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

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

A filter object is an iterator containing the items in the specified iterable that satisfy the condition of the function passed to the filter() function.

We can solve the error by converting the filter object to a list object using the built-in list() method.

For example,

names = ["Ilya", "Georgios", "Ewan", "Meghan"]

selected_names = list(filter(lambda x: x[0].lower() in 'aeiou', names))

first = selected_names[0]

This tutorial will detail the error and how to solve it with code examples.

---

TypeError: ‘filter’ 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 “filter object” tells us the error concerns an illegal operation for the filter object returned by the built-in filter() method.

The part “is not subscriptable” tells us we cannot access an element of the filter 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 filter object and a str object to see their attributes.

names = ["Ilya", "Georgios", "Ewan", "Meghan"]

selected_names = filter(lambda x: x[0].lower() in 'aeiou', names)

print(dir(selected_names))

['__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__lt__', '__ne__', '__new__', '__next__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__']

We can see that __getitems__ is not present in the list of attributes for the filter 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']

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

names = ["Ilya", "Georgios", "Ewan", "Meghan"]

selected_names = filter(lambda x: x[0].lower() in 'aeiou', names)

# Check type of object

print(type(selected_names))

# Check membership of attribute

print('__getitem__' in dir(selected_names))

<class 'filter'>
False

The variable selected_names is an object of the filter class. We can see that __getitem__ is not an attribute of the filter 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 in a filter object using indexing. First, we will create the function to pass to filter().

def large_square(number):

    squared = number ** 2

    if squared > 50:

        return True

    else:

        return False

The above function squares a number and returns True if the squared value is greater than 50. Otherwise, the function returns False.

Next, we will use the filter() function to filter values in a list of integers. The filter function takes a function and a sequence as arguments and returns an iterator containing the items for which the function returns True. If we pass None instead of a function to filter() then all of the items in the sequence which evaluate to False are removed.

The syntax of the filter() function is:

filter(function or None, iterable) -> filter object

numbers = [2, 3, 4, 7, 8, 10, 17]

filtered_numbers = filter(large_square, numbers)

Next, we will try to access the first element from the filter object and print it to the console

first_filtered_number = filtered_numbers[0]

print(first_filtered_number)

Let’s run the code to see what happens:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [33], in <cell line: 1>()
----> 1 first_filtered_number = filtered_numbers[0]
      3 print(first_filtered_number)

TypeError: 'filter' object is not subscriptable

The error occurs because we are trying to access the first element using indexing, which is not possible with filter objects.

Solution

We can solve this error by converting the filter object to a list using the built-in list() method. Let’s look at the revised code:

numbers = [2, 3, 4, 7, 8, 10, 17]

filtered_numbers = list(filter(large_square, numbers))

first_filtered_number = filtered_numbers[0]

print(first_filtered_number)

Let’s run the code to get the result:

8

The first number in the list that gives a squared value greater than 50 is 8.

Summary

Congratulations on reading to the end of this tutorial!

For further reading on AttributeErrors, go to the articles:

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

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!