Covid19 insights.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_data = pd.read_csv(\"time_series_covid_19_deaths.csv\")\n",
    "covid_data.set_index(\"Country/Region\",inplace = True)\n",
    "#Making a new dataframe with just the scandinavian countries\n",
    "covid_data_scandinavia = covid_data.loc[[\"Norway\",\"Sweden\",\"Denmark\",\"Finland\"]]\n",
    "#Selecting only the region that are parts of scandinavia proper, so no Greenland and Faroe islands\n",
    "covid_data_scandinavia = covid_data_scandinavia[covid_data_scandinavia[\"Province/State\"].isnull()]\n",
    "#Transposing the dataframa, so the countries are the columns, and then removing unnecesary rows\n",
    "covid_data_scandinavia.drop([\"Province/State\",\"Lat\",\"Long\"],inplace = True,axis = 1)\n",
    "#Transform dates from columns to rows\n",
    "covid_data_scandinavia = pd.melt(covid_data_scandinavia.reset_index(),\n",
    "                                id_vars = [\"Country/Region\"],\n",
    "                                var_name=\"Date\", \n",
    "                                value_name=\"Total dead\")\n",
    "\n",
    "\n",
    "covid_data_scandinavia[\"Date\"] = pd.to_datetime(covid_data_scandinavia[\"Date\"]) # coverts to datetime format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mDYeV/4BzHoSb37FE0UxYQ4LNzIEDB+jduzfLly+vsc+JaGc/wwhRhc/+Bh886vQ1feUk6F53P/Em9hytIUE7s2hGPvroI0455RTuu+++mEwUJkIO7IZpo2HWL6DHOXDXp5YomqGw3rMQkS3AfiAA+FU1R0TaAm8A3YAtwDWquset/zBwu1v/flV93y0fALwCJAPvAj/RpnpKFEYXXHBB9bsbxtTLN4vgrTugpMB5b2LQ3dZUeDPVGGcW56pqdshpzUPAbFXtCcx2pxGR04DrgCxgJPCciFS1LvY8cCfQ0/2MbIS4jWm+ggH45PfwysXgjYc7PnTem7BE0WxF4jLU5UBV58tTgCtCyqepaoWqbgY2AgNFpAPQUlUXuWcTr4YsY4xpaAd2w6uXw5wn4PSr4Mfz4Ht19/9umrZwPzqrwAciosCLqjoJyFTVfABVzReR9m7djsDikGXz3DKfO354uTGmoe3Lh9d+CLu/hsv/6vRSZ2cThvAni7NVdbubED4UkS9rqVvTb6TWUn7kCkTuxLlcRZcuXY41VmOat92b4bUroHQXjH7TuZltjCusl6FUdbs73An8CxgIFLiXlnCHO93qeUBok5SdgO1ueacaymva3iRVzVHVnIyMjIb8Kg3miSeeICsriz59+pCdnc2SJUsabN1paWkNti7TzGxdApMvgvJiGDPTEoU5QtiShYikikiLqnHgImA1MBO42a12M1DVDOpM4DoRSRSR7jg3sj9zL1ntF5FB4rR/MSZkmZiyaNEi3nnnHZYvX86qVav46KOP6NzZmmw2EfbFm/DKJZCYBre+B50GRDoiE4XCeWaRCXwqIp8DnwH/VdX3gAnAhSKyAbjQnUZV1wDTgbXAe8A9qlrVTsVY4O84N72/BmaFMe6wyc/PJz09ncTERADS09PJy8vjyiuvBGDGjBkkJydTWVlJeXk5PXr0AODrr79m5MiRDBgwgKFDh/Lll87VvM2bNzN48GDOOOMMfv3rXx+yrT/84Q+cccYZ9OnTh/HjxwOwZcsWTj31VH70ox+RlZXFRRddRFlZWWN9fRNtVGHJi86jsZ0Hwo8+hvanRDoqE61UtUl+BgwYoIdbu3btEWWNaf/+/dq3b1/t2bOnjh07VufOnas+n0+7deumqqo/+9nPNCcnRz/99FOdO3euXnfddaqqet555+n69etVVXXx4sV67rnnqqrqpZdeqlOmTFFV1WeffVZTU1NVVfX999/XH/3oRxoMBjUQCOgll1yin3zyiW7evFm9Xq+uWLFCVVVHjRqlr732WqPug+8q0j/DJqPygOrbP1Yd31L1H9c508aoKpCrNRxTm21Dgsx6CHZ80bDrPKE3fH/CUWenpaWxbNky5s+fz5w5c7j22muZMGECJ510EuvWreOzzz7jgQceYN68eQQCAYYOHUpJSQkLFy5k1KhR1eupqKgAYMGCBbz11luA05rsgw8+CMAHH3zABx98QL9+zuOOJSUlbNiwgS5dutC9e3eys7MBGDBgAFu2bGnYfWCi396tMO0G2LEahj8Mw34JHmvMwdSu+SaLCPF6vQwfPpzhw4fTu3dvpkyZwtChQ5k1axbx8fFccMEF3HLLLQQCASZOnEgwGKR169asXLmyxvVJDY81qioPP/wwP/7xjw8p37JlS/UlsKpY7DJUM1P4Fbx6BVSWwg1vQK8RkY7IxIjmmyxqOQMIl6+++gqPx0PPnj0BWLlyJV27dmXYsGGMGTOGMWPGkJGRQVFRETt27CArKwsRoXv37vzzn/9k1KhRqCqrVq2ib9++nH322UybNo0bb7yRqVOnVm9nxIgR/PrXv2b06NGkpaWxbds24uPjG/37miizexO88gNn/NZ34YTTIxuPiSnNN1lEQElJCffddx979+4lLi6Ok046iUmTJpGamkpBQQHDhjmNs/Xp04f27dtXnzVMnTqVsWPH8tvf/hafz8d1111H3759eeqpp7jhhht46qmnuOqqq6q3c9FFF7Fu3ToGDx4MOJe/Xn/99eqe9kwztHMd/N/1EPTBbR9ARq9IR2RijDVRbmKK/QyPw+LnnT6yE1vADdOh8xmRjshEsaM1UW5nFsY0Vaow90n45Hdw8iVw2dOQmh7pqEyMsmRhTFOkCu8/Aoufg+wbnUThscuQ5vjZ83LGNDWq8P6vnERx5l1w2TOWKMx3ZmcWxjQ1n/4ZFv8VBt4JIydYq7GmQViyMKYpmfcH+Pi3Tj8UI39nicI0GEsWxjQFqk5nRfP+AL2vgSuet7eyTYOy36ZG5vV6yc7OJisri759+/KnP/2JYDAY6bCqWTPnMUgVPvx/TqLodyP88AXw2t+BpmHZb1QjS05Orm66Y+fOndxwww0UFxfz+OOPRzSuqsbCTIxRhfcegiUvQM7tcPFEO6MwYWG/VRHUvn17Jk2axLPPPouqEggE+MUvflHdtPiLL74IwNy5cxk+fDhXX301p5xyCqNHj64+sHfr1o1HHnmEwYMHk5OTw/LlyxkxYgQnnngiL7zwAuC8OX7++efTv39/evfuzYwZTncgVU2W33333fTv359vv/22OrZdu3YxePBg/vvf/zbyXjH1FgzAOz91EsWgu+GSP1qiMOFTU1O0TeETjU2Uq2p1M+KhWrdurTt27NAXX3xRf/Ob36iqanl5uQ4YMEA3bdqkc+bM0ZYtW+q3336rgUBABw0apPPnz1dV1a5du+pzzz2nqqrjxo3T3r176759+3Tnzp2akZGhqqo+n0+Li4tVVbWwsFBPPPFEDQaDunnzZhURXbRo0SHx7dixQwcOHKgffPBBWPfF8YiGn2FU2L1FdfJIp4nxD8erBoORjsg0EVgT5Yf63We/48vdtXUJfuxOaXsKDw588JiXU/cs4YMPPmDVqlW8+eabABQXF7NhwwYSEhIYOHAgnTo5vctmZ2ezZcsWhgwZAsBll10GQO/evSkpKaFFixa0aNGCpKQk9u7dS2pqKo888gjz5s3D4/Gwbds2CgoKAOjatSuDBg2qjsXn83H++efz17/+lXPOsa41o9KeLfDS96GyBK54AfpeZ089mbBrtskiWmzatAmv10v79u1RVZ555hlGjDi02ei5c+ce0bS43++vnq6a5/F4Dqnn8Xjw+/1MnTqVwsJCli1bRnx8PN26daO8vByA1NTUQ7YVFxfHgAEDeP/99y1ZRKN922HKZeA7ALe9B5lZkY7INBPNNlkczxlAQyssLOSuu+7i3nvvRUQYMWIEzz//POeddx7x8fGsX7+ejh07fuftFBcX0759e+Lj45kzZw7ffPPNUeuKCC+99BKjRo1iwoQJPPTQQ995+6aBlBY5fVEcKIIxMy1RmEbVbJNFpJSVlZGdnY3P5yMuLo6bbrqJBx54AIA77riDLVu20L9/f1SVjIwM/v3vf3/nbY4ePZpLL72UnJwcsrOzOeWU2vtZ9nq9TJs2jUsvvZSWLVty9913f+cYzHcUDMLbdziXoG58CzoNiHREppmxJspNTGm2P8P5f4LZj8MP/gw5t0U6GtOEHa2JcnvOzphot3Wx04RH1g9hwK2RjsY0U5YsjIlmB3bDm7dD685w6VP21JOJGLtnYUy0UoUZ90BJAdz+ASS1inREphmzZGFMtFryAnz1Lox4Ejr2j3Q0ppmzy1DGRKNty51+s0++GAaNjXQ0xoQ/WYiIV0RWiMg77nRbEflQRDa4wzYhdR8WkY0i8pWIjAgpHyAiX7jznhaxC7emCSvOg3/eDGnt4fK/2n0KExUa48ziJ8C6kOmHgNmq2hOY7U4jIqcB1wFZwEjgORGp6gvyeeBOoKf7GdkIcYdFVRPlVZ8tW7Zw1lln1bnc8OHDOfxR4OPVrVs3du3a1SDrMg1sXz68cgmU7YVrXoOUtpGOyBggzPcsRKQTcAnwBPCAW3w5MNwdnwLMBR50y6epagWwWUQ2AgNFZAvQUlUXuet8FbgCmBXO2MMltInyKgsXLoxQNCaqVN/Q3gk3v2Mv3pmoEu4zi78AvwRCe/fJVNV8AHfY3i3vCHwbUi/PLevojh9e3mRUdThUW1PkocaOHUtOTg5ZWVmMHz++urxbt26MHz++uinyL790GkosKirioosuol+/fvz4xz+2fiui1YrX4OvZcMHjlihM1AlbshCRHwA7VXVZfRepoUxrKa9pm3eKSK6I5BYWFtZzs42rqrmP7OxsfvjDHx4xf8WKFfzlL39h7dq1bNq0iQULFhxR54knniA3N5dVq1bxySefsGrVqup56enpLF++nLFjxzJx4kQAHn/8cYYMGcKKFSu47LLL2Lp1a/i+oDk+O9fBrAeh21A4445IR2PMEcJ5Geps4DIRuRhIAlqKyOtAgYh0UNV8EekA7HTr5wGdQ5bvBGx3yzvVUH4EVZ0ETAKnuY/agtvxv/9LxbqGbaI88dRTOOGRR2qtU9NlqFC1NUVeZfr06UyaNAm/309+fj5r166lT58+AFx55ZUADBgwgLfffhuAefPmVY9fcskltGnTBhNFyvfBGzdBQhpc9XfrwMhEpbD9Vqrqw6raSVW74dy4/lhVbwRmAje71W4GZrjjM4HrRCRRRLrj3Mj+zL1UtV9EBrlPQY0JWabJqa0pcoDNmzczceJEZs+ezapVq7jkkkuqmxsPXf7wZe0BsigVDMK/7oLdm+Dql6DFCZGOyJgaReKlvAnAdBG5HdgKjAJQ1TUiMh1YC/iBe1Q14C4zFngFSMa5sf2db27XdQYQrfbt20dqaiqtWrWioKCAWbNmMXz48FqXGTZsGFOnTuXRRx9l1qxZ7Nmzp3GCNXWb9wf46r8wcgJ0HxrpaIw5qkZJFqo6F+epJ1S1CDj/KPWewHly6vDyXOD08EUYO/r27Uu/fv3IysqiR48enH322XUuM378eK6//nr69+/POeecQ5cuXRohUlOnL9+Fuf8Lfa+HM++KdDTG1MqaKDcxpcn8DAvXw9/Og3YnOj3exSdHOiJjAGui3JjoUV4M066HuES4bqolChMTrCFBYxpTMAhv3+n0eDdmJrTqVOcixkQDSxbGNKb5E2H9e/D9P0C3uu83GRMtmt1lqKZ6j6Y5iPmf3faVMHcC9B4FA38U6WiMOSbNKlkkJSVRVFQU+wedZkhVKSoqIikpKdKhHJ+AD/59N6RmwMV/sJZkTcxpVpehOnXqRF5eHtHaFIipXVJSUvXb7THns0mwcw1cOxWS7Q16E3uaVbKIj4+ne/fukQ7DNDf7C5zLTyddCKdcEulojDkuR00WInJlbQuq6tsNH44xTdBHj4GvzHlL2y4/mRhV25nFpe6wPXAW8LE7fS7O29iWLIypy7efwef/gLPHQfpJkY7GmON21GShqrcCuN2hnlbVB4XbUuxfGyc8Y2JYMADv/gJadIBhv4h0NMZ8J/W5Z9GtKlG4CoBeYYrHmKZjxWuQvxKu/DskpkU6GmO+k/oki7ki8j7wfzidDl0HzAlrVMbEugO7Yfb/QJezoPfVkY7GmO+szmShqve6N7ur2k+epKr/Cm9YxsS4d3/htAFl71SYJqJej866Tz7ZDW1j6mPtTFj9Jpz7KJxgLeubpqHON7jdHuqWikiJiFSKSEBE9jVGcMbEnIAPPvx/kHk6DPlppKMxpsHU58ziWZz7FP8EcnC6NbVnAI2pyefTYM9muH4aeJvVO6+miavvZaiNIuJ1uzl9WUQWhjkuY2KPrxw++T18rx/0GhnpaIxpUPVJFgdEJAFYKSK/B/KB1PCGZUwM+uxFKN4Klz1tN7VNk1OfVmdvcuvdC5QCnYGrwhmUMTGndBfMmwg9R8CJ50Y6GmMaXH0enf1GRJKBDqr6eCPEZEzsmfskVJbCRb+JdCTGhEV9noa6FFgJvOdOZ4vIzHAHZkzM2Pkl5L4MObdBxsmRjsaYsKjPZajHgIHAXgBVXQl0C19IxsSY2Y9DQhoMfyjSkRgTNvVJFn5VLQ57JMbEovxV8NW7cNa9kJoe6WiMCZv6PA21WkRuALwi0hO4H7BHZ40BmD8RElvCwDsjHYkxYVWfM4v7gCygAqcxwX3AuLoWEpEkEflMRD4XkTUi8rhb3lZEPhSRDe6wTcgyD4vIRhH5SkRGhJQPEJEv3HlPi9hziSYK7HXGk0kAAB93SURBVFjtNO1xxh2Q3DrS0RgTVnUmC1U9oKq/UtUzVDXHHS+vx7orgPNUtS+QDYwUkUHAQ8BsVe0JzHanEZHTcN4UzwJGAs+JiNdd1/PAnUBP92NvPJnI+/DXkNQKzr4/0pEYE3a1dav6H5wmyWukqpfVtmJVVaDEnYx3PwpcDgx3y6fg9Lr3oFs+TVUrgM0ishEYKCJbgJaqusiN61XgCmBW7V/NmDDaOBu+/hgu+i0kt6m7vjExrrZ7FhPd4ZXACcDr7vT1wJb6rNw9M1iG05bUX1V1iYhkVnWmpKr5ItLerd4RWByyeJ5b5nPHDy83JjKCAfhwPLTuYvcqTLNRW7eqnwCIyG9UdVjIrP+IyLz6rNxtSypbRFoD/xKR2tprruk+hNZSfuQKRO7EuVxFly5d6hOiMcdu1RtQ8AVcNRniEiMdjTGNoj43uDNEpEfVhIh0BzKOZSOquhfnctNIoMDtx7uqP++dbrU8nKZEqnQCtrvlnWoor2k7k9z7KjkZGccUojH146+Aj59wGgvMujLS0RjTaOqTLH6K07XqXBGZi9Ol6k/qWkhEMtwzCtzmQi4AvgRmAje71W4GZrjjM4HrRCTRTUg9gc/cS1b73X41BKeJ9BkYEwnLX4V9eXD+/wNPff77GNM01KdtqPfc9ytOcYu+dG9C16UDMMW9b+EBpqvqOyKyCJguIrcDW4FR7nbWiMh0YC3gB+5xL2MBjAVeAZJxbmzbzW3T+HzlMP+P0GUw9LDGAk3zUt/+LCqAz49lxaq6CuhXQ3kRcP5RlnkCeKKG8lzA+qc0kbV8CuzPhx++aE2Qm2bHzqONqQ9fmXNW0XUIdB9Wd31jmhjr99GY+sh9CUoK4OqX7KzCNEu1vZTXv7YFVXV5w4djTBSqLIVP/+ycUXQbEulojImI2s4s/ljLPAXOa+BYjIlOSydDaSEMfy3SkRgTMbW9lGePexhTUQILnnKefuo6ONLRGBMx9bpn4b55fRqQVFWmqq+GKyhjosbSv8GBXXDuI5GOxJiIqjNZiMh4nIb/TgPeBb4PfApYsjBNW8V+56zipAuh88BIR2NMRNXn0dmrcd6L2KGqtwJ9AWsQxzR9i56Dsj0w/OFIR2JMxNUnWZSpahDwi0hLnLacetSxjDGxbX+Bc1Zx6mXQaUCkozEm4upzzyLXbePpbzjNjZcAn4U1KmMibe7/QqACLngs0pEYExXq0zbU3e7oCyLyHk5HRKvCG5YxEbRzndNg4MA7od2JkY7GmKhQ52UoEZldNa6qW1R1VWiZMU3Oh+MhoQUM+2WkIzEmatT2BncSkAKki0gbDnZC1BL4XiPEZkzj2/QJbHgfLngcUttFOhpjokZtl6F+DIzDSQyhTXvsA/4azqCMiYhgED54FFp1hjPvinQ0xkSV2t7gfgp4SkTuU9VnGjEmYyLji3/CjlVw5d8gPqnu+sY0I/V5GupFEbkfqGqXeS7woqr6whaVMY0tGIR5f4DM0+H0qyMdjTFRpz7J4jkg3h0C3AQ8D9wRrqCMaXRfvQtFG+CqydZdqjE1qO0Gd5yq+oEzVLVvyKyPReSYes0zJqqpwoK/QOuucNoVkY7GmKhU259QVS/eBUSk+mFzEekBBGpexJgY9M1CyFsKZ90HXusPzJia1PY/o+pR2Z8Dc0RkkzvdDbg1nEEZ06g+/TOkpEO/GyMdiTFRq7ZkkSEiD7jjLwJeoBSnmfJ+wJwwx2ZM+O34AjZ+COc+CvHJkY7GmKhVW7LwAmkcPMPAnQZoEbaIjGlMHz8Bia1goD2vYUxtaksW+ar6P40WiTGNbesSWD8Lzvs1JLeJdDTGRLXabnBLLfOMiW3BAHzwK0htD4PGRjoaY6JebWcW5zdaFMY0tsXPO09AXfk3SEiNdDTGRL2jnlmo6u7vsmIR6Swic0RknYisEZGfuOVtReRDEdngDtuELPOwiGwUka9EZERI+QAR+cKd97SI2FmPOX778uHj30Kv70PvUZGOxpiYEM5XVf3Az1T1VGAQcI+InAY8BMxW1Z7AbHcad951QBYwEnhORLzuup4H7gR6up+RYYzbNHWLnnU6Nhr5JNjfHcbUS9iSharmq+pyd3w/sA7oCFwOTHGrTQGqXpm9HJimqhWquhnYCAwUkQ44HS4tUlUFXg1Zxphjc2A35L4Mp18FbbtHOhpjYkajNIIjIt1w3s1YAmSqaj44CQVo71brCHwbslieW9bRHT+83Jhj99kk8JXCkJ9GOhJjYkrYk4WIpAFvAeNUdV9tVWso01rKa9rWnSKSKyK5hYWFxx6sadoqSmDJC869isysSEdjTEwJa7IQkXicRDFVVd92iwvcS0u4w51ueR7QOWTxTsB2t7xTDeVHUNVJqpqjqjkZGRkN90VM07B8CpTtgaEP1F3XGHOIsCUL94mlycA6Vf1TyKyZwM3u+M3AjJDy60QkUUS649zI/sy9VLVfRAa56xwTsowx9eOvgIXPQNch0HlgpKMxJuaEs4nNs3H6vvhCRFa6ZY8AE4DpInI7sBUYBaCqa0RkOrAW50mqe1S1qnXbscArQDIwy/0YU38rp8L+fLji+UhHYkxMEucBo6YnJydHc3NzIx2GiQYBHzzT33lb+46P7HFZY2ohIstUNefwcusSzDR9X/wT9m6FYb+wRGHMcbJkYZq2YADm/xFO6A29RtRd3xhTI0sWpmlb8y8o2mhnFcZ8R5YsTNO2dDK0OwlOuTTSkRgT0yxZmKZrzzewdSFk3wAe+1U35ruw/0Gm6fpiujO0lmWN+c4sWZimSRVWTYeuZ0PrLpGOxpiYZ8nCNE3bV8Cu9dDnmkhHYkyTYMnCNE2rpoM3AU67PNKRGNMkWLIwTU/AD6vfhF4jIblN3fWNMXWyZGGanq8/htJC6HNtpCMxpsmwZGGankXPQtoJ0PPCSEdiTJNhycI0LdtXwOZPYNBYiEuMdDTGNBmWLEzTsuApSGwJObdGOhJjmhRLFqbp2L0J1s5wEkVSq0hHY0yTYsnCNB0LnwVPHJw5NtKRGNPkWLIwTUPpLqc3vD7XQssOkY7GmCbHkoVpGla8Dv5yGHxPpCMxpkmyZGFiXzAIy1522oFqf2qkozGmSbJkYWLfpo9hzxbIuS3SkRjTZFmyMLEv92VISYdTrYMjY8LFkoWJbcXb4KtZ0O9GewnPmDCyZGFi24rXQIMw4JZIR2JMk2bJwsQufyUsewVOPA/ado90NMY0aZYsTOz64p+wPx8G3R3pSIxp8sKWLETkJRHZKSKrQ8raisiHIrLBHbYJmfewiGwUka9EZERI+QAR+cKd97SISLhiNjEkGISFT0Pm6XDS+ZGOxpgmL5xnFq8AIw8rewiYrao9gdnuNCJyGnAdkOUu85yIeN1lngfuBHq6n8PXaZqjDe9D4Zdw9k/A/n4wJuzClixUdR6w+7Diy4Ep7vgU4IqQ8mmqWqGqm4GNwEAR6QC0VNVFqqrAqyHLmOZswVPQqgtk/TDSkRjTLDT2PYtMVc0HcIft3fKOwLch9fLcso7u+OHlpjnbugS2LnKa9vDGRzoaY5qFaLnBXdN1BK2lvOaViNwpIrkikltYWNhgwZkos/ivTt/a/W+KdCTGNBuNnSwK3EtLuMOdbnke0DmkXidgu1veqYbyGqnqJFXNUdWcjIyMBg3cRImyvc5LeH2ug4TUSEdjTLPR2MliJnCzO34zMCOk/DoRSRSR7jg3sj9zL1XtF5FB7lNQY0KWMc3Ruv9AoBJ6j4p0JMY0K3HhWrGI/B8wHEgXkTxgPDABmC4itwNbgVEAqrpGRKYDawE/cI+qBtxVjcV5sioZmOV+THP1xXRo2wM69o90JMY0K2FLFqp6/VFm1fhQvKo+ATxRQ3kucHoDhmZi1e7NsHk+nPNLe1zWmEYWLTe4janbwqedp58G3BrpSIxpdixZmNiwvwBWTIXsG6zbVGMiwJKFiQ2L/wpBH5x1f6QjMaZZsmRhol/ZXlj6Epx2BbQ7MdLRGNMsWbIw0W/p36FyPwz5aaQjMabZsmRholvlAVj8PJx0IXToE+lojGm2wvborDENYsXrcGAXDH0g0pEYE1VUFUUPDlGcf0q8J56G7s3BkoWJXgGf87hs5zOhy+BIR9MsqSoBDVARqKDcX44v6COgAYLBIH71E9SgM101DDpDf9BPQAPOJ3hweMQywcAh00ENoqgzdA+CRy1TJYg7dOsENFAdc9WwpnWFLlPfeaGfw9cPHPXAHdQg5f5ySv2llPvLq9ddNf+Q5WpJAKHldcm9MZdEb8P2SW/JwkSvZa9A8bdwyR/tJbw6qCpl/jJKfaWU+Eo44DtAia+EUl9p9efw6eqySmdYWFZIhb/ikANnLBAEEcEjHjx48Hq8CIJXvAfLxYMg1UMRdxk8znjIOqrHq+a5873irV5X6Kdqe4Lg/Du4vqqylLgUUuJSSIpLOjQGt63Uw2OpcT1uvdCyo9X1SMPfYbBkYaLTgd3w8W+h+zDoeVGkowmbQDBAia+EgAYoPFDItpJt7Dyw85CD+e7y3Wwq3kRxRfFR/7qtCFRU/4VbG694SY1PJS0+jZT4FNLi02iV1IqOLTpydvLZJMclH3HQERGSvckkxiWS4EmoPkB6xVt9AK0eerzESRwej1MW54lz6oXUP2QZj+eQg7BXvIce/EMP3ocdyKtiM43DkoWJTnMnQMU+GDkh5s4qVJVdZbtYt3sda4vWsq1kG0VlRRSVF1FUVoSIkJmSSVFZETsO7MAf9Ne4Ho94SI1PpU1iG7q36k7v9N7Vfz0f/tdtUlxSdRJIjU895BOaGBK9iXaANcfFkoWJPsV5sOxl6HcjZGZFOppaBYIBNu7dyMqdK1lZuJLPCz9nR+kOfEEf4FweyEjOoF1yO9olt6Nn654ENUjBgQJ6Z/RmROoI2iW3wyMe0pPT6ZjWkcyUTNIS0kjyJtmB3UQNSxYm+sybCKow7BeRjqRGBaUFrC5azYJtC3h/y/vsq9wHQHpyOtkZ2VzQ9QIyUzI5pe0pnNL2FFLjrd8NE/ssWZjoUrgeVrwG/W+G1l0iFkZQg+wu301BaQH5pfms37OetUVrWVu0lsIypxfGJG8S53U5jyEdh5DdPptOaZ3sTMA0WZYsTPRQhfcfhvgUGP5wo29+X+U+Vu5cyZL8Jby3+T12lu2snucRD91bdmdgh4H0Tu9NVrssTml7CklxSY0epzGRYMnCRI91M2HjR3DRE5AW/m5xd5TuYMXOFSwvWM7yncvZsGcDihLniWNox6Hc3uF2OqR2IDM1k24tu5ESnxL2mIyJVpYsTHQo+hpm3Avf6wcD7wzLJkp9pSzJX8KCbQtYsH0B20q2Ac4z8H0z+nJh9oX0b9+f09NPt8RgaqSqVSMHC4NB1OdzPoGAMy8YBHVeonNeV1GnPOSjRy2veZnqcnXXHQweMh0q6fTTEU/DvmthycJEXtlemDYaxAOjpkBcQoOsVlXZsHcD8/Pms2D7AlYUrMCvflLiUjizw5nceOqN9M/sT682vYjzRNd/BQ0G0YoKguXlaFkZwfJyggfK0MoK1O8Hvx8NBFCfj2BJCYF9+wmWlh48aPl8BMsOENhVBB4PEheHVlYQLK9AKyrQykpUgxB0DmzV44FAzeVV40rIgdCdhkMPVocfUGuYd8ihrR71Q8u0lnnh2masOfnzlUiivcFtmhJ/JUy/CYo2wI1vQZuu32l1lYFKcnfkMjdvLvPy5lWfPZzc5mTGZI1xbkZnZBPvjW+I6I+ZBoME9u7Ft207ZV+swvfNVnwFBfgLCggUFxPYv4/g/hK0vPz4NxIXhyQk4ElIwJvezvkD1e/Dk5iEJCYiiQl4UpLB4wWPIOIBj/MRjzhJ2+sJKXdehKNqWnD/ahXnHZiqm/qh9/bl4BvHR8ysrl9TWdXkISurpb7UUK2GebWugyPm1SvukOU8CQlIfLyzT0VC9plwcD85dY8sd9/ClpBpain3iLP/q38mcsSDFRLX8Id2SxYmclRh5n2weR5c8QL0GH5cq9lRuoOF2xfy6bZPWbBtAQf8B0jyJjGowyBu7307wzoOIzM1s0FDr4n6/fh2FODbvg3ftu348rfjL9iJv7Dw4GfXLvAffAlPkpOJP+EE4jIzSezVC2+LFnjS0vAkJyPJSXiSkvGkJCNJSU5ZQiISF4fEecHrReLi8bZIw9OyJZ7UVCQ+vkEvP6gqgaDiDypBdzyouO0nQVCdcpTq6UBQKS7zsa/M55yIuJdclKrG73DrH1muh5RVtbfk1CWkLBB0tx1UAlWxuDFWnxiEfIeD34fqZkxC6x0c1yNOKPSwddZUN/RsJ/Rk62jbCqriCwQJBBVfQAkEgvgDzn72B0PHFX/1vCD+4JHx1eTNs6FhzyssWZhICQadJ59WTYNzH4Xs649p8QO+A/x38395c/2brC1aC0D75PZc3ONihncazsAOA0mOSw5H5GgggC8/H9+27QT27qXs888pXbqUinXrDkkEANqyFdounWCbdvhPy6YkrTW7E1uwM6kVOzt0p7hFW/yKc0AOHH6gCOI/oARKFF9QCQSD+AMBIFB9wHEOPrsOPQBz6IFWNfRgHFLOoQdnfyBIUMEfDBIMusPYvRITMVUnEc64HHHiIghejxDnEeK8gtfjId7rlMV7PYfMi/N4qsdT4uKI8wqeejyeLdRd51hZsjCNz18B/7oL1rwNg+6GYT+v12KBYIAv93zJvzb8i3c2vUOpr5ST25zMTwf8lCEdh9Czdc9jfs9BVanwB8kvLmf73jK27S0jf285u7YXEr8jj+Ti3cTt2UXinl202ltI+707aL+vkISAr3odPvHyVdsurOk+jO2p7diZ0oadKW0oTG6Nr6bLXT5o4Y0jKT9IXEFRjQcJb9VBwiMkxntI8XiI9wgej3MY8IiEXMEQ9+qEVF3pcIdycHjwKojbvtKR5XEeJ4aqT5zHOTDFudutmvZ4BI+7PY+7cOi0ILRMjqdVcjxez6HbwN32obEe+l0OH/eELFf1fbwi7hUyJ1ZPSAzV+8RZzBnU4wBe8xUqqXP55vJujSUL07jK9sAbN8GW+XDh/zh9atfxn23Dng28suYVPvzmQ8r8ZSR4EhjZfSSjeo2ib0bfev1nLfcFWL2tmB37yjlQEaCwpIL/fL6drwr2owoeDXLRN59x9vYvOL10F98rLTpkeb83jpJW7SjO6Mj60/pTmtmJioxMNK0FFd/rQlxKMh29HrrHeUiI85DgdYeHTaenJXJCyySSE7zfaTca09gsWZjGk/+5kyj2bYcfToK+1x61qqqyrGAZL695mXl580iOS+aSHpfQv31/hnYcSuuk1jUuU1hSwcadJWwqLGXr7gMs2VTExp0llFYGjqjft1Mrft6nJafN+gdtN60jYVcB0q07KdkDSDn9dBJ7nkR8ZiZxJ5yAt3XrBn8U0ZhYYsnChF8wCCtehXd/CSnt4NZ3ofPAGquW+cv45NtPeG3ta6zatYq2SW25N/terj352kMSRGmFn9xv9rChYD8bCkrYWFjChoL97Cs/eM8g3iv06dSaa87oTKvkeLK+14qu7VJISfCS5oHkoh18e9dYAkVFpAweRKtLH6HFRRc2m8sKxhyLmEkWIjISeArwAn9X1QkRDsnURRW+ng2z/8c5q+gxHK6aDKnp1VUO+A7wxa4v2Lp/K0vylzAvbx5l/jI6t+jMrwf9mstOvIykuCQK91fwydZCtuwq5ZP1hXy6cReVfqf/hnapCZzUPo1L+36Pnu3TOKl9C05sn0r75Dh8Wzbj27oVf9FuKpd/Q+WmTZRv3sT+b/MgGMSTkkKXl18iuW/fyOwjY2KEaH2ew4owEfEC64ELgTxgKXC9qq492jI5OTmam5vbSBGaar5y2JYLX8+BDR/AjlXQqguc+wj0uYa9lftZu3stuTtyWbpjKat3rcavztlA26S2DEgfRqeEMwkc6MHGnQfYVFhKUWklu0srqzfRuW0y3+/eknNbVNI9WUnaXYh/506CZQeo3LwFX16e85hqUREEDl5+ksREErp1I6F7dxK6dyO+QwdScs4gsUf3xt5LxkQtEVmmqjlHlMdIshgMPKaqI9zphwFU9cmjLWPJIkyCASgvdm5Ul+2Fsj0Ei77mwPZllOWvwrfna/aLsjsunvI2J7K+/WnkJiTw1b517K/ci18rkaCS7PNwgr8L6b7upJZ1oGxXAiVFQRIrK0gOVJLiL6dDvJKZEKSl+miNnzbiI6myDNmWh3/nziNjEyG+S2cSOnchrn174jIySDyxBwk9TiSubRviMjMRr91YNqY2R0sWsXIZqiPwbch0HnBmODY08+LTabHX+Wu0tivXUkuOPdq80HI9bP1V81SOXP6QOOq53apltJZ5da2j3vMOKU9HcH6x0iiiP/PprwJ4QIW4oJLkd94VgK/dTy3bTE7Gk5qKJyXFGaamkHDWWST06EFCt654W7YkLjOT+A4dkIQEuwltTJjESrKo6fh2xKFLRO4E7gTo0uX4+kIoa5NMwFtR85ZrOcoefvDXo90kPaxYQ2fUehQPXcZ5zrtqmxqyuB5tocPXLTUVuuuvDkUOJht33UHxoOJFxUMAL0gceBIRbzJx3rjqPpgTvE7H9EneZJLjUw8+PpqUQFLLFiS1TCMuLe3QRBCSEDypqc4by3YmYExUiJVkkQd0DpnuBGw/vJKqTgImgXMZ6ng2dO3UpcezmDHGNGmxcs6+FOgpIt1FJAG4DpgZ4ZiMMabZiIkzC1X1i8i9wPs4j86+pKprIhyWMcY0GzGRLABU9V3g3UjHYYwxzVGsXIYyxhgTQZYsjDHG1MmShTHGmDpZsjDGGFMnSxbGGGPqFBNtQx0PESkEvmnkzaYDuxp5m8ci2uMLFQuxxkKMVWIl1liJE2In1mONs6uqZhxe2GSTRSSISG5NDXBFi2iPL1QsxBoLMVaJlVhjJU6InVgbKk67DGWMMaZOliyMMcbUyZJFw5oU6QDqEO3xhYqFWGMhxiqxEmusxAmxE2uDxGn3LIwxxtTJziyMMcbUqVknCxHpLCJzRGSdiKwRkZ+45W1F5EMR2eAO27jlF4rIMhH5wh2eF7KuAW75RhF5WqTm3o+OVk9EHhCRtSKySkRmi0jXxtgHDSWa9mXI/KtFREUk6p9YOVy07U8Rucb9/VwjIv8I9/dvaNG0P0WkixvLCvf/+8WNsQ++M1Vtth+gA9DfHW8BrAdOA34PPOSWPwT8zh3vB3zPHT8d2Bayrs+AwTidys0Cvn+UbdZYDzgXSHHHxwJvRHr/xOq+DIlhHrAYyIn0/onl/Qn0BFYAbdzp9pHePzG+PycBY93x04Atkd4/9dqHkQ4gmj7ADOBC4CugQ8gv2Vc11BWgCEh063wZMu964MUalqlvvX7Agkjvj1jel8BfgB8Ac2MxWUTT/nQPqHdEeh80of35IvCgOz4YWBjp/VGfT7O+DBVKRLrhHKSXAJmqmg/gDtvXsMhVwApVrQA64nT9WiXPLTtcfevdjvOXSEyK9L4UkX5AZ1V95zt9kSgR6f0J9AJ6icgCEVksIiOP/9tEXhTsz8eAG0UkD6ePnvuO86s0qpjp/CicRCQNeAsYp6r7jnIJMrR+FvA74KKqohqq1fSYWZ31RORGIAc4p46wo1Kk96WIeIA/A7fUN+ZoFun96Q7jcC5FDQc6AfNF5HRV3VvnF4gyUbI/rwdeUdU/ishg4DV3fwbr8x0ipdmfWYhIPM4vz1RVfdstLhCRDu78DsDOkPqdgH8BY1T1a7c4D+c/UZVOwHYR8YrISvfzP0erF7LuC4BfAZe5f8XElCjZly1wrjHPFZEtwCBgZoze5I6G/Vm1jhmq6lPVzTiXbno25HdtDFG0P28HpgOo6iIgCaf9pugW6etgkfzgZP9Xgb8cVv4HDr3p9Xt3vDXwOXBVDetainNgqrqZdfFRtlljPZzT4q+BnpHeL7G+Lw+rM5cYvGcRTfsTGAlMccfTgW+BdpHeRzG8P2cBt7jjp+IkEYn0PqpzH0Y6gAj/Ag3BOTVcBax0PxcD7YDZwAZ32Nat/yhQGlJ3Je6TITiXjla7B/xnj/bDP1o94COgIGS9MyO9f2J1Xx5WZy6xmSyiZn+6B7s/AWuBL4DrIr1/Ynx/ngYswElGK4GLIr1/6vOxN7iNMcbUqdnfszDGGFM3SxbGGGPqZMnCGGNMnSxZGGOMqZMlC2OMMXWyZGFMAxCRgPtC1hoR+VycVoRr/f8lIt1E5IbGitGY78KShTENo0xVs1U1C6eBuouB8XUs0w2wZGFigr1nYUwDEJESVU0Lme6B8wZvOtAVeA1IdWffq6oLRWQxzhu8m4EpwNPABJw2mBKBv6rqi432JYyphSULYxrA4cnCLdsDnALsB4KqWi4iPYH/U9UcERkO/FxVf+DWvxPnLeHfikgizlu+o9Rpj8mYiLJWZ40Jn6qWR+OBZ0UkGwjgNPldk4uAPiJytTvdCqfBPksWJuIsWRgTBu5lqABOK6bjcdr96otzn7D8aIsB96nq+40SpDHHwG5wG9PARCQDeAF4Vp3rvK2AfHX6K7gJ8LpV9+M0qV7lfWCs25Q2ItJLRFIxJgrYmYUxDSNZRFbiXHLy49zQ/pM77zngLREZBczBac0UnBZQ/SLyOfAK8BTOE1LLxemVpxC4orG+gDG1sRvcxhhj6mSXoYwxxtTJkoUxxpg6WbIwxhhTJ0sWxpj/314dCAAAAAAI8rceYYGSCJYsAFiyAGDJAoAlCwBWxEO/QO76HUgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sb.lineplot(x = \"Date\", y=\"Total dead\", \n",
    "                   hue = \"Country/Region\",\n",
    "                   data = covid_data_scandinavia.reset_index(),\n",
    "                   estimator = None)\n",
    "for ind, label in enumerate(plot.get_xticklabels()):\n",
    "    if ind % 2 == 0:  # every 2th label is kept\n",
    "        label.set_visible(True)\n",
    "    else:\n",
    "        label.set_visible(False)\n",
    "# Her kan vi se antall døde over tid blant de ulike skandinaviske landene\n",
    "# Vi kan konkludere med at det definitivt har død flest i Sverige hittil.\n",
    "#Det har død omtrent like mange blant de resterende skandinaviske landene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "list_data = pd.read_csv(\"COVID19_line_list_data.csv\")\n",
    "age_plot = sb.countplot(x = \"age\",data = list_data)\n",
    "for ind, label in enumerate(age_plot.get_xticklabels()):\n",
    "    if ind % 7 == 0:  # every 2th label is kept\n",
    "        label.set_visible(True)\n",
    "    else:\n",
    "        label.set_visible(False)\n",
    "#Vi kan se de aller fleste pasientene er eldre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = sb.countplot(x=\"gender\",data=list_data)\n",
    "value_count = list_data['gender'].value_counts()\n",
    "total = float(len(list_data[\"gender\"])) #Number of patients\n",
    "#Putting percentage on the top of both bars\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x()+p.get_width()/2.,\n",
    "            height + 3,\n",
    "            '{:1.2f}'.format(height/total),\n",
    "            ha=\"center\") \n",
    "    \n",
    "#Vi kan se at det er flere menn enn kvinner som blir syke\n",
    "#Summen av prosentene blir ikke hundre noe som vil si at jeg har gjort noe feil,\n",
    "#eller at ikke alle pasientenes kjønn er identifisert i dataen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}