python-basic-hw24_covis-19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Province/State         Country/Region        Lat       Long  \\\n",
      "0                          NaN            Afghanistan  33.000000  65.000000   \n",
      "1                          NaN                Albania  41.153300  20.168300   \n",
      "2                          NaN                Algeria  28.033900   1.659600   \n",
      "3                          NaN                Andorra  42.506300   1.521800   \n",
      "4                          NaN                 Angola -11.202700  17.873900   \n",
      "..                         ...                    ...        ...        ...   \n",
      "259  Saint Pierre and Miquelon                 France  46.885200 -56.315900   \n",
      "260                        NaN            South Sudan   6.877000  31.307000   \n",
      "261                        NaN         Western Sahara  24.215500 -12.885800   \n",
      "262                        NaN  Sao Tome and Principe   0.186360   6.613081   \n",
      "263                        NaN                  Yemen  15.552727  48.516388   \n",
      "\n",
      "     1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  4/14/20  \\\n",
      "0          0        0        0        0        0        0  ...      714   \n",
      "1          0        0        0        0        0        0  ...      475   \n",
      "2          0        0        0        0        0        0  ...     2070   \n",
      "3          0        0        0        0        0        0  ...      659   \n",
      "4          0        0        0        0        0        0  ...       19   \n",
      "..       ...      ...      ...      ...      ...      ...  ...      ...   \n",
      "259        0        0        0        0        0        0  ...        1   \n",
      "260        0        0        0        0        0        0  ...        4   \n",
      "261        0        0        0        0        0        0  ...        6   \n",
      "262        0        0        0        0        0        0  ...        4   \n",
      "263        0        0        0        0        0        0  ...        1   \n",
      "\n",
      "     4/15/20  4/16/20  4/17/20  4/18/20  4/19/20  4/20/20  4/21/20  4/22/20  \\\n",
      "0        784      840      906      933      996     1026     1092     1176   \n",
      "1        494      518      539      548      562      584      609      634   \n",
      "2       2160     2268     2418     2534     2629     2718     2811     2910   \n",
      "3        673      673      696      704      713      717      717      723   \n",
      "4         19       19       19       24       24       24       24       25   \n",
      "..       ...      ...      ...      ...      ...      ...      ...      ...   \n",
      "259        1        1        1        1        1        1        1        1   \n",
      "260        4        4        4        4        4        4        4        4   \n",
      "261        6        6        6        6        6        6        6        6   \n",
      "262        4        4        4        4        4        4        4        4   \n",
      "263        1        1        1        1        1        1        1        1   \n",
      "\n",
      "     4/23/20  \n",
      "0       1279  \n",
      "1        663  \n",
      "2       3007  \n",
      "3        723  \n",
      "4         25  \n",
      "..       ...  \n",
      "259        1  \n",
      "260        5  \n",
      "261        6  \n",
      "262        4  \n",
      "263        1  \n",
      "\n",
      "[264 rows x 97 columns]\n"
     ]
    }
   ],
   "source": [
    "# https://github.com/CSSEGISandData/COVID-19\n",
    "# 全球/地區-國家/每日累積 確認 病例 (每日更新)\n",
    "# https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\n",
    "\n",
    "# 直接取用，做「資料梳理」\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "download_url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'\n",
    "row = pd.read_csv(download_url, encoding = 'utf-8')\n",
    "\n",
    "print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "# download_url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'\n",
    "row = pd.read_csv('time_series_covid19_confirmed_global.csv', encoding = 'utf-8')\n",
    "\n",
    "# row.info()\n",
    "# row.head()\n",
    "# 全世界\n",
    "row.loc['264','1/22/20':]= row.sum(axis=0)\n",
    "row.loc['264','Country/Region']='Global_total'\n",
    "# 全世界新增\n",
    "# row.info()\n",
    "\n",
    "# 新增\n",
    "row.loc['265','1/22/20':] = 0 \n",
    "row.loc['265','Country/Region']='Global_add'\n",
    "#row.iloc[265,5:101]\n",
    "for i in range(5,101):\n",
    "    row.iloc[265,i] = row.iloc[264,i] - row.iloc[264,i-1]\n",
    "# 整數\n",
    "row.loc[:,'1/22/20':]=row.loc[:,'1/22/20':].astype(int)\n",
    "# 另存\n",
    "row.to_csv('./covid19_confirmed_global.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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"
    },
    {
     "data": {
      "image/png": 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4P/7HEg7t141vnH5E1NXpdFpzS1sRkS7p7hfX8GZ5FX/+4skU5GZHXZ1OR0cgIiLN2F1Tz+//9SYfGl3EGUcNjLo6nZISiIhIM/78n7XsqK7nisljoq5Kp6UEIiLSxI7qOm5/bjWTxw3SJUv2QwlERKSJ259fze7aBr4zaXTUVenUlEBERJJsq6zlz/9Zy8eOG8LYIb2irk6npgQiIpLkj/9+k5r6GN/+iI4+WqIEIiISrK+oZsZL6zjvhGEcObBr3ue8LZRARESC/31yBQZcPllHH63RYgIxs7vMbIuZLUmKXWtmG8xsYXickzTvKjMrNbMVZnZWUnxKiJWa2ZVJ8ZFmNs/MVpnZX80sL8Tzw+vSMH9ES9sQEUnVwvU7mLlwI1/54OEc0qdb1NVJC605ArkbmNJM/GZ3Hx8eswHMbBwwDTg6LPN7M8s2s2zgd8DZwDjgglAW4OdhXaOA7cAlIX4JsN3djwRuDuX2uY227baIyLvcnRsfW86Awjy+pkuWtFqLCcTdnwMqWrm+qcAD7l7r7muAUmBCeJS6+2p3rwMeAKZa4rKWZwIPheVnAOcmrWtGmH4I+HAov69tiIikZM7SzbyytoLvTBpNYb6u8NRaB9IHcpmZLQqnuPqG2FBgfVKZshDbV7w/sMPdG5rE91pXmL8zlN/Xut7DzKabWYmZlZSXl6e2lyKS0eoa4vz8iTc4cmAhny0+NOrqpJVUE8gfgCOA8cAm4Fch3tyF8j2FeCrrem/Q/TZ3L3b34qIi3QRGRN7r3pfXsWZrFT885yhysjWuqC1Sai133+zuMXePA7fz7imkMiA5hQ8DNu4nvhXoY2Y5TeJ7rSvM703iVNq+1iUi0ibbq+q45anEnQbPGKMLJrZVSgnEzIYkvTwPaByhNQuYFkZQjQRGAa8A84FRYcRVHolO8Fnu7sCzwPlh+YuBmUnrujhMnw88E8rvaxsiIm1yy9OrqKxt4OqPjtOdBlPQYm+Rmf0FOB0YYGZlwDXA6WY2nsSpo7XAVwHcfamZPQgsAxqAS909FtZzGTAHyAbucvelYRM/AB4wsxuA14A7Q/xO4F4zKyVx5DGtpW2IiLRW6ZZK7n15HdMmDGfM4J5RVyctWeJLfeYrLi72kpKSqKshIp3El++ez/w1FTz7vdMZUJgfdXU6LTNb4O7Fzc1Tj5GIdDnPrtjCM29s4dIzj1TyOABKICLSpVTXNXD1I0s4oqgHX3r/iKirk9b0ixkR6VJunruSDTv28OBXTyU/RxexOBA6AhGRLmPJhp3c+cIaLpgwnAkj+0VdnbSnBCIiXUJDLM6VDy+if2E+V559VNTVyQg6hSUiXcKf/7OWJRt28bsLT6R3t9yoq5MRdAQiIhlvdXkl//vkCj4ydhDnHDs46upkDCUQEclosbjz/YcWkZ+TxY3nHaNfnB9ESiAiktFmvLiWknXbuebjRzOwV0HU1ckoSiAikrHWbq3iF3Pe4IwxRXzyxGbv+iAHQAlERDJSPO784O+LyM3O4mefPE6nrtqBEoiIZKQH5q9n3poKfnTOWAb31qmr9qAEIiIZ5+2dNfxs9nJOPbw/nz1ZdxlsL0ogIpJR3J0fz1xCXSzOzz55rE5dtSMlEBHJKI8veZu5yzbznUmjGTGgR9TVyWhKICKSMXbV1PM/M5dyzNBe/NcHRkZdnYynS5mISMa45alVbKuq5a4vFpOTre/H7U0tLCIZYeXm3dz94lqmnTyc44b1ibo6XYISiIikPXfnmplLKczP4XtnjYm6Ol2GEoiIpL3Zi9/mpdXbuGLyaPr1yIu6Ol2GEoiIpLXqugZueGwZ44b04sJTDou6Ol2KOtFFJK398V9vsmlnDbdecALZWfrNR0fSEYiIpK2y7dX86bnVfPz4Qzh5hG5R29GUQEQkbf3s8TcwQ7eojYgSiIikpVfWVPDYok189bQjGNqnW9TV6ZJaTCBmdpeZbTGzJUmxfmY218xWhee+IW5mdquZlZrZIjM7MWmZi0P5VWZ2cVL8JDNbHJa51cKFa1LZhoh0DbG4c90/lzKkdwFf+9ARUVeny2rNEcjdwJQmsSuBp919FPB0eA1wNjAqPKYDf4BEMgCuAU4BJgDXNCaEUGZ60nJTUtmGiHQdDy1Yz9KNu7jy7KPolpcddXW6rBYTiLs/B1Q0CU8FZoTpGcC5SfF7POFloI+ZDQHOAua6e4W7bwfmAlPCvF7u/pK7O3BPk3W1ZRsi0gXs3FPPL55YQfFhffnE8YdEXZ0uLdU+kEHuvgkgPA8M8aHA+qRyZSG2v3hZM/FUtvEeZjbdzErMrKS8vLxNOygindMtT62iorqOaz9xtC7VHrGD3Yne3P+mpxBPZRvvDbrf5u7F7l5cVFTUwmpFpLNbuXk3M15aywUThnPM0N5RV6fLSzWBbG48bRSet4R4GZB8+69hwMYW4sOaiaeyDRHJYO6JjvPC/ByumKzrXXUGqSaQWUDjSKqLgZlJ8YvCSKmJwM5w+mkOMNnM+obO88nAnDBvt5lNDKOvLmqyrrZsQ0Qy2Jylb/Of0m1crutddRotXsrEzP4CnA4MMLMyEqOpbgIeNLNLgLeAT4fis4FzgFKgGvgSgLtXmNlPgPmh3PXu3tgx/3USI726AY+HB23dhohkrsraBq775zKOGtyTCycMj7o6Elhi8FPmKy4u9pKSkqirISIpuHbWUma8tJa/f/19nDi8b4vl5eAxswXuXtzcPP0SXUQ6tYXrdzDjpbV8YeJhSh6djBKIiHRa9bE4Vz28mEE9C3SjqE5Il3MXkU7rzhfWsHzTLv70hZPoWZAbdXWkCR2BiEintG5bFb95aiWTxw3irKMHR10daYYSiIh0Ou7O1f9YQk5WFtdNPTrq6sg+KIGISKczc+FGnl+1le9PGcOQ3rpUe2elBCIincr2qjquf3QZJwzvw+d0j/NOTQlERDqVn85ezq499fzsk8fqHuednBKIiHQac5dt5qEFZUw/7XCOGtwr6upIC5RARKRTeGtbNZc/uJBjh/bmWx8ZFXV1pBWUQEQkcjX1Mb5x/wIAfv+5E8nP0V0G04F+SCgikfvJo8tYsmEXt19UzKH9ukddHWklHYGISKQefrWM++a9xVc/dDiTxg2KujrSBkogIhKZJRt2ctXDizllZD++p5tEpR0lEBGJxLbKWr567wL698jjd587kZxsfRylG/WBiEiHa4jFuez+1yivrOWhr53KgML8qKskKVDKF5EO98snV/DS6m3ceN6xHDesT9TVkRQpgYhIh3qxdCu3PbeaC08ZzvknDYu6OnIAlEBEpMPsrK7n8r+9zsgBPfjxR8dFXR05QOoDEZEO4e788B+LKd9dy8PfeB/d8vRjwXSnIxAR6RD/WLiBxxZt4juTRqvfI0MogYhIu1u7tYof/2MpJ4/oy9c+dETU1ZGDRAlERNpVbUOMS+9/lews4zfTTtAl2jOI+kBEpF3d+Nhylm7cxR0XFTO0j+4umEl0BCIi7eaJJZuY8dI6LvnASD6i61xlnANKIGa21swWm9lCMysJsX5mNtfMVoXnviFuZnarmZWa2SIzOzFpPReH8qvM7OKk+Elh/aVhWdvfNkSk83hrWzXfe2gRxx/ahx9MOSrq6kg7OBhHIGe4+3h3Lw6vrwSedvdRwNPhNcDZwKjwmA78ARLJALgGOAWYAFyTlBD+EMo2LjelhW2ISCfQeH8PA357wQnk5ehkRyZqj//VqcCMMD0DODcpfo8nvAz0MbMhwFnAXHevcPftwFxgSpjXy91fcncH7mmyrua2ISKdwPXh/h6//sx43d8jgx1oAnHgSTNbYGbTQ2yQu28CCM8DQ3wosD5p2bIQ21+8rJn4/raxFzObbmYlZlZSXl6e4i6KSFs8/GoZ9897i6+ffoT6PTLcgY7Cer+7bzSzgcBcM3tjP2WbG7vnKcRbzd1vA24DKC4ubtOyItJ2Szbs5EePLOGUkf24fNLoqKsj7eyAjkDcfWN43gI8QqIPY3M4/UR43hKKlwGHJi0+DNjYQnxYM3H2sw0RiUjplt1cdNcr9O2ey/9dcILu79EFpPw/bGY9zKxn4zQwGVgCzAIaR1JdDMwM07OAi8JorInAznD6aQ4w2cz6hs7zycCcMG+3mU0Mo68uarKu5rYhIhFYt62KC2+fR5YZ931lIgN7FURdJekAB3IKaxDwSBhZmwPc7+5PmNl84EEzuwR4C/h0KD8bOAcoBaqBLwG4e4WZ/QSYH8pd7+4VYfrrwN1AN+Dx8AC4aR/bEJEOtnHHHi68fR71sTgPTD+VkQN6RF0l6SCWGOCU+YqLi72kpCTqaohklCUbdnLJjPlU18a4/ysTOXZY76irJAeZmS1I+pnGXnSSUkRS8sSSTZz/xxfJycriwa+dquTRBelaWCLSJu7O7//1Jr+cs4IThvfhti8UU9RT9zTvipRARKTV6mNxrn5kCX8tWc/U8Yfw808dR0GubgzVVSmBiEir7K6p5xv3vcrzq7by32ceyXcnjSYMopEuSglERFq04u3dfPMvr/FmeSW/+NRxfObkQ1teSDKeEoiI7FMs7tzx/Gp+9eRKehbk8OcvncwHRxVFXS3pJJRARKRZ67ZVccXfXmf+2u1MHjeIGz95LAMK1Vku71ICEZG9uDv3zXuLG2cvJ9uMX336eD554lD1d8h7KIGIyDs27tjDD/6+iOdXbeUDRw7gF+cfxyG6Da3sgxKIiNAQizPjpXX8+skVxB1+cu4xfP6U4TrqkP1SAhHp4haV7eCHjyxmyYZdnD6miJ9MPUY3gZJWUQIR6aK2VtbyyydW8OCC9QwozOd3F57IOccO1lGHtJoSiEgXs72qjgdL1vPbZ0vZUxfjvz4wkv/+8Ch6FeRGXTVJM0ogIl1AXUOc51aW8/dXy3hq+WbqY85po4v4n4+N48iBhVFXT9KUEohIhqqpj/H8qq08vngTc5dvZndNA/175HHRqSP41InDGHdIr6irKGlOCUQkg2zYsYd/ryjnmTc280LpVmrq4/TulsuUowdz9rGD+eCoInJ1q1k5SJRARNJUPO6s3LKbkrXbmb+2gvlrKti4swaAYX27Me3k4Zx51EBOPaK/koa0CyUQkTSyq6aeJ5a8zeOLN1Gybju7axoAKOqZz4QR/Zg+oi+nHjGA0YMKNZpK2p0SiEgnUx+Ls25bNavLK9m8u5aq2gYqaxoo3VLJMyu2UNcQZ3i/7nz8+EM4aXhfikf0ZXi/7koY0uGUQEQ6gLtTWdtARVUdWyvrqKiqY3tVHduq6qioqmXzrlre3lXD5l01lG3fQyzuey2fZYmjjAsnDGfq+EMYf2gfJQyJnBKISCu5O3vqY1TWNFBZ20BVbYyqugaqahvYXdPA7pp6dtU0sL0qkSC2NT5X1rK1qo66hniz683PyWJQrwIG9yrguGF9+NhxQzh8QCFHDCzkkN4FFBbk0C03WwlDOh0lEOm03J1Y3Im5E48nhqXuqY9RUx8jFnfiDvHGMu+Uc+pjidf18TgNMachFqcuFqeu4d3n2obG5xi19XH2hHXvqYtRVRejqjaRGKrr3o1X1zXQ5MCgWd1ys+nXI4/+hXkMKMxjzOCe9O+RR78eeQwozKdfYd47r/v3yKdbnm4JK+lJCUQOWHVdA+u2VfNWRTXbq+qoqouxpy7x4VtdF6OytoHq8HpP0gdyYzKoa4jvlQzefW7/umcZ5Odk0z0vm4LcbLrlZdMjP4fC/Gz69ehO97x35xXm5yQeBTn0yMuhR34OPfKy6Z6fQ8+CxKNXQa7uES5dhhKItFl9LM681RU8sXQTz75RzoYde5otl5ttdM9LfMh2y8ume14O3fIS38679018KBfkZpOXnUWWGVkG2VlGVpaRbYnnnCwjOzwKcrLeWSYn295ZxixRrnG5nGwjJyuLnGwjNyuL3JzE6/ycLPJyssjLTjzn52SRo+GtIilTAulC6mNxtlfXsbM6ca6+sjact9/TwM499eyqqX/n/P7umnAKpz5xNFHXEH/nVNLOPfVU1jbQLTebD40u4sJThnNY/+4c1q8HA3rm0T03kSjycvThLJLJ0jqBmNkU4BYgG7jD3W+KuEodwt2pqY+zc089O/bUUVFZx9bQWajONm4AAAiISURBVLu9qo6K6kTn7Y7qenbuCY/qenbXNux3vTlZRs+CxCmawvxcCvOz6d0tlyG9CsjLyUocHZjRPS+bD44awGmji3S6RqQLS9sEYmbZwO+ASUAZMN/MZrn7smhr9l7uTlVd4+idemob4sTjEHOnriFOdV1D6KSNUdMQo6Y+Tk197J1+g+raGNuqatmyu5bNu2rYXlVPXaz5ET1m0Ld7Hn2659K3ex6DehUwelBPenfLpV+PPPp2z6V397xwvj6RKHp3y6VXN430EZG2SdsEAkwASt19NYCZPQBMBQ5qAvn3ynJ+8ugy3BM9uh7+ibvjQCzuuPPOKKB3RgTFnfpYnFjcaUixNzjLoEdSv8GgXgUcNbgn/QvzEx/6Bbn06Z5L/zDip1+PRDw7S0lARNpfOieQocD6pNdlwCnJBcxsOjAdYPjw4SltpDA/hzGDeoYVJp6yzDAS3/YbO3sbO4CzsxIdtllm5GaH19lZFOZnU5ifS4/8bPJzskNZyM3Oontezl6jfQpyssnPTXTy6ohARDqrdE4gzX2y7vVV391vA24DKC4uTukw4KTD+nLSYX1TWVREJKOl8zCZMuDQpNfDgI0R1UVEpMtJ5wQyHxhlZiPNLA+YBsyKuE4iIl1G2p7CcvcGM7sMmENiGO9d7r404mqJiHQZaZtAANx9NjA76nqIiHRF6XwKS0REIqQEIiIiKVECERGRlCiBiIhISqzxEh2ZzszKgXUpLj4A2HoQq5OO1AZqA1AbdMX9P8zdi5qb0WUSyIEwsxJ3L466HlFSG6gNQG3Q1fe/KZ3CEhGRlCiBiIhISpRAWue2qCvQCagN1AagNujq+78X9YGIiEhKdAQiIiIpUQIREZGUZHQCMbO7zGyLmS1pEj/VzG43s0lmtsDMFofnM8P87mb2mJm9YWZLzeymJssPMbMnzWy8mb0Uyiwys88mlRlpZvPMbJWZ/TVccr7DmdmhZvasmS0P9fxW0rzGdugfylSa2W+bLP9TM1tvZpXNrDtt2iGpPgVm9oqZvR7qe13SvAvM7Edm9rmwH4vM7EUzO77JOv5kZu83s1+G98giM3vEzPoklbnKzErNbIWZndWR+9jUgezz/t4/Yf5+/5ZCmZNCvNTMbrUIb7NpZtlm9pqZPZoUa2yDo8L7uNbMrmiyXLOfJWFeWrXBQeXuGfsATgNOBJY0iV8HfAo4ATgkxI4BNoTp7sAZYToPeB44O2n5LwGXA6OBUSF2CLAJ6BNePwhMC9N/BL4eURsMAU4M0z2BlcC4Ju3QA/gA8DXgt02WnxjWUdnMutOmHZLqbEBhmM4F5gETw+sZwEnA+4C+IXY2MK/JOhaSuIXAZCAnxH4O/DxMjwNeB/KBkcCbQHY67vP+3j+t+VsKr18BTg31eDz5bymCtvgucD/waFKssQ0GAicDPwWuaLJcs58l6dgGB7U9o65AB7xhRjT9TwdeAHo3iRmwDchvZh23AF9Jev3X5D+ipPjrwKiwrq1JHy6nAnOibotQl5nApObaAfgiTRJI0rzmEkjatkOoT3fgVeCUUNfXCQNLksr0bfJBMBZ4sJl1nQfcF6avAq5KmjcHODXq/U11n/f1/mnuPRRi7/wtkUhAbyTNuwD4U0T7Pgx4GjizMYE01wbAtU0TSIi/57Mk3drgYD8y+hRWc8xsAFDv7jubzPoU8Jq71zYp3wf4OIk3HmaWDYxx92VNyk0gcbTyJtAf2OHuDWF2GTD0YO9LW5nZCBLfkubtpx1au650bodsM1sIbAHmuvs8Eu3yuoe/8CSXkPjG2Ohs4IlmVvvlpHJDgfVJ8yLf7wPc58Z1jAjLzAuvW/O3NJTE/jeKsi1+A3wfiCfF9tUGrZKGbXBQpfUNpVI0GXgyOWBmR5M4BTG5STwH+Atwq7uvDuFTCH9ASeWGAPcCF7t7fB/nNyMdL21mhcDfgW+7+y4zu5Am7dBGadkOAO4eA8aHLwePmNkxwBSafGia2RkkPkw/kBQ+i8Spu+RyPwIagPsaQ81t9uDUPjUHuM/vef+EcGv+ljpFW5jZx4At7r7AzE5PmvWeNmijtGmD9tDljkBo8g3SzIYBjwAXufubTcreBqxy99/sZ/lewGPA1e7+cghvBfqEBASJQ+eNB3Uv2sDMckn88d/n7g+H8L6+SbdW2rVDU+6+A/gXiQ+RvT4IzOw44A5gqrtvC7HuJPp2NiaVuxj4GPC5pG+xZcChSZvqNPvd1n0O8ebeP9C6v6UyEvvfKKq2eD/wCTNbCzwAnGlm/49mEkAbpVMbHHxRn0Nr7wdJ5y1pcr4T6BNef6qZ5W4g8UeT1ST+ItArTOeROLX17WaW/xt7dx5/I6L9N+Ae4DdNYs2d+/4irewDSbd2SKpPEe928HcjMUBiKvBCUpnhQCnwvibLfhS4Ken1FGAZUNSk3NHs3Ym+mmg70Q9kn9/z/mnuPdTC39J8EoMxGjuQz4n4PXA68CjQO7kNkuZfSyv6QNK5DQ5aW0ZdgXZ+o/yFxIigehLfAn4A3J00/2qgisSomsbHQBLfEBxYnhT/r/CH+EzS8p8P605efnyYdziJkRel4UP0PZ3zHdQGHwj7siipjv+T3A6h3FqgAqgMbdU4UusX4XU8PF+bju2QVNfjgNdCeywJbXE+cG1SmTuA7Un7UhLivwVOTypXSqKvo7HcH5Pm/YhEP9AKIh5xc4D73Nz75xyguDV/S2Fecdjum6ENraP2fR/tcTqJBNK0DQaH9/guYEeYbvyS1PSz5JJ0boOD9ehSlzIxs6uBUnd/IMXlPw8Mc/ebWizciakd9mZmdwB3+Lun3vZV7lXgFHev75iatZ/W7vN+lj+g91BnoDY4cF0qgYiIyMHTFTvRRUTkIFACERGRlCiBiIhISpRAREQkJUogIiKSEiUQERFJyf8HKbE5NYRqkFEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>...</th>\n",
       "      <th>4/18/20</th>\n",
       "      <th>4/19/20</th>\n",
       "      <th>4/20/20</th>\n",
       "      <th>4/21/20</th>\n",
       "      <th>4/22/20</th>\n",
       "      <th>4/23/20</th>\n",
       "      <th>4/24/20</th>\n",
       "      <th>4/25/20</th>\n",
       "      <th>4/26/20</th>\n",
       "      <th>4/27/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>933</td>\n",
       "      <td>996</td>\n",
       "      <td>1026</td>\n",
       "      <td>1092</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>548</td>\n",
       "      <td>562</td>\n",
       "      <td>584</td>\n",
       "      <td>609</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2534</td>\n",
       "      <td>2629</td>\n",
       "      <td>2718</td>\n",
       "      <td>2811</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>704</td>\n",
       "      <td>713</td>\n",
       "      <td>717</td>\n",
       "      <td>717</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>261</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>24.215500</td>\n",
       "      <td>-12.885800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>262</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>0.186360</td>\n",
       "      <td>6.613081</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>263</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Global_total</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>555</td>\n",
       "      <td>654</td>\n",
       "      <td>941</td>\n",
       "      <td>1434</td>\n",
       "      <td>2118</td>\n",
       "      <td>...</td>\n",
       "      <td>2317339</td>\n",
       "      <td>2400843</td>\n",
       "      <td>2471759</td>\n",
       "      <td>2548821</td>\n",
       "      <td>2624107</td>\n",
       "      <td>2707742</td>\n",
       "      <td>2811603</td>\n",
       "      <td>2897624</td>\n",
       "      <td>2972363</td>\n",
       "      <td>3041764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Global_add</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>287</td>\n",
       "      <td>493</td>\n",
       "      <td>684</td>\n",
       "      <td>...</td>\n",
       "      <td>77616</td>\n",
       "      <td>83504</td>\n",
       "      <td>70916</td>\n",
       "      <td>77062</td>\n",
       "      <td>75286</td>\n",
       "      <td>83635</td>\n",
       "      <td>103861</td>\n",
       "      <td>86021</td>\n",
       "      <td>74739</td>\n",
       "      <td>69401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0 Province/State         Country/Region        Lat       Long  \\\n",
       "0             0            NaN            Afghanistan  33.000000  65.000000   \n",
       "1             1            NaN                Albania  41.153300  20.168300   \n",
       "2             2            NaN                Algeria  28.033900   1.659600   \n",
       "3             3            NaN                Andorra  42.506300   1.521800   \n",
       "4             4            NaN                 Angola -11.202700  17.873900   \n",
       "..          ...            ...                    ...        ...        ...   \n",
       "261         261            NaN         Western Sahara  24.215500 -12.885800   \n",
       "262         262            NaN  Sao Tome and Principe   0.186360   6.613081   \n",
       "263         263            NaN                  Yemen  15.552727  48.516388   \n",
       "264         264            NaN           Global_total        NaN        NaN   \n",
       "265         265            NaN             Global_add        NaN        NaN   \n",
       "\n",
       "     1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  ...  4/18/20  4/19/20  \\\n",
       "0          0        0        0        0        0  ...      933      996   \n",
       "1          0        0        0        0        0  ...      548      562   \n",
       "2          0        0        0        0        0  ...     2534     2629   \n",
       "3          0        0        0        0        0  ...      704      713   \n",
       "4          0        0        0        0        0  ...       24       24   \n",
       "..       ...      ...      ...      ...      ...  ...      ...      ...   \n",
       "261        0        0        0        0        0  ...        6        6   \n",
       "262        0        0        0        0        0  ...        4        4   \n",
       "263        0        0        0        0        0  ...        1        1   \n",
       "264      555      654      941     1434     2118  ...  2317339  2400843   \n",
       "265        0       99      287      493      684  ...    77616    83504   \n",
       "\n",
       "     4/20/20  4/21/20  4/22/20  4/23/20  4/24/20  4/25/20  4/26/20  4/27/20  \n",
       "0       1026     1092     1176     1279     1351     1463     1531     1703  \n",
       "1        584      609      634      663      678      712      726      736  \n",
       "2       2718     2811     2910     3007     3127     3256     3382     3517  \n",
       "3        717      717      723      723      731      738      738      743  \n",
       "4         24       24       25       25       25       25       26       27  \n",
       "..       ...      ...      ...      ...      ...      ...      ...      ...  \n",
       "261        6        6        6        6        6        6        6        6  \n",
       "262        4        4        4        4        4        4        4        4  \n",
       "263        1        1        1        1        1        1        1        1  \n",
       "264  2471759  2548821  2624107  2707742  2811603  2897624  2972363  3041764  \n",
       "265    70916    77062    75286    83635   103861    86021    74739    69401  \n",
       "\n",
       "[266 rows x 102 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "df = pd.read_csv('covid19_confirmed_global.csv')\n",
    "df\n",
    "data_add = df.loc[265, '1/22/20':'4/25/20']\n",
    "data_total = df.loc[264, '1/22/20':'4/25/20']\n",
    "#data.info()\n",
    "#data = data.set_index('date')\n",
    "data_add.plot(kind='bar')\n",
    "plt.show()\n",
    "data_total.plot(kind='line')\n",
    "plt.show()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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