{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading data\n",
    "import os\n",
    "import git\n",
    "import shutil\n",
    "import tempfile\n",
    "\n",
    "# Create temporary dir\n",
    "t = tempfile.mkdtemp()\n",
    "d = 'lwc/topics/covid19/covid-model'\n",
    "# Clone into temporary dir\n",
    "git.Repo.clone_from('http://gmarx.jumpingcrab.com:8088/COVID-19/covid19-data.git', \n",
    "                    t, branch='master', depth=1)\n",
    "# Delete files\n",
    "#os.remove('README.txt')\n",
    "shutil.rmtree('data')\n",
    "#shutil.rmtree('secondTest')\n",
    "# Copy desired file from temporary dir\n",
    "shutil.move(os.path.join(t, 'data'), '.')\n",
    "# Remove temporary dir\n",
    "shutil.rmtree(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "def loadData(path, file):\n",
    "    csvPath=os.path.join(path, file)\n",
    "    return pd.read_csv(csvPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country/Region Province/State   Lat  Long  Confirmed  Recovered  \\\n",
       "0  2020-01-22    Afghanistan            NaN  33.0  65.0          0        0.0   \n",
       "1  2020-01-23    Afghanistan            NaN  33.0  65.0          0        0.0   \n",
       "2  2020-01-24    Afghanistan            NaN  33.0  65.0          0        0.0   \n",
       "3  2020-01-25    Afghanistan            NaN  33.0  65.0          0        0.0   \n",
       "4  2020-01-26    Afghanistan            NaN  33.0  65.0          0        0.0   \n",
       "\n",
       "   Deaths  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import jtplot submodule from jupyterthemes\n",
    "from jupyterthemes import jtplot\n",
    "PATH=os.path.join(\"data\")\n",
    "covid_data=loadData(PATH,\"time-series-19-covid-combined.csv\")\n",
    "covid_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11e46ca50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x116caea90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11e65dd10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11e6a16d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11e6d3ed0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11e716710>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 8)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt \n",
    "covid_mexico = covid_data[covid_data['Country/Region']=='Mexico']\n",
    "covid_mexico.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b7c9910>"
      ]
     },
     "execution_count": 134,
     "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": [
    "covid_data.plot(kind=\"scatter\", x=\"Long\", y=\"Lat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9954</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9955</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9956</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9957</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9958</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</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>10012</th>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>164</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10013</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>203</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10014</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>251</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10015</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>316</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10016</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Country/Region Province/State      Lat      Long  Confirmed  \\\n",
       "9954   2020-01-22         Mexico            NaN  23.6345 -102.5528          0   \n",
       "9955   2020-01-23         Mexico            NaN  23.6345 -102.5528          0   \n",
       "9956   2020-01-24         Mexico            NaN  23.6345 -102.5528          0   \n",
       "9957   2020-01-25         Mexico            NaN  23.6345 -102.5528          0   \n",
       "9958   2020-01-26         Mexico            NaN  23.6345 -102.5528          0   \n",
       "...           ...            ...            ...      ...       ...        ...   \n",
       "10012  2020-03-20         Mexico            NaN  23.6345 -102.5528        164   \n",
       "10013  2020-03-21         Mexico            NaN  23.6345 -102.5528        203   \n",
       "10014  2020-03-22         Mexico            NaN  23.6345 -102.5528        251   \n",
       "10015  2020-03-23         Mexico            NaN  23.6345 -102.5528        316   \n",
       "10016  2020-03-24         Mexico            NaN  23.6345 -102.5528        367   \n",
       "\n",
       "       Recovered  Deaths  \n",
       "9954         0.0       0  \n",
       "9955         0.0       0  \n",
       "9956         0.0       0  \n",
       "9957         0.0       0  \n",
       "9958         0.0       0  \n",
       "...          ...     ...  \n",
       "10012        4.0       1  \n",
       "10013        4.0       2  \n",
       "10014        4.0       2  \n",
       "10015        4.0       3  \n",
       "10016        NaN       4  \n",
       "\n",
       "[63 rows x 8 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "#covid_mexico['Date'] =pd.to_datetime(covid_mexico.Date, format=\"%Y-%m-%d\")\n",
    "mexico_sort=covid_mexico.sort_values(by='Date', ascending=True)\n",
    "mexico_sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10001</th>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10002</th>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10003</th>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10004</th>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10005</th>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10006</th>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>26</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10007</th>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>41</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10008</th>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>53</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10009</th>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>82</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10010</th>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>93</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10011</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>118</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10012</th>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>164</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10013</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>203</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10014</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>251</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10015</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>316</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10016</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6345</td>\n",
       "      <td>-102.5528</td>\n",
       "      <td>367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Country/Region Province/State      Lat      Long  Confirmed  \\\n",
       "9991   2020-02-28         Mexico            NaN  23.6345 -102.5528          1   \n",
       "9992   2020-02-29         Mexico            NaN  23.6345 -102.5528          4   \n",
       "9993   2020-03-01         Mexico            NaN  23.6345 -102.5528          5   \n",
       "9994   2020-03-02         Mexico            NaN  23.6345 -102.5528          5   \n",
       "9995   2020-03-03         Mexico            NaN  23.6345 -102.5528          5   \n",
       "9996   2020-03-04         Mexico            NaN  23.6345 -102.5528          5   \n",
       "9997   2020-03-05         Mexico            NaN  23.6345 -102.5528          5   \n",
       "9998   2020-03-06         Mexico            NaN  23.6345 -102.5528          6   \n",
       "9999   2020-03-07         Mexico            NaN  23.6345 -102.5528          6   \n",
       "10000  2020-03-08         Mexico            NaN  23.6345 -102.5528          7   \n",
       "10001  2020-03-09         Mexico            NaN  23.6345 -102.5528          7   \n",
       "10002  2020-03-10         Mexico            NaN  23.6345 -102.5528          7   \n",
       "10003  2020-03-11         Mexico            NaN  23.6345 -102.5528          8   \n",
       "10004  2020-03-12         Mexico            NaN  23.6345 -102.5528         12   \n",
       "10005  2020-03-13         Mexico            NaN  23.6345 -102.5528         12   \n",
       "10006  2020-03-14         Mexico            NaN  23.6345 -102.5528         26   \n",
       "10007  2020-03-15         Mexico            NaN  23.6345 -102.5528         41   \n",
       "10008  2020-03-16         Mexico            NaN  23.6345 -102.5528         53   \n",
       "10009  2020-03-17         Mexico            NaN  23.6345 -102.5528         82   \n",
       "10010  2020-03-18         Mexico            NaN  23.6345 -102.5528         93   \n",
       "10011  2020-03-19         Mexico            NaN  23.6345 -102.5528        118   \n",
       "10012  2020-03-20         Mexico            NaN  23.6345 -102.5528        164   \n",
       "10013  2020-03-21         Mexico            NaN  23.6345 -102.5528        203   \n",
       "10014  2020-03-22         Mexico            NaN  23.6345 -102.5528        251   \n",
       "10015  2020-03-23         Mexico            NaN  23.6345 -102.5528        316   \n",
       "10016  2020-03-24         Mexico            NaN  23.6345 -102.5528        367   \n",
       "\n",
       "       Recovered  Deaths  \n",
       "9991         0.0       0  \n",
       "9992         0.0       0  \n",
       "9993         0.0       0  \n",
       "9994         0.0       0  \n",
       "9995         1.0       0  \n",
       "9996         1.0       0  \n",
       "9997         1.0       0  \n",
       "9998         1.0       0  \n",
       "9999         1.0       0  \n",
       "10000        1.0       0  \n",
       "10001        1.0       0  \n",
       "10002        4.0       0  \n",
       "10003        4.0       0  \n",
       "10004        4.0       0  \n",
       "10005        4.0       0  \n",
       "10006        4.0       0  \n",
       "10007        4.0       0  \n",
       "10008        4.0       0  \n",
       "10009        4.0       0  \n",
       "10010        4.0       0  \n",
       "10011        4.0       1  \n",
       "10012        4.0       1  \n",
       "10013        4.0       2  \n",
       "10014        4.0       2  \n",
       "10015        4.0       3  \n",
       "10016        NaN       4  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mexico_filter = mexico_sort[mexico_sort['Confirmed']!=0]\n",
    "mexico_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n=mexico_filter.shape[0]\n",
    "days=np.arange(1,n+1,1)\n",
    "days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12acc8290>"
      ]
     },
     "execution_count": 119,
     "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['Confirmed']!=0]\n",
    "plt.scatter(x=days, y=mexico_filter['Confirmed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.07768657,  0.22640743, -3.90363561])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "def exponential(x, a,k, b):\n",
    "    return a*np.exp(x*k) + b\n",
    "\n",
    "potp, pcov = curve_fit(exponential, days, mexico_filter['Confirmed'])\n",
    "potp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12b888fd0>]"
      ]
     },
     "execution_count": 133,
     "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": [
    "# Plot outputs\n",
    "plt.scatter(days, mexico_filter['Confirmed'],  color='black')\n",
    "plt.plot(days,exponential(days,*potp), color='blue', linewidth=2)"
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}