{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Concentration de CO2 dans l'atmosphère depuis 1958"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Nous importons les modules nécessaires pour cette étude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "On télécharge le fichier *weekly_in_situ_co2_mlo.csv* en local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('weekly_in_situ_co2_mlo.csv', header=44, names=['week', 'concentration'])\n",
    "# Ici header=44 permet de sauter les premières lignes du csv qui ne contiennent pas de données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "On affiche le dataframe obtenu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            week  concentration\n",
      "0     1958-04-05         317.31\n",
      "1     1958-04-12         317.69\n",
      "2     1958-04-19         317.58\n",
      "3     1958-04-26         316.48\n",
      "4     1958-05-03         316.95\n",
      "5     1958-05-17         317.56\n",
      "6     1958-05-24         317.99\n",
      "7     1958-07-05         315.85\n",
      "8     1958-07-12         315.85\n",
      "9     1958-07-19         315.46\n",
      "10    1958-07-26         315.59\n",
      "11    1958-08-02         315.64\n",
      "12    1958-08-09         315.10\n",
      "13    1958-08-16         315.09\n",
      "14    1958-08-30         314.14\n",
      "15    1958-09-06         313.54\n",
      "16    1958-11-08         313.05\n",
      "17    1958-11-15         313.26\n",
      "18    1958-11-22         313.57\n",
      "19    1958-11-29         314.01\n",
      "20    1958-12-06         314.56\n",
      "21    1958-12-13         314.41\n",
      "22    1958-12-20         314.77\n",
      "23    1958-12-27         315.21\n",
      "24    1959-01-03         315.24\n",
      "25    1959-01-10         315.50\n",
      "26    1959-01-17         315.69\n",
      "27    1959-01-24         315.86\n",
      "28    1959-01-31         315.42\n",
      "29    1959-02-14         316.94\n",
      "...          ...            ...\n",
      "3301  2022-11-26         417.73\n",
      "3302  2022-12-17         418.41\n",
      "3303  2022-12-24         419.29\n",
      "3304  2022-12-31         419.19\n",
      "3305  2023-01-07         419.09\n",
      "3306  2023-01-14         419.32\n",
      "3307  2023-01-21         419.18\n",
      "3308  2023-01-28         419.66\n",
      "3309  2023-02-04         419.68\n",
      "3310  2023-02-11         420.63\n",
      "3311  2023-02-18         420.19\n",
      "3312  2023-02-25         420.76\n",
      "3313  2023-03-04         421.12\n",
      "3314  2023-03-11         420.19\n",
      "3315  2023-03-18         419.96\n",
      "3316  2023-03-25         420.87\n",
      "3317  2023-04-01         421.33\n",
      "3318  2023-04-08         422.20\n",
      "3319  2023-04-15         423.02\n",
      "3320  2023-04-22         422.99\n",
      "3321  2023-04-29         423.95\n",
      "3322  2023-05-06         423.76\n",
      "3323  2023-05-13         423.78\n",
      "3324  2023-05-20         422.77\n",
      "3325  2023-05-27         424.44\n",
      "3326  2023-06-03         424.40\n",
      "3327  2023-06-10         424.01\n",
      "3328  2023-06-17         422.93\n",
      "3329  2023-06-24         422.21\n",
      "3330  2023-07-01         422.80\n",
      "\n",
      "[3331 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "On vérifie qu'il y a bien des données pour toutes les lignes et colonnes : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       week  concentration\n",
      "0     False          False\n",
      "1     False          False\n",
      "2     False          False\n",
      "3     False          False\n",
      "4     False          False\n",
      "5     False          False\n",
      "6     False          False\n",
      "7     False          False\n",
      "8     False          False\n",
      "9     False          False\n",
      "10    False          False\n",
      "11    False          False\n",
      "12    False          False\n",
      "13    False          False\n",
      "14    False          False\n",
      "15    False          False\n",
      "16    False          False\n",
      "17    False          False\n",
      "18    False          False\n",
      "19    False          False\n",
      "20    False          False\n",
      "21    False          False\n",
      "22    False          False\n",
      "23    False          False\n",
      "24    False          False\n",
      "25    False          False\n",
      "26    False          False\n",
      "27    False          False\n",
      "28    False          False\n",
      "29    False          False\n",
      "...     ...            ...\n",
      "3301  False          False\n",
      "3302  False          False\n",
      "3303  False          False\n",
      "3304  False          False\n",
      "3305  False          False\n",
      "3306  False          False\n",
      "3307  False          False\n",
      "3308  False          False\n",
      "3309  False          False\n",
      "3310  False          False\n",
      "3311  False          False\n",
      "3312  False          False\n",
      "3313  False          False\n",
      "3314  False          False\n",
      "3315  False          False\n",
      "3316  False          False\n",
      "3317  False          False\n",
      "3318  False          False\n",
      "3319  False          False\n",
      "3320  False          False\n",
      "3321  False          False\n",
      "3322  False          False\n",
      "3323  False          False\n",
      "3324  False          False\n",
      "3325  False          False\n",
      "3326  False          False\n",
      "3327  False          False\n",
      "3328  False          False\n",
      "3329  False          False\n",
      "3330  False          False\n",
      "\n",
      "[3331 rows x 2 columns]\n",
      "week             False\n",
      "concentration    False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull())\n",
    "print(df.isnull().any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "week              object\n",
      "concentration    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "La colonne \"week\" n'apparait pas au format date, donc on transforme le format de cette colonne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "df['week'] = pd.to_datetime(df['week'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        week  concentration\n",
      "0 1958-04-05         317.31\n",
      "1 1958-04-12         317.69\n",
      "2 1958-04-19         317.58\n",
      "3 1958-04-26         316.48\n",
      "4 1958-05-03         316.95\n",
      "5 1958-05-17         317.56\n",
      "6 1958-05-24         317.99\n",
      "7 1958-07-05         315.85\n",
      "8 1958-07-12         315.85\n",
      "9 1958-07-19         315.46\n"
     ]
    }
   ],
   "source": [
    "print(df[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "On vérifie maintenant que le nouveau format est bien un format date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "week             datetime64[ns]\n",
      "concentration           float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "On peut réaliser un premier affichage des données : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdc0126d550>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot('week', 'concentration', figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Cet affichage fait apparaître un phénomène périodique en dents de scie couplé à une augmentation linéaire de la concentration en $CO_{2}$ dans le temps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "On se concentre sur une période de temps plus courte pour essayer de comprends le phénomène périodique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdbfee570b8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[:200].plot('week', 'concentration', figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il apparaît clairement que la période du motif périodique est de 1 an, avec un minimum en novembre et un maximum en juin.\n",
    "Pour n'étudier que la tendance haussière et pas le motif périodique, on va ne garder qu'une seule date par an correspondant à la première semaine de novembre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "df['year'] = df['week'].apply(lambda x:x.year) # on ajoute une colonne \"year\" qui correspond à l'année de la date \n",
    "df['month'] = df['week'].apply(lambda x:x.month) # on ajoute une colonne \"month\" qui correspond au mois de la date\n",
    "df_november = df[df[\"month\"] == 11] # on ne conserve dans un premier temps que les dates de novembre\n",
    "df_first_november = df_november.drop_duplicates(subset=\"year\", keep='first') # on ne garde ensuite que la première semaine de novembre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut maintenat afficher les données réduites aux premières semaines de novembre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdbff1a92e8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_first_november.plot('week', 'concentration', figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le motif périodique a clairement disparu. Il ne reste maintenant qu'une tendance haussière, qui peut être approximée par une droite dans un premier temps. On va chercher à estimer les coefficients de cette droite afin d'effectuer des prédictions pour des dates futures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16      313.05\n",
      "64      314.42\n",
      "116     314.50\n",
      "168     315.82\n",
      "217     315.94\n",
      "266     316.63\n",
      "297     317.77\n",
      "349     318.30\n",
      "398     319.59\n",
      "447     320.01\n",
      "499     320.70\n",
      "551     322.21\n",
      "604     323.69\n",
      "656     324.36\n",
      "708     326.33\n",
      "760     327.60\n",
      "812     328.00\n",
      "864     329.36\n",
      "916     329.85\n",
      "968     332.14\n",
      "1020    333.59\n",
      "1072    334.74\n",
      "1124    336.67\n",
      "1177    337.89\n",
      "1229    338.51\n",
      "1281    340.81\n",
      "1329    342.06\n",
      "1380    343.36\n",
      "1432    345.15\n",
      "1485    347.07\n",
      "         ...  \n",
      "1798    354.59\n",
      "1850    356.81\n",
      "1902    359.05\n",
      "1954    359.81\n",
      "2006    361.60\n",
      "2059    364.95\n",
      "2111    366.14\n",
      "2163    367.69\n",
      "2215    368.79\n",
      "2267    371.60\n",
      "2316    373.44\n",
      "2369    375.01\n",
      "2417    377.73\n",
      "2467    379.64\n",
      "2518    381.73\n",
      "2570    383.58\n",
      "2623    385.60\n",
      "2675    387.98\n",
      "2727    389.70\n",
      "2777    391.83\n",
      "2829    394.47\n",
      "2881    396.61\n",
      "2934    399.46\n",
      "2986    403.08\n",
      "3038    404.21\n",
      "3090    406.76\n",
      "3142    409.79\n",
      "3194    412.30\n",
      "3246    414.32\n",
      "3298    416.30\n",
      "Name: concentration, Length: 65, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df_first_november['concentration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.6126097  303.83802797]\n"
     ]
    }
   ],
   "source": [
    "coef_reg = np.polyfit(np.arange(len(df_first_november['concentration'])), df_first_november['concentration'], 1) # on obtient les coefficients de la régression\n",
    "print(coef_reg)\n",
    "poly1d_fn = np.poly1d(coef_reg) # poly1d_fn est une fonction qui permet de faire une nouvelle prédiction avec les coefficients obtenus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "df_filtered = df_first_november[['week', 'concentration']] # on ne conserve que les colonnes week et concentration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>concentration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1958-11-08</td>\n",
       "      <td>313.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1959-11-07</td>\n",
       "      <td>314.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1960-11-05</td>\n",
       "      <td>314.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>1961-11-04</td>\n",
       "      <td>315.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>1962-11-03</td>\n",
       "      <td>315.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          week  concentration\n",
       "16  1958-11-08         313.05\n",
       "64  1959-11-07         314.42\n",
       "116 1960-11-05         314.50\n",
       "168 1961-11-04         315.82\n",
       "217 1962-11-03         315.94"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va maintenant effectuer les prédictions pour 3 dates futures : novembre 2023, 2024 et 2025"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "df_future = pd.DataFrame({'week':[datetime.datetime(2023, 11, 4), datetime.datetime(2024, 11, 2), datetime.datetime(2025, 11, 1)], \n",
    "                         \"concentration\":[None, None, None]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>concentration</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>2023-11-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>2024-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>2025-11-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  concentration       week\n",
       "0          None 2023-11-04\n",
       "1          None 2024-11-02\n",
       "2          None 2025-11-01"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "df_concat = pd.concat([df_filtered, df_future]) # on concatène les dates actuelles avec les dates futures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      concentration       week\n",
      "16           313.05 1958-11-08\n",
      "64           314.42 1959-11-07\n",
      "116          314.50 1960-11-05\n",
      "168          315.82 1961-11-04\n",
      "217          315.94 1962-11-03\n",
      "266          316.63 1963-11-02\n",
      "297          317.77 1964-11-07\n",
      "349          318.30 1965-11-06\n",
      "398          319.59 1966-11-12\n",
      "447          320.01 1967-11-04\n",
      "499          320.70 1968-11-02\n",
      "551          322.21 1969-11-01\n",
      "604          323.69 1970-11-07\n",
      "656          324.36 1971-11-06\n",
      "708          326.33 1972-11-04\n",
      "760          327.60 1973-11-03\n",
      "812          328.00 1974-11-02\n",
      "864          329.36 1975-11-01\n",
      "916          329.85 1976-11-06\n",
      "968          332.14 1977-11-05\n",
      "1020         333.59 1978-11-04\n",
      "1072         334.74 1979-11-03\n",
      "1124         336.67 1980-11-01\n",
      "1177         337.89 1981-11-07\n",
      "1229         338.51 1982-11-06\n",
      "1281         340.81 1983-11-05\n",
      "1329         342.06 1984-11-03\n",
      "1380         343.36 1985-11-02\n",
      "1432         345.15 1986-11-01\n",
      "1485         347.07 1987-11-07\n",
      "...             ...        ...\n",
      "1954         359.81 1996-11-02\n",
      "2006         361.60 1997-11-01\n",
      "2059         364.95 1998-11-07\n",
      "2111         366.14 1999-11-06\n",
      "2163         367.69 2000-11-04\n",
      "2215         368.79 2001-11-03\n",
      "2267         371.60 2002-11-02\n",
      "2316         373.44 2003-11-01\n",
      "2369         375.01 2004-11-06\n",
      "2417         377.73 2005-11-05\n",
      "2467         379.64 2006-11-04\n",
      "2518         381.73 2007-11-03\n",
      "2570         383.58 2008-11-01\n",
      "2623         385.60 2009-11-07\n",
      "2675         387.98 2010-11-06\n",
      "2727         389.70 2011-11-05\n",
      "2777         391.83 2012-11-03\n",
      "2829         394.47 2013-11-02\n",
      "2881         396.61 2014-11-01\n",
      "2934         399.46 2015-11-07\n",
      "2986         403.08 2016-11-05\n",
      "3038         404.21 2017-11-04\n",
      "3090         406.76 2018-11-03\n",
      "3142         409.79 2019-11-02\n",
      "3194         412.30 2020-11-07\n",
      "3246         414.32 2021-11-06\n",
      "3298         416.30 2022-11-05\n",
      "0               NaN 2023-11-04\n",
      "1               NaN 2024-11-02\n",
      "2               NaN 2025-11-01\n",
      "\n",
      "[68 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df_concat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "df_concat[\"predictions\"] = poly1d_fn(np.arange(len(df_concat))) # on effectue les prédictions pour toutes les dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      concentration       week  predictions\n",
      "16           313.05 1958-11-08   303.838028\n",
      "64           314.42 1959-11-07   305.450638\n",
      "116          314.50 1960-11-05   307.063247\n",
      "168          315.82 1961-11-04   308.675857\n",
      "217          315.94 1962-11-03   310.288467\n",
      "266          316.63 1963-11-02   311.901076\n",
      "297          317.77 1964-11-07   313.513686\n",
      "349          318.30 1965-11-06   315.126296\n",
      "398          319.59 1966-11-12   316.738906\n",
      "447          320.01 1967-11-04   318.351515\n",
      "499          320.70 1968-11-02   319.964125\n",
      "551          322.21 1969-11-01   321.576735\n",
      "604          323.69 1970-11-07   323.189344\n",
      "656          324.36 1971-11-06   324.801954\n",
      "708          326.33 1972-11-04   326.414564\n",
      "760          327.60 1973-11-03   328.027174\n",
      "812          328.00 1974-11-02   329.639783\n",
      "864          329.36 1975-11-01   331.252393\n",
      "916          329.85 1976-11-06   332.865003\n",
      "968          332.14 1977-11-05   334.477612\n",
      "1020         333.59 1978-11-04   336.090222\n",
      "1072         334.74 1979-11-03   337.702832\n",
      "1124         336.67 1980-11-01   339.315441\n",
      "1177         337.89 1981-11-07   340.928051\n",
      "1229         338.51 1982-11-06   342.540661\n",
      "1281         340.81 1983-11-05   344.153271\n",
      "1329         342.06 1984-11-03   345.765880\n",
      "1380         343.36 1985-11-02   347.378490\n",
      "1432         345.15 1986-11-01   348.991100\n",
      "1485         347.07 1987-11-07   350.603709\n",
      "...             ...        ...          ...\n",
      "1954         359.81 1996-11-02   365.117197\n",
      "2006         361.60 1997-11-01   366.729806\n",
      "2059         364.95 1998-11-07   368.342416\n",
      "2111         366.14 1999-11-06   369.955026\n",
      "2163         367.69 2000-11-04   371.567635\n",
      "2215         368.79 2001-11-03   373.180245\n",
      "2267         371.60 2002-11-02   374.792855\n",
      "2316         373.44 2003-11-01   376.405465\n",
      "2369         375.01 2004-11-06   378.018074\n",
      "2417         377.73 2005-11-05   379.630684\n",
      "2467         379.64 2006-11-04   381.243294\n",
      "2518         381.73 2007-11-03   382.855903\n",
      "2570         383.58 2008-11-01   384.468513\n",
      "2623         385.60 2009-11-07   386.081123\n",
      "2675         387.98 2010-11-06   387.693733\n",
      "2727         389.70 2011-11-05   389.306342\n",
      "2777         391.83 2012-11-03   390.918952\n",
      "2829         394.47 2013-11-02   392.531562\n",
      "2881         396.61 2014-11-01   394.144171\n",
      "2934         399.46 2015-11-07   395.756781\n",
      "2986         403.08 2016-11-05   397.369391\n",
      "3038         404.21 2017-11-04   398.982000\n",
      "3090         406.76 2018-11-03   400.594610\n",
      "3142         409.79 2019-11-02   402.207220\n",
      "3194         412.30 2020-11-07   403.819830\n",
      "3246         414.32 2021-11-06   405.432439\n",
      "3298         416.30 2022-11-05   407.045049\n",
      "0               NaN 2023-11-04   408.657659\n",
      "1               NaN 2024-11-02   410.270268\n",
      "2               NaN 2025-11-01   411.882878\n",
      "\n",
      "[68 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df_concat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a maintenant tout ce qu'il faut pour afficher à la fois les vraies données et nos prédictions pour celles ci :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py:1716: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  series.name = label\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdc01512e80>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_concat.plot(x=\"week\", y=[\"concentration\", \"predictions\"], figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "On aperçoit que la droite colle assez bien à nos données. Néanmoins, elle sous-estime un peu pour les dates anciennes (années 1960)\n",
    "et les dates récentes (à partir des années 2010). Ainsi, la prédiction pour novembre 2025 d'une concentration de **412 ppm** est sans doute une sous-estimation.\n",
    "Pour y remédier, on pourrait n'effectuer la régression que sur les années récentes, par ex à partir des années 2000.\n",
    "On aurait ainsi une estimation plus précise."
   ]
  }
 ],
 "metadata": {
  "hide_code_all_hidden": false,
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}