As I understand, the list
type in Python is a dynamic pointer array, which will increase it's capacity when items are appended to it. And an array in NumPy uses a continuous memory area to hold all the data of the array.
Are there any types that dynamically increase its capacity as a list, and stores the value as a NumPy array? Something like List in C#. And it's great if the type has the same interface as a NumPy array.
I can create a class which wraps a NumPy array inside, and resize this array when it's full, such as:
class DynamicArray(object): def __init__(self): self._data = np.zeros(100) self._size = 0 def get_data(self): return self._data[:self._size] def append(self, value): if len(self._data) == self._size: self._data = np.resize(self._data, int(len(self._data)*1.25)) self._data[self._size] = value self._size += 1
but DynamicArray can't be used as a NumPy array, and I think all the views returned by get_data() before np.resize() will hold the old array.
Edit: array type in array module is dynamic array. The following program test the increase factor of list and array:
from array import array import time import numpy as np import pylab as pl def test_time(func): arrs = [func() for i in xrange(2000)] t = [] for i in xrange(2000): start = time.clock() for a in arrs: a.append(i) t.append(time.clock()-start) return np.array(t) t_list = test_time(lambda:[]) t_array = test_time(lambda:array("d")) pl.subplot(211) pl.plot(t_list, label="list") pl.plot(t_array, label="array") pl.legend() pl.subplot(212) pl.plot(np.where(t_list>2*np.median(t_list))[0]) pl.plot(np.where(t_array>2*np.median(t_array))[0]) pl.show()
from the graph: the increase factor of list is bigger than array.
A dynamic array is a random access, variable-size list data structure that allows elements to be added or removed. It is supplied with standard libraries in many modern programming languages. Dynamic arrays overcome a limit of static arrays, which have a fixed capacity that needs to be specified at allocation.
In C++, a dynamic array can be created using new keyword and can be deleted it by using delete keyword.
We can create an array of pointers also dynamically using a double pointer. Once we have an array pointers allocated dynamically, we can dynamically allocate memory and for every row like method 2.
You may be interested to know that the Python standard library also includes an array module which sounds like just what you want:
This module defines an object type which can compactly represent an array of basic values: characters, integers, floating point numbers. Arrays are sequence types and behave very much like lists, except that the type of objects stored in them is constrained.
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