ValueError: setting 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)

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.

In my case , I got this Error in Tensorflow , Reason was i was trying to feed a array with different length or sequences :

example :

import tensorflow as tf

input_x = tf.placeholder(tf.int32,[None,None])



word_embedding = tf.get_variable('embeddin',shape=[len(vocab_),110],dtype=tf.float32,initializer=tf.random_uniform_initializer(-0.01,0.01))

embedding_look=tf.nn.embedding_lookup(word_embedding,input_x)

with tf.Session() as tt:
    tt.run(tf.global_variables_initializer())

    a,b=tt.run([word_embedding,embedding_look],feed_dict={input_x:example_array})
    print(b)

And if my array is :

example_array = [[1,2,3],[1,2]]

Then i will get error :

ValueError: setting an array element with a sequence.

but if i do padding then :

example_array = [[1,2,3],[1,2,0]]

Now it's working.

In my case, the problem was another. I was trying convert lists of lists of int to array. The problem was that there was one list with a different length than others. If you want to prove it, you must do:

print([i for i,x in enumerate(list) if len(x) != 560])

In my case, the length reference was 560.