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()