![]() It expects whatever you give it to evaluate to a single number, if it doesn't, Numpy responds that it doesn't know how to set an array element with a sequence. X = np.array() #Fail, can't convert the numpy array to fitĪ numpy array is being created, and numpy doesn't know how to cram multivalued tuples or arrays into single element slots. By trying to cram a numpy array length > 1 into a numpy array element: x = np.array() You will be better of to define b as a list. Since your np.where statement sometimes returns an empty array or even possibly an array with more scalars in it, it is a bit problematic. ![]() we can fix this error by matching the data-type of value and array and then assign it as element of array. The problem lies with your array b.Since it is an array, it is not as flexible as a list. Here we have seen that this error is cause because we are assigning array as a element to array which accept string data-type. Numpy.array() #Fail, can't convert a list into a numpyĢ. ValueError: setting an array element with a sequence. an() #Fail, can't convert a tuple into a numpy Numpy.array() #Fail, can't convert a tuple into a numpy Before downvoting this question and marked as duplicate, let me just explain the issue, i tried all the possible solutions with similar question here on stack, but none of them worked. When you pass a python tuple or list to be interpreted as a numpy array element: import numpy It can be thrown under various circumstances.ġ. Means exactly what it says, you're trying to cram a sequence of numbers into a single number slot. The first one stacks along the first axis (vstack) and the hstack by the zeroth-axis (which is what you want) Share. ![]() The Python ValueError: ValueError: setting an array element with a sequence. 1 2 import numpy as np print(np.array ( 1, 2, 3, 4, 5,dtype int)) Output: Explanation: Firstly, we have imported the numpy library with an alias name as np. scores np.hstack ( x, np.oneslike (x), 0.2 np.oneslike (x)) verify the shape of these arrays by printing scores.shape really helps you find such errors by yourself. ![]()
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