model-mexico/covid-model.ipynb

{
 "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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
      "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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
      "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": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        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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
      "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": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD7CAYAAACRxdTpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de3wV9Z3/8dcnIVwCKqApIORCvVStdalG1C5br1y8ol212Fjx8jPdrba6tW7RrFu3boq2VtRqbUNFoR6L1iu6ritSWmtbL8EiImhFTQKIEEW5GCQk+fz+mImcQC4nyTmZk5P38/GYx3znOzPnfMbz8JPhO9/5fs3dERGRzJIVdQAiIpJ8Su4iIhlIyV1EJAMpuYuIZCAldxGRDKTkLiKSgRJO7maWbWZ/M7Mnw+2xZvaima0yswfMrH9YPyDcXhXuL0pN6CIi0pbO3LlfAayM274JmOXu+wMfAZeE9ZcAH4X1s8LjRESkB1kiLzGZ2RhgLlAOfA84HagFRrp7g5kdA1zv7pPN7P/C8l/NrB/wPpDn7XzRPvvs40VFRd2/GhGRPmTJkiUfuHtea/v6JfgZtwL/DuwRbu8NfOzuDeH2GmB0WB4NrAYIE/+m8PgP2vrwoqIiKisrEwxFREQAzKy6rX0dNsuY2WnABndfkuSgSs2s0swqa2trk/nRIiJ9XiJt7v8InGFmVcB84ATgNmBo2OwCMAZYG5bXAvkA4f69gA93/VB3r3D3Yncvzstr9V8VIiLSRR0md3e/xt3HuHsRMA34vbuXAIuBs8PDpgOPh+UF4Tbh/t+3194uIiLJ151+7j8Avmdmqwja1O8O6+8G9g7rvwfM6F6IIiLSWYk+UAXA3f8A/CEsvwOMb+WYT4FzkhCbiIh0kd5QFRHJQEruIiIRSeXTSCV3EZEIvPkmHHgg/OhHqfl8JXcRkQg88QSsWgVvvZWaz1dyFxGJwIIFwfr001Pz+UruIiI97MMP4c9/hpwcmDw5Nd+h5C4i0sOeegqamuDYY2GvvVLzHUruIiI97IkngvUZZ6TuO5TcRUR6UH09PP10UE5VezsouYuI9Kg//hG2bIH8/I847rgisrKyKCoqIhaLJfV7OjX8gIiIdE9zL5l162bT0BAMx15dXU1paSkAJSUlSfke3bmLiPQQ953t7Q0ND7fYV1dXR1lZWdK+S8ldRKSHvPYaVFdDMPvoy7vtr6mpSdp3KbmLiPSQ5rv2IUP+AOw+sExBQUHSvkvJXUSkhzS3t1966Shyc3Nb7MvNzaW8vDxp36XkLiLSA95/H156CQYOhBtuOJaKigoKCwsxMwoLC6moqEjaw1RQbxkRkR7x5JPB+sQTYfDgoFdMMpP5rjq8czezgWb2kpm9amavm9l/hfX3mtm7ZrY0XMaF9WZmt5vZKjNbZmaHpyx6EZFeoifeSo2XyJ37duAEd99qZjnA82b2v+G+q939oV2OPxk4IFyOAu4K1yIifdK2bbBwYVA+7bSe+c4O79w9sDXczAmX9uYPmQrMC897ARhqZqO6H6qISO+0aFGQ4IuLYd99e+Y7E3qgambZZrYU2AAsdPcXw13lYdPLLDMbENaNBlbHnb4mrBMR6ZNSPXZ7axJK7u7e6O7jgDHAeDM7FLgGOAg4EhgO/KAzX2xmpWZWaWaVtbW1nQxbRKR3aGra+TA17ZJ7M3f/GFgMTHH3dWHTy3bgHmB8eNhaID/utDFh3a6fVeHuxe5enJeX17XoRUTS3JIlsG4djBkD48b13Pcm0lsmz8yGhuVBwETgjeZ2dDMz4ExgeXjKAuCCsNfM0cAmd1+XkuhFRNJccy+Z008Hs5773kR6y4wC5ppZNsEfgwfd/Ukz+72Z5QEGLAX+JTz+KeAUYBVQB1yU/LBFRHqH5vb2nuoC2azD5O7uy4Avt1J/QhvHO3BZ90MTEendamrg1VeDl5aOO65nv1vDD4iIpEhzk8zkycGwAz1JyV1EJEXi29t7mpK7iEgKbNkCixcHD1FPPbXnv1/JXUQkBZ55JpgM+5hjIIre3kruIiIpEFUvmWZK7iIiSdbYCP/zP0E5ivZ2UHIXEUm6v/4VPvwQ9tsPDj44mhiU3EVEkiyqt1LjKbmLiCRZ1O3toOQuIpIUsViMoqIizA7kjTcgN7eeCROii0fJXUSkm2KxGKWlpVRXVwPBVEvbtz/Ggw/GIotJyV1EpJvKysqoq6sLt4LuMY2Nj1JWVhZZTImMCikiIu2oqakJS0OBfwJ2AE9TU7Mpsph05y4i0k0FBQVh6WSCe+Y/AR/H1fc8JXcRkW4qLy8nNzcXaO4es4Dc3FzKy8sji0nNMiIi3VRSUkJDQxYXXXQK7rDvvq/wk59UUFJSEllMHSZ3MxsIPAcMCI9/yN1/aGZjgfnA3sAS4JvuXm9mA4B5wBHAh8DX3b0qRfGLiKSFMWPOwx0OOQRef/25qMNJqFlmO3CCu/8DMA6YEs6NehMwy933Bz4CLgmPvwT4KKyfFR4nIpLRfve7YB3VWDK76jC5e2BruJkTLg6cADwU1s8lmCQbYGq4Tbj/xHASbRGRjLRtG8yfH5TPPz/aWJol9EDVzLLNbCmwAVgIvA187O4N4SFrgNFheTSwGiDcv4mg6UZEJCM9+ihs2gTFxXDooVFHE0goubt7o7uPA8YA44GDuvvFZlZqZpVmVllbW9vdjxMRicw99wTriy+ONo54neoK6e4fA4uBY4ChZtb8QHYMsDYsrwXyAcL9exE8WN31syrcvdjdi/OimKZERCQJqqth0SIYMACmTYs6mp06TO5mlmdmQ8PyIGAisJIgyZ8dHjYdeDwsLwi3Cff/3t09mUGLiKSLuXPBHc46C4YNizqanRLp5z4KmGtm2QR/DB509yfNbAUw38z+G/gbcHd4/N3Ab8xsFbARSKO/ZSIiydPUBPfeG5QvuijSUHbTYXJ392XAl1upf4eg/X3X+k+Bc5ISnYhIGnvuOXj3XRgzBk48MepoWtLwAyIiXdT8IHX6dMjOjjaWXSm5i4h0webN8FD4ps+FF0YaSquU3EVEuuDBB6GuDr76Vdh//6ij2Z2Su4hIFzQ3yaTbg9RmSu4iIp305pvwl7/A4MFw9tkdHx8FJXcRkU5q7v547rkwZEikobRJyV1EpBMaG2HevKCcrk0yoOQuItIpzzwD770XPESdMCHqaNqm5C4i0glz5gTriy6CdB7MXMldRCRBH34ICxZAVhZccEHU0bRPyV1EJEH33w/19TBxYjDkQDpTchcRSVC6922Pp+QuIpKAV1+Fv/0tGNZ36tSoo+mYkruISAKa79q/8Q0YODDaWBKh5C4i0oH6erjvvqDcG5pkQMldRKRDTzwR9JT50pfg8MOjjiYxSu4iIh2If5Cazn3b4yUyh2q+mS02sxVm9rqZXRHWX29ma81sabicEnfONWa2yszeNLPJqbwAEZFki8ViFBUVkZWVxZgxR/LUU0306wfnnx91ZIlLZA7VBuAqd3/FzPYAlpjZwnDfLHe/Of5gMzuEYN7ULwL7As+a2YHu3pjMwEVEUiEWi1FaWkpdXR0Aa9ceD2QxblwNeXkF0QbXCR3eubv7Ond/JSxvAVYCo9s5ZSow3923u/u7wCpamWtVRCQdlZWVfZbYA8ET1Orq/4omoC7qVJu7mRURTJb9Ylh1uZktM7M5ZjYsrBsNrI47bQ3t/zEQEUkbNTU1cVtHAQcD71NbOy+iiLom4eRuZkOAh4Er3X0zcBewHzAOWAf8rDNfbGalZlZpZpW1tbWdOVVEJGUKCuKbXi4O17+hsLB33aMmlNzNLIcgscfc/REAd1/v7o3u3gTMZmfTy1ogP+70MWFdC+5e4e7F7l6cl5fXnWsQEUma8vJycnNzgUEEjw9h4MD5lJeXRxpXZ3X4QNXMDLgbWOnut8TVj3L3deHmWcDysLwAuN/MbiF4oHoA8FJSoxYRSZGSkhIArrjiZT78cE/691/Kr3/9vc/qe4tEesv8I/BN4DUzWxrWXQucZ2bjAAeqgG8BuPvrZvYgsIKgp81l6ikjIr3JN75Rwh13lPD
      "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
}