Statistiques sur liste d'entrée, exo 2 2

parent 0e5a4c3f
{
"cells": [],
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculs statistiques sur un tableau\n",
"Le but de cet exercice est d'examiner les différentes statistiques telles que la moyenne, écart-type etc. d'un tableau donné au préalable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Données d'entrées"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"table = 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])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calcul de la moyenne\n",
"Pour calculer la moyenne de la liste, il suffit d'appeler la fonction _mean_ de la bibliotèque _numpy_."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"mean_table = np.mean(table)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La __moyenne__ de la liste d'entrée est donc de"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean value = 14.11\n"
]
}
],
"source": [
"print(\"mean value = \", round(mean_table,2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calcul du minimum et du maximum\n",
"Tout comme pour la moyenne, on peut directement appeler les fonctions _min_ et _max_ de _numpy_ pour connaître la valeur minimale et maximale de la liste d'entrée. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"min_table = np.min(table)\n",
"max_table = np.max(table)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les valeurs minimale et maximale de la liste d'entrée sont donc respectivement :"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"min value = 2.8\n",
"max value = 23.4\n"
]
}
],
"source": [
"print(\"min value = \", round(min_table,2))\n",
"print(\"max value = \", round(max_table,2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calcul de la médiane\n",
"_numpy_ fournit également un fonction permettant de calculer la médiane d'une liste. Dans le cas de notre liste celle-ci vaut :"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"median value = 14.5\n"
]
}
],
"source": [
"median_table = np.median(table)\n",
"print(\"median value = \", round(median_table,2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calcul de l'écart-type\n",
"L'écart-type (ou *standard deviation*) est également une fonction intégrée dans la bibliothèque *numpy*. Celle-ci nous donne un écart-type pour la liste donnée en entrée de :"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"standard deviation = 4.33\n"
]
}
],
"source": [
"ecart_type_table = np.std(table, ddof=1)\n",
"print(\"standard deviation = \", round(ecart_type_table,2))"
]
}
],
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"display_name": "Python 3",
......@@ -16,10 +180,9 @@
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