ValueError: setting an array element with a sequence

Question

Why do the following code samples:

np.array([[1, 2], [2, 3, 4]])

np.array([1.2, "abc"], dtype=float)

...all give the following error?

ValueError: setting an array element with a sequence.

Answer 1

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)

Answer 2

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.

Answer 3

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.

Answer 4

for those who are having trouble with similar problems in Numpy, a very simple solution would be:

defining dtype=object when defining an array for assigning values to it. for instance:

out = np.empty_like(lil_img, dtype=object)

Answer 5

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.

Answer 6

In my case, the problem was with a scatterplot of a dataframe X[]:

ax.scatter(X[:,0],X[:,1],c=colors,    
       cmap=CMAP, edgecolor='k', s=40)  #c=y[:,0],

#ValueError: setting an array element with a sequence.
#Fix with .toarray():
colors = 'br'
y = label_binarize(y, classes=['Irrelevant','Relevant'])
ax.scatter(X[:,0].toarray(),X[:,1].toarray(),c=colors,   
       cmap=CMAP, edgecolor='k', s=40)

Answer 7

When the shape is not regular or the elements have different data types, the dtype argument passed to np.array only can be object.

import numpy as np

# arr1 = np.array([[10, 20.], [30], [40]], dtype=np.float32)  # error
arr2 = np.array([[10, 20.], [30], [40]])  # OK, and the dtype is object
arr3 = np.array([[10, 20.], 'hello'])     # OK, and the dtype is also object

``

Answer 8

In my case, I had a nested list as the series that I wanted to use as an input.

First check: If

df['nestedList'][0]

outputs a list like [1,2,3], you have a nested list.

Then check if you still get the error when changing to input df['nestedList'][0].

Then your next step is probably to concatenate all nested lists into one unnested list, using

[item for sublist in df['nestedList'] for item in sublist]

This flattening of the nested list is borrowed from How to make a flat list out of list of lists?.

Answer 9

The error is because the dtype argument of the np.array function specifies the data type of the elements in the array, and it can only be set to a single data type that is compatible with all the elements. The value "abc" is not a valid float, so trying to convert it to a float results in a ValueError. To avoid this error, you can either remove the string element from the list, or choose a different data type that can handle both float values and string values, such as object.

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