Unhashable type numpy ndarray ошибка

Have you ever seen the message “TypeError: unhashable type” when running your Python program? Do you know what to do to fix it?

The message “TypeError: unhashable type” appears in a Python program when you try to use a data type that is not hashable in a place in your code that requires hashable data. For example, as an item of a set or as a key of a dictionary.

This error can occur in multiple scenarios and in this tutorial we will analyse few of them to make sure you know what to do when you see this error.

Let’s fix it now!

To understand when this error occurs let’s replicate it in the Python shell.

We will start from a dictionary that contains one key:

>>> country = {"name": "UK"}
>>> country
{'name': 'UK'} 

Now add a second item to the dictionary:

>>> country["capital"] = "London"
>>> country
{'name': 'UK', 'capital': 'London'} 

All good so far, but here is what happens if by mistake we use another dictionary as key:

>>> info = {"language": "english"}
>>> country[info] = info["language"]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'dict' 

The error unhashable type: ‘dict’ occurs because we are trying to use a dictionary as key of a dictionary item. By definition a dictionary key needs to be hashable.

What does it mean?

When we add a new key / value pair to a dictionary, the Python interpreter generates a hash of the key. To give you an idea of how a hash looks like let’s have a look at what the hash() function returns.

>>> hash("language")
-79422224077228785
>>> hash({"language": "english"})
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'dict' 

You can see that we got back a hash for a string but when we tried to pass a dictionary to the hash function we got back the same “unhashable type” error we have seen before.

The unhashable type: ‘dict’ error is caused by the fact that mutable objects like dictionaries are not hashable.

Unhashable Type ‘numpy.ndarray’ Python Error

Let’s have a look at a similar error but this time for a numpy.ndarray (N-dimensional array).

>>> import numpy as np
>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> type(x)
<class 'numpy.ndarray'> 

After defining an array using NumPy, let’s find out what happens if we try to convert the array into a set.

>>> set(x)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'numpy.ndarray' 

We see the “unhashable type” error again, I want to confirm if once again we see the same behaviour when we try to apply the hash() function to our ndarray.

>>> hash(x)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'numpy.ndarray' 

The error is exactly the same, but why are we seeing this error when converting the array into a set?

Let’s try something else…

The array we have defined before was bi-dimensional, now we will do the same test with a uni-dimensional array.

>>> y = np.array([1, 2, 3]) 
>>> y
array([1, 2, 3])
>>> type(y)
<class 'numpy.ndarray'>
>>> set(y)
{1, 2, 3} 

It worked this time.

The reason why the first conversion to a set has failed is that we were trying to create a set of NumPy arrays but a NumPy array is mutable and hence it cannot be used as element of a set.

>>> my_set = {np.array([1, 2, 3]), np.array([4, 5, 6])}
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'numpy.ndarray' 

The items in a set have to be hashable. Only immutable types are hashable while mutable types like NumPy arrays are not hashable because they could change and break the lookup based on the hashing algorithm.

For example, strings are immutable so an array of strings should get converted to a set without any errors:

>>> z = np.array(['one', 'two', 'three'])
>>> type(z)
<class 'numpy.ndarray'>
>>> set(z)
{'one', 'two', 'three'} 

All good. The same behaviour we have seen also applies to normal Python lists instead of NumPy arrays.

Unhashable Type ‘Slice’ Error in Python

The error unhashable type: ‘slice’ occurs if you try to use the slice operator with a data type that doesn’t support it.

For example, you can use the slice operator to get a slice of a Python list.

But what happens if you apply the slice operator to a dictionary?

Let’s find out…

>>> user = {"name": "John", "age": 25, "gender": "male"}
>>> user[1:3]
Traceback (most recent call last):
  File "", line 1, in 
    user[1:3]
TypeError: unhashable type: 'slice'         

The slice works on indexes and that’s why it works on lists and it doesn’t work on dictionaries.

Dictionaries are made of key-value pairs and this allows to access any value by simply using the associated dictionary key.

>>> user["name"]
'John'
>>> user["age"]
25

Unhashable Type ‘List’ in Python

Here is when you can get the unhashable type ‘list’ error in Python…

Let’s create a set of numbers:

>>> numbers = {1, 2, 3, 4}
>>> type(numbers)
<class 'set'> 

All good so far, but what happens if one of the elements in the set is a list?

>>> numbers = {1, 2, 3, 4, [5, 6]}
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'list' 

We get back the unhashable type error, and that’s because…

The items of a Python set have to be immutable but a list is mutable. This is required because the items of a set need to be hashable and a mutable data type is not hashable considering that its value can change at any time.

The tuple is similar to a list but is immutable, let’s see if we can create a set a provide a tuple instead of a list as one of its items:

>>> numbers = {1, 2, 3, 4, (5, 6)}
>>> numbers
{1, 2, 3, 4, (5, 6)} 

No error this time.

The difference between a list and a tuple in Python is that a list is mutable, is enclosed in square brackets [ ] and is not hashable. A tuple is immutable, is enclosed in parentheses () and is hashable.

Unhashable Type ‘Set’ Python Error

It’s time to find out how you can also encounter the unhashable type error when trying to use a set as item of another set.

First of all let’s define a list of sets:

>>> numbers = [{1, 2}, {3, 4}]
>>> numbers
[{1, 2}, {3, 4}]
>>> type(numbers[0])
<class 'set'> 

It works fine because the elements of a list can be mutable.

Now, instead of defining a list of sets we will try to define a set of sets.

Start by creating an empty set:

>>> numbers = set()
>>> type(numbers)
<class 'set'> 

Then we will use the set add method to add a first item of type set to it.

>>> item = {1,2}
>>> type(item)
<class 'set'>
>>> numbers.add(item)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'set' 

We get back the error unhashable type: ‘set’ because, as explained before, the items of a set have to be immutable and hashable (e.g. strings, integers, tuples).

As a workaround we can use a different datatype provided by Python: the frozenset.

The frozenset is an immutable version of the Python set data type.

Let’s convert the item set to a frozenset:

>>> item
{1, 2}
>>> type(item)
<class 'set'>
>>> frozen_item = frozenset(item)
>>> type(frozen_item)
<class 'frozenset'> 

And now add the frozenset to the empty set we have defined before:

>>> numbers
set()
>>> numbers.add(frozen_item)
>>> numbers
{frozenset({1, 2})} 

It worked!

Hash Function For Different Data Types

We have seen that the unhashable type error occurs when we use a data type that doesn’t support hashing inside a data structure that requires hashing (e.g. inside a set or as a dictionary key).

Let’s go through several Python data types to verify which ones are hashable (they provide a __hash__ method).

Mutable data types are not hashable: list, set, dictionary.

>>> my_list = []
>>> print(my_list.__hash__)
None

>>> my_set = set()
>>> print(my_set.__hash__)
None

>>> my_dict = {}
>>> print(my_dict.__hash__)
None 

As you can see above, all three data types don’t provide the __hash__ method (None returned).

Immutable data types are hashable: string, integer, float, tuple, frozenset.

>>> my_string = ''
>>> print(my_string.__hash__)
<method-wrapper '__hash__' of str object at 0x7ffc1805a2f0>

>>> my_integer = 1
>>> print(my_integer.__hash__)
<method-wrapper '__hash__' of int object at 0x103255960>

>>> my_float = 3.4
>>> print(my_float.__hash__)
<method-wrapper '__hash__' of float object at 0x7ffc0823b610>

>>> my_tuple = (1, 2)
>>> print(my_tuple.__hash__)
<method-wrapper '__hash__' of tuple object at 0x7ffc08344940>

>>> my_frozenset = frozenset({1, 2})
>>> print(my_frozenset.__hash__)
<method-wrapper '__hash__' of frozenset object at 0x7ffc0837a9e0> 

All these data types have an implementation of the __hash__ function, they are hashable.

Conclusion

We have seen several circumstances in which the unhashable type error can occur in your Python code.

Specific Python data types require hashable data, for example the items of a set have to be hashable or the keys of a Python dictionary have to be hashable.

If unhashable data is used where hashable data is required the unhashable type error is raised by the Python interpreter.

You now know how to find out the cause of the error and how to solve it potentially by replacing a Python data type that is unhashable with a data type that is hashable.

The error TypeError: unhashable type: ‘numpy.ndarray’ occurs when trying to get a hash of a NumPy ndarray. For example, using an ndarray as a key in a Python dictionary because you can only use hashable data types as a key.

We can use the update() method to add a ndarray directly to a set. We can use the elements of an ndarray as the keys of a dictionary, provided the elements are hashable.

This tutorial will go through the error in detail and how to solve it with the help of code examples.

TypeError: unhashable type: ‘numpy.ndarray’

What Does TypeError Mean?

TypeError occurs whenever you try to perform an illegal operation for a specific data type object. In the example, the illegal operation is hashing, and the data type is numpy.ndarray.

What Does Unhashable Mean?

By definition, a dictionary key needs to be hashable. An object is hashable if it has a hash value that remains the same during its lifetime. A hash value is an integer Python uses to compare dictionary keys while looking at a dictionary.

When we add a new key:value pair to a dictionary, the Python interpreter generates a hash of the key.

Similarly, we can think of a set as a dictionary that only contains the keys, so it also requires hashable items.

We can only hash particular objects in Python, like strings or integers. All immutable built-in objects in Python are hashable, for example, tuple, and mutable containers are not hashable, for example, list.

Example #1: Converting a Multi-dimension NumPy array to a Set

We can convert an iterable object like a list or a NumPy array to a set using the built-in set() method. When we call the set() method on the iterable object, the Python interpreter checks whether the elements in the iterable are hashable or not. If the elements are hashable, we can successfully convert the iterable object to a set object.

Let’s look at an example where we convert a one-dimension NumPy ndarray to a set:

import numpy as np

arr = np.array([1, 3, 5, 7])

print(set(arr))

{1, 3, 5, 7}

We successfully get a set because the array elements are of integer type. In Python, int is a hashable type.

Next, let’s try to convert a multi-dimensional ndarray to a set:

import numpy as np

arr = np.array([[1, 3, 5, 7],[1, 4, 5, 8]])

print(set(arr))

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
      3 arr = np.array([[1, 3, 5, 7],[1, 4, 5, 8]])
      4 
----≻ 5 print(set(arr))

TypeError: unhashable type: 'numpy.ndarray'

We raise the error because the elements of the array is a ndarray array object, and Ndarray objects are not hashable.

print(type(arr[0]))
print(type(arr[1]))

≺class 'numpy.ndarray'≻
≺class 'numpy.ndarray'≻

Solution

We separate the multi-dimensional array into its component arrays and add their values to the set to solve this error. Let’s look at the code:

import numpy as np

arr = np.array([[1, 3, 5, 7],[1, 4, 5, 8]])

a_set = set()

for i in arr:

    a_set.update(set(i))

print(a_set)

In the above code, we use a for loop to iterate over the component arrays in the multi-dimensional array; we convert each array to a set and call the update() method on a set object to contain the values for all the arrays. Let’s run the code to see the result:

{1, 3, 4, 5, 7, 8}

Example #2: Using a NumPy NDarray as a Key in a Dictionary

We can only use hashable objects as a key in a Python dictionary. If we use any unhashable objects as a dictionary key, we will raise the TypeError. Let’s look at an example:

import numpy as np

arr = np.array([0])

a_dict = dict()

a_dict[arr] = "X"

print(a_dict)

In the above code, we define a numpy array with one element and attempt to use it as a key in a dictionary. Let’s run the code to see the result:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
----≻ 1 a_dict[arr] = "X"

TypeError: unhashable type: 'numpy.ndarray'

Solution

To solve this error, we need to access the element of the array as they are unhashable and use this as the key to the dictionary. Let’s look at the revised code:

import numpy as np

arr = np.array([0])

a_dict = dict()

a_dict[arr[0]] = "X"

print(a_dict)

We can get the elements of an array using the index operator []. Let’s run the code to get the result:

{0: 'X'}

Example #3: Adding a NumPy NDarray to a Set

We can think of a Python set as a dictionary with only keys; therefore, set objects can only contain hashable elements. Let’s look at an example of adding a NumPy array to a set:

import numpy as np

arr = np.array([1, 3, 3, 5, 5, 7, 7])

a_set = set()

a_set.add(arr)

print(a_set)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
      5 a_set = set()
      6 
----≻ 7 a_set.add(arr)

TypeError: unhashable type: 'numpy.ndarray'

The error occurs because the set.add() method adds the array object to the set instead of the array elements.

Solution

To solve this error, we can use the update() method instead of add

import numpy as np

arr = np.array([1, 3, 3, 5, 5, 7, 7])

a_set = set()

a_set.update(arr)

Let’s run the code to see the result:

{1, 3, 5, 7}

Summary

Congratulations on reading to the end of this article! The error: TypeError: unhashable type: ‘numpy.ndarray’ occurs when trying to get the hash value of a NumPy ndarray.

You can add a numpy array directly to a set using the update() method. You cannot add a multi-dimensional ndarray directly to a set, you have to iterate over the ndarray and add each component array to the set using update().

If you want to use the values in a ndarray as keys in a dictionary, you have to extract the values from the array using the indexing operator [].

For further reading on TypeError with unhashable data types, go to the articles:

• How to Solve Python TypeError: unhashable type: ‘list’.
• How to Solve Python TypeError: unhashable type ‘set’,

For further reading on errors involving NumPy, go to the article: How to Solve Python ValueError: all the input arrays must have the same number of dimensions.

Have fun and happy researching!

TypeError: unhashable type: ‘numpy.ndarray’ error typically occurs when you are attempting to use a NumPy ndarray in a data structure that requires hashable elements, such as a dictionary or a set.

Reproduce the error

import numpy as np

arr = np.array([11, 21, 19, 46])

my_dict = {}
my_dict[arr] = "value"

print(my_dict)

Output

TypeError: unhashable type: 'numpy.ndarray'

In this example, we are attempting to use a NumPy array as a key to a dictionary. 

Numpy.ndarray has properties such as shape, size, ndim, dtype, data, and dsize. You can access and modify various aspects of the array using these properties.

NumPy arrays are mutable objects and, therefore, cannot be used as dictionary keys or set elements, which must be immutable.

Since arrays are immutable, we cannot use them as dictionary keys; hence, we get the TypeError.

Here are the ways to fix the TypeError: unhashable type: ‘numpy.ndarray’ error.

1. Converting a numpy array to a tuple using the “tuple()” method.
2. Using a hashable object as a key.
3. Using a custom class that defines the __hash__ and __eq__ methods.

Solution 1: Converting the numpy array to a tuple

Converting the NumPy array to a tuple or a list before using it as a dictionary key or set element will resolve the TypeError.

Use the tuple() function to convert an array to a tuple in Python.

import numpy as np

arr = np.array([11, 21, 19, 46])

tup = tuple(arr)

my_dict = {}
my_dict[tup] = "value"

print(my_dict)

Output

{(11, 21, 19, 46): 'value'}

In this example, we converted an array to a hashable tuple and used the tuple as a key to the dictionary, which won’t throw an error, and we resolved the error.

Solution 2: Use hashable as a key

When you use a dictionary or set in Python, the keys or elements must be hashable objects.

Use a different type of object as the key or element. For example:

1. Strings
2. Integers
3. Tuples of immutable elements (such as strings or integers)

my_dict = {}
my_dict[1] = "value"

print(my_dict)

Output

This code uses the integer data type as a key to the dictionary, which completely avoids the TypeError.

Solution 3: Use a custom class that defines __hash__ and __eq__

If there is a necessity where you need to use a NumPy array as a key in a dictionary and you cannot convert it to a tuple or a list, define a custom class that implements the __hash__ and __eq__ methods.

The __hash__ and __eq__ methods let you define how the objects should be hashed.

This solution is rare, and I recommend you don’t use it unless necessary.

import numpy as np


class CustomClass:
  def __init__(self, arr):
    self.arr = arr

  def __hash__(self):
    return hash(tuple(self.arr))

  def __eq__(self, other):
    return np.array_equal(self.arr, other.arr)


arr = np.array([11, 21, 19, 46])
key = CustomClass(arr)

my_dict = {key: 'value'}
print(my_dict)

Output

{<__main__.CustomClass object at 0x10234cfd0>: 'value'}

That’s it.

Your variable energies probably has the wrong shape:

>>> from numpy import array
>>> set([1,2,3]) & set(range(2, 10))
set([2, 3])
>>> set(array([1,2,3])) & set(range(2,10))
set([2, 3])
>>> set(array([[1,2,3],])) & set(range(2,10))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'numpy.ndarray'

And that’s what happens if you read columnar data using your approach:

>>> data
array([[  1.,   2.,   3.],
       [  3.,   4.,   5.],
       [  5.,   6.,   7.],
       [  8.,   9.,  10.]])
>>> hsplit(data,3)[0]
array([[ 1.],
       [ 3.],
       [ 5.],
       [ 8.]])

Probably you can simply use

>>> data[:,0]
array([ 1.,  3.,  5.,  8.])

instead.

(P.S. Your code looks like it’s undecided about whether it’s data or elementdata. I’ve assumed it’s simply a typo.)

The typeerror: unhashable type: ‘numpy.ndarray’ error can easily be fixed by properly indexing each array’s value. This is a Python error that web developers usually face once entering a list of arrays with multiple containers and functions.

Lucky for you, the process of debugging this unhashable type of error is easy and only consists of a couple of steps.

Keep reading our complete guide that contains all the details, tips and tricks that will make you an expert at removing the error message from your program entirely.

Why Is the Typeerror: Unhashable Type: ‘numpy.ndarray’ Error Happening?

The typeerror: unhashable error type: ‘numpy.ndarray’ error occurs because the web developer passes certain lists of NumPy arrays inside the mode function with hashable data. All the changes are taking place inside a separate Python directory, where you can store all the data, arrays, functions and specific values for each program. If you look closely at the difference between a generic dtype list and a Python directory or a NumPy array, you can easily tell the difference.

In other words, this error usually appears when users try to move data of unhashable form inside a Python directory that requires the use of hashable type. Python uses NumPy to introduce more advanced array structures and create the specific ndarray.

Consequently, there are several instances where this error might occur, so keep reading the following section of this article where we teach you how to fix it.

How To Solve This Error in Python

Solving this unhashable error type may sometimes be as simple as fixing a type you made while working with the ndarray. For example, all you are supposed to do is hash the specific array as a whole unit instead of each item individually. Web developers use this method to specify the hash function and location inside the correct target of the program. However, this is not the only way you can use to fix this error.

As we have previously explained, this error may also happen when the array contains elements and functions that are incompatible with hash. In this case, all you are supposed to do is remove them from the directory and completely remove the error. However, in practice, things may not always be that easy and simple because there are different cases you can come across.

We are going to show you specific examples that are going to clarify our explanations, so keep reading for more.

– Error of the Unhashable Directory in Python

To easily understand why this problem is happening and what is the easiest way to debug it, we will first recreate it. We will create a directory that only contains a single key, as shown in the following example:

>>> country = {“name”: “UK”}
>>> country
{‘name’: ‘UK’}

Now we will add a different item inside the same directory. This will create an additional function inside the same array, as shown here:

>>> country[“capital”] = “London”
>>> country
{‘name’: ‘UK’, ‘capital’: ‘London’}

So far, nothing will be wrong with the code if you use these functions and arrays. However, if by mistake you use a different directory as the primary key, the program is going to show the error.

Let us see the incorrect syntax shown in the following example:

>>> info = {“language”: “english”}
>>> country[info] = info[“language”]
Traceback (most recent call last):
File “<stdin>”, line 1, in <module>
TypeError: unhashable error type: ‘dict’

The web developer will be presented with the error because the specific directory key must be hashable, or the program will render the information incorrectly. Lucky for you, there is an easy solution to this problem, and it only consists of a simple change in the code. The solution to this problem is provided in the following section of this article.

Solution to the Error of the Directory in Python

Once we have successfully recreated the problem, it is time to provide a simple solution. All you are supposed to do is provide a new key or a value pair directly to the directory. Python takes notice and generates a hash key that is going to remove the error completely. The hash() function is going to return a correct value.

Learn how to write the correct syntax by looking at the following code:

>>> hash(“language”)
-79422224077228785
>>> hash({“language”: “english”})
Traceback (most recent call last):
File “<stdin>”, line 1, in <module>
TypeError: unhashable error type: ‘dict’

This is all it takes to easily solve this error from your document without affecting the rest of the functions. However, this is just a fraction of all the possible solutions you can do to debug the unhashable error from your program. Keep reading the following section of the article to learn more about the second method.

– Numpy Ndarray Error in Python

The NumPy ndarray error in Python refers to a bug in your program that contains an N-dimensional array. The values inside the array may vary, and you can use as many arrays as you like. To understand why this problem occurs, let us take a closer look at the following example that contains the necessary functions and arrays:

>>> import numpy as np
>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> type(x)
<class ‘numpy.ndarray’>

This part of the syntax is complete. However, something strange happens if the user attempts to convert the same array into a different set. The following example captures this process and converts the array used in the previous example:

>>> set(x)
Traceback (most recent call last):
File “<stdin>”, line 1, in <module>
TypeError: unhashable error type: ‘numpy.ndarray’

Though you might think there is nothing wrong with the syntax and the function, the unhashable error will appear. To confirm that there is something wrong with the array and its function, we are going to introduce the hash() function inside the ndarray.

The following example shows how this process looks:

>>> hash(x)
Traceback (most recent call last):
File “<stdin>”, line 1, in <module>
TypeError: unhashable error type: ‘numpy.ndarray’

The complete problem and why it happens are shown in this section. So, now, it is time to show you how you can fix it easily by implementing different arrays. Keep reading the following section to learn more.

Solution to the Numpy Ndarray Error in Python

In the previous examples, we have used arrays that are bi-dimensional. Unfortunately, this is one of the main reasons why the problem happens. This time, however, we are going to use a uni-dimensional array, which is going to remove the error completely.

Take a closer look at the following example to learn more:

>>> y = np.array([1, 2, 3])
>>> y
array([1, 2, 3])
>>> type(y)
<class ‘numpy.ndarray’>
>>> set(y)
{1, 2, 3}

The error will not appear this time because we have used a completely different array for the same function. Now, we are going to show you hashable functions that are usually used to fix the error by fixing the algorithm of values.

The following example shows how you should do it:

>>> my_set = {np.array([1, 2, 3]), np.array([4, 5, 6])}
Traceback (most recent call last):
File “<stdin>”, line 1, in <module>
TypeError: unhashable error type: ‘numpy.ndarray’

Finally, you are going to create immutable strings of arrays that will be converted without any errors or bugs. The behavior of the NumPy arrays will not change, and the values will be applied to the normal Python function.

This is the final example that captures the correct syntax:

>>> z = np.array([‘one’, ‘two’, ‘three’])
>>> type(z)
<class ‘numpy.ndarray’>
>>> set(z)
{‘one’, ‘two’, ‘three’}

This section proved that fixing the second error does not have to be complicated. Next, let us look at the third problem and how you are supposed to fix it.

– Unhashable Slice Error in Python

The same error may also appear when web developers try to use a slice operator containing a data type that the function does not support. One of the most common applications is using a slice operator to obtain a section of a Python slice. However, the directory faces certain problems, and the error is the first message that the user sees.

Let us take a look at the following syntax that recreates this problem:

>>> user = {“name”: “Luke”, “age”: 25, “gender”: “male”}
>>> user[1:3]
Traceback (most recent call last):
File “”, line 1, in
user[1:3]
TypeError: unhashable type: ‘slice’

Slices always work on specific indexes. This reason is the main factor why web developers can apply their functions on lists but can never work on directories. Lucky for you, fixing it is easy. Keep reading for more.

Solution to the Unhashable Slice Error in Python

Unlike the previous solutions, this solution’s syntax is short and does not require a lot of effort to complete. All you are supposed to do is introduce certain key-value pairs that allow access to the value function.

The following syntax teaches you how to fix this error:

>>> user[“name”]
‘Luke’
>>> user[“age”]
25

You now understand a lot more about the steps you should take to remove this slice error from your program completely. Let us move on to the last example in this article.

– Set Type Error in Python

Finally, we are going to show you an example syntax that recreates the same unhashable type error in your program. It is also known as the set type error in Python. Recreating this syntax consists of several steps where you are supposed to specify certain sets.

The following example shows the first step that defines a list of sets:

>>> numbers = [{1, 2}, {3, 4}]
>>> numbers
[{1, 2}, {3, 4}]
>>> type(numbers[0])
<class ‘set’>

Next, you are supposed to define and create a set of sets that will initiate the function. However, first, we are going to create an empty set.

Learn how to create an empty set of sets by looking at the following example:

>>> numbers = set()
>>> type(numbers)
<class ‘set’>

We are finally ready to insert the set add method inside the syntax. Adding this method is going to reproduce the unhashable bug in your program, as shown here:

>>> item = {1,2}
>>> type(item)
<class ‘set’>
>>> numbers.add(item)
Traceback (most recent call last):
File “<stdin>”, line 1, in <module>
TypeError: unhashable type: ‘set’

All that is left now is to learn how to fix and debug this error from your program properly. More on the solution in the following section of this article.

Solution to the Set Type Error in Python

To provide a proper solution to this error, all you are supposed to do is include a different datatype with the frozenset function. But first, we are going to convert the item inside the list, which is the first step of the process.

Take a closer look at the following example to learn more:

>>> item
{1, 2}
>>> type(item)
<class ‘set’>
>>> frozen_item = frozenset(item)
>>> type(frozen_item)
<class ‘frozenset’>

In the second and final step of the process, web developers are supposed to introduce a frozenset inside the empty set defined in the previous section:

>>> numbers
set()
>>> numbers.add(frozen_item)
>>> numbers
{frozenset({1, 2})}

You will no longer see the annoying error in your program. Consequently, you will have a more pleasant programming experience with Python. We will now summarize all the important points from this article.

Remember the Following Important Points

The typeerror: unhashable type: ‘numpy.ndarray’ error can easily be fixed by properly indexing each array’s value. Besides teaching you how to effortlessly fix this error in your program, this guide also covered the following important points:

• This error usually appears when web developers introduce the list of arrays with different functions and values.
• It is important to tell the difference between hashable and unhashable arrays and the way you can implement them in your program.
• The simplest solution requires revising the complete syntax and specifying where the array is incorrect.
• Other functions can recreate the same error in your program, so it is crucial to know how to fix them.

Debugging an error inside Python may sometimes be more complex than you think. However, this article was the best place to learn everything about this error and how you are supposed to fix it.