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://www.canva.com/design/DAFwVICi7qs/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://www.canva.com/design/DAFwVICi7qs/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://www.canva.com/design/DAFtE_0BP_c/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://www.canva.com/design/DAFtE_0BP_c/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://www.canva.com/design/DAFwVICi7qs/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://www.canva.com/design/DAFwVICi7qs/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> [](https://twitter.com/halleyuis?lang=es) [](https://class.redclara.net/halley/moncora/intro.html)