Getting error messages is a common but annoying part of programming. This is often the case when you are dealing with a new library of functions. It is particularly true with Python’s Numpy library. Numpy is Python’s fundamental scientific computing package, so it is a necessity if you are going to use Python for scientific work.
What is this error?
The valueerror: setting an array element with a sequence error message is a frequent error that occurs when using an np array from the Numpy Library when the numpy array element is not set up properly, such as the numpy array element has a different length, or is a multi dimensional array which does not work with that particular python function. It can result from having an inconsistent number of elements, different length or string values, or some other value error in a multi dimensional array or a data type mismatch. In either case, it results is from a problem with your python code and different data types. Specifically, in this case, it means that there is a problem with the content of an object that you have created.
Why does this happen?
A valueerror can occur in am np array when the number of sequence elements in multi-dimensional arrays is not the same on different levels, or if the type of an element does not match that of the object. Here are a couple of examples.
- np.array([[7, 4], [2, 4, 9]])
- np.array([3.1, 2.5, “Tony”], dtype=float)
Each input example producers our python code error message, but for different reasons. The first one is a multidimensional array where the number of elements in each level does not match. The second example tries to insert string values into a numeric object.
Fixing the valueerror: setting an array element with a sequence error messages is a simple matter of making small adjustments to the object, scalar, or vector producing it. Because it can result from a simple coding mistake in the array or matrix function the solution is a simple matter of fixing those mistakes.
- np.array([[7, 4], [2, 4, 9]]) – Produces the error message
- np.array([[7, 4, 0], [2, 4, 9]]) – Does not produce the error message
- np.array([3.1, 2.5, “Tony”], dtype=float) – Produces the error message
- np.array([3.1, 2.5, “Tony”], dtype=object) – Does not produce the error message
In the first case, it is just a matter of adding a zero to balance out the array. In the second chase, it is a simple matter of changing the data type of the object. The valueerror: setting an array element with a sequence error message, results from a very simple mistake, but fortunately it is also easy to fix. Fixing this problem only requires some minor adjustments to the python code and your program will be good to go.
Looking for more help with Python problems? Check out these great articles:
- invalid literal for int() with base 10
- typeerror: a bytes-like object is required, not ‘str’
- indentationerror: unindent does not match any outer indentation level
- the truth value of an array with more than one element is ambiguous. use a.any() or a.all()
- only integer scalar arrays can be converted to a scalar index