Update Yelpdata_663.py

Yelpdata_663.py

@@ -30,6 +30,7 @@ import os
 from typing import List
 import datasets
 import logging
+import pandas as pd
 
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
@@ -105,26 +106,32 @@ class YelpDataset(datasets.GeneratorBasedBuilder):
           citation=_CITATION,
       )
 
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-      urls_to_download = self._URLS
-      downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-      return [
-          datasets.SplitGenerator(name=datasets.Split.BUSINESS, gen_kwargs={"filepath": downloaded_files["business"]}),
-          datasets.SplitGenerator(name=datasets.Split.REVIEW, gen_kwargs={"filepath": downloaded_files["review"]}),
-      ]
-
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logging.info("generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as csv_file:
-            reader = csv.DictReader(csv_file)
-            for i, row in enumerate(reader):
-                # Handle missing values for float fields
-                for key, value in row.items():
-                    if value == '':
-                        # Assuming all fields that can be empty are floats; adjust as needed
-                        row[key] = None
-                yield i, row
+
+    def _generate_examples(self, filepaths):
+        logging.info("Generating examples from = %s", filepaths)
+    
+    # Load JSON files into pandas DataFrames
+        business_df = pd.read_json(filepaths['business'], lines=True)
+        review_df = pd.read_json(filepaths['review'], lines=True)
+    
+    # Merge DataFrames on 'business_id'
+        merged_df = pd.merge(business_df, review_df, on='business_id')
+    
+    # Filter out entries where 'categories' does not contain 'Restaurants'
+        filtered_df = merged_df[merged_df['categories'].str.contains("Restaurants", na=False)]
+    
+    # Convert to CSV (optional step if you need CSV output)
+    # filtered_df.to_csv('filtered_dataset.csv', index=False)
+    
+    # Generate examples
+        for index, row in filtered_df.iterrows():
+        # Handle missing values for float fields
+            for key, value in row.items():
+                if pd.isnull(value):
+                    row[key] = None  # or appropriate handling of nulls based on your requirements
+        
+        # Yield each row as an example
+            yield index, row.to_dict()
+
+