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Commit 80ad5711 authored by 1c81ef35986da123fb66944be6aa226c's avatar 1c81ef35986da123fb66944be6aa226c
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M module2/exo2/exercice_yas.ipynb

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module2/exo2/exercice.ipynb
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{ {
"cells": [], "cells": [
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.8"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
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"source": [
"min([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",
"])"
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},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23.4"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
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"source": [
"max([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])"
]
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"outputs": [
{
"data": {
"text/plain": [
"100"
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},
"execution_count": 14,
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"source": [
"len([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])"
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"data": {
"text/plain": [
"1411.3000000000006"
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"cell_type": "code",
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"outputs": [
{
"data": {
"text/plain": [
"14.113000000000007"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum([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])/100"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'numpy' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-24-e20f5adcda28>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m12.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.9\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36m14.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m22.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m23.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'numpy' is not defined"
]
}
],
"source": [
"a = numpy.average([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": 25,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"a = np.average([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": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.113000000000001"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"a=np.std( [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": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.312369534258399\n"
]
}
],
"source": [
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.312369534258399\n"
]
}
],
"source": [
"print(np.std([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": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
...@@ -16,10 +229,9 @@ ...@@ -16,10 +229,9 @@
"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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