feat: seem to be able to write to a db.

db_schema.sql

@@ -39,3 +39,6 @@ CREATE TABLE IF NOT EXISTS data (
     fact_description TEXT,
     fact_unit VARCHAR(255)
 )
+
+-- @block
+CREATE TABLE IF NOT EXISTS data ();

new_write.py

@@ -1,26 +1,40 @@
 import json
 import pandas as pd
+from sqlalchemy import create_engine
+from sqlalchemy.types import Integer, String, Float
+from dotenv import load_dotenv
+import os
 
-# Step 1: Load the JSON data
+# Load environment variables from .env file
-with open('CIK0000320193.json') as f:
+load_dotenv()
+
+# Database connection
+DB_USER = os.getenv('DB_USER')
+DB_PASSWORD = os.getenv('DB_PASSWORD')
+DB_HOST = os.getenv('DB_HOST')
+DB_PORT = os.getenv('DB_PORT')
+DB_NAME = os.getenv('DB_NAME')
+
+# Create the connection string
+db_connection_str = f'mysql+pymysql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_NAME}'
+engine = create_engine(db_connection_str)
+
+# Load the JSON data as before
+with open('CIK0001937441.json') as f:
     data = json.load(f)
 
-# Step 2: Extract the relevant fields
 cik = data.get('cik')
 entity_name = data.get('entityName')
 facts = data.get('facts')
 
-# Prepare a list to hold the rows
 rows = []
 
-# Traverse through the facts dictionary
 for taxonomy, fact_items in facts.items():
     for fact_id, fact_data in fact_items.items():
         label = fact_data.get('label')
         description = fact_data.get('description')
         units = fact_data.get('units')
         
-        # For each unit and its details, add a new row
         for unit, details_list in units.items():
             for details in details_list:
                 row = {
@@ -32,15 +46,33 @@ for taxonomy, fact_items in facts.items():
                     'fact_description': description,
                     'fact_unit': unit
                 }
-                
-                # Include the additional details in the row
                 row.update(details)
-                
-                # Append the row to the rows list
                 rows.append(row)
 
-# Step 3: Create the DataFrame
+# Create DataFrame from collected rows
 df = pd.DataFrame(rows)
 
-# Step 4: Output the head of the DataFrame
+# Define DataFrame to SQL types (optional, but recommended for performance)
-print(df.head())
+dtype_map = {
+    'entity_cik': String(10),
+    'entity_name': String(255),
+    'fact_id': String(255),
+    'fact_taxonomy': String(255),
+    'fact_label': String(255),
+    'fact_description': String(255),
+    'fact_unit': String(50),
+    'start': String(10),
+    'end': String(10),
+    'val': Float,
+    'accn': String(50),
+    'fy': Integer,
+    'fp': String(10),
+    'form': String(10),
+    'filed': String(10),
+    'frame': String(50)
+}
+
+# Write DataFrame to the 'data' table in the MariaDB database, create the table if it doesn't exist
+df.to_sql('data', con=engine, if_exists='replace', index=False, dtype=dtype_map)
+
+print("Data successfully written to the database.")