如何在 Python 中获取多维 Numpy 数组元素的类型
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How to get type of multidimensional Numpy array elements in Python
提问by froggy
How can I get the type of a multidimensional array?
如何获取多维数组的类型？
I treat arrays but considering data type: string, float, Boolean, I have to adapt code so I would have to get the type regardless of dimension that can be one two dimensions or more.
我处理数组，但考虑到数据类型： string, float, Boolean，我必须调整代码，因此我必须获得类型，而不管维度可以是一维还是更多维。
Data can be 1d of real, 3D of string ...
数据可以是实数的 1d，字符串的 3D ...
I would like to recover type of Array, is it a real , is it a string is it a boolean ... without doing Array[0] or Array [0][0][0][0] because dimension can be various. Or a way to get the first element of an array whatever the dimensions.
我想恢复数组的类型，它是一个真实的，它是一个字符串还是一个布尔值......不做 Array[0] 或 Array [0][0][0][0] 因为维度可以是多种多样的. 或者一种获取数组第一个元素的方法，无论维度如何。
It works with np.isreal a bit modified , but I don't found equivalent like isastring or isaboolean ...
它适用于 np.isreal 有点修改，但我没有找到像 isastring 或 isaboolean 这样的等效项......
采纳答案by Aaron Hall
Use the dtypeattribute:
使用dtype属性：
>>> import numpy
>>> ar = numpy.array(range(10))
>>> ar.dtype
dtype('int32')
Explanation
解释
Python lists are like arrays:
Python 列表就像数组：
>>> [[1, 2], [3, 4]]
[[1, 2], [3, 4]]
But for analysis and scientific computing, we typically use the numpy package's arrays for high performance calculations:
但是对于分析和科学计算，我们通常使用 numpy 包的数组进行高性能计算：
>>> import numpy as np
>>> np.array([[1, 2], [3, 4]])
array([[1, 2],
[3, 4]])
If you're asking about inspecting the type of the data in the arrays, we can do that by using the index of the item of interest in the array (here I go sequentially deeper until I get to the deepest element):
如果您要检查数组中数据的类型，我们可以通过使用数组中感兴趣的项目的索引来完成（这里我按顺序深入，直到到达最深的元素）：
>>> ar = np.array([[1, 2], [3, 4]])
>>> type(ar)
<type 'numpy.ndarray'>
>>> type(ar[0])
<type 'numpy.ndarray'>
>>> type(ar[0][0])
<type 'numpy.int32'>
We can also directly inspect the datatype by accessing the dtypeattribute
我们也可以通过访问dtype属性直接检查数据类型
>>> ar.dtype
dtype('int32')
If the array is a string, for example, we learn how long the longest string is:
例如，如果数组是一个字符串，我们学习最长的字符串有多长：
>>> ar = numpy.array([['apple', 'b'],['c', 'd']])
>>> ar
array([['apple', 'b'],
['c', 'd']],
dtype='|S5')
>>> ar = numpy.array([['apple', 'banana'],['c', 'd']])
>>> ar
array([['apple', 'banana'],
['c', 'd']],
dtype='|S6')
>>> ar.dtype
dtype('S6')
I tend not to alias my imports so I have the consistency as seen here, (I usually do import numpy).
我倾向于不别名我的进口，所以我有在这里看到的一致性，（我通常这样做import numpy）。
>>> ar.dtype.type
<type 'numpy.string_'>
>>> ar.dtype.type == numpy.string_
True
But it is common to import numpy as np(that is, alias it):
但它是常见的import numpy as np（即别名）：
>>> import numpy as np
>>> ar.dtype.type == np.string_
True
回答by Ankit Aranya
fruits = [['banana'], [1], [11.12]]
for first_array in range(len(fruits)):
for second_array in range(len(fruits[first_array])):
print('Type :', type(fruits[first_array][second_array]), 'data:', fruits[first_array][second_array])
That show the data type of each values.
显示每个值的数据类型。
