225 lines
62 KiB
Plaintext
225 lines
62 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "spread-happiness",
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import torch\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from IPython.display import Markdown, display, HTML\n",
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"\n",
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"# Fix the dying kernel problem (only a problem in some installations - you can remove it, if it works without it)\n",
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"import os\n",
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"os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "adult-compensation",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Numpy array\n",
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"[[1. 2. 3.]\n",
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" [4. 5. 6.]\n",
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" [7. 8. 9.]]\n",
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"\n",
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"Pandas DataFrame\n",
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" A B C\n",
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"0 1.0 2.0 3.0\n",
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"1 4.0 5.0 6.0\n",
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"2 7.0 8.0 9.0\n",
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"\n",
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"Pretty display of pandas DataFrame\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th>A</th>\n",
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" <th>B</th>\n",
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" <th>C</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>1.0</td>\n",
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" <td>2.0</td>\n",
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" <td>3.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>4.0</td>\n",
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" <td>5.0</td>\n",
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" <td>6.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>7.0</td>\n",
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" <td>8.0</td>\n",
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" <td>9.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"PyTorch tensor\n",
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"tensor([[1., 2., 3.],\n",
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" [4., 5., 6.],\n",
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" [7., 8., 9.]], dtype=torch.float64)\n",
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"\n",
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"Matplolib chart\n"
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]
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Seaborn chart\n"
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]
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"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
|
||
|
|
}
|