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Commit bb4b83d8 authored by a7253e7718fcaf362de01f5bbf92fedd's avatar a7253e7718fcaf362de01f5bbf92fedd
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exercie 2.2

parent 4982053d
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module2/exo2/exercice.ipynb
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{
"cells": [],
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from statistics import median\n",
"\n",
"from math import isnan\n",
"\n",
"from itertools import filterfalse"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"data = [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": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2.8,\n",
" 2.9,\n",
" 3.9,\n",
" 4.7,\n",
" 6.3,\n",
" 6.4,\n",
" 6.8,\n",
" 7.3,\n",
" 7.6,\n",
" 8.2,\n",
" 8.9,\n",
" 8.9,\n",
" 9.4,\n",
" 9.7,\n",
" 9.8,\n",
" 9.9,\n",
" 9.9,\n",
" 9.9,\n",
" 10.2,\n",
" 10.2,\n",
" 10.9,\n",
" 11.2,\n",
" 11.3,\n",
" 11.3,\n",
" 11.7,\n",
" 11.9,\n",
" 12.1,\n",
" 12.1,\n",
" 12.1,\n",
" 12.2,\n",
" 12.2,\n",
" 12.3,\n",
" 12.4,\n",
" 12.5,\n",
" 12.5,\n",
" 12.8,\n",
" 12.8,\n",
" 13.1,\n",
" 13.2,\n",
" 13.2,\n",
" 13.5,\n",
" 13.6,\n",
" 13.6,\n",
" 13.7,\n",
" 14.0,\n",
" 14.0,\n",
" 14.0,\n",
" 14.3,\n",
" 14.4,\n",
" 14.4,\n",
" 14.6,\n",
" 14.6,\n",
" 14.6,\n",
" 14.9,\n",
" 14.9,\n",
" 15.0,\n",
" 15.1,\n",
" 15.2,\n",
" 15.4,\n",
" 15.4,\n",
" 15.5,\n",
" 15.5,\n",
" 15.7,\n",
" 15.7,\n",
" 15.7,\n",
" 15.8,\n",
" 15.8,\n",
" 16.0,\n",
" 16.1,\n",
" 16.2,\n",
" 16.2,\n",
" 16.3,\n",
" 16.4,\n",
" 16.8,\n",
" 16.8,\n",
" 16.8,\n",
" 16.9,\n",
" 16.9,\n",
" 17.0,\n",
" 17.6,\n",
" 17.9,\n",
" 18.0,\n",
" 18.1,\n",
" 18.4,\n",
" 18.7,\n",
" 18.7,\n",
" 19.1,\n",
" 19.1,\n",
" 19.6,\n",
" 19.9,\n",
" 19.9,\n",
" 20.3,\n",
" 20.4,\n",
" 20.4,\n",
" 20.5,\n",
" 21.0,\n",
" 21.4,\n",
" 21.7,\n",
" 22.4,\n",
" 23.4]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(data)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.5"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"median(data)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'std' 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-7-9c6f672b38f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mddof\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'std' is not defined"
]
}
],
"source": [
"std(data,ddof=1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.113000000000001"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.average(data)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.334094455301447"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.std(data,ddof=1)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.5"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.median(data)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.8"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.min(data)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23.4"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.max(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
......@@ -16,10 +315,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.4"
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},
"nbformat": 4,
"nbformat_minor": 2
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