NumPy is one of the most useful additions to the Python language. It’s an important library that essentially elevates Python into a platform that is capable of advanced mathematical data type functions. However, there are instances where NumPy’s status as an add-on rather than something integral to Python is readily apparent. For example, a typeerror: unhashable type: ‘numpy.ndarray’ can be a little confusing at first. But this seemingly mysterious hashable objects error makes a lot more sense by looking a bit deeper into the key relationship between Python’s system and NumPy.
What is this error?
An error message in Python’s system usually points to one of a limited number of problems. A type error with an unhashable object suggests a mismatch between two of the elements we’re working with. For example, if you tried to add a string and integer together you might receive a TypeError. This is because the two types need to be compatible. We’d also raise a TypeError if we tried to treat that string as a function. It’s similar to trying to change the nature of an immutable object such as an integer.
Consider a situation where we have a string file column called hello which contains “HelloWorld”. If we typed print(hello) it would return “HelloWorld”. But if we called it by typing print(hello()) we’d get a TypeMismatch. This is due to the fact that we tried to call the string to return text instead of just referencing it as a string variable.
The next part of the hash argument datatype error elaborates on the elements Python’s having trouble with. We see that the interpreter reports an attempt to hash argument something that’s unhashable. A hash is an alphanumeric value that represents a singular dataset or datatype. If you’ve MD5 to verify a download then you’ve seen what a hashing function can do in real-world situations. But not everything in your dataframe can be hashed.
What is causing it?
Normally it’s fairly easy to tell when we’ve incorrectly hashed something. But the moment we type import NumPy we’re changing the nature of the programming language. This is particularly true when looking at the difference between a standard dtype list or NumPy array, Python dictionary and a NumPy array. This is where the numpy.ndarray dataframe issue in the error message comes into play.
Python code using NumPy can use advanced array structures in the form of an ndarray. This is a multidimensional array filled with items that are the same size and type. The data set in these arrays should be fairly predictable in many ways. The value within it can change, but the dtype of data shouldn’t.
The attribute of the ndarray and the items within it can both point to an unhashable object or immutable objects. But we have to keep in mind that the ndarray itself is unhashable. Trying to do so is like hashing the actual string object rather than a variable of that type. This might also occur when using a function that needs to work with a hash value. We might see a hash error rather than an error with the function.
How do I fix it?
The biggest impediment to fixing the error comes from the fact that we might want to do so in different ways depending on what we need from the ndarray. This error most commonly comes from a simple mistake or typo when working with an ndarray. For example, trying to hash the ndarray as a whole rather than an item within it. In that case it’s simply a matter of pointing the hash function at the correct target.
The problem can also come from the fact that an ndarray can contain elements that aren’t compatible with a hash. Or we might have data that could be hashed if it was properly formatted. For example, an ndarray might be usable if presented as a flat list. In both of these cases we’d need to do a conversion first. For example, imagine if we had a multidimensional array generating the error. We could first flatten the array with ravel. Then we could use tolist to make it a duplicate list that we can properly hash.