{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Création de données sur la taille des poissons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = np.random.normal(60, 15, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = d.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w = [round(l/9+np.random.normal(),2) for l in d]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dt = {\"Taille\": d,\n",
    "     \"Poids\": w}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.11 & 81.0 \\\\\n",
      "\\hline\n",
      "9.66 & 78.0 \\\\\n",
      "\\hline\n",
      "9.55 & 80.0 \\\\\n",
      "\\hline\n",
      "7.87 & 79.0 \\\\\n",
      "\\hline\n",
      "8.24 & 64.0 \\\\\n",
      "\\hline\n",
      "5.46 & 50.0 \\\\\n",
      "\\hline\n",
      "5.89 & 56.0 \\\\\n",
      "\\hline\n",
      "7.9 & 78.0 \\\\\n",
      "\\hline\n",
      "6.98 & 75.0 \\\\\n",
      "\\hline\n",
      "5.37 & 49.0 \\\\\n",
      "\\hline\n",
      "6.03 & 53.0 \\\\\n",
      "\\hline\n",
      "7.29 & 63.0 \\\\\n",
      "\\hline\n",
      "3.98 & 38.0 \\\\\n",
      "\\hline\n",
      "6.08 & 40.0 \\\\\n",
      "\\hline\n",
      "7.02 & 69.0 \\\\\n",
      "\\hline\n"
     ]
    }
   ],
   "source": [
    "for d in df.values:\n",
    "    print(f\"{d[0]} & {d[1]} \\\\\\\\\")\n",
    "    print(\"\\\\hline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
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 "nbformat": 4,
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