Reserve memory for list in Python?

When programming in Python, is it possible to reserve memory for a list that will be populated with a known number of items, so that the list will not be reallocated several times while building it? I've looked through the docs for a Python list type, and have not found anything that seems to do this. However, this type of list building shows up in a few hotspots of my code, so I want to make it as efficient as possible.

Edit: Also, does it even make sense to do something like this in a language like Python? I'm a fairly experienced programmer, but new to Python and still getting a feel for its way of doing things. Does Python internally allocate all objects in separate heap spaces, defeating the purpose of trying to minimize allocations, or are primitives like ints, floats, etc. stored directly in lists?

Solution 1:

Here's four variants:

an incremental list creation
"pre-allocated" list
array.array()
numpy.zeros()

python -mtimeit -s"N=10**6" "a = []; app = a.append;"\
    "for i in xrange(N):  app(i);"
10 loops, best of 3: 390 msec per loop

python -mtimeit -s"N=10**6" "a = [None]*N; app = a.append;"\
    "for i in xrange(N):  a[i] = i"
10 loops, best of 3: 245 msec per loop

python -mtimeit -s"from array import array; N=10**6" "a = array('i', [0]*N)"\
    "for i in xrange(N):" "  a[i] = i"
10 loops, best of 3: 541 msec per loop

python -mtimeit -s"from numpy import zeros; N=10**6" "a = zeros(N,dtype='i')"\
    "for i in xrange(N):" "  a[i] = i"
10 loops, best of 3: 353 msec per loop

It shows that [None]*N is the fastest and array.array is the slowest in this case.

Solution 2:

you can create list of the known length like this:

>>> [None] * known_number

Solution 3:

Take a look at this:

In [7]: %timeit array.array('f', [0.0]*4000*1000)
1 loops, best of 3: 306 ms per loop

In [8]: %timeit array.array('f', [0.0])*4000*1000
100 loops, best of 3: 5.96 ms per loop

In [11]: %timeit np.zeros(4000*1000, dtype='f')
100 loops, best of 3: 6.04 ms per loop

In [9]: %timeit [0.0]*4000*1000
10 loops, best of 3: 32.4 ms per loop

So don't ever use array.array('f', [0.0]*N), use array.array('f', [0.0])*N or numpy.zeros.

Solution 4:

In most of everyday code you won't need such optimization.

However, when list efficiency becomes an issue, the first thing you should do is replace generic list with typed one from array module which is much more efficient.

Here's how list of 4 million floating point numbers cound be created:

import array
lst = array.array('f', [0.0]*4000*1000)

Solution 5:

If you're wanting to manipulate numbers efficiently in Python then have a look at NumPy ( http://numpy.scipy.org/). It let's you do things extremely fast while still getting to use Python.

To do what your asking in NumPy you'd do something like

import numpy as np
myarray = np.zeros(4000)

which would give you an array of floating point numbers initialized to zero. You can then do very cool things like multiply whole arrays by a single factor or by other arrays and other stuff (kind of like in Matlab if you've ever used that) which is very fast (most of the actual work is happening in the highly optimized C part of the NumPy library).

If it's not arrays of numbers your after then you're probably not going to find a way to do what you want in Python. A Python list of objects is a list of points to objects internally (I think so anyway, I'm not an expert of Python internals) so it would still be allocating each of its members as you create them.