This error occurs when you pass a generator object to a len()
method call. The generator
object is a type of lazy iterator containing a sequence of values. In Python, iterators do not have length.
We can solve the error by converting the generator
object to a list
object using the built-in list()
method.
For example,
# A generator function def generator_func(): yield 1 yield 2 yield 3 # x is a generator object x = list(generator_func()) print(len(x))
This tutorial will go through how to solve the error with code examples.
TypeError: object of type ‘generator’ has no len()
We raise a Python TypeError when attempting to perform an illegal operation for a specific type. In this case, the type is generator
.
The part ‘has no len()
‘ tells us the map object does not have a length, and therefore len()
is an illegal operation for the generator
object.
Retrieving the length of an object is only suitable for iterable objects, like a list
or a tuple
.
A generator
function returns a generator
object, an iterator containing a sequence of values. We can access the values in a generator object using a for
loop or by calling next()
.
All iterators have the __iter__
dunder method, which we can check by passing the iterator to the dir()
method:
# A generator function def generator_func(): yield 1 yield 2 yield 3 # x is a generator object x = generator_func() print(type(x)) print('__iter__' in dir(x))
<class 'generator'> True
The len()
method implicitly calls the dunder method __len__()
which returns a positive integer representing the length of the object on which it is called. All iterable objects have __len__
as an attribute. Let’s check if __len__
is in the list of attributes for the generator
object and the list object using the built-in dir()
method.
# A generator function def generator_func(): yield 1 yield 2 yield 3 # x is a generator object x = generator_func() print(type(x)) print('__len__' in dir(x))
<class 'generator'> False
We can see that __len__
is not present in the attributes of the generator
object.
lst = ["Einstein", "Feynman", "Dirac"] print(type(lst)) print('__len__' in dir(lst))
<class 'list'> True
We can see that __len__
is present in the attributes of the list
object.
Example
Let’s look at an example of trying to get the length of a generator
object. First, we will create the generator()
function.
Generator functions allow us to declare a function that behaves like an iterator. We use a yield
statement rather than a return
statement in a generator
function.
def generator_func(): yield 2 yield 3 yield 8
Next, we will assign the generator
object returned by the generator
function to a variable,
x = generator_func()
Next, we will try to get the length of the generator object and print it to the console.
print(len(x))
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Input In [7], in <cell line: 1>() ----> 1 print(len(x)) TypeError: object of type 'generator' has no len()
The error occurs because the generator
object is an iterator and does not have a length.
Solution
We can solve the error by converting the generator
object to a list
, which is an iterable data type. We can convert a generator
object to a list using the built-in list()
method. Let’s look at the revised code:
def generator_func(): yield 2 yield 3 yield 8 x = list(generator_func()) print(type(x)) print(len(x))
Let’s run the code to get the length of the list:
<class 'list'> 3
We successfully converted the generator
object to a list and then used the len()
method to get the length of the list.
Summary
Congratulations on reading to the end of this tutorial!
For further reading on the has no len()
TypeErrors, go to the article:
- How to Solve Python TypeError: object of type ‘builtin_function_or_method’ has no len()
- How to Solve Python TypeError: object of type ‘filter’ has no len()
- How to Solve Python TypeError: object of type ‘int’ has no len()
To learn more about Python for data science and machine learning, go to the online courses page on Python, which provides the best, easy-to-use online courses.
Suf is a senior advisor in data science with deep expertise in Natural Language Processing, Complex Networks, and Anomaly Detection. Formerly a postdoctoral research fellow, he applied advanced physics techniques to tackle real-world, data-heavy industry challenges. Before that, he was a particle physicist at the ATLAS Experiment of the Large Hadron Collider. Now, he’s focused on bringing more fun and curiosity to the world of science and research online.