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Commit 484ce04f authored by d195ad838ca814ed5d135603ed8b731b's avatar d195ad838ca814ed5d135603ed8b731b
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exercice fini

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module2/exo2/exercice.ipynb
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{ {
"cells": [], "cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Données"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([14. , 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9,\n",
" 18.1, 7.3, 9.8, 10.9, 12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7,\n",
" 13.1, 13.2, 12.3, 11.7, 16. , 12.4, 17.9, 12.2, 16.2, 18.7, 8.9,\n",
" 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7,\n",
" 14. , 13.6, 18. , 13.6, 19.9, 13.7, 17. , 20.5, 9.9, 12.5, 13.2,\n",
" 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6,\n",
" 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15. , 14.3, 16.8, 14. ,\n",
" 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8,\n",
" 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9,\n",
" 21. ])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"L = 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,\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, 16.0, 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,\n",
" 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,\n",
" 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,\n",
" 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,\n",
" 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,\n",
" 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])\n",
"L"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Extrema\n",
"\n",
"## Minimum"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.8"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.min(L)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.8"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"min = L[0]\n",
"\n",
"for i in range(1, len(L)):\n",
" if L[i] < min:\n",
" min = L[i]\n",
"\n",
"min "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Maximum"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23.4"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.max(L)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23.4"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max = L[0]\n",
"\n",
"for i in range(1, len(L)):\n",
" if L[i] > max:\n",
" max = L[i]\n",
"\n",
"max"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Médiane"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.5"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.median(L)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Moyenne\n",
"\n",
"## Calcul de la moyenne avec la fonction *mean* de *numpy*"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.113000000000001"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean = np.mean(L)\n",
"mean"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calcul de la moyenne *à la main*"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.113000000000001"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean2 = np.sum(L)/len(L)\n",
"mean2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparaison"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean == mean2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Écart-type\n",
"\n",
"## Calcul de l'écart-type (corrigé) avec la fonction *std* de *numpy*"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.334094455301447"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SD = np.std(L, ddof=1)\n",
"SD"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calcul de l'écart-type *à la main*"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.3340944553014475"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from math import *\n",
"\n",
"V = 0\n",
"\n",
"for i in range(len(L)):\n",
" V += (L[i]-mean2)**2\n",
"\n",
"V /= len(L)-1\n",
"\n",
"SD2 = sqrt(V)\n",
"SD2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparaison"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SD == SD2"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-8.881784197001252e-16"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SD - SD2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bien que les deux résultats ne soient pas **strictement** égaux, la différence est particulièrement faible (correspondant peut-être à la précision machine)."
]
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
...@@ -16,10 +404,9 @@ ...@@ -16,10 +404,9 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 2
} }
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