Only size 1 arrays can be converted to python scalars как исправить

Python has helped thousands of communities to create solutions for their real-life problems. With thousands of useful modules, Python has proved to be one of the top versatile languages in the coding world. In addition, writing in Python is similar to writing in English, and using it is even simpler. With simple single command module installs, you can install almost every dependency to control your problems. Numpy is one of those important modules, and Only Size 1 Arrays Can Be Converted To Python Scalars Error appears while using this module.

Only Size 1 Arrays Can Be Converted To Python Scalars Error is a typical error that appears as a TypeError form in the terminal. This error’s main cause is passing an array to a parameter that accepts a scalar value. In various numpy methods, acceptable parameters are only a scalar value. Hence, if you pass a single dimensional array or multidimensional array in the method, it’ll throw this error. With increasing methods accepting a single parameter, you can expect this error to appear many times.

This post will guide you with the causes of the error and it’s solutions.

Only Size 1 Arrays Error is a TypeError that gets triggered when you enter an array as a parameter in a function or method which accepts a single scalar value. Many functions have inbuilt error handling methods to avoid crashing programs and validate the inputs given for the function. Without the validation, the python program will crash immediately, which can cause issues.

numpy.int() and numpy.float() shows this error due to single-valued parameters. As TypeError originates from an invalid data type, you can get this error by sending an array as a parameter.. There are different ways to avoid this error, which we’ll discuss in the post’s bottom section.

Why do I get Only Size 1 Arrays Can Be Converted To Python Scalars Error?

Errors are an integral part of the programming, and they should be handled properly. With the management of error handling, your program can not only avoid harmful vulnerabilities, but it can also perform in a better way. As a result, you can get the Only Size 1 Arrays error while using numpy. This module’s developers have categorized this error into TypeError, which describes that you supplied the wrong data to the function/method.

Causes of Only Size 1 Arrays Can Be Converted To Python Scalars Error

There are several ways the error can appear while using numpy. All of these errors are solvable by one or the other means. This error is a user sided error and passing appropriate parameters can prevent this error. Following are the causes of this error –

Incorrect Datatype

In Python, every data type has different methods and attributes. Each of these data types has different usage. In many numpy methods, the primary parameter required is a single value. With such methods, if you pass a numpy array as a parameter, Only Size 1 Arrays Can Be Converted To Python Scalars Error can appear.

Example:

import numpy as np

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

Output:

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

Explanation:

In the program, x is a simple array with integer elements. If you try to convert this array into int form, it will throw an error because np.int() accept a single value.

Using Single Conversion Function

Single Conversion Functions are the functions that accept a single-valued datatype and convert it to another data type. For example, converting a string to int is a single-valued conversion. In numpy, these functions accept the single numpy element and change its datatype from within. If you pass a numpy array as a parameter, you’ll get an error in such methods.

Example –

import numpy as np

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

Output –

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

Explanation –

In the above example, we wanted to convert all the integers from the numpy array to float values. np.float() throws TypeError as it accepts single-valued parameters.

Solutions for Only Size 1 Arrays Can Be Converted To Python Scalars Error

There are multiple ways of solving the TypeError in Python. Most importantly, Numpy modules provide some inbuilt functions which you can use to create a suitable datatype before using it in a method. Following are the solutions to this error –

1. Using Numpy Vectorize Function

In layman’s terms, Vectorize means applying an algorithm to a set of values rather than applying them on a single value. As the TypeError occurs due to its usage on sets of values, you can use numpy.vectorize() in between the algorithm and methods. This method acts like a python map function over a numpy array.

Code –

import numpy as np

vector = np.vectorize(np.float)
x = np.array([1, 2, 3])
x = vector(x)
print(x)

Output –

[1. 2. 3.]

Explanation –

Firstly, we started by creating a vector that accepts np.float as a parameter. To apply a method on all the numpy array elements, we’ll use this vector. Nextly, we created a numpy array and then used our vector() to apply np.float over all the values. This method avoids all sorts of TypeError and also converts all the values into the float.

2. Using Map() Function

Surely, the map is the basic inbuilt function in python that applies a function over all array elements. map() function accepts two major parameters. The first one is the function you need to apply over sets of values. The second one is an array that you want to change. Let’s jump on an example –

Code –

import numpy as np

x = np.array([1, 2, 3])
x = np.array(list(map(np.float, x)))
print(x)

Output –

[1. 2. 3.]

Explanation –

Firstly, we’ve created a simple integer array and used map(np.float, x) to convert all the elements from numpy array to float. As the map function returns a map object, we need to convert it back to the list and numpy array to restore its datatype. By using this method, you can avoid getting TypeError.

3. Using Loops

Loops are the most brute force methods to apply a function over a set of values. But it provides us control over all parts of the elements and can be used to customize elements if we need.

Code –

import numpy as np

x = np.array([1, 2, 3])
y = np.array([None]*3)
for i in range(3):
    y[i] = np.float(x[i])
print(y)

Output –

[1.0 2.0 3.0]

Explanation –

In the above example, we’ve used indexing to fetch the initial integer from the numpy array. Then applied the np.float() method to convert it from float to int. Furthermore, we’ve created a dummy numpy array y, which stores the float values after changing.

4. Using apply_along_axis

Apply_along_axis is a Numpy method that allows the users to apply a function over a numpy array along a specific axis. As numpy is looped according to axis number, you can use it to apply a function over sets of values.

Code –

import numpy as np

x = np.array([1, 2, 3])
app = lambda y: [np.float(i) for i in y]
x = np.apply_along_axis(app, 0, x)
print(x)

Output –

[1. 2. 3.]

Explanation –

In this example, we’ll use the lambda function to create a function’s vectorized function. Then we’ll use np.apply_along_axis to apply the lambda function over the specific numpy array. You can also specify the axis along which you need to apply the function.

plt.barh TypeError: Only size-1 arrays can be converted to Python scalars

plt.barh is a popular function that is used in Matplotlib to plot a horizontal bar graph. This function requires two arrays as input. The first one being the labels on Y-axis and the second one being the numeric values that are extended on the X-axis. In many cases, we try to convert the values of the list to integers by using np.int(). This issue creates a TypeError in the code as the list type of data cannot be converted into an int.

Error Example –

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(10, 5))
plt.barh(np.array(["C", "C++", "Python"]), np.int(np.array(["10", "15", "20"])))
plt.show()

Possible Solution –

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(10, 5))
plt.barh(np.array(["C", "C++", "Python"]), np.array(list(map(np.int, np.array(["10", "15", "20"])))))
plt.show()

Explanation –

Using map() function along with np.int will properly convert each value from str to integer in the array. This method can be used in plt.barh function to avoid TypeError.

Conclusion

Numpy module has provided thousands of useful methods that easily solve hard problems. These methods have their own sets of instructions and parameters that we need to follow properly. Only Size 1 Arrays Can Be Converted To Python Scalars Error appears when you provide an invalid data type to a function that accepts a scalar value. By following the solutions and alternatives given in the post, you can solve this error in no time!

However, if you have any doubts or questions, do let me know in the comment section below. I will try to help you as soon as possible.

Happy Pythoning!

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In this short tutorial, we look at the typeerror «Only size-1 arrays can be converted to Python scalars» in Python. This typeerror is quite common however there are only a handful of tutorials explaining it. In case you are looking for an in-depth explanation of the error I would recommend you follow step by step. If not, feel free to check out the solutions directly.

Table of Contents — Typeerror: only size-1 arrays can be converted to Python scalars

• What does «Only size-1 arrays can be converted to Python scalars» signify?
• Solutions 1: Code & Explanation
• Solutions 2: Code & Explanation

What does «Typeerror: Only size-1 arrays can be converted to Python scalars» signify?

You are likely to face the above typeerror while working with NumPy and matplotlib.pyplot. This error arises when you pass an array into a function that is expecting a single value (i.e., scalar value). Python generally works with only a handful of scalar values (Int, float, bool, etc) however while dealing with NumPy we have another 24 new fundamental Python types to describe different types of scalars. You can read more about them here.

Due to the above errors, you ought to be extra cautious while using NumPy. A common error I came across was this:

import numpy as ny
import matplotlib.pyplot as matplot


def ycord(xcord):
    return ny.int(xcord)


xcord = ny.arange(1, 5, 2.7)
matplot.plot(xcord, ycord(xcord))
matplot.show()

For the above input, this is the output we receive:

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

This is because the .int function only accepts single values and in our case we are trying to pass x which is an array.

Solution 1: Code and Explanation using the .vectorize function.

In the first method, we use the .vectorize function. The .vectorize function is essentially a for loop that takes in an array and returns a single numpy array. Using this method we iterate over the array x and return a single value. Thus we do not encounter the «only size-1 arrays can be converted to Python scalars» typeerror.

import numpy as ny
import matplotlib.pyplot as matplot

xcord = ny.arange(1, 5, 2.5)
ycord = ny.vectorize(ny.int)
matplot.plot(xcord, ycord(xcord))
matplot.show()

As you can see we no longer need to define a function as we have used the .int function inside the .vectorize function, which loops through the array and returns a single array that is accepted by the .int function.

Solution 2: Code and Explanation using the .astype method

Another method I came across utilizes the .astype method, this method casts the array into a specified type in our case int. Although this method works, I personally recommend the first solution as this method casts a string into an int which is not a desirable method.

import numpy as ny
import matplotlib.pyplot as matplot


def ycord(xcord):
    return xcord.astype(int)


xcord = ny.arange(1, 5, 2.5)
matplot.plot(xcord, ycord(xcord))
matplot.show()

These are two solutions that you can be use when faced with the Typeerror: Only size-1 arrays can be converted to Python scalars error.

A popular TypeError in Python is TypeError only length-1 arrays can be converted to python scalar. You might encounter when working with NumPy and Matplotlib libraries. This occurs when the function that you have defined or have created is expecting a single parameter but gets an array instead. You can start fixing this error by passing a single value to the function.

In this article, we will understand more about this error and its solution.

Example:

# Import numpy & matplotlib
import numpy
import matplotlib.pyplot

# Define your custome function
def myfunction(x):
  return numpy.int(x)

x = numpy.arange(1, 15.1, 0.1)
matplotlib.pyplot.plot(x, myfunction(x))
matplotlib.pyplot.show()

Output:

Traceback (most recent call last):
  File "error-1.py", line 13, in <module>
    matplotlib.pyplot.plot(x, myfunction(x))
  File "error-1.py", line 7, in myfunction
    return numpy.int(x)
TypeError: only size-1 arrays can be converted to Python scalars

This error occurs as the function called np.int() takes in a single parameter as per its function definition. But if you are passing an array, it will not work and throw the error.

Solution:

Besides passing in a single element to the function you can also try using the np.vectorize() function. Use the following code to assign the np.int() method to this function:

# import numpy & matplotlib
import numpy
import matplotlib.pyplot

# define your custom function
def myfunction(x):
  return numpy.int(x)

myfunction2 = numpy.vectorize(myfunction)
x = numpy.arange(1, 15.1, 0.1)
matplotlib.pyplot.plot(x, myfunction2(x))
matplotlib.pyplot.show()

Output:

The np.vectorize() function works as a «for loop» that allows the function passed into it to accept a single element from an array. So, a single element is passed onto the np.int() method every time.

Conclusion

You must pass single values to functions that accept a single value. As mentioned earlier, the best way to solve this error is to use numpy.vectorize() method. This will convert the array you pass to the function, into a vector.