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f08f63d75da0658a492e80a20b022848
mooc-rr
Commits
e567a89d
Commit
e567a89d
authored
Nov 28, 2023
by
f08f63d75da0658a492e80a20b022848
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exercice.ipynb
module2/exo3/exercice.ipynb
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module2/exo3/exercice.ipynb
View file @
e567a89d
...
...
@@ -60,6 +60,32 @@
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig2, ax2 = plt.subplots(figsize = (10,8))\n",
"\n",
"ax2.hist(valeursY, bins = 10, linewidth = 0.5, edgecolor = \"black\")\n",
"ax2.set(xlim = (0, 25), ylim = (0, 25))\n",
"plt.grid(linestyle = \"--\", linewidth = 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
...
...
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