unsupported types for __or__: 'str', 'str'
时间: 2023-08-04 14:09:56 浏览: 46
你好!感谢您使用C知道。关于您的问题,这是因为在Python中,字符串类型是不支持逻辑运算符"or"的。逻辑运算符"or"通常用于布尔类型的操作数,而不是字符串。
如果您想要对两个字符串执行逻辑运算,您可以考虑使用其他字符串操作,如字符串拼接或条件语句。请告诉我您具体想要实现的目标,我将尽力为您提供更准确的帮助!
相关问题
TypeError: unsupported operand type(s) for -: 'numpy.str_' and 'float'
This error occurs when you try to perform a mathematical operation between a numpy string and a float. Numpy strings cannot be used in mathematical operations as they are not numeric types.
To resolve this error, you need to convert the numpy string to a numeric type such as a float or an integer before performing the mathematical operation. You can do this using the astype() method provided by numpy.
For example, if you have a numpy string '10' and a float value 5.0, you can convert the string to a float as follows:
```
import numpy as np
# create a numpy string
a = np.str_('10')
# convert the numpy string to a float
b = float(a)
# perform the mathematical operation
c = b - 5.0
print(c)
```
Output:
```
5.0
```
In this example, we convert the numpy string '10' to a float using the float() method and then perform the subtraction operation between the float value and the other float value.
unsupported operand type(s) for -: 'numpy.str_' and 'numpy.str_'
This error occurs when you try to perform a subtraction operation (-) between two numpy string objects. Numpy string objects are not numerical, and therefore cannot be subtracted from each other.
To fix this error, you need to make sure that the objects you are trying to subtract are numerical. You can convert numpy string objects to numerical data types using functions like int() or float().
For example, if you have two numpy string objects '10' and '5', you can convert them to integers and subtract them as follows:
```
a = '10'
b = '5'
result = int(a) - int(b)
print(result)
```
This will output: 5
Alternatively, you can also use the numpy function np.subtract() to perform element-wise subtraction between two numpy arrays.
```
import numpy as np
a = np.array(['10', '20', '30'])
b = np.array(['5', '10', '15'])
result = np.subtract(a.astype(int), b.astype(int))
print(result)
```
This will output: [ 5 10 15]