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Jupyter notebooks and labs
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"id": "bb333be6-11c7-44c4-956a-052ef170c09d", | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"id": "a16da80f-8483-4704-979d-f202ea33b3ca", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"[<matplotlib.lines.Line2D at 0x7fab8abdd340>]" | |
] | |
}, | |
"execution_count": 5, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"plt.plot(np.arange(10), np.random.rand(10))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "850f328f-bd77-45ed-8323-47f0b2f5a638", | |
"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": "3.9.4" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 5 | |
} |
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