diff --git a/dbt/.sqlfluff b/dbt/.sqlfluff
index 936f1df6e..f8ce53e30 100644
--- a/dbt/.sqlfluff
+++ b/dbt/.sqlfluff
@@ -20,4 +20,8 @@ align_within = select_clause
 # in this example would likely be the boundary of a CTE. Stopping
 # when we hit brackets is usually a good rule of thumb for this
 # configuration.
-align_scope = bracketed
\ No newline at end of file
+align_scope = bracketed
+
+[sqlfluff:indentation]
+indent_unit = space
+tab_space_size = 2
\ No newline at end of file
diff --git a/dbt/models/fraud/trips/_simultaneous_group_trips.yml b/dbt/models/fraud/trips/_simultaneous_group_trips.yml
new file mode 100644
index 000000000..b34814eaa
--- /dev/null
+++ b/dbt/models/fraud/trips/_simultaneous_group_trips.yml
@@ -0,0 +1,28 @@
+version: 2
+models:
+  - name: simultaneous_group_trips
+    columns:
+      - name: start_geo_code
+        data_type: character varying
+      - name: end_geo_code
+        data_type: character varying
+      - name: start_datetime
+        data_type: timestamp with time zone
+      - name: distance_50m
+        data_type: integer
+      - name: total_driver_revenue
+        data_type: bigint
+      - name: total_passengers_contributions
+        data_type: bigint
+      - name: total_incentives
+        data_type: numeric
+      - name: num_incitations
+        data_type: numeric
+      - name: operators
+        data_type: ARRAY
+      - name: num_fraud_journeys
+        data_type: bigint
+      - name: num_anomaly_journeys
+        data_type: bigint
+      - name: has_fraud_labels
+        data_type: text
diff --git a/dbt/models/fraud/trips/simultaneous_group_trips.sql b/dbt/models/fraud/trips/simultaneous_group_trips.sql
new file mode 100644
index 000000000..0860bfba9
--- /dev/null
+++ b/dbt/models/fraud/trips/simultaneous_group_trips.sql
@@ -0,0 +1,132 @@
+{{
+  config(
+    materialized = 'incremental',
+    unique_key = [
+      'start_geo_code',
+      'end_geo_code',
+      'start_datetime',
+      'distance_50m'
+    ],
+    indexes = [
+      {
+        'columns':[
+          'start_geo_code',
+          'end_geo_code',
+          'start_datetime',
+          'distance_50m'
+        ],
+        'unique':true
+
+      }
+    ]
+    )
+}}
+
+with incentives as (
+  select
+    carpool_id,
+    sum(i.amount)         as total_amount,
+    count(distinct i._id) as num_incitations
+  from {{ source('carpool', 'operator_incentives') }} as i
+  inner join
+    {{ source('carpool', 'carpools') }} as c
+    on
+      i.carpool_id = c._id
+      {% if is_incremental() %}
+        and c.start_datetime
+        >= (
+          select date_trunc('week', max(start_datetime))
+          from {{ this }}
+        )
+      {% else %}
+              and c.start_datetime >= now() - interval '1 year'
+      {% endif %}
+  group by 1
+),
+
+groups as (
+  select
+    g.start_geo_code,
+    g.end_geo_code,
+    c.start_datetime,
+    (
+      c.distance / 50
+    )::int as distance_50m,
+    count(
+      *
+    )      as num_journeys,
+    sum(
+      driver_revenue
+    )      as total_driver_revenue,
+    sum(
+      passenger_contribution
+    )      as total_passengers_contributions,
+    sum(
+      i.total_amount
+    )      as total_incentives,
+    sum(
+      i.num_incitations
+    )      as num_incitations,
+    array_agg(
+      distinct c.operator_id
+    )      as operators,
+    sum(
+      (s.fraud_status = 'failed')::int
+    )      as num_fraud_journeys,
+    sum(
+      (s.anomaly_status = 'failed')::int
+    )      as num_anomaly_journeys,
+    max(
+      case when tvel._id is not null then 'yes' else 'no' end
+    )      as has_fraud_labels,
+    json_agg(tvel.labels) filter (
+      where tvel.labels is not null
+    )      as fraud_labels,
+    1      as link
+  from
+    {{ source('carpool', 'carpools') }} as c
+  inner join {{ source('carpool', 'geo') }} as g
+    on
+      c._id = g.carpool_id
+  left join incentives as i
+    on
+      c._id = i.carpool_id
+  left join
+    {{ source('carpool', 'status') }} as s
+    on
+      c._id = s.carpool_id
+  left join
+    {{ source('carpool', 'terms_violation_error_labels') }} as tvel
+    on c._id = tvel.carpool_id
+  {% if is_incremental() %}
+    where
+      c.start_datetime
+      >= (select date_trunc('week', max(start_datetime)) from {{ this }})
+  {% else %}
+            where c.start_datetime >= now() - interval '1 year'
+        {% endif %}
+  group by
+    1,
+    2,
+    3,
+    4
+  having
+    count(*) >= 3
+)
+
+select
+  start_geo_code,
+  end_geo_code,
+  start_datetime,
+  distance_50m,
+  num_journeys,
+  total_driver_revenue,
+  total_passengers_contributions,
+  total_incentives,
+  num_incitations,
+  operators,
+  num_fraud_journeys,
+  num_anomaly_journeys,
+  has_fraud_labels
+from groups
+order by date_trunc('week', start_datetime) desc, total_driver_revenue desc
diff --git a/dbt/models/fraud/_users_intraday_role_change_stats.yml b/dbt/models/fraud/users/_users_intraday_role_change_stats.yml
similarity index 100%
rename from dbt/models/fraud/_users_intraday_role_change_stats.yml
rename to dbt/models/fraud/users/_users_intraday_role_change_stats.yml
diff --git a/dbt/models/fraud/_users_statistics.yml b/dbt/models/fraud/users/_users_statistics.yml
similarity index 100%
rename from dbt/models/fraud/_users_statistics.yml
rename to dbt/models/fraud/users/_users_statistics.yml
diff --git a/dbt/models/fraud/users_intraday_role_change_stats.sql b/dbt/models/fraud/users/users_intraday_role_change_stats.sql
similarity index 91%
rename from dbt/models/fraud/users_intraday_role_change_stats.sql
rename to dbt/models/fraud/users/users_intraday_role_change_stats.sql
index 455d38fdd..07e22623d 100644
--- a/dbt/models/fraud/users_intraday_role_change_stats.sql
+++ b/dbt/models/fraud/users/users_intraday_role_change_stats.sql
@@ -50,6 +50,8 @@ select
   avg(
     count_consecutive_changes
   )                              as avg_daily_consecutives_intraday_role_changes,
-  max(count_consecutive_changes) as max_daily_consecutives_intraday_role_changes
+  max(
+    count_consecutive_changes
+  )                              as max_daily_consecutives_intraday_role_changes
 from intraday_stats
 group by 1
diff --git a/dbt/models/fraud/users_statistics.sql b/dbt/models/fraud/users/users_statistics.sql
similarity index 100%
rename from dbt/models/fraud/users_statistics.sql
rename to dbt/models/fraud/users/users_statistics.sql
diff --git a/dbt/models/fraud/users_trips.sql b/dbt/models/fraud/users/users_trips.sql
similarity index 100%
rename from dbt/models/fraud/users_trips.sql
rename to dbt/models/fraud/users/users_trips.sql
diff --git a/dbt/models/sources/carpool.yml b/dbt/models/sources/carpool.yml
index 1c289efba..b02282e63 100644
--- a/dbt/models/sources/carpool.yml
+++ b/dbt/models/sources/carpool.yml
@@ -6,7 +6,7 @@ sources:
 
     tables:
       - name: carpools
-        tags: ['carpools_source']
+        tags: [carpools_source]
         meta:
           elementary: # default config
             timestamp_column: start_datetime
@@ -22,7 +22,7 @@ sources:
             seasonality: day_of_week
         tests:
           - elementary.dimension_anomalies:
-              name: "volume de trajets par opérateur"
+              name: volume de trajets par opérateur
               dimensions:
                 - operator_id
               config:
@@ -135,7 +135,7 @@ sources:
           - name: legacy_id
             data_type: bigint
       - name: geo
-        tags: ['geo_source']
+        tags: [geo_source]
         meta:
           elementary: # default config
             timestamp_column: updated_at
@@ -150,24 +150,24 @@ sources:
               count: 1
         tests:
           - elementary.dimension_anomalies:
-              name: "volume de trajets par O/D"
+              name: volume de trajets par O/D
               timestamp_column: updated_at
               dimensions:
                 - start_geo_code
                 - end_geo_code
-              exclude_final_results: 'average > 50'
+              exclude_final_results: average > 50
           - elementary.dimension_anomalies:
-              name: "volume de trajets par origine"
+              name: volume de trajets par origine
               timestamp_column: updated_at
               dimensions:
                 - start_geo_code
-              exclude_final_results: 'average > 50'
+              exclude_final_results: average > 50
           - elementary.dimension_anomalies:
-              name: "volume de trajets par destination"
+              name: volume de trajets par destination
               timestamp_column: updated_at
               dimensions:
                 - end_geo_code
-              exclude_final_results: 'average > 50'
+              exclude_final_results: average > 50
         columns:
           - name: _id
             data_type: integer
@@ -219,3 +219,13 @@ sources:
             data_type: USER-DEFINED
           - name: anomaly_status
             data_type: USER-DEFINED
+      - name: terms_violation_error_labels
+        columns:
+          - name: _id
+            data_type: integer
+          - name: created_at
+            data_type: timestamp with time zone
+          - name: carpool_id
+            data_type: integer
+          - name: labels
+            data_type: ARRAY
diff --git a/dbt/models/sources/policy.yml b/dbt/models/sources/policy.yml
index 2ede9da88..ae1924712 100644
--- a/dbt/models/sources/policy.yml
+++ b/dbt/models/sources/policy.yml
@@ -57,6 +57,6 @@ sources:
           - name: incentive_sum
             data_type: integer
           - name: max_amount
-            data_type: integer
+            data_type: bigint
           - name: tz
             data_type: character varying
diff --git a/notebooks/.pre-commit-config.yaml b/notebooks/.pre-commit-config.yaml
index b8562edcc..ecc3d7dd2 100644
--- a/notebooks/.pre-commit-config.yaml
+++ b/notebooks/.pre-commit-config.yaml
@@ -4,6 +4,6 @@ repos:
       - id: jupyter-nb-clear-output
         name: jupyter-nb-clear-output
         files: \.ipynb$
-        stages: [commit]
+        stages: [pre-commit]
         language: system
         entry: jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace
diff --git a/notebooks/analytics/cee/cee_analysis.ipynb b/notebooks/analytics/cee/cee_analysis.ipynb
index a0306680c..eae0dfee5 100644
--- a/notebooks/analytics/cee/cee_analysis.ipynb
+++ b/notebooks/analytics/cee/cee_analysis.ipynb
@@ -5,14 +5,22 @@
    "id": "88314698-ff9d-4314-b576-cd6bc2456ab3",
    "metadata": {},
    "source": [
-    "# Imports"
+    "# Imports\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "069a1698-fb73-4445-b1e9-ec019f349be3",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-05T10:13:37.056696Z",
+     "iopub.status.busy": "2025-02-05T10:13:37.056287Z",
+     "iopub.status.idle": "2025-02-05T10:13:47.780935Z",
+     "shell.execute_reply": "2025-02-05T10:13:47.780693Z",
+     "shell.execute_reply.started": "2025-02-05T10:13:37.056664Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -33,14 +41,22 @@
    "id": "a5e803f4-ce31-4c93-8edd-d2341869b576",
    "metadata": {},
    "source": [
-    "# DB"
+    "# DB\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "b52ccb03-1fab-4750-a3e4-bdac2ba9c3f7",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-05T10:13:47.781693Z",
+     "iopub.status.busy": "2025-02-05T10:13:47.781551Z",
+     "iopub.status.idle": "2025-02-05T10:13:47.783222Z",
+     "shell.execute_reply": "2025-02-05T10:13:47.782976Z",
+     "shell.execute_reply.started": "2025-02-05T10:13:47.781684Z"
+    }
+   },
    "outputs": [],
    "source": [
     "DATABASE_URL = os.environ[\"DB_URL_RPC\"]"
@@ -50,7 +66,15 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "78cd6961-0676-4e96-ad56-23b568f1e01a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-05T10:13:47.783635Z",
+     "iopub.status.busy": "2025-02-05T10:13:47.783572Z",
+     "iopub.status.idle": "2025-02-05T10:13:47.786014Z",
+     "shell.execute_reply": "2025-02-05T10:13:47.785793Z",
+     "shell.execute_reply.started": "2025-02-05T10:13:47.783628Z"
+    }
+   },
    "outputs": [],
    "source": [
     "OUTPUT_PATH = Path(\"outputs\")\n",
@@ -59,9 +83,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "34d842df-ffce-4110-9aab-a727a7a66d0b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-05T10:13:47.786828Z",
+     "iopub.status.busy": "2025-02-05T10:13:47.786739Z",
+     "iopub.status.idle": "2025-02-05T10:13:47.891509Z",
+     "shell.execute_reply": "2025-02-05T10:13:47.891197Z",
+     "shell.execute_reply.started": "2025-02-05T10:13:47.786821Z"
+    }
+   },
    "outputs": [],
    "source": [
     "db_engine = create_engine(DATABASE_URL)"
@@ -80,14 +112,22 @@
     }
    },
    "source": [
-    "# Paramètres"
+    "# Paramètres\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "4d8b7f78",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-05T10:13:47.891874Z",
+     "iopub.status.busy": "2025-02-05T10:13:47.891800Z",
+     "iopub.status.idle": "2025-02-05T10:13:47.894077Z",
+     "shell.execute_reply": "2025-02-05T10:13:47.893714Z",
+     "shell.execute_reply.started": "2025-02-05T10:13:47.891865Z"
+    }
+   },
    "outputs": [],
    "source": [
     "aom_with_incentives = [\n",
@@ -130,7 +170,7 @@
    "id": "ef966c8a-5764-409b-a32a-9c8e5ddadfe7",
    "metadata": {},
    "source": [
-    "# Couleurs"
+    "# Couleurs\n"
    ]
   },
   {
@@ -402,7 +442,7 @@
    "id": "df757060-db5d-4844-a63a-0f26a065acfb",
    "metadata": {},
    "source": [
-    "# Statistiques cohorte 2022"
+    "# Statistiques cohorte 2022\n"
    ]
   },
   {
@@ -460,7 +500,7 @@
    "id": "0abcf0d8-6dce-4470-aa2b-c46c9fc1cc55",
    "metadata": {},
    "source": [
-    "## Nombre de CEE par mois et opérateur"
+    "## Nombre de CEE par mois et opérateur\n"
    ]
   },
   {
@@ -531,7 +571,7 @@
    "id": "26a2c669-93b4-4672-8096-344d31b9aaf9",
    "metadata": {},
    "source": [
-    "## Part des trjets CEE vs trajets non CEE"
+    "## Part des trjets CEE vs trajets non CEE\n"
    ]
   },
   {
@@ -725,7 +765,7 @@
     }
    },
    "source": [
-    "## Part des opérateurs par cohorte CEE"
+    "## Part des opérateurs par cohorte CEE\n"
    ]
   },
   {
@@ -808,7 +848,7 @@
    "id": "5b47b941-04f2-443a-9c08-1f4e2d244621",
    "metadata": {},
    "source": [
-    "## Evolution de la part de CEE par opérateur"
+    "## Evolution de la part de CEE par opérateur\n"
    ]
   },
   {
@@ -912,7 +952,7 @@
    "id": "dd7fab2a-0be8-4838-841f-8f6fc071d823",
    "metadata": {},
    "source": [
-    "### Part des conducteurs CEE qui ont obtenus leurs bonus sur l'ensemble des conducteurs CEE :"
+    "### Part des conducteurs CEE qui ont obtenus leurs bonus sur l'ensemble des conducteurs CEE :\n"
    ]
   },
   {
@@ -930,7 +970,7 @@
    "id": "5cebf6e2-385d-4883-8e8f-5855efec61ed",
    "metadata": {},
    "source": [
-    "### Comparaison par opérateur"
+    "### Comparaison par opérateur\n"
    ]
   },
   {
@@ -1036,7 +1076,7 @@
    "id": "dcdec20a-c647-4f11-8564-d6e6006d6da8",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -1084,7 +1124,7 @@
    "id": "4cc33648-88bf-4fce-9f31-51ab80975ec4",
    "metadata": {},
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -1152,7 +1192,7 @@
    "id": "f8dd35a4-d3de-4bd3-b98d-035862f8507c",
    "metadata": {},
    "source": [
-    "### AOM"
+    "### AOM\n"
    ]
   },
   {
@@ -1310,7 +1350,7 @@
    "id": "eeba0204-a469-412d-a075-60bd2efeebd3",
    "metadata": {},
    "source": [
-    "## Moyenne par cohorte"
+    "## Moyenne par cohorte\n"
    ]
   },
   {
@@ -1340,7 +1380,7 @@
    "id": "df55f0d2-4889-499f-b5ff-0d91c4c5d6c2",
    "metadata": {},
    "source": [
-    "## Visualisation 2022"
+    "## Visualisation 2022\n"
    ]
   },
   {
@@ -1366,7 +1406,7 @@
     "    elif breakpoint == np.inf:\n",
     "        return \"Plus de 55\"\n",
     "    else:\n",
-    "        return f\"{breakpoint-4:.0f}-{breakpoint:.0f}\"\n",
+    "        return f\"{breakpoint - 4:.0f}-{breakpoint:.0f}\"\n",
     "\n",
     "\n",
     "df_trips_by_drivers_2022_agg = (\n",
@@ -1427,7 +1467,7 @@
     "        .with_columns(\n",
     "            pl.col(\"breakpoint\")\n",
     "            .cast(pl.String)\n",
-    "            .replace(np.inf, f\"{bins[-1]+1}+\")\n",
+    "            .replace(np.inf, f\"{bins[-1] + 1}+\")\n",
     "            .str.replace(\"(\\.0)\", \"\"),\n",
     "            (100 * pl.col(\"count\") / pl.col(\"count\").sum()).alias(\"share\"),\n",
     "        )\n",
@@ -1927,7 +1967,7 @@
    "id": "89456da2-b0a5-4994-bb7b-11c437ecba1d",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -1973,7 +2013,7 @@
    "id": "b24c4909-0c91-4610-b6ba-f32d18df2201",
    "metadata": {},
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -2030,7 +2070,7 @@
    "id": "70082ce9-83aa-4903-98dc-10166b5b231a",
    "metadata": {},
    "source": [
-    "### AOM"
+    "### AOM\n"
    ]
   },
   {
@@ -2182,7 +2222,7 @@
    "id": "d43f1018-5bb4-4658-b84c-e47dd2be529b",
    "metadata": {},
    "source": [
-    "## Moyennes"
+    "## Moyennes\n"
    ]
   },
   {
@@ -2250,7 +2290,7 @@
     "        .with_columns(\n",
     "            pl.col(\"breakpoint\")\n",
     "            .cast(pl.String)\n",
-    "            .replace(np.inf, f\"{bins[-1]+1}\")\n",
+    "            .replace(np.inf, f\"{bins[-1] + 1}\")\n",
     "            .str.replace(\"(\\.0)\", \"\"),\n",
     "            (100 * pl.col(\"count\") / pl.col(\"count\").sum()).alias(\"share\"),\n",
     "        )\n",
@@ -2643,7 +2683,7 @@
    "id": "809ef076-f8b6-4cec-abd3-e50cc55e34f5",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -2688,7 +2728,7 @@
    "id": "8fae42cc-8425-4628-adbc-bd031dc09a6e",
    "metadata": {},
    "source": [
-    "On filtre les trajets > 100km :"
+    "On filtre les trajets > 100km :\n"
    ]
   },
   {
@@ -2706,7 +2746,7 @@
    "id": "e04691f7-cc40-4eb2-8395-1fbd7728ff55",
    "metadata": {},
    "source": [
-    "Part des trajets >100km :"
+    "Part des trajets >100km :\n"
    ]
   },
   {
@@ -2728,7 +2768,7 @@
    "id": "26ccad3a-5983-496e-ab96-824a9425eddd",
    "metadata": {},
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -2779,7 +2819,7 @@
    "id": "9848a3af-4393-4b34-ac1a-5cb244b912ed",
    "metadata": {},
    "source": [
-    "### AOM"
+    "### AOM\n"
    ]
   },
   {
@@ -2919,7 +2959,7 @@
    "id": "c481e858-3c5d-4304-ae19-f32bf4adda61",
    "metadata": {},
    "source": [
-    "## Moyennes"
+    "## Moyennes\n"
    ]
   },
   {
@@ -2927,7 +2967,7 @@
    "id": "9f22be16-b3f0-4ef1-aca5-33326ac22c81",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -2945,7 +2985,7 @@
    "id": "75b1865f-0c62-4927-a4b8-8628dc643562",
    "metadata": {},
    "source": [
-    "Part des trajets >100km :"
+    "Part des trajets >100km :\n"
    ]
   },
   {
@@ -2967,7 +3007,7 @@
    "id": "0fde838c-5013-4106-a75e-1fd4deaf1b92",
    "metadata": {},
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -2987,7 +3027,7 @@
    "id": "f231e6f0-4122-4c16-99e9-137bb81709da",
    "metadata": {},
    "source": [
-    "On filtre les trajets > 100km :"
+    "On filtre les trajets > 100km :\n"
    ]
   },
   {
@@ -3029,7 +3069,7 @@
    "id": "896da050-9423-4700-97a9-b13150154bf3",
    "metadata": {},
    "source": [
-    "Part des trajets >100km :"
+    "Part des trajets >100km :\n"
    ]
   },
   {
@@ -3629,7 +3669,7 @@
    "id": "5af0572d-29e6-4222-89ed-c55646fe8a49",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -3679,7 +3719,7 @@
    "id": "b5f73f33-4699-44dc-a800-418eb3fda060",
    "metadata": {},
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -3734,7 +3774,7 @@
    "id": "5e3e2ed4-8aba-4580-8675-6d9d81ca94c6",
    "metadata": {},
    "source": [
-    "### AOM"
+    "### AOM\n"
    ]
   },
   {
@@ -3917,7 +3957,7 @@
    "id": "b363bf3d-c946-484d-8edd-138f916fd4fc",
    "metadata": {},
    "source": [
-    "## Moyennes"
+    "## Moyennes\n"
    ]
   },
   {
@@ -3956,7 +3996,7 @@
     "        .with_columns(\n",
     "            pl.col(\"breakpoint\")\n",
     "            .cast(pl.String)\n",
-    "            .replace(np.inf, f\"{bins[-1]+1}\")\n",
+    "            .replace(np.inf, f\"{bins[-1] + 1}\")\n",
     "            .str.replace(\"(\\.0)\", \"\"),\n",
     "            (100 * pl.col(\"count\") / pl.col(\"count\").sum()).alias(\"share\"),\n",
     "        )\n",
@@ -4328,7 +4368,7 @@
    "id": "aea6233d-35e4-421e-b9d8-06ba8abf86d1",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -4386,7 +4426,7 @@
    "id": "8db415f1-c0f7-4f10-a8c3-c7d9cd3f22be",
    "metadata": {},
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -4744,7 +4784,7 @@
    "id": "283f4d91-e461-44e7-af18-91674d711247",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -4822,7 +4862,7 @@
    "id": "bee54326-db67-409b-a54f-82d163463a8e",
    "metadata": {},
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -4901,7 +4941,7 @@
    "id": "501092b8-3877-4884-b3f5-cab62916afb6",
    "metadata": {},
    "source": [
-    "### AOM"
+    "### AOM\n"
    ]
   },
   {
@@ -5496,7 +5536,7 @@
    "id": "fe2d6a74-0cb7-46d2-ab0f-e7c4769fdaa9",
    "metadata": {},
    "source": [
-    "## Application au nombre de trajets effectués"
+    "## Application au nombre de trajets effectués\n"
    ]
   },
   {
@@ -5504,7 +5544,7 @@
    "id": "74805c9f-edde-40f1-a68d-4edec31d9173",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -5586,7 +5626,7 @@
    "id": "d3bfd58d-06ad-42ad-82e4-4951f37fe947",
    "metadata": {},
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -5647,7 +5687,7 @@
    "id": "01114a32-1c54-46b3-bab6-cdd2b2293aea",
    "metadata": {},
    "source": [
-    "### Moyenne 2023"
+    "### Moyenne 2023\n"
    ]
   },
   {
@@ -5692,7 +5732,7 @@
    "id": "15a8f2ff-f0ce-4728-94e8-9e717e17ce58",
    "metadata": {},
    "source": [
-    "### Stats par cohortes"
+    "### Stats par cohortes\n"
    ]
   },
   {
@@ -5729,9 +5769,7 @@
     "    )\n",
     "    .alias(\"churn_estimatation_mean_trips\")\n",
     "    .round(1),\n",
-    ").sort(\n",
-    "    pl.col(\"cohorte\").str.reverse()\n",
-    ")"
+    ").sort(pl.col(\"cohorte\").str.reverse())"
    ]
   },
   {
@@ -5755,7 +5793,7 @@
    "id": "22f7b45b-448e-46d6-9a2b-48ccf471d4fd",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -5791,7 +5829,7 @@
    "id": "7391236e-bdfc-4243-8b4e-ebf629fc8318",
    "metadata": {},
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -5873,7 +5911,7 @@
     "        .with_columns(\n",
     "            pl.col(\"breakpoint\")\n",
     "            .cast(pl.String)\n",
-    "            .replace(np.inf, f\"{bins[-1]+1}+\")\n",
+    "            .replace(np.inf, f\"{bins[-1] + 1}+\")\n",
     "            .str.replace(\"(\\.0)\", \"\"),\n",
     "            (100 * pl.col(\"count\") / pl.col(\"count\").sum()).alias(\"share\"),\n",
     "        )\n",
@@ -6063,7 +6101,7 @@
    "id": "6fe11250-1408-49c5-a6a3-c5cdd5fd2b84",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -6138,7 +6176,7 @@
    "id": "7c025adf-0138-4e03-958e-d7c69e0fe24f",
    "metadata": {},
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -6218,7 +6256,7 @@
    "id": "78ffe334-d038-4988-8a13-53a396e9f69d",
    "metadata": {},
    "source": [
-    "### AOM"
+    "### AOM\n"
    ]
   },
   {
@@ -6520,7 +6558,7 @@
    "id": "9fd467ed-ae43-4d28-92a0-1168fb94b284",
    "metadata": {},
    "source": [
-    "### Distribution des distance économisées"
+    "### Distribution des distance économisées\n"
    ]
   },
   {
@@ -6623,7 +6661,7 @@
    "id": "f585048b-a222-469d-baf1-e7702558e06c",
    "metadata": {},
    "source": [
-    "### Moyenne par cohorte"
+    "### Moyenne par cohorte\n"
    ]
   },
   {
@@ -6725,7 +6763,7 @@
    "id": "694f2c4c-ceb3-4e6b-b065-d1e2f6bdb5ad",
    "metadata": {},
    "source": [
-    "### Médiane par cohorte"
+    "### Médiane par cohorte\n"
    ]
   },
   {
@@ -6827,7 +6865,7 @@
    "id": "c4b7c55e-9a7b-4bdd-9c7a-92722d9e596f",
    "metadata": {},
    "source": [
-    "### Par cohortes hebdomadaires"
+    "### Par cohortes hebdomadaires\n"
    ]
   },
   {
@@ -6962,6 +7000,246 @@
     "## Comparaison par AOM\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0715a98d-75e2-4ffc-a7f7-74595a75d203",
+   "metadata": {},
+   "source": [
+    "## Par niveau de pratique\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a37edd5d-9036-475e-ac67-f372012066c6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-05T10:39:37.964058Z",
+     "iopub.status.busy": "2025-02-05T10:39:37.963490Z",
+     "iopub.status.idle": "2025-02-05T10:40:07.787110Z",
+     "shell.execute_reply": "2025-02-05T10:40:07.786613Z",
+     "shell.execute_reply.started": "2025-02-05T10:39:37.964030Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_distance_eco_cee_by_month = pl.read_database(\n",
+    "    \"\"\"\n",
+    "select \n",
+    "\tc.trip_id,\n",
+    "\tmax(cdv.\"uuid\"::text) filter (where c.is_driver) as \"uuid\",\n",
+    "\tmin(cdv.date_first_cee) as date_first_cee,\n",
+    "\tmin(c.datetime) as start_datetime,\n",
+    "\tsum(coalesce (distance,(c.meta->>'calc_distance')::int) * (case when seats=0 then 1 else seats end)) as distance_eco\n",
+    "from carpool.carpools c \n",
+    "inner join carpool.identities i on c.identity_id = i.\"_id\" \n",
+    "left join luis.cee_drivers_v4 cdv on i.\"uuid\" = cdv.\"uuid\"\n",
+    "where cdv.cohorte in ('t1_23','t2_23')\n",
+    "and c.datetime between cdv.date_first_cee and cdv.date_first_cee + interval '23 month'\n",
+    "group by 1\n",
+    "    \"\"\",\n",
+    "    connection=db_engine,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06b99ca8-e400-4f40-9e0b-94bb30cdb304",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-05T10:40:25.460896Z",
+     "iopub.status.busy": "2025-02-05T10:40:25.459002Z",
+     "iopub.status.idle": "2025-02-05T10:40:25.473438Z",
+     "shell.execute_reply": "2025-02-05T10:40:25.472344Z",
+     "shell.execute_reply.started": "2025-02-05T10:40:25.460798Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_distance_eco_cee_by_month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "546ff184-01ce-46fa-b743-ae46ee9003fb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-05T10:40:30.414750Z",
+     "iopub.status.busy": "2025-02-05T10:40:30.414263Z",
+     "iopub.status.idle": "2025-02-05T10:40:30.662285Z",
+     "shell.execute_reply": "2025-02-05T10:40:30.661785Z",
+     "shell.execute_reply.started": "2025-02-05T10:40:30.414718Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_distance_eco_cee_by_month.select(pl.col(\"uuid\").n_unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a79b30c2-a83f-4b92-99ca-104b96986967",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-05T13:02:33.563164Z",
+     "iopub.status.busy": "2025-02-05T13:02:33.561637Z",
+     "iopub.status.idle": "2025-02-05T13:02:34.356764Z",
+     "shell.execute_reply": "2025-02-05T13:02:34.356449Z",
+     "shell.execute_reply.started": "2025-02-05T13:02:33.563085Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "df_distance_eco_cee_by_month_agg = (\n",
+    "    df_distance_eco_cee_by_month.group_by(\"uuid\")\n",
+    "    .agg(\n",
+    "        pl.col(\"date_first_cee\").min(),\n",
+    "        pl.col(\"trip_id\")\n",
+    "        .filter(\n",
+    "            pl.col(\"start_datetime\") <= pl.col(\"date_first_cee\").dt.offset_by(\"6mo\")\n",
+    "        )\n",
+    "        .n_unique()\n",
+    "        .alias(\"num_trips_6mo\"),\n",
+    "    )\n",
+    "    .with_columns(\n",
+    "        pl.date_ranges(\n",
+    "            pl.col(\"date_first_cee\").dt.truncate(\"1mo\"),\n",
+    "            pl.col(\"date_first_cee\").dt.offset_by(\"23mo\"),\n",
+    "            \"1mo\",\n",
+    "        ).alias(\"month\")\n",
+    "    )\n",
+    "    .explode(\"month\")\n",
+    "    .with_columns(\n",
+    "        pl.col(\"month\")\n",
+    "        .rank()\n",
+    "        .over(partition_by=\"uuid\", order_by=\"month\")\n",
+    "        .alias(\"month_rank\")\n",
+    "    )\n",
+    "    .filter(pl.col(\"month\") <= datetime(2024, 9, 30))\n",
+    "    .join(\n",
+    "        df_distance_eco_cee_by_month.group_by(\n",
+    "            [\"uuid\", pl.col(\"start_datetime\").dt.truncate(\"1mo\")]\n",
+    "        ).agg(pl.col(\"distance_eco\").sum()),\n",
+    "        left_on=[\"uuid\", \"month\"],\n",
+    "        right_on=[\"uuid\", pl.col(\"start_datetime\").cast(pl.Date)],\n",
+    "        how=\"left\",\n",
+    "    )\n",
+    ")\n",
+    "df_distance_eco_cee_by_month_agg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "a955b38d-189e-46c1-806a-1277b68588f0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-05T14:57:39.234336Z",
+     "iopub.status.busy": "2025-02-05T14:57:39.233833Z",
+     "iopub.status.idle": "2025-02-05T14:57:39.292762Z",
+     "shell.execute_reply": "2025-02-05T14:57:39.292386Z",
+     "shell.execute_reply.started": "2025-02-05T14:57:39.234309Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_distance_eco_cee_by_month_agg.group_by(\"month_rank\").agg(\n",
+    "    pl.col(\"uuid\").filter(pl.col(\"distance_eco\").is_not_null()).n_unique()\n",
+    ").sort(pl.col(\"month_rank\")).write_clipboard()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a82c408f-714d-4327-9fc8-c846eca0604f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-05T14:45:39.094616Z",
+     "iopub.status.busy": "2025-02-05T14:45:39.092408Z",
+     "iopub.status.idle": "2025-02-05T14:45:39.451594Z",
+     "shell.execute_reply": "2025-02-05T14:45:39.451295Z",
+     "shell.execute_reply.started": "2025-02-05T14:45:39.094492Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "traces = []\n",
+    "colors = dict(\n",
+    "    zip(\n",
+    "        [1, 10, 20, 30, 40, 50, 60, 70, 80],\n",
+    "        [\n",
+    "            \"#9da7f5\",\n",
+    "            \"#7a88f2\",\n",
+    "            \"#5669f0\",\n",
+    "            \"#314bef\",\n",
+    "            \"#112fe9\",\n",
+    "            \"#0d28c7\",\n",
+    "            \"#0921a5\",\n",
+    "            \"#061a82\",\n",
+    "            \"#04135e\",\n",
+    "        ],\n",
+    "    )\n",
+    ")\n",
+    "for num_trips in [1, 10, 20, 30, 40, 50, 60, 70, 80]:\n",
+    "    data = (\n",
+    "        df_distance_eco_cee_by_month_agg.filter(\n",
+    "            (pl.col(\"num_trips_6mo\") >= num_trips) & (pl.col(\"month_rank\") != 21)\n",
+    "        )\n",
+    "        .with_columns(pl.col(\"distance_eco\").fill_null(0))\n",
+    "        .group_by(\"month_rank\")\n",
+    "        .agg((pl.col(\"distance_eco\").mean() / 1000).round(1))\n",
+    "        .sort(pl.col(\"month_rank\"))\n",
+    "    )\n",
+    "\n",
+    "    data.write_excel(OUTPUT_PATH / f\"df_{num_trips}.xlsx\")\n",
+    "\n",
+    "    trace = go.Scatter(\n",
+    "        x=data[\"month_rank\"],\n",
+    "        y=data[\"distance_eco\"],\n",
+    "        name=f\"{num_trips}+\",\n",
+    "        mode=\"lines+markers\",\n",
+    "        line_color=colors[num_trips],\n",
+    "    )\n",
+    "\n",
+    "    traces.append(trace)\n",
+    "\n",
+    "fig_distance_eco_mensuelle = go.Figure(traces)\n",
+    "\n",
+    "fig_distance_eco_mensuelle.update_layout(\n",
+    "    plot_bgcolor=\"white\",\n",
+    "    legend_title=\"Nombre de trajets effectués<br> (6 premiers mois)\",\n",
+    "    title=\"Distance moyenne économisée en fonction du mois covoituré\",\n",
+    "    margin_t=80,\n",
+    "    height=600,\n",
+    ")\n",
+    "fig_distance_eco_mensuelle.update_yaxes(\n",
+    "    showgrid=True,\n",
+    "    griddash=\"dashdot\",\n",
+    "    gridwidth=1,\n",
+    "    gridcolor=\"gray\",\n",
+    "    title=\"Distance économisée (km)\",\n",
+    "    zeroline=True,\n",
+    "    zerolinecolor=\"black\",\n",
+    "    zerolinewidth=1,\n",
+    ")\n",
+    "\n",
+    "fig_distance_eco_mensuelle.update_xaxes(title=\"Mois n°\", ticks=\"outside\")\n",
+    "fig_distance_eco_mensuelle.write_html(\n",
+    "    OUTPUT_PATH / \"fig_distance_eco_mensuelle_par_niveau_pratique.html\"\n",
+    ")\n",
+    "fig_distance_eco_mensuelle.write_image(\n",
+    "    OUTPUT_PATH / \"fig_distance_eco_mensuelle_par_niveau_pratique.svg\",\n",
+    "    format=\"svg\",\n",
+    "    width=1280,\n",
+    "    height=720,\n",
+    ")\n",
+    "fig_distance_eco_mensuelle.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f0b35594",
@@ -7142,7 +7420,7 @@
    "id": "2d8ed25a-cf31-4436-bcfc-bd4c5be66a94",
    "metadata": {},
    "source": [
-    "# KwhCumac"
+    "# KwhCumac\n"
    ]
   },
   {
@@ -7150,7 +7428,7 @@
    "id": "c99a204c-34eb-4205-84ff-86b894cc3cc6",
    "metadata": {},
    "source": [
-    "## Requetes"
+    "## Requetes\n"
    ]
   },
   {
@@ -7158,7 +7436,7 @@
    "id": "01a98a2d-f611-4aef-865b-24cf86c40a94",
    "metadata": {},
    "source": [
-    "## 2022"
+    "## 2022\n"
    ]
   },
   {
@@ -7406,7 +7684,7 @@
    "id": "b102ef82-c9c1-4f77-8846-53cf1549b08c",
    "metadata": {},
    "source": [
-    "## CEE"
+    "## CEE\n"
    ]
   },
   {
@@ -7486,7 +7764,7 @@
    "id": "abbf4153-40fd-4ae0-acb8-b22922a90eb2",
    "metadata": {},
    "source": [
-    "### Courbe de churn"
+    "### Courbe de churn\n"
    ]
   },
   {
@@ -7507,7 +7785,7 @@
    "id": "c1251002-b53a-4a54-8cf3-181452c65634",
    "metadata": {},
    "source": [
-    "### Stats 2023"
+    "### Stats 2023\n"
    ]
   },
   {
@@ -7532,7 +7810,7 @@
    "id": "0a56ad57-9e04-4980-98b8-ca2801e475af",
    "metadata": {},
    "source": [
-    "### Stats par cohortes"
+    "### Stats par cohortes\n"
    ]
   },
   {
@@ -7676,7 +7954,7 @@
    "id": "6ca341e9-63ea-4914-b648-68ae6a952353",
    "metadata": {},
    "source": [
-    "## Visualisation"
+    "## Visualisation\n"
    ]
   },
   {
@@ -7738,7 +8016,7 @@
    "id": "0803075e-7d63-4c60-82cb-7f612d6df78d",
    "metadata": {},
    "source": [
-    "## Analayse de l'effet de seuil"
+    "## Analayse de l'effet de seuil\n"
    ]
   },
   {
@@ -7815,7 +8093,7 @@
     "        ],\n",
     "        (100 * pl.col(\"num_drivers\") / pl.col(\"num_drivers\").max()).alias(\n",
     "            \"share_drivers\"\n",
-    "        )\n",
+    "        ),\n",
     "    )\n",
     ")\n",
     "cummulative_distance_eco_cee_df_agg"
@@ -7915,7 +8193,7 @@
    "id": "30077ed2-e07c-4091-a6a1-2c177acbefc4",
    "metadata": {},
    "source": [
-    "# KwhCumac mensuel"
+    "# KwhCumac mensuel\n"
    ]
   },
   {
@@ -7923,7 +8201,7 @@
    "id": "7618446c-0a23-4734-a17a-4eeed63cc286",
    "metadata": {},
    "source": [
-    "## Requêtes"
+    "## Requêtes\n"
    ]
   },
   {
@@ -7931,7 +8209,7 @@
    "id": "c46c6c61-f4bb-4220-ba84-1ea8614d93c6",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -8092,7 +8370,7 @@
     "    ),\n",
     "    x=\"num_mois\",\n",
     "    y=\"kwhcumac\",\n",
-    "    template=\"simple_white\"\n",
+    "    template=\"simple_white\",\n",
     ")"
    ]
   },
@@ -8101,7 +8379,7 @@
    "id": "819e9ca2-be09-467a-aedb-b75c0bd776bd",
    "metadata": {},
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -8190,7 +8468,7 @@
    "id": "3e0fa541-154d-4d66-a33a-1c52d56fc8c8",
    "metadata": {},
    "source": [
-    "## Visualisation"
+    "## Visualisation\n"
    ]
   },
   {
@@ -8216,15 +8494,11 @@
     "    template=\"simple_white\",\n",
     "    labels={\"num_mois\": \"Mois n°\", \"cohorte\": \"Cohorte\", \"kwhcumac\": \"kWh cumac\"},\n",
     "    title=\"Comparaison de khwcumac économisé au fil des mois par les différentes cohortes\",\n",
-    "    color_discrete_map={\n",
-    "       **cohortes_color_mapping,\"t1_23\":\"#82c4ad\"\n",
-    "    },\n",
+    "    color_discrete_map={**cohortes_color_mapping, \"t1_23\": \"#82c4ad\"},\n",
     ")\n",
     "\n",
     "fig_kwhcumac_mensuel.write_html(OUTPUT_PATH / \"fig_kwhcumac_mensuel_multi.html\")\n",
-    "fig_kwhcumac_mensuel.update_traces(\n",
-    "    textposition = \"top center\"\n",
-    ")\n",
+    "fig_kwhcumac_mensuel.update_traces(textposition=\"top center\")\n",
     "fig_kwhcumac_mensuel.update_layout(\n",
     "    hovermode=\"x unified\",\n",
     ")\n",
@@ -8415,7 +8689,7 @@
    "id": "b2890235-e3dd-4fab-9aab-925f253e1f41",
    "metadata": {},
    "source": [
-    "# Territoires"
+    "# Territoires\n"
    ]
   },
   {
@@ -8439,7 +8713,7 @@
    "id": "a330d8f3-4ac4-483a-a336-b67886931a43",
    "metadata": {},
    "source": [
-    "## Requêtes"
+    "## Requêtes\n"
    ]
   },
   {
@@ -8447,7 +8721,7 @@
    "id": "eb4d1ef1-d7ef-4311-9a32-5fcdaad625f9",
    "metadata": {},
    "source": [
-    "### 2022"
+    "### 2022\n"
    ]
   },
   {
@@ -8504,7 +8778,7 @@
     }
    },
    "source": [
-    "### CEE"
+    "### CEE\n"
    ]
   },
   {
@@ -8555,7 +8829,7 @@
    "id": "db38ef33-8679-4baf-b605-0a75c93a47d5",
    "metadata": {},
    "source": [
-    "## Visualisation"
+    "## Visualisation\n"
    ]
   },
   {
@@ -8566,7 +8840,6 @@
    "outputs": [],
    "source": [
     "def preprocess_territories_df(df: pl.DataFrame) -> pl.DataFrame:\n",
-    "\n",
     "    agg_exprs = [pl.len()]\n",
     "    group_by_exprs = [\"TYPE_COMMUNE_UU\"]\n",
     "    if \"cohorte\" in df.columns:\n",
@@ -8687,7 +8960,7 @@
    "id": "0706aed4-64cd-4512-8ed8-980ecbb4ff80",
    "metadata": {},
    "source": [
-    "## Requêtes"
+    "## Requêtes\n"
    ]
   },
   {
@@ -8695,7 +8968,7 @@
    "id": "43ca9beb-aebf-41db-85f2-54aead9c3111",
    "metadata": {},
    "source": [
-    "### Stats"
+    "### Stats\n"
    ]
   },
   {
@@ -8759,7 +9032,7 @@
    "id": "10587d99-7c63-47a0-aec2-5c383f386012",
    "metadata": {},
    "source": [
-    "### Distance économisée"
+    "### Distance économisée\n"
    ]
   },
   {
@@ -8854,7 +9127,7 @@
    "id": "dbbf418c-969b-4457-af41-c3b65f8d2e7b",
    "metadata": {},
    "source": [
-    "### Trajets"
+    "### Trajets\n"
    ]
   },
   {
@@ -8923,7 +9196,7 @@
    "id": "0e123d7b-305f-434c-9f0d-bdfd9dc1782b",
    "metadata": {},
    "source": [
-    "## Nombre moyen de trajets effectués par semaine"
+    "## Nombre moyen de trajets effectués par semaine\n"
    ]
   },
   {
@@ -8993,7 +9266,7 @@
    "id": "68e0e91a-f279-4827-9eb8-72c5e98c150f",
    "metadata": {},
    "source": [
-    "## Nombre de conducteurs loyaux par opérateurs"
+    "## Nombre de conducteurs loyaux par opérateurs\n"
    ]
   },
   {
@@ -9703,7 +9976,7 @@
    "id": "c2f36535-026c-4455-bf7d-c0af50d04db7",
    "metadata": {},
    "source": [
-    "### Analyse du TOP 30 des Unités Urbaines"
+    "### Analyse du TOP 30 des Unités Urbaines\n"
    ]
   },
   {
@@ -9824,7 +10097,7 @@
    "id": "d57dd8e4-3c4b-47b0-979c-9a7338e31eca",
    "metadata": {},
    "source": [
-    "### Analyse du TOP 30 par communes"
+    "### Analyse du TOP 30 par communes\n"
    ]
   },
   {
@@ -10027,7 +10300,7 @@
    "id": "1295f9c4-80c4-4bb6-a4bc-aa3ffee8d2b4",
    "metadata": {},
    "source": [
-    "## Nombre de passagers différents"
+    "## Nombre de passagers différents\n"
    ]
   },
   {
@@ -10271,7 +10544,7 @@
    "id": "d18c9f55-e76a-48df-8f30-b693a7c1e8b9",
    "metadata": {},
    "source": [
-    "## Conducteurs CEE ayant covoiturés avec un autre conducteur CEE"
+    "## Conducteurs CEE ayant covoiturés avec un autre conducteur CEE\n"
    ]
   },
   {
@@ -10373,7 +10646,7 @@
    "id": "3a1dc631-9d4b-4b2d-9ade-0141fd2278c2",
    "metadata": {},
    "source": [
-    "## Analyse en réseau"
+    "## Analyse en réseau\n"
    ]
   },
   {
@@ -10563,7 +10836,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,