model-mexico/covid-model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "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('firstTest')\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": 6,
   "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": 10,
   "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": 10,
     "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": 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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11a434290>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1194add50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1194d3410>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1194f1a90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x119516150>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1195337d0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "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": 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": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "The descartes package is required for plotting polygons in geopandas. You can install it using 'conda install -c conda-forge descartes' or 'pip install descartes'.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m~/lwc/topics/covid19/covid/lib/python3.7/site-packages/geopandas/plotting.py\u001b[0m in \u001b[0;36mplot_polygon_collection\u001b[0;34m(ax, geoms, values, color, cmap, vmin, vmax, **kwargs)\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0;32mfrom\u001b[0m \u001b[0mdescartes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPolygonPatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'descartes'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-0ad2a8a84de6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgeopandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mworld\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'naturalearth_lowres'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\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/geopandas/geodataframe.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    654\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mthere\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m         \"\"\"\n\u001b[0;32m--> 656\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    657\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m     \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_dataframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\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/geopandas/plotting.py\u001b[0m in \u001b[0;36mplot_dataframe\u001b[0;34m(df, column, cmap, color, ax, cax, categorical, legend, scheme, k, vmin, vmax, markersize, figsize, legend_kwds, classification_kwds, missing_kwds, **style_kwds)\u001b[0m\n\u001b[1;32m    545\u001b[0m             \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m             \u001b[0mmarkersize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmarkersize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m             \u001b[0;34m**\u001b[0m\u001b[0mstyle_kwds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m         )\n\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/lwc/topics/covid19/covid/lib/python3.7/site-packages/geopandas/plotting.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(s, cmap, color, ax, figsize, **style_kwds)\u001b[0m\n\u001b[1;32m    371\u001b[0m         \u001b[0mvalues_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpoly_idx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcmap\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    372\u001b[0m         plot_polygon_collection(\n\u001b[0;32m--> 373\u001b[0;31m             \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpolys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mstyle_kwds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    374\u001b[0m         )\n\u001b[1;32m    375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/lwc/topics/covid19/covid/lib/python3.7/site-packages/geopandas/plotting.py\u001b[0m in \u001b[0;36mplot_polygon_collection\u001b[0;34m(ax, geoms, values, color, cmap, vmin, vmax, **kwargs)\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         raise ImportError(\n\u001b[0;32m---> 84\u001b[0;31m             \u001b[0;34m\"The descartes package is required for plotting polygons in geopandas. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m             \u001b[0;34m\"You can install it using 'conda install -c conda-forge descartes' or \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m             \u001b[0;34m\"'pip install descartes'.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: The descartes package is required for plotting polygons in geopandas. You can install it using 'conda install -c conda-forge descartes' or 'pip install descartes'."
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "world.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_data.plot(kind=\"scatter\", x=\"Long\", y=\"Lat\")"
   ]
  },
  {
   "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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
      "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"
   ]
  }
 ],
 "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
}