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Commit bb2491a6 authored by ec05da226ab8240df36990c29f8ac768's avatar ec05da226ab8240df36990c29f8ac768
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plot 1 et 2

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module2/exo2/exercice.ipynb
View file @ bb2491a6
{ {
"cells": [], "cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'mean' 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-4-10ccf06f9db5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean\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 \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.0\u001b[0m\u001b[0;34m,\u001b[0m \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[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'mean' is not defined"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.113000000000001"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as nb\n",
"nb.mean([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"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.8"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as nb\n",
"nb.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"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23.4"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as nb\n",
"nb.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])\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.5"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as nb\n",
"nb.median([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"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.334094455301447"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as nb\n",
"nb.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],ddof=1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
...@@ -16,10 +150,9 @@ ...@@ -16,10 +150,9 @@
"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
} }
module2/exo3/exercice.ipynb
View file @ bb2491a6
{ {
"cells": [], "cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"a=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": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 4., 3., 5., 9., 16., 20., 22., 9., 8., 4.]),\n",
" array([ 2.8 , 4.86, 6.92, 8.98, 11.04, 13.1 , 15.16, 17.22, 19.28,\n",
" 21.34, 23.4 ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.hist(a)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 100)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(a)\n",
"plt.xlim([0,100])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
...@@ -16,10 +103,9 @@ ...@@ -16,10 +103,9 @@
"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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