创建numpy数组时dtype = object是什么意思? [英] What does dtype=object mean while creating a numpy array?

问题描述

我正在尝试使用numpy数组并创建了一个numpy的字符串数组:

I was experimenting with numpy arrays and created a numpy array of strings:

ar1 = np.array(['avinash', 'jay'])

正如我从他们的官方指南中所读到的那样，对numpy数组的操作会传播到各个元素.所以我这样做了:

As I have read from from their official guide, operations on numpy array are propagated to individual elements. So I did this:

ar1 * 2

但随后出现此错误:

TypeError                                 Traceback (most recent call last)
<ipython-input-22-aaac6331c572> in <module>()
----> 1 ar1 * 2

TypeError: unsupported operand type(s) for *: 'numpy.ndarray' and 'int'

但是当我使用dtype=object

ar1 = np.array(['avinash', 'jay'], dtype=object)

在创建数组时，我可以执行所有操作.

while creating the array I am able to do all operations.

有人可以告诉我为什么会这样吗?

Can anyone tell me why this is happening?

推荐答案

NumPy数组存储为连续的内存块.它们通常只有一个数据类型(例如整数，浮点数或固定长度的字符串)，然后将内存中的位解释为具有该数据类型的值.

NumPy arrays are stored as contiguous blocks of memory. They usually have a single datatype (e.g. integers, floats or fixed-length strings) and then the bits in memory are interpreted as values with that datatype.

使用dtype=object创建数组是不同的.现在，数组占用的内存充满了指针，这些指针指向存储在内存中其他地方的Python对象(很像Python list实际上只是一个指针列表)对象，而不是对象本身.

Creating an array with dtype=object is different. The memory taken by the array now is filled with pointers to Python objects which are being stored elsewhere in memory (much like a Python list is really just a list of pointers to objects, not the objects themselves).

诸如*的算术运算符不能与具有string_数据类型的诸如ar1的数组一起使用(取而代之的是特殊功能-参见下文). NumPy只是将内存中的位视为字符，而*运算符在这里没有意义.但是，这行

Arithmetic operators such as * don't work with arrays such as ar1 which have a string_ datatype (there are special functions instead - see below). NumPy is just treating the bits in memory as characters and the * operator doesn't make sense here. However, the line

np.array(['avinash','jay'], dtype=object) * 2

之所以起作用，是因为现在该数组是Python字符串(指向)的数组.对于这些Python字符串对象，*运算符定义良好.在内存中创建新的Python字符串，并返回引用了新字符串的新object数组.

works because now the array is an array of (pointers to) Python strings. The * operator is well defined for these Python string objects. New Python strings are created in memory and a new object array with references to the new strings is returned.

如果您有一个string_或unicode_ dtype的数组，并且想重复每个字符串，则可以使用np.char.multiply:

If you have an array with string_ or unicode_ dtype and want to repeat each string, you can use np.char.multiply:

In [52]: np.char.multiply(ar1, 2)
Out[52]: array(['avinashavinash', 'jayjay'], 
      dtype='<U14')

NumPy还有许多其他的向量化的字符串方法

NumPy has many other vectorised string methods too.

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