NumPy数组与Python数组 [英] Numpy arrays vs Python arrays

问题描述

我注意到，Python中用于数组操作的事实标准是通过出色的numpy库实现的.但是，我知道Python标准库有一个array模块，在我看来，它的用例与Numpy类似.

I noticed that the de facto standard for array manipulation in Python is through the excellent numpy library. However, I know that the Python Standard Library has an array module, which seems to me to have a similar use-case as Numpy.

在现实世界中，有没有哪个示例比numpy或普通list更适合array?

Is there any actual real-world example where array is desirable over numpy or just plain list?

根据我的幼稚解释，array只是用于存储同类数据的内存有效容器，但无法提高计算效率.

From my naive interpretation, array is just memory-efficient container for homogeneous data, but offers no means of improving computational efficiency.

编辑

出于好奇，我通过Github和import array搜索Python命中186'721的计数，而import numpy发现8'062'678的计数.

Just out of curiosity, I searched through Github and import array for Python hits 186'721 counts, while import numpy hits 8'062'678 counts.

但是，我找不到使用array的流行存储库.

However, I could not find a popular repository using array.

推荐答案

为了解numpy和array之间的区别，我进行了一些定量测试.

To understand the differences between numpy and array, I ran a few more quantitative test.

我发现的是，对于我的系统(Ubuntu 18.04，Python3)，array从range生成器生成大型数组的速度似乎是numpy的两倍(尽管numpy专用的np.arange()似乎更快-实际上太快了，也许它正在测试期间缓存某些东西)，但是比使用list慢了一倍.

What I have found is that, for my system (Ubuntu 18.04, Python3), array seems to be twice as fast at generating a large array from the range generator compared to numpy (although numpy's dedicated np.arange() seems to be much faster -- actually too fast, and perhaps it is caching something during tests), but twice as slow than using list.

但是，非常令人惊讶，array个对象似乎比numpy个对象大. 相反，list对象比array对象大大约8-13％(显然，这将随单个项目的大小而变化). 与list相比，array提供了一种控制数字对象大小的方法.

However, quite surprisingly, array objects seems to be larger than the numpy counterparts. Instead, the list objects are roughly 8-13% larger than array objects (this will vary with the size of the individual items, obviously). Compared to list, array offers a way to control the size of the number objects.

因此，也许array的唯一合理用例实际上是当numpy不可用时.

So, perhaps, the only sensible use case for array is actually when numpy is not available.

为完整起见，这是我用于测试的代码:

For completeness, here is the code that I used for the tests:

import numpy as np
import array
import sys

num = int(1e6)
num_i = 100
x = np.logspace(1, int(np.log10(num)), num_i).astype(int)

%timeit list(range(num))
# 10 loops, best of 3: 32.8 ms per loop

%timeit array.array('l', range(num))
# 10 loops, best of 3: 86.3 ms per loop

%timeit np.array(range(num), dtype=np.int64)
# 10 loops, best of 3: 180 ms per loop

%timeit np.arange(num, dtype=np.int64)
# 1000 loops, best of 3: 809 µs per loop


y_list = np.array([sys.getsizeof(list(range(x_i))) for x_i in x])
y_array = np.array([sys.getsizeof(array.array('l', range(x_i))) for x_i in x])
y_np = np.array([sys.getsizeof(np.array(range(x_i), dtype=np.int64)) for x_i in x])

import matplotlib.pyplot as plt

plt.figure(figsize=(12, 6))
plt.plot(x, y_list, label='list')
plt.plot(x, y_array, label='array')
plt.plot(x, y_np, label='numpy')
plt.legend()
plt.show()

这篇关于NumPy数组与Python数组的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！