typeerror: unhashable type: numpy.ndarray

Guri Bee

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19-10-2024

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Unraveling the "TypeError: unhashable type: 'numpy.ndarray'"

The "TypeError: unhashable type: 'numpy.ndarray'" error in Python is a common issue that arises when you try to use a NumPy array as a key in a dictionary. This article will delve into the reason behind this error, provide solutions, and explore alternative approaches for working with NumPy arrays in dictionaries.

Understanding the Error

Dictionaries in Python are designed to store key-value pairs. The keys must be hashable, meaning they need to be immutable and have a unique hash value. This allows for efficient lookup and retrieval of values based on their corresponding keys.

NumPy arrays, on the other hand, are mutable data structures, meaning their contents can be changed after they are created. This mutability makes them unhashable. Consequently, using a NumPy array as a key in a dictionary will result in the "TypeError: unhashable type: 'numpy.ndarray'" error.

Solutions

Here are some solutions to address this error:

1. Convert the NumPy Array to a Tuple:

Tuples in Python are immutable, making them suitable as dictionary keys. You can convert your NumPy array to a tuple using the tuple() function.

import numpy as np

arr = np.array([1, 2, 3])
my_dict = {tuple(arr): "value"}
print(my_dict)  # Output: {(1, 2, 3): 'value'} 

2. Use the Array's Hash as the Key:

Another solution is to use the hash of the NumPy array as the dictionary key. You can obtain the hash using the hash() function.

import numpy as np

arr = np.array([1, 2, 3])
my_dict = {hash(arr.tobytes()): "value"}
print(my_dict) # Output: {4472598249701648778: 'value'}

3. Use a Custom Hash Function:

If your array contains complex data types or you need a more tailored hashing approach, you can create a custom hash function for your NumPy array. This allows you to define specific rules for generating the hash value based on your array's contents.

4. Avoid Using NumPy Arrays as Dictionary Keys:

If possible, consider using alternative data structures that do not require the use of NumPy arrays as dictionary keys. You can explore using lists or other suitable data structures depending on your application.

Example and Additional Insights

Let's say you want to store data associated with specific locations represented by NumPy arrays. Here's how you can implement this using the solutions discussed above:

import numpy as np

locations = [np.array([1, 2]), np.array([3, 4]), np.array([1, 2])]
data = ["location 1", "location 2", "location 3"]

# Using tuples
location_data = {}
for i, location in enumerate(locations):
    location_data[tuple(location)] = data[i]

print(location_data)  # Output: {(1, 2): 'location 1', (3, 4): 'location 2'}

# Using array hash
location_data = {}
for i, location in enumerate(locations):
    location_data[hash(location.tobytes())] = data[i]
print(location_data)  # Output: {1689980143592759162: 'location 1', 8716604258067496562: 'location 2'}

Key Takeaways

• The "TypeError: unhashable type: 'numpy.ndarray'" error arises because NumPy arrays are mutable and cannot be used as dictionary keys.
• You can overcome this issue by converting the array to a tuple, using its hash, or defining a custom hash function.
• Consider alternative data structures if you can avoid using NumPy arrays as dictionary keys.

By understanding the cause of the error and implementing the appropriate solutions, you can effectively work with NumPy arrays and dictionaries in your Python projects. Remember to choose the approach that best suits your specific needs and data format.