{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Étude sur l'usure de mes crayons à papier\n",
    "## 1 présentation des données\n",
    "\n",
    "La présente étude porte sur les crayons à papier retrouvés dans les quatre tiroirs de mon bureau lors de son dernier rangement en date.\n",
    "\n",
    "Elle a pour objectif d'évaluer l'état général du stock et de fournir des indications sur l'usure relative des crayons.\n",
    "\n",
    "Le paramètres pris en compte est la **longueur** du crayon de graphite, exprimée en millimètres.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "# chargement des bibliothèques\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taille des crayons trouvés :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[160 132 136 108  54  77  77  79  92 104 160 160 160 160 160 115 128 129\n",
      " 144 129 132 140 160]\n"
     ]
    }
   ],
   "source": [
    "# création du tableau\n",
    "t = np.array([160, 132, 136, 108, 54, 77, 77, 79, 92, 104, 160, 160, 160, 160, 160, 115, 128, 129, 144, 129, 132, 140, 160])\n",
    "print (t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'histogramme ci-dessous montre comment se distribuent mes crayons selon leur taille"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "%matplotlib inline\n",
    "plt.hist(t)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 calculs\n",
    "### 2.1 nombre total de crayons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n"
     ]
    }
   ],
   "source": [
    "print (t.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "soit une taille totale en millimètres de :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2896"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Sur les plus petits crayons\n",
    "Taille du plus petit crayon et nombre de crayons ayant cette taille"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.amin(t)\n",
    "cp = np.amin(t)\n",
    "print (cp)\n",
    "np.sum(t==cp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Sur les plus grands crayons\n",
    "Taille du plus grand crayon et nombre de crayons ayant cette taille \n",
    "(correspond à un crayon encore neuf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.amax(t)\n",
    "cg = np.amax(t)\n",
    "print (cg)\n",
    "np.sum(t==cg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 taille moyenne des crayons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125.91304347826087"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "soit en pourcentage de la taille du crayon neuf :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.69565217391305\n"
     ]
    }
   ],
   "source": [
    "cm = t.mean()\n",
    "pcm = (cm/cg*100)\n",
    "print (pcm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La quantité de crayons de taille supérieure ou égale à la taille moyenne est : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "ncm = np.sum(t>=cm)\n",
    "print (ncm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "soit en pourcentage du nombre total de crayons :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65.21739130434783\n"
     ]
    }
   ],
   "source": [
    "ntc = (t.size)\n",
    "pgc = (ncm/ntc*100)\n",
    "print (pgc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "Bon, ça va, j'ai encore de quoi écrire. "
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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