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Commit f98fe0d5 authored by 5956bcdb06c2088a97b35e01dc67db8c's avatar 5956bcdb06c2088a97b35e01dc67db8c
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Deuxième commit de l'exo 2 du module 2

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module2/exo2/exercice.ipynb
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{
"cells": [],
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[14. , 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2],\n",
" [14.9, 18.1, 7.3, 9.8, 10.9, 12.2, 9.9, 2.9, 2.8, 15.4],\n",
" [15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16. , 12.4, 17.9, 12.2],\n",
" [16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9],\n",
" [16.8, 11.3, 14.4, 15.7, 14. , 13.6, 18. , 13.6, 19.9, 13.7],\n",
" [17. , 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6],\n",
" [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],\n",
" [20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2],\n",
" [20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21. ]])"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"A = np.array([[14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2],\n",
" [14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4],\n",
" [15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2],\n",
" [16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9],\n",
" [16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7],\n",
" [17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6],\n",
" [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, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9],\n",
" [20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2],\n",
" [20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]])\n",
"A"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.113000000000001"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"moyenne_A = np.mean(A)\n",
"moyenne_A"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.334094455301447"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"std_A = np.std(A,ddof=1)\n",
"std_A"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.8"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"min_A = np.min(A)\n",
"min_A"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23.4"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_A = np.max(A)\n",
"max_A"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.5"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"med_A = np.median(A) \n",
"med_A"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10, 10)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.shape(A)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
......@@ -16,10 +184,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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