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a6b043a967b7461961cf4321689a5db1
mooc-rr
Commits
97ccb125
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Commit
97ccb125
authored
Nov 20, 2024
by
a6b043a967b7461961cf4321689a5db1
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Exercice de traitement de données (avg, min/max, median, std)
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exercice.ipynb
module2/exo2/exercice.ipynb
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module2/exo2/exercice.ipynb
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97ccb125
{
{
"cells": [],
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Traitement de données\n",
"## Import des données dans un tableau"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[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"
]
}
],
"source": [
"import numpy as np\n",
"a = [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 (a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calcul de la moyenne"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"14.113000000000001\n"
]
}
],
"source": [
"avg = np.average(a)\n",
"print(avg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recherche du minimum"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.8\n"
]
}
],
"source": [
"min = np.min(a)\n",
"print(min)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recherche du maximum"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"23.4\n"
]
}
],
"source": [
"max = np.max(a)\n",
"print(max)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calcul de la médiane"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"14.5\n"
]
}
],
"source": [
"med = np.median(a)\n",
"print(med)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calcul de l'écart-type"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.334094455301447\n"
]
}
],
"source": [
"std = np.std(a, ddof=1)\n",
"print(std)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"metadata": {
"kernelspec": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3",
...
@@ -16,10 +176,9 @@
...
@@ -16,10 +176,9 @@
"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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