covid-19 death comparison cdmx.ipynb

{
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   "cell_type": "code",
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   "source": [
    "# Libraries\n",
    "\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from dateutil.parser import parse\n",
    "from pandas import Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data from contralacorrupcion.mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EDAD</th>\n",
       "      <th>SEMANA</th>\n",
       "      <th>MES</th>\n",
       "      <th>RAZON</th>\n",
       "      <th>ACTA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FECHA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>41 AÑOS</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>CHOQUE SEPTICO, NEUMONIA POR COVID 19 POR SARS...</td>\n",
       "      <td>7129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>61 AÑOS</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>INSUFICIENCIA RESPIRATORIA AGUDA, NEUMONIA VIR...</td>\n",
       "      <td>4459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>60 AÑOS</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>SINDROME DE INSUFICIENCIA RESPIRATORIA AGUDA, ...</td>\n",
       "      <td>4591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>37 AÑOS</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>NEUMONIA POR CORONAVIRUS</td>\n",
       "      <td>7879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>63 AÑOS</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>CERVICOVAGINITIS PURULENTA, CARCINOMA EPIDERMO...</td>\n",
       "      <td>7829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               EDAD  SEMANA  MES  \\\n",
       "FECHA                              \n",
       "2020-03-18  41 AÑOS      12    3   \n",
       "2020-03-23  61 AÑOS      13    3   \n",
       "2020-03-26  60 AÑOS      13    3   \n",
       "2020-03-26  37 AÑOS      13    3   \n",
       "2020-03-26  63 AÑOS      13    3   \n",
       "\n",
       "                                                        RAZON  ACTA  \n",
       "FECHA                                                                \n",
       "2020-03-18  CHOQUE SEPTICO, NEUMONIA POR COVID 19 POR SARS...  7129  \n",
       "2020-03-23  INSUFICIENCIA RESPIRATORIA AGUDA, NEUMONIA VIR...  4459  \n",
       "2020-03-26  SINDROME DE INSUFICIENCIA RESPIRATORIA AGUDA, ...  4591  \n",
       "2020-03-26                           NEUMONIA POR CORONAVIRUS  7879  \n",
       "2020-03-26  CERVICOVAGINITIS PURULENTA, CARCINOMA EPIDERMO...  7829  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the dataset\n",
    "covid19 = pd.read_csv(\"actas-defuncion-covid-19-cdmx1.csv\",parse_dates=['FECHA'], index_col = \"FECHA\")\n",
    "covid19.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>FECHA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
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       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
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       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
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       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
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       "      <th>2020-03-26</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-12</th>\n",
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       "    <tr>\n",
       "      <th>2020-05-12</th>\n",
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       "    <tr>\n",
       "      <th>2020-05-12</th>\n",
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       "<p>4579 rows × 0 columns</p>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [2020-03-18 00:00:00, 2020-03-23 00:00:00, 2020-03-26 00:00:00, 2020-03-26 00:00:00, 2020-03-26 00:00:00, 2020-03-27 00:00:00, 2020-03-27 00:00:00, 2020-03-28 00:00:00, 2020-03-29 00:00:00, 2020-03-29 00:00:00, 2020-03-30 00:00:00, 2020-03-30 00:00:00, 2020-03-30 00:00:00, 2020-03-31 00:00:00, 2020-03-31 00:00:00, 2020-03-31 00:00:00, 2020-03-31 00:00:00, 2020-04-01 00:00:00, 2020-04-01 00:00:00, 2020-04-01 00:00:00, 2020-04-02 00:00:00, 2020-04-02 00:00:00, 2020-04-02 00:00:00, 2020-04-02 00:00:00, 2020-04-03 00:00:00, 2020-04-03 00:00:00, 2020-04-04 00:00:00, 2020-04-04 00:00:00, 2020-04-04 00:00:00, 2020-04-04 00:00:00, 2020-04-04 00:00:00, 2020-04-04 00:00:00, 2020-04-04 00:00:00, 2020-04-04 00:00:00, 2020-04-04 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-05 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-06 00:00:00, 2020-04-07 00:00:00, 2020-04-07 00:00:00, 2020-04-07 00:00:00, 2020-04-07 00:00:00, 2020-04-07 00:00:00, 2020-04-07 00:00:00, 2020-04-07 00:00:00, 2020-04-07 00:00:00, 2020-04-07 00:00:00, 2020-04-07 00:00:00, 2020-04-08 00:00:00, 2020-04-08 00:00:00, 2020-04-08 00:00:00, 2020-04-08 00:00:00, 2020-04-08 00:00:00, 2020-04-08 00:00:00, 2020-04-08 00:00:00, 2020-04-08 00:00:00, 2020-04-08 00:00:00, 2020-04-08 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, 2020-04-09 00:00:00, ...]\n",
       "\n",
       "[4579 rows x 0 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "number_by_date = covid19.drop(['EDAD', 'SEMANA', 'MES', 'RAZON', 'ACTA'], axis=1)\n",
    "number_by_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_by_date['MUERTES'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>MUERTES</th>\n",
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       "    <tr>\n",
       "      <th>FECHA</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2020-03-18</th>\n",
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       "      <th>2020-03-23</th>\n",
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       "      <th>2020-03-26</th>\n",
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       "      <th>2020-03-26</th>\n",
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       "      <th>...</th>\n",
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       "      <th>2020-05-12</th>\n",
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       "<p>4579 rows × 1 columns</p>\n",
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      "text/plain": [
       "            MUERTES\n",
       "FECHA              \n",
       "2020-03-18        0\n",
       "2020-03-23        0\n",
       "2020-03-26        0\n",
       "2020-03-26        0\n",
       "2020-03-26        0\n",
       "...             ...\n",
       "2020-05-12        0\n",
       "2020-05-12        0\n",
       "2020-05-12        0\n",
       "2020-05-12        0\n",
       "2020-05-12        0\n",
       "\n",
       "[4579 rows x 1 columns]"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "number_by_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_by_date.drop(number_by_date.head(2).index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>MUERTES</th>\n",
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       "    <tr>\n",
       "      <th>FECHA</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2020-03-26</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2020-03-28</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-29</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            MUERTES\n",
       "FECHA              \n",
       "2020-03-26        3\n",
       "2020-03-27        2\n",
       "2020-03-28        1\n",
       "2020-03-29        2\n",
       "2020-03-30        3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_by_date = number_by_date.groupby('FECHA').count()\n",
    "number_by_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nplt.subplot(211)\\nplt.plot(number_by_date)\\nplt.xlabel(\"Fecha\")\\nplt.ylabel(\"Muertes\")\\nplt.show()\\n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "plt.subplot(211)\n",
    "plt.plot(number_by_date)\n",
    "plt.xlabel(\"Fecha\")\n",
    "plt.ylabel(\"Muertes\")\n",
    "plt.show()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data from gob.mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FECHA_ACTUALIZACION</th>\n",
       "      <th>ID_REGISTRO</th>\n",
       "      <th>ORIGEN</th>\n",
       "      <th>SECTOR</th>\n",
       "      <th>ENTIDAD_UM</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>ENTIDAD_NAC</th>\n",
       "      <th>ENTIDAD_RES</th>\n",
       "      <th>MUNICIPIO_RES</th>\n",
       "      <th>TIPO_PACIENTE</th>\n",
       "      <th>...</th>\n",
       "      <th>CARDIOVASCULAR</th>\n",
       "      <th>OBESIDAD</th>\n",
       "      <th>RENAL_CRONICA</th>\n",
       "      <th>TABAQUISMO</th>\n",
       "      <th>OTRO_CASO</th>\n",
       "      <th>RESULTADO</th>\n",
       "      <th>MIGRANTE</th>\n",
       "      <th>PAIS_NACIONALIDAD</th>\n",
       "      <th>PAIS_ORIGEN</th>\n",
       "      <th>UCI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>11e989</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>MÃ©xico</td>\n",
       "      <td>99</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1aad65</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>MÃ©xico</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>04f631</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>MÃ©xico</td>\n",
       "      <td>99</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>02556b</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>MÃ©xico</td>\n",
       "      <td>99</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>0356d5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>MÃ©xico</td>\n",
       "      <td>99</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>1d2dfb</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>MÃ©xico</td>\n",
       "      <td>99</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>1b3e2b</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>MÃ©xico</td>\n",
       "      <td>99</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>0c0eef</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>114</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>MÃ©xico</td>\n",
       "      <td>99</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>043ea2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>MÃ©xico</td>\n",
       "      <td>99</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>0bd39a</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>MÃ©xico</td>\n",
       "      <td>99</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  FECHA_ACTUALIZACION ID_REGISTRO  ORIGEN  SECTOR  ENTIDAD_UM  SEXO  \\\n",
       "0          2020-05-21      11e989       2       3          27     2   \n",
       "1          2020-05-21      1aad65       2       4          19     2   \n",
       "2          2020-05-21      04f631       2       4          14     1   \n",
       "3          2020-05-21      02556b       2       4          15     1   \n",
       "4          2020-05-21      0356d5       2       4           9     1   \n",
       "5          2020-05-21      1d2dfb       2       4          25     2   \n",
       "6          2020-05-21      1b3e2b       2       4           9     2   \n",
       "7          2020-05-21      0c0eef       2       4          21     1   \n",
       "8          2020-05-21      043ea2       2       4          27     2   \n",
       "9          2020-05-21      0bd39a       2       4           8     1   \n",
       "\n",
       "   ENTIDAD_NAC  ENTIDAD_RES  MUNICIPIO_RES  TIPO_PACIENTE  ... CARDIOVASCULAR  \\\n",
       "0           27           27              4              2  ...              2   \n",
       "1            5            5             18              2  ...              2   \n",
       "2           14           14             67              1  ...              2   \n",
       "3           15           15            110              2  ...              2   \n",
       "4            9            9              5              2  ...              2   \n",
       "5           14           25              1              1  ...              2   \n",
       "6            7            9              2              1  ...              2   \n",
       "7           21           21            114              2  ...              2   \n",
       "8           27           27              4              1  ...              2   \n",
       "9            8            8             17              1  ...              2   \n",
       "\n",
       "  OBESIDAD RENAL_CRONICA  TABAQUISMO  OTRO_CASO  RESULTADO  MIGRANTE  \\\n",
       "0        2             2           2          1          1        99   \n",
       "1        2             2           2         99          1        99   \n",
       "2        2             2           2         99          1        99   \n",
       "3        2             2           2         99          1        99   \n",
       "4        2             2           1         99          1        99   \n",
       "5        2             2           2         99          1        99   \n",
       "6        2             2           2         99          1        99   \n",
       "7        1             2           2         99          1        99   \n",
       "8        2             2           2         99          1        99   \n",
       "9        2             2           2         99          1        99   \n",
       "\n",
       "   PAIS_NACIONALIDAD  PAIS_ORIGEN  UCI  \n",
       "0            MÃ©xico           99    2  \n",
       "1            MÃ©xico           99    1  \n",
       "2            MÃ©xico           99   97  \n",
       "3            MÃ©xico           99    2  \n",
       "4            MÃ©xico           99    2  \n",
       "5            MÃ©xico           99   97  \n",
       "6            MÃ©xico           99   97  \n",
       "7            MÃ©xico           99    2  \n",
       "8            MÃ©xico           99   97  \n",
       "9            MÃ©xico           99   97  \n",
       "\n",
       "[10 rows x 35 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid19_oficial = pd.read_csv(\"200521COVID19MEXICO.csv\", sep = \",\",parse_dates = [\"FECHA_DEF\"], encoding ='latin1')\n",
    "covid19_oficial.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = covid19_oficial[['FECHA_DEF', 'ENTIDAD_RES']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = temp.set_index('FECHA_DEF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENTIDAD_RES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FECHA_DEF</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-27</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-20</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201838 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ENTIDAD_RES\n",
       "FECHA_DEF              \n",
       "2020-04-27           27\n",
       "2020-04-03            5\n",
       "9999-99-99           14\n",
       "2020-04-20           15\n",
       "9999-99-99            9\n",
       "...                 ...\n",
       "2020-05-14           15\n",
       "9999-99-99           26\n",
       "9999-99-99           26\n",
       "9999-99-99           15\n",
       "9999-99-99            9\n",
       "\n",
       "[201838 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_cdmx = temp.loc[(temp['ENTIDAD_RES'] == 9)] #Number nine corresponds to CDMX as stated in the data dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENTIDAD_RES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FECHA_DEF</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>9</td>\n",
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       "      <th>9999-99-99</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-22</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2020-05-05</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999-99-99</th>\n",
       "      <td>9</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>46594 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ENTIDAD_RES\n",
       "FECHA_DEF              \n",
       "9999-99-99            9\n",
       "9999-99-99            9\n",
       "9999-99-99            9\n",
       "9999-99-99            9\n",
       "2020-03-22            9\n",
       "...                 ...\n",
       "2020-05-05            9\n",
       "9999-99-99            9\n",
       "9999-99-99            9\n",
       "9999-99-99            9\n",
       "9999-99-99            9\n",
       "\n",
       "[46594 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from_cdmx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "from_cdmx.drop(['ENTIDAD_RES'], axis=1, inplace=True, errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>9999-99-99</th>\n",
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       "    <tr>\n",
       "      <th>2020-03-22</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2020-05-05</th>\n",
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       "      <th>9999-99-99</th>\n",
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       "      <th>9999-99-99</th>\n",
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       "<p>46594 rows × 0 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 2020-03-22, 9999-99-99, 9999-99-99, 2020-04-08, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 2020-04-12, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 2020-04-22, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 2020-04-14, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 2020-04-29, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 2020-04-25, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 2020-04-30, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 2020-05-02, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, 9999-99-99, ...]\n",
       "\n",
       "[46594 rows x 0 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from_cdmx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "from_cdmx['MUERTES'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_cdmx = from_cdmx.groupby('FECHA_DEF').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>MUERTES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FECHA_DEF</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-22</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MUERTES\n",
       "FECHA_DEF          \n",
       "2020-03-16        1\n",
       "2020-03-22        1\n",
       "2020-03-23        1\n",
       "2020-03-25        1\n",
       "2020-03-26        3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from_cdmx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_cdmx.drop(from_cdmx.tail(1).index, inplace=True) #9999-99-99 date is dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_cdmx = from_cdmx.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_cdmx = from_cdmx.loc[(from_cdmx['FECHA_DEF'] >= '2020-03-26') &  (from_cdmx['FECHA_DEF'] <= '2020-05-12')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_cdmx = from_cdmx.set_index('FECHA_DEF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            MUERTES\n",
      "FECHA_DEF          \n",
      "2020-03-26        3\n",
      "            MUERTES\n",
      "FECHA_DEF          \n",
      "2020-05-12       60\n"
     ]
    }
   ],
   "source": [
    "print(from_cdmx.head(1))\n",
    "print(from_cdmx.tail(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_cdmx = from_cdmx.reset_index()\n",
    "from_cdmx['FECHA_DEF'] = pd.to_datetime(from_cdmx['FECHA_DEF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_cdmx = from_cdmx.set_index('FECHA_DEF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize':(14, 7)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "start, end = '2020-03', '2020-05'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1946b7e5a58>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(from_cdmx.loc[start:end],\n",
    "marker='o', markersize=8, linestyle='-', label='gob.mx')\n",
    "ax.plot(number_by_date.loc[start:end],\n",
    "marker='o', markersize=8, linestyle='-', label='contralacorrupcion.mx')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Number of Deaths')\n",
    "ax.set_title('COVID-19 death comparison in CDMX')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  }
 ],
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