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Commit b7507f28 authored by 7c8b98dd05a86d272dc44ef63cc416c0's avatar 7c8b98dd05a86d272dc44ef63cc416c0
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first part of the exercice

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module2/exo2/exercice.ipynb
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{
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"# Savoir faire un calcul simple par soi-même\n",
"L'idée est de savoir calculer la **moyenne** et **l'écart-type**, le **min**, la **médiane** et le **max** des données suivantes :\n",
"\n",
" 14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[14. 7.6 11.2 12.8 12.5 9.9 14.9 9.4 16.9 10.2 14.9 18.1 7.3 9.8\n",
" 10.9 12.2 9.9 2.9 2.8 15.4 15.7 9.7 13.1 13.2 12.3 11.7 16. 12.4\n",
" 17.9 12.2 16.2 18.7 8.9 11.9 12.1 14.6 12.1 4.7 3.9 16.9 16.8 11.3\n",
" 14.4 15.7 14. 13.6 18. 13.6 19.9 13.7 17. 20.5 9.9 12.5 13.2 16.1\n",
" 13.5 6.3 6.4 17.6 19.1 12.8 15.5 16.3 15.2 14.6 19.1 14.4 21.4 15.1\n",
" 19.6 21.7 11.3 15. 14.3 16.8 14. 6.8 8.2 19.9 20.4 14.6 16.4 18.7\n",
" 16.8 15.8 20.4 15.8 22.4 16.2 20.3 23.4 12.1 15.5 15.4 18.4 15.7 10.2\n",
" 8.9 21. ]\n"
]
}
],
"source": [
"import numpy as np\n",
"arr = np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])\n",
"print(arr)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"14.113000000000001\n"
]
}
],
"source": [
"moyenne = np.mean(arr)\n",
"print(moyenne)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.334094455301447\n"
]
}
],
"source": [
"ecartType = np.std(arr, ddof=1)\n",
"print(ecartType)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"14.5\n"
]
}
],
"source": [
"mediane = np.median(arr)\n",
"print(mediane)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.8\n"
]
}
],
"source": [
"minimum = np.min(arr)\n",
"print(minimum)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"23.4\n"
]
}
],
"source": [
"maximum = np.max(arr)\n",
"print(maximum)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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...@@ -16,10 +150,9 @@ ...@@ -16,10 +150,9 @@
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