Update README.md

README.md

@@ -92,61 +92,90 @@ def load_csvs_from_huggingface(start_date, end_date):
     huggingface_dataset_name = "amylonidis/PatClass2011"
 
     column_types = {
-        "ucid": "string",
+        "ucid": "string[pyarrow]",
         "country": "category",
-        "doc_number": "int64",
         "kind": "category",
         "lang": "category",
-        "date": "int32",
-        "application_date": "int32",
-        "date_produced": "int32",
+        "date": "Int32",
+        "application_date": "Int32",
+        "date_produced": "Int32",
         "status": "category",
-        "main_code": "string",
-        "further_codes": "string",
-        "ipcr_codes": "string",
-        "ecla_codes": "string",
-        "title": "string",
-        "abstract": "string",
-        "description": "string",
-        "claims": "string",
-        "applicants": "string",
-        "inventors": "string",
+        "main_code": "string[pyarrow]",
+        "further_codes": "string[pyarrow]",
+        "ipcr_codes": "string[pyarrow]",
+        "ecla_codes": "string[pyarrow]",
+        "title": "string[pyarrow]",
+        "abstract": "string[pyarrow]",
+        "description": "string[pyarrow]",
+        "claims": "string[pyarrow]",
+        "applicants": "string[pyarrow]",
+        "inventors": "string[pyarrow]",
+        "patent_number": "Int64",
     }
 
-    dataset_years = ['1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',
-                     '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',
-                     '1996','1997', '1998', '1999', '2000', '2001', '2002','2003', '2004', '2005']
+    dataset_years = [str(year) for year in range(1978, 2006)]
 
     start_date_int = int(datetime.strptime(start_date, "%Y-%m-%d").strftime("%Y%m%d"))
     end_date_int = int(datetime.strptime(end_date, "%Y-%m-%d").strftime("%Y%m%d"))
 
     start_year, end_year = str(start_date_int)[:4], str(end_date_int)[:4]
     given_years = [str(year) for year in range(int(start_year), int(end_year) + 1)]
-    matching_years = [f  for f in dataset_years for year in given_years if f==year]
+    matching_years = [f for f in dataset_years if f in given_years]
 
     if not matching_years:
         raise ValueError(f"No matching CSV files found for {start_date} to {end_date}")
 
     df_list = []
+    
     for year in matching_years:
         filepath = f"data/years/{year}/clefip2011_en_classification_{year}_validated.csv"
 
         try:
-            dataset = load_dataset(huggingface_dataset_name, data_files=filepath, split="train")
-            df = dataset.to_pandas().astype(column_types)
-            mask = (df["date"] >= start_date_int) & (df["date"] <= end_date_int)
-            df_filtered = df[mask].copy()
-
-
-            if not df_filtered.empty:
-                df_list.append(df_filtered)
-
-            del df, dataset, df_filtered, mask
+            # 1. Load the dataset (This stays on disk as an Arrow memory-map, NOT in RAM)
+            dataset = load_dataset(
+                huggingface_dataset_name, 
+                data_files=filepath, 
+                split="train",
+                sep=";",
+                on_bad_lines="skip"
+            )
+            
+            # 2. Tell HuggingFace to output Pandas dataframes when sliced
+            dataset = dataset.with_format("pandas")
+            
+            # 3. CHUNKING: Process exactly 10,000 rows at a time
+            chunk_size = 10000
+            for i in range(0, len(dataset), chunk_size):
+                # Only these 10,000 rows are loaded into RAM
+                df_chunk = dataset[i : i + chunk_size]
+                df_chunk.columns = df_chunk.columns.str.strip()
+                
+                # Filter this specific chunk
+                if "date" in df_chunk.columns:
+                    temp_dates = pd.to_numeric(df_chunk["date"], errors="coerce")
+                    mask = (temp_dates >= start_date_int) & (temp_dates <= end_date_int)
+                    df_filtered = df_chunk[mask].copy()
+                else:
+                    df_filtered = df_chunk.copy()
+                
+                # Apply types and append
+                if not df_filtered.empty:
+                    valid_column_types = {col: dtype for col, dtype in column_types.items() if col in df_filtered.columns}
+                    df_filtered = df_filtered.astype(valid_column_types)
+                    df_list.append(df_filtered)
+                
+                # Clear chunk from memory immediately
+                del df_chunk, df_filtered
+                gc.collect()
+
+            # Clear the dataset object from memory before the next year
+            del dataset
             gc.collect()
 
         except Exception as e:
             print(f"Error processing {filepath}: {e}")
 
+    # Combine all the tiny, filtered chunks at the very end
     return pd.concat(df_list, ignore_index=True) if df_list else pd.DataFrame()
 
 
@@ -179,7 +208,7 @@ This will load the dataset into a `DatasetDict` object, please make sure you hav
 
 You can also use the following Google Colab notebooks to explore the Analytics that were performed to the dataset.
 
-- [Date Analytics](https://colab.research.google.com/drive/1N2w5F1koWmZOyQaf0ZTB3gighPTXtUzD?usp=sharing)
+- [Date Analytics](https://colab.research.google.com/drive/17KXRabtBLPiPmgVIl2N30MoRqzOqlX-N?usp=sharing)
 - [Applicant - Inventor Name Analytics](https://colab.research.google.com/drive/1y1nEGrl40IjsUrFKgVsmYyuHyve40mfk?usp=sharing)
 - [Main Codes Analytics](https://colab.research.google.com/drive/1fhAgrSAsO5Q2TzFlFywI6Bl0D2w-cfoL?usp=sharing)
 - [Section Title Analytics](https://colab.research.google.com/drive/126zqwzdt2nZDF5N7mdl4n8XadCnwav12?usp=sharing)