# Solve IndexError: Arrays Used as Indices Must Be of Integer (Or Boolean) Type

When working with Numpy arrays in Python, you might experience different error messages dealing with Index or Type issues. In these many error types, `IndexError: arrays used as indices must be of integer (or boolean) type`

can be tricky.

When we face `IndexError`

error messages, we use the wrong Type. In this case, we were supposed to use `Integer`

or `Boolean`

, but the array index receives another data type (string or float).

In this article, we will explain how to deal with `IndexError: arrays used as indices must be of integer (or boolean) type`

error messages when working with numbers in Numpy.

## Use `astype()`

to Solve `IndexError: arrays used as indices must be of integer (or boolean) type`

in Numpy

Numpy only works with two types, Integer or Boolean. Therefore, if there is a type it doesn’t understand, it will throw an error.

Let’s recreate the error message to understand this error message better. To recreate the error message, we need to generate two Numpy arrays, `index`

and `array`

, extract values from `index`

, and use the extracted values to access the values in `array`

.

For the extracted values, we will use the first column values.

```
import numpy as np
index = np.array([[0, 1, 2.1], [1, 2, 3.4]])
array = np.array([[1, 2, 3], [4, 5, 6]])
indices = index[:, 0]
print(array[indices])
```

Output:

```
Traceback (most recent call last):
File "temp.py", line 7, in <module>
print(array[indices])
IndexError: arrays used as indices must be of integer (or boolean) type
```

From the `IndexError: arrays used as indices must be of integer (or boolean) type`

error message, we know the problem stems from the `print(array[indices])`

section.

Since we know that it is syntactically correct, we know that the issue we are looking for is present in what we are parsing to the `array`

binding. That brings us to the `indices`

binding.

From what we know from the error message, the element in the `indices`

binding might not be `integer`

or `Boolean`

. The `dtype`

property is useful to check the type of elements within `indices`

.

```
print(indices.dtype)
```

Output:

```
float64
```

Now, that confirms the cause of the issue we are facing. The values we pass to the indices of the `array`

binding are `float64`

instead of `Boolean`

.

To solve this, we need to convert the values in `indices`

to `Integer`

or `Boolean`

. It makes more sense to convert them to `Integer`

.

Converting them to `Boolean`

might be useful another time.

The `astype()`

method helps modify the `dtype`

property of a Numpy array. To modify the `dtype`

of the `indices`

binding, we can use the below.

```
indices = index[:, 0].astype(int)
```

We get the below if we check for the `dtype`

property using the `indices.dtype`

expression.

```
int32
```

Now, our code becomes:

```
import numpy as np
index = np.array([[0, 1, 2.1], [1, 2, 3.4]])
array = np.array([[1, 2, 3], [4, 5, 6]])
indices = index[:, 0].astype(int)
print(array[indices])
```

Output:

```
[[1 2 3]
[4 5 6]]
```

We could have converted the values of `indices`

to `Boolean`

. Let’s experiment with that.

To do so, we have a Numpy array with two Booleans.

```
indices = index[:, 0].astype(bool)
print(indices)
```

Output:

```
[False True]
```

The values of the `indices`

binding were `[0. 1.]`

, and when converting `0`

to Boolean, it gives `False`

, and any other number gives `True`

. Let’s run everything together.

```
import numpy as np
index = np.array([[0, 1, 2.1], [1, 2, 3.4]])
array = np.array([[1, 3, 5], [7, 9, 11]])
indices = index[:, 0].astype(bool)
print(array[indices])
```

Output:

```
[[ 7 9 11]]
```

That’s because it processes only the `True`

value.

Therefore, when you face an `IndexError: arrays used as indices must be of integer (or boolean) type`

error message, know there is a wrong `dtype`

somewhere. Trace your code, and convert the necessary values.

**Olorunfemi Akinlua**

Olorunfemi is a lover of technology and computers. In addition, I write technology and coding content for developers and hobbyists. When not working, I learn to design, among other things.

LinkedIn## Related Article - Python Error

- Can Only Concatenate List (Not Int) to List in Python
- Invalid Syntax in Python
- Value Error Need More Than One Value to Unpack in Python
- ValueError Arrays Must All Be the Same Length in Python
- Fix the TypeError: Object of Type 'Int64' Is Not JSON Serializable
- Fix the TypeError: 'float' Object Cannot Be Interpreted as an Integer in Python