diff --git a/notebooks/create_tmdb_subsets.ipynb b/notebooks/create_tmdb_subsets.ipynb
index d432166..e62e131 100644
--- a/notebooks/create_tmdb_subsets.ipynb
+++ b/notebooks/create_tmdb_subsets.ipynb
@@ -47,10 +47,10 @@
     "\n",
     "EXPORT_TMDB_SUBSETS_TO = Path(\"../data/tmdb_subsets\").resolve()\n",
     "\n",
-    "MOVIES_COLUMNS_OF_INTEREST = ['title', 'original_title', 'release_date', 'production_countries', 'genres', 'production_companies', 'vote_average', 'revenue']\n",
+    "MOVIES_COLUMNS_OF_INTEREST = ['title', 'original_title', 'release_date', 'production_countries', 'genres', 'production_companies']\n",
     "NB_MOVIES_SUBSET = 5000\n",
     "\n",
-    "TVSHOWS_COLUMNS_OF_INTEREST = ['name', 'original_name', 'first_air_date', 'production_countries', 'genres', 'production_companies', 'vote_average']\n",
+    "TVSHOWS_COLUMNS_OF_INTEREST = ['name', 'original_name', 'first_air_date', 'production_countries', 'genres', 'production_companies']\n",
     "NB_TVSHOWS_SUBSET = 5000"
    ]
   },
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "id": "bf19fe99",
    "metadata": {},
    "outputs": [],
@@ -92,6 +92,35 @@
     "df_tvshows = pd.read_csv(EXTRACT_TWSHOWS_ZIP_TO / os.listdir(EXTRACT_TWSHOWS_ZIP_TO)[0])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ad152395",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6497 movies are note uniquely identify by original_title & release_year on 797541 movies (0.81%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df_movies[(df_movies['status'] == 'Released') & \n",
+    "               (~df_movies['adult']) &\n",
+    "               (~df_movies['release_date'].isna())].copy()\n",
+    "\n",
+    "df['release_year'] = df['release_date'].apply(lambda date : date[0:4]).astype(int)\n",
+    "\n",
+    "df_by_title_year = df.groupby(by=['original_title', 'release_year']).id.count()\n",
+    "\n",
+    "nb_duplicates_title_year = df_by_title_year[df_by_title_year > 1].shape[0]\n",
+    "nb_total_movies = df.shape[0]\n",
+    "print(f\"{nb_duplicates_title_year} movies are note uniquely identify by original_title & release_year on {nb_total_movies} movies ({(100 * nb_duplicates_title_year / nb_total_movies):.2f}%)\")\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -99,12 +128,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_movies.dropna(axis=0, how='any', subset=MOVIES_COLUMNS_OF_INTEREST, inplace=True)\n",
+    "df_movies_subset = df_movies.dropna(axis=0, how='any', subset=MOVIES_COLUMNS_OF_INTEREST)\n",
     "\n",
-    "df_movies_subset = df_movies[(df_movies['status'] == 'Released') & \n",
-    "                             (~df_movies['adult']) &\n",
-    "                             (df_movies['release_date'] < '2024-03-01') &\n",
-    "                             (df_movies['original_language'].isin(['fr', 'en']))].sort_values(by='release_date', ascending=False).iloc[0:NB_MOVIES_SUBSET]\n",
+    "df_movies_subset = df_movies_subset[(df_movies_subset['status'] == 'Released') & \n",
+    "                                    (~df_movies_subset['adult']) &\n",
+    "                                    (df_movies_subset['release_date'] < '2024-03-01') &\n",
+    "                                    (df_movies_subset['original_language'].isin(['fr', 'en']))].sort_values(by='release_date', ascending=False).iloc[0:NB_MOVIES_SUBSET]\n",
     "          \n",
     "df_movies_subset.to_csv(EXPORT_TMDB_SUBSETS_TO / \"tmdb_movies_subset.csv\")"
    ]
@@ -116,12 +145,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_tvshows.dropna(axis=0, how='any', subset=TVSHOWS_COLUMNS_OF_INTEREST, inplace=True)\n",
+    "df_tvshows_subset = df_tvshows.dropna(axis=0, how='any', subset=TVSHOWS_COLUMNS_OF_INTEREST)\n",
     "\n",
-    "df_tvshows_subset = df_tvshows[(df_tvshows['status'] == 'Ended') & \n",
-    "                             (~df_tvshows['adult']) &\n",
-    "                             (df_tvshows['last_air_date'] < '2024-03-01') &\n",
-    "                             (df_tvshows['original_language'].isin(['fr', 'en']))].sort_values(by='last_air_date', ascending=False).iloc[0:NB_TVSHOWS_SUBSET]\n",
+    "df_tvshows_subset = df_tvshows_subset[(df_tvshows_subset['status'] == 'Ended') & \n",
+    "                                      (~df_tvshows_subset['adult']) &\n",
+    "                                      (df_tvshows_subset['last_air_date'] < '2024-03-01') &\n",
+    "                                      (df_tvshows_subset['original_language'].isin(['fr', 'en']))].sort_values(by='last_air_date', ascending=False).iloc[0:NB_TVSHOWS_SUBSET]\n",
     "          \n",
     "df_tvshows_subset.to_csv(EXPORT_TMDB_SUBSETS_TO / \"tmdb_tvshows_subset.csv\")"
    ]