Python 创建 numpy 数组时 dtype=object 是什么意思？
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What does dtype=object mean while creating a numpy array?
提问by Avinash Pandey
I was experimenting with numpy arrays and created a numpy array of strings:
我正在试验 numpy 数组并创建了一个 numpy 字符串数组：
ar1 = np.array(['avinash', 'jay'])
As I have read from from their official guide, operations on numpy array are propagated to individual elements. So I did this:
正如我从他们的官方指南中读到的那样，对 numpy 数组的操作会传播到各个元素。所以我这样做了：
ar1 * 2
But then I get this error:
但是后来我收到了这个错误：
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'
But when I used dtype=object
但是当我使用 dtype=object
ar1 = np.array(['avinash', 'jay'], dtype=object)
while creating the array I am able to do all operations.
在创建数组时，我可以执行所有操作。
Can anyone tell me why this is happening?
谁能告诉我为什么会这样？
采纳答案by Alex Riley
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.
NumPy 数组存储为连续的内存块。它们通常具有单一数据类型（例如整数、浮点数或固定长度的字符串），然后内存中的位被解释为具有该数据类型的值。
Creating an array with dtype=objectis different. The memory taken by the array now is filled with pointersto Python objects which are being stored elsewherein memory (much like a Python listis really just a list of pointers to objects, not the objects themselves).
创建一个数组dtype=object是不同的。数组占用的内存现在充满了指向存储在内存中其他地方的Python 对象的指针（就像 Pythonlist实际上只是指向对象的指针列表，而不是对象本身）。
Arithmetic operators such as *don't work with arrays such as ar1which 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
算术运算符（例如）*不适ar1用于具有string_数据类型的数组（有特殊功能 - 请参见下文）。NumPy 只是将内存中的位视为字符，*运算符在这里没有意义。然而，该行
np.array(['avinash','jay'], dtype=object) * 2
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 objectarray with references to the new strings is returned.
之所以有效，是因为现在该数组是一个（指向）Python 字符串的数组。*为这些 Python 字符串对象定义了良好的运算符。在内存中创建新的 Python 字符串，并返回一个object包含对新字符串的引用的新数组。
If you have an array with string_or unicode_dtype and want to repeat each string, you can use np.char.multiply:
如果您有一个带有string_或unicode_dtype的数组并且想要重复每个字符串，则可以使用np.char.multiply：
In [52]: np.char.multiply(ar1, 2)
Out[52]: array(['avinashavinash', 'jayjay'],
dtype='<U14')
NumPy has many other vectorised string methodstoo.
NumPy 也有许多其他向量化字符串方法。
