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tailwind.config.cjs
prix_carburant.ipynb NaN GiB
{
"cells": [
{
"cell_type": "markdown",
"id": "b9c36fa1-d0ac-4202-91ba-236830bd3d1b",
"metadata": {},
"source": [
"# Prix Carburant"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b887ef4a-6f33-4152-af83-daeeffba5a79",
"metadata": {},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.8.10 64-bit' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: '/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
]
}
],
"source": [
"import zipfile\n",
"import os\n",
"from urllib.request import urlretrieve\n",
"from datetime import date\n",
"\n",
"import tempfile\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4268984b-2876-498d-85e1-110f242376f4",
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'prix_litre_mensuel_carburant.csv'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-e86df01e3e85>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Si il y a déjà des fichiers avec ces noms, le script ne les remplaces pas, donc il faut les suprimer au debut.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"prix_litre_mensuel_carburant.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"prix_hectolitre_mensuel_carburant.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"prix_litre_annuel_carburant.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"prix_hectolitre_annuel_carburant.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'prix_litre_mensuel_carburant.csv'"
]
}
],
"source": [
"# Si il y a déjà des fichiers avec ces noms, le script ne les remplaces pas, donc il faut les suprimer au debut.\n",
"os.remove(\"prix_litre_mensuel_carburant.csv\")\n",
"os.remove(\"prix_hectolitre_mensuel_carburant.csv\")\n",
"os.remove(\"prix_litre_annuel_carburant.csv\")\n",
"os.remove(\"prix_hectolitre_annuel_carburant.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "36d19b81-57cc-4b3a-be5c-2c5813367b6f",
"metadata": {},
"outputs": [],
"source": [
"carburants = {\"diesel\":\"000442588\",\n",
" \"super_98\":\"000442589\",\n",
" \"super_95\":\"000849411\",\n",
" \"super_plombe\":\"000442587\",\n",
" \"super_95_e10\":\"010596132\"} ##le 95_e10 j'ai repris l'écriture de l'IPP, mais à voir si c'est pas juste super_e10"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5fbf5893-de0b-4522-91e6-49bf992cb768",
"metadata": {},
"outputs": [],
"source": [
"def get_df(carburant,id_url):\n",
" directory_to_extract_to = tempfile.mkdtemp(prefix=\"prix_carburants_\")\n",
" path_to_zip_file = os.path.join(directory_to_extract_to, f\"{carburant}.zip\")\n",
" urlretrieve(f\"https://www.insee.fr/fr/statistiques/serie/telecharger/csv/{id_url}?ordre=antechronologique&transposition=donneescolonne&periodeDebut=1&anneeDebut=1992&periodeFin={date.today().month}&anneeFin={date.today().year}&revision=sansrevisions\", path_to_zip_file)\n",
" with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref:\n",
" zip_ref.extractall(directory_to_extract_to)\n",
" df = pd.read_csv(os.path.join(directory_to_extract_to, \"valeurs_mensuelles.csv\"), sep=\";\")\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1925b873-dc73-4d8b-b837-463f5f846f47",
"metadata": {},
"outputs": [],
"source": [
"def creat_df(carburant,df):\n",
" df.columns = ['date', f'{carburant}_ttc', 'codes']\n",
" df = df.dropna(subset = ['codes'])\n",
" del df['codes']\n",
" df['date'] = df['date'].astype(str) + '-01'\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f1f6d31d-ed11-4d44-9a2c-d11a0ca2b8fb",
"metadata": {},
"outputs": [],
"source": [
"def clean_df(carburant,df):\n",
" df[f'{carburant}_ttc'] = pd.to_numeric(df[f'{carburant}_ttc'])\n",
" return df "
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f566506b-95b9-4390-a6a6-f4ebfd844469",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-6-d23337e5192c>:5: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['date'] = df['date'].astype(str) + '-01'\n",
"<ipython-input-6-d23337e5192c>:5: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['date'] = df['date'].astype(str) + '-01'\n",
"<ipython-input-6-d23337e5192c>:5: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['date'] = df['date'].astype(str) + '-01'\n",
"<ipython-input-6-d23337e5192c>:5: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['date'] = df['date'].astype(str) + '-01'\n",
"<ipython-input-6-d23337e5192c>:5: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['date'] = df['date'].astype(str) + '-01'\n"
]
}
],
"source": [
"df_mens = pd.DataFrame({'date':[\"1992-01-01\"]})\n",
"for carburant, id_url in carburants.items():\n",
" df = get_df(carburant,id_url)\n",
" df_creat = creat_df(carburant,df)\n",
" df_mens = df_mens.merge(df_creat, on=['date','date'], how='outer')\n",
" df_mens = clean_df(carburant,df_mens)\n",
" df_mens.sort_values(by='date',ascending = False, inplace= True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a7354947-55e1-47e5-9fcd-7be37b00131c",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>diesel_ttc</th>\n",
" <th>super_98_ttc</th>\n",
" <th>super_95_ttc</th>\n",
" <th>super_plombe_ttc</th>\n",
" <th>super_95_e10_ttc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2022-04-01</td>\n",
" <td>1.87</td>\n",
" <td>1.87</td>\n",
" <td>1.82</td>\n",
" <td>NaN</td>\n",
" <td>1.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2022-03-01</td>\n",
" <td>2.02</td>\n",
" <td>2.05</td>\n",
" <td>2.00</td>\n",
" <td>NaN</td>\n",
" <td>1.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2022-02-01</td>\n",
" <td>1.72</td>\n",
" <td>1.86</td>\n",
" <td>1.80</td>\n",
" <td>NaN</td>\n",
" <td>1.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2022-01-01</td>\n",
" <td>1.63</td>\n",
" <td>1.77</td>\n",
" <td>1.71</td>\n",
" <td>NaN</td>\n",
" <td>1.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2021-12-01</td>\n",
" <td>1.54</td>\n",
" <td>1.70</td>\n",
" <td>1.64</td>\n",
" <td>NaN</td>\n",
" <td>1.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>359</th>\n",
" <td>1992-05-01</td>\n",
" <td>0.54</td>\n",
" <td>0.78</td>\n",
" <td>NaN</td>\n",
" <td>0.81</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360</th>\n",
" <td>1992-04-01</td>\n",
" <td>0.53</td>\n",
" <td>0.77</td>\n",
" <td>NaN</td>\n",
" <td>0.81</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361</th>\n",
" <td>1992-03-01</td>\n",
" <td>0.54</td>\n",
" <td>0.77</td>\n",
" <td>NaN</td>\n",
" <td>0.81</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362</th>\n",
" <td>1992-02-01</td>\n",
" <td>0.54</td>\n",
" <td>0.78</td>\n",
" <td>NaN</td>\n",
" <td>0.81</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>363</th>\n",
" <td>1992-01-01</td>\n",
" <td>0.54</td>\n",
" <td>0.78</td>\n",
" <td>NaN</td>\n",
" <td>0.80</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>364 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" date diesel_ttc super_98_ttc super_95_ttc super_plombe_ttc \\\n",
"0 2022-04-01 1.87 1.87 1.82 NaN \n",
"1 2022-03-01 2.02 2.05 2.00 NaN \n",
"2 2022-02-01 1.72 1.86 1.80 NaN \n",
"3 2022-01-01 1.63 1.77 1.71 NaN \n",
"4 2021-12-01 1.54 1.70 1.64 NaN \n",
".. ... ... ... ... ... \n",
"359 1992-05-01 0.54 0.78 NaN 0.81 \n",
"360 1992-04-01 0.53 0.77 NaN 0.81 \n",
"361 1992-03-01 0.54 0.77 NaN 0.81 \n",
"362 1992-02-01 0.54 0.78 NaN 0.81 \n",
"363 1992-01-01 0.54 0.78 NaN 0.80 \n",
"\n",
" super_95_e10_ttc \n",
"0 1.76 \n",
"1 1.96 \n",
"2 1.77 \n",
"3 1.69 \n",
"4 1.61 \n",
".. ... \n",
"359 NaN \n",
"360 NaN \n",
"361 NaN \n",
"362 NaN \n",
"363 NaN \n",
"\n",
"[364 rows x 6 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_mens.to_csv(\"prix_litre_mensuel_carburant.csv\", index=False, index_label=False)\n",
"df_mens"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "49128573-c7ca-4d64-bac1-d84edcf65742",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 364 entries, 0 to 363\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 date 364 non-null object \n",
" 1 diesel_ttc 364 non-null float64\n",
" 2 super_98_ttc 364 non-null float64\n",
" 3 super_95_ttc 244 non-null float64\n",
" 4 super_plombe_ttc 157 non-null float64\n",
" 5 super_95_e10_ttc 40 non-null float64\n",
"dtypes: float64(5), object(1)\n",
"memory usage: 19.9+ KB\n"
]
}
],
"source": [
"df_mens.describe()\n",
"df_mens.info()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "8edc5227-ee31-45fd-8d94-a7e803012312",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>diesel_ttc</th>\n",
" <th>super_98_ttc</th>\n",
" <th>super_95_ttc</th>\n",
" <th>super_plombe_ttc</th>\n",
" <th>super_95_e10_ttc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>2022</td>\n",
" <td>1.81</td>\n",
" <td>1.89</td>\n",
" <td>1.83</td>\n",
" <td>NaN</td>\n",
" <td>1.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2021</td>\n",
" <td>1.44</td>\n",
" <td>1.62</td>\n",
" <td>1.56</td>\n",
" <td>NaN</td>\n",
" <td>1.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2020</td>\n",
" <td>1.27</td>\n",
" <td>1.42</td>\n",
" <td>1.37</td>\n",
" <td>NaN</td>\n",
" <td>1.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2019</td>\n",
" <td>1.44</td>\n",
" <td>1.56</td>\n",
" <td>1.51</td>\n",
" <td>NaN</td>\n",
" <td>1.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2018</td>\n",
" <td>1.44</td>\n",
" <td>1.57</td>\n",
" <td>1.51</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2017</td>\n",
" <td>1.23</td>\n",
" <td>1.45</td>\n",
" <td>1.38</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2016</td>\n",
" <td>1.11</td>\n",
" <td>1.35</td>\n",
" <td>1.32</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2015</td>\n",
" <td>1.17</td>\n",
" <td>1.41</td>\n",
" <td>1.37</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2014</td>\n",
" <td>1.30</td>\n",
" <td>1.54</td>\n",
" <td>1.50</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2013</td>\n",
" <td>1.36</td>\n",
" <td>1.60</td>\n",
" <td>1.56</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2012</td>\n",
" <td>1.41</td>\n",
" <td>1.63</td>\n",
" <td>1.59</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2011</td>\n",
" <td>1.34</td>\n",
" <td>1.55</td>\n",
" <td>1.50</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2010</td>\n",
" <td>1.16</td>\n",
" <td>1.40</td>\n",
" <td>1.36</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2009</td>\n",
" <td>1.01</td>\n",
" <td>1.25</td>\n",
" <td>1.22</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2008</td>\n",
" <td>1.28</td>\n",
" <td>1.42</td>\n",
" <td>1.38</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2007</td>\n",
" <td>1.10</td>\n",
" <td>1.32</td>\n",
" <td>1.29</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2006</td>\n",
" <td>1.09</td>\n",
" <td>1.29</td>\n",
" <td>1.25</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2005</td>\n",
" <td>1.03</td>\n",
" <td>1.21</td>\n",
" <td>1.18</td>\n",
" <td>1.24</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2004</td>\n",
" <td>0.89</td>\n",
" <td>1.09</td>\n",
" <td>1.08</td>\n",
" <td>1.16</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2003</td>\n",
" <td>0.80</td>\n",
" <td>1.04</td>\n",
" <td>1.02</td>\n",
" <td>1.11</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2002</td>\n",
" <td>0.77</td>\n",
" <td>1.03</td>\n",
" <td>1.00</td>\n",
" <td>1.10</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2001</td>\n",
" <td>0.80</td>\n",
" <td>1.05</td>\n",
" <td>NaN</td>\n",
" <td>1.13</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2000</td>\n",
" <td>0.85</td>\n",
" <td>1.11</td>\n",
" <td>NaN</td>\n",
" <td>1.18</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1999</td>\n",
" <td>0.69</td>\n",
" <td>0.96</td>\n",
" <td>NaN</td>\n",
" <td>1.01</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1998</td>\n",
" <td>0.64</td>\n",
" <td>0.93</td>\n",
" <td>NaN</td>\n",
" <td>0.96</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1997</td>\n",
" <td>0.68</td>\n",
" <td>0.96</td>\n",
" <td>NaN</td>\n",
" <td>0.99</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1996</td>\n",
" <td>0.66</td>\n",
" <td>0.92</td>\n",
" <td>NaN</td>\n",
" <td>0.96</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1995</td>\n",
" <td>0.59</td>\n",
" <td>0.87</td>\n",
" <td>NaN</td>\n",
" <td>0.90</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1994</td>\n",
" <td>0.60</td>\n",
" <td>0.82</td>\n",
" <td>NaN</td>\n",
" <td>0.87</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1993</td>\n",
" <td>0.56</td>\n",
" <td>0.79</td>\n",
" <td>NaN</td>\n",
" <td>0.84</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992</td>\n",
" <td>0.54</td>\n",
" <td>0.78</td>\n",
" <td>NaN</td>\n",
" <td>0.81</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date diesel_ttc super_98_ttc super_95_ttc super_plombe_ttc \\\n",
"32 2022 1.81 1.89 1.83 NaN \n",
"31 2021 1.44 1.62 1.56 NaN \n",
"30 2020 1.27 1.42 1.37 NaN \n",
"29 2019 1.44 1.56 1.51 NaN \n",
"28 2018 1.44 1.57 1.51 NaN \n",
"27 2017 1.23 1.45 1.38 NaN \n",
"26 2016 1.11 1.35 1.32 NaN \n",
"25 2015 1.17 1.41 1.37 NaN \n",
"24 2014 1.30 1.54 1.50 NaN \n",
"23 2013 1.36 1.60 1.56 NaN \n",
"22 2012 1.41 1.63 1.59 NaN \n",
"21 2011 1.34 1.55 1.50 NaN \n",
"20 2010 1.16 1.40 1.36 NaN \n",
"19 2009 1.01 1.25 1.22 NaN \n",
"18 2008 1.28 1.42 1.38 NaN \n",
"17 2007 1.10 1.32 1.29 NaN \n",
"16 2006 1.09 1.29 1.25 NaN \n",
"15 2005 1.03 1.21 1.18 1.24 \n",
"14 2004 0.89 1.09 1.08 1.16 \n",
"13 2003 0.80 1.04 1.02 1.11 \n",
"12 2002 0.77 1.03 1.00 1.10 \n",
"11 2001 0.80 1.05 NaN 1.13 \n",
"10 2000 0.85 1.11 NaN 1.18 \n",
"9 1999 0.69 0.96 NaN 1.01 \n",
"8 1998 0.64 0.93 NaN 0.96 \n",
"7 1997 0.68 0.96 NaN 0.99 \n",
"6 1996 0.66 0.92 NaN 0.96 \n",
"5 1995 0.59 0.87 NaN 0.90 \n",
"4 1994 0.60 0.82 NaN 0.87 \n",
"3 1993 0.56 0.79 NaN 0.84 \n",
"2 1992 0.54 0.78 NaN 0.81 \n",
"\n",
" super_95_e10_ttc \n",
"32 1.80 \n",
"31 1.54 \n",
"30 1.35 \n",
"29 1.49 \n",
"28 NaN \n",
"27 NaN \n",
"26 NaN \n",
"25 NaN \n",
"24 NaN \n",
"23 NaN \n",
"22 NaN \n",
"21 NaN \n",
"20 NaN \n",
"19 NaN \n",
"18 NaN \n",
"17 NaN \n",
"16 NaN \n",
"15 NaN \n",
"14 NaN \n",
"13 NaN \n",
"12 NaN \n",
"11 NaN \n",
"10 NaN \n",
"9 NaN \n",
"8 NaN \n",
"7 NaN \n",
"6 NaN \n",
"5 NaN \n",
"4 NaN \n",
"3 NaN \n",
"2 NaN "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_ann = df_mens.copy()\n",
"df_ann[['annee','mois','jour']] = df_ann['date'].str.split('-',expand=True)\n",
"df_ann.drop(['date','mois','jour'], axis=1, inplace=True)\n",
"df_ann = df_ann.groupby(['annee']).agg({'diesel_ttc': ['mean'],'super_98_ttc': ['mean'],\n",
" 'super_95_ttc': ['mean'],'super_plombe_ttc': ['mean'],\n",
" 'super_95_e10_ttc': ['mean']}).round(2)\n",
"df_ann.to_csv(\"prix_annuel_carburant.csv\")\n",
"df_ann = pd.read_csv(\"prix_annuel_carburant.csv\", sep=\",\")\n",
"os.remove(\"prix_annuel_carburant.csv\")\n",
"df_ann = df_ann.iloc[2:, :]\n",
"df_ann = df_ann.rename(columns={'Unnamed: 0': 'date'})\n",
"for carburant, id_url in carburants.items():\n",
" df_ann = clean_df(carburant,df_ann)\n",
"df_ann.sort_values(by='date',ascending = False, inplace= True)\n",
"df_ann.to_csv(\"prix_litre_annuel_carburant.csv\", index=False, index_label=False)\n",
"df_ann"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ca94602c-9898-479d-aa7b-1ede366e6850",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 31 entries, 32 to 2\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 date 31 non-null object \n",
" 1 diesel_ttc 31 non-null float64\n",
" 2 super_98_ttc 31 non-null float64\n",
" 3 super_95_ttc 21 non-null float64\n",
" 4 super_plombe_ttc 14 non-null float64\n",
" 5 super_95_e10_ttc 4 non-null float64\n",
"dtypes: float64(5), object(1)\n",
"memory usage: 1.7+ KB\n"
]
}
],
"source": [
"df_ann.describe()\n",
"df_ann.info()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "94720f2b-1184-4b0d-8d74-e03c5b80fd39",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\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>date</th>\n",
" <th>diesel_ttc</th>\n",
" <th>super_98_ttc</th>\n",
" <th>super_95_ttc</th>\n",
" <th>super_plombe_ttc</th>\n",
" <th>super_95_e10_ttc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2022-04-01</td>\n",
" <td>1.87</td>\n",
" <td>1.87</td>\n",
" <td>1.82</td>\n",
" <td>NaN</td>\n",
" <td>1.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2022-03-01</td>\n",
" <td>2.02</td>\n",
" <td>2.05</td>\n",
" <td>2.00</td>\n",
" <td>NaN</td>\n",
" <td>1.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2022-02-01</td>\n",
" <td>1.72</td>\n",
" <td>1.86</td>\n",
" <td>1.80</td>\n",
" <td>NaN</td>\n",
" <td>1.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2022-01-01</td>\n",
" <td>1.63</td>\n",
" <td>1.77</td>\n",
" <td>1.71</td>\n",
" <td>NaN</td>\n",
" <td>1.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2021-12-01</td>\n",
" <td>1.54</td>\n",
" <td>1.70</td>\n",
" <td>1.64</td>\n",
" <td>NaN</td>\n",
" <td>1.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>359</th>\n",
" <td>1992-05-01</td>\n",
" <td>0.54</td>\n",
" <td>0.78</td>\n",
" <td>NaN</td>\n",
" <td>0.81</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360</th>\n",
" <td>1992-04-01</td>\n",
" <td>0.53</td>\n",
" <td>0.77</td>\n",
" <td>NaN</td>\n",
" <td>0.81</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361</th>\n",
" <td>1992-03-01</td>\n",
" <td>0.54</td>\n",
" <td>0.77</td>\n",
" <td>NaN</td>\n",
" <td>0.81</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362</th>\n",
" <td>1992-02-01</td>\n",
" <td>0.54</td>\n",
" <td>0.78</td>\n",
" <td>NaN</td>\n",
" <td>0.81</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>363</th>\n",
" <td>1992-01-01</td>\n",
" <td>0.54</td>\n",
" <td>0.78</td>\n",
" <td>NaN</td>\n",
" <td>0.80</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>364 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" date diesel_ttc super_98_ttc super_95_ttc super_plombe_ttc \\\n",
"0 2022-04-01 1.87 1.87 1.82 NaN \n",
"1 2022-03-01 2.02 2.05 2.00 NaN \n",
"2 2022-02-01 1.72 1.86 1.80 NaN \n",
"3 2022-01-01 1.63 1.77 1.71 NaN \n",
"4 2021-12-01 1.54 1.70 1.64 NaN \n",
".. ... ... ... ... ... \n",
"359 1992-05-01 0.54 0.78 NaN 0.81 \n",
"360 1992-04-01 0.53 0.77 NaN 0.81 \n",
"361 1992-03-01 0.54 0.77 NaN 0.81 \n",
"362 1992-02-01 0.54 0.78 NaN 0.81 \n",
"363 1992-01-01 0.54 0.78 NaN 0.80 \n",
"\n",
" super_95_e10_ttc \n",
"0 1.76 \n",
"1 1.96 \n",
"2 1.77 \n",
"3 1.69 \n",
"4 1.61 \n",
".. ... \n",
"359 NaN \n",
"360 NaN \n",
"361 NaN \n",
"362 NaN \n",
"363 NaN \n",
"\n",
"[364 rows x 6 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_mens"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "02992af3-7110-4f17-9ac2-f4f0f2fdc4de",
"metadata": {},
"outputs": [],
"source": [
"df_mens.loc[:,['diesel_ttc','super_98_ttc','super_95_ttc','super_plombe_ttc','super_95_e10_ttc']] *= 100\n",
"df_mens[['diesel_ttc','super_98_ttc','super_95_ttc','super_plombe_ttc','super_95_e10_ttc']] = df_mens[['diesel_ttc','super_98_ttc','super_95_ttc','super_plombe_ttc','super_95_e10_ttc']].round(0)\n",
"df_mens.to_csv(\"prix_hectolitre_mensuel_carburant.csv\", index=False, index_label=False)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "62b9b211-5aa0-4180-8eea-33b6bc2ac4eb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
"\n",
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" 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>date</th>\n",
" <th>diesel_ttc</th>\n",
" <th>super_98_ttc</th>\n",
" <th>super_95_ttc</th>\n",
" <th>super_plombe_ttc</th>\n",
" <th>super_95_e10_ttc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2022-04-01</td>\n",
" <td>187.0</td>\n",
" <td>187.0</td>\n",
" <td>182.0</td>\n",
" <td>NaN</td>\n",
" <td>176.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2022-03-01</td>\n",
" <td>202.0</td>\n",
" <td>205.0</td>\n",
" <td>200.0</td>\n",
" <td>NaN</td>\n",
" <td>196.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2022-02-01</td>\n",
" <td>172.0</td>\n",
" <td>186.0</td>\n",
" <td>180.0</td>\n",
" <td>NaN</td>\n",
" <td>177.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2022-01-01</td>\n",
" <td>163.0</td>\n",
" <td>177.0</td>\n",
" <td>171.0</td>\n",
" <td>NaN</td>\n",
" <td>169.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2021-12-01</td>\n",
" <td>154.0</td>\n",
" <td>170.0</td>\n",
" <td>164.0</td>\n",
" <td>NaN</td>\n",
" <td>161.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>359</th>\n",
" <td>1992-05-01</td>\n",
" <td>54.0</td>\n",
" <td>78.0</td>\n",
" <td>NaN</td>\n",
" <td>81.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360</th>\n",
" <td>1992-04-01</td>\n",
" <td>53.0</td>\n",
" <td>77.0</td>\n",
" <td>NaN</td>\n",
" <td>81.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361</th>\n",
" <td>1992-03-01</td>\n",
" <td>54.0</td>\n",
" <td>77.0</td>\n",
" <td>NaN</td>\n",
" <td>81.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362</th>\n",
" <td>1992-02-01</td>\n",
" <td>54.0</td>\n",
" <td>78.0</td>\n",
" <td>NaN</td>\n",
" <td>81.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>363</th>\n",
" <td>1992-01-01</td>\n",
" <td>54.0</td>\n",
" <td>78.0</td>\n",
" <td>NaN</td>\n",
" <td>80.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>364 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" date diesel_ttc super_98_ttc super_95_ttc super_plombe_ttc \\\n",
"0 2022-04-01 187.0 187.0 182.0 NaN \n",
"1 2022-03-01 202.0 205.0 200.0 NaN \n",
"2 2022-02-01 172.0 186.0 180.0 NaN \n",
"3 2022-01-01 163.0 177.0 171.0 NaN \n",
"4 2021-12-01 154.0 170.0 164.0 NaN \n",
".. ... ... ... ... ... \n",
"359 1992-05-01 54.0 78.0 NaN 81.0 \n",
"360 1992-04-01 53.0 77.0 NaN 81.0 \n",
"361 1992-03-01 54.0 77.0 NaN 81.0 \n",
"362 1992-02-01 54.0 78.0 NaN 81.0 \n",
"363 1992-01-01 54.0 78.0 NaN 80.0 \n",
"\n",
" super_95_e10_ttc \n",
"0 176.0 \n",
"1 196.0 \n",
"2 177.0 \n",
"3 169.0 \n",
"4 161.0 \n",
".. ... \n",
"359 NaN \n",
"360 NaN \n",
"361 NaN \n",
"362 NaN \n",
"363 NaN \n",
"\n",
"[364 rows x 6 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_mens"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "372a7fe6-f63d-496b-87b8-171c1888ae81",
"metadata": {},
"outputs": [],
"source": [
"df_ann.loc[:,['diesel_ttc','super_98_ttc','super_95_ttc','super_plombe_ttc','super_95_e10_ttc']] *= 100\n",
"df_ann[['diesel_ttc','super_98_ttc','super_95_ttc','super_plombe_ttc','super_95_e10_ttc']] = df_ann[['diesel_ttc','super_98_ttc','super_95_ttc','super_plombe_ttc','super_95_e10_ttc']].round(0)\n",
"df_ann.to_csv(\"prix_hectolitre_annuel_carburant.csv\", index=False, index_label=False)"
]
},
{
"cell_type": "code",
"execution_count": 19,
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" <th></th>\n",
" <th>date</th>\n",
" <th>diesel_ttc</th>\n",
" <th>super_98_ttc</th>\n",
" <th>super_95_ttc</th>\n",
" <th>super_plombe_ttc</th>\n",
" <th>super_95_e10_ttc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>2022</td>\n",
" <td>181.0</td>\n",
" <td>189.0</td>\n",
" <td>183.0</td>\n",
" <td>NaN</td>\n",
" <td>180.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2021</td>\n",
" <td>144.0</td>\n",
" <td>162.0</td>\n",
" <td>156.0</td>\n",
" <td>NaN</td>\n",
" <td>154.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2020</td>\n",
" <td>127.0</td>\n",
" <td>142.0</td>\n",
" <td>137.0</td>\n",
" <td>NaN</td>\n",
" <td>135.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2019</td>\n",
" <td>144.0</td>\n",
" <td>156.0</td>\n",
" <td>151.0</td>\n",
" <td>NaN</td>\n",
" <td>149.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2018</td>\n",
" <td>144.0</td>\n",
" <td>157.0</td>\n",
" <td>151.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2017</td>\n",
" <td>123.0</td>\n",
" <td>145.0</td>\n",
" <td>138.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2016</td>\n",
" <td>111.0</td>\n",
" <td>135.0</td>\n",
" <td>132.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2015</td>\n",
" <td>117.0</td>\n",
" <td>141.0</td>\n",
" <td>137.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2014</td>\n",
" <td>130.0</td>\n",
" <td>154.0</td>\n",
" <td>150.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2013</td>\n",
" <td>136.0</td>\n",
" <td>160.0</td>\n",
" <td>156.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2012</td>\n",
" <td>141.0</td>\n",
" <td>163.0</td>\n",
" <td>159.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2011</td>\n",
" <td>134.0</td>\n",
" <td>155.0</td>\n",
" <td>150.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2010</td>\n",
" <td>116.0</td>\n",
" <td>140.0</td>\n",
" <td>136.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2009</td>\n",
" <td>101.0</td>\n",
" <td>125.0</td>\n",
" <td>122.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2008</td>\n",
" <td>128.0</td>\n",
" <td>142.0</td>\n",
" <td>138.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2007</td>\n",
" <td>110.0</td>\n",
" <td>132.0</td>\n",
" <td>129.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2006</td>\n",
" <td>109.0</td>\n",
" <td>129.0</td>\n",
" <td>125.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2005</td>\n",
" <td>103.0</td>\n",
" <td>121.0</td>\n",
" <td>118.0</td>\n",
" <td>124.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2004</td>\n",
" <td>89.0</td>\n",
" <td>109.0</td>\n",
" <td>108.0</td>\n",
" <td>116.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2003</td>\n",
" <td>80.0</td>\n",
" <td>104.0</td>\n",
" <td>102.0</td>\n",
" <td>111.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2002</td>\n",
" <td>77.0</td>\n",
" <td>103.0</td>\n",
" <td>100.0</td>\n",
" <td>110.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2001</td>\n",
" <td>80.0</td>\n",
" <td>105.0</td>\n",
" <td>NaN</td>\n",
" <td>113.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2000</td>\n",
" <td>85.0</td>\n",
" <td>111.0</td>\n",
" <td>NaN</td>\n",
" <td>118.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1999</td>\n",
" <td>69.0</td>\n",
" <td>96.0</td>\n",
" <td>NaN</td>\n",
" <td>101.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1998</td>\n",
" <td>64.0</td>\n",
" <td>93.0</td>\n",
" <td>NaN</td>\n",
" <td>96.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1997</td>\n",
" <td>68.0</td>\n",
" <td>96.0</td>\n",
" <td>NaN</td>\n",
" <td>99.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1996</td>\n",
" <td>66.0</td>\n",
" <td>92.0</td>\n",
" <td>NaN</td>\n",
" <td>96.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1995</td>\n",
" <td>59.0</td>\n",
" <td>87.0</td>\n",
" <td>NaN</td>\n",
" <td>90.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1994</td>\n",
" <td>60.0</td>\n",
" <td>82.0</td>\n",
" <td>NaN</td>\n",
" <td>87.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1993</td>\n",
" <td>56.0</td>\n",
" <td>79.0</td>\n",
" <td>NaN</td>\n",
" <td>84.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992</td>\n",
" <td>54.0</td>\n",
" <td>78.0</td>\n",
" <td>NaN</td>\n",
" <td>81.0</td>\n",
" <td>NaN</td>\n",
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"text/plain": [
" date diesel_ttc super_98_ttc super_95_ttc super_plombe_ttc \\\n",
"32 2022 181.0 189.0 183.0 NaN \n",
"31 2021 144.0 162.0 156.0 NaN \n",
"30 2020 127.0 142.0 137.0 NaN \n",
"29 2019 144.0 156.0 151.0 NaN \n",
"28 2018 144.0 157.0 151.0 NaN \n",
"27 2017 123.0 145.0 138.0 NaN \n",
"26 2016 111.0 135.0 132.0 NaN \n",
"25 2015 117.0 141.0 137.0 NaN \n",
"24 2014 130.0 154.0 150.0 NaN \n",
"23 2013 136.0 160.0 156.0 NaN \n",
"22 2012 141.0 163.0 159.0 NaN \n",
"21 2011 134.0 155.0 150.0 NaN \n",
"20 2010 116.0 140.0 136.0 NaN \n",
"19 2009 101.0 125.0 122.0 NaN \n",
"18 2008 128.0 142.0 138.0 NaN \n",
"17 2007 110.0 132.0 129.0 NaN \n",
"16 2006 109.0 129.0 125.0 NaN \n",
"15 2005 103.0 121.0 118.0 124.0 \n",
"14 2004 89.0 109.0 108.0 116.0 \n",
"13 2003 80.0 104.0 102.0 111.0 \n",
"12 2002 77.0 103.0 100.0 110.0 \n",
"11 2001 80.0 105.0 NaN 113.0 \n",
"10 2000 85.0 111.0 NaN 118.0 \n",
"9 1999 69.0 96.0 NaN 101.0 \n",
"8 1998 64.0 93.0 NaN 96.0 \n",
"7 1997 68.0 96.0 NaN 99.0 \n",
"6 1996 66.0 92.0 NaN 96.0 \n",
"5 1995 59.0 87.0 NaN 90.0 \n",
"4 1994 60.0 82.0 NaN 87.0 \n",
"3 1993 56.0 79.0 NaN 84.0 \n",
"2 1992 54.0 78.0 NaN 81.0 \n",
"\n",
" super_95_e10_ttc \n",
"32 180.0 \n",
"31 154.0 \n",
"30 135.0 \n",
"29 149.0 \n",
"28 NaN \n",
"27 NaN \n",
"26 NaN \n",
"25 NaN \n",
"24 NaN \n",
"23 NaN \n",
"22 NaN \n",
"21 NaN \n",
"20 NaN \n",
"19 NaN \n",
"18 NaN \n",
"17 NaN \n",
"16 NaN \n",
"15 NaN \n",
"14 NaN \n",
"13 NaN \n",
"12 NaN \n",
"11 NaN \n",
"10 NaN \n",
"9 NaN \n",
"8 NaN \n",
"7 NaN \n",
"6 NaN \n",
"5 NaN \n",
"4 NaN \n",
"3 NaN \n",
"2 NaN "
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