How to fix Python error – “only integer scalar arrays can be converted to a scalar index”

Errors are like your best friend in programming—they will never leave you. No matter how expert someone is at coding, they will face some sort of error while doing so. The best programmer always knows how to solve this issue. The errors become more common as Python becomes more popular. If you have ever worked with arrays, lists, or dictionaries, then you might have come across the error “Only Integer Scalar Arrays Can be Converted to a Scalar Index.” Let us discuss this error and check out why it happens and how you can prevent it. 

What is an “integer scalar arrays” error?

There are two common scenarios when this error in Python can occur. 

1. When you are working on Array indexing on a list 
2. When you are trying to concatenate two matrices together. 

Usually, it is a form of syntax error, so if you ever see one in your code, try to look at your syntax. Now, let us see two of the scenarios where this mistake can happen and how you can prevent it. 

How to Fix “integer scalar arrays” error?

As we have said before, there are two main reasons behind this error. Now, we will intentionally get this error and we will also try to resolve the issue. This will help us to understand the reason behind this error better and how you can avoid this issue when you will try to code.

Array Indexing

Let us try to plot a line graph with matplotlib. 

import NumPy as np
data = [1, 2, 3, 4, 5, 6, 7, 8]
random_values = np.random.choice(range(len(data)), size=3)
random_vals = data[random_values.astype(int)]
random_vals
Code language: Python (python)

when we try to run the program. Alas! Here is the error we were talking about: “Only integer scalar arrays can be converted to a scalar index.” So, what happened? Let us try to analyze the code. 

This is very common, because in Python, what you know as an array in other programming languages is known as a list. On the other hand, when you want to work with Numpy, that list becomes an array with a slight difference, i.e., an extra parenthesis is added to that list. 

How about the same code when we write like this? 

import numpy as np
data = [1, 2, 3, 4, 5, 6, 7, 8]
random_values = np.random.choice(range(len(data)), size=3)
random_vals = np.array(data)[random_values.astype(int)]
random_vals
Code language: Python (python)

Now we will get three random values from our desired output. See, now we have no problem, and we get our desired output. All we had to do was convert our list into a Numpy array. 

Concatenate Two Matrices

Let us try to concatenate two Numpy matrices together. 

import numpy as np<br>matrics1 = np.matrix([[1, 2], [3, 4]])<br>matrics2 = np.matrix([[5, 6], [7, 8]])<br>np.concatenate(matrics1, matrics2)
Code language: Python (python)

Well, here we go again; the same error has hit us again. What happened this time? 

Let us get back into our code and try to figure out the problem. Notice the line where we have written the code to concatenate both of these arrays; did you get it? As we have used only one pair of parenthesis, our code was unable to form a Tuple using those two matrices. If we just add another pair of parentheses, we are good to go. 

Let’s give it a shot. 

import numpy as np<br>matrics1 = np.matrix([[1, 2], [3, 4]])<br>matrics2 = np.matrix([[5, 6], [7, 8]])<br>np.concatenate((matrics1, matrics2))
Code language: Python (python)

See, now we have our desired output and no errors at all. Now, both of our matrices are concatenated together and forever.

Conclusion 

A better programmer can not only create something from scratch but also troubleshoot any issues that arise. So, finding the reason behind an error is something everyone should master. If you are a Python programmer and working on data science then knowing to solve this problem will be a great help on your way forward.