diff --git a/.gitignore b/.gitignore
index 17e8394517ba6db43fd2ee968803ea9754de001c..6a5e218066c2a5bc04f640284227489da79e649a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
notebooks/.venv/
+.venv/
diff --git a/notebooks/prix_annuel_carburant.csv b/notebooks/prix_annuel_carburant.csv
index 3a8aeb081acd1232cc411da6dff6b9e91181c1b4..747a64cc5d995d4764772c41908756599c61eae6 100644
--- a/notebooks/prix_annuel_carburant.csv
+++ b/notebooks/prix_annuel_carburant.csv
@@ -29,4 +29,5 @@ date,diesel_ttc,super_98_ttc,super_95_ttc,super_plombe_ttc,super_95_e10_ttc
2019,1.44,1.56,1.51,,1.49
2020,1.27,1.42,1.37,,1.35
2021,1.44,1.62,1.56,,1.54
-2022,1.79,1.89,1.84,,1.81
+2022,1.86,1.88,1.83,,1.78
+2023,1.8,1.96,1.9,,1.88
diff --git a/notebooks/prix_carburant.ipynb b/notebooks/prix_carburant.ipynb
index 314f2fc4639ab5684d83627eefb82ec5464c9d99..8fb2661abd3b4c7119aa55d6c03b35ea7ff019e7 100644
--- a/notebooks/prix_carburant.ipynb
+++ b/notebooks/prix_carburant.ipynb
@@ -23,7 +23,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 2,
"id": "b887ef4a-6f33-4152-af83-daeeffba5a79",
"metadata": {},
"outputs": [],
@@ -40,19 +40,7 @@
"execution_count": 3,
"id": "4268984b-2876-498d-85e1-110f242376f4",
"metadata": {},
- "outputs": [
- {
- "ename": "FileNotFoundError",
- "evalue": "[Errno 2] No such file or directory: 'prix_annuel_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-3-3b04b83e7eb9>\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[1;32m 2\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"prix_mensuel_carburant.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\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_annuel_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[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'prix_annuel_carburant.csv'"
- ]
- }
- ],
+ "outputs": [],
"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_mensuel_carburant.csv\")\n",
@@ -61,7 +49,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 4,
"id": "36d19b81-57cc-4b3a-be5c-2c5813367b6f",
"metadata": {},
"outputs": [],
@@ -75,7 +63,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 5,
"id": "5fbf5893-de0b-4522-91e6-49bf992cb768",
"metadata": {},
"outputs": [],
@@ -92,7 +80,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 6,
"id": "1925b873-dc73-4d8b-b837-463f5f846f47",
"metadata": {},
"outputs": [],
@@ -107,7 +95,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 7,
"id": "f566506b-95b9-4390-a6a6-f4ebfd844469",
"metadata": {},
"outputs": [
@@ -115,31 +103,31 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "<ipython-input-17-42f2542d49df>:5: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_395451/3498542900.py: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-17-42f2542d49df>:5: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_395451/3498542900.py: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-17-42f2542d49df>:5: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_395451/3498542900.py: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-17-42f2542d49df>:5: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_395451/3498542900.py: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-17-42f2542d49df>:5: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_395451/3498542900.py: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",
@@ -161,7 +149,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 8,
"id": "fa856347-5f85-41d2-a725-4cc392aa2019",
"metadata": {},
"outputs": [
@@ -197,48 +185,48 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>2022-03-01</td>\n",
- " <td>2.02</td>\n",
- " <td>2.05</td>\n",
- " <td>2.0</td>\n",
+ " <td>2023-08-01</td>\n",
+ " <td>1.85</td>\n",
+ " <td>1.99</td>\n",
+ " <td>1.94</td>\n",
" <td>NaN</td>\n",
- " <td>1.96</td>\n",
+ " <td>1.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>2022-02-01</td>\n",
+ " <td>2023-07-01</td>\n",
" <td>1.72</td>\n",
- " <td>1.86</td>\n",
- " <td>1.8</td>\n",
+ " <td>1.91</td>\n",
+ " <td>1.85</td>\n",
" <td>NaN</td>\n",
- " <td>1.77</td>\n",
+ " <td>1.84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>2022-01-01</td>\n",
- " <td>1.63</td>\n",
- " <td>1.77</td>\n",
- " <td>1.71</td>\n",
+ " <td>2023-06-01</td>\n",
+ " <td>1.7</td>\n",
+ " <td>1.94</td>\n",
+ " <td>1.88</td>\n",
" <td>NaN</td>\n",
- " <td>1.69</td>\n",
+ " <td>1.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>2021-12-01</td>\n",
- " <td>1.54</td>\n",
- " <td>1.7</td>\n",
- " <td>1.64</td>\n",
+ " <td>2023-05-01</td>\n",
+ " <td>1.69</td>\n",
+ " <td>1.93</td>\n",
+ " <td>1.87</td>\n",
" <td>NaN</td>\n",
- " <td>1.61</td>\n",
+ " <td>1.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>2021-11-01</td>\n",
- " <td>1.57</td>\n",
- " <td>1.74</td>\n",
- " <td>1.68</td>\n",
+ " <td>2023-04-01</td>\n",
+ " <td>1.81</td>\n",
+ " <td>2.0</td>\n",
+ " <td>1.95</td>\n",
" <td>NaN</td>\n",
- " <td>1.65</td>\n",
+ " <td>1.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
@@ -250,7 +238,7 @@
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>358</th>\n",
+ " <th>375</th>\n",
" <td>1992-05-01</td>\n",
" <td>0.54</td>\n",
" <td>0.78</td>\n",
@@ -259,7 +247,7 @@
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>359</th>\n",
+ " <th>376</th>\n",
" <td>1992-04-01</td>\n",
" <td>0.53</td>\n",
" <td>0.77</td>\n",
@@ -268,7 +256,7 @@
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>360</th>\n",
+ " <th>377</th>\n",
" <td>1992-03-01</td>\n",
" <td>0.54</td>\n",
" <td>0.77</td>\n",
@@ -277,7 +265,7 @@
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>361</th>\n",
+ " <th>378</th>\n",
" <td>1992-02-01</td>\n",
" <td>0.54</td>\n",
" <td>0.78</td>\n",
@@ -286,7 +274,7 @@
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>362</th>\n",
+ " <th>379</th>\n",
" <td>1992-01-01</td>\n",
" <td>0.54</td>\n",
" <td>0.78</td>\n",
@@ -296,40 +284,40 @@
" </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>363 rows × 6 columns</p>\n",
+ "<p>380 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" date diesel_ttc super_98_ttc super_95_ttc super_plombe_ttc \\\n",
- "0 2022-03-01 2.02 2.05 2.0 NaN \n",
- "1 2022-02-01 1.72 1.86 1.8 NaN \n",
- "2 2022-01-01 1.63 1.77 1.71 NaN \n",
- "3 2021-12-01 1.54 1.7 1.64 NaN \n",
- "4 2021-11-01 1.57 1.74 1.68 NaN \n",
+ "0 2023-08-01 1.85 1.99 1.94 NaN \n",
+ "1 2023-07-01 1.72 1.91 1.85 NaN \n",
+ "2 2023-06-01 1.7 1.94 1.88 NaN \n",
+ "3 2023-05-01 1.69 1.93 1.87 NaN \n",
+ "4 2023-04-01 1.81 2.0 1.95 NaN \n",
".. ... ... ... ... ... \n",
- "358 1992-05-01 0.54 0.78 NaN 0.81 \n",
- "359 1992-04-01 0.53 0.77 NaN 0.81 \n",
- "360 1992-03-01 0.54 0.77 NaN 0.81 \n",
- "361 1992-02-01 0.54 0.78 NaN 0.81 \n",
- "362 1992-01-01 0.54 0.78 NaN 0.8 \n",
+ "375 1992-05-01 0.54 0.78 NaN 0.81 \n",
+ "376 1992-04-01 0.53 0.77 NaN 0.81 \n",
+ "377 1992-03-01 0.54 0.77 NaN 0.81 \n",
+ "378 1992-02-01 0.54 0.78 NaN 0.81 \n",
+ "379 1992-01-01 0.54 0.78 NaN 0.8 \n",
"\n",
" super_95_e10_ttc \n",
- "0 1.96 \n",
- "1 1.77 \n",
- "2 1.69 \n",
- "3 1.61 \n",
- "4 1.65 \n",
+ "0 1.93 \n",
+ "1 1.84 \n",
+ "2 1.86 \n",
+ "3 1.85 \n",
+ "4 1.93 \n",
".. ... \n",
- "358 NaN \n",
- "359 NaN \n",
- "360 NaN \n",
- "361 NaN \n",
- "362 NaN \n",
+ "375 NaN \n",
+ "376 NaN \n",
+ "377 NaN \n",
+ "378 NaN \n",
+ "379 NaN \n",
"\n",
- "[363 rows x 6 columns]"
+ "[380 rows x 6 columns]"
]
},
- "execution_count": 19,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -340,7 +328,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 9,
"id": "8edc5227-ee31-45fd-8d94-a7e803012312",
"metadata": {},
"outputs": [],
@@ -356,7 +344,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 10,
"id": "cd984e50-fd5f-476e-aee3-59ceef07f30f",
"metadata": {},
"outputs": [
@@ -365,18 +353,18 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
- "Int64Index: 363 entries, 0 to 362\n",
+ "RangeIndex: 380 entries, 0 to 379\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
- " 0 diesel_ttc 363 non-null float64\n",
- " 1 super_98_ttc 363 non-null float64\n",
- " 2 super_95_ttc 243 non-null float64\n",
+ " 0 diesel_ttc 380 non-null float64\n",
+ " 1 super_98_ttc 380 non-null float64\n",
+ " 2 super_95_ttc 260 non-null float64\n",
" 3 super_plombe_ttc 157 non-null float64\n",
- " 4 super_95_e10_ttc 39 non-null float64\n",
- " 5 annee 363 non-null object \n",
+ " 4 super_95_e10_ttc 56 non-null float64\n",
+ " 5 annee 380 non-null object \n",
"dtypes: float64(5), object(1)\n",
- "memory usage: 19.9+ KB\n"
+ "memory usage: 17.9+ KB\n"
]
}
],
@@ -387,7 +375,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 11,
"id": "ca94602c-9898-479d-aa7b-1ede366e6850",
"metadata": {},
"outputs": [],
@@ -399,7 +387,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 12,
"id": "1b441ed2-b73d-4b61-8f34-b3e3df97267a",
"metadata": {},
"outputs": [],
@@ -414,7 +402,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 13,
"id": "a03acbd2-8b0a-45b9-b446-ab2504d9c6dc",
"metadata": {},
"outputs": [
@@ -721,11 +709,20 @@
" <tr>\n",
" <th>32</th>\n",
" <td>2022</td>\n",
- " <td>1.79</td>\n",
- " <td>1.89</td>\n",
- " <td>1.84</td>\n",
+ " <td>1.86</td>\n",
+ " <td>1.88</td>\n",
+ " <td>1.83</td>\n",
" <td>NaN</td>\n",
- " <td>1.81</td>\n",
+ " <td>1.78</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33</th>\n",
+ " <td>2023</td>\n",
+ " <td>1.8</td>\n",
+ " <td>1.96</td>\n",
+ " <td>1.9</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1.88</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -763,7 +760,8 @@
"29 2019 1.44 1.56 1.51 NaN \n",
"30 2020 1.27 1.42 1.37 NaN \n",
"31 2021 1.44 1.62 1.56 NaN \n",
- "32 2022 1.79 1.89 1.84 NaN \n",
+ "32 2022 1.86 1.88 1.83 NaN \n",
+ "33 2023 1.8 1.96 1.9 NaN \n",
"\n",
" super_95_e10_ttc \n",
"2 NaN \n",
@@ -796,10 +794,11 @@
"29 1.49 \n",
"30 1.35 \n",
"31 1.54 \n",
- "32 1.81 "
+ "32 1.78 \n",
+ "33 1.88 "
]
},
- "execution_count": 27,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -810,18 +809,61 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
+ "id": "9447ff41",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_ann.diesel_ttc = df_ann.diesel_ttc.astype(float)\n",
+ "df_ann.super_98_ttc = df_ann.super_98_ttc.astype(float)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
"id": "4960738b-ba5d-4ec4-a937-e136fa36b116",
"metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: title={'center': 'Prix annuel du super_98_ttc en euros'}, xlabel='date'>"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_ann.diesel_ttc.atype = 'float64'\n",
+ "df_ann.plot.line(x=\"date\", y=\"super_98_ttc\", title=\"Prix annuel du super_98_ttc en euros\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4e3a3250",
+ "metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "indirect-taxation-kernel",
+ "display_name": ".venv",
"language": "python",
- "name": "indirect-taxation-kernel"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -833,7 +875,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.10"
+ "version": "3.11.4"
},
"toc-autonumbering": true,
"toc-showmarkdowntxt": false
diff --git a/notebooks/prix_mensuel_carburant.csv b/notebooks/prix_mensuel_carburant.csv
index 2bbb9daa77bf4f5a93637663bcd4d9ec5fd71821..f02a8b4a85d8951b6b800fe617f4299a341ca19d 100644
--- a/notebooks/prix_mensuel_carburant.csv
+++ b/notebooks/prix_mensuel_carburant.csv
@@ -1,4 +1,21 @@
date,diesel_ttc,super_98_ttc,super_95_ttc,super_plombe_ttc,super_95_e10_ttc
+2023-08-01,1.85,1.99,1.94,,1.93
+2023-07-01,1.72,1.91,1.85,,1.84
+2023-06-01,1.7,1.94,1.88,,1.86
+2023-05-01,1.69,1.93,1.87,,1.85
+2023-04-01,1.81,2.0,1.95,,1.93
+2023-03-01,1.85,1.98,1.92,,1.9
+2023-02-01,1.86,1.98,1.92,,1.89
+2023-01-01,1.92,1.94,1.87,,1.86
+2022-12-01,1.78,1.76,1.71,,1.65
+2022-11-01,1.88,1.79,1.74,,1.68
+2022-10-01,1.87,1.73,1.69,,1.62
+2022-09-01,1.74,1.63,1.59,,1.51
+2022-08-01,1.87,1.88,1.82,,1.77
+2022-07-01,2.0,2.07,2.01,,1.95
+2022-06-01,2.09,2.19,2.12,,2.08
+2022-05-01,1.9,1.98,1.92,,1.89
+2022-04-01,1.87,1.87,1.82,,1.76
2022-03-01,2.02,2.05,2.0,,1.96
2022-02-01,1.72,1.86,1.8,,1.77
2022-01-01,1.63,1.77,1.71,,1.69