diff --git a/ejercicio2.ipynb b/ejercicio2.ipynb
index 1c5d45a7e35a140eff7aafe00acedd98431e3471..d1b9094ea94ac286ab9ce01801f36f43b590f6ea 100644
--- a/ejercicio2.ipynb
+++ b/ejercicio2.ipynb
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -129,7 +129,7 @@
       "Introduzca un numero entero mayor que cero;\n",
       "se retornará la fila n del triangulo de Pascal\n",
       "\n",
-      " 8\n"
+      " 3\n"
      ]
     },
     {
@@ -137,7 +137,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "[1, 7, 21, 35, 35, 21, 7, 1]\n"
+      "[1, 2, 1]\n"
      ]
     }
    ],
@@ -157,7 +157,7 @@
    "source": [
     "---\n",
     "\n",
-    "## La segunda parte del ejercicio lo lamé Triangulo de Pascal modificado, y consiste en...\n",
+    "## La segunda parte del ejercicio lo llamé Triangulo de Pascal modificado, y consiste en...\n",
     "\n",
     "Modifique la rutina anterior para que reciba un número variable de argumentos: n1, n2, n3,... y retorne una lista cuyo primer elemento es una lista conteniendo los números en la fila n1 del triángulo de Pascal, el segundo elemento una lista con los números en la fila n2, y así sucesivamente.\n",
     "___"
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -192,7 +192,8 @@
       "e.g. n1,n2,n3\n",
       "Se retornará una lista que contiene las filas n1,n2 y n3 del\n",
       "triangulo de Pascal\n",
-      " 10,20,33\n"
+      "\n",
+      " 2,4,6\n"
      ]
     },
     {
@@ -202,9 +203,9 @@
       "\n",
       "\n",
       "[\n",
-      "[1, 9, 36, 84, 126, 126, 84, 36, 9, 1]\n",
-      "[1, 19, 171, 969, 3876, 11628, 27132, 50388, 75582, 92378, 92378, 75582, 50388, 27132, 11628, 3876, 969, 171, 19, 1]\n",
-      "[1, 32, 496, 4960, 35960, 201376, 906192, 3365856, 10518300, 28048800, 64512240, 129024480, 225792840, 347373600, 471435600, 565722720, 601080390, 565722720, 471435600, 347373600, 225792840, 129024480, 64512240, 28048800, 10518300, 3365856, 906192, 201376, 35960, 4960, 496, 32, 1]\n",
+      "[1, 1]\n",
+      "[1, 3, 3, 1]\n",
+      "[1, 5, 10, 10, 5, 1]\n",
       "]\n"
      ]
     }
@@ -215,6 +216,7 @@
     "e.g. n1,n2,n3\n",
     "Se retornará una lista que contiene las filas n1,n2 y n3 del\n",
     "triangulo de Pascal\n",
+    "\n",
     "\"\"\")\n",
     "\n",
     "lista_de_numeros_string=varios_numeros.split(\",\")\n",
@@ -265,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -311,22 +313,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "range(1, 5)\n",
-      "[[1], [1, 1], [1, 2, 1], [1, 3, 3, 1]]\n",
+      "range(1, 10)\n",
+      "[[1], [1, 1], [1, 2, 1], [1, 3, 3, 1], [1, 4, 6, 4, 1], [1, 5, 10, 10, 5, 1], [1, 6, 15, 20, 15, 6, 1], [1, 7, 21, 35, 35, 21, 7, 1], [1, 8, 28, 56, 70, 56, 28, 8, 1]]\n",
       "\n",
-      "[1, 11, 121, 1331]\n"
+      "[1, 11, 121, 1331, 14641, 15101051, 1615201561, 172135352171, 18285670562881]\n"
      ]
     }
    ],
    "source": [
-    "x=range(1,5)\n",
+    "x=range(1,10)\n",
     "\n",
     "print(x)\n",
     "\n",
@@ -342,12 +344,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -361,7 +363,7 @@
    "source": [
     "#plt.plot(x,y,\"b-*\")\n",
     "#plt.show()\n",
-    "plt.loglog(x,y)\n",
+    "plt.loglog(x,y,\"b-o\")\n",
     "plt.show()"
    ]
   },