diff --git a/PENDULO_ENTREGA_1.ipynb b/PENDULO_ENTREGA_1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dc14e5da6ce3a9fd449835f5f31a0417a96f3bf3
--- /dev/null
+++ b/PENDULO_ENTREGA_1.ipynb
@@ -0,0 +1,559 @@
+{
+  "metadata": {
+    "language_info": {
+      "codemirror_mode": {
+        "name": "python",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8"
+    },
+    "kernelspec": {
+      "name": "python",
+      "display_name": "Python (Pyodide)",
+      "language": "python"
+    }
+  },
+  "nbformat_minor": 4,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": "import numpy as np\nimport matplotlib.pyplot as plt",
+      "metadata": {},
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "datos= np.genfromtxt('MassB.txt')",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 301,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "datos",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 302,
+      "outputs": [
+        {
+          "execution_count": 302,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([[4.  , 4.02, 4.04, 3.95],\n       [4.16, 4.18, 4.17, 4.18],\n       [4.42, 4.41, 4.43, 4.4 ],\n       [4.87, 4.92, 4.88, 4.88],\n       [5.14, 5.13, 5.16, 5.14],\n       [5.26, 5.17, 5.15, 5.24],\n       [5.43, 5.44, 5.43, 5.46],\n       [5.7 , 5.74, 5.71, 5.73]])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "t=datos/5\nt",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 303,
+      "outputs": [
+        {
+          "execution_count": 303,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([[0.8  , 0.804, 0.808, 0.79 ],\n       [0.832, 0.836, 0.834, 0.836],\n       [0.884, 0.882, 0.886, 0.88 ],\n       [0.974, 0.984, 0.976, 0.976],\n       [1.028, 1.026, 1.032, 1.028],\n       [1.052, 1.034, 1.03 , 1.048],\n       [1.086, 1.088, 1.086, 1.092],\n       [1.14 , 1.148, 1.142, 1.146]])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "source": "# datos promedios del Periodo",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": "tp=t[0]\nT1=np.mean(tp)\nT1\n#14cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 297,
+      "outputs": [
+        {
+          "execution_count": 297,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "0.8005"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp2=t[1]\nT2=np.mean(tp2)\nT2\n\n#16cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 283,
+      "outputs": [
+        {
+          "execution_count": 283,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "0.8345"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp3=t[2]\nT3=np.mean(tp3)\nT3\n#18cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 282,
+      "outputs": [
+        {
+          "execution_count": 282,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "0.883"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp4=t[3]\nT4=np.mean(tp4)\nT4\n#22cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 304,
+      "outputs": [
+        {
+          "execution_count": 304,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "0.9775"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp5=t[4]\nT5=np.mean(tp5)\nT5\n#24cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 280,
+      "outputs": [
+        {
+          "execution_count": 280,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "1.0285000000000002"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp6=t[5]\nT6=np.mean(tp6)\nT6\n#26cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 279,
+      "outputs": [
+        {
+          "execution_count": 279,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "1.0410000000000001"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp7=t[6]\nT7=np.mean(tp7)\nT7\n#28cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 278,
+      "outputs": [
+        {
+          "execution_count": 278,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "1.088"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp8=t[7]\nT8=np.mean(tp8)\nT8\n#30cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 236,
+      "outputs": [
+        {
+          "execution_count": 236,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "1.1440000000000001"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "def gravedad (l,T_p):\n    g=4*np.pi**2*l/T_p**2\n    return (g)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 284,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "g1=gravedad(0.14,T1)\nprint (g1)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 298,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "8.625119082912407\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g2=gravedad(0.16,T2)\nprint (g2)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 286,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "9.070412483082855\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g3=gravedad(0.18,T3)\nprint (g3)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 287,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "9.11403799307716\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g4=gravedad(0.24,T4)\nprint (g4)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 288,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "10.018247540046165\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": " g5=gravedad(0.22,T5)\nprint (g5)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 305,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "8.210579781959067\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g6=gravedad(0.24,T6)\nprint (g6)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 290,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "8.743182011169136\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g7=gravedad(0.26,T7)\nprint (g7)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 291,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "8.671121946242542\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g8=gravedad(0.30,T8)\nprint (g8)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 292,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "9.049590812285462\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "G=[g1,g2,g3,g4,g5,g6,g7,g8]\nG\n",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 308,
+      "outputs": [
+        {
+          "execution_count": 308,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "[8.625119082912407,\n 9.070412483082855,\n 9.11403799307716,\n 10.018247540046165,\n 8.210579781959067,\n 8.743182011169136,\n 8.671121946242542,\n 9.049590812285462]"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g_prom=np.mean(G)\ng_prom",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 309,
+      "outputs": [
+        {
+          "execution_count": 309,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "8.93778645634685"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "T_p=np.array([T1,T2,T3,T4,T5,T6,T7,T8])\nT_p",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 210,
+      "outputs": [
+        {
+          "execution_count": 210,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([0.8055, 0.8345, 0.883 , 0.9725, 1.0275, 1.041 , 1.088 , 1.144 ])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "t_err=t.std(axis=1)\nt_err\n",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 310,
+      "outputs": [
+        {
+          "execution_count": 310,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([0.00668954, 0.00165831, 0.00223607, 0.00384057, 0.00217945,\n       0.00921954, 0.00244949, 0.00316228])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "l=np.arange(14,31,2)\nL=l/100\nL_list=L.tolist()\nL_list",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 311,
+      "outputs": [
+        {
+          "execution_count": 311,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "[0.14, 0.16, 0.18, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3]"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "del L_list[3]\nL_list\nL_p=np.array(L_list)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 312,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "l_err=L_p.std()\nl_err",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 313,
+      "outputs": [
+        {
+          "execution_count": 313,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "0.054256336035526764"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "plt.figure()\nplt.errorbar(T_p,L_p,l_err,t_err,fmt='o',color='k')\nplt.xlabel('T(s)')\nplt.ylabel('l(m)')\nplt.show()",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 316,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 640x480 with 1 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "Y= np.log(np.abs(T_p))\nY\n",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 318,
+      "outputs": [
+        {
+          "execution_count": 318,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([-0.21629208, -0.18092254, -0.12443008, -0.0278852 ,  0.02712867,\n        0.04018179,  0.08434115,  0.13453089])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "X=np.log(2*np.pi*np.sqrt(L_p/g_prom))\nX",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 326,
+      "outputs": [
+        {
+          "execution_count": 326,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([-0.24032334, -0.17355764, -0.11466613, -0.01433078,  0.02917491,\n        0.06919626,  0.10625025,  0.14074668])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "plt.figure()\nplt.plot(X,Y)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 328,
+      "outputs": [
+        {
+          "execution_count": 328,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "[<matplotlib.lines.Line2D at 0x5ae46e0>]"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 640x480 with 1 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "",
+      "metadata": {},
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file