Python's "in" and "not in" Operators: Check for Membership

Python's "in" and "not in" Operators: Check for Membership

Python’s in and not in operators allow you to quickly determine if a given value is or isn’t part of a collection of values. This type of check is common in programming, and it’s generally known as a membership test in Python. Therefore, these operators are known as membership operators.

In this tutorial, you’ll learn how to:

  • Perform membership tests using the in and not in operators
  • Use in and not in with different data types
  • Work with operator.contains(), the equivalent function to the in operator
  • Provide support for in and not in in your own classes

To get the most out of this tutorial, you’ll need basic knowledge of Python, including built-in data types, such as lists, tuples, ranges, strings, sets, and dictionaries. You’ll also need to know about Python generators, comprehensions, and classes.

Getting Started With Membership Tests in Python

Sometimes you need to find out whether a value is present in a collection of values or not. In other words, you need to check if a given value is or is not a member of a collection of values. This kind of check is commonly known as a membership test.

Arguably, the natural way to perform this kind of check is to iterate over the values and compare them with the target value. You can do this with the help of a for loop and a conditional statement.

Consider the following is_member() function:

Python
>>> def is_member(value, iterable):
...     for item in iterable:
...         if value is item or value == item:
...             return True
...     return False
...

This function takes two arguments, the target value and a collection of values, which is generically called iterable. The loop iterates over iterable while the conditional statement checks if the target value is equal to the current value. Note that the condition checks for object identity with is or for value equality with the equality operator (==). These are slightly different but complementary tests.

If the condition is true, then the function returns True, breaking out of the loop. This early return short-circuits the loop operation. If the loop finishes without any match, then the function returns False:

Python
>>> is_member(5, [2, 3, 5, 9, 7])
True

>>> is_member(8, [2, 3, 5, 9, 7])
False

The first call to is_member() returns True because the target value, 5, is a member of the list at hand, [2, 3, 5, 9, 7]. The second call to the function returns False because 8 isn’t present in the input list of values.

Membership tests like the ones above are so common and useful in programming that Python has dedicated operators to perform these types of checks. You can get to know the membership operators in the following table:

Operator Description Syntax
in Returns True if the target value is present in a collection of values. Otherwise, it returns False. value in collection
not in Returns True if the target value is not present in a given collection of values. Otherwise, it returns False. value not in collection

As with Boolean operators, Python favors readability by using common English words instead of potentially confusing symbols as operators.

Like many other operators, in and not in are binary operators. That means you can create expressions by connecting two operands. In this case, those are:

  1. Left operand: The target value that you want to look for in a collection of values
  2. Right operand: The collection of values where the target value may be found

The syntax of a membership test looks something like this:

Python
value in collection

value not in collection

In these expressions, value can be any Python object. Meanwhile, collection can be any data type that can hold collections of values, including lists, tuples, strings, sets, and dictionaries. It can also be a class that implements the .__contains__() method or a user-defined class that explicitly supports membership tests or iteration.

If you use the in and not in operators correctly, then the expressions that you build with them will always evaluate to a Boolean value. In other words, those expressions will always return either True or False. On the other hand, if you try and find a value in something that doesn’t support membership tests, then you’ll get a TypeError. Later, you’ll learn more about the Python data types that support membership tests.

Now that you know what membership operators are, it’s time to learn the basics of how they work.

Python’s in Operator

To better understand the in operator, you’ll start by writing some small demonstrative examples that determine if a given value is in a list:

Python
>>> 5 in [2, 3, 5, 9, 7]
True

>>> 8 in [2, 3, 5, 9, 7]
False

The first expression returns True because 5 appears inside your list of numbers. The second expression returns False because 8 isn’t present in the list.

According to the in operator documentation, an expression like value in collection is equivalent to the following code:

Python
any(value is item or value == item for item in collection)

The generator expression wrapped in the call to any() builds a list of the Boolean values that result from checking if the target value has the same identity or is equal to the current item in collection. The call to any() checks if any one of the resulting Boolean values is True, in which case the function returns True. If all the values are False, then any() returns False.

Python’s not in Operator

The not in membership operator does exactly the opposite. With this operator, you can check if a given value is not in a collection of values:

Python
>>> 5 not in [2, 3, 5, 9, 7]
False

>>> 8 not in [2, 3, 5, 9, 7]
True

In the first example, you get False because 5 is in [2, 3, 5, 9, 7]. In the second example, you get True because 8 isn’t in the list of values. This negative logic may seem like a tongue twister. To avoid confusion, remember that you’re trying to determine if the value is not part of a given collection of values.

With this quick overview of how membership operators work, you’re ready to go to the next level and learn how in and not in work with different built-in data types.

Using in and not in With Different Python Types

All built-in sequences—such as lists, tuples, range objects, and strings—support membership tests with the in and not in operators. Collections like sets and dictionaries also support these tests. By default, membership operations on dictionaries check whether the dictionary has a given key or not. However, dictionaries also have explicit methods that allow you to use the membership operators with keys, values, and key-value pairs.

In the following sections, you’ll learn about a few particularities of using in and not in with different built-in data types. You’ll start with lists, tuples, and range objects to kick things off.

Lists, Tuples, and Ranges

So far, you’ve coded a few examples of using the in and not in operators to determine if a given value is present in an existing list of values. For these examples, you’ve explicitly used list objects. So, you’re already familiar with how membership tests work with lists.

With tuples, the membership operators work the same as they would with lists:

Python
>>> 5 in (2, 3, 5, 9, 7)
True

>>> 5 not in (2, 3, 5, 9, 7)
False

There are no surprises here. Both examples work the same as the list-focused examples. In the first example, the in operator returns True because the target value, 5, is in the tuple. In the second example, not in returns the opposite result.

For lists and tuples, the membership operators use a search algorithm that iterates over the items in the underlying collection. Therefore, as your iterable gets longer, the search time increases in direct proportion. Using Big O notation, you’d say that membership operations on these data types have a time complexity of O(n).

If you use the in and not in operators with range objects, then you get a similar result:

Python
>>> 5 in range(10)
True

>>> 5 not in range(10)
False

>>> 5 in range(0, 10, 2)
False

>>> 5 not in range(0, 10, 2)
True

When it comes to range objects, using membership tests may seem unnecessary at first glance. Most of the time, you’ll know the values in the resulting range beforehand. But what if you’re using range() with arguments that are determined at runtime?

Consider the following examples, which use random numbers to determine arguments at runtime:

Python
>>> from random import randint

>>> 50 in range(0, 100, randint(1, 10))
False

>>> 50 in range(0, 100, randint(1, 10))
False

>>> 50 in range(0, 100, randint(1, 10))
True

>>> 50 in range(0, 100, randint(1, 10))
True

On your machine, you might get different results because you’re working with random ranges. In these specific examples, step is the only value that varies. In real code, you could have varying values for start and stop as well.

For range objects, the algorithm behind the membership tests computes the presence of a given value using the expression (value - start) % step) == 0, which depends on the arguments used to create the range at hand. This makes membership tests very efficient when they operate on range objects. In this case, you’d say that their time complexity is O(1).

Remember that the target value in a membership test can be of any type. The test will check if that value is or isn’t in the target collection. For example, say that you have a hypothetical app where the users authenticate with a username and a password. You can have something like this:

Python
# users.py

username = input("Username: ")
password = input("Password: ")

users = [("john", "secret"), ("jane", "secret"), ("linda", "secret")]

if (username, password) in users:
    print(f"Hi {username}, you're logged in!")
else:
    print("Wrong username or password")

This is a naive example. It’s unlikely that anyone would handle their users and passwords like this. But the example shows that the target value can be of any data type. In this case, you use a tuple of strings representing the username and the password of a given user.

Here’s how the code works in practice:

Shell
$ python users.py
Username: john
Password: secret
Hi john, you're logged in!

$ python users.py
Username: tina
Password: secret
Wrong username or password

In the first example, the username and password are correct because they’re in the users list. In the second example, the username doesn’t belong to any registered user, so the authentication fails.

In these examples, it’s important to note that the order in which the data is stored in the login tuple is critical because something like ("john", "secret") isn’t equal to ("secret", "john") in tuple comparison even if they have the same items.

In this section, you’ve explored examples that showcase the core behavior of membership operators with common Python built-in sequences. However, there’s a built-in sequence left. Yes, strings! In the next section, you’ll learn how membership operators work with this data type in Python.

Strings

Python strings are a fundamental tool in every Python developer’s tool kit. Like tuples, lists, and ranges, strings are also sequences because their items or characters are sequentially stored in memory.

You can use the in and not in operators with strings when you need to figure out if a given character is present in the target string. For example, say that you’re using strings to set and manage user permissions for a given resource:

Python
>>> class User:
...     def __init__(self, username, permissions):
...         self.username = username
...         self.permissions = permissions
...

>>> admin = User("admin", "wrx")
>>> john = User("john", "rx")

>>> def has_permission(user, permission):
...     return permission in user.permissions
...

>>> has_permission(admin, "w")
True
>>> has_permission(john, "w")
False

The User class takes two arguments, a username and a set of permissions. To provide the permissions, you use a string in which w means that the user has write permission, r means that the user has read permission, and x implies execution permissions. Note that these letters are the same ones that you’d find in the Unix-style file-system permissions.

The membership test inside has_permission() checks whether the current user has a given permission or not, returning True or False accordingly. To do this, the in operator searches the permissions string to find a single character. In this example, you want to know if the users have write permission.

However, your permission system has a hidden issue. What would happen if you called the function with an empty string? Here’s your answer:

Python
>>> has_permission(john, "")
True

Because an empty string is always considered a substring of any other string, an expression like "" in user.permissions will return True. Depending on who has access to your users’ permissions, this behavior of membership tests may imply a security breach in your system.

You can also use the membership operators to determine if a string contains a substring:

Python
>>> greeting = "Hi, welcome to Real Python!"

>>> "Hi" in greeting
True
>>> "Hi" not in greeting
False

>>> "Hello" in greeting
False
>>> "Hello" not in greeting
True

For the string data type, an expression like substring in string is True if substring is part of string. Otherwise, the expression is False.

An important point to remember when using membership tests on strings is that string comparisons are case-sensitive:

Python
>>> "PYTHON" in greeting
False

This membership test returns False because strings comparisons are case-sensitive, and "PYTHON" in uppercase isn’t present in greeting. To work around this case sensitivity, you can normalize all your strings using either the .upper() or .lower() method:

Python
>>> "PYTHON".lower() in greeting.lower()
True

In this example, you use .lower() to convert the target substring and the original string into lowercase letters. This conversion tricks the case sensitivity in the implicit string comparison.

Generators

Generator functions and generator expressions create memory-efficient iterators known as generator iterators. To be memory efficient, these iterators yield items on demand without keeping a complete series of values in memory.

In practice, a generator function is a function that uses the yield statement in its body. For example, say that you need a generator function that takes a list of numbers and returns an iterator that yields square values from the original data. In this case, you can do something like this:

Python
>>> def squares_of(values):
...     for value in values:
...         yield value ** 2
...

>>> squares = squares_of([1, 2, 3, 4])

>>> next(squares)
1
>>> next(squares)
4
>>> next(squares)
9
>>> next(squares)
16
>>> next(squares)
Traceback (most recent call last):
    ...
StopIteration

This function returns a generator iterator that yields square numbers on demand. You can use the built-in next() function to retrieve consecutive values from the iterator. When the generator iterator is completely consumed, it raises a StopIteration exception to communicate that no more values are left.

You can use the membership operators on a generator function like squares_of():

Python
>>> 4 in squares_of([1, 2, 3, 4])
True
>>> 9 in squares_of([1, 2, 3, 4])
True
>>> 5 in squares_of([1, 2, 3, 4])
False

The in operator works as expected when you use it with generator iterators, returning True if the value is present in the iterator and False otherwise.

However, there’s something you need to be aware of when checking for membership on generators. A generator iterator will yield each item only once. If you consume all the items, then the iterator will be exhausted, and you won’t be able to iterate over it again. If you consume only some items from a generator iterator, then you can iterate over the remaining items only.

When you use in or not in on a generator iterator, the operator will consume it while searching for the target value. If the value is present, then the operator will consume all the values up to the target value. The rest of the values will still be available in the generator iterator:

Python
>>> squares = squares_of([1, 2, 3, 4])

>>> 4 in squares
True

>>> next(squares)
9
>>> next(squares)
16
>>> next(squares)
Traceback (most recent call last):
    ...
StopIteration

In this example, 4 is in the generator iterator because it’s the square of 2. Therefore, in returns True. When you use next() to retrieve a value from square, you get 9, which is the square of 3. This result confirms that you no longer have access to the first two values. You can continue calling next() until you get a StopIteration exception when the generator iterator is exhausted.

Likewise, if the value isn’t present in the generator iterator, then the operator will consume the iterator completely, and you won’t have access to any of its values:

Python
>>> squares = squares_of([1, 2, 3, 4])

>>> 5 in squares
False

>>> next(squares)
Traceback (most recent call last):
    ...
StopIteration

In this example, the in operator consumes squares completely, returning False because the target value isn’t in the input data. Because the generator iterator is now exhausted, a call to next() with squares as an argument raises StopIteration.

You can also create generator iterators using generator expressions. These expressions use the same syntax as list comprehensions but replace the square brackets ([]) with round brackets (()). You can use the in and not in operators with the result of a generator expression:

Python
>>> squares = (value ** 2 for value in [1, 2, 3, 4])
>>> squares
<generator object <genexpr> at 0x1056f20a0>

>>> 4 in squares
True

>>> next(squares)
9
>>> next(squares)
16
>>> next(squares)
Traceback (most recent call last):
    ...
StopIteration

The squares variable now holds the iterator that results from the generator expression. This iterator yields square values from the input list of numbers. Generator iterators from generator expressions work the same as generator iterators from generator functions. So, the same rules apply when you use them in membership tests.

Another critical issue can arise when you use the in and not in operators with generator iterators. This issue can appear when you’re working with infinite iterators. The function below returns an iterator that yields infinite integers:

Python
>>> def infinite_integers():
...     number = 0
...     while True:
...         yield number
...         number += 1
...

>>> integers = infinite_integers()
>>> integers
<generator object infinite_integers at 0x1057e8c80>

>>> next(integers)
0
>>> next(integers)
1
>>> next(integers)
2
>>> next(integers)
3
>>> next(integers)

The infinite_integers() function returns a generator iterator, which is stored in integers. This iterator yields values on demand, but remember, there will be infinite values. Because of this, it won’t be a good idea to use the membership operators with this iterator. Why? Well, if the target value isn’t in the generator iterator, then you’ll run into an infinite loop that’ll make your execution hang.

Dictionaries and Sets

Python’s membership operators also work with dictionaries and sets. If you use the in or not in operators directly on a dictionary, then it’ll check whether the dictionary has a given key or not. You can also do this check using the .keys() method, which is more explicit about your intentions.

You can also check if a given value or key-value pair is in a dictionary. To do these checks, you can use the .values() and .items() methods, respectively:

Python
>>> likes = {"color": "blue", "fruit": "apple", "pet": "dog"}

>>> "fruit" in likes
True
>>> "hobby" in likes
False
>>> "blue" in likes
False

>>> "fruit" in likes.keys()
True
>>> "hobby" in likes.keys()
False
>>> "blue" in likes.keys()
False

>>> "dog" in likes.values()
True
>>> "drawing" in likes.values()
False

>>> ("color", "blue") in likes.items()
True
>>> ("hobby", "drawing") in likes.items()
False

In these examples, you use the in operator directly on your likes dictionary to check whether the "fruit", "hobby", and "blue" keys are in the dictionary or not. Note that even though "blue" is a value in likes, the test returns False because it only considers the keys.

Next up, you use the .keys() method to get the same results. In this case, the explicit method name makes your intentions much clearer to other programmers reading your code.

To check if a value like "dog" or "drawing" is present in likes, you use the .values() method, which returns a view object with the values in the underlying dictionary. Similarly, to check if a key-value pair is contained in likes, you use .items(). Note that the target key-value pairs must be two-item tuples with the key and value in that order.

If you’re using sets, then the membership operators work as they would with lists or tuples:

Python
>>> fruits = {"apple", "banana", "cherry", "orange"}

>>> "banana" in fruits
True
>>> "banana" not in fruits
False

>>> "grape" in fruits
False
>>> "grape" not in fruits
True

These examples show that you can also check whether a given value is contained in a set by using the membership operators in and not in.

Now that you know how the in and not in operators work with different built-in data types, it’s time to put these operators into action with a couple of examples.

Putting Python’s in and not in Operators Into Action

Membership tests with in and not in are pretty common operations in programming. You’ll find these kinds of tests in many existing Python codebases, and you’ll use them in your code as well.

In the following sections, you’ll learn how to replace Boolean expressions based on the or operator with membership tests. Because membership tests can be quite common in your code, you’ll also learn how to make these tests more efficient.

Replacing Chained or Operators

Using a membership test to replace a compound Boolean expression with several or operators is a useful technique that allows you to simplify your code and make it more readable.

To see this technique in action, say that you need to write a function that takes a color name as a string and determines whether it’s a primary color. To figure this out, you’ll use the RGB (red, green, and blue) color model:

Python
>>> def is_primary_color(color):
...     color = color.lower()
...     return color == "red" or color == "green" or color == "blue"
...

>>> is_primary_color("yellow")
False

>>> is_primary_color("green")
True

In is_primary_color(), you use a compound Boolean expression that uses the or operator to check if the input color is either red, green, or blue. Even though this function works as expected, the condition may be confusing and difficult to read and understand.

The good news is that you can replace the above condition with a compact and readable membership test:

Python
>>> def is_primary_color(color):
...     primary_colors = {"red", "green", "blue"}
...     return color.lower() in primary_colors
...

>>> is_primary_color("yellow")
False

>>> is_primary_color("green")
True

Now your function uses the in operator to check whether the input color is red, green, or blue. Assigning the set of primary colors to a properly named variable like primary_colors also helps to make your code more readable. The final check is pretty clear now. Anyone reading your code will immediately understand that you’re trying to determine if the input color is a primary color according to the RGB color model.

If you look at the example again, then you’ll notice that the primary colors have been stored in a set. Why? You’ll find your answer in the following section.

Writing Efficient Membership Tests

Python uses a data structure called a hash table to implement dictionaries and sets. Hash tables have a remarkable property: looking for any given value in the data structure takes about the same time, no matter how many values the table has. Using Big O notation, you’ll say that value lookups in hash tables have a time complexity of O(1), which makes them super fast.

Now, what does this feature of hash tables have to do with membership tests on dictionaries and sets? Well, it turns out that the in and not in operators work very quickly when they operate on these types. This detail allows you to optimize your code’s performance by favoring dictionaries and sets over lists and other sequences in membership tests.

To have an idea of how much more efficient than a list a set can be, go ahead and create the following script:

Python
# performance.py

from timeit import timeit

a_list = list(range(100_000))
a_set = set(range(100_000))

list_time = timeit("-1 in a_list", number=1, globals=globals())
set_time = timeit("-1 in a_set", number=1, globals=globals())

print(f"Sets are {(list_time / set_time):.2f} times faster than Lists")

This script creates a list of integer numbers with one hundred thousand values and a set with the same number of elements. Then the script computes the time that it takes to determine if the number -1 is in the list and the set. You know up front that -1 doesn’t appear in the list or set. So, the membership operator will have to check all the values before getting a final result.

As you already know, when the in operator searches for a value in a list, it uses an algorithm with a time complexity of O(n). On the other hand, when the in operator searches for a value in a set, it uses the hash table lookup algorithm, which has a time complexity of O(1). This fact can make a big difference in terms of performance.

Go ahead and run your script from the command line using the following command:

Shell
$ python performance.py
Sets are 1563.33 times faster than Lists

Although your command’s output may be slightly different, it’ll still show a significant performance difference when you use a set instead of a list in this specific membership test. With a list, the processing time will be proportional to the number of values. With a set, the time will be pretty much the same for any number of values.

This performance test shows that when your code is doing membership checks on large collections of values, you should use sets instead of lists whenever possible. You’ll also benefit from sets when your code performs several membership tests during its execution.

However, note that it’s not a good idea to convert an existing list into a set just to perform a few membership tests. Remember that converting a list into a set is an operation with O(n) time complexity.

Using operator.contains() for Membership Tests

The in operator has an equivalent function in the operator module, which comes in the standard library. The function is called contains(). It takes two arguments—a collection of values and a target value. It returns True if the input collection contains the target value:

Python
>>> from operator import contains

>>> contains([2, 3, 5, 9, 7], 5)
True

>>> contains([2, 3, 5, 9, 7], 8)
False

The first argument to contains() is the collection of values, and the second argument is the target value. Note that the order of arguments differs from a regular membership operation, where the target value comes first.

This function comes in handy when you’re using tools like map(), or filter() to process iterables in your code. For example, say you have a bunch of Cartesian points stored as tuples inside a list. You want to create a new list containing only the points that aren’t over the coordinate axis. Using the filter() function, you can come up with the following solution:

Python
>>> points = [
...     (1, 3),
...     (5, 0),
...     (3, 7),
...     (0, 6),
...     (8, 3),
...     (2, 0),
... ]

>>> list(filter(lambda point: not contains(point, 0), points))
[(1, 3), (3, 7), (8, 3)]

In this example, you use filter() to retrieve the points that don’t contain a 0 coordinate. To do this, you use contains() in a lambda function. Because filter() returns an iterator, you wrap up everything in a call to list() to convert the iterator into a list of points.

Even though the construct in the above example works, it’s quite complex because it implies importing contains(), creating a lambda function on top of it, and calling a couple of functions. You can get the same result using a list comprehension either with contains() or the not in operator directly:

Python
>>> [point for point in points if not contains(point, 0)]
[(1, 3), (3, 7), (8, 3)]

>>> [point for point in points if 0 not in point]
[(1, 3), (3, 7), (8, 3)]

The above list comprehensions are shorter and arguably more readable than the equivalent filter() call from the previous example. They’re also less complex because you don’t need to create a lambda function or call list(), so you’re reducing the knowledge requirements.

Supporting Membership Tests in User-Defined Classes

Providing a .__contains__() method is the most explicit and preferred way to support membership tests in your own classes. Python will automatically call this special method when you use an instance of your class as the right operand in a membership test.

You’ll likely add a .__contains__() method only to classes that’ll work as collections of values. That way, the users of your class will be able to determine if a given value is stored in a specific instance of your class.

As an example, say that you need to create a minimal stack data structure to store values following the LIFO (last in, first out) principle. One requirement of your custom data structure is to support membership tests. So, you end up writing the following class:

Python
# stack.py

class Stack:
    def __init__(self):
        self.items = []

    def push(self, item):
        self.items.append(item)

    def pop(self):
        return self.items.pop()

    def __contains__(self, item):
        return item in self.items

Your Stack class supports the two core functionalities of stack data structures. You can push a value to the top of the stack and pop a value from the top of the stack. Note that your data structure uses a list object under the hood to store and manipulate the actual data.

Your class also supports membership tests with the in and not in operators. To do this, the class implements a .__contains__() method that relies on the in operator itself.

To try out your class, go ahead and run the following code:

Python
>>> from stack import Stack

>>> stack = Stack()
>>> stack.push(1)
>>> stack.push(2)
>>> stack.push(3)

>>> 2 in stack
True
>>> 42 in stack
False
>>> 42 not in stack
True

Your class fully supports the in and not in operators. Great job! You now know how to support membership tests in your own classes.

Note that if a given class has a .__contains__() method, then the class doesn’t have to be iterable for the membership operators to work. In the example above, Stack isn’t iterable, and the operators still work because they retrieve their result from the .__contains__() method.

There are at least two more ways to support membership tests in user-defined classes apart from providing a .__contains__() method. If your class has either an .__iter__() or a .__getitem__() method, then the in and not in operators also work.

Consider the following alternative version of Stack:

Python
# stack.py

class Stack:
    def __init__(self):
        self.items = []

    def push(self, item):
        self.items.append(item)

    def pop(self):
        return self.items.pop()

    def __iter__(self):
        yield from self.items

The .__iter__() special method makes your class iterable, which is enough for membership tests to work. Go ahead and give it a try!

Another way to support membership tests is to implement a .__getitem__() method that handles indexing operations using zero-based integer indices in your classes:

Python
# stack.py

class Stack:
    def __init__(self):
        self.items = []

    def push(self, item):
        self.items.append(item)

    def pop(self):
        return self.items.pop()

    def __getitem__(self, index):
        return self.items[index]

Python automatically calls the .__getitem__() method when you perform indexing operations on the underlying object. In this example, when you do stack[0], you’ll get the first item in the Stack instance. Python takes advantage of .__getitem__() to make the membership operators work correctly.

Conclusion

Now you know how to perform membership tests using Python’s in and not in operators. This type of test allows you to check if a given value is present in a collection of values, which is a pretty common operation in programming.

In this tutorial, you’ve learned how to:

  • Run membership tests using Python’s in and not in operators
  • Use the in and not in operators with different data types
  • Work with operator.contains(), the equivalent function to the in operator
  • Support in and not in in your own classes

With this knowledge, you’re good to go with membership tests using Python’s in and not in operators in your code.

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About Leodanis Pozo Ramos

Leodanis Pozo Ramos Leodanis Pozo Ramos

Leodanis is an industrial engineer who loves Python and software development. He's a self-taught Python developer with 6+ years of experience. He's an avid technical writer with a growing number of articles published on Real Python and other sites.

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