ProjetPics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align=\"center\">\n",
    "    <h1><u>Projet Pics</u></h1>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Ce projet consiste à créer 3 représentations graphiques différentes à partir d'un base 4 fichiers CSV."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Importation des bibliothèques</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Importation des fichiers CSV utilisés</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fic_chaussures = pd.read_table(\n",
    "        \"Fichiers/Chaussures.csv\",\n",
    "        decimal = \",\",\n",
    "        sep = \";\"\n",
    ")\n",
    "\n",
    "fic_pic = pd.read_table(\n",
    "        \"Fichiers/Pic.csv\",\n",
    "        decimal = \",\",\n",
    "        sep = \";\"\n",
    ")\n",
    "\n",
    "fic_origines = pd.read_table(\n",
    "        \"Fichiers/Origine.csv\",\n",
    "        decimal = \",\",\n",
    "        sep = \";\"\n",
    ")\n",
    "\n",
    "fic_sommets = pd.read_table(\n",
    "        \"Fichiers/Sommet.csv\",\n",
    "        decimal = \",\",\n",
    "        sep = \";\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Analyse bivariée</h4>\n",
    "<h4>Répartition des sexes en fonction du pic gravis</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Répartition des sexes en fonction du pic gravis')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Sexe = fic_pic['Sexe']\n",
    "\n",
    "corresp = {'Aneto' : 'Aneto',\n",
    "           'Aragüells' : 'Autre',\n",
    "           'Aspe' : 'Autre',\n",
    "           'Bachimala' : 'Autre',\n",
    "           'Bacías' : 'Autre',\n",
    "           'Bisaurín' : 'Bisaurín',\n",
    "           'Castillo de Acher' : 'Castillo de Acher',\n",
    "           'Cilindro' : 'Autre',\n",
    "           'Collarada' : 'Autre',\n",
    "           'Cotiella' : 'Autre',\n",
    "           'Garmo Negro' : 'Garmo Negro',\n",
    "           'Infiernos' : 'Autre',\n",
    "           'La Facha' : 'Autre',\n",
    "           'La Munia' : 'Autre',\n",
    "           'Monte Perdido' : 'Monte Perdido',\n",
    "           'Mulleres' : 'Autre',\n",
    "           'Petrechema' : 'Petrechema',\n",
    "           'Peña Forca' : 'Autre',\n",
    "           'Pico de Alba' : 'Autre',\n",
    "           'Posets' : 'Posets',\n",
    "           'Punta Escuzana' : 'Autre',\n",
    "           'Punta Las Olas' : 'Autre',\n",
    "           'Punta Suelza' : 'Autre',\n",
    "           'Robiñera' : 'Autre',\n",
    "           'Tendeñera' : 'Autre',\n",
    "           'Vallibierna' : 'Vallibierna'\n",
    "}\n",
    "\n",
    "fic_pic['pico'] = fic_pic['pico'].map(corresp)\n",
    "\n",
    "Pico = fic_pic['pico']\n",
    "\n",
    "SexePico = pd.crosstab(Pico, Sexe, normalize=\"index\")\n",
    "\n",
    "SexePico.sort_values(\"F\", ascending=False).plot(\n",
    "    kind=\"bar\",\n",
    "    stacked=True\n",
    ")\n",
    "\n",
    "plt.xticks(rotation=70)\n",
    "plt.xlabel('')\n",
    "\n",
    "plt.legend(bbox_to_anchor =(1, 1), ncol = 1)\n",
    "\n",
    "plt.title(\"Répartition des sexes en fonction du pic gravis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Altitude moyenne en fonction du sommet (Diagramme en barre)</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Vallee = fic_sommets['Zone (Carte)']\n",
    "\n",
    "df = fic_sommets\n",
    "\n",
    "Altitude = df.groupby(['Zone (Carte)']).mean()\n",
    "\n",
    "df_sorted_desc = df.sort_values('Altitude', ascending=False)\n",
    "\n",
    "plt.bar('Zone (Carte)', 'Altitude',data=df_sorted_desc)\n",
    "\n",
    "plt.title(\"Altitude moyenne en fonction de la vallée\")\n",
    "\n",
    "plt.legend(['Altitude'])\n",
    "\n",
    "plt.xticks(rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Altitude moyenne en fonction du sommet (Boîte à moustache)</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = fic_sommets\n",
    "\n",
    "g = df.groupby(\"Zone (Carte)\")\n",
    "\n",
    "df = pd.DataFrame({col: val['Altitude'] for col, val in g})\n",
    "\n",
    "median = df.median()\n",
    "\n",
    "median.sort_values(ascending=False, inplace=True)\n",
    "\n",
    "df = df[median.index]\n",
    "\n",
    "df.boxplot()\n",
    "\n",
    "plt.title(\"Altitude moyenne en fonction de la vallée\")\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "7e1998ff7f8aa20ada591c520b972326324e5ea05489af9e422744c7c09f6dad"
  },
  "kernelspec": {
   "display_name": "Python 3.10.2 64-bit",
   "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": "3.10.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}