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mooc-rr
Commit 95da642b authored by 6f892419cc99326ee525ed439d8ff5df's avatar 6f892419cc99326ee525ed439d8ff5df
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module2/exo2/exercice.ipynb
View file @ 95da642b
......@@ -9,11 +9,12 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
"import numpy as np\n",
"import matplotlib as plt"
]
},
{
......@@ -39,143 +40,41 @@
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(14.0,\n",
" 7.6,\n",
" 11.2,\n",
" 12.8,\n",
" 12.5,\n",
" 9.9,\n",
" 14.9,\n",
" 9.4,\n",
" 16.9,\n",
" 10.2,\n",
" 14.9,\n",
" 18.1,\n",
" 7.3,\n",
" 9.8,\n",
" 10.9,\n",
" 12.2,\n",
" 9.9,\n",
" 2.9,\n",
" 2.8,\n",
" 15.4,\n",
" 15.7,\n",
" 9.7,\n",
" 13.1,\n",
" 13.2,\n",
" 12.3,\n",
" 11.7,\n",
" 16.0,\n",
" 12.4,\n",
" 17.9,\n",
" 12.2,\n",
" 16.2,\n",
" 18.7,\n",
" 8.9,\n",
" 11.9,\n",
" 12.1,\n",
" 14.6,\n",
" 12.1,\n",
" 4.7,\n",
" 3.9,\n",
" 16.9,\n",
" 16.8,\n",
" 11.3,\n",
" 14.4,\n",
" 15.7,\n",
" 14.0,\n",
" 13.6,\n",
" 18.0,\n",
" 13.6,\n",
" 19.9,\n",
" 13.7,\n",
" 17.0,\n",
" 20.5,\n",
" 9.9,\n",
" 12.5,\n",
" 13.2,\n",
" 16.1,\n",
" 13.5,\n",
" 6.3,\n",
" 6.4,\n",
" 17.6,\n",
" 19.1,\n",
" 12.8,\n",
" 15.5,\n",
" 16.3,\n",
" 15.2,\n",
" 14.6,\n",
" 19.1,\n",
" 14.4,\n",
" 21.4,\n",
" 15.1,\n",
" 19.6,\n",
" 21.7,\n",
" 11.3,\n",
" 15.0,\n",
" 14.3,\n",
" 16.8,\n",
" 14.0,\n",
" 6.8,\n",
" 8.2,\n",
" 19.9,\n",
" 20.4,\n",
" 14.6,\n",
" 16.4,\n",
" 18.7,\n",
" 16.8,\n",
" 15.8,\n",
" 20.4,\n",
" 15.8,\n",
" 22.4,\n",
" 16.2,\n",
" 20.3,\n",
" 23.4,\n",
" 12.1,\n",
" 15.5,\n",
" 15.4,\n",
" 18.4,\n",
" 15.7,\n",
" 10.2,\n",
" 8.9,\n",
" 21.0)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"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": 8,
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"list=[]"
"list = (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": 9,
"execution_count": 21,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"la moyenne des nombres est : 14.113000000000007\n",
"la moyenne de 1 et 2 est : 1.5\n",
"le minimum est : 2.8\n",
"le maximum est : 23.4\n"
]
}
],
"source": [
"def moyenne(liste=[]) :\n",
" somme = sum(liste)\n",
" nb_elements = len(liste)\n",
"def moyenne(l):\n",
" somme = sum(l)\n",
" nb_elements = len(l)\n",
" moyenne = somme / nb_elements\n",
" return moyenne"
" return moyenne\n",
"\n",
"print(\"la moyenne des nombres est : \", moyenne(list))\n",
"print(\"la moyenne de 1 et 2 est : \", moyenne((1, 2)))\n",
"\n",
"print(\"le minimum est : \", min(list))\n",
"print(\"le maximum est : \", max(list))"
]
},
{
......@@ -326,28 +225,25 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"calcul de la moyenne\n"
"calcul de la moyenne\n",
"Entrer une note ou écrire fin s'il n'y a plus de notes à entrer : \n",
"fin\n",
"Vous avez entré 100 notes\n",
"La moyenne de cette série est 14.113000000000007\n"
]
}
],
"source": [
"print (\"calcul de la moyenne\")\n",
"list=[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",
"n=0\n",
"while n!=\"fin\":\n",
" n=input((\"Entrer une note ou écrire fin s'il n'y a plus de notes à entrer : \\n\"))\n",
" if n!=\"fin\":\n",
" n=float(n)\n",
" liste.append(n)\n",
"print (\"Vous avez entré\", len(liste), \" notes\")\n",
"m=sum(list)/len(liste)\n",
"m=sum(list)/len(list)\n",
"print(\"La moyenne de cette série est \", m)"
]
},
......@@ -389,24 +285,29 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 38,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calcul de la médiane.\n",
"15.35\n",
"Vous avez entré 100 valeurs\n",
"La médiane de votre série est 15.35\n"
]
}
],
"source": [
"Print (\"Calcul de la médiane.\")\n",
"liste=[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",
"n=0\n",
"while n!=\"fin\":\n",
" n=input((Entrer une valeur de la série ou écrire fin s'il n'y a plus de valeur à entrer : \\n))\n",
" if n!= \"fin\":\n",
" n=float(n)\n",
" liste.append(n)\n",
"liste.sort()\n",
"if len(liste)%2==0 :\n",
" m=((liste[(len(liste)-1)//2]+liste[len(liste)//2])/2)\n",
"else :\n",
" m=liste[len(liste)//2]\n",
"print (\"Vous avez entré \", len(liste), \"valeurs\")\n",
"print(\"Calcul de la médiane.\")\n",
"sorted_list = sorted(list)\n",
"print((list[49] + list[50]) / 2)\n",
"if len(sorted_list) % 2 == 0:\n",
" m = ((list[(len(sorted_list) - 1) // 2] + list[len(sorted_list) // 2]) / 2)\n",
"else:\n",
" m = list[len(sorted_list) / 2]\n",
"print (\"Vous avez entré \", len(list), \"valeurs\")\n",
"print(\"La médiane de votre série est \", m)"
]
},
......@@ -427,7 +328,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
......@@ -437,19 +338,34 @@
" if liste_len < 1:\n",
" return None\n",
" if liste_len % 2 == 0 :\n",
" return ( l[(liste_len-1)/2] + l[(liste_len+1)/2] ) / 2.0\n",
" return ( l[(liste_len-1)/2] + l[(liste_len)/2] ) / 2.0\n",
" else:\n",
" return liste[(liste_len-1)/2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 49,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"18.784374747474743"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [1]\n",
"print calculate_median(l)"
"import pandas as pd\n",
"\n",
"df = pd.Series(list)\n",
"df.describe()\n",
"df.std()**2"
]
},
{
......@@ -463,12 +379,42 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 28,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"14.5\n"
]
}
],
"source": [
"l = [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(np.median(np.array(list)))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.5"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import statistics\n",
"l=[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 np.median(np.array(l))"
"statistics.median(list)"
]
},
{
......@@ -484,18 +430,6 @@
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
......
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