nuevo_ejemplo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hola mundo\n"
     ]
    }
   ],
   "source": [
    "print('Hola mundo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "print(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejemplo usando pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Fecha</th>\n",
       "      <th>Producto</th>\n",
       "      <th>Cantidad</th>\n",
       "      <th>Precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>Producto A</td>\n",
       "      <td>10</td>\n",
       "      <td>25.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>Producto B</td>\n",
       "      <td>5</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>Producto C</td>\n",
       "      <td>7</td>\n",
       "      <td>20.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-01-04</td>\n",
       "      <td>Producto A</td>\n",
       "      <td>3</td>\n",
       "      <td>25.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-01-05</td>\n",
       "      <td>Producto B</td>\n",
       "      <td>12</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fecha    Producto  Cantidad  Precio\n",
       "0  2024-01-01  Producto A        10   25.50\n",
       "1  2024-01-02  Producto B         5   15.00\n",
       "2  2024-01-03  Producto C         7   20.75\n",
       "3  2024-01-04  Producto A         3   25.50\n",
       "4  2024-01-05  Producto B        12   15.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Cargar archivo CSV\n",
    "data = pd.read_csv('datos.csv')\n",
    "\n",
    "# Mostrar primeras filas\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Crear gráfico simple\n",
    "data['Cantidad'].plot(kind='line')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1\n",
    "b = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.0\n"
     ]
    }
   ],
   "source": [
    "def calcular_area(base, altura):\n",
    "    return base * altura / 2\n",
    "\n",
    "area = calcular_area(base=10, altura=5)\n",
    "print(area)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "notebooks_env",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}