clean slate

db_schema.sql

@@ -1,3 +1,13 @@
+
+-- @block
+-- Reset db
+SET FOREIGN_KEY_CHECKS = 0;
+DROP TABLE IF EXISTS num;
+DROP TABLE IF EXISTS pre;
+DROP TABLE IF EXISTS sub;
+DROP TABLE IF EXISTS tag;
+SET FOREIGN_KEY_CHECKS = 1;
+-- @end
 -- @block
 -- Create tables
 -- Create SUB table
@@ -90,17 +100,6 @@ CREATE TABLE pre (
 );
 -- @end
 
--- @block
--- Reset db
-SET FOREIGN_KEY_CHECKS = 0;
-DROP TABLE IF EXISTS num;
-DROP TABLE IF EXISTS pre;
-DROP TABLE IF EXISTS sub;
-DROP TABLE IF EXISTS tag;
-SET FOREIGN_KEY_CHECKS = 1;
--- @end\
-
-
 -- baph is 12 in new 20 in old
 -- doc is 2048 in new unlimitted in old
 

write_to_db.py

@@ -19,80 +19,43 @@ connection_string = f"mariadb+pymysql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{DB_PO
 # Create the SQLAlchemy engine
 engine = create_engine(connection_string)
 
-# Define a list of file paths and corresponding table names
+# Define a list of file paths and corresponding table names with primary keys
 file_paths = [
-    ('sec_data/2015q1/sub.txt', 'sub'),
+    ('sec_data/2015q1/sub.txt', 'sub', ['adsh']),
-    ('sec_data/2015q1/tag.txt', 'tag'),
+    ('sec_data/2015q1/tag.txt', 'tag', ['tag', 'version']),
-    ('sec_data/2015q1/num.txt', 'num'),
+    ('sec_data/2015q1/num.txt', 'num', ['adsh', 'tag', 'version', 'coreg', 'ddate', 'qtrs', 'uom']),
-    ('sec_data/2015q1/pre.txt', 'pre')
+    ('sec_data/2015q1/pre.txt', 'pre', ['adsh', 'report', 'line'])
 ]
 
-# Define the expected column names for each table
-expected_columns = {
-    'sub': [
-        'adsh', 'cik', 'name', 'sic', 'countryba', 'stprba', 'cityba', 'zipba',
-        'bas1', 'bas2', 'baph', 'countryma', 'stprma', 'cityma', 'zipma',
-        'mas1', 'mas2', 'countryinc', 'stprinc', 'ein', 'former', 'changed',
-        'afs', 'wksi', 'fye', 'form', 'period', 'fy', 'fp', 'filed',
-        'accepted', 'prevrpt', 'detail', 'instance', 'nciks', 'aciks'],
-    'tag': [
-        'tag', 'version', 'custom', 'abstract', 'datatype', 'iord', 'crdr', 
-        'tlabel', 'doc'],
-    'num': [
-        'adsh', 'tag', 'version', 'coreg', 'ddate', 'qtrs', 'uom', 'value',
-        'footnote'],
-    'pre': [
-        'adsh', 'report', 'line', 'stmt', 'inpth', 'rfile', 'tag', 
-        'version', 'plabel']
-}
-
 # Loop through each file and write the data to the database
-for i, (file_path, table_name) in enumerate(file_paths):
+for i, (file_path, table_name, primary_keys) in enumerate(file_paths):
     print(f"\nAnalyzing {file_path} (File {i+1}/4)...")
     
     # Read the data into a Pandas DataFrame
     df = pd.read_csv(file_path, sep='\t')
     
-    # Inspect the DataFrame
-    print("First rows of the DataFrame:")
-    print(df.head(10))
-    
     # Get the DataFrame Information
     print("\nSummary Information:")
     print(df.info())
     
-    # Check if there are any missing values in the DataFrame
-    missing_values = df.isnull().sum()
-    print("\nMissing Values:")
-    print(missing_values)
-    
-    # Check if the column names match the expected columns
-    if table_name in expected_columns:
-        expected = expected_columns[table_name]
-        print("\nEnsuring that columns match for table:", table_name)
-        
-        df.columns = [col.lower() for col in df.columns]
-        if set(df.columns) != set(expected):
-            missing_cols = set(expected) - set(df.columns)
-            extra_cols = set(df.columns) - set(expected)
-            if missing_cols:
-                print(f"Missing columns in {table_name}: {missing_cols}")
-            if extra_cols:
-                print(f"Extra columns found in {table_name}: {extra_cols}")
-                df = df.drop(columns=list(extra_cols))
-            
-            df = df.reindex(columns=expected)
-        else:
-            print(f"Column names in {table_name} match the expected schema.")
-    
     # If the file being processed is 'num.txt', fix the `coreg` column
     if table_name == 'num':
         df['coreg'] = df['coreg'].fillna('nocoreg')
         print("\nUpdated 'coreg' column (NaN values replaced with 'nocoreg'):")
         print(df[['coreg']].head(10))  # Display first 10 rows of the 'coreg' column for verification
     
-    # Write the DataFrame to the corresponding table in the MariaDB database
+    # Dropping rows with any missing values in the primary keys and NOT NULL columns
-    # df.to_sql(table_name, con=engine, if_exists='append', index=False)
+    df.dropna(subset=primary_keys, inplace=True)
-    # print(f"\nData from {file_path} written to the '{table_name}' table in the database.")
 
-# print("\nAll files have been processed and written to the database.")
+    # Dropping duplicate rows based on primary keys
+    df.drop_duplicates(subset=primary_keys, keep='first', inplace=True)
+    
+    # Get Updated Information
+    print("\nUpdated Information:")
+    print(df.info())
+
+    # Write the cleaned DataFrame to the corresponding table in the MariaDB database
+    df.to_sql(table_name, con=engine, if_exists='append', index=False)
+    print(f"\nCleaned data from {file_path} has been written to the '{table_name}' table in the database.\n")
+
+print("\nAll files have been processed and cleaned data has been written to the database.")