与常规Python列表相比，NumPy有什么优势? [英] What are the advantages of NumPy over regular Python lists?

问题描述

与常规Python列表相比， NumPy 有什么优势?

What are the advantages of NumPy over regular Python lists?

我有大约100个金融市场系列，我将创建一个100x100x100 = 1百万个单元的多维数据集阵列.我将每个x与y和z回归(3变量)，以用标准误差填充数组.

I have approximately 100 financial markets series, and I am going to create a cube array of 100x100x100 = 1 million cells. I will be regressing (3-variable) each x with each y and z, to fill the array with standard errors.

我听说对于大型矩阵"，出于性能和可伸缩性的原因，我应该使用NumPy而不是Python列表.事实是，我知道Python列表，它们似乎对我有用.

I have heard that for "large matrices" I should use NumPy as opposed to Python lists, for performance and scalability reasons. Thing is, I know Python lists and they seem to work for me.

如果我转到NumPy，会有什么好处?

What will the benefits be if I move to NumPy?

如果我有1000个序列(即立方体中有10亿个浮点单元)怎么办?

What if I had 1000 series (that is, 1 billion floating point cells in the cube)?

推荐答案

NumPy的数组比Python列表更紧凑-您所描述的列表列表在Python中至少需要20 MB左右，而NumPy单元格中具有单精度浮点数的3D数组可容纳4 MB.使用NumPy可以更快地读取和写入项目.

NumPy's arrays are more compact than Python lists -- a list of lists as you describe, in Python, would take at least 20 MB or so, while a NumPy 3D array with single-precision floats in the cells would fit in 4 MB. Access in reading and writing items is also faster with NumPy.

也许您只关心一百万个单元格就不会那么在意，但是您肯定会关心十亿个单元格-两种方法都不适合32位体系结构，但是使用64位版本，NumPy可以摆脱4大约需要1 GB的内存，仅Python就需要至少12 GB(很多指针的大小加倍)，这是一个昂贵得多的硬件！

Maybe you don't care that much for just a million cells, but you definitely would for a billion cells -- neither approach would fit in a 32-bit architecture, but with 64-bit builds NumPy would get away with 4 GB or so, Python alone would need at least about 12 GB (lots of pointers which double in size) -- a much costlier piece of hardware!

差异主要是由于间接性"造成的-Python列表是指向Python对象的指针的数组，每个指针至少4个字节，对于最小的Python对象至少也要16个字节(类型指针为4个，引用为4个)计数(4为值)-内存分配器舍入为16). NumPy数组是统一值的数组-单精度数字每个占用4个字节，双精度数字每个占用8个字节.灵活性较差，但是您需要为标准Python列表的灵活性付出巨大的代价！

The difference is mostly due to "indirectness" -- a Python list is an array of pointers to Python objects, at least 4 bytes per pointer plus 16 bytes for even the smallest Python object (4 for type pointer, 4 for reference count, 4 for value -- and the memory allocators rounds up to 16). A NumPy array is an array of uniform values -- single-precision numbers takes 4 bytes each, double-precision ones, 8 bytes. Less flexible, but you pay substantially for the flexibility of standard Python lists!

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