Update utils.py

utils.py

@@ -25,6 +25,7 @@ def load_and_preprocess_data():
     # Use only positive quantites. This is not a robust approach,
     # but to keep things simple it quite good.
     df = df[df["Book-Rating"] > 0]
+    
 
     # Parse the date column and add 10 years, just to better visualization
     #df["InvoiceDate"] = pd.to_datetime(df["InvoiceDate"]).dt.floor(   "d") + pd.offsets.DateOffset(years=10)
@@ -54,9 +55,7 @@ def load_and_preprocess_data():
     user_idx = df["User-ID"].astype(product_cat).cat.codes
     product_idx = df["ISBN"].astype(product_cat).cat.codes
 
-    # Add the categorical index to the starting dataframe
-    #df["CustomerIndex"] = user_idx
-    
+    # Add the categorical index to the starting dataframe  
     # Merging both DataFrames based on respective common columns
     merged_df = pd.merge(df, df_users[['User-ID', 'Location', 'Age']], on='User-ID', how='left')
     merged_df = pd.merge(merged_df, df_books[['ISBN', 'Book-Title', 'Book-Author', 'Year-Of-Publication']], on='ISBN', how='left')