{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "import numpy as np\n",
    "import isoweek  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\"\n",
    "data = \"monthly_in_situ_co2_mlo.csv\"\n",
    "# Vérifier si le fichier local existe, et s'il n'existe pas, le télécharger depuis l'URL\n",
    "import os\n",
    "import urllib.request\n",
    "\n",
    "# Vérifier si le fichier local n'existe pas\n",
    "if not os.path.exists(data):\n",
    "    # Télécharger les données depuis l'URL et les enregistrer dans le fichier local\n",
    "    urllib.request.urlretrieve(data_url, data)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['  Yr', ' Mn', '    Date', '      Date', '     CO2', 'seasonally',\n",
      "       '        fit', '  seasonally', '      CO2', ' seasonally', ' Sta'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    " # Lire les données depuis le fichier local CSV en sautant la première ligne (commentaire)\n",
    "raw_data = pd.read_csv(data, skiprows=57)\n",
    "\n",
    "# Afficher les données brutes\n",
    "raw_data\n",
    " # Sélectionner les lignes contenant au moins une valeur manquante (NaN)\n",
    "raw_data[raw_data.isnull().any(axis=1)]\n",
    " # Supprimer les lignes contenant des valeurs manquantes (NaN) à partir des données brutes\n",
    "data = raw_data.dropna().copy()\n",
    "\n",
    "# Afficher les données nettoyées (sans valeurs manquantes) et en créer une copie\n",
    "data\n",
    "data = data.dropna(subset=['     CO2'])\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f46b4233240>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Grouper par année (Yr) et sommer les valeurs de CO2\n",
    "annees = data['  Yr'].unique()  # Obtenir la liste des années uniques\n",
    "\n",
    "# Initialiser les listes pour stocker les années et les émissions de CO2 annuelles\n",
    "year = []  # Liste des années\n",
    "yearly_CO2 = []  # Liste des émissions annuelles de CO2\n",
    "\n",
    "for annee in annees:\n",
    "    # Filtrer les données pour l'année spécifique\n",
    "    donnees_annee = data[data['  Yr'] == annee]\n",
    "    \n",
    "    # Somme des émissions de CO2 pour l'année spécifique\n",
    "    donnees_annee['     CO2'] = donnees_annee['     CO2'].astype(float)\n",
    "    somme_co2_annee = donnees_annee['     CO2'].sum()\n",
    "    \n",
    "    # Ajouter l'année et la somme des émissions à leurs listes respectives\n",
    "    year.append(annee)\n",
    "    yearly_CO2.append(somme_co2_annee)\n",
    "\n",
    "# Créer une série pandas avec les données annuelles et les années comme index\n",
    "yearly_CO2 = pd.Series(data=yearly_CO2, index=year)\n",
    "# Tracer un graphique de dispersion des données d'incidence annuelle avec un style en étoile\n",
    "yearly_CO2.plot(style='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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