如果要使用数组，请考虑numpy或scipy包，它们为数组提供了更大的灵活性。

Array只能用于特定类型，而list可以用于任何对象。

数组也只能有一种类型的数据，而列表可以有各种对象类型的条目。

数组对于某些数值计算也更有效。

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. float or uint64_t).

数组。另一方面，数组类型只是C数组的精简包装。它只能保存同构数据(也就是说，所有的数据类型都是相同的)，因此它只使用sizeof(一个对象)* length字节的内存。通常，当需要将C数组公开给扩展或系统调用(例如，ioctl或ftnl)时，应该使用它。

数组中。array也是Python 2中表示可变字符串的合理方式。x(数组('B'，字节))。然而，Python 2.6+和3。X提供了一个可变字节字符串bytearray。

但是，如果您想在数值数据的齐次数组上进行数学运算，那么最好使用NumPy，它可以自动向量化复杂多维数组上的操作。

长话短说:数组。当你出于数学以外的原因需要一个同构C数据数组时，数组是很有用的。

这个答案将总结几乎所有关于何时使用List和Array的查询:

The main difference between these two data types is the operations you can perform on them. For example, you can divide an array by 3 and it will divide each element of array by 3. Same can not be done with the list. The list is the part of python's syntax so it doesn't need to be declared whereas you have to declare the array before using it. You can store values of different data-types in a list (heterogeneous), whereas in Array you can only store values of only the same data-type (homogeneous). Arrays being rich in functionalities and fast, it is widely used for arithmetic operations and for storing a large amount of data - compared to list. Arrays take less memory compared to lists.

如果要使用数组，请考虑numpy或scipy包，它们为数组提供了更大的灵活性。

在性能方面，这里有一些比较python列表、数组和numpy数组的数字(都是2017年Macbook Pro上的python 3.7)。 最终的结果是python列表对于这些操作是最快的。

# Python list with append()
np.mean(timeit.repeat(setup="a = []", stmt="a.append(1.0)", number=1000, repeat=5000)) * 1000
# 0.054 +/- 0.025 msec

# Python array with append()
np.mean(timeit.repeat(setup="import array; a = array.array('f')", stmt="a.append(1.0)", number=1000, repeat=5000)) * 1000
# 0.104 +/- 0.025 msec

# Numpy array with append()
np.mean(timeit.repeat(setup="import numpy as np; a = np.array([])", stmt="np.append(a, [1.0])", number=1000, repeat=5000)) * 1000
# 5.183 +/- 0.950 msec

# Python list using +=
np.mean(timeit.repeat(setup="a = []", stmt="a += [1.0]", number=1000, repeat=5000)) * 1000
# 0.062 +/- 0.021 msec

# Python array using += 
np.mean(timeit.repeat(setup="import array; a = array.array('f')", stmt="a += array.array('f', [1.0]) ", number=1000, repeat=5000)) * 1000
# 0.289 +/- 0.043 msec

# Python list using extend()
np.mean(timeit.repeat(setup="a = []", stmt="a.extend([1.0])", number=1000, repeat=5000)) * 1000
# 0.083 +/- 0.020 msec

# Python array using extend()
np.mean(timeit.repeat(setup="import array; a = array.array('f')", stmt="a.extend([1.0]) ", number=1000, repeat=5000)) * 1000
# 0.169 +/- 0.034