{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Générer les données pour le chapitre de Stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Découverte de la médiane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.32,  7.78, 10.43, 10.53, 10.83, 10.97,  9.02, 10.34, 11.01,\n",
       "        9.12, 10.13,  8.9 , 10.72,  9.22, 12.88,  9.29])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "picsou = np.random.normal(10, 1, 16).round(2)\n",
    "picsou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.85, 1.54, 1.72, 1.68, 1.48, 1.54, 1.57, 1.55, 1.8 , 1.75, 1.79,\n",
       "       1.57, 1.57, 1.52, 1.5 , 1.74, 1.58, 1.59, 1.73, 1.53])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sport = np.random.normal(1.6, 0.1, 20).round(2)\n",
    "sport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4033, 1178, 9512, 7896, 3253, 8789, 2494, 5446, 6015, 5977, 1738,\n",
       "       5501, 6654, 9135, 6954, 9867, 9144, 2386])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes = np.random.randint(0, 10000, 18)\n",
    "votes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Découverte de l'étendu\n",
    "\n",
    "Deux groupes avec même médiane et même moyenne mais 2 étendues différentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "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": "3.7.3"
  }
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 "nbformat": 4,
 "nbformat_minor": 2
}