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Take a look at SoX.

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There are some problems:

Here's your code with a few fixes, it prints out only primes:

import math""def main():"    count = 3"    "    while True:"        isprime = True"        "        for x in range(2, int(math.sqrt(count) + 1)):"            if count % x == 0: "                isprime = False"                break"        "        if isprime:"            print count"        "        count += 1

For much more efficient prime generation, see the Sieve of Eratosthenes, as others have suggested. Here's a nice, optimized implementation with many comments:

# Sieve of Eratosthenes"# Code by David Eppstein, UC Irvine, 28 Feb 2002"# http://code.activestate.com/recipes/117119/""def gen_primes():"    """ Generate an infinite sequence of prime numbers."    """"    # Maps composites to primes witnessing their compositeness."    # This is memory efficient, as the sieve is not "run forward""    # indefinitely, but only as long as required by the current"    # number being tested."    #"    D = {}"    "    # The running integer that's checked for primeness"    q = 2"    "    while True:"        if q not in D:"            # q is a new prime."            # Yield it and mark its first multiple that isn't"            # already marked in previous iterations"            # "            yield q"            D[q * q] = [q]"        else:"            # q is composite. D[q] is the list of primes that"            # divide it. Since we've reached q, we no longer"            # need it in the map, but we'll mark the next "            # multiples of its witnesses to prepare for larger"            # numbers"            # "            for p in D[q]:"                D.setdefault(p + q, []).append(p)"            del D[q]"        "        q += 1

Note that it returns a generator.

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Player.objects.values('player_type').order_by().annotate(Count('player_type'))

I think that B is problematic. The thread would only run on one CPU, and even if it runs a process, the thread is still running. A may be better.

It is best to try and measure both in terms of time and see which one is faster and which one scales well. However, I'll reiterate that I highly doubt that B will scale well.

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Try hasattr():

if hasattr(a, 'property'):"    a.property

EDIT: See zweiterlinde's answer below, who offers good advice about asking forgiveness! A very pythonic approach!

The general practice in python is that, if the property is likely to be there most of the time, simply call it and either let the exception propagate, or trap it with a try/except block. This will likely be faster than hasattr. If the property is likely to not be there most of the time, or you're not sure, using hasattr will probably be faster than repeatedly falling into an exception block.

You could use an in-memory sqlite database as a data structure, and define the desired operations as SQL queries.

import sqlite3""c = sqlite3.Connection(':memory:')"c.execute('CREATE TABLE data (a, b, c)')""c.executemany('INSERT INTO data VALUES (?, ?, ?)', ["    (1, None,    1),"    (1,    0,    3),"    (1,    0,    3),"    (1,    2,    3),"    (2, None,    1),"    (2,    0, None),"    (2,    2,    2),"    (2,    2,    4),"    (2,    2, None),"])""# queries"# ...

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