You've given us a small piece of your problem to review, and Janne's suggestion seems reasonable if that piece is considered on its own. But I have the feeling that this isn't the only bit of analysis that you are doing on your data, and if so, you probably want to think about using a proper database.
Python comes with a built-in relational database engine in the form of the sqlite3 module. So you could easily read your CSV directly into a SQLite table, either using the .import command in SQLite's command-line shell, or via Python if you need more preprocessing:
import sqlite3
import csv
def load_flight_csv(db_filename, csv_filename):
"""
Load flight data from `csv_filename` into the SQLite database in
`db_filename`.
"""
with sqlite3.connect(db_filename) as conn, open(csv_filename, 'rb') as f:
c = conn.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS flight
(id INTEGER PRIMARY KEY AUTOINCREMENT, fid TEXT)''')
c.execute('''CREATE INDEX IF NOT EXISTS flight_fid ON flight (fid)''')
c.executemany('''INSERT INTO flight (fid) VALUES (?)''', csv.reader(f))
conn.commit()
And then you can analyze the data by issuing SQL queries:
>>> db = 'flight.db'
>>> load_flight_csv(db, 'flight.csv')
>>> conn = sqlite3.connect(db)
>>> from pprint import pprint
>>> pprint(conn.execute('''SELECT MIN(id), fid FROM flight GROUP BY fid''').fetchall())
[(1, u'20110117559515'),
(4, u'20110117559572'),
(7, u'20110117559574'),
(8, u'20110117559587'),
(9, u'20110117559588')]