When mentioning Python, ArrowHiTech thinks there are various people who will immediately think about an advanced yet really easy-to-use programming language. In fact, working with it is quite simple, however, if you aren’t careful, you will easily get hard problems. Then, one of the most common errors in Python is Typeerror: Only size-1 arrays can be converted to python scalars. What is this error and how to fix it quickest are the issues that many people consider. In this blog, ArrowHiTech will make this topic clearer. So, let’s explore right now!

## What is Typeerror: Only size-1 arrays can be converted?

Firstly, this error in Python will occur as soon as you pass an array as an argument to a function or method that only accepts a single scalar value. Besides, many functions contain built-in error handling capabilities to prevent programs from crashing and to ensure that the inputs given to the function are correct. Moreover, in case the validation is not performed, the Python program will surely crash which can cause problems.

What’s more, due to single-valued parameters, numpy.int() and numpy.float() create the Only size-1 arrays can be converted. Because the main reason causes TypeError is an improper data type, it can be caused by passing an array as a parameter.

Also read: Python memory error: Definition, types and how to solve it

## What causes the Typeerror: only size-1 arrays can be converted to python scalars?

First and foremost, when using numpy, this error can appear in a variety of ways. However, you don’t need to worry because all of these mistakes can be fixed in one way or another. This is a user-side error that can be avoided by giving sufficient parameters. Then, let’s take a look at the causes of the Typeerror: Only size-1 arrays can be converted below.

### #1. Incorrect Datatype

As you probably know, each datatype in Python all includes its own set of functions and properties. Not only that, you can uese every data type in a unique way. The primary parameter in many numpy methods is a single value. If you send a numpy array as a parameter to such methods, this error may arise. Then, in order to know this first cause of Only size-1 arrays can be converted error in detail, let’s explore from the following instance:

1 import numpy as np

2

3 x = np.array([1, 2, 3, 4])

4 x = np.int(x)

As a result, you will get the output like:

TypeError: only size-1 arrays can be converted to Python scalars

### #2. Applying Single Conversion Function

In fact, in Python, functions that accept a single-valued datatype and convert it to another data type are known as single conversion functions. Besides, transforming a string to an int, for example, is a single-valued conversion. Furthermore, these functions in numpy take a single numpy element and change its datatype from within the function. In such procedures, passing a numpy array as a parameter will result in an error.

Now, you should have a glance at the example below:

1 import numpy as np

2

3 x = np.array([1, 2, 3, 4])

4 x = np.float(x)

Then, the outcome will display:

TypeError: Only size-1 arrays can be converted

## How to solve the Typeerror: Only size-1 arrays can be converted?

In the process of working with Python, if you are getting this error, you don’t need to worry. Because in reality, there are several simple solutions that you can adapt to fix it.

### #1. Using Numpy Vectorize Function

Vectorize, in layman’s terms, refers to the process of applying an algorithm to a group of values instead of a single value. Besides, you are able to use numpy.vectorize() in between the algorithm and methods to avoid the TypeError: Only size-1 arrays can be converted that occurs when it is used on sets of values. This solution works over a numpy array like a python map function.

Now, let’s refer to the following codes:

1 import numpy as np

2

3 vector = np.vectorize(np.float)

4 x = np.array([1, 2, 3])

5 x = vector(x)

6 print(x)

Then, the result will look like:

[1. 2. 3.]

### #2. Utilizing Map() function to solve Only size-1 arrays can be converted error

The map is, without a doubt, Python’s most fundamental built-in function for applying a function to all array items. In fact, there are two major parameters to the Map() function. For more details, the first is a function that must be applied to a set of values. Also, an array that you’d like to modify is the second one. Now, coming to the example below:

1 import numpy as np

2

3 x = np.array([1, 2, 3])

4 x = np.array(list(map(np.float, x)))

5 print(x)

Then, below is the result:

[1. 2. 3.]

### #3. Making use of Loops

The most brute-force solution to solve Only size-1 arrays can be converted of applying a function to a group of values is to use loops. However, it gives us complete control over all of the pieces and may be utilized to change them if necessary. As a result, thanks to using Loops, you can avoid this error with ease.

Now, the codes below will show you the specific example:

1 import numpy as np

2

3 x = np.array([1, 2, 3])

4 y = np.array([None]*3)

5 for i in range(3):

6 y[i] = np.float(x[i])

7 print(y)

After that, the outcome will not surely disappointed you:

[1.0 2.0 3.0]

### #4. Using apply_along_axis

Coming to the final method to prevent the Typeerror: Only size-1 arrays can be converted effortlessly. It is thanks to taking advantage of using apply_along_axis.

Simply speaking, by the apply_along_axis Numpy solution, users can easily apply a function to a numpy array along a given axis. Then, you can use numpy to apply a function over sets of values because it is looped according to the axis number.

1 import numpy as np

2

3 x = np.array([1, 2, 3])

4 app = lambda y: [np.float(i) for i in y]

5 x = np.apply_along_axis(app, 0, x)

6 print(x)

Finally, the output will become:

[1. 2. 3.]

## What is plt.barh TypeError: only size-1 arrays can be converted to python scalars?

At first, in Matplotlib, if you want to plot a horizontal bar graph, the plt.barh is the perfect option for you. In addition, it requires two arrays as input to this function. The first are the labels on the Y-axis, and the second are the expanded numeric values on the X-axis. In many circumstances, we try to use np.int to convert the list’s values to integers (). Not only that, because the list type of data cannot be turned into an int, this issue will cause a plt.barh TypeError: Only size-1 arrays can be converted in the code.

Now, let’s examine the following error example:

1 import numpy as np

2 import matplotlib.pyplot as plt

3

4 fig = plt.figure(figsize=(10, 5))

5 plt.barh(np.array([“C”, “C++”, “Python”]), np.int(np.array([“10”, “15”, “20”])))

6 plt.show()

Then, in order to fix this error, you should follow the possible solution below:

1 import numpy as np

2 import matplotlib.pyplot as plt

3

4 fig = plt.figure(figsize=(10, 5))

5 plt.barh(np.array([“C”, “C++”, “Python”]), np.array(list(map(np.int, np.array([“10”, “15”, “20”])))))

6 plt.show()

## In conclusion

In short, through this article above, ArrowHiTech showed you the main causes to appear Typeerror: Only size-1 arrays can be converteds and the smart solutions to fix it with ease. Besides, in case you get this error, let’s follow our instructions and let us know what’s effectiveness. All in all, ArrowHiTech hopes you will acquire a lot of helpful information about Python programming language. We are experienced professionals in the field of ECommerce, Web/mobile apps, CMS website development as well as Salesforce and Software Consultant & Development.