Update README.md

README.md

@@ -79,13 +79,13 @@ import pandas as pd
 from datetime import datetime
 import gc
 
-def load_csvs_from_huggingface(start_date, end_date):
+def load_csvs_from_huggingface(start_date, end_date, columns_to_keep=None):
     """
     Load only the necessary CSV files from a Hugging Face dataset repository.
 
     :param start_date: str, the start date in 'YYYY-MM-DD' format (inclusive)
     :param end_date: str, the end date in 'YYYY-MM-DD' format (inclusive)
-
+    :param columns_to_keep: list of str, optional. Specific columns to load (e.g., ["date", "lang"]).
     :return: pd.DataFrame, combined data from selected CSVs
     """
 
@@ -140,6 +140,13 @@ def load_csvs_from_huggingface(start_date, end_date):
                 on_bad_lines="skip"
             )
             
+            # Select Columns Before Chunking
+            if columns_to_keep is not None:
+                # Safety check: Only select columns that actually exist in this specific file
+                valid_columns = [col for col in columns_to_keep if col in dataset.column_names]
+                if valid_columns:
+                    dataset = dataset.select_columns(valid_columns)
+
             # 2. Tell HuggingFace to output Pandas dataframes when sliced
             dataset = dataset.with_format("pandas")
             
@@ -175,8 +182,18 @@ def load_csvs_from_huggingface(start_date, end_date):
         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()
+
+    if not df_list:
+        return pd.DataFrame()
+    
+    # Combine chunks
+    final_df = pd.concat(df_list, ignore_index=True)
+    
+    # Destroy the list
+    del df_list
+    gc.collect()
+    
+    return final_df
 
 
 ```