class_01a_jupyter_test.ipynb

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     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'torch'",
     "output_type": "error",
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      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_5188/278862110.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torch'"
     ]
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   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from IPython.display import Markdown, display, HTML\n",
    "\n",
    "# Fix the dying kernel problem (only a problem in some installations - you can remove it, if it works without it)\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adult-compensation",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "a = np.array(\n",
    "    [[1.0, 2.0, 3.0], \n",
    "     [4.0, 5.0, 6.0], \n",
    "     [7.0, 8.0, 9.0]]\n",
    ")\n",
    "\n",
    "print(\"Numpy array\")\n",
    "print(a)\n",
    "print()\n",
    "\n",
    "df = pd.DataFrame(a, columns=['A', 'B', 'C'])\n",
    "\n",
    "print(\"Pandas DataFrame\")\n",
    "print(df)\n",
    "print()\n",
    "\n",
    "print(\"Pretty display of pandas DataFrame\")\n",
    "display(HTML(df.to_html(index=False)))\n",
    "print()\n",
    "\n",
    "tensor = torch.from_numpy(a)\n",
    "\n",
    "print(\"PyTorch tensor\")\n",
    "print(tensor)\n",
    "print()\n",
    "\n",
    "# Matplotlib\n",
    "\n",
    "print(\"Matplolib chart\")\n",
    "\n",
    "# Prepare the data\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "# Plot the data\n",
    "plt.plot(x, x, label='linear')\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# Seaborn\n",
    "\n",
    "print(\"Seaborn chart\")\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "\n",
    "# Load the example Titanic dataset (the dataset may load some time)\n",
    "df = sns.load_dataset(\"titanic\")\n",
    "\n",
    "# Make a custom palette with gendered colors\n",
    "pal = dict(male=\"#6495ED\", female=\"#F08080\")\n",
    "\n",
    "# Show the survival probability as a function of age and sex\n",
    "g = sns.lmplot(x=\"age\", y=\"survived\", col=\"sex\", hue=\"sex\", data=df,\n",
    "               palette=pal, y_jitter=.02, logistic=True, truncate=False)\n",
    "g.set(xlim=(0, 80), ylim=(-.05, 1.05))\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "apparent-first",
   "metadata": {},
   "outputs": [],
   "source": []
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