COVID-19
/
model-mexico

{ "cells": [  {   "cell_type": "code",   "execution_count": 150,   "metadata": {},   "outputs": [    {     "data": {      "text/html": [       "<div>\n",       "<style scoped>\n",       "    .dataframe tbody tr th:only-of-type {\n",       "        vertical-align: middle;\n",       "    }\n",       "\n",       "    .dataframe tbody tr th {\n",       "        vertical-align: top;\n",       "    }\n",       "\n",       "    .dataframe thead th {\n",       "        text-align: right;\n",       "    }\n",       "</style>\n",       "<table border=\"1\" class=\"dataframe\">\n",       "  <thead>\n",       "    <tr style=\"text-align: right;\">\n",       "      <th></th>\n",       "      <th>DateRep</th>\n",       "      <th>Day</th>\n",       "      <th>Month</th>\n",       "      <th>Year</th>\n",       "      <th>Cases</th>\n",       "      <th>Deaths</th>\n",       "      <th>Countries and territories</th>\n",       "      <th>GeoId</th>\n",       "    </tr>\n",       "  </thead>\n",       "  <tbody>\n",       "    <tr>\n",       "      <th>0</th>\n",       "      <td>24/03/2020</td>\n",       "      <td>24</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>6</td>\n",       "      <td>1</td>\n",       "      <td>Afghanistan</td>\n",       "      <td>AF</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>1</th>\n",       "      <td>23/03/2020</td>\n",       "      <td>23</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>10</td>\n",       "      <td>0</td>\n",       "      <td>Afghanistan</td>\n",       "      <td>AF</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>2</th>\n",       "      <td>22/03/2020</td>\n",       "      <td>22</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>0</td>\n",       "      <td>0</td>\n",       "      <td>Afghanistan</td>\n",       "      <td>AF</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>3</th>\n",       "      <td>21/03/2020</td>\n",       "      <td>21</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>2</td>\n",       "      <td>0</td>\n",       "      <td>Afghanistan</td>\n",       "      <td>AF</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>4</th>\n",       "      <td>20/03/2020</td>\n",       "      <td>20</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>0</td>\n",       "      <td>0</td>\n",       "      <td>Afghanistan</td>\n",       "      <td>AF</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>...</th>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>6546</th>\n",       "      <td>19/03/2020</td>\n",       "      <td>19</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>2</td>\n",       "      <td>0</td>\n",       "      <td>Zambia</td>\n",       "      <td>ZM</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>6547</th>\n",       "      <td>24/03/2020</td>\n",       "      <td>24</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>0</td>\n",       "      <td>1</td>\n",       "      <td>Zimbabwe</td>\n",       "      <td>ZW</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>6548</th>\n",       "      <td>23/03/2020</td>\n",       "      <td>23</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>0</td>\n",       "      <td>0</td>\n",       "      <td>Zimbabwe</td>\n",       "      <td>ZW</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>6549</th>\n",       "      <td>22/03/2020</td>\n",       "      <td>22</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>1</td>\n",       "      <td>0</td>\n",       "      <td>Zimbabwe</td>\n",       "      <td>ZW</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>6550</th>\n",       "      <td>21/03/2020</td>\n",       "      <td>21</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>1</td>\n",       "      <td>0</td>\n",       "      <td>Zimbabwe</td>\n",       "      <td>ZW</td>\n",       "    </tr>\n",       "  </tbody>\n",       "</table>\n",       "<p>6551 rows × 8 columns</p>\n",       "</div>"      ],      "text/plain": [       "         DateRep  Day  Month  Year  Cases  Deaths Countries and territories  \\\n",       "0     24/03/2020   24      3  2020      6       1               Afghanistan   \n",       "1     23/03/2020   23      3  2020     10       0               Afghanistan   \n",       "2     22/03/2020   22      3  2020      0       0               Afghanistan   \n",       "3     21/03/2020   21      3  2020      2       0               Afghanistan   \n",       "4     20/03/2020   20      3  2020      0       0               Afghanistan   \n",       "...          ...  ...    ...   ...    ...     ...                       ...   \n",       "6546  19/03/2020   19      3  2020      2       0                    Zambia   \n",       "6547  24/03/2020   24      3  2020      0       1                  Zimbabwe   \n",       "6548  23/03/2020   23      3  2020      0       0                  Zimbabwe   \n",       "6549  22/03/2020   22      3  2020      1       0                  Zimbabwe   \n",       "6550  21/03/2020   21      3  2020      1       0                  Zimbabwe   \n",       "\n",       "     GeoId  \n",       "0       AF  \n",       "1       AF  \n",       "2       AF  \n",       "3       AF  \n",       "4       AF  \n",       "...    ...  \n",       "6546    ZM  \n",       "6547    ZW  \n",       "6548    ZW  \n",       "6549    ZW  \n",       "6550    ZW  \n",       "\n",       "[6551 rows x 8 columns]"      ]     },     "execution_count": 150,     "metadata": {},     "output_type": "execute_result"    }   ],   "source": [    "# Reading data\n",    "import pandas as pd\n",    "covid_data = pd.read_csv('https://worldhealthorg-my.sharepoint.com/personal/garnicacarrenoj_who_int/_layouts/15/Doc.aspx?sourcedoc=%7B87BD9C0A-2E91-4BE3-8308-B0B545B6DFB6%7D&file=CSV%20as%20at%2024%20March%202020-Daily%20additions.csv&action=default&mobileredirect=true&CT=1585104198257&OR=ItemsView')\n",    "covid_data"   ]  },  {   "cell_type": "code",   "execution_count": 151,   "metadata": {},   "outputs": [],   "source": [    "from sklearn.model_selection import train_test_split \n",    "train_set, test_set=train_test_split(covid_data,test_size=0.2,random_state=42)\n",    "train_cp=train_set.copy()"   ]  },  {   "cell_type": "code",   "execution_count": 152,   "metadata": {},   "outputs": [    {     "data": {      "text/plain": [       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x13ceded90>,\n",       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13caacad0>],\n",       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x13cc2fd50>,\n",       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13cc897d0>],\n",       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x13bd9e950>,\n",       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13bd2ea50>]],\n",       "      dtype=object)"      ]     },     "execution_count": 152,     "metadata": {},     "output_type": "execute_result"    },    {     "data": {      "image/png": 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     "text/plain": [       "<Figure size 432x288 with 6 Axes>"      ]     },     "metadata": {      "needs_background": "light"     },     "output_type": "display_data"    }   ],   "source": [    "%matplotlib inline\n",    "covid_data.hist()\n"   ]  },  {   "cell_type": "code",   "execution_count": 153,   "metadata": {},   "outputs": [    {     "data": {      "text/plain": [       "(77, 8)"      ]     },     "execution_count": 153,     "metadata": {},     "output_type": "execute_result"    }   ],   "source": [    "%matplotlib inline \n",    "import matplotlib.pyplot as plt \n",    "covid_mexico = covid_data[covid_data['GeoId']=='MX']\n",    "covid_mexico.shape"   ]  },  {   "cell_type": "code",   "execution_count": 154,   "metadata": {},   "outputs": [    {     "data": {      "text/plain": [       "<matplotlib.axes._subplots.AxesSubplot at 0x13d347490>"      ]     },     "execution_count": 154,     "metadata": {},     "output_type": "execute_result"    },    {     "data": {      "image/png": 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     "text/plain": [       "<Figure size 432x288 with 1 Axes>"      ]     },     "metadata": {      "needs_background": "light"     },     "output_type": "display_data"    }   ],   "source": [    "covid_mexico.plot(kind=\"scatter\", x=\"Month\", y=\"Cases\")"   ]  },  {   "cell_type": "code",   "execution_count": 168,   "metadata": {},   "outputs": [    {     "data": {      "text/html": [       "<div>\n",       "<style scoped>\n",       "    .dataframe tbody tr th:only-of-type {\n",       "        vertical-align: middle;\n",       "    }\n",       "\n",       "    .dataframe tbody tr th {\n",       "        vertical-align: top;\n",       "    }\n",       "\n",       "    .dataframe thead th {\n",       "        text-align: right;\n",       "    }\n",       "</style>\n",       "<table border=\"1\" class=\"dataframe\">\n",       "  <thead>\n",       "    <tr style=\"text-align: right;\">\n",       "      <th></th>\n",       "      <th>DateRep</th>\n",       "      <th>Day</th>\n",       "      <th>Month</th>\n",       "      <th>Year</th>\n",       "      <th>Cases</th>\n",       "      <th>Deaths</th>\n",       "      <th>Countries and territories</th>\n",       "      <th>GeoId</th>\n",       "    </tr>\n",       "  </thead>\n",       "  <tbody>\n",       "    <tr>\n",       "      <th>3985</th>\n",       "      <td>2019-12-31</td>\n",       "      <td>31</td>\n",       "      <td>12</td>\n",       "      <td>2019</td>\n",       "      <td>0</td>\n",       "      <td>0</td>\n",       "      <td>Mexico</td>\n",       "      <td>MX</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>3984</th>\n",       "      <td>2020-01-01</td>\n",       "      <td>1</td>\n",       "      <td>1</td>\n",       "      <td>2020</td>\n",       "      <td>0</td>\n",       "      <td>0</td>\n",       "      <td>Mexico</td>\n",       "      <td>MX</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>3953</th>\n",       "      <td>2020-01-02</td>\n",       "      <td>1</td>\n",       "      <td>2</td>\n",       "      <td>2020</td>\n",       "      <td>0</td>\n",       "      <td>0</td>\n",       "      <td>Mexico</td>\n",       "      <td>MX</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>3924</th>\n",       "      <td>2020-01-03</td>\n",       "      <td>1</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>2</td>\n",       "      <td>0</td>\n",       "      <td>Mexico</td>\n",       "      <td>MX</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>3972</th>\n",       "      <td>2020-01-13</td>\n",       "      <td>13</td>\n",       "      <td>1</td>\n",       "      <td>2020</td>\n",       "      <td>0</td>\n",       "      <td>0</td>\n",       "      <td>Mexico</td>\n",       "      <td>MX</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>...</th>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "      <td>...</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>3974</th>\n",       "      <td>2020-11-01</td>\n",       "      <td>11</td>\n",       "      <td>1</td>\n",       "      <td>2020</td>\n",       "      <td>0</td>\n",       "      <td>0</td>\n",       "      <td>Mexico</td>\n",       "      <td>MX</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>3943</th>\n",       "      <td>2020-11-02</td>\n",       "      <td>11</td>\n",       "      <td>2</td>\n",       "      <td>2020</td>\n",       "      <td>0</td>\n",       "      <td>0</td>\n",       "      <td>Mexico</td>\n",       "      <td>MX</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>3973</th>\n",       "      <td>2020-12-01</td>\n",       "      <td>12</td>\n",       "      <td>1</td>\n",       "      <td>2020</td>\n",       "      <td>0</td>\n",       "      <td>0</td>\n",       "      <td>Mexico</td>\n",       "      <td>MX</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>3942</th>\n",       "      <td>2020-12-02</td>\n",       "      <td>12</td>\n",       "      <td>2</td>\n",       "      <td>2020</td>\n",       "      <td>0</td>\n",       "      <td>0</td>\n",       "      <td>Mexico</td>\n",       "      <td>MX</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>3921</th>\n",       "      <td>2020-12-03</td>\n",       "      <td>12</td>\n",       "      <td>3</td>\n",       "      <td>2020</td>\n",       "      <td>4</td>\n",       "      <td>0</td>\n",       "      <td>Mexico</td>\n",       "      <td>MX</td>\n",       "    </tr>\n",       "  </tbody>\n",       "</table>\n",       "<p>77 rows × 8 columns</p>\n",       "</div>"      ],      "text/plain": [       "        DateRep  Day  Month  Year  Cases  Deaths Countries and territories  \\\n",       "3985 2019-12-31   31     12  2019      0       0                    Mexico   \n",       "3984 2020-01-01    1      1  2020      0       0                    Mexico   \n",       "3953 2020-01-02    1      2  2020      0       0                    Mexico   \n",       "3924 2020-01-03    1      3  2020      2       0                    Mexico   \n",       "3972 2020-01-13   13      1  2020      0       0                    Mexico   \n",       "...         ...  ...    ...   ...    ...     ...                       ...   \n",       "3974 2020-11-01   11      1  2020      0       0                    Mexico   \n",       "3943 2020-11-02   11      2  2020      0       0                    Mexico   \n",       "3973 2020-12-01   12      1  2020      0       0                    Mexico   \n",       "3942 2020-12-02   12      2  2020      0       0                    Mexico   \n",       "3921 2020-12-03   12      3  2020      4       0                    Mexico   \n",       "\n",       "     GeoId  \n",       "3985    MX  \n",       "3984    MX  \n",       "3953    MX  \n",       "3924    MX  \n",       "3972    MX  \n",       "...    ...  \n",       "3974    MX  \n",       "3943    MX  \n",       "3973    MX  \n",       "3942    MX  \n",       "3921    MX  \n",       "\n",       "[77 rows x 8 columns]"      ]     },     "execution_count": 168,     "metadata": {},     "output_type": "execute_result"    }   ],   "source": [    "from datetime import datetime\n",    "mexico['DateRep'] =pd.to_datetime(mexico.DateRep, format=\"%d/%m/%Y\")\n",    "mexico_sort=mexico.sort_values(by='DateRep', ascending=True)\n",    "mexico_sort"   ]  },  {   "cell_type": "code",   "execution_count": null,   "metadata": {},   "outputs": [],   "source": []  },  {   "cell_type": "code",   "execution_count": 156,   "metadata": {},   "outputs": [    {     "data": {      "text/plain": [       "<matplotlib.axes._subplots.AxesSubplot at 0x13ba35150>"      ]     },     "execution_count": 156,     "metadata": {},     "output_type": "execute_result"    },    {     "data": {      "image/png": "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      "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}