Upload 5 files

app.py

@@ -0,0 +1,59 @@
+import streamlit as st
+import csv
+import os
+import re
+
+def delete_existing_state_csv_files():
+    """Delete all CSV files with 6-character names in the current directory."""
+    for file in os.listdir('.'):
+        if re.match(r'^[A-Z]{2}\.csv$', file, re.I):
+            os.remove(file)
+
+def clean_column_name(column_name):
+    """Remove spaces and punctuation from column names, keeping only alphabets."""
+    return re.sub(r'[^a-zA-Z]', '', column_name)
+
+def process_file(input_file_path):
+    # Delete existing state CSV files before processing
+    delete_existing_state_csv_files()
+    
+    # Open the input file for reading
+    with open(input_file_path, mode='r', encoding='utf-8') as input_file:
+        reader = csv.DictReader(input_file)
+        # Prepare headers with cleaned column names and truncate to the first 107 fields
+        headers = [clean_column_name(header) for header in reader.fieldnames[:107]]
+        
+        state_files = {}  # Dictionary to keep track of open file handles for each state
+        
+        for row in reader:
+            state = row['Provider Business Mailing Address State Name']
+            
+            # Skip if state is not exactly two letters
+            if not re.match(r'^[A-Z]{2}$', state, re.I):
+                continue
+            
+            # Generate the file name based on the state
+            file_name = f'{state}.csv'
+            
+            # Check if we've already opened this state file
+            if state not in state_files:
+                # Open a new file for the state and write the header
+                state_file = open(file_name, mode='w', newline='', encoding='utf-8')
+                writer = csv.DictWriter(state_file, fieldnames=headers)
+                writer.writeheader()
+                state_files[state] = state_file
+            else:
+                # Get the writer for the already opened file
+                writer = csv.DictWriter(state_files[state], fieldnames=headers)
+            
+            # Write the current row to the state's file, cleaning and truncating each row to the first 50 fields
+            cleaned_row = {clean_column_name(k): v for k, v in list(row.items())[:50]}
+            writer.writerow(cleaned_row)
+    
+    # Close all open state files
+    for file in state_files.values():
+        file.close()
+
+if __name__ == "__main__":
+    input_file_path = 'npidata_pfile_20050523-20240107.csv'  # Replace with the path to your large file
+    process_file(input_file_path)