meh/recommender-systems-class-master/jupyter_test.ipynb
2021-07-07 20:03:54 +02:00

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62 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "spread-happiness",
"metadata": {},
"outputs": [],
"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": 2,
"id": "adult-compensation",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Numpy array\n",
"[[1. 2. 3.]\n",
" [4. 5. 6.]\n",
" [7. 8. 9.]]\n",
"\n",
"Pandas DataFrame\n",
" A B C\n",
"0 1.0 2.0 3.0\n",
"1 4.0 5.0 6.0\n",
"2 7.0 8.0 9.0\n",
"\n",
"Pretty display of pandas DataFrame\n"
]
},
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"PyTorch tensor\n",
"tensor([[1., 2., 3.],\n",
" [4., 5., 6.],\n",
" [7., 8., 9.]], dtype=torch.float64)\n",
"\n",
"Matplolib chart\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Seaborn chart\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}