TypeError only integer scalar arrays can be converted to a scalar index occurs because of using NumPy utilities in the wrong way. There can be many reasons for this error and this can be time-consuming to find and fix it. In this article, we will try to solve TypeError: only integer scalar arrays can be converted to a scalar index error by taking various cases. Moreover, we will also discuss the reason for these errors as well and cover how to understand errors in Python.

## TypeError only integer scalar arrays can be converted to a scalar index

The error clearly states that we are trying to use the array’s index incorrectly. We are trying to use indexing that is not an integer type and in NumPy, only integers can be set as indexes.

Usually, this error occurs when you tried to concatenate two NumPy arrays together. So, while concatenating, we need to keep in mind the type for indexing.

Let us take an example and see how the error occurs and how we can solve the issue.

```
# import numpy
import numpy
# Create array
ar1 = numpy.array(['Red', 'Blue'])
ar2 = numpy.array(['Black', 'Yellow'])
# Concatenate array ar1 & ar2
ar3 = numpy.concatenate(ar1, ar1)
```

Output:

As you can see, we get TypeError: only integer scalar arrays can be converted to a scalar index because of the type.

In Python, the TypeError is a specific type of exception that occurs when an operation is misapplied to the object. So, this clearly means we are misapplying the concatenation function on the array.

The error can be solved using the following methods:

- Error with concatenation
- Using a tuple
- Error with ndindex
- See the source of the error

Let us now go through each of the possible solutions.

### Error With Concatenation

As we have seen in the above example we get the error when we tried to concatenate two arrays. If you are getting this error because of a concatenation issue, then you need to convert the array type to list and then concatenate because you can not concatenate two arrays directly. We need to pass the arrays using iterables.

For example, let us first use a tuple and pass the two arrays through a tuple to the concatenation method.

```
# Create array
ar1 = numpy.array(['Red', 'Blue'])
ar2 = numpy.array(['Black', 'Yellow'])
# Concatenate array ar1 & ar2
ar3 = numpy.concatenate([ar1, ar1])
# printing
print(ar3)
```

Output:

`['Red' 'Blue' 'Red' 'Blue']`

As you can see, we were able to concatenate the two arrays using concatenate method without getting any errors.

### Using a Tuple

Another way to solve TypeError: only integer scalar arrays can be converted to a scalar index is using a tuple. We can pass tuples instead of lists to concatenate the arrays.

```
# Create array
ar1 = numpy.array(['Red', 'Blue'])
ar2 = numpy.array(['Black', 'Yellow'])
# Concatenate arrays using tuple
ar3 = numpy.concatenate((ar1, ar1))
# printing
print(ar3)
```

Output:

`['Red' 'Blue' 'Red' 'Blue']`

As you can see, we solved the problem using the tuple.

### Error With ndindex

Sometimes, we get the same error because of ndindex function. For example, see the following Python code which returns an error.

```
# importing the numpy
import numpy as np
# using ndindex
x = np.ndindex(np.random.rand(128,128))
```

Output:

```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_46339/3145723907.py in <module>
3
4 # using ndindex
----> 5 x = np.ndindex(np.random.rand(128,128))
/usr/lib/python3/dist-packages/numpy/lib/index_tricks.py in __init__(self, *shape)
657 if len(shape) == 1 and isinstance(shape[0], tuple):
658 shape = shape[0]
--> 659 x = as_strided(_nx.zeros(1), shape=shape,
660 strides=_nx.zeros_like(shape))
661 self._it = _nx.nditer(x, flags=['multi_index', 'zerosize_ok'],
/usr/lib/python3/dist-packages/numpy/lib/stride_tricks.py in as_strided(x, shape, strides, subok, writeable)
102 interface['strides'] = tuple(strides)
103
--> 104 array = np.asarray(DummyArray(interface, base=x))
105 # The route via `__interface__` does not preserve structured
106 # dtypes. Since dtype should remain unchanged, we set it explicitly.
TypeError: only integer scalar arrays can be converted to a scalar index
```

As you can see, we get this error.

To solve this error in ndindex case, we need to reshape the array as shown below:

```
# solving ndindex error typeError
x = np.ndindex(np.random.rand(128,128).shape)
```

As you can see, we solved the error using the shape method.

### See the Source of the Error

The error usually occurs from the NumPy source code file which is located in the following directory:

`numpy/core/src/multiarray/number.c`

Here is the code snipped from the NumPy code file:

```
static PyObject *
array_index(PyArrayObject *v)
{
if (!PyArray_ISINTEGER(v) || PyArray_NDIM(v) != 0) {
PyErr_SetString(PyExc_TypeError,
"only integer scalar arrays can be converted to a scalar index");
return NULL;
}
return PyArray_GETITEM(v, PyArray_DATA(v));
}
```

array_index is used whenever we refer to the array index in Numpy. The function array_index is used as follows in the same file.

```
NPY_NO_EXPORT PyNumberMethods array_as_number = {
.nb_add = array_add,
.nb_subtract = array_subtract,
.nb_multiply = array_multiply,
.nb_remainder = array_remainder,
.nb_divmod = array_divmod,
.nb_power = (ternaryfunc)array_power,
.nb_negative = (unaryfunc)array_negative,
.nb_positive = (unaryfunc)array_positive,
.nb_absolute = (unaryfunc)array_absolute,
.nb_bool = (inquiry)_array_nonzero,
.nb_invert = (unaryfunc)array_invert,
.nb_lshift = array_left_shift,
.nb_rshift = array_right_shift,
.nb_and = array_bitwise_and,
.nb_xor = array_bitwise_xor,
.nb_or = array_bitwise_or,
.nb_int = (unaryfunc)array_int,
.nb_float = (unaryfunc)array_float,
.nb_index = (unaryfunc)array_index,
.nb_inplace_add = (binaryfunc)array_inplace_add,
.nb_inplace_subtract = (binaryfunc)array_inplace_subtract,
```

Hopefully, now you will get rid of TypeError: only integer scalar arrays can be converted to a scalar index error.

## Understanding the only integer scalar arrays can be converted to a scalar index error in Python

In Python, it is easy to understand and interpret the errors. There are mainly two parts of errors in Python. The first part of the error gives information about the category of the error. For example, in our case, the category of the error is TypeError which means there is something wrong with the data type.

The second part of the error gives more specific information about the error. For example, in our case, it says, TypeError: only integer scalar arrays can be converted to a scalar index which means only integers can be used as indexes for arrays.

The following are some of the main causes of TypeError: only integer scalar arrays can be converted to a scalar index error:

- Using floating numbers as indexes for arrays
- Using lists or tuples as index values for arrays.
- Passing a string or other data types that are not integer as the index value

### What is TypeError In Python?

The TypeError is an exception in Python that occurs when we tried to use the wrong data type or apply a function to an incorrect data type. For example, if the function can take only string values as arguments and when we provide integer values, we will get a TypeError message.

For example, let us assume that we have two different data-typed variables and we want to add them.

```
# defining variables
integer = 234
string = "Bashir"
# result
a = string + integer
```

This will raise TypeError because both variables have different data types.

### What is an Array in Python?

An array is a basic data structure in Python that can contain a sequence of values. Unlike, other programming languages, we don’t need to fix the size and type of the array before initializing it. In Python, we can use the NumPy module to create an array for us.

Let us see, how we can create a simple array in Python using the NumPy module.

```
# importing the numpy module
import numpy as np
# creating numpy array
a = np.array([1, 2, 3, 4])
# printing
print(a)
```

Output:

`[1 2 3 4]`

As you can see, we have created a one-dimensional array. In a similar way, we can create a multi-dimensional array as well.

```
# importing the numpy module
import numpy as np
# creating numpy array
a = np.array([[1, 2],[ 3, 4]])
# printing
print(a)
```

Output:

```
[[1 2]
[3 4]]
```

Notice that we have created a multi-dimensional NumPy array.

### How Does the Indexing of an Array Work in Python?

The indexing in Python starts from 0. This means the first element in any sequence can be accessed in Python by an index value of 0.

Similarly, the elements of the array in Python can be accessed by the index values. The index is a very important operation as it helps us to select specific elements from the array.

By default, the indexing starts from 0 and goes on. It can only be an integer value. If we try to use indexing other than integers, then we will get TypeError: only integer scalar arrays can be converted to a scalar index error.

## Summary

In this short article, we learned how we can solve TypeError: only integer scalar arrays can be converted to a scalar index error. We covered 4 different methods to solve this problem. Moreover, we covered how to understand errors in Python by taking only integer scalar arrays that can be converted to a scalar index as an example.

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