ValueError: setting an array element with a sequence

The Python ValueError:

ValueError: setting an array element with a sequence.

Means exactly what it says, you're trying to cram a sequence of numbers into a single number slot. It can be thrown under various circumstances.

1. When you pass a python tuple or list to be interpreted as a numpy array element:

import numpy

numpy.array([1,2,3])               #good

numpy.array([1, (2,3)])            #Fail, can't convert a tuple into a numpy 
                                   #array element


numpy.mean([5,(6+7)])              #good

numpy.mean([5,tuple(range(2))])    #Fail, can't convert a tuple into a numpy 
                                   #array element


def foo():
    return 3
numpy.array([2, foo()])            #good


def foo():
    return [3,4]
numpy.array([2, foo()])            #Fail, can't convert a list into a numpy 
                                   #array element

2. By trying to cram a numpy array length > 1 into a numpy array element:

x = np.array([1,2,3])
x[0] = np.array([4])         #good



x = np.array([1,2,3])
x[0] = np.array([4,5])       #Fail, can't convert the numpy array to fit 
                             #into a numpy array element

A numpy array is being created, and numpy doesn't know how to cram multivalued tuples or arrays into single element slots. 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.

Possible reason 1: trying to create a jagged array

You may be creating an array from a list that isn't shaped like a multi-dimensional array:

numpy.array([[1, 2], [2, 3, 4]])         # wrong!

numpy.array([[1, 2], [2, [3, 4]]])       # wrong!

In these examples, the argument to numpy.array contains sequences of different lengths. Those will yield this error message because the input list is not shaped like a "box" that can be turned into a multidimensional array.

Possible reason 2: providing elements of incompatible types

For example, providing a string as an element in an array of type float:

numpy.array([1.2, "abc"], dtype=float)   # wrong!

If you really want to have a NumPy array containing both strings and floats, you could use the dtype object, which allows the array to hold arbitrary Python objects:

numpy.array([1.2, "abc"], dtype=object)