Basically, Python lists are very flexible and can hold completely heterogeneous, arbitrary data, and they can be appended to very efficiently, in amortized constant time. If you need to shrink and grow your list time-efficiently and without hassle, they are the way to go. But they use a lot more space than C arrays, in part because each item in the list requires the construction of an individual Python object, even for data that could be represented with simple C types (e.g.
array.array type, on the other hand, is just a thin wrapper on C arrays. It can hold only homogeneous data (that is to say, all of the same type) and so it uses only
sizeof(one object) * length bytes of memory. Mostly, you should use it when you need to expose a C array to an extension or a system call (for example,
array.array is also a reasonable way to represent a mutable string in Python 2.x (
array('B', bytes)). However, Python 2.6+ and 3.x offer a mutable byte string as
However, if you want to do math on a homogeneous array of numeric data, then you’re much better off using NumPy, which can automatically vectorize operations on complex multi-dimensional arrays.
To make a long story short:
array.array is useful when you need a homogeneous C array of data for reasons other than doing math.