diff --git a/LabAvanzado1/EfectoFotoelectrico/code/fotoelectrico.ipynb b/LabAvanzado1/EfectoFotoelectrico/code/fotoelectrico.ipynb
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index 0000000000000000000000000000000000000000..7930dcbb25e1118fca5bf27785fe8bef24fd2672
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@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Efecto Fotoelectrico"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\nicom\\AppData\\Local\\Temp\\ipykernel_2784\\3256740604.py:20: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
+      "  datos['U'] = datos[['U1', 'U2', 'U3', 'U4', 'U5']].mean(axis=1)\n"
+     ]
+    },
+    {
+     "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>lambda</th>\n",
+       "      <th>U1</th>\n",
+       "      <th>U2</th>\n",
+       "      <th>U3</th>\n",
+       "      <th>U4</th>\n",
+       "      <th>U5</th>\n",
+       "      <th>U</th>\n",
+       "      <th>f</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>578</td>\n",
+       "      <td>0.227</td>\n",
+       "      <td>0.231</td>\n",
+       "      <td>0.221</td>\n",
+       "      <td>0.220</td>\n",
+       "      <td>0.210</td>\n",
+       "      <td>0.2220</td>\n",
+       "      <td>518685.121107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>436</td>\n",
+       "      <td>0.197</td>\n",
+       "      <td>0.200</td>\n",
+       "      <td>0.199</td>\n",
+       "      <td>0.202</td>\n",
+       "      <td>0.197</td>\n",
+       "      <td>0.1990</td>\n",
+       "      <td>687614.678899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>546</td>\n",
+       "      <td>0.197</td>\n",
+       "      <td>0.202</td>\n",
+       "      <td></td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.1995</td>\n",
+       "      <td>549084.249084</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>405</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>740246.913580</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   lambda     U1     U2     U3     U4     U5       U              f\n",
+       "0     578  0.227  0.231  0.221  0.220  0.210  0.2220  518685.121107\n",
+       "1     436  0.197  0.200  0.199  0.202  0.197  0.1990  687614.678899\n",
+       "2     546  0.197  0.202           NaN    NaN  0.1995  549084.249084\n",
+       "3     405    NaN    NaN    NaN    NaN    NaN     NaN  740246.913580"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Importamos las librerias necesarias\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Cargamos los datos\n",
+    "datos = pd.read_csv('../data/datos.csv', usecols=[0,1,2,3,4,5])\n",
+    "\n",
+    "# Definimos constantes\n",
+    "h = 6.626e-34 # Constante de Planck\n",
+    "dh = 0.001e-34 # Incertidumbre de la constante de Planck\n",
+    "e = 1.602e-19 # Carga del electron\n",
+    "de = 0.001e-19 # Incertidumbre de la carga del electron\n",
+    "c = 2.998e8 # Velocidad de la luz\n",
+    "dc = 0.001e8 # Incertidumbre de la velocidad de la luz\n",
+    "dU = 0.001 # Incertidumbre de la diferencia de potencial\n",
+    "dL = 1e-9 # Incertidumbre de la longitud de onda\n",
+    "\n",
+    "# Agregamos el promedio\n",
+    "datos['U'] = datos[['U1', 'U2', 'U3', 'U4', 'U5']].mean(axis=1)\n",
+    "\n",
+    "# Calculamos la frecuencia a partir de lambda\n",
+    "datos['f'] = c / datos['lambda']\n",
+    "\n",
+    "datos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Graficamos el potencial en función de la frecuencia\n",
+    "plt.errorbar(datos['f'], datos['U'], xerr=c/datos['lambda']**2*dL, yerr=dU, fmt='o')\n",
+    "plt.xlabel('Frecuencia (Hz)')\n",
+    "plt.ylabel('Potencial (V)')\n",
+    "plt.title('Potencial en función de la frecuencia')\n",
+    "plt.grid()\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/LabAvanzado1/EfectoFotoelectrico/data/datos.csv b/LabAvanzado1/EfectoFotoelectrico/data/datos.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2e008a7a02939c4ae8a69664b79d2103456c488a
--- /dev/null
+++ b/LabAvanzado1/EfectoFotoelectrico/data/datos.csv
@@ -0,0 +1,5 @@
+lambda,U1,U2,U3,U4,U5,t
+578,0.227,0.231,0.221,0.220,0.210,4mins 
+436,0.197,0.200,0.199,0.202,0.197,3mins
+546,0.197, 0.202, 
+405
\ No newline at end of file