When working with NumPy, one common and sometimes confusing error you may encounter is ValueError: setting an array element with a sequence. This error typically arises when you attempt to create or modify a NumPy array but the data types of the elements you provide don’t align with the expected data type of the array.

Why does this error occur ?

This error occurs when NumPy expects a single value like a number or string but receives a sequence like a list or array instead. It often happens with:

• Mixed data types in the input list
• Uneven nested lists
• Assigning a list to an element that expects a scalar

NumPy cannot automatically convert these sequences into a fixed, uniform structure so it raises a ValueError.

Common Situations and How to Fix the Error

1. Using common data-type

When you try to create a NumPyarray from a list containing both numbers and nested lists and specify a strict data type like int, it causes a ValueError.

import numpy as np
a = [1, 2, 4, [5, [6, 7]]]
b = np.array(a, dtype=int) 

Output

Hangup (SIGHUP)
Traceback (most recent call last):
  File "/home/guest/sandbox/Solution.py", line 4, in <module>
    b = np.array(a, dtype=int)
ValueError: setting an array element with a sequence.

Solution: Use a more flexible data type like object which supports mixed types and nested lists.

import numpy as np

a = [1, 2, 4, [5, [6, 7]]]
b = np.array(a, dtype=object)
print(b)

[1 2 4 list([5, [6, 7]])]

2. Assigning list to string

If the array expects strings dtype=str, but you try to assign a list to an element, it causes a ValueError.

import numpy as np
a = np.array(["Geeks", "For"], dtype=str)
a[1] = ["for", "Geeks"] 

Output

Hangup (SIGHUP)
Traceback (most recent call last):
  File "/home/guest/sandbox/Solution.py", line 4, in <module>
    arr[1] = ["for", "Geeks"]
    ~~~^^^
ValueError: setting an array element with a sequence

Solution: Make sure the element assigned matches the data type. If you need to assign a list, create the array with dtype=object or convert the list to a string.

import numpy as np

# Use dtype=object
a = np.array(["Geeks", "For"], dtype=object)
a[1] = ["for", "Geeks"]
print(a)

# Convert list to string
a = np.array(["Geeks", "For"], dtype=str)
a[1] = str(["for", "Geeks"])
print(a)

['Geeks' list(['for', 'Geeks'])]
['Geeks' "['for"]

3. Unequal nested lists

If the array expects strings dtype=str , but you try to assign a list to an element, it causes a ValueError.

import numpy as np

a = [[1, 2], [3, 4, 5]]  
np_array = np.array(a, dtype=int)  

Output

Hangup (SIGHUP)
Traceback (most recent call last):
  File "/home/guest/sandbox/Solution.py", line 4, in <module>
    np_array = np.array(a, dtype=int)
ValueError: setting an array element with a sequence. 

Solution: Use dtype=object to allow arrays of nested lists with different lengths, or pad the nested lists to equal lengths before creating the array.

import numpy as np
a = [[1, 2], [3, 4, 5]]  

res = np.array(a, dtype=object)
print(res)

[list([1, 2]) list([3, 4, 5])]

4. Assigning list to number

When you assign a list to a position in an array that expects a single number e.g., int , it raises a TypeError.

import numpy as np

arr = np.zeros((3, 3), dtype=int)
arr[0, 0] = [1, 2]

Output

Hangup (SIGHUP)
Traceback (most recent call last):
  File "/home/guest/sandbox/Solution.py", line 4, in <module>
    arr[0, 0] = [1, 2]
    ~~~^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'list'

Solution: Assign only single numeric values to array elements with numeric data types. If you want to store lists, use dtype=object.

import numpy as np
a = np.zeros((3, 3), dtype=int)

# Assign a single number
a[0, 0] = 1

# Use dtype=object 
a_obj = np.zeros((3, 3), dtype=object)
a_obj[0, 0] = [1, 2]
print(a_obj)

[[list([1, 2]) 0 0]
 [0 0 0]
 [0 0 0]]