Sometimes in the process of writing programs, you will encounter the error “**How to fix “ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()”**”. We can use one of two ways to handle the error: “Using numpy.logical_and() and numpy.logical_or() function” or “Using &, any() and all() function”. This article will guide you in detail on how to handle it.

## What causes ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()?

First, to make it easier for you to determine what’s causing the error, we’ll talk a little bit about ValueError. ValueError is considered one of the most common exception errors for programmers. ValueError occurs when you pass a function with the correct data type argument but the argument’s value is incorrect/inappropriate.

In this situation, when the python interpreter sends you the following error message:

**Input**

```
import numpy as np
tax = np.array([[2000, 3000], [4000, 5000]])
print((tax[0] < 3500) and (tax[1] > 3500))
```

**Output**

```
ValueError Traceback (most recent call last)
<ipython-input-38-91a3fe76dc28> in <module>
1 import numpy as np
2 tax = np.array([[2000, 3000], [4000, 5000]])
----> 3 print((tax[0] < 3500) and (tax[1] > 3500))
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```

The cause of the error is that you declare multiple elements for the Numpy array which causes Python to return a ValueError.

## How to fix it?

Here we will introduce to you two commonly used methods to solve the error “**ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()**”:

- Using
`any()`

and`all()`

- Using
`numpy.logical_and()`

and`numpy.logical_or()`

### Solution 1: Using &, any() and all() function

**Any()** function returns `True`

if any element is true, if the input boolean string is empty or false, **any()** function will return False. Calling **any()** is quite similar to performing a sequence of consecutive OR operations, on a given set of boolean values.

**Syntax: **

`any(`*iterable*)

**All()** function returns True if all the boolean values in the value string passed to the** all() **function are true, otherwise it returns false. The** all()** function is akin to performing a sequence of consecutive AND logical operations, on a set of provided boolean values.

**Syntax:**

`all(`*iterable*)

Combining the two functions above you can check the quartiles and shorten the process.

** Code sample:**

```
import numpy as np
tax = np.array([[2000, 3000], [4000, 5000]])
print(((tax[0] < 3500) & (tax[1] > 3500)).any())
print(((tax[0] < 3500) & (tax[1] > 3500)).all())
```

The result is

```
True
True
```

Thus, the interpreter can clearly identify the objects.

### Solution 2: Using numpy.logical_and() and numpy.logical_or() function

With this method, the results returned to the browser can determine the correct result from which to determine the true or false of the element. Follow the example below:

```
import numpy as np
tax = np.array([[2000, 3000], [4000, 5000]])
print(np.logical_and(tax[0] < 3500 , tax[1] > 3500).any())
print(np.logical_or(tax[0] < 1000 , tax[1] > 3500).all())
```

The result is

```
True
True
```

## Conclusion

We hope you enjoyed our article about fixing the error “**ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()**”. We are confident that with this information, you will be able to make the best method of your own and get the relevant information about this topic.

If you are still having any problems with your subjects, please feel free to leave us a comment. Thank you for reading!

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