diff --git a/split_csv.py b/split_csv.py
new file mode 100644
index 0000000..8eb8b81
--- /dev/null
+++ b/split_csv.py
@@ -0,0 +1,112 @@
+import pandas as pd
+import os
+import sys
+from pathlib import Path
+
+def split_csv_hierarchically(input_file, output_dir='output'):
+    """
+    Split a CSV file by Cluster, then Type, then Indicator.
+    Each split removes the splitting column and adds it to the filename.
+    
+    Args:
+        input_file: Path to the input CSV file
+        output_dir: Directory where output files will be saved
+    """
+    # Create output directory if it doesn't exist
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
+    
+    # Read the CSV file with low_memory=False to avoid dtype warnings
+    print(f"Reading {input_file}...")
+    df = pd.read_csv(input_file, low_memory=False)
+    
+    # Strip whitespace from all column headers
+    df.columns = df.columns.str.strip()
+    print("Stripped whitespace from column headers")
+    
+    # Convert Year column to integer if it exists, handling missing values
+    if 'Year' in df.columns:
+        # Convert to nullable integer type (Int64 instead of int64)
+        df['Year'] = pd.to_numeric(df['Year'], errors='coerce')
+        df['Year'] = df['Year'].astype('Int64')
+        print("Converted 'Year' column to integers (preserving empty values)")
+    
+    # Clean and convert Value column if it exists
+    if 'Value' in df.columns:
+        # Remove commas and other non-numeric characters, then convert to number
+        df['Value'] = df['Value'].astype(str).str.replace(',', '', regex=False)
+        df['Value'] = df['Value'].str.replace(' ', '', regex=False)
+        df['Value'] = pd.to_numeric(df['Value'], errors='coerce')
+        print("Cleaned and converted 'Value' column to numbers")
+    
+    # Verify required columns exist
+    required_columns = ['Cluster', 'Type', 'Indicator']
+    for col in required_columns:
+        if col not in df.columns:
+            raise ValueError(f"Column '{col}' not found in CSV. Available columns: {df.columns.tolist()}")
+    
+    # Get unique values for each splitting column
+    clusters = df['Cluster'].unique()
+    
+    print(f"Found {len(clusters)} unique clusters")
+    
+    # Split by Cluster
+    for cluster in clusters:
+        cluster_df = df[df['Cluster'] == cluster].copy()
+        
+        # Get unique Types for this cluster
+        types = cluster_df['Type'].unique()
+        
+        # Split by Type
+        for type in types:
+            type_df = cluster_df[cluster_df['Type'] == type].copy()
+            
+            # Get unique Indicators for this Type
+            indicators = type_df['Indicator'].unique()
+            
+            # Split by Indicator
+            for indicator in indicators:
+                indicator_df = type_df[type_df['Indicator'] == indicator].copy()
+                
+                # Remove the splitting columns
+                indicator_df = indicator_df.drop(columns=['Cluster', 'Type', 'Indicator'])
+                
+                # Create filename with all three values
+                # Clean the values to make them filesystem-safe
+                clean_cluster = str(cluster).replace('/', '_').replace('\\', '_').replace(' ', '_')
+                clean_type = str(type).replace('/', '_').replace('\\', '_').replace(' ', '_')
+                clean_indicator = str(indicator).replace('/', '_').replace('\\', '_').replace(' ', '_')
+                
+                filename = f"{clean_cluster}_{clean_type}_{clean_indicator}.csv"
+                output_path = os.path.join(output_dir, filename)
+                
+                # Save the split CSV
+                indicator_df.to_csv(output_path, index=False)
+                print(f"Created: {filename} ({len(indicator_df)} rows)")
+    
+    print(f"\nAll files saved to '{output_dir}' directory")
+
+# Main execution
+if __name__ == "__main__":
+    # Check if file path was provided
+    if len(sys.argv) < 2:
+        print("Usage: python split_csv.py <input_file.csv> [output_directory]")
+        print("\nExamples:")
+        print("  python split_csv.py mydata.csv")
+        print("  python split_csv.py mydata.csv split_output")
+        print("  python split_csv.py /path/to/mydata.csv /path/to/output")
+        sys.exit(1)
+    
+    # Get input file from command line argument
+    input_csv = sys.argv[1]
+    
+    # Get output directory (optional, defaults to 'output')
+    output_dir = sys.argv[2] if len(sys.argv) > 2 else 'output'
+    
+    try:
+        split_csv_hierarchically(input_csv, output_dir=output_dir)
+    except FileNotFoundError:
+        print(f"Error: File '{input_csv}' not found.")
+        sys.exit(1)
+    except Exception as e:
+        print(f"Error: {e}")
+        sys.exit(1)