fixes merging different length recommended lists

Author: justin-cechmanek

python-recipes/recommendation-systems/collaborative_filtering.ipynb

@@ -1473,7 +1473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1517,14 +1517,14 @@
     "\n",
     "top_picks_for_you = get_unique_recommendations(user_id=user_id, num_results=5)  # general SVD results, no filter\n",
     "block_buster_hits = get_unique_recommendations(user_id=user_id, filters=block_buster_filter, num_results=5)\n",
-    "classics = get_unique_recommendations(user_id=user_id, filters=classics_filter, num_results=5)\n",
+    "classic_movies = get_unique_recommendations(user_id=user_id, filters=classics_filter, num_results=5)\n",
     "whats_popular = get_unique_recommendations(user_id=user_id, filters=popular_filter, num_results=5)\n",
     "indie_hits = get_unique_recommendations(user_id=user_id, filters=indie_filter, num_results=5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "vscode": {
      "languageId": "ruby"
@@ -1627,13 +1627,13 @@
    ],
    "source": [
     "# put all these titles into a single pandas dataframe , where each column is one category\n",
-    "all_recommendations = pd.DataFrame(columns=[\"top picks\", \"block busters\", \"classics\", \"what's popular\", \"indie hits\"])\n",
-    "all_recommendations[\"top picks\"] = [m[0] for m in top_picks_for_you]\n",
-    "all_recommendations[\"block busters\"] = [m[0] for m in block_buster_hits]\n",
-    "all_recommendations[\"classics\"] = [m[0] for m in classics]\n",
-    "all_recommendations[\"what's popular\"] = [m[0] for m in whats_popular]\n",
-    "all_recommendations[\"indie hits\"] = [m[0] for m in indie_hits]\n",
+    "top_picks = pd.DataFrame({\"top picks\":[m[0] for m in top_picks_for_you]})\n",
+    "block_busters = pd.DataFrame({\"block busters\": [m[0] for m in block_buster_hits]})\n",
+    "classics = pd.DataFrame({\"classics\": [m[0] for m in classic_movies]})\n",
+    "popular = pd.DataFrame({\"what's popular\": [m[0] for m in whats_popular]})\n",
+    "indies = pd.DataFrame({\"indie hits\": [m[0] for m in indie_hits]})\n",
     "\n",
+    "all_recommendations = pd.concat([top_picks, block_busters, classics, popular, indies], axis=1)\n",
     "all_recommendations.head()"
    ]
   },