Update README.md

README.md

@@ -74,8 +74,10 @@ that were published from March until April in 1985.
 
 ```python
 
+from datasets import load_dataset
 import pandas as pd
 from datetime import datetime
+import gc
 
 def load_csvs_from_huggingface(start_date, end_date):
     """
@@ -86,30 +88,30 @@ def load_csvs_from_huggingface(start_date, end_date):
 
     :return: pd.DataFrame, combined data from selected CSVs
     """
+
     huggingface_dataset_name = "amylonidis/PatClass2011"
 
     column_types = {
-      "ucid": "string",
-      "country": "category",
-      "doc_number": "int64",
-      "kind": "category",
-      "lang": "category",
-      "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",
-  }
-
+        "ucid": "string",
+        "country": "category",
+        "doc_number": "int64",
+        "kind": "category",
+        "lang": "category",
+        "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",
+    }
 
     dataset_years = ['1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',
                      '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',
@@ -123,27 +125,29 @@ def load_csvs_from_huggingface(start_date, end_date):
     matching_years = [f  for f in dataset_years for year in given_years if f==year]
 
     if not matching_years:
-        raise ValueError(f"No matching CSV files found in dataset for the given dates")
+        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)
-            df = dataset["train"].to_pandas().astype(column_types)
-            df_list.append(df)
-        except Exception as e:
-            print(f"Error loading {filepath}: {e}")
+            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 df_list:
-        df = pd.concat(df_list, ignore_index=True)
-        df["date"] = df["date"].astype(float).astype(int)
-        df_filtered = df[(df["date"] >= start_date_int) & (df["date"] <= end_date_int)]
-        return df_filtered
-    else:
-        return pd.DataFrame()
+            if not df_filtered.empty:
+                df_list.append(df_filtered)
+
+            del df, dataset, df_filtered, mask
+            gc.collect()
+
+        except Exception as e:
+            print(f"Error processing {filepath}: {e}")
+
+    return pd.concat(df_list, ignore_index=True) if df_list else pd.DataFrame()
 
 
 ```