOecy1HB77Mws05JVwErgArgP81sU6FzOOec\ny11JNCQoqRl4NeYYhwPvxZwBiiNHMWSA4shRDBmgOHIUQwYojhzFkAHgZDMbn48Zlcod3K/mq+XE\n4ZK0Nu4MxZKjGDIUS45iyFAsOYohQ7HkKIYM6Rz5mpe30e2ccy4rLxbOOeeyKpVisTjuABRHBiiO\nHMWQAYojRzFkgOLIUQwZoDhyFEMGyGOOkjjB7ZxzLl6lsmfhnHMuRl4snHPOZRVbsZDUJemFjJ8z\nJW2V9KGMae5I93ch6frQ/8Wrkj6XMc0EScskvSLpZUkzCplB0sl95rFX0jdiWhdXS9okaaOkeyWN\njSHDP4flbxrqehhqDkmHSfq9pBZJt/WZz8clbQgZF2kInYDnMcN3Jb0lqSWO9SBpnKRfh8/GJkk3\nxZEjTLNc0vqQ40eKuiooaIaMaR+WtDHGdfF4+Nyk53NkDBmqJS2WtDn8f/xV1oWbWSw/QMsA4/4B\n+FkYPh14Eagi6vdiPVADHAe8DlSE6e4GrgjD1cCEQmfIeG0FsBOYXOh1QdRy71YgEV5zP/DVAmeY\nCmwExhHdw/NbYMoorota4JPh+dv6vGY1MIOo4cpHgQtjyDAdOHqg+RUiQ3gfPp3x2Vg5lPWQ53VR\nF34LeBCYVegMYdq/BP4b2BjHexKmexw4Y6jLz3OG+cCNYXgMcHi2ZRfbYajFwAmSPg3cBlxlZh1E\n/V3cZ2b7zWwrsAU4U1IdcC7wYwAzS5nZnkJm6PPa84DXzezNEWYYbo5KICGpkmhD0a+BxlHO8BHg\nGTPbZ2adwB+AvxhhhkFzmFmrmT0JtGdOLOlooo3T0xZ9Gn4KfLGQGQDM7Bkz2zHC5Q47Q3gffh+G\nU8DzRA13FjRHWP7eMFhJVLhGemXNkDNISgLXADeOcNkjyjEKhpNhDvCvAGbWbWZZ7zaPs1gkMnal\nfg5RaOAfib55bDazJ8K0g/WBcTywC/iJpHWSlkiqLXCGTLOAe4ew/LzlMLPtwC3AH4EdQJOZPVbI\nDER7FeeG3d9xwOfp3cJwvnMMpiFk6puvkBlGKq8ZJE0ALgb+N64cklYA7wLNwLIYMnwH+D6wbwjL\nHo0cEG2zXpA0T8r9EGk+MoT/BYDvSHpe0gOSjsq24Dib+2gzs2l9R5rZC+F44h0ZowfrA6OSaLfr\n62b2rKR/A74FzCtghujJqAXdLwDX57jsvOaQdAjRN/7jgD3AA5L+2sx+VqgMZvaypAXAb4AWokNV\nnTkufzg5BpNTnymjnGGk8pYh7GneCywyszfiymFmn1N0Hu0e4M+I/k8KkkHSNOBEM7ta0rG5Zs53\njuArZrZd0niiDfxsor3fQmWoJNrDXGVm10i6huiL5uwDvajYDkOldYeftMH6wGgEGs3s2TB+GVHx\nKGSGtAuB583snTwtf6g5zge2mtmucKjoIeDsAmfAzH5sZqeb2bnAB8BrecowUI7BNNL7cMuAfaaM\ncobRNNQMi4HXzOyHMefAzNqJuiTIV1fKuWaYAXxc0jbgSeAkSY/nKcNQchCOAmBmzUTnT/oezh7t\nDO8T7V39PDx+gBy2m8VaLPp6GJglqUbSccAUYLWZ7QTeknRymO484KVCZsh4/ssM7xBUvnL8EZiu\n6AoYEa2LlwucgfSVHZI+THQysRDrpJdwnqBZ0vSwLv4G+GWhcxQDSTcC9cCQr0zLY4ZkOI+U3sv5\nPPBKITOY2X+Y2UQzO5bopO9mM5tZyAwQ/f2SDg/DVcCfEx2+LZhwHu8RYGYYldN2syRanTWzTZLu\nJ/qDOoErzawrPP114J5wGOgN4G8LnSEcn/8M8PejsewcczwraRnRScxOYB2j1ORAlvfjQUmHAR1h\n/O7RyJAWvinWAdWSvgh81sxeIjqGuxRIEF0N9WihM0haCFwGjJPUCCwxsxsKlQHYC8wl2jA/Hw6N\n32ZmS0YjwwFyvA88LKmG6Kq53wE/KmSG8D9RUIOsizeBFaFQVBBdMXhXITOEdXEd8F+Sfkh03jfr\ndtOb+3DOOZdVqRyGcs45FyMvFs4557LyYuGccy4rLxbOOeey8mLhnHMuKy8Wrqyof6ucx0o6Q9Ki\nPC7jBknf7DPus5KeTjfdIKkiLD9fN0Y6F6uSuM/CuSEYqDmEbcDa0VyomT0maQ5wObCE6P6fNWb2\n1EjmK6kyNMroXKx8z8KVPUkzJf0qDH8qY69jXWifB0n/oqj/i/UK/T5I+pqkNWHcg+HmywO5Grhe\n0p8AVxHd+ISkoyQ9JGmtpNWSpofx08PeyDpJqyRNCeOvkHRfyPyopAZJT4bMG31vxcXB9yxcuUlI\neiEMbzWzvk2kf5PozvJVipqsbpd0IVET5meZ2T5Jh4ZpHzKzu6Cn2YzLgX8fbMFmtiPcEfs08E9m\n9kF4ahGw0MyeCY3Y/Yqo74+XgU+aWZekC4iazr40vGYGMM3Mdku6DnjEzBYo6jQoMaw149wIeLFw\n5WbAVjkzrAJulXQPUTFolHQ+8BMz2weQsZGfGorEBCAJrMhh+bcDN5nZ0oxx5wMn6/9boj5EUiLM\n96eSThhgPo9lNJWyBrhTUYutvzCz9TnkcC6v/DCUO6iY2U3AFUTfzp+RdApRk+YDtXuzlKgjmY8R\n9SyWtZva0LdA33kJONPMpoWfBjNrA74LrDCzqUR7Npnzb82Y5++IGn3bQdQO2ldy+VudyycvFu6g\nIukEM9tgZguITnqfAjwGzEmfk8g4DDUe2BEafRvJBvq3wJUZGdJ7PvXA9jD81QNkngzsNLPFRAXs\ntBFkcW5YvFi4g803wkni9UAb8KiZLSdqdn1tON+Rvix2HvAsUSc9I2lS+0rgHEkvSnoJ+FoYvwC4\nWdKqLK8/D1gvaR1RPxCDnjdxbrR4q7POOeey8j0L55xzWXmxcM45l5UXC+ecc1l5sXDOOZeVFwvn\nnHNZebFwzjmXlRcL55xzWf0fHoBuuSSyn24AAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.6.1",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"gist": {
"id": "f6c5ed234f193704d857469a97b1d8a9",
"data": {
"description": "Tourist Visa Refusals by Country",
"public": true
}
},
"_draft": {
"nbviewer_url": "https://gist.github.com/f6c5ed234f193704d857469a97b1d8a9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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