{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateRep</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Countries and territories</th>\n",
       "      <th>GeoId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24/03/2020</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23/03/2020</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22/03/2020</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21/03/2020</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20/03/2020</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6546</th>\n",
       "      <td>19/03/2020</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6547</th>\n",
       "      <td>24/03/2020</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6548</th>\n",
       "      <td>23/03/2020</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6549</th>\n",
       "      <td>22/03/2020</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6550</th>\n",
       "      <td>21/03/2020</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6551 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         DateRep  Day  Month  Year  Cases  Deaths Countries and territories  \\\n",
       "0     24/03/2020   24      3  2020      6       1               Afghanistan   \n",
       "1     23/03/2020   23      3  2020     10       0               Afghanistan   \n",
       "2     22/03/2020   22      3  2020      0       0               Afghanistan   \n",
       "3     21/03/2020   21      3  2020      2       0               Afghanistan   \n",
       "4     20/03/2020   20      3  2020      0       0               Afghanistan   \n",
       "...          ...  ...    ...   ...    ...     ...                       ...   \n",
       "6546  19/03/2020   19      3  2020      2       0                    Zambia   \n",
       "6547  24/03/2020   24      3  2020      0       1                  Zimbabwe   \n",
       "6548  23/03/2020   23      3  2020      0       0                  Zimbabwe   \n",
       "6549  22/03/2020   22      3  2020      1       0                  Zimbabwe   \n",
       "6550  21/03/2020   21      3  2020      1       0                  Zimbabwe   \n",
       "\n",
       "     GeoId  \n",
       "0       AF  \n",
       "1       AF  \n",
       "2       AF  \n",
       "3       AF  \n",
       "4       AF  \n",
       "...    ...  \n",
       "6546    ZM  \n",
       "6547    ZW  \n",
       "6548    ZW  \n",
       "6549    ZW  \n",
       "6550    ZW  \n",
       "\n",
       "[6551 rows x 8 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data\n",
    "import pandas as pd\n",
    "covid_data = pd.read_csv('https://worldhealthorg-my.sharepoint.com/personal/garnicacarrenoj_who_int/_layouts/15/Doc.aspx?sourcedoc=%7B87BD9C0A-2E91-4BE3-8308-B0B545B6DFB6%7D&file=CSV%20as%20at%2024%20March%202020-Daily%20additions.csv&action=default&mobileredirect=true&CT=1585104198257&OR=ItemsView')\n",
    "covid_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "train_set, test_set=train_test_split(covid_data,test_size=0.2,random_state=42)\n",
    "train_cp=train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x13ceded90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13caacad0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x13cc2fd50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13cc897d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x13bd9e950>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13bd2ea50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "covid_data.hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77, 8)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt \n",
    "covid_mexico = covid_data[covid_data['GeoId']=='MX']\n",
    "covid_mexico.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13d347490>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ekTzpWcmIuC4ihiNieGxsbB4re2Pzck5J7Zrv4H8xIs4DKB4PnOyNmbk9M4cyc2jVqlXzVuAbnWP8kto138F/N7C5eL4Z2DXPx6881+OX1K7S1uqJiK8C7wVWRsQo8DvATcAdEXEtsBe4pqzjL2Suxy+pHaUFf2Z+9CQvXVHWMevE9fglnSln7kpSzRj8klQzBr8k1YzBL0k1Y/BLUs0Y/JJUMwa/JNWMwV9R45NTPLbvoIuzSZqz0iZwqTy79uxny84Rent6mG402LZpPRsH13S7LEkVYY+/YsYnp9iyc4Qj0w0OTR3lyHSDG3aO2POX1DKDv2JclllSuwz+ihno7+Pw9NFZbYenj7oss6SWGfwVFBGn3JakUzH4K2Z04jBLFp01q23JorMc6pHUMoO/YrwDl6R2GfwV4x24JLXL6/g7aHxyal7uirVxcA1vefMSHnrmJd5z0UqGLlxR2rEkLTwGf4fM56Sq//hnj/OVb/0AgC888Cz/6l0X8J+u+oelHEvSwuNQTwfM56SqZ1889JPQP+Yrf/UDnn3xUMePJWlhMvg7YD4nVe3Zd3BO7ZJ0PIO/A+bzSpvB85fPqV2Sjmfwd8B8XmnTv/Rseo6br9UTzXZJakVXgj8iroyIv42IZyNia1nHefun72Ht1nt4+6fvKesQP7FxcA1n9zTH+M/uaZR2Ynd04jBLz559Tn7p2YucwCUtMGUuvT7vwR8RZwH/HfinwMXARyPi4k4fZ+3We3hluvn8lenmdpnWbr2Hl4vfz8tT5R3PCVzSwrdrz342fPYBfvnmh9nw2Qe4e8/+jv78bvT4LwOezcznMvPHwO3AVZ08wMl6+GX1/AdvvHdO7e1wApe0sM3HVYLduI5/DbBvxvYo8I+Of1NEXAdcB3DBBRfM6QDHevqttrfr4JGcU3u7Ng6uYcO6lfMyWUzS/Dp2leARXvuf/bGrBDv1b/0Ne3I3M7dn5lBmDq1atWpO+y7tnVt7u5YvOfHqmCdr74QVyxZzyfnLDX1pgZmP4dxuBP9+4PwZ2wNFW8c8+bsfmlN7u/bc+ME5tUvSyczHcG5kljMccdIDRiwCvgtcQTPw/wb4l5n55Mn2GRoayuHh4Tkf6+2fbp7gXdpbXujPNHjjvRw8kixfEoa+pLZ0Yu2viHgkM4de1z7fwV8U80Hgc8BZwK2Z+V9O9f4zDX5JqrOTBX9XFmnLzHuBzl/yIkk6rTfsyV1JUjkMfkmqGYNfkmrG4JekmunKVT1zFRFjwN5u19GilcBL3S6iJH626lrIn8/PdnI/nZmvmwFbieCvkogYPtHlUwuBn626FvLn87PNnUM9klQzBr8k1YzB33nbu11Aifxs1bWQP5+fbY4c45ekmrHHL0k1Y/BLUs0Y/B0QEedHxIMR8VREPBkRn+x2TZ0WEWdFxKMR8Y1u19JpEbE8Iu6MiO9ExNMR8a5u19QpEfHrxd/JJyLiqxGxpNs1tSMibo2IAxHxxIy2cyPi/oh4pnjs72aNZ+okn+33ir+XIxFxV0Qs78SxDP7OOAr8ZmZeDFwO/GoZN5Dvsk8CT3e7iJJ8HvhmZr4NuIQF8jkjYg3wa8BQZr6D5jLoH+luVW37MnDlcW1bgd2ZeRGwu9iuoi/z+s92P/COzFxP8z4mn+rEgQz+DsjM5zPz28XzQzSDY013q+qciBgAPgTc3O1aOi0i3gy8B7gFIDN/nJkHu1tVRy0C+oobIP0U8P+6XE9bMvMh4O+Pa74K2FE83wFcPa9FdciJPltm/nlmHi02v0XzjoVtM/g7LCLWApcCD3e3ko76HHAD0DjdGyvoQmAM+ONiKOvmiFja7aI6ITP3A/8N+AHwPPDDzPzz7lZVitWZ+Xzx/AVgdTeLKdG/Be7rxA8y+DsoIpYBO4HrM/PlbtfTCRHxYeBAZj7S7VpKsgh4J/DFzLwUeIXqDhXMUox1X0Xzy+0twNKI+OXuVlWubF6fvuCuUY+I/0BzSPm2Tvw8g79DIqKXZujflplf73Y9HbQB2BgR3wduB94fEf+zuyV11CgwmpnH/od2J80vgoXgF4DvZeZYZk4DXwd+vss1leHFiDgPoHg80OV6Oioi/jXwYeBj2aGJVwZ/B0RE0Bwjfjozf7/b9XRSZn4qMwcycy3NE4MPZOaC6TVm5gvAvoh4a9F0BfBUF0vqpB8Al0fETxV/R69ggZy4Ps7dwObi+WZgVxdr6aiIuJLmMOvGzPxRp36uwd8ZG4CP0+wN7yn+fLDbRallnwBui4gRYBD4r12upyOK/8XcCXwbeJzmv/dKL28QEV8F/gp4a0SMRsS1wE3AByLiGZr/y7mpmzWeqZN8tj8EzgHuL3Llf3TkWC7ZIEn1Yo9fkmrG4JekmjH4JalmDH5JqhmDX5JqxuCXgIjImRPTImJRRIyd6WqkxYqfvzJj+70LcWVTVZPBLzW9ArwjIvqK7Q8A+9v4ecuBXzntu6QuMPil19xLcxVSgI8CXz32QrHm+58V66J/KyLWF+03Fuuo/0VEPBcRv1bschPws8Wkm98r2pbNWPf/tmI2rTTvDH7pNbcDHyluVrKe2SusfgZ4tFgX/beBr8x47W3ALwKXAb9TrNu0Ffi7zBzMzN8q3ncpcD1wMfAzNGd8S/PO4JcKmTkCrKXZ27/3uJffDfxJ8b4HgBUR8abitXsycyozX6K5QNjJlgX+68wczcwGsKc4ljTvFnW7AOkN5m6aa9i/F1jR4j5TM56/ysn/XbX6PqlU9vil2W4FPpOZjx/X/n+Aj0HzCh3gpdPcc+EQzcW1pDccexzSDJk5CnzhBC/dCNxarOD5I15bBvhkP2c8Iv6yuHH2fcA9na5VOlOuzilJNeNQjyTVjMEvSTVj8EtSzRj8klQzBr8k1YzBL0k1Y/BLUs38f4NOeoUsM4iTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "covid_mexico.plot(kind=\"scatter\", x=\"Month\", y=\"Cases\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateRep</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Countries and territories</th>\n",
       "      <th>GeoId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3985</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3984</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3953</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3924</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3972</th>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3974</th>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3943</th>\n",
       "      <td>2020-11-02</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3973</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3942</th>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3921</th>\n",
       "      <td>2020-12-03</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DateRep  Day  Month  Year  Cases  Deaths Countries and territories  \\\n",
       "3985 2019-12-31   31     12  2019      0       0                    Mexico   \n",
       "3984 2020-01-01    1      1  2020      0       0                    Mexico   \n",
       "3953 2020-01-02    1      2  2020      0       0                    Mexico   \n",
       "3924 2020-01-03    1      3  2020      2       0                    Mexico   \n",
       "3972 2020-01-13   13      1  2020      0       0                    Mexico   \n",
       "...         ...  ...    ...   ...    ...     ...                       ...   \n",
       "3974 2020-11-01   11      1  2020      0       0                    Mexico   \n",
       "3943 2020-11-02   11      2  2020      0       0                    Mexico   \n",
       "3973 2020-12-01   12      1  2020      0       0                    Mexico   \n",
       "3942 2020-12-02   12      2  2020      0       0                    Mexico   \n",
       "3921 2020-12-03   12      3  2020      4       0                    Mexico   \n",
       "\n",
       "     GeoId  \n",
       "3985    MX  \n",
       "3984    MX  \n",
       "3953    MX  \n",
       "3924    MX  \n",
       "3972    MX  \n",
       "...    ...  \n",
       "3974    MX  \n",
       "3943    MX  \n",
       "3973    MX  \n",
       "3942    MX  \n",
       "3921    MX  \n",
       "\n",
       "[77 rows x 8 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "mexico['DateRep'] =pd.to_datetime(mexico.DateRep, format=\"%d/%m/%Y\")\n",
    "mexico_sort=mexico.sort_values(by='DateRep', ascending=True)\n",
    "mexico_sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13ba35150>"
      ]
     },
     "execution_count": 156,
     "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": [
    "mexico_filter = mexico_sort[mexico_sort['Cases']!=0]\n",
    "mexico_filter.plot(kind=\"scatter\", x=\"DateRep\", y=\"Cases\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateRep</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Countries and territories</th>\n",
       "      <th>GeoId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3924</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3923</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3925</th>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3920</th>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3919</th>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3918</th>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3917</th>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3916</th>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3915</th>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3913</th>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3912</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3911</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3910</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3909</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3922</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3921</th>\n",
       "      <td>2020-12-03</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DateRep  Day  Month  Year  Cases  Deaths Countries and territories  \\\n",
       "3924 2020-01-03    1      3  2020      2       0                    Mexico   \n",
       "3923 2020-02-03    2      3  2020      1       0                    Mexico   \n",
       "3925 2020-02-29   29      2  2020      2       0                    Mexico   \n",
       "3920 2020-03-13   13      3  2020      5       0                    Mexico   \n",
       "3919 2020-03-14   14      3  2020     10       0                    Mexico   \n",
       "3918 2020-03-15   15      3  2020     15       0                    Mexico   \n",
       "3917 2020-03-16   16      3  2020     12       0                    Mexico   \n",
       "3916 2020-03-17   17      3  2020     29       0                    Mexico   \n",
       "3915 2020-03-18   18      3  2020     11       0                    Mexico   \n",
       "3914 2020-03-19   19      3  2020     25       0                    Mexico   \n",
       "3913 2020-03-20   20      3  2020     46       0                    Mexico   \n",
       "3912 2020-03-21   21      3  2020     39       2                    Mexico   \n",
       "3911 2020-03-22   22      3  2020     48       0                    Mexico   \n",
       "3910 2020-03-23   23      3  2020     65       0                    Mexico   \n",
       "3909 2020-03-24   24      3  2020     51       2                    Mexico   \n",
       "3922 2020-09-03    9      3  2020      2       0                    Mexico   \n",
       "3921 2020-12-03   12      3  2020      4       0                    Mexico   \n",
       "\n",
       "     GeoId  \n",
       "3924    MX  \n",
       "3923    MX  \n",
       "3925    MX  \n",
       "3920    MX  \n",
       "3919    MX  \n",
       "3918    MX  \n",
       "3917    MX  \n",
       "3916    MX  \n",
       "3915    MX  \n",
       "3914    MX  \n",
       "3913    MX  \n",
       "3912    MX  \n",
       "3911    MX  \n",
       "3910    MX  \n",
       "3909    MX  \n",
       "3922    MX  \n",
       "3921    MX  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mexico_filter.head(77)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "367"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(mexico_filter.Cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3985   2019-12-31\n",
       "3954   2020-01-31\n",
       "3955   2020-01-30\n",
       "3925   2020-02-29\n",
       "3956   2020-01-29\n",
       "          ...    \n",
       "3952   2020-02-02\n",
       "3983   2020-02-01\n",
       "3924   2020-01-03\n",
       "3953   2020-01-02\n",
       "3984   2020-01-01\n",
       "Name: DateRep, Length: 77, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mexico.DateRep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "# Select a linear model\n",
    "lin_reg_model = sklearn.linear_model.LinearRegression()\n",
    "# Train the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.\n 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36.\n 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54.\n 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72.\n 73. 74. 75. 76. 77.].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-161-44b24316a33b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m77\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m77\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmexico\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlin_reg_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/lwc/topics/covid19/covid/lib/python3.7/site-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0mn_jobs_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[0;32m--> 492\u001b[0;31m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/lwc/topics/covid19/covid/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    753\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    754\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 755\u001b[0;31m                     estimator=estimator)\n\u001b[0m\u001b[1;32m    756\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32m~/lwc/topics/covid19/covid/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    554\u001b[0m                     \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    555\u001b[0m                     \u001b[0;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 556\u001b[0;31m                     \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[1;32m    557\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    558\u001b[0m         \u001b[0;31m# in the future np.flexible dtypes will be handled like object dtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.\n 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36.\n 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54.\n 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72.\n 73. 74. 75. 76. 77.].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "X = np.linspace(1,77,77, axis=0)\n",
    "y = mexico.Cases\n",
    "lin_reg_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y.shape"
   ]
  }
 ],
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