is there a way to make this more efficient

Below is a snip it of python/psycopg2 code. It is inefficient when
compared with parallel logic that creates a file and then updates the
table in postgres sql from the file. In a test data base it takes 24
seconds to update 100,000 records. The actual database is over 5,000,000
records. The data base is on a solid state drive, I would imagine it
would be very inefficient on a hard drive.

# init lines
     conn_string = "host='localhost' dbname='opace0421' user='dan'
port=5432"
     print ("Connecting to database\n    ->%s" % (conn_string))
     conn = psycopg2.connect(conn_string)
     cursori = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
     cursoro = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
     work_mem = 2048
     cursori.execute('SET work_mem TO %s', (work_mem,))
     cursori.execute('select address_2, row_num from opace')
     i = 1
     while i != 100000:
         records = cursori.fetchone()
         record = records['address_2']
         rn = str(records['row_num'])

#python code to create replacement string s

         cursoro.execute("UPDATE opace SET p_norm_add = %s WHERE row_num
= %s", (s, rn,))
         i = i+1
     conn.commit()