diff --git a/Book/Jupyter_Notebooks/.ipynb_checkpoints/apiMakeSens-checkpoint.ipynb b/Book/Jupyter_Notebooks/.ipynb_checkpoints/apiMakeSens-checkpoint.ipynb
index 8f745c7c6a57f2f63cf65de3512107aa32347c41..b67a3a57908ab6df028203fe27bb7b8511dd7858 100644
--- a/Book/Jupyter_Notebooks/.ipynb_checkpoints/apiMakeSens-checkpoint.ipynb
+++ b/Book/Jupyter_Notebooks/.ipynb_checkpoints/apiMakeSens-checkpoint.ipynb
@@ -507,14 +507,37 @@
    "execution_count": 52,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "Resumiendo:"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe width=\"560\" height=\"315\" src=\"https:&#x2F;&#x2F;www.canva.com&#x2F;design&#x2F;DAFwVICi7qs&#x2F;view?embed\" title=\"Pandas Review\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "HTML('<iframe width=\"560\" height=\"315\" src=\"https:&#x2F;&#x2F;www.canva.com&#x2F;design&#x2F;DAFwVICi7qs&#x2F;view?embed\" title=\"Pandas Review\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>')"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/Book/Jupyter_Notebooks/Fundamentos.ipynb b/Book/Jupyter_Notebooks/Fundamentos.ipynb
index 2aa0812a0260eb67782507ce038d949fdadcd034..2173d7eeab3255df020c000f80b726e6207dcdb4 100644
--- a/Book/Jupyter_Notebooks/Fundamentos.ipynb
+++ b/Book/Jupyter_Notebooks/Fundamentos.ipynb
@@ -969,7 +969,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.7"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,
diff --git a/Book/Jupyter_Notebooks/Pandas.ipynb b/Book/Jupyter_Notebooks/Pandas.ipynb
index d63a668705378d05902af35d7d9a7f998d5050a5..8f057e6b14afe857f59fc06137bc6888c4fbba3e 100644
--- a/Book/Jupyter_Notebooks/Pandas.ipynb
+++ b/Book/Jupyter_Notebooks/Pandas.ipynb
@@ -47,6 +47,34 @@
     "*  Visualización de datos utilizando herramientas integradas o integrando con otras bibliotecas como Matplotlib y Seaborn.\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe width=\"560\" height=\"315\" src=\"https:&#x2F;&#x2F;www.canva.com&#x2F;design&#x2F;DAFtE_0BP_c&#x2F;view?embed\" title=\"Pandas Review\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "HTML('<iframe width=\"560\" height=\"315\" src=\"https:&#x2F;&#x2F;www.canva.com&#x2F;design&#x2F;DAFtE_0BP_c&#x2F;view?embed\" title=\"Pandas Review\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -1688,20 +1716,6 @@
    "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/Book/Jupyter_Notebooks/apiMakeSens.ipynb b/Book/Jupyter_Notebooks/apiMakeSens.ipynb
index 8f745c7c6a57f2f63cf65de3512107aa32347c41..b67a3a57908ab6df028203fe27bb7b8511dd7858 100644
--- a/Book/Jupyter_Notebooks/apiMakeSens.ipynb
+++ b/Book/Jupyter_Notebooks/apiMakeSens.ipynb
@@ -507,14 +507,37 @@
    "execution_count": 52,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "Resumiendo:"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe width=\"560\" height=\"315\" src=\"https:&#x2F;&#x2F;www.canva.com&#x2F;design&#x2F;DAFwVICi7qs&#x2F;view?embed\" title=\"Pandas Review\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "HTML('<iframe width=\"560\" height=\"315\" src=\"https:&#x2F;&#x2F;www.canva.com&#x2F;design&#x2F;DAFwVICi7qs&#x2F;view?embed\" title=\"Pandas Review\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>')"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/Book/images/LogoMoncora.png b/Book/images/LogoMoncora.png
new file mode 100644
index 0000000000000000000000000000000000000000..53d5fe3de67e3adefb2fe9db4abee2f2623f30de
Binary files /dev/null and b/Book/images/LogoMoncora.png differ
diff --git a/README.md b/README.md
index 22d42bf2565d1873ae43001af983245fadd7ed27..a21b821f82f77f563ab252215fba33d65b5b96c1 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,11 @@
-
+<table>
+  <tr>
+    <td> 
 <img src="./Book/LogoHalleyTrans.png" alt="HalleyLogo" title="Halley logo" 
-     width="300px" align="left top" > 
+     width="300px" align="left top" > </td> <td> <img src="./Book/images/LogoMoncora.png" alt="HalleyLogo" title="Moncora logo" 
+     width="150px" align="left top" > </td>
+</tr>
+</table>
 
 
 [![Twitter](https://img.shields.io/twitter/follow/halleyUIS?style=social)](https://twitter.com/halleyuis?lang=es) [![Visita el sitio](https://img.shields.io/badge/Visita-el%20sitio-blue)](https://class.redclara.net/halley/moncora/intro.html)