0.直接上两段代码：

$Code1:$

import numpy as np
a = []
e = 0.3
a.append(['s1', 's2', 's3', float(e)])
a = np.array(a)
print(type(a[0, 3]))

$输出结果为：$
<class 'numpy.str_'>

$Code2:$

import numpy as np
a = []
e = 0.3
a.append(['s1', 's2', 's3', float(e)])
a = np.array(a,dtype=object)
print(type(a[0, 3]))

$输出结果为：$
<class 'float'>

1.解释：

$numpy$数组存储为连续的内存块。它们通常有单一的数据类型(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。
创建$dtype=object$的数组是不同的。数组占用的内存现在充满了存储在内存其他地方的$Python$对象的指针(很像$Python$列表实际上只是对象指针的列表，而不是对象本身)。

2.文档原话:

$numpy$ 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.
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).

3.存在的坑点：

如$Code1$所示，如果多$append$几行后要对$numpy$矩阵按照第三列排序时使用np.argsort()函数时$python$会默认按照str类型的字典序排序，影响最终排序结果！！！！且你如果不懂的话很难发现！！

$\mathrm{22.08.04}$
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