ejemplo_2_covid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cargar Dataset\n",
    "dataset = pd.read_excel('dataset_covid.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = dataset[['Año', 'Fecha','Casos']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Casos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-04-25</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año      Fecha  Casos\n",
       "0  2020 2020-04-23    176\n",
       "1  2020 2020-04-24    110\n",
       "2  2020 2020-04-25    414\n",
       "3  2020 2020-04-26    263\n",
       "4  2020 2020-04-27      0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "heatmap1_data = pd.pivot_table(df1, values='Casos', \n",
    "                     index=['Fecha'], \n",
    "                     columns='Año')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('dark_background')\n",
    "sns.heatmap(heatmap1_data, cmap=\"coolwarm\")\n",
    "plt.savefig('heatmap.png', dpi=200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}