How to Solve Python TypeError: ‘generator’ object is not subscriptable

by | Programming, Python, Tips

In Python, you cannot access values inside a generator object using indexing syntax.

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().

We can solve this error by converting the generator object to a list 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(x[0])

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


TypeError: ‘generator’ object is not subscriptable

Let’s break up the error message to understand what the error means. TypeError occurs whenever you attempt to use an illegal operation for a specific data type. The part “generator object” tells us the error concerns an illegal operation for the generator object.

The part “is not subscriptable” tells us we cannot access an element of the generator object using the subscript operator, which is square brackets [].

A subscriptable object is a container for other objects and implements the __getitem__() method. Examples of subscriptable objects include strings, lists, tuples, and dictionaries.

We can check if an object implements the __getitem__() method by listing its attributes with the dir function. Let’s call the dir function and pass a generator object and a str object to see their attributes.

# A generator function
def generator_func():

    yield 1

    yield 2

    yield 3

  
x = generator_func()

print(dir(x))
['__class__', '__del__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__lt__', '__name__', '__ne__', '__new__', '__next__', '__qualname__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'close', 'gi_code', 'gi_frame', 'gi_running', 'gi_yieldfrom', 'send', 'throw']

We can see that __getitem__ is not present in the list of attributes for the generator object.

string = "Python"

print(dir(string))
['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmod__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'capitalize', 'casefold', 'center', 'count', 'encode', 'endswith', 'expandtabs', 'find', 'format', 'format_map', 'index', 'isalnum', 'isalpha', 'isascii', 'isdecimal', 'isdigit', 'isidentifier', 'islower', 'isnumeric', 'isprintable', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'maketrans', 'partition', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']

We can see that __getitem__ is present in the list of attributes for the str object.

If we want to check if a specific attribute belongs to an object, we can check for membership using the in operator.

# A generator function
def generator_func():

    yield 1

    yield 2

    yield 3
  
x = generator_func()

# Check type of object

print(type(x))

# Check membership of attribute

print('__getitem__' in dir(x))
<class 'generator'>
False

The variable x is an object of the generator class. We can see that __getitem__ is not an attribute of the generator class.

string = "Python"
print(type(string))
print('__getitem__' in dir(string))
<class 'str'>
True

We can see that __getitem__ is an attribute of the str class.

Example

Let’s look at an example of trying to access an element of a generator object using indexing. 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 attempt to access the first item in the generator object using indexing syntax.

first_num = x[0]

print(first_num)

Let’s run the code to see what happens:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [41], in <cell line: 1>()
----> 1 first_num = x[0]
      2 print(first_num)

TypeError: 'generator' object is not subscriptable

The error occurs because the x variable is a generator object, which is an Iterator. Iterators generate values in order as requested by a for loop or by calling next(). We are trying to access values in the object as though it were a list or another subscriptable object.

Solution

We can solve this error by converting the 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())

first_num = x[0]

print(first_num)

Let’s run the code to get the result:

2

The first item in the generator object is the number 2.

Summary

Congratulations on reading to the end of this tutorial!

For further reading on AttributeErrors, go to the articles:

To learn more about Python for data science and machine learning, you can go to the online courses page on Python for the most comprehensive courses.

Have fun and happy researching!

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!

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