The Python Interpreter Notices That

The shape of your variable energy is probably wrong:

>>> 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.])

If we use the hash, we can check if an object is hashable or not. () function. If hash() returns a number, it indicates that the object is hashable.,Understanding the root cause of TypeError: unhashable type: ‘numpy.ndarray’: ,We see TypeError: unhashable type: ‘numpy.ndarray’, in the following cases: This is a case where we see an error. The Python interpreter notices that the element is a ndarray object if it checks if elements of the array are hashable. There is an error because ndarray objects are not hashable.

If we have a string, let’s say we do. When we run the function on the object string we will see what happens.

s = “Finxter”

print(hash(s))

Output:

951412520483326359

When we run the function on the ndarray object, we will see what happens.

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

print(hash(arr))

Now, let’s see what happens when we convert a multi-dimensional array.

import numpy as np

arr = np.array([

[1, 2, 3, 4]

])

print(set(arr))

This is shown in the following code snippet

import numpy as np

arr = np.array([

[1, 2, 3, 4]

])

print(set(arr[0]))

Consider the following example:

import numpy as np

arr = np.array([

[1],

[2],

[3],

[4]

])

a = dict()

# Adding the first element from the array as a dictionary key

a[arr[0]] = “Value”

As shown below, the inner element should be index to fix this.

import numpy as np

arr = np.array([

[1],

[2],

[3],

[4]

])

a = dict()

# Adding the first element from the array as a dictionary key

a[arr[0, 0]] = “Value”

print(a)

If there is an array and you want to add all the elements of it to a set, then this is what you should do.

import numpy as np

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

a = set()

a.add(arr)

To fix this, add elements of the array instead of the array object:

import numpy as np

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

a = set()

for ele in arr:

a.add(ele)

print(a)

When trying to get a hash of a NumPy ndarray, the errorTypeError: unhashable type is present. For example, if you want to use an ndarray as a key in a Python dictionary.

You have to use a data type that is readable by computers. The error is called TypeError: unhashable type.

‘numpy.ndarray’ occurs when trying to get the hash value of a NumPy ndarray. ,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 type error mean?

import numpy as np

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

print(set(arr))

import numpy as np

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

print(set(arr))

{

}

import numpy as np

arr = np.array([

[1, 3, 5, 7],

[1, 4, 5, 8]

])

print(set(arr))

The elements of the array are a ndarray object, and the objects in the array are not hashable:

print(type(arr[0]))

print(type(arr[1]))

print(type(arr[0]))

print(type(arr[1]))

≺

class ‘numpy.ndarray’≻≺

class ‘numpy.ndarray’≻

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 array in the multi-dimensional array; we convert each array to a set and call the update method on a set object to get the values for all the array. To see the results, run the code.

{

}

import numpy as np

arr = np.array([0])

a_dict = dict()

a_dict[arr] = “X”

print(a_dict)

In the above code, we attempt to use the one element in the numpy array as a key in the dictionary. To see the result, we need to run the code.

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — –

TypeError Traceback(most recent call last)

— –≻1 a_dict[arr] = “X”

TypeError: unhashable type: ‘numpy.ndarray’

import numpy as np

arr = np.array([0])

a_dict = dict()

a_dict[arr[0]] = “X”

print(a_dict)

Using the index operator, we can get elements of an array. Let’s use the code to get the result.

{

0: ‘X’

}

import numpy as np

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

a_set = set()

a_set.add(arr)

print(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()

— –≻7 a_set.add(arr)

TypeError: unhashable type: ‘numpy.ndarray’

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:

{

}

Unhashable type ‘Set’, Unhashable type ‘Dict’, Unhashable type ‘Slice’ Error in Python, Unhashable type ‘numpy.ndarray’ Error in Python.

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’

}

This is what happens if we use another dictionary as a key:

>>> info = {“language”: “english”}

>>> country[info] = info[“language”]

Traceback (most recent call last):

File “<stdin>”, line 1, in <module>

TypeError: unhashable type: ‘dict’

There is a similar error but this time it is for a numpy.ndarray. (N-dimensional array).

>>> import numpy as np

>>> x = np.array([[1, 2, 3], [4, 5, 6]])

>>> type(x)

Let’s find out what happens if we 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 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)

>>> set(y)

{1, 2, 3}

The first conversion to a set failed due to the fact that we were trying to create a set of NumPy array but it couldn’t be used as an element of a set because it was mutable.

>>> 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’

For example, An array of strings should be able to be converted to a set without errors.

>>> z = np.array([‘one’, ‘two’, ‘three’])

>>> type(z)

>>> set(z)

{‘one’, ‘two’, ‘three’}

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’

Key-value pairs are used to make dictionaries and they allow access to any value by simply using the associated dictionary key.

>>> user[“name”]

‘John’ >>>

user[“age”]

Let’s create a set of numbers:

>>> numbers = {1, 2, 3, 4}

>>> type(numbers)

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’

Let’s see if we can create a set that provides a tuple instead of a list as one of the items:

>>> numbers = {

(5, 6)

} >>>

numbers {

(5, 6)

}

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

>>> numbers = [{1, 2}, {3, 4}]

>>> numbers

[{1, 2}, {3, 4}]

>>> type(numbers[0])

Start by creating an empty set:

>>> numbers = set()

>>> type(numbers)

The set add method will be used to add a first item of type.

>>> item = {1,2}

>>> type(item)

>>> numbers.add(item)

Traceback (most recent call last):

File “<stdin>”, line 1, in <module>

TypeError: unhashable type: ‘set’

Let’s Convert The Item Set To A Frozenset:

>>> item

{1, 2}

>>> type(item)

>>> frozen_item = frozenset(item)

>>> type(frozen_item)

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

>>> numbers

set() >>>

numbers.add(frozen_item) >>>

numbers {

frozenset({

})

}

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

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>

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