Week_7_Exercise.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dk': 24021133506, 'se': 65074283493, 'no': 44369180306, 'fi': 32100084252}\n"
     ]
    },
    {
     "data": {
      "image/png": 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+gMsdNvsk0SFSGZXuKGioU2QAP/jRkcaqTCOL21lnh2f0ly4z3qQdGfiyO17PmQpu6diOC9vb0RYM4oSNrfj3wAAWuZz4zqaN+O7mTSiSZZwTObKuK6Tiyj2Whh3MOy4XJpstKJIVZBsMOMJiwZmbNwMAbPuZkkgQBgC/Fx0ilTHO6SfSWGuoU47NUHDl02dZzjHJLO7HpIR0aA84v7Xtb+bLqg596+R0dtMrW3/S/nHFoW9Jhqnevs7xuegQqYhGujHWUKdYAZx/8eFKiYjCBQBZguF/cpdULQjettWke1URGUjS+7noAKmKSjf2ZhsYMudWydMPfdPRdWL21opl0nWuanXjgOgsJOmc47DZY7rMkYRR6cZQQ52iAGg4d5I8JsvEEmJ3/rFm/5i3rHdZTve/sU10FpJUJAA3iw6Riqh0Y2sqgJxvj5dniA6yJ7OBmx7J/Uf5/YH72xktKyNDd6nDZj/4xskkalS6MRLZK/esE2sMGaO5LnckLsxprlqkX7czP9RFy8rIUFgAXCs6RKqh0o0dG4CxZ9uUIe2VK4o9Y7BkmelmXh/4ZKfoLCQpXOuw2YW8IZyqqHRjIHL12XfH5TFWmcNqRec5lBxFy/hH9p8Lr/f/rV10FpLwCgB8T3SIVEKlGxsVACadbVMqDnDsWcIxMEg3575X9bx68zaL5k7Ia1JJwrhcdIBUQqUbG3MAhI4oMRwuOki0jsnaUf6ufL2nNrgu4XbXJgljLi0fix0q3RGKLBObe0ylQc41s4MeNpmoSkyBvDcyfp11rv+VoV3fStKGDu5tK8LHD5wrnSU6S6qg/XRHzgbAcsp4+TDRQUbCZODKg7kvV8wZXNN+q3F+hcaM9IKcpjjnfItJ71xyGNj7c5UxAat0NIAiAA+KzpYKqHRHbq4iIWArkCaLDhIL5+asqzrce33Xxdrd1p1yWaboPCR++pne/+E4PvjmcYai3mLTfy97rJ3yzJTpzfOaVwgJl0KodEegoU7JAnBkQ52cYRa0z8JoqLW6it4J3ea92v2Tzo/Mx9Km1inMz3XfV0V616I5cub6ScZ8AAc7xO1CAFS6I0Q/Qo7M4QDYrLEGu+ggsZYla9Zncx4rvsX/aDvnCbv3KxkGjXNtnTW07eFj9Y7LbpFND11hrlo/SR7K+xHnT3lmSnIsz0lgNNIdmRMZ4KzOje35Z4lCYmDX5S6vmuXesP1ydk+B25BjOvS9SKLqNGhd79rgWzJXLnHnKuXDeIhyADMB0JaPI0ClO0yRU35rZlcYVIvCUvoU1VmZXWOXBX46OC/wc7fDeFhSrtBIV27ozk/L9b435spjOqpMRTF4yBNApTsiVLrDNwEAjio3TBAdJB6KTMGc/yi/Cd3tPHPLi+bzK0XnIQemch5ck6d1vllvMK+cKhdBkmJ5WvTxAH4Xw8dLO1S6wzcNQKAuPzWnFvbHKEG+P/c/lUcPrm2/Ubm9XJVMBtGZSBjnnLeb9c53piD03tFKWdCqjNYL4zFTnpkiN89r3v+Z9OSQqHSHoaFOMQCYlmdm7qIMlnZHxpye01o1yXdd98XqnaYOpTKWoygSpT6m9X04Hs5FxxmK+4v2WeY1GjIBzADwaRyeKyVR6Q5POQDTyeMMYwwSS8sVIOMsnsIlof/xXe++vGOZ+cSyQ9+DxIqf694VxXr3wqPlrFabaQyAMXGOcAKodIeNSnd46gCwyUWGGtFBRMqQdcuTOU+anxpc1Xav6afVLD1ff+JC41xbn6ntWDxdkj6tl0t1WRJ56OgJAO4X+PxJjUp3eOoBDJZlsbQf4UkM7Me5X1TPcP+041L8Km9QzkuZi0QSwQ45vMzr7blyqTdnWMu8RsOcKc9MUZrnNdOhp8NApRulhjrFDKBGlrAt38roaq2IaZm9ZcuCP3Ne5rvRs9o0tUB0nmTmgu78pIL3LZpryO+ojMkyr1jLADALwHLRQZIRlW70SgHo00qkAlli9Pe3h3yjmv2K/IB2n/M7WxaYL6FlZVFQOQ80j9F2Lqo3WFYfIRfGeJnXaDgBVLrDQqURvTIA0uQiQ9pPLeyPLMFwV+6blUc5v95yvXxXWUCy0L+xA9DDy7x2LDmC6R/MkUuDllFb5jUaZokOkKzoGyJ6tQAC1bmJefhkojglu71yie+63ovUO+StSk2O6DyJpFfSej8YD/ebx8tFAwVKsr54J/yxVImKSjd6dQDc9CbaoVVafPlva3cEbnRdsv0t86ljRecRycd174oSvXvhMXL2xommfADJfjn1uCnPTJGa5zXTbkhRotKNQuRNtGKJYWu+lRWLzpMMLAZueizn2bHPDa5uv9t0cyVnhrTZpUrjPNSSpXW+NUMyfD5TLhG8zCvWjAAqAbQJzpF0qHSjUwJAr8llWbLEFNFhkgVjwA9zV1Yd6blhxw9xT06focAqOtNo6pC1ncsmIfDOXLnUm5Uwy7xGQy2odKNGpRudUgBSZY5Ec5TDMDmjv3SpepP7Cu8NXStMMxJxKdSwuaAPflyp9y+cK+d3VpjS5aegCQCWiA6RbKh0o1MMQC/LknJFB0lWeUoo86WsP1p/P3hS++OWHyX1j9tBzgOr87Wdi44yWNYcYSwEkG4vxvRm2jBQ6UanBIC/MIOl2zdXTBkkSPPzllYd5WzZepV8V4lfykyaqRqdc73None+fQTTP5wjl6rmpFrmFWtpsa1prFHpRqcYgD/fwmikGwPHZ2+rWOa/oe/i4C+kTcbahP477ZG03g9q4X5zrlw0mLzLvGKNRrrDQKUbnSIAA7lmGunGSpnZP2aR8svgra4LtjWaz0ioN518XPd8Uar3vHGMnN1WmxLLvGKNlo0NA5XuEDXUKSYAVgDdWSYq3VgyG7jxT7kvlh89uLr9F8ZbK3SmCNuuLMR5qCVb2/HWDEn+YkbKLfOKNSPCU24dooMkEyrdocsBoAOAVUGW4Cwp6fyctVWHe2/YeYn2y8weuThu585xztFh1HcunYTA0mPlUl+WknYb049ASp8POBqodIcuFwAHAEUCnYo7SuzWweJl6i2eH3uv2fmZafaoLr0ahD7wcbU+sGiuXLBzbNos84o12sozSlS6Q5cJgGUaIafraRHxkq1oGf/I/rPlTwOr2h+2XBXTH++DnPtX5Wtdi2YbrGsPNxYg/GJKhi+lL3QZDVS6Q2cCwPLMjEa5cWBgkG7M+6Cq3rV+2xXSPUVeQ5ZxuI+lc65vtmg7Fk+V+Edz5LKQKa2XecUajXSjRKU7dCYALNPIhv3NT6I3J6uz/N3A9QOXBOa71xttUZ0F1i1pPe9PhOet4+Ri5xglrTfcGUU00o0Sle7QZQDQzTLo2PE4KzYFc1+Xf63e7jp368vmcw76JpcXuvvzUr1n4bFybvt4UwEAOsVidNFIN0pUukNnBqCZZTotQgSTgSt/yH25YmbnV+7bs+/ca3QV4lx15Gidb86QlBUz5BJukDJF5UxDNNKNEhXI0BkB6IqBRroinV+yKXOm8+q+F/SjAtsUrXPpZASXHiOX+jNpmZcgNNKNEpXu0BkBcE0HXX0j2Lhs/5i6GZ9oN9mKC0VnITTSjRYtfRo6BYDuC/GQ6CAEONkQyOc656JzEJhFB0g2VLpDpwFgXhWq6CAEGCNDMntC3aJzEHhFB0g2VLpDFwAgeVUa6SaKOnfAJzoDgUt0gGRDpTt0fgAGd5BGuonipIAvafbhTWFUulGi0h26AADJHeRUugniDN2XzznN6wpGpRslKt2h8wOQghp0nb7RE0Ihg8no1XpF50hzVLpRotIdut0jXFVDQGQQ8o0Jbr9HdIY0Ry96UaLSHToVka0dPSqcgrOQiON9frpYRawu0QGSDZXu0AUQKV1XgFPpJogG3TeGZnuEUQH0iw6RbKh0h86JSOk6A3xQcBYSUc64VfFpfaJzpKnu5nnN9IoXJSrdoXMCYADQ76eRbiKpcQXozRwxaGphGKh0h24Qkb+vHi+NdBPJXL+P/h2L0So6QDKif6xD1NiiBgD4AMidbhrpJpIzQ/6oNjcnMdMsOkAyotKNTg8A89ZBnUa6CaRG0jMknzYgOkcaotIdBird6HQDMLX06v0hnfZgSCSVrgD99BF/VLrDQKUbnU4AppAO3ufjO0WHId84xudjojOkGS+ATaJDJCMq3ehsRWTj90437xScheyhIeTLEZ0hzaxtntdMG/oPA5VudDoRWau7ZVDfITgL2YNd0rOZX6OlY/GzRnSAZEWlG51OhNfqsnU9Oo10E8xYV5Cujoofms8dJirdKDS2qH4AOwFYvuzQduqc049XCWS2j/Y0jyMq3WGi0o1eK4BMr4rQgB89osOQbzSEfNmiM6QRKt1hotKN3npEDuPrcOnbBGche5jKtFwEdbfoHGlgU/O8Zlq9M0x0BHv0diDyZtqaLn3T5CLDkYLzkD2UuAL9nfmWTBHPHewNYvsT2xEaDAEMyDs+DwWnFAAAepf0ondpL5jEkHVEFkrOL9nn/j2Le9D/fj/AAHO5GWN/NBaSUULnS51wrXbBUmlB+U/KAQD9y/uhebTdjx9nb4p40lRBI93odSDyZtqyzaFNdIpEYqn3+oXNszMDQ8kFJai9vxbj7hyHvqV98G/3w+1ww9nkxIRfT0DtfbUo+M6+Ran2q+hd0ovxvxyP2t/Uguscg58NQvNq8LZ6UXtv+M/8W/3QgzoGPhpA/on5Ar5KAMAiUU+cCqh0o9TYou5aFJ7d6ea+Xi+npWMJ5HRV3LyukqvAUm0BABgsBpjKTAj1h9C3rA+F3y2EpIS/3eTs/f+AyXUOPaiDaxw8yCHnyQADeIiDcw6ucjADQ8+bPcj/Vj6YHP/rQTjnfgDvxv2JUwiV7vB8ASAbAFr79I2Cs5A9HMVCeVB1r+gcwe4g/O1+WMZbEOwMwrPeg42/2ohN92+Cd9O+8ZQ8BQWnFmD9zeux7mfrIFkkZE3OgsFiQPaMbGy8ayOUAgWSVYJvkw/ZR4p5bWGMvd88r5mWiYwAle7wrN/1iy87NCrdBFPgCgrd1Fzza9jyyBaU/KAEBosBXOfQPBrG3TkOJeeXYOtftuK/Z6U0jwZXkwsTfz8Rtv+1QQ/oGPg4vIdP4WmFmPDrCSi9sBRdr3Sh6Jwi9L3fhy2PbkFXY9y3tKWphRGi0h2erQgfVSK/365tDWo8GO8A/hDHrCfcOOJxNw77ixt3v+sHAKzq1DD7KQ+mPObGGS964QzsO+V8oPsCwG1L/Dj8MTd++Oo3g5nnVgXx8KfJcxbnTK9fE/XcPMSx9ZGtyJ2di5wZ4SuTlTwF2dOzwRiDdZwVYIDm2juie60bSoECOVsGkxmyZ2TD27r3iNjXHv5/YioxYWD5ACqvrURgWwCBzrj+v6E30UaISncYGlvUEIDVAPKCGvRtTh73jT9MBmDZvAysuioTK6/MwFsbQ/h0WwhXvO7Db08yofnqTJxtk/H75ft+Qx7ovoN+jo+3aVh9dSY0ztG8U4NP5Xh6lYprZhrj/SUO22lBn5DVC5xzbF+wHaZSEwpO/ebNsuwjs+FxhA8tDnQGwDUOQ9be52kq+Qp8G33QAzo45/B87YGp1LTXbbpe6ULR2UXgIQ7sertQAvRgfN475Jy3Ns9r3hCXJ0thVLrD9xUACwB8tk2L+3XojDFkGsNvpKg6oGrhJRUtPTrmVoW/ob81Tsa/HfvuQHmg+0oMCGrhN218KqAYgN9/HMQNs4xQDMmzidcxCI7hIT3uQ3PvBi8GPh6A2+FG652taL2zFa5VLuTOzUWwO4gNt2/A1se2ovyKcjDGoParaPtjGwDAOt6K7JnZaL27Fa13tILrHHnH5+1+bOcKJyw1Fih5CgwZBlgmWLDhjnD/WSotcfn6GGM0yo0BWqc7fK2ILB37T4vacu4kOWA0MNOh7hRLms4x/W8etPbpuHamEfXlMiYXGdDYEsKZNgX/+lrFVuf+R0H7uy8AnGtXMO2vHpxUIyPHxPBFh4a7jovrlzViMmNsjEvt6c8zjY3n82ZMzMDkpyfv93MVV1bs82dKnoLqm6p3/7747GIUn1283/tnT89G9vRv3jwrvaB0ZGGHh0o3BmikO0yNLWoPwkvHcrwqQq19uiPeGQwSw8qrMrHtpix83qFhTR6eKi0AABFoSURBVJeGBWea8egXQUz/mxuuAGA8wAh1f/cFgFuPNmHlVZl48Ntm3PluAL863oQnvwri+//y4t4Pkmded5rHr4rOkEo4510A3hGdIxVQ6Y7MMkSWjr3fpq0WFSLXzHB8lYy3WkOwFRjw9iUZWPGTTFw4Rcb4vINPC+x53z017QiX8MR8Cc+uUvHS96xY06VhQ6+w96iiclrQlyE6QyphjC1ontdML2QxQKU7Ms0IXxIsLd4YavOqPG77uXZ7dAz4wysTfCrHO5tDsBVI6PKEpxN0znHvB0FcNWPfN8AOdN893fluAL86wQRVB7TIAgiJAd4k+bY7HoF8LmBVSSri4fVtfxOdI1VQ6Y5AY4vqBLAKQL7Owdd26XF7Q22Hm+OEZzw4/DE3Zj7hwbfGyTh9ooIXm1VM/LMbtkc8KMtiuGyqAgDocOk47QXvQe+7y2vrVMwsM6AsS0KumWF2uQFTHnODMeCIEsN+8yQaE2NSjivYKzpHStCxtHle82bRMVIFo60DRqahTpkG4HoAW2aWSUV3Hme+WnQmEna1Mafto7E51aJzpICzm+c1vyY6RKqgke7IOQCEAMhfdOhdHS69TXAeEnFq0BeftVQpjOt8J4A3ROdIJVS6IxQ5TWI5gCIAWNwa+kRsIrLLKXqggGt834XKZOgY/to8r5n+DmOISjc23gVgBMBeWxdaP+DnNJeYACwSM2S6VTrdY5g45xpj7EnROVINlW5sbEV4miGfA/hoC412E8Vkjz95FhcnGh2Lm+c1bxUdI9VQ6cZAY4vKASwEkAEA/2hWV/lDXPj2ggQ4JeBLrsvpEggzsD+JzpCKqHRj52sAXQCy3EGEVnRoX4oORIDv8EAB13lyXNGRQHRVb2qe17xYdI5URKUbI40tqg7gPwDGAMALzernIZ0nyaUEqSuLQba6QzSvGyVmYLeJzpCqqHRjawUALwDTNif3fL5do7ndBGD3+P2HvhXZRQ/oX625bM0S0TlSFZVuDDW2qAGEd9YvAYC/fhlcTnO74p0c8CmHvhXZhcnsFtEZUhmVbuy9C8ADwNLvR/C9ttD7ogOlu9M1fwGnSy+HRPfry9dcvoYOnhxFVLoxFjkt+GUAxQDw5Ffql84A7xebKr3lSTCaPTSveyicc50p7CrROVIdle7oWA6gG0B2UIO+aENoqehA6a7WHaBpnkPQvforay5fM6RNmxhj1YwxB2PsCcbYWsbY24wxC2NsKmPsU8bYasbYq4yxvEM/Wnqh0h0FjS2qCuBFAPkA8GKzurbbo3eITZXeTvT76JSUg+AaDzITuyHKu9UCeJRzfhiAAQDnAngWwG2c88MR3vr07tgmTX5UuqNnJcInS+RzAM+tVt+kaUVxGnR/Ps3rHpjm0x5a+6O1O6K822bO+crIr1cAGA8gl3O+632MZwDMjVXGVEGlO0oi63b/D0AWAPZem7Zt9U6dLpgQpJhxs9Gr9YnOkYg0n9YmZ8p3DOOue15irQHIjVGklEalO7rWA/gCQCkAPPRp8J14ni5B9jbeHXCLzpBouM51tV+9KEZH8QwC6GeMHRv5/SUAaPXOf6HSHUWRPRn+D+Ejfcy9Ph54+Wt1keBYaet4ny85jr2Io2BX8IkNv9jwcQwfch6A3zPGVgOYCuBXMXzslEAnR8RBQ51yPIBLAbQBwB9OMZ07Md+w/7O6yajZypn3tHEVVtE5EkXIFWr3tfkmtP2hjfbLjSMa6cbHhwBaEdno/A8fBxf5VO4RGyn9VDBuNXhDtGYaANe4pvap51Phxh+V7hAxxjIYYwsZY6sYY2sYY+czxqYzxt5njK1gjC1mjJXu776NLaoGYAEAEwBjp5v7Xlqrvh7XL4AAAKpdAafoDIkg2B18pPWu1s9E50hHVLpDdyqADs75EZzzyQDeAvBnAOdxzqcjXKq/OdCdG1vUDgD/BDAWAP7tCLV82UEb4sTbXL8/7f/Nh5yhjSFn6CbROdJV2v8DjEIzgJMZY7+LvDtbAWAygCWMsZUA7gBQfojHWIbwioZiALj/w8A7nW6dduaPowbNl9ZXSPEQV9U+9bxNv9mki86Srqh0h4hzvh7AdITL936Er75ZyzmfGvmYwjk/5WCP0diihgD8FeHVDJmqDv2+DwP/ovnd+JnA9EzJrw2KziGKv8P/y9a7W1ce+pZktFDpDhFjrAyAl3P+PIA/AKgHUMgYmx35vMIYO+xQj9PYovYA+AuAQgBy2wB3Pb1S/bdOy0jiptwVSMvS9bX5Fna90nW/6Bzpjkp36KYA+DwylXA7gLsAnAfgd4yxVQhf9jtnKA/U2KKuAfAqwlMUeLM1tPmjLdp7oxGa7OtoX/rtaR7YEWjpeavnQmeTk17cBaN1uoI01CkygJ8hvGnIDgbg4e+YL6zOlSaKTZb61nKD84JxY7NF54gXdUDt6n2nd073690bRWchNNIVJjK/+yQAH4AcDuD2pf6Xuzz6drHJUt9hTMtmAS0tLsfWfJpn8PPB71HhJg4qXYEaW9QBAI8ivFGI2RWEeueywD8G/bxXcLSUV+oKDojOMNr0kB5yrnD+bMcLOz4QnYV8g0pXsMYWdQPCKxpKASg73Nx77weB570qp81ZRtFsry+ll0xxzrl7tfth55fOp0RnIXuj0k0AjS3qpwD+gfAba4aWXn3goU+DLwQ1HhQcLWWdEfLliM4wmrzrvW/0f9A/n944SzxUuonjbQALAVQCYJ9u0zoXNKn/1HSe0iMyUaYzLRdBPSXXR3taPB/1vNlzobPJSfsqJCAq3QQR2QbyZYTPV6sEgEUbQpueX62+pOlcExouRRW7gim3+Y2r2fVpz5s95zqbnCn5gpIKqHQTSGRjnKcBfI3IGt5/O0ItC5rUF0M6p1FLjM3y+lLmxYxzDudXzuV9S/sucjY5u0TnIQdGpZtgGlvUIMJXrLUhUryvrw9tfPzL4PMqzfHG1OmqL0t0hljgnHPnl873+j/ov8jZ5NwkOg85OCrdBNTYonoA/BHhzXEqAeDtjVr7w58Fnw2EePpdTjVKjoI6BqruE51jJLjO+eCng0sGlg9c4mxytovOQw6NSjdBRYr3YQBrAVQDYB+0a9sf/CT4jD/EvULDpQiJMeS7gkl7WCXXuT7w8cCiwc8GL3U2ObeJzkOGhko3gTW2qH4AjyB8vHUVIqsa7vsw8HdngKfcm0AiTPf6Y3EgY9xxjWv9H/T/x/ml83JnkzPao9OJQFS6Ca6xRQ0AeBzAJwiPeKWVnXrPzYv9T2x36puFhksBpwV9maIzREsP6L7eJb0vula6fkxvmiUf2vAmSUQ2yLkIwEkAtgEIKhKkO+aavj2t1DBLbLrkFeKcT62sCDJZMonOMhTqgNrdvbD7RbVbvdvZ5Ez5S5lTEZVuEmmoUxjCpXsJgG4AbgC44kjlyO/WyqcZJEZHjA/DsbnFHQN5pjLROQ7F1+7b2L2o+xke4A87m5x01luSotJNQg11yiQA1wPQAPQAwEk1hsorZxjPN8uMjhiP0vXGnLb3xuZUi85xIFznmnOFc8XA8oE/AXjJ2eRMynloEkalm6Qa6pQSAD9F+ASKbQBQk8uyfn606ezybKlGaLgk8yaM3bfWlBSKzrE/mlcb7Hm7531/m/9BAB/SXgrJj0o3iTXUKRkArgBwJICtAEIMwLWzjEedWGM4SZaYLDRgkghwrk+vqtSYgSmis+zJ3+Hf3LOwZ7Hm0R50NjlbRechsUGlm+Qa6hQDgNMBnA1gIPKB6aVS4XWzjOfkW6USkfmSxZy8kh2uXGOp6BwAwEM84FzhbBr4ZOBfAJ5yNjnT8ky3VEWlmyIa6pQJAK4EMAbAdgC6WYbhptnG42eNNRwtMcbEJkxsVxlz25ePza4SncPf4V/f+3bvytBA6BkAi51NzpTZH4KEUemmkIY6xQrgewivcNgJwAOE32S75Ajjd8dYWJHIfInsVZh23lVTXCzq+TWf1t//Yf8Xnq89XwN4zNnkXC8qCxldVLopJrKs7HAAPwFgBNABgCsSpB9PN848odpwvElmZqEhE5BX59qsmkrO4jwPznWue9d7m3qX9Tp4kL8B4HVnk5Mu805hVLopqqFOyQXwQwDTAfQCcAJAWRazXjfLePJhhdI0mnHYW31+Sac32xi3OXC1X93au6T3q0BH4CsAz9CGNemBSjeFRUa9UxAu3wKER71BADi20lA2b6pyWlGGNFZgxIRyuSm3/Yuy0Z/X1VXd61zh/HLw08F1AF5EeCkYzd2mCSrdNNBQp5gQnuc9GwAHsAMAZwB+MEWZfMp4eW6ehSXkOtV4ehGmHffVFI/aCgY9qLs96zxfDSwf2KYH9A8B/NPZ5KSNi9IMlW4aaahTCgCcB2A2gEEAfQDAAHz/MHnSqRPkuflWSdibSaINcqhHV1cYmMRiuhGU5tcGPQ7P5wPLB3bwEO8G8HcAX9OFDumJSjfNRKYcJgK4EOFdy5zYo3zPsct1p9XKxxVmSAmxZjXeZhaUdvmzlJis8tB8Wp97jfvzgU8HdkKDC8BrAJY7m5y0EX0ao9JNUw11igTADuAcAOMR3jynZ9fnz6yTa0+dIM8Zmy1Vi0koxiXmvPaVpVkjmtfVPFqXa7Xri8EvBnugox/AqwA+czY5A7FJSZIZlW6ai4x8bQjP905EeG1v967PTy2RCs6yKTMmF0lTjQaWFNsfjsTTMHc8WFMU9Y5jXOdasCe43vO1Z71rpWsA4b/DVwB8SRvUkD1R6RIAu8u3FsCZACYBUAF0AQgBQKYR8jl2ZdLscsO0sixWnarLzfp0BOeOq1DYEL5AzjlCg6F23ybfaucK53bNo1kQXiHybwCrnE1OOsGZ7INKl+wlUr7lAOYCOA6AgvB+Druv/7cVSLmn1cpTJhVKdYVWNjbVCnh6YWlPMFMpONDnNY/W7dviW+38ytmqdqtGhKfDNwJ4HUCzs8mpxysrST5UuuSAIpcVHwngFISPgw8hPO+7e26yOpdlnTJennh4scE2NovVpMJG6hdYxrSvLcnca15X82sDgR0Bh7vZ7fBt8ukAZIQvOnkHQJOzyblTRFaSfKh0ySHtMfo9CuERcAYAHUA/Ivs7AECeGcZvT5AnTC0xTCzPliqyTWyMkMAj9Fdm2f6nsfl5ap+6OdAR2Ozd4G0PdARkACYAXgDvA/gcQDst+yLRotIlUYlsJVkJYDKAYxDeRB0ILz0bRPjiCwDhS45nlxsq7IVSRUW2VFGYwcoSdY9fZ4APfNTDpS1+tnxxN9qW7oQbQC7CI1odwGcAlgPYQG+MkZGg0iXDFhkBFyO89OxoAOPwTem6Ih+7L281yzDMGmsoGZ8nFZZmsfxCq5SfZ2H52SaMiUcZc87hUTHY6+XdXR7evd2ld2/q17ubd+rdvT4urVfkb3XJ8pYQY30Ir11eAWAtgFbahIbECpUuiZmGOiUT4bnfGoT3fJgAQEL4jSY/wlMRPoRHjrsxAOPHSNm1Y6T8kkyWk2VilgwFFqvCLBYFZovMLGYZlsjuaAzh/uR8j/8CQEjnqleFxxPkHleQe1wBeAYD3Nvn455uD3c7erQ+ZwAhABYA1sgHjzxmX78k7VhvVL7wSdJaAH00dUBGA5UuGTWRY+NLES7iSQCqAJQgXMRAuOx0hIvYj/AytRD+q5SjxBBecWHc478m7DHtgfBSuHYAmxDeh2IrgMHGFpW+Gcioo9IlcRW5Ei4HQD7Cp1yUIFzGRQAyIx+75lGj/ccpRe7jQnh+eQDhN/u2IrypezeA3sYWldbPEmGodElCicwTywhPAez5AYQLdX8fGsJTFx4APhqxkkRGpUsIIXEU0y3sCCGEHByVLiGExBGVLiGExBGVLiGExBGVLiGExBGVLiGExBGVLiGExBGVLiGExBGVLiGExBGVLiGExBGVLiGExBGVLiGExBGVLiGExBGVLiGExBGVLiGExNH/A79wRQIEgi5tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise from Cloudy Addition\n",
    "\n",
    "#Hvilket land i Norden (Danmark, Finland, Island, Norge og Sverige) har samlet set flest \n",
    "#‘Video Views’ beregnet ud fra hvert lands top 250? Lav et pie chart, som viser fordelingen.\n",
    "#Brug URLs: “[...]/top/country/{countryCode}/mostviewed”\n",
    "\n",
    "base_url = \"https://socialblade.com/youtube/top/\"\n",
    "\n",
    "import bs4\n",
    "import requests\n",
    "import re\n",
    "from concurrent.futures import ThreadPoolExecutor\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "countries = [\"dk\", \"se\", \"no\", \"fi\"]\n",
    "\n",
    "country_views = {}\n",
    "\n",
    "headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36'}\n",
    "\n",
    "executor = ThreadPoolExecutor(len(countries))\n",
    "\n",
    "def scrape(country):\n",
    "    r = requests.get('https://socialblade.com/youtube/top/country/'+country+'/mostviewed', headers=headers)\n",
    "    r.raise_for_status()\n",
    "\n",
    "    soup = bs4.BeautifulSoup(r.text, 'html.parser')\n",
    "\n",
    "    divs = soup.find_all('div', attrs={'style': 'float: left; width: 150px;'})\n",
    "    \n",
    "    counter = 0\n",
    "    for div in divs:\n",
    "        spans = div.select('span')\n",
    "        for span in spans:\n",
    "            number = re.sub('[,-]', '', span.text)\n",
    "            if number:\n",
    "                counter += int(number)\n",
    "    country_views[country]=counter\n",
    "\n",
    "for country in countries:\n",
    "    executor.submit(scrape(country))\n",
    "\n",
    "print(country_views)\n",
    "\n",
    "labels = country_views.keys()\n",
    "amounts = country_views.values()\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(amounts, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'autos': 79816947613, 'comedy': 351982476689, 'education': 612873287708, 'entertainment': 1655617170476, 'film': 663649600931, 'games': 523790108023, 'howto': 265331616522, 'made-for-kids': 1376114934680, 'music': 1741632722280, 'news': 429954619861, 'nonprofit': 34004761376, 'people': 349282623007, 'animals': 86533795352, 'tech': 112025448524, 'shows': 353914851, 'sports': 228583311280, 'travel': 72819251264}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],\n",
       " <a list of 17 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 38,
     "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": [
    "#Hvilken ‘Channel Type’ har samlet set flest uploads ud fra top 250 for hver ‘Channel Type’?\n",
    "#Vis fordelingen af uploads pr. ‘Channel Type’ med et bar plot.\n",
    "#Brug URLs: [...]/top/category/{channelType}\n",
    "\n",
    "channel_types = ['autos', 'comedy', 'education', 'entertainment', 'film', 'games', 'howto', 'made-for-kids',\n",
    "                'music', 'news', 'nonprofit', 'people', 'animals', 'tech', 'shows', 'sports', 'travel']\n",
    "\n",
    "channel_views = {}\n",
    "\n",
    "headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36'}\n",
    "\n",
    "executor = ThreadPoolExecutor(len(channel_types))\n",
    "\n",
    "def scrape(channel):\n",
    "    r = requests.get('https://socialblade.com/youtube/top/category/'+channel, headers=headers)\n",
    "    r.raise_for_status()\n",
    "\n",
    "    soup = bs4.BeautifulSoup(r.text, 'html.parser')\n",
    "\n",
    "    divs = soup.find_all('div', attrs={'style': 'float: left; width: 150px;'})\n",
    "    \n",
    "    counter = 0\n",
    "    for div in divs:\n",
    "        spans = div.select('span')\n",
    "        for span in spans:\n",
    "            number = re.sub('[,-]', '', span.text)\n",
    "            if number:\n",
    "                counter += int(number)\n",
    "    channel_views[channel]=counter\n",
    "\n",
    "for channel in channel_types:\n",
    "    executor.submit(scrape(channel))\n",
    "\n",
    "print(channel_views)\n",
    "\n",
    "views = channel_views.values()\n",
    "\n",
    "\n",
    "plt.bar(channel_types, views)\n",
    "plt.title('Views for top 250 for different channel types')\n",
    "plt.xlabel('Channels')\n",
    "plt.ylabel('Views')\n",
    "plt.xticks(rotation=70, horizontalalignment='right')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "ename": "HTTPError",
     "evalue": "403 Client Error: Forbidden for url: https://socialblade.com/youtube/top/country/dk/mostsubscribed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-66-c1101b7293fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'https://socialblade.com/youtube/top/country/dk/mostsubscribed'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0msoup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbs4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBeautifulSoup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'html.parser'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 941\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    942\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    943\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://socialblade.com/youtube/top/country/dk/mostsubscribed"
     ]
    }
   ],
   "source": [
    "#Hvordan er fordelingen af ‘Channel Type’ på danmarks top 250 youtube kanaler fordelt på most subscribers?\n",
    "#Vis dette med et bar plot.\n",
    "#Brug URLs: [...]/top/country/{countryCode}/mostsubscribed og: /user/{channelName} for hver kanal på listen\n",
    "\n",
    "headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36'}\n",
    "\n",
    "channel_types_in_top_250 = {}\n",
    "channel_names = []\n",
    "\n",
    "r = requests.get('https://socialblade.com/youtube/top/country/dk/mostsubscribed', headers=headers)\n",
    "r.raise_for_status()\n",
    "\n",
    "soup = bs4.BeautifulSoup(r.text, 'html.parser')\n",
    "\n",
    "divs = soup.find_all('div', attrs={'style': 'float: left; width: 350px; line-height: 25px;'})\n",
    "    \n",
    "for div in divs:\n",
    "    for a in div.find_all('a', href=True):\n",
    "        channel_names.append(a['href'])\n",
    "\n",
    "\n",
    "def scrape(channel):\n",
    "    print(channel)\n",
    "    r = requests.get('https://socialblade.com'+channel, headers=headers)\n",
    "    r.raise_for_status()\n",
    "\n",
    "    soup = bs4.BeautifulSoup(r.text, 'html.parser')\n",
    "\n",
    "    channel_type = soup.find(id='youtube-user-page-channeltype')\n",
    "    print(channel_type.text)\n",
    "    chan = channel_types_in_top_250.get(channel_type)\n",
    "    if chan == None:\n",
    "        channel_types_in_top_250[channel_type] = 1\n",
    "    else: \n",
    "        channel_types_in_top_250[channel_type] += 1\n",
    "    \n",
    "executor = ThreadPoolExecutor(len(channel_names))\n",
    "\n",
    "for channel in channel_names:\n",
    "    executor.submit(scrape(channel))\n",
    "\n",
    "print(channel_types_in_top_250)\n"
   ]
  }
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