How to Solve Python TypeError: cannot unpack non-iterable builtin_function_or_method object

by | Programming, Python, Tips

In Python, you can unpack iterable objects and assign their elements to multiple variables in the order they appear. If you try to unpack a built-in function or a method, you will throw the error TypeError: cannot unpack non-iterable builtin_function_or_method object. A method is not a sequence which we can loop over.

If the built-in function or method returns an iterable object, you can call the method before performing unpacking. For example,

particle_dict = {"name":"muon", "charge":-1, "mass":105.7}

name, charge, mass = particle_dict.values()


This tutorial will go through how to solve the error with code examples.

TypeError: cannot unpack non-iterable builtin_function_or_method object

TypeError occurs in Python when you perform an illegal operation for a specific data type. A builtin_function_or_method is a method or a function that is built into the Python interpreter, and we cannot iterate over it.

Unpacking is only suitable for iterable objects.

What is an Iterable Object in Python?

An iterable is an object that can be “iterated over“, for example in a for loop. In terms of dunder methods under the hood, an object can be iterated over with “for” if it implements __iter__() or __getitem__().

An iterator returns the next value in the iterable object. An iterable generates an iterator when it is passed to the iter() method.

In terms of dunder methods under the hood, an iterator is an object that implements the __next__() method.

A for loop automatically calls the iter() method to get an iterator and then calls next over and over until it reaches the end of the iterable object.

Unpacking requires an iteration in order to assign values to variables in order, and as such requires iterable objects.

What is Unpacking in Python?

Unpacking is the process of splitting packed values into individual elements. The packed values can be a string, list, tuple, set or dictionary. During unpacking, the elements on the right-hand side of the statement are split into the values on the left-hand side based on their relative positions. Let’s look at the unpacking syntax with an example:

values = [10, 20, 30]

x, y, z = values

print(f'x: {x}, y: {y}, z: {z}')

The above code assigns the integer values in the value list to three separate variables. The value of x is 10, y is 20, and the value of z is 30. Let’s run the code to get the result:

x: 10, y: 20, z: 30

We can also unpack sets and dictionaries. Dictionaries are only ordered for Python version 3.7 and above but are unordered for 3.6 and below. Generally, it is not recommended to unpack unordered collections of elements as there is no guarantee of the order of the unpacked elements.

We cannot unpack an Integer because it is not an iterable object, and an iterable is a Python object that we can iterate over.


Let’s look at an example of attempting to unpack a built-in method object.

First, we will define a dictionary containing information about the electron particle:

particle_dict = {"name":"electron", "charge":-1, "mass":0.51, "spin": "1/2"}

Then we will try to unpack the values in the dictionary from the view object returned by the dict method values().

name, charge, mass, spin = particle_dict.values


Let’s run the code to see what happens:

TypeError                                 Traceback (most recent call last)
Input In [4], in <cell line: 1>()
----> 1 name, charge, mass, spin = particle_dict.values
      3 print(name)
      4 print(charge)

TypeError: cannot unpack non-iterable builtin_function_or_method object

The error occurs because we did not call the values() method and Python interpets this as trying to unpack the values() method.


We can solve this error by calling the method values(). We can call a method by specifying the method name and putting parentheses after the name. Let’s look at the revised code:

name, charge, mass, spin = particle_dict.values()


Let’s run the code to get the values in the dictionary:



Congratulations on reading to the end of this tutorial!

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Research Scientist at Moogsoft | + posts

Suf is a research scientist at Moogsoft, specializing in Natural Language Processing and Complex Networks. Previously he was a Postdoctoral Research Fellow in Data Science working on adaptations of cutting-edge physics analysis techniques to data-intensive problems in industry. In another life, he was an experimental particle physicist working on the ATLAS Experiment of the Large Hadron Collider. His passion is to share his experience as an academic moving into industry while continuing to pursue research. Find out more about the creator of the Research Scientist Pod here and sign up to the mailing list here!