E3 sur les stats des poissons pour les 302
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3e/Gestion_donnees/Statistiques/E3_analyse_merou.pdf
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3e/Gestion_donnees/Statistiques/E3_analyse_merou.pdf
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3e/Gestion_donnees/Statistiques/E3_analyse_merou.tex
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3e/Gestion_donnees/Statistiques/E3_analyse_merou.tex
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\documentclass[a4paper,10pt]{article}
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\usepackage{myXsim}
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\usepackage{multirow}
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\title{EPI Lagon}
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\tribe{Troisième}
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\date{Mars 2018}
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%\geometry{left=15mm,right=15mm, bottom= 10mm, top=10mm}
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\pagestyle{empty}
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\begin{document}
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\begin{exercise}[subtitle={Le mérou\Cal \Rep}]
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Voici les relevés (fictifs) de la taille et du poids de méroux.
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\begin{minipage}{0.3\textwidth}
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\begin{tabular}{|c|c|}
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\hline
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Taille (en cm) & Poids (en kg) \\
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\hline
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9.11 & 81.0 \\
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\hline
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9.66 & 78.0 \\
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\hline
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9.55 & 80.0 \\
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\hline
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7.87 & 79.0 \\
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\hline
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8.24 & 64.0 \\
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\hline
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\end{tabular}
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\end{minipage}
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\begin{minipage}{0.3\textwidth}
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\begin{tabular}{|c|c|}
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\hline
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Taille (en cm) & Poids (en kg) \\
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\hline
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5.46 & 50.0 \\
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\hline
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5.89 & 56.0 \\
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\hline
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7.9 & 78.0 \\
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\hline
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6.98 & 75.0 \\
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\hline
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5.37 & 49.0 \\
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\hline
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\end{tabular}
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\end{minipage}
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\begin{minipage}{0.3\textwidth}
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\begin{tabular}{|c|c|}
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\hline
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Taille (en cm) & Poids (en kg) \\
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\hline
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6.03 & 53.0 \\
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\hline
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7.29 & 63.0 \\
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\hline
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3.98 & 38.0 \\
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\hline
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6.08 & 40.0 \\
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\hline
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7.02 & 69.0 \\
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\hline
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\end{tabular}
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\end{minipage}
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\begin{enumerate}
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\item Pour la taille, calculer la moyenne, l'étendue et la médiane de ces données.
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\item Même question pour le poids.
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\item Pensez vous que d'après ces données, le poids du poisson est proportionnel à sa taille?
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\end{enumerate}
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\end{exercise}
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\vfill
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\printexercise{exercise}{1}
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\vfill
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\printexercise{exercise}{1}
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\vfill
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\end{document}
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%%% Local Variables:
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%%% mode: latex
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%%% TeX-master: "master"
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%%% End:
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151
3e/Gestion_donnees/Statistiques/Taille et poids des merous.ipynb
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3e/Gestion_donnees/Statistiques/Taille et poids des merous.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Création de données sur la taille des poissons"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"d = np.random.normal(60, 15, 15)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"d = d.astype(int)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"w = [round(l/9+np.random.normal(),2) for l in d]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"dt = {\"Taille\": d,\n",
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" \"Poids\": w}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.DataFrame(dt)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"9.11 & 81.0 \\\\\n",
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"\\hline\n",
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"9.66 & 78.0 \\\\\n",
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"\\hline\n",
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"9.55 & 80.0 \\\\\n",
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"\\hline\n",
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"7.87 & 79.0 \\\\\n",
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"\\hline\n",
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"8.24 & 64.0 \\\\\n",
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"\\hline\n",
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"5.46 & 50.0 \\\\\n",
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"\\hline\n",
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"5.89 & 56.0 \\\\\n",
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"\\hline\n",
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"7.9 & 78.0 \\\\\n",
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"\\hline\n",
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"6.98 & 75.0 \\\\\n",
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"\\hline\n",
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"5.37 & 49.0 \\\\\n",
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"\\hline\n",
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"6.03 & 53.0 \\\\\n",
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"\\hline\n",
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"7.29 & 63.0 \\\\\n",
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"\\hline\n",
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"3.98 & 38.0 \\\\\n",
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"\\hline\n",
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"6.08 & 40.0 \\\\\n",
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"\\hline\n",
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"7.02 & 69.0 \\\\\n",
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"\\hline\n"
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]
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}
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],
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"source": [
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"for d in df.values:\n",
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" print(f\"{d[0]} & {d[1]} \\\\\\\\\")\n",
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" print(\"\\\\hline\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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